World Economic Outlook 2014 (IMF)

WORLD ECONOMIC OUTLOOK
April 2014
Recovery Strengthens, Remains Uneven
International Monetary Fund
W o r l d E c o n o m i c a n d F i n a n c i a l S u r v e y s

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Recovery Strengthens, Remains Uneven (Washington, April 2014).
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International Monetary Fund|April 2014 iii
Assumptions and Conventions ix
Further Information and Data xi
Preface xii
Foreword xiii
Executive Summary xv
Chapter 1. Recent Developments and Prospects 1
The Demand and Activity Perspective 1
The External Sector Perspective 12
Downside Risks 13
Policies 19
Special Feature: Commodity Prices and Forecasts 25
Box 1.1. Credit Supply and Economic Growth 32
Box 1.2. Is China’s Spending Pattern Shifting (away from Commodities)? 36
Box 1.3. Anchoring Inflation Expectations When Inflation Is Undershooting 41
Box 1.4. Exchange Rate Regimes and Crisis Susceptibility in Emerging Markets 44
References 47
Chapter 2. Country and Regional Perspectives 49
The United States and Canada: Firming Momentum 49
Europe 53
Asia: Steady Recovery 57
Latin America and the Caribbean: Subdued Growth 60
Commonwealth of Independent States: Subdued Prospects 63
The Middle East and North Africa: Turning the Corner? 65
Sub-Saharan Africa: Accelerating Growth 68
Spillover Feature: Should Advanced Economies Worry about Growth Shocks
in Emerging Market Economies? 72
References 79
Chapter 3. Perspectives on Global Real Interest Rates 81
Stylized Facts: Measuring Real Rates and the Cost of Capital 83
Determinants of Real Rates: A Saving-Investment Framework 86
Which Factors Contributed to the Decline in Real Interest Rates? 88
Should We Expect a Large Reversal in Real Rates? 96
Summary and Policy Conclusions 97
Appendix 3.1. Model-Based Inflation and Dividend Growth Expectations 99
Appendix 3.2. Investment Profitability 99
Appendix 3.3. Fiscal Indicator 100
Appendix 3.4. The Effect of Financial Crises on Investment and Saving 101
CONTENTS

WORLD ECONOMIC OUTLOOK: RECOVERY STRENGTHENS, REMAINS UNEVEN
iv International Monetary Fund|April 2014
Appendix 3.5. Sensitivity of Saving and Investment to Real Rates 101
Appendix 3.6. Saving and Growth with Consumption Habit 102
Appendix 3.7. Sample of Countries Used in Tables and Figures 102
Box 3.1. Saving and Economic Growth 107
References 111
Chapter 4. On the Receiving End? External Conditions and Emerging Market Growth Before, During,
and After the Global Financial Crisis 113
Effects of External Factors on Emerging Market Growth 116
Global Chain or Global China? Quantifying China’s Impact 124
Growth Effects: The Long and the Short of It 126
Shifting Gears: Have Emerging Markets’ Growth Dynamics Changed since the Global Financial Crisis? 128
Policy Implications and Conclusions 133
Appendix 4.1. Data Definitions, Sources, and Descriptions 133
Appendix 4.2. Estimation Approach and Robustness Checks 137
Box 4.1. The Impact of External Conditions on Medium-Term Growth in Emerging Market Economies 145
References 150
Annex: IMF Executive Board Discussion of the Outlook, March 2014 153
Statistical Appendix 155
Assumptions 155
What’s New 156
Data and Conventions 156
Classification of Countries 157
General Features and Composition of Groups in the World Economic Outlook Classification 157
Table A. Classification by World Economic Outlook Groups and Their Shares in Aggregate GDP,
Exports of Goods and Services, and Population, 2013 159
Table B. Advanced Economies by Subgroup 160
Table C. European Union 160
Table D. Emerging Market and Developing Economies by Region and Main Source of Export Earnings 161
Table E. Emerging Market and Developing Economies by Region, Net External Position,
Status as Heavily Indebted Poor Countries, and Low-Income Developing Countries 162
Table F. Key Data Documentation 164
Box A1. Economic Policy Assumptions Underlying the Projections for Selected Economies 174
List of Tables 179
Output (Tables A1–A4) 180
Inflation (Tables A5–A7) 187
Financial Policies (Table A8) 192
Foreign Trade (Table A9) 193
Current Account Transactions (Tables A10–A12) 195
Balance of Payments and External Financing (Tables A13–A14) 201
Flow of Funds (Table A15) 203
Medium-Term Baseline Scenario (Table A16) 207
World Economic Outlook, Selected Topics 209

CONTENTS
International Monetary Fund|April 2014 v
Tables
Table 1.1. Overview of the World Economic Outlook Projections 2
Table 1.SF.1. Root-Mean-Squared Errors across Forecast Horizons h (Relative to the Random
Walk Model) 31
Table 1.3.1. Consensus Consumer Price Index Inflation Expectations 42
Table 2.1. Selected Advanced Economies: Real GDP, Consumer Prices, Current Account Balance, and
Unemployment 52
Table 2.2. Selected European Economies: Real GDP, Consumer Prices, Current Account Balance, and
Unemployment 54
Table 2.3. Selected Asian Economies: Real GDP, Consumer Prices, Current Account Balance, and
Unemployment 59
Table 2.4. Selected Western Hemisphere Economies: Real GDP, Consumer Prices, Current Account
Balance, and Unemployment 62
Table 2.5. Commonwealth of Independent States: Real GDP, Consumer Prices, Current Account
Table 2.6. Selected Middle East and North African Economies: Real GDP, Consumer Prices, Current
Account Balance, and Unemployment 67
Table 2.7. Selected Sub-Saharan African Economies: Real GDP, Consumer Prices, Current Account
Table 2.SF.1. Exports to Emerging Market Economies, 1995 versus 2008 74
Table 3.1. Alternative Hypotheses Explaining a Decline in Real Interest Rates 87
Table 3.2. Factors Affecting Real Interest Rates 96
Table 3.3. Investment (Saving) and the Real Interest Rate, Reduced-Form Equations 102
Table 3.4. Data Coverage for Global Interest Rates, Investment, and Saving 103
Table 3.1.1. Saving and Growth: Granger Causality Tests 108
Table 3.1.2. Determinants of the Evolution in Saving-to-GDP Ratios 110
Table 4.1. Impulse Responses to Shocks within the External Block: Baseline Model 119
Table 4.2. Impulse Responses to Shocks within the External Block: Modified Baseline Model
with China Real GDP Growth 126
Table 4.3. Share of Output Variance Due to External Factors 128
Table 4.4. Data Sources 134
Table 4.5 Sample of Emerging Market Economies and International Organization for Standardization
Country Codes 135
Table 4.6. Correlations of Domestic Real GDP Growth with Key Variables, 1998–2013 138
Table 4.1.1. Growth Regressions for Emerging Markets, 1997–2011 146
Table 4.1.2. Growth Regressions for Emerging Markets: Brazil, China, India, Russia, and South Africa
versus Other Emerging Market Partner Growth, 1997–2011 148
Table 4.1.3. Growth Regressions for Emerging Markets 149
Table A1. Summary of World Output 180
Table A2. Advanced Economies: Real GDP and Total Domestic Demand 181
Table A3. Advanced Economies: Components of Real GDP 182
Table A4. Emerging Market and Developing Economies: Real GDP 184
Table A5. Summary of Inflation 187
Table A6. Advanced Economies: Consumer Prices 188
Table A7. Emerging Market and Developing Economies: Consumer Prices 189
Table A8. Major Advanced Economies: General Government Fiscal Balances and Debt 192
Table A9. Summary of World Trade Volumes and Prices 193
Table A10. Summary of Balances on Current Account 195
Table A11. Advanced Economies: Balance on Current Account 197

vi International Monetary Fund|April 2014
Table A12. Emerging Market and Developing Economies: Balance on Current Account 198
Table A13. Emerging Market and Developing Economies: Net Financial Flows 201
Table A14. Emerging Market and Developing Economies: Private Financial Flows 202
Table A15. Summary of Sources and Uses of World Savings 203
Table A16. Summary of World Medium-Term Baseline Scenario 207
Online Tables
Table B1. Advanced Economies: Unemployment, Employment, and Real GDP per Capita
Table B2. Emerging Market and Developing Economies: Real GDP
Table B3. Advanced Economies: Hourly Earnings, Productivity, and Unit Labor Costs in Manufacturing
Table B4. Emerging Market and Developing Economies: Consumer Prices
Table B5. Summary of Fiscal and Financial Indicators
Table B6. Advanced Economies: General and Central Government Net Lending/Borrowing
and Excluding Social Security Schemes
Table B7. Advanced Economies: General Government Structural Balances
Table B8. Emerging Market and Developing Economies: General Government Net Lending/
Borrowing and Overall Fiscal Balance
Table B9. Emerging Market and Developing Economies: General Government Net Lending/
Borrowing
Table B10. Advanced Economies: Exchange Rates
Table B11. Emerging Market and Developing Economies: Broad Money Aggregates
Table B12. Advanced Economies: Export Volumes, Import Volumes, and Terms of Trade
in Goods and Services
Table B13. Emerging Market and Developing Economies by Region: Total Trade in Goods
Table B14. Emerging Market and Developing Economies by Source of Export Earnings: Total Trade in Goods
Table B15. Advanced Economies: Current Account Transactions
Table B16. Emerging Market and Developing Economies: Balances on Current Account
Table B17. Emerging Market and Developing Economies by Region: Current Account Transactions
Table B18. Emerging Market and Developing Economies by Analytical Criteria: Current
Account Transactions
Table B19. Summary of Balance of Payments, Financial Flows, and External Financing
Table B20. Emerging Market and Developing Economies by Region: Balance of Payments
and External Financing
Table B21. Emerging Market and Developing Economies by Analytical Criteria:
Balance of Payments and External Financing
Table B22. Summary of External Debt and Debt Service
Table B23. Emerging Market and Developing Economies by Region: External Debt by Maturity
and Type of Creditor
Table B24. Emerging Market and Developing Economies by Analytical Criteria: External Debt
by Maturity and Type of Creditor
Table B25. Emerging Market and Developing Economies: Ratio of External Debt to GDP
Table B26. Emerging Market and Developing Economies: Debt-Service Ratios
Table B27. Emerging Market and Developing Economies, Medium-Term Baseline Scenario:
Selected Economic Indicators
Figures
Figure 1.1. Global Activity Indicators 3
Figure 1.2. GDP Growth Forecasts 3
Figure 1.3. Monetary Conditions in Advanced Economies 4

CONTENTS
Figure 1.4. Fiscal Policies 5
Figure 1.5. Global Inflation 6
Figure 1.6. Capacity, Unemployment, and Output Trend 7
Figure 1.7. Overheating Indicators for the Group of Twenty Economies 9
Figure 1.8. Financial Market Conditions in Advanced Economies 10
Figure 1.9. Financial Conditions and Capital Flows in Emerging Market Economies 11
Figure 1.10. Monetary Policies and Credit in Emerging Market Economies 11
Figure 1.11. Exchange Rates and Reserves 12
Figure 1.12. External Sector 13
Figure 1.13. Risks to the Global Outlook 14
Figure 1.14. Recession and Deflation Risks 14
Figure 1.15. Slower Growth in Emerging Market Economies and a Faster Recovery in the United States 18
Figure 1.SF.1. Commodity Market Developments 26
Figure 1.SF.2. Brent Forecast Errors and Futures 27
Figure 1.SF.3. Vector Autoregression and Combination Forecasts 29
Figure 1.SF.4. Rolling Root-Mean-Squared Errors: Recursive Estimation 30
Figure 1.1.1. Cumulative Responses of GDP to a 10 Percentage Point Tightening of Lending Standards 33
Figure 1.1.2. Credit Supply Shocks 34
Figure 1.1.3. Contribution of Credit Supply Shocks to GDP 34
Figure 1.2.1. China: Real GDP Growth and Commodity Prices 36
Figure 1.2.2. Growth Rate of Global Commodity Consumption 37
Figure 1.2.3. Actual and Predicted Per Capita Commodity Consumption 38
Figure 1.2.4. Spending Patterns 39
Figure 1.3.1. Inflation Expectations in Euro Area, United States, Japan, and Norway 41
Figure 1.4.1. Distribution of Exchange Rate Regimes in Emerging Markets, 1980–2011 44
Figure 1.4.2. Predicted Crisis Probability in Emerging Markets, 1980–2011 45
Figure 1.4.3. Probability of Banking or Currency Crisis 46
Figure 2.1. 2014 GDP Growth Forecasts and the Effects of a Plausible Downside Scenario 50
Figure 2.2. United States and Canada: Recovery Firming Up 51
Figure 2.3. Advanced Europe: From Recession to Recovery 55
Figure 2.4. Emerging and Developing Europe: Recovery Strengthening, but with Vulnerabilities 56
Figure 2.5. Asia: Steady Recovery 58
Figure 2.6. Latin America and the Caribbean: Subdued Growth 61
Figure 2.7. Commonwealth of Independent States: Subdued Prospects 64
Figure 2.8. Middle East, North Africa, Afghanistan, and Pakistan: Turning a Corner? 66
Figure 2.9. Sub-Saharan Africa: Accelerating Growth 70
Figure 2.SF.1. Real Trade Linkages between Advanced Economies and Emerging Market Economies 73
Figure 2.SF.2. Financial Exposure of Advanced Economies to Emerging Market Economies 74
Figure 2.SF.3. Event Studies around Downturn Episodes in Emerging Market Economies 75
Figure 2.SF.4. Peak Effect of a Growth Shock to Emerging Market Economies on Advanced
Economies’ Output Growth 77
Figure 2.SF.5. Model Simulations of Potential Growth Spillover Effects from Emerging Market
Economies on Advanced Economies 78
Figure 3.1. Ten-Year Interest Rate on Government Bonds and Inflation 81
Figure 3.2. Real Interest Rate Comparison 84
Figure 3.3. Real Interest Rates, Real Returns on Equity, and Cost of Capital 85
Figure 3.4. Common Factors in Real Interest Rates 85
Figure 3.5. Real Interest Rate and Shifts in Demand for and Supply of Funds 87
Figure 3.6. Investment-to-GDP Ratios 88
Figure 3.7. Investment Shifts in Advanced Economies 89
International Monetary Fund|April 2014 vii

Figure 3.8. Saving Shifts in Emerging Markets 90
Figure 3.9. Effect of Fiscal Policy on Real Interest Rates 91
Figure 3.10. Effect of U.S. Monetary Policy Shocks on Real Interest Rates 92
Figure 3.11. Real Long-Term Interest Rates and Real Returns on Equity 93
Figure 3.12. Portfolio Shifts and Relative Demand for Bonds versus Equity 94
Figure 3.13. Portfolio Shifts and Relative Riskiness of Bonds versus Equity, 1980–2013 94
Figure 3.14. Effect of Financial Crises on Saving- and Investment-to-GDP Ratios 95
Figure 3.15. Implications of Lower Real Interest Rates for Debt Sustainability 97
Figure 3.16. Investment Shifts in Advanced Economies 100
Figure 3.17. Global Long-Term Real Interest Rates 106
Figure 3.18. Convergence of Real Interest Rates in the Euro Area 106
Figure 3.1.1. Saving Rate and Accelerations (Decelerations) in GDP 109
Figure 3.1.2. Total Saving: Actual versus Conditional Forecasts 109
Figure 4.1. Growth Developments in Advanced and Emerging Market and Developing Economies 114
Figure 4.2. Average Country Rankings, 2000–12 118
Figure 4.3. Impulse Responses of Domestic Real GDP Growth to External Demand Shocks 120
Figure 4.4. Impulse Responses to External Financing Shock 120
Figure 4.5. Impulse Responses to U.S. High-Yield Spread Shock 121
Figure 4.6. Correlations between Growth Responses to External Shocks and Country-Specific
Characteristics 122
Figure 4.7. Impulse Responses of Domestic Real GDP Growth to Terms-of-Trade Growth Shock 123
Figure 4.8. Historical Decompositions of Real GDP Growth into Internal and External Factors 124
Figure 4.9. Impulse Responses to Real GDP Growth Shock in China 125
Figure 4.10. Historical Decomposition of Real GDP Growth with China as an Explicit External Factor 127
Figure 4.11. Emerging Markets’ Output and Growth Performance after Global Recessions 129
Figure 4.12. Out-of-Sample Growth Forecasts Conditional on External Factors, by Country 131
Figure 4.13. Conditional Forecast and Actual Growth since the Global Financial Crisis, by Country 132
Figure 4.14. Domestic Real GDP Growth across Emerging Markets versus United States and China 136
Figure 4.15. Average Growth for Regional Groups of Emerging Market Economies 137
Figure 4.16. Impact of Prior Choice on Average Impulse Responses 139
Figure 4.17. Average Impulse Responses to Shocks from Alternative U.S. Monetary Policy Variables 140
Figure 4.18. Domestic Real GDP Growth Response to U.S. Federal Funds Rate and 10-Year
U.S. Treasury Bond Rate under Alternative Specifications 141
Figure 4.19. Average Impulse Responses to Alternative Measures of U.S. Monetary Policy Shock 142
Figure 4.20. Alternative Monetary Policy Shocks 142
Figure 4.21. Impulse Response of Domestic Real GDP Growth to External Financing Shocks 143
Figure 4.22. Average Impulse Responses of Domestic Real GDP Growth to Shocks under Alternative
Vector Autoregression Specifications 143
Figure 4.23. Brazil: Comparison of Responses under the Baseline Model with Responses from
Model with Sample Beginning in the First Quarter of 1995 144
Figure 4.24. Comparison of Impulse Responses from Panel Vector Autoregression with Responses
from the Baseline Model 144
Figure 4.1.1. Export Partner Growth Elasticity 147
Figure 4.1.2. Export Partner Growth 147
viii International Monetary Fund|April 2014

Editor’s notes:
(April 8, 2014)
Note 7 in Figure 1.3 on page 4 has been corrected to remove Colombia from the list of upward pressure
countries.
(April 10, 2014)
Panel 3 of Figure 4.2 (page 118) and panel 2 of Figure 4.6 (page 122) have been replaced to correct errors in
the underlying data.
(April 11, 2014)
Panel 1 of Figure 1.3 has been revised to change the underlying data for the October 2013 WEO projections
for the United States from overnight swap rates to federal funds rate futures.
(April 21, 2014)
In Statistical Table A15 on page 204, the first instance of “Emerging and Developing Europe” has been cor-
rected to read “Emerging and Developing Asia.”

International Monetary Fund|April 2014 ix
A number of assumptions have been adopted for the projections presented in the World Economic Outlook (WEO). It
has been assumed that real effective exchange rates remained constant at their average levels during January 31–Febru-
ary 28, 2014, except for those for the currencies participating in the European exchange rate mechanism II (ERM II),
which are assumed to have remained constant in nominal terms relative to the euro; that established policies of national
authorities will be maintained (for specific assumptions about fiscal and monetary policies for selected economies, see
Box A1 in the Statistical Appendix); that the average price of oil will be $104.17 a barrel in 2014 and $97.92 a barrel in
2015 and will remain unchanged in real terms over the medium term; that the six-month London interbank offered rate
(LIBOR) on U.S. dollar deposits will average 0.4 percent in 2014 and 0.8 percent in 2015; that the three-month euro
deposit rate will average 0.3 percent in 2014 and 0.4 percent in 2015; and that the six-month Japanese yen deposit rate
will yield on average 0.2 percent in 2014 and 2015. These are, of course, working hypotheses rather than forecasts, and
the uncertainties surrounding them add to the margin of error that would in any event be involved in the projections.
The estimates and projections are based on statistical information available generally through March 24, 2014.
The following conventions are used throughout the WEO:
. . . to indicate that data are not available or not applicable;
– between years or months (for example, 2013–14 or January–June) to indicate the years or months cov-
ered, including the beginning and ending years or months;
/ between years or months (for example, 2013/14) to indicate a fiscal or financial year.
“Billion” means a thousand million; “trillion” means a thousand billion.
“Basis points” refer to hundredths of 1 percentage point (for example, 25 basis points are equivalent to ¼ of 1
percentage point).
For some countries, the figures for 2013 and earlier are based on estimates rather than actual outturns.
Data refer to calendar years, except in the case of a few countries that use fiscal years. Please refer to Table F in
the Statistical Appendix, which lists the reference periods for each country.
Projections for Ukraine are excluded due to the ongoing crisis.
The consumer price projections for Argentina are excluded because of a structural break in the data. Please refer
to note 6 in Table A7 for further details.
Korea’s real GDP series is based on the reference year 2005. This does not reflect the revised national accounts
released on March 26, 2014, after the WEO was finalized for publication. These comprehensive revisions include
implementing the 2008 System of National Accounts and updating the reference year to 2010. As a result of these
revisions, real GDP growth in 2013 was revised up to 3 percent from 2.8 percent (which is the figure included in
Tables 2.3 and A2).
On January 1, 2014, Latvia became the 18th country to join the euro area. Data for Latvia are not included in
the euro area aggregates, because the database has not yet been converted to euros, but are included in data aggre-
gated for advanced economies.
Starting with the April 2014 WEO, the Central and Eastern Europe and Emerging Europe regions have been
renamed Emerging and Developing Europe. The Developing Asia region has been renamed Emerging and Devel-
oping Asia.
Cape Verde is now called Cabo Verde.
As in the October 2013 WEO, data for Syria are excluded for 2011 onward because of the uncertain political
situation.
If no source is listed on tables and figures, data are drawn from the WEO database.
When countries are not listed alphabetically, they are ordered on the basis of economic size.
Minor discrepancies between sums of constituent figures and totals shown reflect rounding.
ASSUMPTIONS AND CONVENTIONS

x International Monetary Fund|April 2014
As used in this report, the terms “country” and “economy” do not in all cases refer to a territorial entity that is
a state as understood by international law and practice. As used here, the term also covers some territorial entities
that are not states but for which statistical data are maintained on a separate and independent basis.
Composite data are provided for various groups of countries organized according to economic characteristics or
region. Unless noted otherwise, country group composites represent calculations based on 90 percent or more of
the weighted group data.
The boundaries, colors, denominations, and any other information shown on the maps do not imply, on the
part of the International Monetary Fund, any judgment on the legal status of any territory or any endorsement or
acceptance of such boundaries.

International Monetary Fund|April 2014 xi
WORLD ECONOMIC OUTLOOK: TENSIONS FROM THE TWO-SPEED RECOVERY
FURTHER INFORMATION AND DATA
This version of the World Economic Outlook (WEO) is available in full through the IMF eLibrary (www.elibrary.
imf.org) and the IMF website (www.imf.org). Accompanying the publication on the IMF website is a larger com-
pilation of data from the WEO database than is included in the report itself, including files containing the series
most frequently requested by readers. These files may be downloaded for use in a variety of software packages.
The data appearing in the World Economic Outlook are compiled by the IMF staff at the time of the WEO exer-
cises. The historical data and projections are based on the information gathered by the IMF country desk officers
in the context of their missions to IMF member countries and through their ongoing analysis of the evolving situ-
ation in each country. Historical data are updated on a continual basis as more information becomes available, and
structural breaks in data are often adjusted to produce smooth series with the use of splicing and other techniques.
IMF staff estimates continue to serve as proxies for historical series when complete information is unavailable.
As a result, WEO data can differ from those in other sources with official data, including the IMF’s International
Financial Statistics.
The WEO data and metadata provided are “as is” and “as available,” and every effort is made to ensure, but not
guarantee, their timeliness, accuracy, and completeness. When errors are discovered, there is a concerted effort to
correct them as appropriate and feasible. Corrections and revisions made after publication are incorporated into the
electronic editions available from the IMF eLibrary (www.elibrary.imf.org) and on the IMF website (www.imf.org).
All substantive changes are listed in detail in the online tables of contents.
For details on the terms and conditions for usage of the WEO database, please refer to the IMF Copyright and
Usage website (www.imf.org/external/terms.htm).
Inquiries about the content of the World Economic Outlook and the WEO database should be sent by mail, fax,
or online forum (telephone inquiries cannot be accepted):
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Online Forum: www.imf.org/weoforum

xii International Monetary Fund|April 2014
The analysis and projections contained in the World Economic Outlook are integral elements of the IMF’s surveil-
lance of economic developments and policies in its member countries, of developments in international financial
markets, and of the global economic system. The survey of prospects and policies is the product of a comprehen-
sive interdepartmental review of world economic developments, which draws primarily on information the IMF
staff gathers through its consultations with member countries. These consultations are carried out in particular by
the IMF’s area departments—namely, the African Department, Asia and Pacific Department, European Depart-
ment, Middle East and Central Asia Department, and Western Hemisphere Department—together with the
Strategy, Policy, and Review Department; the Monetary and Capital Markets Department; and the Fiscal Affairs
Department.
The analysis in this report was coordinated in the Research Department under the general direction of Olivier
Blanchard, Economic Counsellor and Director of Research. The project was directed by Thomas Helbling, Divi-
sion Chief, Research Department, and Jörg Decressin, Deputy Director, Research Department.
The primary contributors to this report are Abdul Abiad, Aseel Almansour, Aqib Aslam, Samya Beidas-Strom,
John Bluedorn, Rupa Duttagupta, Davide Furceri, Andrea Pescatori, Marco E. Terrones, and Juan Yepez Albornoz.
Other contributors include Ali Alichi, Angana Banerji, Benjamin Beckers, Alberto Behar, Sami Ben Naceur,
Patrick Blagrave, Kevin Clinton, Alexander Culiuc, Joshua Felman, Emilio Fernandez Corugedo, Roberto Garcia-
Saltos, Roberto Guimarães-Filho, Keiko Honjo, Benjamin Hunt, Dora Iakova, Deniz Igan, Gregorio Impavido,
Zoltan Jakab, Douglas Laxton, Lusine Lusinyan, Andre Meier, Pritha Mitra, Dirk Muir, Jean-Marc Natal, Marco
Pani, Mahvash Qureshi, Jesmin Rahman, Marina Rousset, Damiano Sandri, John Simon, Serhat Solmaz, Shane
Streifel, Yan Sun, Li Tang, Boqun Wang, and Shengzu Wang.
Gohar Abajyan, Gavin Asdorian, Shan Chen, Tingyun Chen, Angela Espiritu, Madelyn Estrada, Sinem Kilic
Celik, Mitko Grigorov, Cleary A. Haines, Pavel Lukyantsau, Olivia Ma, Tim Mahedy, Anayo Osueke, Katherine
Pan, Sidra Rehman, Daniel Rivera Greenwood, Carlos Rondon, Yang Yang, and Fan Zhang provided research
assistance. Luis Cubeddu provided comments and suggestions. Mahnaz Hemmati, Toh Kuan, Emory Oakes, and
Richard Watson provided technical support. Skeeter Mathurin and Anduriña Espinoza-Wasil were responsible for
word processing. Linda Griffin Kean and Michael Harrup of the Communications Department edited the manu-
script and coordinated production of the publication with assistance from Lucy Scott Morales and Sherrie Brown.
The Core Data Management team from the IMF’s IT department and external consultant Pavel Pimenov provided
additional technical support.
The analysis has benefited from comments and suggestions by staff members from other IMF departments, as
well as by Executive Directors following their discussion of the report on March 21, 2014. However, both projec-
tions and policy considerations are those of the IMF staff and should not be attributed to Executive Directors or to
their national authorities.
PREFACE

T
he dynamics that were emerging at the
time of the October 2013 World Economic
Outlook are becoming more visible:
The recovery then starting to take hold
in advanced economies is becoming broader. Fiscal
consolidation is slowing, and investors are less wor-
ried about debt sustainability. Banks are gradually
becoming stronger. Although we are far short of a full
recovery, the normalization of monetary policy—both
conventional and unconventional—is now on the
agenda.
These dynamics imply a changing environment for
emerging market and developing economies. Stron-
ger growth in advanced economies implies increased
demand for their exports. The normalization of mon-
etary policy, however, implies tighter financial condi-
tions and a tougher financial environment. Investors
will be less forgiving, and macroeconomic weaknesses
will become more costly.
Acute risks have decreased, but risks have not
disappeared. In the United States, the recovery seems
solidly grounded. In Japan, Abenomics still needs
to translate into stronger domestic private demand
for the recovery to be sustained. Adjustment in
the south of Europe cannot be taken for granted,
especially if Euro wide inflation is low. As discussed
in the April 2014 Global Financial Stability Report,
financial reform is incomplete, and the financial
system remains at risk. Geopolitical risks have arisen,
although they have not yet had global macroeco-
nomic repercussions.
Looking ahead, the focus must increasingly turn to
the supply side:
Potential growth in many advanced economies is
very low. This is bad on its own, but it also makes
fiscal adjustment more difficult. In this context,
measures to increase potential growth are becoming
more important—from rethinking the shape of labor
market institutions, to increasing competition and
productivity in a number of nontradables sectors, to
rethinking the size of the government, to examining
the role of public investment.
Although the evidence is not yet clear, potential
growth in many emerging market economies also
appears to have decreased. In some countries, such as
China, this may be in part a desirable byproduct of
more balanced growth. In others, there is clearly scope
for some structural reforms to improve the outcome.
Finally, as the effects of the financial crisis slowly
diminish, another trend may come to dominate the
scene, namely, increased income inequality. Though
inequality has always been perceived to be a central
issue, until recently it was not believed to have major
implications for macroeconomic developments.
This belief is increasingly called into question. How
inequality affects both the macroeconomy and the
design of macroeconomic policy will likely be increas-
ingly important items on our agenda.
Olivier Blanchard
Economic Counsellor
FOREWORD
International Monetary Fund|April 2014 xiii

G
lobal activity has broadly strengthened and
is expected to improve further in 2014–15,
with much of the impetus coming from
advanced economies. Inflation in these
economies, however, has undershot projections,
reflecting still-large output gaps and recent commod-
ity price declines. Activity in many emerging market
economies has disappointed in a less favorable external
financial environment, although they continue to
contribute more than two-thirds of global growth.
Their output growth is expected to be lifted by stron-
ger exports to advanced economies. In this setting,
downside risks identified in previous World Economic
Outlook reports have diminished somewhat. There are
three caveats: emerging market risks have increased,
there are risks to activity from lower-than-expected
inflation in advanced economies, and geopolitical risks
have resurfaced. Overall, the balance of risks, while
improved, remains on the downside.
The renewed increase in financial volatility in late
January of this year highlights the challenges for
emerging market economies posed by the changing
external environment. The proximate cause seems to
have been renewed market concern about emerging
market fundamentals. Although market pressures were
relatively broadly based, countries with higher inflation
and wider current account deficits were generally more
affected. Some of these weaknesses have been present
for some time, but with prospects of improved returns
in advanced economies, investor sentiment is now less
favorable toward emerging market risks. In view of pos-
sible capital flow reversals, risks related to sizable external
funding needs and disorderly currency depreciations are
a concern. Some emerging market economies have tight-
ened macroeconomic policies to shore up confidence
and strengthen their commitment to policy objectives.
Overall, financial conditions have tightened further in
some emerging market economies compared with the
October 2013 World Economic Outlook. The cost of
capital has increased as a result, and this is expected to
dampen investment and weigh on growth.
Looking ahead, global growth is projected to
strengthen from 3 percent in 2013 to 3.6 percent in
2014 and 3.9 percent in 2015, broadly unchanged
from the October 2013 outlook. In advanced
economies, growth is expected to increase to about
2¼ percent in 2014–15, an improvement of about
1 percentage point compared with 2013. Key drivers
are a reduction in fiscal tightening, except in Japan,
and still highly accommodative monetary condi-
tions. Growth will be strongest in the United States at
about 2¾ percent. Growth is projected to be positive
but varied in the euro area: stronger in the core, but
weaker in countries with high debt (both private and
public) and financial fragmentation, which will both
weigh on domestic demand. In emerging market and
developing economies, growth is projected to pick up
gradually from 4.7 percent in 2013 to about 5 percent
in 2014 and 5¼ percent in 2015. Growth will be
helped by stronger external demand from advanced
economies, but tighter financial conditions will
dampen domestic demand growth. In China, growth
is projected to remain at about 7½ percent in 2014
as the authorities seek to rein in credit and advance
reforms while ensuring a gradual transition to a more
balanced and sustainable growth path.
The global recovery is still fragile despite improved
prospects, and significant downside risks—both old
and new—remain. Recently, some new geopolitical
risks have emerged. On old risks, those related to
emerging market economies have increased with the
changing external environment. As highlighted in the
April 2014 Global Financial Stability Report, unexpect-
edly rapid normalization of U.S. monetary policy or
renewed bouts of high risk aversion on the part of
investors could result in further financial turmoil. This
would lead to difficult adjustments in some emerg-
ing market economies, with a risk of contagion and
broad-based financial stress, and thus lower growth.
In advanced economies, risks to activity associated
with very low inflation have come to the fore, espe-
cially in the euro area, where large output gaps have
contributed to low inflation. With inflation likely to
remain below target for some time, longer-term infla-
tion expectations might drift down, leading to even
lower inflation than is currently expected, or possibly
International Monetary Fund|April 2014 xv
EXECUTIVE SUMMARY

to deflation if other downside risks to activity mate-
rialize. The result would be higher real interest rates,
an increase in private and public debt burdens, and
weaker demand and output.
The strengthening of the recovery from the Great
Recession in the advanced economies is a welcome
development. But growth is not evenly robust across
the globe, and more policy efforts are needed to fully
restore confidence, ensure robust growth, and lower
downside risks.
Policymakers in advanced economies need to
avoid a premature withdrawal of monetary accom-
modation. In an environment of continued fiscal
consolidation, still-large output gaps, and very low
inflation, monetary policy should remain accommoda-
tive. In the euro area, more monetary easing, includ-
ing unconventional measures, is necessary to sustain
activity and help achieve the European Central Bank’s
price stability objective, thus lowering risks of even
lower inflation or outright deflation. Sustained low
inflation would not likely be conducive to a suitable
recovery of economic growth. In Japan, implementa-
tion of the remaining two arrows of Abenomics—
structural reform and plans for fiscal consolidation
beyond 2015—is essential to achieve the inflation
target and higher sustained growth. The need for
credible medium-term fiscal plans, however, extends
beyond Japan. The April 2014 Fiscal Monitor high-
lights that the combination of large public debt stocks
and the absence of medium-term adjustment plans
that include specific measures and strong entitlement
reforms is the main factor behind important medium-
term fiscal risks in advanced economies, including in
the United States. In the euro area, repairing bank
balance sheets in the context of a credible asset quality
review and recapitalizing weak banks will be critical if
confidence is to improve and credit is to revive. Also
essential for achieving these goals is progress on com-
pleting the banking union—including an independent
Single Resolution Mechanism with the capacity to
undertake timely bank resolution and common back-
stops to sever the link between sovereigns and banks.
More structural reforms are needed to lift investment
and activity prospects.
Emerging market economies will have to weather
turbulence and maintain high medium-term growth.
The appropriate policy measures will differ across these
economies. However, many of them have some policy
priorities in common. First, policymakers should allow
exchange rates to respond to changing fundamentals
and facilitate external adjustment. Where international
reserves are adequate, foreign exchange interventions
can be used to smooth volatility and avoid financial
disruption. Second, in economies in which inflation
is still relatively high or the risks that recent currency
depreciation could feed into underlying inflation are
high, further monetary policy tightening may be neces-
sary. If policy credibility is a problem, strengthening
the transparency and consistency of policy frameworks
may be necessary for tightening to be effective. Third,
on the fiscal front, policymakers must lower budget
deficits, although the urgency for action varies across
economies. Early steps are required if public debt is
already elevated and the associated refinancing needs
are a source of vulnerability. Fourth, many economies
need a new round of structural reforms that include
investment in public infrastructure, removal of bar-
riers to entry in product and services markets, and
in China, rebalancing growth away from investment
toward consumption.
Low-income countries will need to avoid a buildup
of external and public debt. Many of these countries
have succeeded in maintaining strong growth, partly
reflecting better macroeconomic policies, but their
external environment has also been changing. Foreign
direct investment has started to moderate with declin-
ing commodity prices, and commodity-related budget
revenues and foreign exchange earnings are at risk.
Timely policy adjustments will be important to avoid a
buildup in external debt and public debt.
xvi International Monetary Fund|April 2014

1
CHAPTER
International Monetary Fund|April 2014 1
1
CHAPTER
RECENT DEVELOPMENTS AND PROSPECTS
Global activity strengthened during the second half of
2013 and is expected to improve further in 2014–15.
The impulse has come mainly from advanced economies,
although their recoveries remain uneven. With supportive
monetary conditions and a smaller drag from fiscal con-
solidation, annual growth is projected to rise above trend
in the United States and to be close to trend in the core
euro area economies. In the stressed euro area economies,
however, growth is projected to remain weak and fragile as
high debt and financial fragmentation hold back domes-
tic demand. In Japan, fiscal consolidation in 2014–15
is projected to result in some growth moderation. Growth
in emerging market economies is projected to pick up only
modestly. These economies are adjusting to a more difficult
external financial environment in which international
investors are more sensitive to policy weakness and vulner-
abilities given prospects for better growth and monetary pol-
icy normalization in some advanced economies. As a result,
financial conditions in emerging market economies have
tightened further compared with the October 2013 World
Economic Outlook (WEO), while they have been broadly
stable in advanced economies. Downside risks continue to
dominate the global growth outlook, notwithstanding some
upside risks in the United States, the United Kingdom,
and Germany. In advanced economies, major concerns
include downside risks from low inflation and the possibil-
ity of protracted low growth, especially in the euro area
and Japan. While output gaps generally remain large, the
monetary policy stance should stay accommodative, given
continued fiscal consolidation. In emerging market econo-
mies, vulnerabilities appear mostly localized. Nevertheless, a
still-greater general slowdown in these economies remains a
risk, because capital inflows could slow or reverse. Emerging
market and developing economies must therefore be ready to
weather market turmoil and reduce external vulnerabilities.
The Demand and Activity Perspective
Global growth picked up in the second half of 2013,
averaging 3⅔ percent—a marked uptick from the
2⅔ percent recorded during the previous six months.
Advanced economies accounted for much of the
pickup, whereas growth in emerging markets increased
only modestly (Figure 1.1, panel 2). The strengthening
in activity was mirrored in global trade and industrial
production (Figure 1.1, panel 1).
The latest incoming data suggest a slight modera-
tion in global growth in the first half of 2014. The
stronger-than-expected acceleration in global activity in
the latter part of 2013 was partly driven by increases in
inventory accumulation that will be reversed. Overall,
however, the outlook remains broadly the same as in
the October 2013 WEO: global growth is projected to
strengthen to 3.6 percent in 2014 and then to increase
further to 3.9 percent in 2015 (Table 1.1).
• A major impulse to global growth has come from
the United States, whose economy (Figure 1.2, panel
1) grew at 3¼ percent in the second half of 2013—
stronger than expected in the October 2013 WEO.
Some of the upside surprise was due to strong
export growth and temporary increases in inventory
demand. Recent indicators suggest some slowing in
early 2014. Much of this seems related to unusually
bad weather, although some payback from previous
inventory demand increases may also be contribut-
ing. Nevertheless, annual growth in 2014–15 is
projected to be above trend at about 2¾ percent
(Table 1.1). More moderate fiscal consolidation
helps; it is estimated that the change in the primary
structural balance will decline from slightly more
than 2 percent of GDP in 2013 to about ½ percent
in 2014–15. Support also comes from accommoda-
tive monetary conditions as well as from a real estate
sector that is recovering after a long slump (Figure
1.3, panel 5), higher household wealth (Figure 1.3,
panel 3), and easier bank lending conditions.
• In the euro area, growth has turned positive. In
Germany, supportive monetary conditions, robust
labor market conditions, and improving confidence
have underpinned a pickup in domestic demand,
reflected mainly in higher consumption and a tenta-
tive revival in investment but also in housing. Across
the euro area, a strong reduction in the pace of fiscal

2 International Monetary Fund|April 2014
Table 1.1. Overview of the World Economic Outlook Projections
(Percent change unless noted otherwise)
Year over Year
Difference from
January 2014 WEO
Update
Q4 over Q4
Projections Estimates Projections
2012 2013 2014 2015 2014 2015 2013 2014 2015
World Output1 3.2 3.0 3.6 3.9 –0.1 –0.1 3.3 3.6 3.7
Advanced Economies 1.4 1.3 2.2 2.3 0.0 0.0 2.1 2.1 2.4
United States 2.8 1.9 2.8 3.0 0.0 0.0 2.6 2.7 3.0
Euro Area2 –0.7 –0.5 1.2 1.5 0.1 0.1 0.5 1.3 1.5
Germany 0.9 0.5 1.7 1.6 0.2 0.1 1.4 1.6 1.7
France 0.0 0.3 1.0 1.5 0.1 0.0 0.8 1.2 1.6
Italy –2.4 –1.9 0.6 1.1 0.0 0.0 –0.9 0.7 1.4
Spain –1.6 –1.2 0.9 1.0 0.3 0.2 –0.2 1.1 0.9
Japan 1.4 1.5 1.4 1.0 –0.3 0.0 2.5 1.2 0.5
United Kingdom 0.3 1.8 2.9 2.5 0.4 0.3 2.7 3.0 1.9
Canada 1.7 2.0 2.3 2.4 0.1 0.0 2.7 2.1 2.4
Other Advanced Economies3 1.9 2.3 3.0 3.2 0.1 0.0 2.9 2.7 3.6
Emerging Market and Developing Economies4 5.0 4.7 4.9 5.3 –0.2 –0.1 4.8 5.2 5.3
Commonwealth of Independent States 3.4 2.1 2.3 3.1 –0.3 0.1 1.3 2.0 2.5
Russia 3.4 1.3 1.3 2.3 –0.6 –0.2 1.1 1.6 2.5
Excluding Russia 3.3 3.9 5.3 5.7 1.2 1.4 . . . . . . . . .
Emerging and Developing Asia 6.7 6.5 6.7 6.8 0.0 0.0 6.4 6.7 6.8
China 7.7 7.7 7.5 7.3 0.0 0.0 7.7 7.6 7.2
India5 4.7 4.4 5.4 6.4 0.0 0.0 4.7 5.7 6.5
ASEAN-56 6.2 5.2 4.9 5.4 –0.2 –0.2 . . . . . . . . .
Emerging and Developing Europe 1.4 2.8 2.4 2.9 –0.5 –0.2 3.6 2.5 2.9
Latin America and the Caribbean 3.1 2.7 2.5 3.0 –0.4 –0.3 1.9 3.1 2.5
Brazil 1.0 2.3 1.8 2.7 –0.5 –0.2 1.9 2.0 2.9
Mexico 3.9 1.1 3.0 3.5 0.0 0.0 0.6 4.5 2.4
Middle East, North Africa, Afghanistan, and Pakistan 4.2 2.4 3.2 4.4 –0.1 –0.4 . . . . . . . . .
Sub-Saharan Africa 4.9 4.9 5.4 5.5 –0.7 –0.3 . . . . . . . . .
South Africa 2.5 1.9 2.3 2.7 –0.5 –0.6 2.1 2.1 3.0
Memorandum
European Union –0.3 0.2 1.6 1.8 0.2 0.1 1.1 1.7 1.7
Low-Income Developing Countries 5.7 6.1 6.3 6.5 –0.3 0.1 . . . . . . . . .
Middle East and North Africa 4.1 2.2 3.2 4.5 –0.2 –0.5 . . . . . . . . .
World Growth Based on Market Exchange Rates 2.5 2.4 3.1 3.3 0.0 0.0 2.8 3.0 3.2
World Trade Volume (goods and services) 2.8 3.0 4.3 5.3 –0.1 0.1 . . . . . . . . .
Imports
Advanced Economies 1.1 1.4 3.5 4.5 0.1 0.3 . . . . . . . . .
Emerging Market and Developing Economies 5.8 5.6 5.2 6.3 –0.7 –0.1 . . . . . . . . .
Exports
Advanced Economies 2.1 2.3 4.2 4.8 0.2 0.1 . . . . . . . . .
Emerging Market and Developing Economies 4.2 4.4 5.0 6.2 –0.4 –0.1 . . . . . . . . .
Commodity Prices (U.S. dollars)
Oil7 1.0 –0.9 0.1 –6.0 0.4 –0.8 2.6 –2.3 –6.3
Nonfuel (average based on world commodity export weights) –10.0 –1.2 –3.5 –3.9 2.7 –1.5 –3.0 –3.2 –3.0
Consumer Prices
Advanced Economies 2.0 1.4 1.5 1.6 –0.2 –0.1 1.2 1.6 1.7
Emerging Market and Developing Economies4 6.0 5.8 5.5 5.2 –0.2 –0.1 5.3 5.1 4.7
London Interbank Offered Rate (percent)
On U.S. Dollar Deposits (six month) 0.7 0.4 0.4 0.8 0.0 0.3 . . . . . . . . .
On Euro Deposits (three month) 0.6 0.2 0.3 0.4 –0.1 –0.2 . . . . . . . . .
On Japanese Yen Deposits (six month) 0.3 0.2 0.2 0.2 0.0 0.0 . . . . . . . . .
Note: Real effective exchange rates are assumed to remain constant at the levels prevailing during January 31–February 28, 2014. When economies are not listed alphabetically,
they are ordered on the basis of economic size. The aggregated quarterly data are seasonally adjusted. Projections for Ukraine are excluded in the April 2014 WEO due to the
ongoing crisis but were included in the January 2014 WEO Update. Latvia is included in the advanced economies; in the January 2014 WEO Update, it was included in the
emerging and developing economies.
1The quarterly estimates and projections account for 90 percent of the world purchasing-power-parity weights.
2Excludes Latvia.
3Excludes the G7 (Canada, France, Germany, Italy, Japan, United Kingdom, United States) and euro area countries but includes Latvia.
4The quarterly estimates and projections account for approximately 80 percent of the emerging market and developing economies.
5For India, data and forecasts are presented on a fiscal year basis and output growth is based on GDP at market prices. Corresponding growth forecasts for GDP at factor cost
are 4.6, 5.4, and 6.4 percent for 2013, 2014, and 2015, respectively.
6Indonesia, Malaysia, Philippines, Thailand, Vietnam.
7Simple average of prices of U.K. Brent, Dubai Fateh, and West Texas Intermediate crude oil. The average price of oil in U.S. dollars a barrel was $104.07 in 2013; the assumed
price based on futures markets is $104.17 in 2014 and $97.92 in 2015.

CHAPTER 1 RECENT DEVELOPMENTS AND PROSPECTS
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2010:
H1
11:H1 12:H113:H114:H1 15:
H2
–5
0
5
10
15
20
25
2010 11 12 13 Feb.
14
Figure 1.1. Global Activity Indicators
1. World Trade, Industrial Production, and Manufacturing PMI
(three-month moving average; annualized percent change)
October 2013 WEO April 2014 WEO
Advanced Economies
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
2010:
H1
11:H112:H113:H114:H1 15:
H2
Emerging Market and
Developing Economies
4. GDP Growth
(annualized semiannual percent change)
–3
–2
–1
0
1
2
3
4
5
2012 13 Feb.
14
2. Manufacturing PMI
(deviations from 50; three-
month moving average)
–6
–3
0
3
6
9
12
15
2012 13 Jan.
14
3. Industrial Production
(three-month moving average;
annualized percent change)
Advanced economies1
Emerging market
economies2
Advanced economies1
Emerging market
economies2
Manufacturing PMI (deviations from 50)
Industrial production
World trade volumes
Sources: CPB Netherlands Bureau for Economic Policy Analysis; Haver Analytics;
Markit Economics; and IMF staff estimates.
Note: IP = industrial production; PMI = purchasing managers’ index.
1
Australia, Canada, Czech Republic, Denmark, euro area, Hong Kong SAR (IP
only), Israel, Japan, Korea, New Zealand, Norway (IP only), Singapore, Sweden (IP
only), Switzerland, Taiwan Province of China, United Kingdom, United States.
2
Argentina (IP only), Brazil, Bulgaria (IP only), Chile (IP only), China, Colombia (IP
only), Hungary, India, Indonesia, Latvia (IP only), Lithuania, Malaysia (IP only),
Mexico, Pakistan (IP only), Peru (IP only), Philippines (IP only), Poland, Romania
(IP only), Russia, South Africa, Thailand (IP only), Turkey, Ukraine (IP only),
Venezuela (IP only).
Global activity strengthened in the second half of 2013, as did world trade, but the
pickup was uneven: broad based in advanced economies, but mixed in emerging
market economies. Although export growth improved, domestic demand growth
remained mostly unchanged.
–4
–2
0
2
4
6
8
10
12
2010 11 12 13 14 15:
Q4
–2
0
2
4
6
8
10
12
14
2010 11 12 13 14 15:
Q4
Figure 1.2. GDP Growth Forecasts
(Annualized quarterly percent change)
–4
–2
0
2
4
6
8
2010 11 12 13 14 15:
Q4
–4
–2
0
2
4
6
8
–8
–4
0
4
8
12
16
2010 11 12 13 14 15:
Q4
1. United States and Japan
2. Euro Area
Source: IMF staff estimates.
3. Emerging and Developing Asia
4. Latin America and the Caribbean
United States (left scale)
Japan (right scale)
Euro area
France and Germany
Spain and Italy
Emerging and developing Asia
China
India
Latin America and the Caribbean
Brazil
Mexico
Advanced economies (left scale)
Growth in advanced economies is projected to strengthen moderately in 2014–15,
building up momentum from the gains in 2013. Growth in the United States will
remain above trend, and growth in Japan is expected to moderate, mostly as the
result of a modest ﬁscal drag. Among emerging market economies, growth is
projected to remain robust in emerging and developing Asia and to recover
somewhat in Latin America and the Caribbean.

tightening from about 1 percent of GDP in 2013
to ¼ percent of GDP is expected to help lift growth
(Figure 1.4, panel 1). Outside the core, contribu-
tions from net exports have helped the turnaround,
as has the stabilization of domestic demand.
•• However, growth in demand is expected to remain
sluggish, given continued financial fragmentation,
tight credit (see Figure 1.3, panel 2), and a high
corporate debt burden. As discussed in Box 1.1, past
credit supply shocks in some economies have not
yet fully reversed and are still weighing on credit
and growth. Credit demand is also weak, however,
because of impaired corporate balance sheets. Overall,
economic growth in the euro area is projected to reach
only 1.2 percent in 2014 and 1½ percent in 2015.
•• In Japan, some underlying growth drivers are
expected to strengthen, notably private invest-
ment and exports, given increased partner country
growth and the substantial yen depreciation over
the past 12 months or so. Nevertheless, activity
overall is projected to slow moderately in response
to a tightening fiscal policy stance in 2014–15. The
tightening is the result of a two-step increase in the
consumption tax rate—to 8 percent from 5 per-
cent in the second quarter of 2014 and then to 10
percent in the fourth quarter of 2015—and to the
unwinding of reconstruction spending and the first
stimulus package of the Abenomics program. How-
ever, at about 1 percent of GDP, the tightening of
the fiscal policy stance in 2014 will be more moder-
ate than was expected in the October 2013 WEO,
as a result of new fiscal stimulus amounting to
about 1 percent of GDP. This stimulus is projected
to lower the negative growth impact of the tighten-
ing by 0.4 percentage point to 0.3 percent of GDP
in 2014. In 2015, the negative growth effect of the
fiscal stance is projected to increase to ½ percent of
GDP. Overall, growth is projected to be 1.4 percent
in 2014 and 1.0 percent in 2015.
In emerging market and developing economies, growth
picked up slightly in the second half of 2013. The weaker
cyclical momentum in comparison with that in the
advanced economies reflects the opposite effects of two
forces on growth. On one hand, export growth increased,
lifted by stronger activity in advanced economies and
by currency depreciation. Fiscal policies are projected
to be broadly neutral (see Figure 1.4, panel 1). On the
other hand, investment weakness continued, and external
funding and domestic financial conditions increasingly
tightened. Supply-side and other structural constraints on
60
80
100
120
140
160
180
2000 02 04 06 08 10 13:
Q3
0
10
20
30
40
50
2007 08 09 10 11 12 Mar.
14
450
500
550
600
650
700
750
800
2000 02 04 06 08 10 13:
Q3
3. Household Net-Worth-to-
Income Ratio
–10
–5
0
5
10
15
20
2006 07 08 09 10 11 12 13:
Q4
2. Nonfinancial Firm and
Household Credit Growth2
(year-over-year percent
change)
5. Real House Price Indices
(2000 = 100)
6. Central Bank Total Assets
(percent of 2008 GDP)
60
70
80
90
100
110
120
130
140
2000 02 04 06 08 10 13:
Q4
4. Household Debt-to-Income
Ratio
Euro area
United States
United States
Euro
area
Fed
ECB8
BOJ
Japan
Advanced economies
experiencing upward
pressure7
United States
EA stressed economies6
Euro area4
Japan
EA core5
United States
Euro area
Japan3
Italy
Spain
Monetary conditions have remained broadly supportive in advanced economies,
but more so in the United States than in the euro area or Japan. Policy rates
remain close to the zero lower bound, but they are expected to rise beginning in
2015, especially in the United States, where household net worth and house
prices have recovered. Household debt has broadly stabilized in the euro area
relative to disposable income, and it has declined markedly in the United States.
Credit to the nonfinancial private sector in the euro area has continued to decline,
reflecting tight lending standards and weak demand.
Figure 1.3. Monetary Conditions in Advanced Economies
Sources: Bank of America/Merrill Lynch; Bank of Italy; Bank of Spain;
Bloomberg, L.P.; Haver Analytics; Organization for Economic Cooperation and
Development; and IMF staff calculations.
Note: BOJ = Bank of Japan; EA = euro area; ECB = European Central Bank;
Fed = Federal Reserve.
1
Expectations are based on the federal funds rate futures for the United States,
the sterling overnight interbank average rate for the United Kingdom, and the
euro interbank offered forward rate for Europe; updated March 26, 2014.
2
Flow-of-funds data are used for the euro area, Spain, and the United States.
Italian bank loans to Italian residents are corrected for securitizations.
3
Interpolated from annual net worth as a percent of disposable income.
4
Euro area includes subsector employers (including own-account workers).
5
Austria, France, Germany, Netherlands, Slovenia. Loans are used for the
Netherlands to calculate the ratio.
6
Greece, Ireland, Italy, Portugal, Spain.
7
Upward pressure countries: Australia, Austria, Belgium, Canada, Hong Kong
SAR, Israel, Norway, Singapore, Sweden, Switzerland.
8
ECB calculations are based on the Eurosystem’s weekly financial statement.
0.0
0.5
1.0
1.5
2.0
2.5
t t + 12 t + 24 t + 36
1. Policy Rate Expectations1
(percent; months on x-axis;
dashed lines are from the
October 2013 WEO)
United States
Europe
United Kingdom

investment and potential output (for example, infrastruc-
ture bottlenecks) are issues in some economies. These
offsetting forces are expected to remain in effect through
much of 2014. Overall, however, emerging market and
developing economies continue to contribute more than
two-thirds of global growth, and their growth is projected
to increase from 4.7 percent in 2013 to 4.9 percent in
2014 and 5.3 percent in 2015.
•• The forecast for China is that growth will remain
broadly unchanged at about 7½ percent in 2014–
15, only a modest decline from 2012–13. This
projection is predicated on the assumption that the
authorities gradually rein in rapid credit growth and
make progress in implementing their reform blue-
print so as to put the economy on a more balanced
and sustainable growth path. For India, real GDP
growth is projected to strengthen to 5.4 percent
in 2014 and 6.4 percent in 2015, assuming that
government efforts to revive investment growth suc-
ceed and export growth strengthens after the recent
rupee depreciation (Figure 1.2, panel 3; Table 1.1).
Elsewhere in emerging and developing Asia, growth
is expected to remain at 5.3 percent in 2014 because
of tighter domestic and external financial condi-
tions before rising to 5.7 percent in 2015, helped by
stronger external demand and weaker currencies.
•• Only a modest acceleration in activity is expected
for regional growth in Latin America, with growth
rising from 2½ percent in 2014 to 3 percent in
2015 (Figure 1.2, panel 4). Some economies have
recently faced strong market pressure, and tighter
financial conditions will weigh on growth. Impor-
tant differences are evident across the major econo-
mies in the region. In Mexico, growth is expected
to strengthen to 3 percent in 2014, resulting from a
more expansionary macroeconomic policy stance, a
reversal of the special factors behind low growth in
2013, and spillovers from higher U.S. growth. It is
expected to increase to 3½ percent in 2015, as the
effect of major structural reforms takes hold. Activ-
ity in Brazil remains subdued. Demand is supported
by the recent depreciation of the real and still-
buoyant wage and consumption growth, but private
investment continues to be weak, partly reflecting
low business confidence. Near-term prospects in
Argentina and Venezuela have deteriorated further.
Both economies continue to grapple with difficult
external funding conditions and the negative impact
on output from pervasive exchange and administra-
tive controls.
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Advanced
economies
excluding euro
area
Emerging market
and developing
economies
France and
Germany
Euro area stressed
economies
1
–12
–10
–8
–6
–4
–2
0
2
4
2001 04 07 10 13 16 19
0
20
40
60
80
100
120
140
160
1950 60 70 80 90 2000 10 19
Figure 1.4. Fiscal Policies
2. Fiscal Balance
(percent of GDP)
3. Public Debt
(percent of GDP)
1. Fiscal Impulse
(change in structural balance as percent of GDP)
2011 2012
2013 2014 (projection)
2015 (projection) October 2013 WEO
Advanced economies
Emerging market and
developing economies
World
Advanced economies
Emerging and developing Asia
G7
2
Other emerging market and
World
1
Greece, Ireland, Italy, Portugal, Spain.
2
The G7 comprises Canada, France, Germany, Italy, Japan, United Kingdom,
and United States.
The fiscal drag in advanced economies is expected to decline in 2014, except in
the case of Japan, and increase in 2015. This increase is largely due to the
second step in the consumption tax increase and the unwinding of fiscal stimulus
in Japan. In emerging market economies, the fiscal stance is projected to remain
broadly neutral in 2014, but it is expected to tighten in 2015, when activity will
have strengthened.

•• In sub-Saharan Africa, growth is expected to increase
from 4.9 percent in 2013 to 5½ percent in 2014–
15. Growth in South Africa is projected to improve
only modestly as the result of stronger external
demand. Commodity-related projects elsewhere in
the region are expected to support higher growth.
Currencies have depreciated substantially in some
economies.
•• In the Middle East and North Africa, regional
growth is projected to rise moderately in 2014–15.
Most of the recovery is due to the oil-exporting
economies, where high public spending contrib-
utes to buoyant non-oil activity in some economies
and oil supply difficulties are expected to be partly
alleviated in others. Many oil-importing economies
continue to struggle with difficult sociopolitical and
security conditions, which weigh on confidence and
economic activity.
•• Near-term prospects in Russia and many other econ-
omies of the Commonwealth of Independent States
have been downgraded, as growth is expected to be
hampered by the fallout from recent developments
in Russia and Ukraine and the related geopolitical
risks. Investment had already been weak, reflecting
in part policy uncertainty. In emerging and devel-
oping Europe, growth is expected to decelerate in
2014 before recovering moderately in 2015 despite
the demand recovery in western Europe, largely
reflecting changing external financial conditions and
recent policy tightening in Turkey.
•• Growth in low-income developing economies
picked up to 6 percent in 2013, driven primarily by
strong domestic demand. A further uptick to about
6½ percent is projected for 2014–15, because of
the support from the stronger recovery in advanced
economies and continued robust expansion of pri-
vate domestic demand.
Inflation Is Low
Inflation pressure is expected to stay subdued (Figure
1.5, panel 1). Activity remains substantially below
potential output in advanced economies, whereas it is
often close to or somewhat below potential in emerging
market and developing economies (Figure 1.6, panel 1).
Declines in the prices of commodities, especially
fuels and food, have been a common force behind
recent decreases in headline inflation across the globe
(Figure 1.5, panel 4). Commodity prices in U.S. dollar
80
100
120
140
160
180
200
220
240
260
2005 06 07 08 09 10 11 12 13 14 15:
Q4
–3
–2
–1
0
1
2
3
4
2009 10 11 12 13 14 15:
Q4
Figure 1.5. Global Inflation
(Year-over-year percent change, unless indicated otherwise)
–2
0
2
4
6
8
10
2005 06 07 08 09 10 11 12 13 14 15:
Q4
1. Global Aggregates: Headline Inflation
4. Commodity Prices
(index; 2005 = 100)
–3
–2
–1
0
1
2
3
4
2009 10 11 12 13 14 15:
Q4
3. GDP Deflator
Emerging market and developing economies
Advanced economies
World
United States Japan1
Euro area2
2. Headline Inflation (dashed
lines are the six- to ten-year
inflation expectations)
United States
Euro area
2
Japan
Food
Energy
Metal
Sources: Consensus Economics; Haver Analytics; IMF, Primary Commodity Price
System; and IMF staff estimates.
1
In Japan, the increase in inflation in 2014 reflects, to a large extent, the increase
in the consumption tax.
2
Excludes Latvia.
Inflation is generally projected to remain subdued in 2014–15 with continued
sizable negative output gaps in advanced economies, weaker domestic demand in
several emerging market economies, and falling commodity prices. In the euro
area and the United States, headline inflation is expected to remain below
longer-term inflation expectations, which could lead to adjustments in expectations
and risks of higher debt burdens and real interest rates.

terms are projected to ease a bit further in 2014–15,
partly reflecting the path implied by commodity futures
prices. As discussed in the Commodity Special Feature,
however, for the specific case of oil prices, forecasts differ
depending on the underlying approach. That said, dif-
ferent forecasting models currently predict flat to falling
oil prices, although the range of uncertainty around
commodity price forecasts generally is large. Even so,
the broader commodity market picture is one in which
supply shifts for many commodities are expected to
more than offset the price effects of the projected
strengthening in global activity. The supply shifts are
most prominent for some food commodities and crude
oil. The lower growth anticipated in China is unlikely to
result in declines in that country’s commodity consump-
tion, which should continue to increase with per capita
income levels projected over the WEO forecast horizon.
However, the growth and composition of commodity
consumption in China should change as the country’s
economy rebalances from investment to more consump-
tion-driven growth (see Box 1.2).
In advanced economies, inflation is currently run-
ning below target and below longer-term inflation
expectations, at about 1½ percent on average (Fig-
ure 1.5, panel 1). The return to target is projected to
be gradual, given that output is expected to return to
potential only slowly (Figure 1.5, panels 2 and 3; Table
A8 in the Statistical Appendix).
•• In the United States, all relevant inflation measures
decreased in the course of 2013, with core inflation
running at rates of less than 1½ percent, notwithstand-
ing continued declines in the unemployment rate. The
lower unemployment rates partly reflect reductions in
labor force participation due to demographic trends as
well as discouraged workers dropping out of the labor
force. A portion of the decline in labor force participa-
tion is expected to be reversed, because some of these
workers are likely to seek employment as labor market
conditions improve. In addition, the long-term unem-
ployment rate remains high compared with historical
standards. As a result, wage growth is expected to be
sluggish even as unemployment declines toward the
natural rate in 2014–15.
•• In the euro area, inflation has steadily declined since
late 2011. Both headline and underlying inflation
have fallen below 1 percent since the fourth quarter
in 2013. Several economies with particularly high
unemployment have seen either inflation close to zero
or outright deflation during the same period. For
Figure 1.6. Capacity, Unemployment, and Output Trend
–18
–15
–12
–9
–6
–3
0
3
6
Advanced
economies
EMDE EDE CIS DA LAC Sub-
Saharan
Africa
1. Output Relative to Precrisis Trends in WEO Estimates in 20141
(percent of potential or precrisis trend GDP)
3. Contribution to Reduction in Emerging Market and Developing Economy
Medium-Term Output4
(percent)
–10
–8
–6
–4
–2
0
2012 13 14 15 16 17 18
Rest EMDE ZA
BR RU
CN IN
EMDE
WEO output gap in 2014
2
4
6
8
10
12
14
Euro area3
Japan US CIS DA EDE LAC MENA
2. Unemployment Rates2
(percent)
2007
2011
2013
Sources: Haver Analytics; IMF, International Financial Statistics; and IMF staff
estimates.
Note: BR = Brazil; BRICS = Brazil, Russia, India, China, South Africa; CIS =
Commonwealth of Independent States; CN = China; DA = developing Asia; EDE =
emerging and developing Europe; EMDE = emerging market and developing
market economies; IN = India; LAC = Latin America and the Caribbean; MENA =
Middle East and North Africa; RU = Russia; US = United States; ZA = South
Africa.
1
Precrisis trend is deﬁned as the geometric average of real GDP level growth
between 1996 and 2006.
2
Sub-Saharan Africa is omitted because of data limitations.
3
Excludes Latvia.
4
Relative to the September 2011 WEO; 2017 and 2018 output ﬁgures for the
September 2011 WEO are extrapolated using 2016 growth rates.
Output in emerging and developing Asia, Latin America, and sub-Saharan
Africa remains above precrisis trend, but WEO output gaps do not indicate
output above capacity. Despite slowing economic growth, unemployment rates
have continued to decline slightly in emerging Asia and Latin America. The IMF
staff has revised down its estimates of medium-term output, responding to
disappointments in the recent past. Sizable revisions to output in the so-called
BRICs economies account for most of the downward revisions to emerging
market and developing economies as a group.

2013 as a whole, inflation was 1.3 percent, which is
closer to the lower end of the range forecast provided
by the European Central Bank (ECB) staff at the end
of 2012 and below the lowest value provided by Con-
sensus Forecast survey participants at the time. Infla-
tion is projected to increase slightly as the recovery
strengthens and output gaps slowly decrease. Under
the current baseline projections, inflation is expected
to remain below the ECB’s price stability objective
until at least 2016.
•• In Japan, inflation started to increase with stronger
growth and the depreciation of the yen during the
past year or so. In 2014–15, it is projected to accel-
erate temporarily in response to increases in the con-
sumption tax. Indications are, however, that labor
market conditions have started to tighten. Nominal
wages have also begun to increase, and underlying
inflation is projected to converge gradually toward
the 2 percent target.
In emerging market and developing economies,
inflation is expected to decline from about 6 percent cur-
rently to about 5¼ percent by 2015 (Figure 1.5, panel
1). Softer world commodity prices in U.S. dollar terms
should help reduce price pressures, although in some
economies, this reduction will be more than offset by
recent exchange rate depreciation. In addition, activity-
related price pressures will ease with the recent growth
declines in many emerging market economies. That
said, this relief will be limited in some emerging market
economies, given evidence of domestic demand pressures
and capacity constraints in some sectors (red and yellow
overheating indicators in Figure 1.7). This picture is
consistent with output remaining above crisis trend and
unemployment having declined further in a number of
emerging market economies (Figure 1.6, panels 1 and 2).
In low-income developing economies, softer com-
modity prices and careful monetary policy tightening
have helped lower inflation from about 9.8 percent in
2012 to 7.8 percent in 2013. Based on current poli-
cies, inflation is expected to decline further to about
6½ percent.
Monetary Policy, Financial Conditions, and Capital Flows
Are Diverging
Monetary conditions have stayed mostly supportive in
advanced economies despite lasting increases in longer-
term interest rates since May 2013, when the Federal
Reserve announced its intention to begin tapering its
asset purchase program (Figure 1.8, panels 2 and 5).
However, longer-term rates are still lower than rates
that would prevail if the term premium had reversed
to precrisis levels, and broad financial conditions have
remained easy—equity markets have rallied and bond
risk spreads remain low (Figure 1.8, panel 3).
Monetary policy stances across advanced economies
are, however, expected to start diverging in 2014–15.
•• Surveys of market participants (such as the Federal
Reserve Bank of New York’s January 2014 Survey
of Primary Dealers) suggest that the policy rate is
expected to increase in the United States in the
second half of 2015. Information based on futures
prices, however, implies that the timing has been
advanced to the first half of 2015 (Figure 1.8,
panel 1). The WEO projections are in line with the
Federal Reserve’s forward guidance for a continued
growth-friendly policy stance and assume that the
first U.S. policy rate hike will take place in the third
quarter of 2015. The projections take into account
that inflation is forecast to remain low, inflation
expectations to stay well anchored, and the unem-
ployment rate to continue its slow decline until
then. The forecasts also assume that the Federal
Reserve will continue tapering asset purchases at the
current pace over the next few months and that the
program will end by late 2014.
•• Markets continue to expect a prolonged period of
low interest rates and supportive monetary policy
for the euro area and Japan (Figure 1.3, panel 1).
Unlike in Europe, Japanese long-term bond yields
have remained virtually unchanged since taper-
ing talk began, reflecting both strong demand for
bonds by nonresidents and residents and the Bank
of Japan’s asset purchases. In the euro area, low
inflation remains the dominant concern, including
deflation pressure in some countries, amid a weak
recovery. The WEO projections assume further
small declines in sovereign spreads in countries
with high debt, consistent with views that sovereign
risks have decreased. The projections also assume,
however, that financial fragmentation will remain
a problem for the transmission of monetary policy
impulses in the euro area. Credit conditions will
thus remain tight, and credit outstanding will
continue to decline for some time, albeit at a slower
pace (Figure 1.3, panel 2). The major contributing
factors are remaining weaknesses in bank balance
sheets and, more generally, the weak economic
environment aggravated by high unemployment and
large debt burdens.

Output
relative
to trend1
Output
gap
Unem-
ployment Inflation2
Summary
Terms
of trade
Capital
inflows3
Current
account Summary
Credit
growth4
House
price4
Share
price4
Summary
Fiscal
Balance5
Real
Interest
Rate 6
Advanced Economies
Japan
Germany
United States
Australia
Canada
France
United Kingdom
Italy
Korea
Emerging Market and Developing Economies
India
Brazil
Indonesia
Argentina7
Saudi Arabia
Turkey
China
Russia
Mexico
South Africa
Greater than or equal to 0.5 but
less than 1.5 standard deviations
Less than 0.5 standard
deviation
Greater than or equal to 1.5 standard
deviations
Financial
2014 estimates above the 1997–2006 average, except as noted below, by
Domestic External
Figure 1.7. Overheating Indicators for the Group of Twenty Economies
Sources: Australian Bureau of Statistics; Bank for International Settlements; CEIC China Database; Global Property Guide; Haver Analytics; IMF, Balance of
Payments Statistics database; IMF, International Financial Statistics database; National Bureau of Statistics of China; Organization for Economic Cooperation and
Development; and IMF staff estimates.
Note: For each indicator, except as noted below, economies are assigned colors based on projected 2014 values relative to their precrisis (1997–2006) average.
Each indicator is scored as red = 2, yellow = 1, and blue = 0; summary scores are calculated as the sum of selected component scores divided by the maximum
possible sum of those scores. Summary blocks are assigned red if the summary score is greater than or equal to 0.66, yellow if greater than or equal to 0.33 but
less than 0.66, and blue if less than 0.33. When data are missing, no color is assigned. Arrows up (down) indicate hotter (colder) conditions compared with the
October 2013 WEO.
1
Output more than 2.5 percent above the precrisis trend is indicated by red. Output more than 2.5 percent below the trend is indicated by blue. Output within
±2.5 percent of the precrisis trend is indicated by yellow.
2
The following scoring methodology is used for the following inflation-targeting economies: Australia, Brazil, Canada, Indonesia, Korea, Mexico, South Africa,
Turkey, and United Kingdom. End-of-period inflation above the country’s target inflation band from the midpoint is assigned yellow; end-of-period inflation more
than two times the inflation band from the midpoint is assigned red. For all other economies in the chart, red is assigned if end-of-period inflation is approxi-
mately 10 percent or higher, yellow if it is approximately 5 to 9 percent, and blue if it is less than approximately 5 percent.
3
Capital inflows refer to the latest available value relative to the 1997–2006 average of capital inflows as a percent of GDP.
4
The indicators for credit growth, house price growth, and share price growth refer to the annual percent change relative to output growth.
5
Arrows in the fiscal balance column represent the forecast change in the structural balance as a percent of GDP over the period 2013–14. An improvement of
more than 0.5 percent of GDP is indicated by an up arrow; a deterioration of more than 0.5 percent of GDP is indicated by a down arrow. A change in fiscal
balance between –0.5 percent of GDP and 0.5 percent of GDP is indicated by a sideways arrow.
6
Real policy interest rates below 0 percent are identified by a down arrow; real interest rates above 3 percent are identified by an up arrow; real interest rates
between 0 and 3 percent are identified by a sideways arrow. Real policy interest rates are deflated by two-year-ahead inflation projections.
7
Calculations are based on Argentina’s official GDP and consumer price index data. See note 5 to Statistical Appendix Table A4 and note 6 to Table A7.
prices in many advanced economies and rising house prices in Germany and
the United States. In emerging market economies, the indicators reflect
continued vulnerabilities from rapid credit growth; developments in other
markets are broadly within historical bounds.
Most indicators point to continued excessive cyclical slack in advanced
economies. In major emerging market economies, some indicators suggest
that capacity constraints are still present, notwithstanding the recent
slowdown in growth. For a number of emerging market economies, indicators
point to continued external vulnerabilities. Financial indicators flag high equity

In emerging market economies, there has been
a tightening of monetary and financial conditions
since May 2013. This is the combined result of
spillovers from rising bond rates and better prospects
in advanced economies, markets’ reassessment of
medium-term growth prospects, and greater investor
concerns about vulnerabilities. Rates on longer-term
local currency bonds in emerging market economies
have risen more than those in advanced economies,
consistent with past patterns—namely, that emerg-
ing market risk is repriced when advanced economy
rates increase (Figure 1.9, panel 2). Equity prices
have moved sideways in local currency, whereas in
U.S. dollar terms—the benchmark for international
investors—they have declined substantially as a result
of widespread currency depreciation. Still, the pass-
through from higher local currency bond yields to
lending rates has often been limited, credit growth has
remained relatively high (Figure 1.10, panels 2 and
3), and the depreciation of nominal exchange rates
against the U.S. dollar and other major currencies has
provided some offset (Figure 1.11, panel 2). Specific
market developments are discussed in more detail in
the April 2014 Global Financial Stability Report.
Despite some retrenchment in capital inflows
since the Federal Reserve’s surprise tapering-related
announcement in May 2013, developments to date
do not portend a sustained reversal of capital flows. In
fact, capital inflows recovered moderately in the latter
part of 2013 from the lows reached in summer 2013
(Figure 1.9, panels 5 and 6). However, they are esti-
mated to have remained below pretapering levels.
The WEO baseline projections assume that capital
inflows to emerging market economies will remain
lower in 2014 than they were in 2013, before recover-
ing modestly in 2015. The projections also assume that
the additional repricing of bonds and equities in some
emerging market economies since October 2013 was
largely a one-off increase in risk premiums on emerg-
ing market economies’ assets. Much of the recent yield
increases and asset price declines will thus be lasting.
This constitutes a broad-based tightening in financial
conditions, which is expected to dampen domestic
demand growth and is one of the main factors con-
tributing to the projected lower growth in emerging
market economies in 2014–15 compared with the
October 2013 WEO (see Table 1.1). The analysis in
Chapter 4 highlights that if the tightening in external
financial conditions for emerging market economies
Figure 1.8. Financial Market Conditions in Advanced
Economies
0
5
10
15
20
25
May
2007
May
08
May
09
May
10
May
11
May
12
Mar.
14
4. Price-to-Earnings Ratios3
0
20
40
60
80
100
120
140
160
2000 02 04 06 08 10 12Mar.
14
3. Equity Markets
(2007 = 100; national currency)
June 29, 2012
0
100
200
300
400
500
2008 09 10 11 12 Feb.
14
6. ECB Gross Claims on
Spanish and Italian Banks
(billions of euros)
0
1
2
3
4
5
6
7
8
9
10
2007 08 09 10 11 12 Mar.
14
5. Government Bond Yields4
(percent)
June 29, 2012
MSCI Emerging Market
SP 500
DJ Euro Stoxx
TOPIX
U.S.
Japan
Germany
Italy
Spain
France
0
1
2
3
4
5
6
7
8
9
2007 08 09 10 11 12 Mar.
14
2. Key Interest Rates2
(percent)
June 29,
2012
Spain
Italy
Japan
U.S.
May 22, 2013
May 22, 2013
May 22,
2013
May 22, 2013
0.0
0.5
1.0
1.5
2.0
2.5
t t + 12 t + 24 t + 36
1. U.S. Policy Rate Expectations1
(percent; months on x-axis)
May 21, 2013
June 21, 2013
September 20, 2013
March 26, 2014
U.S. average
30-year ﬁxed-
rate mortgage
Germany
Italy
Longer-term U.S. interest rates rose immediately after the May 2013 tapering-
related announcement by the Federal Reserve but have broadly stabilized since.
Rates in the core euro area economies and Japan have increased by a fraction.
Equity markets have been buoyant, with price-to-earnings ratios back to precrisis
levels. Spreads on Italian and Spanish bonds have continued to decrease.
Sources: Bloomberg, L.P.; Capital Data; Financial Times; Haver Analytics;
national central banks; Thomson Reuters Datastream; and IMF staff
calculations.
Note: DJ = Dow Jones; ECB = European Central Bank; MSCI = Morgan
Stanley Capital International; SP = Standard Poor’s; TOPIX = Tokyo
Stock Price Index.
1
Expectations are based on the federal funds rate futures for the United
States; updated March 26, 2014.
2
Interest rates are 10-year government bond yields, unless noted otherwise.
3
Some observations for Japan are interpolated because of missing data.
4
Ten-year government bond yields.

2
5
8
11
14
17
2007 08 09 10 11 12 13 Mar.
14
4
5
6
7
8
9
10
11
12
13
2007 08 09 10 11 12 Feb.
14
1. Policy Rate
(percent)
4. Equity Markets
(2007 = 100)
0
100
200
300
400
500
600
700
800
900
2007 08 09 10 11 12 Mar.
14
3. EMBI Sovereign Spreads
(basis points)
Emerging Asia
excluding China
Emerging Europe
Latin America
China
5. Net Flows in Emerging Market
Funds (billions of U.S. dollars)
2. Ten-Year Government Bond Yields
(percent)
6. Capital Inflows Based on
Balance of Payments
(percent of GDP)
Emerging Asia
excluding China
Emerging Europe
Latin America
China
40
60
80
100
120
140
160
2007 08 09 10 11 12 Mar.
14
Emerging Asia
excluding China
Emerging Europe
–15
–10
–5
0
5
10
15
2007 08 09 10 11 12 13:
Q3
Emerging Europe
Emerging Asia
excluding China
Emerging
Europe
Emerging Asia
excluding China
China Latin America
Latin America
China
June 29,
2012
May 22,
2013
Greek
crisis
Irish
crisis
1st ECB
LTROs
Bond
Equity
VXY
China
Latin America
–30
–20
–10
0
10
20
30
2010:
H1
10:
H2
11:
H1
11:
H2
12:
H1
12:
H2
13:
H1
Mar.
14
Sources: Bloomberg, L.P.; EPFR Global; Haver Analytics; IMF, International
Financial Statistics; and IMF staff calculations.
Note: ECB = European Central Bank; EMBI = J.P. Morgan Emerging Markets
Bond Index; LTROs = longer-term refinancing operations; VXY = J.P. Morgan
Emerging Market Volatility Index; emerging Asia excluding China includes India,
Indonesia, Malaysia, Philippines, Thailand; emerging Europe comprises Poland,
Russia, Turkey; Latin America includes Brazil, Chile, Colombia, Mexico, Peru.
Financial conditions in emerging market economies have tightened recently in
response to a more difficult external financial environment. Bond rates and
spreads have increased, and equity markets have moved sideways. Gross capital
inflows have declined, and exchange rates have depreciated. Overall, the cost of
capital in emerging market economies has increased, which will dampen
investment and growth, although increased exports to advanced economies are
expected to provide some offset.
Figure 1.9. Financial Conditions and Capital Flows in Emerging
Market Economies
–3
–2
–1
0
1
2
3
4
5
6
BRA CHL CHN COL IND IDN KOR MEXMYS PER PHL POL RUS THA TUR ZAF
Figure 1.10. Monetary Policies and Credit in Emerging Market
Economies
April 2013 April 2013 average
Latest (February 2014) February 2014 average
1. Real Policy Rates1
(percent; deflated by two-year-ahead WEO inflation projections)
–10
0
10
20
30
40
2009 10 11 12 Dec.
13
IND BRA
CHN HKG
MEX
Real Credit Growth
(year-over-year percent change)
2.
–10
0
10
20
30
40
2009 10 11 12 Dec.
13
3.
IDN
MYS
TUR
80
100
120
140
160
180
200
220
240
10
15
20
25
2006 08 10 12 13:
Q4
20
30
40
50
60
70
2006 08 10 12 13:
Q4
IND BRA
TUR IDN
COL RUS
Bank Credit to GDP
(percent)
MEX (right scale)
HKG
CHN
MYS
4. 5.
COL
RUS
Monetary conditions have tightened in many emerging market economies,
reflecting changes in external funding, but also policy rate increases in some
economies (including Brazil, Indonesia, South Africa, and Turkey); however, real
policy rates remain negative in some emerging markets, in some cases because
of high inflation. Bank credit growth has started to slow in many economies, but
remains at double-digit rates in some, exceeding GDP growth by substantial
margins. Economy-wide leverage continues to rise rapidly, and ratios of bank
credit to GDP have doubled in some economies during the past seven years.
calculations.
Note: BRA = Brazil; CHL = Chile; CHN = China; COL = Colombia; HKG = Hong
Kong SAR; IDN = Indonesia; IND = India; KOR = Korea; MEX = Mexico; MYS =
Malaysia; PER = Peru; PHL = Philippines; POL = Poland; RUS = Russia; THA =
Thailand; TUR = Turkey; ZAF = South Africa.
1
Bank of Indonesia rate for Indonesia; the Central Bank of the Republic of Turkey’s
effective marginal funding cost estimated by the IMF staff for Turkey.

were limited to the higher advanced economy interest
rates associated with faster growth in these economies,
the growth spillovers would be positive. With concur-
rent tightening in other financial conditions, however,
such as risk premiums on emerging market sovereign
debt, the net spillover effects can turn negative.
The External Sector Perspective
Global trade volume growth slowed substantially in the
adjustment after the global financial crisis of 2007–09
and the euro area crisis of 2011–12 (Figure 1.12, pan-
els 1 and 2). This slowing has fueled questions about
whether international trade will remain an engine
of global growth, which are motivated by concerns
about stalling or declining globalization (for example,
because productivity gains from recent trade liberaliza-
tion under the World Trade Organization umbrella are
diminishing). However, data on world trade growth
since 2008 seem to be in line with global output and
investment growth. Moreover, recent forecast errors for
world trade growth are strongly and positively corre-
lated with those for global GDP growth, as in the past.
These factors suggest that the recent trade weakness
has simply mirrored stronger-than-expected declines in
growth across the globe. Indeed, world trade growth
picked up strongly with the strengthening in global
activity in the second half of 2013.
Global current account imbalances narrowed further
in 2013. The narrowing was partly driven by external
adjustment in stressed economies in the euro area—
which increasingly reflects not only import compres-
sion, but also some adjustment in relative prices and
rising exports—although balances in euro area surplus
economies did not decline materially. The narrowing
also reflects larger energy imports in Japan since the
2011 earthquake and tsunami, a decline in net energy
imports in the United States, and a combination of
falling oil export revenues and increased expenditures
in fuel exporters. A modest further narrowing of
imbalances is projected for the medium term, resulting
mostly from lower surpluses of oil exporters (Fig-
ure 1.12, panel 5).
Exchange rate adjustments during the past year or so
have been broadly consistent with a further correction
of external imbalances. Based on the currency assess-
ments in the 2013 Pilot External Sector Report (IMF,
2013b), undervalued currencies, defined by a negative
real effective exchange rate gap in mid-2012, generally
appreciated in real effective terms in 2013, and overval-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
Jul.
2007
Jul. 08 Jul. 09 Jul. 10 Jul. 11 Jul. 12 Jul.
13
Feb.
14
3. International Reserves
(index, 2000 = 100; three-month moving average)
Developing Asia
Middle East and North Africa
Emerging Europe
–20
–10
0
10
20
30
DEU
MYS
CHE
SWE
KOR
NLD
CHN
THA
EA
BEL
MEX
POL
RUS
IND
IDN
ITA
USA
GBR
AUS
FRA
CAN
BRA
TUR
ZAF
ESP
Change in REER between June 2012 and February 2014
REER gap for 2012 (midpoint)
1. Real Effective Exchange Rates1
(percent)
–20
–15
–10
–5
0
5
10
Sur. Def. Aln. MYS CHN EA JPN RUS IND IDN BRA TUR ZAF
2. Nominal Exchange Rates1,2
(percent change from May 22, 2013, to March 21, 2014)
Percent change
from Dec. 18,
2013, to Mar. 21, 2014
Currencies of many major emerging market economies have depreciated against
the U.S. dollar, reflecting a weakening of those economies’ medium-term growth
outlooks vis-à-vis that of advanced economies and tighter external financial
conditions. The broader picture based on the currency assessments in the 2013
Pilot External Sector Report (IMF, 2013b) is that undervalued currencies generally
appreciated in real effective terms in 2013, whereas overvalued currencies
depreciated. The pace of reserve accumulation in emerging market and
developing economies slowed in 2013, reflecting lower capital inflows and
reserve losses from foreign exchange intervention.
Figure 1.11. Exchange Rates and Reserves
Sources: Global Insight; IMF, International Financial Statistics; and IMF staff
calculations.
Note: Aln. = aligned emerging market economies; AUS = Australia; BEL =
Belgium; BRA = Brazil; CAN = Canada; CHE = Switzerland; CHN = China;
Def. = deficit emerging market economies; DEU = Germany; EA = euro
area; ESP = Spain; FRA = France; GBR = United Kingdom; IDN =
Indonesia; IND = India; ITA = Italy; JPN = Japan; KOR = Korea; MEX =
Mexico; MYS = Malaysia; NLD = Netherlands; POL = Poland; REER = real
effective exchange rate; RUS = Russia; Sur. = surplus emerging market
economies; SWE = Sweden; THA = Thailand; TUR = Turkey; USA = United
States; ZAF = South Africa.
1
REER gaps and classifications are based on IMF (2013b).
2
U.S. dollars per national currency.

ued currencies depreciated (Figure 1.11, panel 1). The
main exceptions to this pattern were some advanced
economies affected by safe haven flows (for example, the
United Kingdom) or by capital inflows due to decreases
in perceived sovereign risks (euro area), which saw fur-
ther appreciation of their currencies.
Although exchange rate adjustments have generally
been consistent with corrections of external imbal-
ances, there are conflicting signals for current account
balances. In a number of emerging market economies
in particular, current account deficits increased further
from the underlying norm in 2013 rather than nar-
rowing, despite real exchange rate adjustment in the
correct direction. This deficit widening may be simply
due to delays in the trade and current account response
(the so-called J-curve effects) and lower commodity
prices; it may also indicate that further policy measures
are needed to correct imbalances.
Downside Risks
The balance of risks to WEO projections for global
growth has improved, largely reflecting improving
prospects in the advanced economies. Important
downside risks remain, however, especially for emerg-
ing market economies, for which risks have increased.
A Quantitative Risk Assessment: Uncertainty Has
Narrowed
The fan chart for the global real GDP forecast through
2015 suggests a slightly narrower uncertainty band
around the WEO projections than in the October
2013 WEO (Figure 1.13, panel 1). For 2014, this nar-
rowing reflects primarily the shorter time horizon to
the end of 2014 (“lower baseline uncertainty,” because
there is less uncertainty given that more data affecting
2014 outcomes are known already). The probability
of global growth falling below the 2 percent recession
threshold in 2014 is now estimated to be 0.1 percent,
down from 6 percent in October 2013. For 2015, the
same probability is 2.9 percent, which is appreciably
lower for the next-year forecasts compared with values
in April 2012 and 2013.
The risk of a recession has fallen noticeably in the
major advanced economies while it has remained
broadly unchanged in other economies (Figure 1.14,
panel 1). Specifically, compared with simulations
performed for the October 2013 WEO, the IMF staff’s
Global Projection Model shows a decline in the prob-
Figure 1.12. External Sector
–5
–4
–3
–2
–1
0
1
2
3
4
2000 02 04 06 08 10 12 14 16 18
5. Global Imbalances
(percent of world GDP)
Discrepancy
US OIL
DEU+JPN OCADC
CHN+EMA ROW
3. Current Account Changes
(percent of GDP; 2007 on x-axis
vs. 2013 on y-axis)
–15
–10
–5
0
5
10
15
20
25
–15 –5 5 15 25
AE
EMDE
4. Gross Capital Inflows
(percent of GDP; 2007 on
x-axis vs. 2013 on y-axis)
–5
0
5
10
15
20
25
30
–5 0 5 10 15 20 25 30
AE
EMDE
–15
–10
–5
0
5
10
15
–5.0 –2.5 0.0 2.5 5.0
Changeintradevolumegrowth
Change in GDP growth
1. World Trade Volume and
Global GDP, 1991–2013;
Current WEO
(percent)
y = 3.92x – 0.14
R² = 0.89
2012
2013
2011
–15
–10
–5
0
5
10
15
–5.0 –2.5 0.0 2.5 5.0
Worldtradegrowth
GDP growth
2. WEO Forecast Error Correlation,
1991–2013; April, Next-Year
Forecasts; Current WEO
(percent)
y = 3.51x + 0.62
R² = 0.89
2011
2012
2013
estimates.
Note: AE = advanced economies; CHN+EMA = China, Hong Kong SAR, Indonesia,
Korea, Malaysia, Philippines, Singapore, Taiwan Province of China, Thailand; DEU
+JPN = Germany and Japan; EMDE = emerging market and developing
economies; OCADC = Bulgaria, Croatia, Czech Republic, Estonia, Greece, Hungary,
Ireland, Latvia, Lithuania, Poland, Portugal, Romania, Slovak Republic, Slovenia,
Spain, Turkey, United Kingdom; OIL = oil exporters; ROW = rest of the world; US =
United States.
Global trade volumes rebounded with the strengthening in global activity in the
second half of 2013. The earlier weakening in global trade was broadly consistent
with the slowdown in activity, highlighting the high short-term income elasticities
of exports and imports. Current account balances of most emerging market
economies have declined since the global financial crisis and a few among them
now have excessive deficits.

0
20
40
60
80
100
120
140
0.10
0.15
0.20
0.25
0.30
0.35
0.40
2006 08 10 12 Feb.
14
1
2
3
4
5
6
2010 11 12 13 14 15
–1.2
–0.8
–0.4
0.0
0.4
0.8
1.2
1.6
2.0
Term spread SP 500 Inflation risk Oil price risks
10
20
30
40
50
60
70
80
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2006 08 10 12 Feb.
14
1. Prospects for World GDP Growth1
(percent change)
90 percent confidence interval
90 percent bands from October 2013 WEO
90 percent bands from April 2013 WEO
2. Balance of Risks Associated with Selected Risk Factors2
(coefficient of skewness expressed in units of the underlying
variables)
2014 (October 2013 WEO)
2014 (current WEO)
2015 (current WEO)
Balance of risks for
Dispersion of Forecasts and Implied Volatility3
3. 4.
2000–present
average
2000–present
average
GDP
(right scale)
VIX
(left scale)
Term spread (right
scale)
Oil
4
(left scale)
WEO baseline
The fan chart, which indicates the degree of uncertainty about the global growth
outlook, has narrowed vis-à-vis that in the October 2013 WEO. This suggests a
slightly more benign balance of risks for the global outlook; however, downside
risks remain a concern. Measures of forecast dispersion and implied volatility for
equity and oil prices also suggest a decline in perceived uncertainty about key
variables for the global outlook.
Figure 1.13. Risks to the Global Outlook
Sources: Bloomberg, L.P.; Chicago Board Options Exchange (CBOE); Consensus
Economics; and IMF staff estimates.
1
The fan chart shows the uncertainty around the WEO central forecast with 50,
70, and 90 percent confidence intervals. As shown, the 70 percent confidence
interval includes the 50 percent interval, and the 90 percent confidence interval
includes the 50 and 70 percent intervals. See Appendix 1.2 of the April 2009 WEO
for details. The 90 percent bands for the current-year and one-year-ahead
forecasts from the April 2013 and October 2013 WEO reports are shown relative
to the current baseline.
2
Bars depict the coefficient of skewness expressed in units of the underlying
variables. The values for inflation risks and oil price risks enter with the opposite
sign since they represent downside risks to growth. Note that the risks associated
with the Standard Poor's (SP) 500 for 2014 and 2015 are based on options
contracts for December 2014 and December 2015, respectively.
3
GDP measures the purchasing-power-parity-weighted average dispersion of GDP
forecasts for the G7 economies (Canada, France, Germany, Italy, Japan, United
Kingdom, United States), Brazil, China, India, and Mexico. VIX = Chicago Board
Options Exchange SP 500 Implied Volatility Index. Term spread measures the
average dispersion of term spreads implicit in interest rate forecasts for Germany,
Japan, United Kingdom, and United States. Forecasts are from Consensus
Economics surveys.
4
CBOE crude oil volatility index.
World Greece
Ireland Spain
0
5
10
15
20
25
30
35
United
States
Euro area Japan Emerging
Asia
Latin
America
Remaining
economies
Figure 1.14. Recession and Deflation Risks
0
5
10
15
20
25
30
United
States
Euro area Japan Emerging
Asia
Latin
America
Remaining
economies
0.0
0.2
0.4
0.6
0.8
1.0
2003 05 07 09 11 13 14:
Q4
1. Probability of Recession, 2013:Q4–2014:Q31
(percent)
2. Probability of Deflation, 2014:Q41
(percent)
3. Deflation Vulnerability Index2
High risk
Moderate risk
Low risk
October 2013 WEO:
2013:Q2–2014:Q1
October 2013 WEO
1
Emerging Asia = China, Hong Kong SAR, India, Indonesia, Korea, Malaysia,
Philippines, Singapore, Taiwan Province of China, Thailand; Latin America = Brazil,
Chile, Colombia, Mexico, Peru; Remaining economies = Argentina, Australia,
Bulgaria, Canada, Czech Republic, Denmark, Estonia, Israel, New Zealand,
Norway, Russia, South Africa, Sweden, Switzerland, Turkey, United Kingdom,
Venezuela.
2
For details on the construction of this indicator, see Kumar (2003) and Decressin
and Laxton (2009). The indicator is expanded to include house prices.
The IMF staff’s Global Projection Model suggests that recession risks have
decreased slightly for the major economies and have remained broadly unchanged
for other economies. The probability of a recession for the euro area remains high,
highlighting the fragility of the weak recovery. The risk of deflation also remains
relatively high in the euro area, where it is still about 20 percent, whereas it is
virtually negligible for other economies.

ability of a recession (two successive quarters of nega-
tive growth) in the four quarters ahead. Nevertheless,
recession risks of about 20 percent in the euro area and
Japan—which partly reflect the relatively low growth
projected for these economies—and in the Rest of
the World group highlight that a number of fragilities
remain present in the global recovery.
In most economies, the risk of deflation by the end
of 2014 is virtually negligible, according to the Global
Projection Model simulations. In the euro area, however,
the risk of deflation—estimated at about 20 percent—
remains a concern despite some recent declines (Figure
1.14, panel 2).1 Similarly, broad indicators of deflation
vulnerability, which measure the risk of more persistent
price level declines, remain above or close to the high-risk
threshold for some euro area economies, notwithstanding
recent improvements (Figure 1.14, panel 3). In Japan, the
absence of near-term deflation risks reflects primarily the
price-level effects of the increase in the consumption tax
rate to 8 percent in the second quarter of 2014 from the
previous 5 percent.
A Qualitative Risk Assessment: Some Risks Remain and
New Ones Have Emerged
Some downside risks identified in the October 2013
WEO have become less relevant, notably shorter-term
U.S. fiscal risks because of the two-year budget agree-
ment of December 2013 and the suspension of the
debt ceiling until March 2015. The other risks, how-
ever, remain a concern; new ones have emerged; and
the risks related to emerging market economies have
increased. More recently, developments in Ukraine
have increased geopolitical risks. At the same time,
however, upside risks to growth in some advanced
economies have developed, improving the balance of
risks compared with the October 2013 WEO.
1The probability of deflation increases with a longer forecast
horizon, everything else equal. A longer horizon in this WEO report
compared with the October 2013 WEO (three quarters ahead vs.
one quarter ahead) is an important reason for a higher probability of
deflation in the euro area in panel 2 of Figure 1.14. The comparable
one-quarter-ahead probability for the second quarter of 2014 in this
WEO report would be 9 percent, compared to 15 percent in Octo-
ber. While deflation risks have decreased, the estimated probability
of euro area inflation being above the ECB’s price stability target is
only 28 percent in the fourth quarter of 2015 and 42 percent in the
fourth quarter of 2016 (probabilities calculated as inflation exceeding
1.9 percent).
Advanced economy risks
•• Risks to activity from low inflation: With current
inflation lower than expected in many advanced
economies, there is a risk, albeit a declining one, of
treading into deflation in the event of adverse shocks
to activity. In addition, if inflation stays below target
for an extended period, as it would under the baseline
forecasts, longer-term inflation expectations are likely
to drift down. The main reason to be concerned
about an adverse impact on activity and debt burdens
is that monetary policy will likely be constrained in
lowering nominal interest rates for some time, given
that policy-relevant rates are already close to the zero
lower bound. This risk is primarily a concern in the
euro area and, to a lesser extent, in Japan. In the
euro area, risks are that inflation could undershoot
the ECB’s price stability target by more or for longer
than under the baseline forecasts, given the very high
unemployment and slack in many economies. In
Japan, the issues are entrenched expectations after
a long period of deflation and the ongoing shifts in
employment from regular, full-time positions to non-
regular, part-time positions, which hinder nominal
wage adjustment in response to the Bank of Japan’s
new 2 percent inflation target. More generally, if there
were to be a persistent decline in commodity prices,
possibly because of a larger-than-expected supply
response to recent high prices, risks from low infla-
tion could be broader.
•• Reduced appetite for completing national and euro-
area-wide reforms as the result of improved growth
prospects and reduced market pressures: Downside
risks to euro area growth have decreased relative to
the October 2013 WEO with important progress in
macroeconomic adjustment and improvements in
market confidence, but they remain significant. More
policy action is needed to reduce unemployment and
debt from the current unacceptably high levels and to
preserve market confidence. An important short-term
concern is that progress in banking sector repair and
reform could fall short of what is needed to address
financial fragmentation, restore financial market
confidence, and enable banks to pass on improved
funding conditions and lower policy rates to borrow-
ers. Insufficient bank balance sheet repair could also
hold back the restructuring of debt of nonfinancial
corporations with balance sheet stresses.
•• Risks related to the normalization of monetary policy
in the United States: Tapering risks are expected to

diminish as asset purchases are projected to end in late
2014. The adoption of qualitative forward guidance
in March 2014 can provide the Federal Reserve with
the needed greater flexibility in achieving its inflation
and employment goals on the way to normaliza-
tion, given the increasing difficulties in measuring
slack in the labor market. However, achieving such a
major shift in the monetary policy stance in a smooth
fashion will be challenging and may entail renewed
bouts of financial market volatility. As discussed in
scenario analysis in the April 2013 WEO, the key
concern is that there will be sudden, sharp increases
in interest rates that are driven not by unexpectedly
stronger U.S. activity, but by other factors. These
could include expectations of an earlier monetary
policy tightening because of higher inflation pressures
or financial stability concerns, a portfolio shift leading
to a sizable increase in the term premium, or a shift in
markets’ perception of the Federal Reserve’s intended
policy stance. Should such exit risks materialize, the
impact on U.S. activity and the spillovers on activity
elsewhere would be negative, with the possibility that
contagion will turn problems in specific countries into
a more widespread financial distress.
•• Upside risks to global growth from advanced econo-
mies: Stronger-than-expected growth outcomes in the
second half of 2013 in advanced economies raise this
possibility. It seems most relevant for the United States,
where the fiscal drag will decline in 2014 and pent-up
demand for durables and investment could be stronger
than expected. In Europe, corporate debt overhang
and banking sector weakness continue to weigh on
confidence and demand in some economies. There are,
however, upside risks to growth in Germany, where
crisis legacy effects are largely absent, and in the United
Kingdom, where easier credit conditions have spurred a
rebound in household spending.
Emerging market economy risks
•• Risks of further growth disappointments in emerg-
ing market economies: Downside risks to growth
in emerging market economies have increased even
though earlier risks have partly materialized and have
already resulted in downward revisions to the baseline
forecasts. Many of these economies are still adjusting to
weaker-than-expected medium-term growth prospects.
Foreign investors are also now more sensitive to risks in
these economies, and financial conditions have tight-
ened as a result. The higher cost of capital could lead
to a larger-than-projected slowdown in investment and
durables consumption, with recent monetary policy
tightening in some economies adding to the risk. Risks
could also come from unexpectedly rapid normaliza-
tion of U.S. monetary policy or from other bouts of
risk aversion among investors. Either case could lead to
financial turmoil, capital outflows, and difficult adjust-
ments in some emerging market economies, with a risk
of contagion and broad-based financial and balance of
payments stress. These would lower growth.
•• Lower growth in China: Credit growth and off-
budget borrowing by local governments have both
been high, serving as the main avenues for the siz-
able policy stimulus that has boosted growth since
the global financial crisis. Although a faster-than-
expected unwinding of this stimulus is warranted
to reduce vulnerabilities, such an unwinding would
also lower growth more than currently projected.
•• Geopolitical risks related to Ukraine: The baseline
projections incorporate lower growth in both Russia
and Ukraine and adverse spillovers to the Common-
wealth of Independent States region more broadly
as a result of recent turmoil. Greater spillovers to
activity beyond neighboring trading partners could
emerge if further turmoil leads to a renewed bout of
increased risk aversion in global financial markets,
or from disruptions to trade and finance due to
intensification of sanctions and countersanctions.
In particular, greater spillovers could emerge from
major disruptions in production or the transporta-
tion of natural gas or crude oil, or, to a lesser extent,
corn and wheat.
Medium-term risks
Low interest rates and risks of stagnation
Despite their strengthening recoveries, advanced
economies still face risks of stagnation. As highlighted
in previous WEO reports, the major advanced econo-
mies, especially the euro area and Japan, could face
an extended period of low growth for a number of
reasons, most notably for a failure to address fully the
legacy problems of the recent crisis.
If such a scenario were to materialize, the low growth
would reflect a state of persistently weak demand that
could turn into stagnation—a situation in which affected
economies would not be able to generate the demand
needed to restore full employment through regular
self-correcting forces. The equilibrium real interest rate

consistent with full employment may be too low to be
achieved with the zero lower bound on nominal inter-
est rates. Over time, the growth potential of stagnating
economies would also be adversely affected, because of
lower investment, including in research and development,
and because of lower labor supply as a result of hysteresis
in unemployment—the rise in structural unemployment
from prolonged cyclical unemployment.
The fact that nominal and real interest rates remain
low even though a more definitive recovery is expected
in advanced economies highlights that stagnation risks
cannot be taken lightly. As discussed in Chapter 3, real
interest rates are likely to rise under the WEO baseline,
but they should remain below the average value of about
2 percent recorded in the mid-2000s before the crisis. The
current low rates are resulting from the expectations that
global investment will remain on a lower path than before
the crisis, partly because of persistent postcrisis effects and
partly because of demand rebalancing in China. Although
savings ratios could decrease with lower growth in emerg-
ing market economies and demand rebalancing in China,
demand for safe assets is expected to remain high. As a
result, the precrisis trend of declining safe real interest rates
is not expected to be reversed even as postcrisis brakes ease
and scars heal. Real interest rates thus remain low enough
for the zero-lower-bound issue to reemerge under current
inflation forecasts should low-growth risks materialize.
A hard landing in China
The likelihood of a hard landing in China after over-
investment and a credit boom continues to be small
because the authorities should be in a position to limit
the damage from large-scale asset quality problems
with policy intervention. However, credit continues to
rise rapidly, and fixed capital formation supported by
this rise remains a key source of growth. Risks associ-
ated with asset-quality-related balance sheet problems
in the financial sector are thus building further. The
authorities might find it more difficult to respond
the more these risks continue to build. In that case,
spillovers to the rest of the world, including through
commodity prices, could be significant.
Risk scenarios: Tensions from upside and downside
risks
A more protracted growth slowdown in emerging
market economies remains a key concern. The impact
of such a slowdown on the world economy would
be larger now than it would have been one or two
decades ago. That is because these economies currently
account for a larger share of global production and are
more integrated into both the trade and the financial
spheres (see the Spillover Feature in Chapter 2). At the
same time, there are upside risks from the possibility
of faster growth in advanced economies. The follow-
ing scenario analysis considers the possible interaction
between upside and downside risks.
The upside risk is based on the premise that growth
in the United States will be some ½ percentage point
higher than assumed under the baseline. This is the
standard deviation in the distribution of forecasts for
2014–15 from contributors to the Consensus Econom-
ics survey. The faster U.S. recovery leads the Federal
Reserve, in this scenario, to withdraw monetary
stimulus earlier than in the baseline. All interest rate
changes in the scenario reflect central bank responses
to changes in macroeconomic conditions.
The downside risks are based on the premise that
the downward adjustment in investment in the Group
of Twenty (G20) emerging market economies will go
further than expected under the baseline. This reflects
the interaction of three factors: higher-than-expected
costs of capital due to the change in the external
environment, recent downward revisions to expecta-
tions of growth in partner countries, and a correction
of some past overinvestment. The “shock” is sequen-
tial—the weakness in each period during the five-year
WEO horizon is a surprise. Investment growth in each
economy is roughly 3 percentage points below baseline
every year, resulting in lower investment levels of about
14 percent after five years. Compared with the down-
side scenario for emerging market economies in the
April 2013 WEO, the slowdown is milder but more
persistent, reflecting primarily the fact that some of the
risks have been realized in the meantime and are now
incorporated in the baseline.
The main scenario results are as follows (Figure 1.15):
•• In the first scenario, in which a faster domestic
demand recovery in the United States materializes,
the implied faster U.S. growth and the positive
spillovers to trading partners lead to an increase in
global growth of about 0.2 percentage point in the
first two years (red lines in the figure). The positive
impact is strongest in other advanced economies and
Latin America, reflecting closer trade linkages. With
stronger growth, commodity prices are higher than
under the baseline in this scenario. After the initial
boost to growth in the United States and elsewhere,

–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
1. World: Real GDP Growth
(percentage points)
2. United States: Real GDP Growth
(percentage points)
3. Euro Area: Real GDP Growth
(percentage points)
Source: G20MOD simulations.
Note: AE = advanced economies; EME = emerging market economies.
Faster U.S. recovery Plus emerging markets downturn
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
4. Japan: Real GDP Growth
(percentage points)
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
5. Other AE: Real GDP Growth
(percentage points)
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
6. Oil Exporters: Real GDP Growth
(percentage points)
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
7. China: Real GDP Growth
(percentage points)
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
8. Emerging Asia: Real GDP Growth
(percentage points)
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
9. Emerging Latin America: Real
GDP Growth
(percentage points)
–1.2
–0.8
–0.4
0.0
0.4
0.8
2013 14 15 16 17 18
10. Other EME: Real GDP Growth
(percentage points)
–10
–8
–6
–4
–2
0
2
4
2013 14 15 16 17 18
11. World: Real Price of Oil
(percent)
–10
–8
–6
–4
–2
0
2
4
2013 14 15 16 17 18
12. World: Real Price of Metals
(percent)
Figure 1.15. Slower Growth in Emerging Market Economies and a Faster Recovery in the United States
(Percent or percentage point deviations from the WEO baseline)
Two scenarios generated with G20MOD, the IMF’s model of the Group of
Twenty (G20), are used here to explore the potential implications of a faster
U.S. recovery, coupled with notably slower growth in emerging market
economies. In the ﬁrst scenario (red lines), a faster-than-baseline U.S.
recovery leads the Federal Reserve to withdraw monetary stimulus faster
than in the baseline. In the second scenario (blue lines), weaker-than-
baseline investment growth (roughly 3 percentage points a year below
baseline) in G20 emerging market economies is the key driver of the weaker
growth outcomes. This weaker investment could arise because of revised
expectations of growth in these economies’ export markets, a correction
from a past period of overinvestment, or an expectation of a higher future
cost of capital. In the ﬁrst scenario, the faster U.S. growth and the positive
spillovers to U.S. trading partners lead to an increase in global output
growth in 2014 and 2015 of about 0.2 percentage point. Although the
change in interest rates is the same across emerging markets, because of
spillovers, effects on real GDP are strongest for Latin America, followed by
emerging Asia and then other emerging markets. The front-loading of the
U.S. recovery leads to growth falling slightly in subsequent years.
In the second scenario, as a result of lower investment growth and its
knock-on effects through labor income and private consumption demand,
real GDP growth declines relative to baseline on average by close to 1
percentage point a year in China and 0.6 percentage point in most other
emerging markets. Among the Group of Three (G3), Japan is hit the hardest
by the spillovers, owing to both integration with emerging Asia and the fact
that it has little monetary policy space with which to respond. The euro area
comes next, as limited monetary policy also contains the extent to which the
impact can be offset. The United States, being the least integrated with
emerging markets, has the smallest spillover among the G3.

there is a slight temporary decline relative to the
baseline, reflecting U.S. monetary policy tightening
in response to the higher-than-expected inflation
and growth.
•• In the second scenario, in which upside risks to
U.S. growth materialize along with the downside
risks for emerging market economies, global growth
declines relative to the baseline. This decline reflects
the larger magnitude of the shocks to demand on
the downside and between economic sizes (the G20
emerging market economies are larger than the
U.S. economy in purchasing-power-parity terms).
The impact of the negative surprise to investment
in emerging market economies on growth in these
economies depends on investment shares and the
share of trade with other emerging market econo-
mies in total trade (blue lines in the figure). The
higher the shares, the higher the impact. Reflecting
differences in these shares, growth declines relative
to baseline are largest in China (at about 1 per-
centage point a year) and lower in emerging Asia
and Latin America. Among the major advanced
economies, Japan is hit the hardest by the spillovers,
owing to both its close integration with emerging
market economies in Asia and its limited monetary
policy space to respond with interest rates already
very close to zero. The euro area and the United
States face monetary policy constraints because of
the zero lower bound, but they have smaller trade
links with these emerging market economies. As
commodity prices decline, commodity exporters
perform worse, even though they tend to have more
monetary policy space. Oil exporters are particularly
affected, given their high shares of oil in production.
The second scenario highlights how smaller upside
risks to growth in some major advanced economies
may not be enough to offset the impact of broader
downside risks in major emerging market economies.
As highlighted in the earlier risk discussion and in
scenario analysis in the April 2014 Global Financial
Stability Report, there is a possibility that higher U.S.
longer-term interest rates and a rise in policy rate
expectations in the United States reflect less benign
reasons than faster-than-expected U.S. growth. In this
case, spillovers to output to the rest of the world would
be negative.
The second scenario also illustrates how down-
side risks to emerging market economies can have
important spillovers to advanced economies. Lower-
than-expected growth in the G20 emerging market
economies on its own (without faster U.S. domestic
demand growth) would lead to global growth that
is, on average, roughly 0.3 percentage point less than
baseline each year. In advanced economies, growth is
on average 0.1 percentage point below the baseline.
In emerging market economies, the decline in growth
is 0.7 percentage point on average. Thus, output
spillovers that operate primarily through trade channels
mean that a 1 percentage point decline in emerging
market output growth reduces advanced economy
output by some 0.2 percentage point. As discussed in
the Spillover Feature in Chapter 2, depending on the
nature of the shock and the local impact, there is also
scope for financial channels to play a role in transmit-
ting emerging market economies’ shocks to advanced
economies, given increased financial integration.
Policies
The strengthening of the global recovery from the Great
Recession is evident. However, growth is not yet robust
across the globe, and downside risks to the outlook
remain. In advanced economies, continued—and in
some cases, greater—support for aggregate demand and
more financial sector and structural reforms are needed
to fully restore confidence, foster robust growth, and
lower downside risks. Many emerging market economies
face a less forgiving external financial market environ-
ment; their growth has slowed; and they continue to
face capital flow risks that they must manage. Spillovers,
especially if downside risks were to materialize, could
pose further challenges. Boosting medium-term growth
is a common challenge throughout the world, and dif-
ficult structural reforms are a priority.
Preventing Low Inflation in Advanced Economies
Monetary policy should remain accommodative in
advanced economies. Output gaps are still large and
are projected to close only gradually. Moreover, fiscal
consolidation will continue. That said, the strength of the
expansions differs across advanced economies. Maintain-
ing clear and forward-looking communication about the
path of policy normalization will be a priority for some
central banks. In some other advanced economies, mon-
etary policymakers must consider the cost of persistently
low inflation below target and risks of deflation. Once
inflation expectations start drifting down, reanchor-

ing them to the target could be a long, costly process.
As discussed in Box 1.3, this concern is rooted in the
current constraints on the ability of monetary policy
to lower nominal rates, either because rates are already
close to the zero lower bound or because of financial
fragmentation. As noted earlier, risks from low infla-
tion appear to be most significant in the euro area and,
to a lesser extent, in Japan.
In acknowledgment of such risks, the question is
whether to ease monetary policy now or to use forward
guidance to spell out contingencies for further action if
either inflation or inflation expectations remain below
target.
•• In the euro area, the monetary policy rate is close
to, but not at, zero, and a number of considerations
suggest that more monetary easing, including use
of unconventional measures, is needed now. The
current baseline projections imply that inflation
will undershoot the ECB’s price stability target by
substantial margins for much longer than the usual
horizon of one to two years. In this context, there
are important risks that inflation will turn out even
lower than forecast. Inflation expectations may drift
lower, as discussed in Box 1.3. This in turn would
lead to higher real interest rates, aggravate the debt
burden, and lower growth. In countries that need
to improve competitiveness, and where prices and
wages have to decline further relative to other euro
area countries, this would likely mean greater defla-
tion, and even stronger adverse growth effects.
•• The Bank of Japan should continue with its aggres-
sive quantitative easing policy and further strengthen
its communication strategy, especially in view of the
challenge of assessing underlying inflation following
the consumption tax increase. It will, however, be
important for the bank to specify policy contingen-
cies if inflation or inflation expectations remain
below target for longer than expected.
Risks from low inflation and the need for continued
accommodative monetary policy mean that it will also
be important for many advanced economy central banks
to clarify how they will promote financial stability,
which remains a concern. Long periods of low interest
rates across the entire term structure could encourage
too much risk taking, excessive leverage, and imprudent
maturity mismatches. Banking supervisors and regula-
tory authorities will need to continue to closely monitor
risks to financial stability from monetary policy and
ensure that banks’ activities remain within prudential
regulatory standards. In the euro area, however, credit
has been contracting, and the most pressing issue is to
repair bank balance sheets to increase credit.
Raising Growth and Lowering the Risks of Stagnation
Risks of low growth and stagnation remain a con-
cern, particularly in the euro area and Japan, where a
comprehensive policy response is required to mitigate
these risks. More broadly, however, fiscal policy needs
to play a critical role if growth remains at subpar levels.
In that case, more ambitious measures aimed at raising
the growth potential—including, when relevant, higher
public investment—should be contemplated, with due
consideration for long-term fiscal sustainability.
The euro area has made some progress in addressing
the legacies of the crisis—high public and private debt,
weak balance sheets, and high unemployment—as well
as longer-term impediments to competitiveness and
productivity. Market confidence has been improving,
and growth has started to pick up. However, downside
risks remain—there is still substantial slack, inflation
has been below the ECB’s price stability objective
for some time, and financial fragmentation persists.
Although crisis risks have declined with recent policy
action, risks of persistent low growth remain a concern.
•• Repairing bank balance sheets: Progress has been
made in repairing bank balance sheets. However,
banks have continued to deleverage, and credit to
the private sector is contracting. The ECB’s 2014
asset quality review and stress tests will be a criti-
cal opportunity to move toward completing the
restructuring of bank balance sheets. This exercise,
if executed credibly, will make bank balance sheets
transparent and comparable and identify further
capital needs. With prompt recapitalization if
needed, this exercise will reduce uncertainty about
banking system health and foster bank balance sheet
repair, which should eventually result in a credit
recovery. Although many banks should be able to
resort to market-based recapitalization, the timely
completion of this step might also require recourse
to national and common backstops.
•• Completing the banking union: A more complete
banking union in the euro area is critical to reduce
financial fragmentation and weaken sovereign-bank
links. A key element is to have in place, by the time
the ECB assumes supervisory responsibilities, a
strong, centralized Single Resolution Mechanism to
ensure rapid, least-cost bank resolution. The March
20 agreement between the European Parliament,

Council, and Commission on such a mechanism is
a step toward a fuller banking union. However, the
decision-making process appears complex and may
not provide for timely resolution, especially when
support from the Single Resolution Fund is foreseen.
An even quicker transition period for the mutualiza-
tion of national compartments of the fund, and a
clearer decision on a strong common backstop and
its timing, are required to break sovereign-bank links
effectively, especially in countries where fiscal space
is limited.
•• More demand support: Given weak and fragile
growth and very low inflation, more monetary
easing is needed to raise the prospects of achiev-
ing the ECB’s price stability objective of inflation
below, but close to, 2 percent and support demand.
Among possible further actions would be further
rate cuts, including mildly negative deposit rates,
and unconventional measures, including longer-term
refinancing operations (possibly targeted to small
and medium-sized enterprises), to support demand
and reduce fragmentation. Monetary policy effec-
tiveness would be strengthened by stronger national
insolvency regimes, which would help reduce private
debt overhang, facilitate balance sheet repair, and
lower financial fragmentation. The neutral fiscal
stance planned for the euro area in 2014 is broadly
appropriate. If low growth persists and monetary
policy options are depleted, fiscal policy may need
to use the flexibility available under the current fis-
cal framework to support activity.
•• Advancing structural reforms at the national and
area-wide levels: This is key to boosting productiv-
ity and investment, ensuring higher longer-term
growth, and reducing intra-euro-area imbalances. In
surplus countries, reforms to boost domestic demand,
particularly investment, would help rebalancing. In
deficit countries, further adjustment in relative prices
is needed to achieve resource reallocation from non-
tradables sectors to tradables sectors. Together with
continued labor market reforms at the national level,
opening up product and service markets to competi-
tion could unleash new investment and new jobs.
Growth and investment would be further supported
by lower regulatory hurdles for the entry and exit of
firms, simpler tax systems, a targeted implementation
of the European Union (EU) Services Directive, and
deeper trade integration.
In Japan, the bold monetary easing and new fiscal
stimulus measures under Abenomics lifted growth in
2013 and boosted growth prospects for 2014–15 rela-
tive to the pre-Abenomics baseline forecasts. Longer-
term stagnation risks are present primarily because
of the sizable fiscal consolidation that will be needed
during the next decade or so to ensure the transition
to a sustainable long-term fiscal position in a rapidly
aging society. IMF staff estimates suggest that, in
addition to the consumption tax increase to 8 percent
from 5 percent in the second quarter of 2014 and the
planned further increase to 10 percent in the fourth
quarter of 2015, additional measures yielding 5.5 per-
cent of GDP need to be identified, for public debt to
decline in the medium term. Against this backdrop, it
will be critical to manage this consolidation at a pace
that will not undermine the other goals of Abenom-
ics—sustained growth and a definitive regime change
from deflation to inflation.
In the near term, the additional temporary fiscal
stimulus for 2014 should offset the adverse effects of
the welcome consumption tax increase in the second
quarter of this year. However, the stimulus also adds
to already-elevated fiscal risks and puts a premium
on developing, as quickly as possible, concrete plans
for further consolidation beyond 2015. This should
be supported by ambitious measures to lift potential
growth—the third arrow of Abenomics—during the
Diet session in the first half of 2014.
Managing Capital Flow Risks in Emerging Market and
Developing Economies
The changing external environment increases the
urgency for emerging market economies to address
macroeconomic imbalances and policy weaknesses.
As advanced economies’ assets have become relatively
more attractive, emerging market economies have
experienced lower capital inflows and currency depre-
ciation, and these trends could intensify, including
because of upside risks to growth in advanced econo-
mies, as noted in the risk scenario discussion.
The change in the external environment poses new
challenges for emerging market economies. As recent
developments show, economies with domestic weak-
nesses and vulnerabilities are often more exposed to
market pressure. A number of these weaknesses have
been present for some time, but with better return
prospects in advanced economies, investor sentiment
is now less favorable toward emerging market risks. In
view of possible capital flow reversals, risks related to
sizable external funding needs and disorderly deprecia-

tion are of particular concern given that they affect
returns in investors’ home currencies.
Against this backdrop, emerging market economies
must weather increased risks from sudden capital flow
reversals, recalibrate policies to align them with the
cyclical position if necessary, and raise potential growth
with structural reforms.
Making depreciation manageable
Letting the exchange rate depreciate generally
remains a desirable response to capital flow reversals,
as it facilitates adjustment and lowers the negative
effects on output. In practice, policymakers might
be reluctant to allow for depreciation for a number
of reasons. There is the concern that investors may
overreact and that depreciation may be excessive.
Then there are concerns about the adverse impact on
inflation or financial stability even if depreciation is
not excessive.
If capital flow reversal risks materialize and out-
flows are rapid, policymakers can use foreign exchange
intervention to smooth excessive volatility or pre-
vent financial disruption, adequate levels of foreign
exchange reserves permitting. Such intervention should
not forestall underlying external adjustment in econo-
mies in which current account deficits exceed levels
consistent with fundamentals and desirable macroeco-
nomic policies. Capital flow management measures to
lower or prevent capital outflows might also help in
smoothing excessive exchange rate volatility. In general,
however, relative to capital flow management measures
on inflows, they are less desirable. Expectations of
such measures being put in place could even trigger
outflows in the first place.
Policymakers should also address underlying prob-
lems if there are concerns about large adverse effects of
depreciation. Such measures would help their econo-
mies to be better prepared for weathering increased
risks of capital flow reversals.
•• If the primary concern is inflation, monetary policy
tightening may be required if inflation is running
high. Policymakers may need to consider, how-
ever, that monetary tightening alone might not
be enough. Exchange rate pass-through is also a
function of monetary policy credibility. If exchange
rate depreciation strongly feeds into inflation
expectations, credibility is likely to be low, and
policymakers might need to adopt a more transpar-
ent monetary policy framework or improve the
consistency and transparency of monetary policy
implementation. For example, as discussed in Box
1.4, many emerging market economies have moved
away from free floats to de facto “managed” floating,
in some cases even with narrow limits on the extent
of exchange rate fluctuations. Although managed
floating may lower risks of abrupt exchange rate
movements, it may also undermine the credibility
of inflation targets and delay much-needed external
adjustment.2
•• If the primary concern is financial stability, strong
regulatory and supervisory policy efforts are needed
to ensure that banks address credit quality and prof-
itability problems related to exchange rate and capi-
tal flow risks. Financial stability problems arise from
the negative effects of large, sudden exchange rate
depreciation on balance sheets and cash flows. The
main concerns relate to firms in the domestically
oriented sectors that have foreign currency financing
but that do not enjoy a natural currency hedge in
the form of export sales and to domestically oriented
banks that have foreign currency funding. In both
cases, the debt service burden in domestic currency
increases with depreciation, which in turn can lead
to important asset quality problems. In addition,
regulators must closely monitor possible asset quality
problems arising from recent rapid credit growth
and less favorable medium-term growth prospects.
Recalibrating macroeconomic policies
A key consideration for policy setting is whether mac-
roeconomic policies have contributed to the recent
widening of current account deficits and whether these
deficits are excessive. As noted earlier, some emerging
market economies now run current account deficits, and
in some economies, recent changes have been away from
the underlying equilibrium position (or norm) identified
in the assessments in the 2013 Pilot External Sector Report
(IMF, 2013b). The concern about policies arises because
after the global financial crisis, expansionary macroeco-
nomic policies in emerging market economies boosted
domestic demand and provided for a rapid bounce-back
in activity. In some economies, however, the policy stance
was not fully reversed or was reversed too slowly when
the economies were booming in 2010–12 and output was
above potential. The concurrent deterioration in current
account balances was thus partly the result of overheating,
a process that is now correcting itself.
2See Ostry, Ghosh, and Chamon (2012) for a discussion of mon-
etary and exchange rate policies in emerging market economies.

The main task, therefore, is to recalibrate the macro-
economic policy mix and stance in such a way that they
are credible and consistent with the extent of economic
slack. Specific requirements vary across economies, but
the following general considerations are relevant.
•• Monetary policy: In a number of economies, includ-
ing Brazil, India, and Indonesia, inflation pressure
continues and could be reinforced by currency
depreciation since mid-2013. Although policy rates
were raised in many countries over the past year,
further policy tightening may be needed to rein
in inflation. In other economies, policymakers can
consider slowing the increase in policy rates or can
ease rates if output is below potential. They will,
however, need to be mindful of prospective inflation
pressure, policy credibility, and the possible market
impact in the current environment.
•• Fiscal policy: Policymakers should generally align
the fiscal stance with updated estimates of medium-
term growth potential and recent changes in longer-
term interest rates, as emphasized in previous WEO
reports. Interest rates are appreciably higher in some
economies and are unlikely to change direction soon.
In many emerging market economies, fiscal deficits
remain well above precrisis levels (see Figure 1.4,
panel 2), even though output generally is still above
precrisis trends (Figure 1.6, panel 1). Moreover, debt
dynamics are projected to turn less favorable, given
that real government bond yields are higher than
expected a year ago. Against this backdrop, policy-
makers need to lower budget deficits, as discussed
in the April 2014 Fiscal Monitor. The urgency for
action varies across economies, depending on debt
levels, vulnerabilities, and cyclical positions. In some
economies, increased contingent risks to budgets and
public debt from substantial increases in quasi-fiscal
activity and deficits reinforce the need to adjust the
quasi-fiscal policy stance (Brazil, China, Venezuela).
Policies in low-income countries
Many low-income countries have succeeded in
maintaining strong growth, reflecting more favorable
business and investment regimes and better macro-
economic policies. Among other things, the combina-
tion of high growth and moderate budget deficits has
helped keep public debt levels stable at about 35 per-
cent of GDP. That said, foreign direct investment has
started to moderate with declining commodity prices
and is expected to ease further, and commodity-related
budget revenues and foreign exchange earnings are at
risk. Given these changes in the external environment,
timely adjustments to fiscal policies will be important;
otherwise, external debt and public debt could build
up. Within this broader picture of relative resilience,
some countries face greater challenges. Some low-
income countries with low growth and high public
debt will need stronger fiscal policies to keep debt
levels sustainable. A number of low-income countries
with larger external financial needs that have accessed
international capital markets (“frontier economies”) are
vulnerable to capital flow risks, broadly similar to those
faced by emerging market economies. Addressing these
vulnerabilities might require tighter monetary and fis-
cal policies.
Continuing High Growth in Major Emerging Market
Economies
The major emerging market economies face a common
policy issue: how to achieve robust and sustainable
growth. However, the underlying problems, including
the extent and nature of macroeconomic imbalances,
differ from economy to economy.
Growth in China has decelerated since 2012, and
medium-term growth is now projected to be substan-
tially below the 10 percent average rate recorded dur-
ing the past 30 years. Still, economic activity continues
to be overly dependent on credit-fueled investment,
and vulnerabilities are rising.
The economic policy priority is to achieve a soft
landing on the transition to more inclusive and
sustainable, private-consumption-led growth. This
shift would require liberalizing interest rates to allow
effective pricing of risk; a more transparent, interest-
rate-based monetary policy framework; a more flexible
exchange rate regime; reforms for better governance
and quality of growth; and strengthened financial
sector regulation and supervision. The Third Plenum
of the 18th Central Committee has laid out a reform
blueprint that includes these policy steps. Timely
implementation must be a priority. Encouraging steps
have already been taken in the area of financial sector
policy (announcing a timeline for key reforms such as
introduction of a deposit insurance scheme and further
liberalization of interest rates) and exchange rate policy
(the exchange rate fluctuation zone has been wid-
ened). Reining in rapid credit growth and curtailing
local government off-budget borrowing are near-term
priorities, critical for containing rising risks. Policy-
makers must also address potential challenges from

rapid credit growth in recent years. In particular, bad
loans and other impaired assets, should they emerge,
must be recognized, and the resolution framework for
failed financial institutions should be strengthened.
For downside contingencies, fiscal space can be used to
recapitalize financial institutions where appropriate.
In Brazil, there is a need for continued policy tight-
ening. Despite substantial policy rate increases in the
past year, inflation has remained at the upper bound
of the band. Foreign exchange intervention should be
more selective, used primarily to limit volatility and
prevent disorderly market conditions. Fiscal consoli-
dation would help reduce domestic demand pressure
and lower external imbalances while also contributing
to lowering a relatively high public debt ratio. Supply
bottlenecks must be addressed.
In India, further tightening of the monetary stance
might be needed for a durable reduction in inflation and
inflation expectations. Continued fiscal consolidation
will be essential to lower macroeconomic imbalances.
Policymakers must also concentrate on structural reforms
to support investment, which has slowed markedly. Pri-
orities include market-based pricing of natural resources
to boost investment, addressing delays in the imple-
mentation of infrastructure projects, improving policy
frameworks in the power and mining sectors, reforming
the extensive network of subsidies, and securing passage
of the new goods and services tax to underpin medium-
term fiscal consolidation.
In Russia, the monetary policy regime is in transition
to inflation targeting; thus, anchoring inflation expecta-
tions will have to be a priority in the process. Increased
exchange rate flexibility will help as a shock absorber.
With substantial depreciation, however, some monetary
policy tightening may be required to prevent persistent
increases in inflation. Structural reforms are critical to
increase investment, diversify the economy, and raise
potential growth. Priorities are strengthening the rule of
law and scaling back state involvement in the economy.
In South Africa, the external current account deficit
has been over 5 percent for some time, notwithstand-
ing substantial rand depreciation. Hence, fiscal and
monetary policies may need to be tightened to lower the
country’s vulnerabilities and contain the second-round
impact of the depreciation on inflation. Structural
reforms to reduce the unacceptably high unemployment
rate, which is at 24 percent, are essential.
Global Demand Rebalancing
Hopeful signs of a more sustainable global recovery
are emerging, but robust recovery also requires further
progress on global demand rebalancing. As output
gaps close, external imbalances may increase again. The
materialization of downside risk to emerging markets
could have similar effects if current account balances
were to improve sharply in these economies because of
capital flow reversals.
The challenge is then to implement policy measures
that achieve both strong and balanced growth—put
another way, policies that ensure that growth will
continue without a deterioration of current account
balances. The measures discussed earlier were aimed at
sustaining growth. Some will also further reduce exter-
nal balances. The quantitative implications of some of
these policies, not only for individual countries, but
also for the world economy, are explored in the 2013
Spillover Report (IMF, 2013c).
For example, in economies that have had current
account surpluses, reforms can boost domestic demand
and modify its composition. In China, rebalancing
demand toward consumption by removing financial
distortions, allowing for more market-determined
exchange rates and strengthening social safety nets,
will lead to more balanced growth and smaller external
imbalances. In Germany, an increase in investment,
including public investment, through tax and financial
system reform and services sector liberalization, not
only is desirable on its own, but also will reduce the
large current account surplus. In deficit economies,
structural reforms aimed at improving competitive-
ness (France, South Africa, Spain, United Kingdom)
and removing supply bottlenecks to strengthen exports
(India, South Africa) again not only are good for
growth, but also will help improve external positions
and allow for more sustained growth.

Special Feature: Commodity Prices and Forecasts
SPECIAL FEATURE COMMODITY PRICES AND FORECASTS
Commodity price projections in this and previous World
Economic Outlook (WEO) reports are derived from
commodity futures prices, which currently point to
declining prices and downside risks. Although such a
market-based approach is appealing, its performance
is sometimes questioned. This special feature explores a
model-based oil price forecast with better performance.
Given strengthening global demand, the model forecast
suggests higher oil prices and upside risks. In view of rising
North American oil supply and slowing growth in emerg-
ing markets, there is merit in a forecast that combines the
two approaches as a hedge during a time when the oil
market configuration may be changing. This combina-
tion suggests slightly declining to flat oil prices this year.
Developments in Commodity Markets1
Since the October 2013 WEO, energy prices have been
fairly flat overall (Figure 1.SF.1, panel 1), with falling
prices for crude oil offset by rising prices for natural gas
(extremely cold weather in the United States) and coal
(supply tightness in a number of exporting countries).
Crude oil prices have edged lower, mainly as a result of
the continued supply surge in North America. Non–
Organization of the Petroleum Exporting Countries
(OPEC) supplies increased 1.3 million barrels a day
(mbd) in 2013—slightly faster than the 1.2 mbd growth
in global demand—with all of the net growth due to the
United States (1.2 mbd, mainly shale oil) and Canada
(0.2 mbd, mainly oil sands oil) (Figure 1.SF.1, panel
2). Projections for growth in non-OPEC supply have
been raised to 1.8 mbd in 2014, well above the 1.4 mbd
pace of demand. Prices have been held up by mounting
OPEC supply pressures—notably from disruptions in
Libya, Nigeria, Syria, and Yemen—and from sanctions
against the Islamic Republic of Iran. Oil demand was
relatively weak in the fourth quarter of 2013, with the
United States the exception (Figure 1.SF.1, panel 3).
Despite these pressures, oil prices—based on futures
markets—are projected to decline during the outlook
The author of this feature is Samya Beidas-Strom, with assistance
from Benjamin Beckers and Daniel Rivera Greenwood. Recent
commodity market developments were provided by Marina Rousset
and Shane Streifel. Technical details are given in Beckers and Beidas-
Strom (forthcoming).
1See the “Commodity Market Monthly” and “Commodity Out-
look and Risks” at www.imf.org/commodities.
period, consistent with expanding oil supply and still-
tepid demand.
Metal prices have remained broadly flat since the
October 2013 WEO, at about 30 percent below the
highs of early 2011, with most markets in surplus
(large and rising stocks and steady gains in production).
Global metal demand growth—and metal demand
growth in China—slowed in 2013 (Box 1.2), while sup-
ply grew strongly. Futures prices suggest declining metal
prices through the outlook period, reflecting continuing
albeit diminishing surpluses in a number of markets.
In food markets, the production outlook is favorable
for most major crops. Global output for major grains
and oilseeds is projected to surpass demand growth (Fig-
ure 1.SF.1, panel 4). China expects increased production
of wheat and corn as a result of favorable weather, and
global rice supplies continue to be plentiful. Moreover,
stocks continue to gradually recover, especially stocks of
corn (Figure 1.SF.1, panel 5). In early 2014, concerns
about the effects of adverse weather on South American
harvests have exerted some upward price pressure.
Commodity Price Forecasting
With broadly flat or softening commodity prices in
the second half of 2013, some analysts have predicted
the end of the commodity price supercycle, given the
slowdown in emerging market economies, particularly
China (Box 1.2), and the increase in supplies (namely,
increased U.S. crude oil production, a supply overhang
in most base metals, and increasing grain supplies).
However, during the first quarter of 2014, some prices
firmed with signs of strengthening global activity, albeit
with much price volatility; hence, analysts have become
more circumspect. The motivation for forecasting
commodity prices is thus as relevant as ever, and the
issue becomes how best to do this. Which tools should
policymakers rely on to forecast commodity prices? How
have these forecasting tools performed with regard to
forecast errors and risk assessments after the fact? Are
there other forecasting models that could complement
the policymakers’ toolkit? And which tools are best for
these uncertain economic times? This feature addresses
these four questions as applied to oil prices.2
2The analysis in this feature is focused on oil prices but can be
extended to other commodity prices with futures markets if monthly

What Forecasting Tools Do Policymakers Use?
Since the 1970s epoch of scarcity, when Hotelling-type
(1931) rules were the norm for predicting the price of
an exhaustible commodity, policymakers have gravi-
tated toward a few simple forecasting tools: the long-
data are available for their global demand, supply, and inventories,
and if a leading international price for the commodity prevails (as is
the case for aluminum, copper, lead, nickel, tin, and zinc).
term constant real cost of extracting an exhaustible
commodity, random-walk price models, and futures
prices. Two recent developments have clouded the
usefulness of these approaches—namely, a sustained
price spike during the commodity boom in the middle
of the first decade of the 2000s and the escalation in
extraction costs, which is particularly relevant for oil.
Efforts have been undertaken to assess the predictive
content and statistical performance of these simple
–2
–1
0
1
2
3
4
5
2011:Q4 12:Q1 12:Q2 12:Q3 12:Q4 13:Q1 13:Q2 13:Q3 13:Q4
–2
–1
0
1
2
3
4
5
2011:Q4 12:Q1 12:Q2 12:Q3 12:Q4 13:Q1 13:Q2 13:Q3 13:Q4
80
120
160
200
240
280
2005 06 07 08 09 10 11 12 13 14 15
Figure 1.SF.1. Commodity Market Developments
1. IMF Commodity Price Indices
(2005 = 100)
3. World Oil Demand, Including Natural Gas Liquids
(million barrels a day, year-over-year percent change)
2. World Oil Production
(million barrels a day, year-over-year percent change)
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14
4. Annual Food Production and Consumption1
(billion tons)
0
5
10
15
20
25
30
35
40
Corn Rice Wheat Soybeans Other2
5. Global Food Stock-to-Use Ratios
(inventories as a percent of global consumption)
Food
Energy
Metal
Production
Consumption
2013
2014
1981–2012
average
Commodity prices have been fairly flat since the October 2013 World Economic Outlook, as increases in supplies outpaced tepid demand in most markets.
United States
OPEC
Other non-OPEC
Total
United States Japan China Total
Other advanced economies
Sources: IMF, Primary Commodity Price System; International Energy Agency; U.S. Department of Agriculture; and IMF staff estimates.
Note: OPEC = Organization of the Petroleum Exporting Countries.
1
Sum of data for major grains and oilseeds: barley, corn, millet, rice, rye, sorghum, wheat, palm kernel, rapeseed, soybeans, and sunflower seed.
2
Includes barley, millet, palm kernel, rapeseed, rye, sorghum, and sunflower seed.

forecasting tools (Reeve and Vigfusson, 2011; Reichs-
feld and Roache, 2011; Alquist, Kilian, and Vigfusson,
2013; Chinn and Coibion, 2013) and to resuscitate
the Deaton and Laroque (1996) class of price forma-
tion models with speculative storage. Before examining
forecasting models with speculative storage, however,
this feature explores how the simple forecasting tools
have fared during the last decade, first by focusing on
futures and then by looking at a broader set of models.
How Have Oil Futures Fared as a Forecasting Tool?3
Simple forecast errors
Oil futures have long been used to forecast spot prices
on the premise that the price of a futures contract
equals the discounted value of the expected future
spot price and that, by definition, oil futures include
forward-looking information. As with many com-
modity markets, oil futures markets are frequently in
backwardation.4 This can lead to some downward bias
in the forecasts of future spot prices.
Moreover, the predictive content of commodity
futures (and oil futures in particular) has declined since
the mid-2000s (Chinn and Coibion, 2013), even when
futures were not in backwardation. The forecast error
was more than 100 percent (for futures of the January
2007 vintage relative to the actual outturn of July 2008)
before the global financial crisis (Figure 1.SF.2, panel 1).
This pattern is not unique; the quality of all macroeco-
nomic forecasts tends to deteriorate around recessions
or crises. However, even during the slowdown of 2011,
the forecast error was 38 percent (for futures prices of
the January 2011 vintage relative to the actual outturn
of April 2011). This performance suggests that futures
prices may not fare well as predictors during turbulent
times or periods of structural change.
3For brevity, the analysis focuses on U.K. Brent, the leading
international crude oil benchmark. Results are also available for West
Texas Intermediate (WTI) and Dubai Fateh. A simple average of the
three constitutes the WEO average spot price, forecast to be $104.17
a barrel and $97.92 a barrel in 2014 and 2015, respectively.
4Backwardation describes the market condition wherein the price of
a futures contract is trading below the expected spot price at contract
maturity. The resulting futures curve would typically be downward
sloping (inverted), because contracts for dates further in the future
would typically trade at even lower prices. Keynes (1930) argued that
in commodity markets, backwardation is “normal,” because producers
of commodities are more prone to hedge their price risk than are
consumers. The opposite situation, wherein a futures contract trades at
a premium compared with spot prices, is described as “contango,” as
experienced by WTI futures in early and mid-2013.
Latest forecast
The WEO’s futures-based forecast for the nominal
Brent price is $108 a barrel in 2014, declining to $103
in 2015 (Figure 1.SF.2, panel 2), with risks tilted to
the downside. This forecast implies a small upward
revision compared with the October 2013 WEO, likely
reflecting mostly larger-than-expected increases in non-
OPEC supplies offset by rising geopolitical risks.
Model Forecasts5
Recent evidence
The economic models for determining oil prices pio-
neered by Kilian (2009), and refinements introduced
5The author thanks Christiane Baumeister of the Bank of
Canada for kindly sharing her Matlab code, which was refined and
30
45
60
75
90
105
120
135
150
2005 06 07 08 09 10 11 12 13 Jan.
14
1. Simple Forecast Errors of Brent Spot and Futures
Spot
January 2007 futures
January 2011 futures
2. Brent Oil Price Prospects1
25
50
75
100
125
150
175
200
2007 08 09 10 11 12 13 14 Feb.
15
Futures
Forecast error
of 100 percent
Forecast error
of 38 percent
The predictive content of oil futures has declined, with large forecast errors
evident during the past decade. The World Economic Outlook (futures-based
forecast) projects gradually declining oil prices, with risks tilted to the downside.
Figure 1.SF.2. Brent Forecast Errors and Futures
(U.S. dollars a barrel)
Sources: Bloomberg, L.P.; IMF, Primary Commodity Price System; and IMF staff
estimates.
1
Derived from prices of futures options on February 12, 2014.

thereafter, seem to generate more accurate forecasts.
These models predict future oil prices by combining
global activity measures with changes in oil supply
and in global crude oil inventories (to capture specula-
tive storage or consumption smoothing). They suggest
that vector autoregression (VAR) forecasting models
using monthly data for these aggregates generate more
accurate forecasts than most other approaches (Alquist,
Kilian, and Vigfusson, 2013) and are robust to changes
in model specification and estimation methods (Bau-
meister and Kilian, 2013b). That said, recent evidence
suggests that the use of refined petroleum product
spreads based on commodity futures prices could offer
even better predictive power (Baumeister, Kilian, and
Zhou, 2013).
Model ingredients
Variables that seem relevant for predicting oil prices are
combined to estimate a reduced-form version of the
structural VAR of Beidas-Strom and Pescatori (forth-
coming). The core variables are global crude oil pro-
duction, the WEO global industrial production index,
the real Brent oil price, and petroleum inventories of
the members of the Organization for Economic Coop-
eration and Development (OECD). Three additional
variables are also included: an exchange rate index of
the U.S. dollar weighted against bilateral currencies
of major oil consumers (in the spirit of Chen, Rogoff,
and Rossi, 2010); the U.S. consumer price index; and
a measure of OPEC spare capacity. To these are added
seasonal dummies for the purpose of forecasting the
monthly variation in prices. In addition, the real oil
price is detrended to avoid any potential upward bias
in the forecast given the observed trend since 2000.6
VAR forecast
Out-of-sample forecasts are generated based on the
VAR model estimated recursively on monthly data
from January 1985 through October 2013. The VAR
predicts rising nominal Brent prices over the forecast
horizon (Figure 1.SF.3, panel 1), consistent with the
expected strengthening of global demand reported in
this WEO report (Figure 1.SF.3, panel 2) and the car-
ryover from recent supply and precautionary demand
shocks (Figure 1.SF.3, panel 3). Initially, the Brent
augmented for the purposes of this section and Beckers and Beidas-
Strom (forthcoming).
6The drift without detrending of the real Brent oil price is 3.97
percent.
price is forecast to decline, before rising in the period
after February 2014 to average $114 a barrel during
2014 ($6 higher than futures) and thereafter rising to
an average of $122 a barrel in 2015 ($19 higher than
futures).
Recent shocks
The dynamic effects of shocks are important for oil
price forecasts, given long lags. They depend on the
identification scheme used—here the identification
restricts the influence of noise trading on the real oil
price.7 During the last two quarters of 2013, the real
Brent oil price was held up mostly by OPEC sup-
ply shortages and some impetus from flow demand,
despite the large drawdown of OECD country oil
inventories (Figure 1.SF.3, panel 3). The dynamic
influence of these shocks dissipates gradually (between
12 and 24 months), with the forecast gradually driven
toward the end of the horizon by the model’s param-
eters (from the variables estimated across the entire
sample).
Risks
Prediction intervals are obtained by bootstrapping the
errors of the VAR over the full sample (Figure 1.SF.3,
panel 1, shaded intervals, and panel 4). The shape of the
VAR distribution changes with the horizon, unlike that
for futures prices (which is based on information derived
from oil futures options), and indicates much larger
upside price risks. In practice, this means that the VAR
forecast indicates a 15 percent risk of Brent exceeding
$150 a barrel in January 2015, relative to a less than
5 percent risk suggested by futures. The key message
is that even models that appear relatively successful in
predicting oil prices still imply considerable oil price
forecast uncertainty in both directions (Figure 1.SF.3,
panel 5).8 Upside risks can be attributed to strengthen-
ing global demand and the carryover from some recent
unexpected OPEC supply declines, among other things.
Which Forecasting Method Has the Lowest Error—and
When?
The standard approach for formally assessing forecast-
ing performance is the symmetric root-mean-squared
7See Beidas-Strom and Pescatori (forthcoming) for details.
8A Bayesian VAR narrows the uncertainty range by about 35 per-
cent, without influencing the risk assessment; that is, it remains
upward tilting.

0
50
100
150
200
250
300
2008 09 10 11 12 13 14 Oct.
15
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0 50 100 150 200 250 300 350 400
44
48
52
56
60
64
68
2007 08 09 10 11 12 13
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
0
20
40
60
80
100
120
140
160
2000 01 02 03 04 05 06 07 08 09 10 11 12 13
1. VAR Forecast
3. Historical Decomposition of Shocks1
(contribution of shocks (left scale),
U.S. dollars a barrel (right scale))
4. OECD Inventory Demand Forward Cover
(days)
5. Probability Density Functions of VAR Forecast
(probability)
40
60
80
100
120
140
160
2008 09 10 11 12 13 14 Oct.
15
6. Brent Oil Combination Forecasts
Real Brent price (right scale)
Actual Average of previous five years
3 month
6 month
9 month
12 month
24 month
Historical
Futures
VAR
Combination
80
90
100
110
120
130
2005 06 07 08 09 10 11 12 13 14 Oct.
15
2. World GDP and Industrial Production
(2007 = 100)
VAR forecast
Random walk with drift
Futures
Real GDP
Global industrial production
Flow demand shock Flow oil supply shock
Speculative shock Residual shock
A model-based forecast, based on strengthening global demand, continued small OPEC supply shocks, and a drawdown of oil inventories, suggests higher oil
prices and upside risks over the forecast horizon. However, there is merit in a combination of forecasts from this model and futures, which points to flat prices this
year, rising gradually thereafter.
Figure 1.SF.3. Vector Autoregression and Combination Forecasts
Sources: Bloomberg, L.P.; IMF, Primary Commodity Price System; Organization for Economic Cooperation and Development (OECD); and IMF staff estimates.
Note: OPEC = Organization of the Petroleum Exporting Countries; VAR = vector autoregression.
1
See Beidas-Strom and Pescatori (forthcoming) for more details on the chosen identification.

error (RMSE) of the forecast. The models that were
assessed were the random walk (RW) with and without
drift, futures, simple autoregressive (AR(p)) and mov-
ing average (MA(q)) processes, a combination of these
in the form of ARMA (1,1), and various specifica-
tions of the VAR. The VAR outperforms the RW by
about 20 percent for horizons of 5 to 8 months and
18 months. In the very short term (1 to 2 months)
and at 24 months, the VAR model outperforms the
RW by about 10 to 12 percent. For all other horizons,
the accuracy gains are about 15 percent. Compared
with the futures forecast, the gains from the VAR
forecast are as large as 26 percent for the 1-month
horizon, between 10 and 20 percent for horizons up to
18 months, and 5 percent for the 24-month horizon
(Table 1.SF.1).
In addition to RMSEs of the full sample, two-year
rolling averages are obtained to address potential time
variation of the parameters. These averages indi-
cate that the VAR delivers the lowest RMSE among
comparators, particularly during the global financial
crisis and the subsequent period, including the 2011
slowdown. It is interesting to note, however, that
its performance is no better than futures or the RW
model during the 2001 recession (Figure 1.SF.4).
Which Model Should Be Used?
In view of the considerable forecast uncertainty for
oil prices irrespective of the underlying models, it
could be useful to employ several forecasting methods
to hedge. For oil prices specifically, an abundance of
non-OPEC supplies could presage a change in the oil
market configuration compared with that prevailing
over the past two decades. Indeed, the merits of com-
bination forecasts have long been established (Bates
and Granger, 1969; Diebold and Pauly, 1987; Stock
and Watson, 2004). More recently, it has been argued
that the forecasting model with the lowest RMSE may
potentially be improved by incorporating information
from other models or macroeconomic factors (Bau-
meister and Kilian, 2013a).
A combination forecast is presented (Figure 1.SF.3,
panel 6), based on an inverse weighting of recent
RMSE performance of futures and the VAR model.
Although it is evenly weighted for very short hori-
zons, forecasting performance at the outer end of the
24-month forecast horizon was better for the VAR
model, and hence the combination tends to follow the
VAR forecast more closely at that end. The forecast
combination yields a Brent price of $108 a barrel dur-
ing 2014 ($6 lower than the VAR, but $3 higher than
futures), rising to an average of $114 a barrel in 2015
($8 lower than the VAR, but $14 higher than futures).
0
10
20
30
40
50
0
25
50
75
100
125
150
2000 02 04 06 08 10 12
0
2
4
6
8
10
12
14
16
0
25
50
75
100
125
150
2000 02 04 06 08 10 12
Brent price (right scale) VAR
Futures Random walk
1. Rolling RMSE for the 1-Month Forecast Horizon
2. Rolling RMSE for the 12-Month Forecast Horizon
When comparing the root-mean-squared errors of forecasts with a rolling two-
year window, or as in Table 1.SF.1 over the full forecast horizon, the VAR forecast
performs better than that of other models and futures since 2000, although not in
each year when the rolling window is used.
Figure 1.SF.4. Rolling Root-Mean-Squared Errors: Recursive
Estimation
Note: The line closest to the horizontal axis represents the model with the
smallest forecast errors and thus the one with the best forecasting performance.
RMSE = root-mean-squared errors of the forecast; VAR = vector autoregression.

Table1.SF.1.Root-Mean-SquaredErrorsacrossForecastHorizonsh(RelativetotheRandomWalkModel)
Model
SimpleForecastModelsVARModels
RWRWw/DriftAR(6)MA(3)ARMA(1,1)FuturesABCDEFGHIJ
15.1931.0010.9580.9610.9631.208***0.9190.8940.9461.0080.9270.9490.9781.1450.9890.913
28.6771.0040.9760.9870.9871.0110.8950.8820.9741.0820.9260.9060.9221.1130.9890.888
311.5131.0070.9730.9970.9941.0160.8430.8290.9491.0540.8950.8550.8521.0540.9690.835
413.7991.0100.9751.0081.0031.0150.8350.8260.9771.0780.9030.8520.8291.0230.9630.811
515.6481.0130.9741.0131.0071.0130.8180.8050.9801.1210.9010.8340.8000.9810.9520.784
617.1721.0160.9791.0211.0131.0060.8190.7980.9811.1890.9090.8220.7910.9160.9600.787
718.3371.0180.9821.0281.0160.9980.8220.8030.9881.2330.9190.8150.7870.8590.9690.807
819.2431.0190.9841.0321.0190.9890.8350.8201.0091.2690.9380.8230.8050.8290.9790.838
919.8791.0200.9871.0361.0220.9800.8550.8471.0381.2890.9610.8430.8450.8220.9980.871
1020.2831.0210.9881.0341.0220.9730.8770.8741.0701.2960.9880.8720.8820.8371.0250.898
1120.7061.0210.9871.0321.0220.9640.8830.8811.0861.2621.0000.8880.8990.8461.0490.907
1221.2401.0210.9851.0321.0220.9520.8730.8731.0851.2110.9960.8840.8960.8481.0590.900
1522.5611.0210.9801.0361.0230.9250.8520.8401.1031.2701.0140.8700.8740.8591.0570.862
1823.2761.0180.9811.0321.0210.9180.820*0.796*1.1081.3871.0350.8270.8180.818*1.0550.809**
2123.9291.0080.9821.0181.0100.9260.853*0.842*1.1491.1291.0960.8600.854*0.836**1.1170.864**
2425.3421.0050.9761.0111.0060.9320.8910.8821.1841.0951.1320.8970.8910.8781.1510.924
Source:IMFstaffcalculations.
Note:Valueslessthanoneindicatesuperiorityoftheforecastmodelcomparedwiththerandomwalk.Boldfacevaluesindicatethebestforecastmodel.Valueswith*,**,and***indicaterejectionofthenullhypothesisofequal
predictiveabilityofthecandidatemodelandtherandomwalkmodelbytheDiebold-Marianotestatthe10,5,and1percentlevels,respectively.Allvectorautoregression(VAR)modelsAthroughJareinlogdifferences,except
modelE,whichisinloglevels.Allhave6lags,exceptmodelD,whichhas12.ModelBincludestheexchangerateindex.ModelFdifferentiatesbetweenemergingmarketindustrialproductionandadvancedeconomyindustrial
production.ModelsGandHdisaggregateoilproductionbetweenregions.ModelJistheonepresentedinthisSpecialFeature,withthedetrendedrealoilprice.SeeBeckersandBeidas-Strom(forthcoming)formoredetails.
Rowsrepresenthorizoninmonths.AR=autoregression;ARMA=autoregressionandmovingaverage;MA=movingaverage;RW=randomwalk.

The financial nature of the recent global crisis has
led to renewed interest in understanding the impor-
tance of credit supply conditions for economic growth.
This issue remains relevant today inasmuch as several
countries are still dealing with residual weaknesses in
the banking sector. In particular, the ongoing contrac-
tion of bank lending to nonfinancial firms in the euro
area is raising concerns that tight lending conditions
may still be acting as a drag on economic growth. This
box presents an empirical assessment of the impor-
tance of credit supply shocks in constraining economic
growth since the beginning of 2008 in the United
States; the four largest economies of the euro area
(France, Germany, Italy, Spain); and Ireland, which
experienced a severe banking crisis. The findings reveal
that Germany and the United States have almost
entirely reversed the credit supply tightening expe-
rienced during the crisis. In contrast, further policy
action to revive credit supply in France, Ireland, Italy,
and Spain could increase GDP by 2 percent or more.
Identifying credit supply shocks is not a simple task
because variables that are commonly used to monitor
credit conditions, such as credit growth and lending
rates, reflect both demand and supply factors. This
box isolates credit supply conditions by relying on
measures of bank lending standards that reflect lending
terms and the criteria used by banks for the approval
of loans.1
Even these measures, however, cannot be treated
as pure measures of credit supply shocks—banks
can adjust lending standards not only in response to
changes in their own risk attitudes, regulatory require-
ments, or exogenous shocks to their balance sheets,
but also because of variations in credit demand and
borrowers’ creditworthiness. For example, banks are
likely to tighten lending standards when an ongoing or
incipient recession reduces credit demand and under-
mines borrowers’ repayment capacity.
To address this identification problem, a parsimo-
nious vector autoregression (VAR) is estimated at
quarterly frequency from the first quarter of 2003
to the third quarter of 2013. The VAR includes real
GDP growth, expected GDP growth for the next
The authors of this box are Andrea Pescatori and Damiano
Sandri.
1Lending standards have been used in similar analyses of both
the United States (Lown and Morgan, 2006; Bassett and others,
forthcoming) and the euro area (de Bondt and others, 2010).
quarter, and changes in bank lending standards on
loans to firms. Credit supply shocks are isolated by
imposing in the VAR that they result in an immediate
change in lending standards without a contempora-
neous impact on current or expected GDP growth.
Shocks that move lending standards as well as actual
or expected GDP growth within the same quarter
are not interpreted as credit supply shocks. They are
instead a hodgepodge of domestic and nondomestic
shocks that, by affecting current and expected output,
may also induce changes in lending standards. For
example, news about an incipient recession that results
in a downward revision of expected GDP growth and
a tightening of lending standards is not considered a
credit shock.
There are three main concerns with regard to pos-
sible limitations of the identification strategy. On the
one hand, the identification restriction may be very
conservative. A credit supply shock, especially if real-
ized at the beginning of the quarter, is likely to have
already had some effects on GDP within the same
quarter, or at least on the expectations of next-quarter
GDP. Ignoring this likelihood introduces a downward
bias in the estimates; thus the estimation framework
provides a conservative assessment of the effects of
credit supply shocks on GDP growth. On the other
hand, current and expected GDP growth may not
fully capture banks’ perceptions of borrowers’ cred-
itworthiness. In this case, the estimation framework
risks overestimating the role of credit supply shocks.
Finally, the estimation results could be affected by
omitted variable bias because the limited time series of
lending standards (available only from 2003 onward)
does not allow for a larger-scale VAR or by structural
breaks in the credit-activity nexus after the global
financial crisis.
Figure 1.1.1 shows the cumulative effect on real
GDP of a credit supply shock that causes a 10 per-
centage point tightening of lending standards. This
is similar to the cross-country average of the shocks
experienced at the time of the Lehman Brothers bank-
ruptcy shown in Figure 1.1.2. The estimated impact
on GDP is negative and statistically significant across
all countries. In France, Italy, and the United States,
the shock leads to a total cumulative contraction in
GDP of about 1 percent. Credit supply shocks seem to
have a stronger effect on GDP in Germany (1.8 per-
cent) and especially in Spain and Ireland (2.2 percent
and 4.0 percent, respectively), where nonfinancial
Box 1.1. Credit Supply and Economic Growth

firms have been much more dependent on bank
credit. However, the confidence bars show that these
cross-country differences are generally not statistically
significant.
Figure 1.1.1 also shows that credit supply shocks
have a more immediate effect in France, Germany,
and Italy, where the maximum contraction in GDP
is reached within 6 quarters. The effect is more
delayed in the United States and especially in Ireland
and Spain, where credit supply shocks continue to
reduce GDP for up to 16 quarters. It is interesting to
note that in all countries credit supply shocks have a
permanent effect on GDP, suggesting that unresolved
problems in the banking sector may have an enduring
detrimental effect on output.
In assessing the importance of credit supply shocks
in reducing growth since 2008, it is important to con-
sider not only how a given shock affects GDP, but also
the size and frequency of shocks. Figure 1.1.2 plots the
credit supply shocks identified by the VAR; positive
values indicate a tightening of credit conditions. The
figure shows significant differences across countries
that are broadly in line with anecdotal evidence about
the nature of the crisis. In France, Germany, and the
United States, the greatest tightening of credit supply
took place in the second half of 2008 at the time
of the Lehman Brothers bankruptcy. From then on,
credit conditions remained relatively stable, especially
in Germany (Figure 1.1.2, panel 1). In contrast,
Ireland, Italy, and Spain endured the largest shocks
later in the crisis. In Ireland credit supply contracted
sharply at the end of 2009, and experienced a large
negative shock at the time of Greece’s bailout. Italy
suffered a major credit supply contraction at the end
of 2011, when sovereign yields reached their peak.
Combining the size and frequency of credit supply
shocks (from Figure 1.1.2) with the impact that these
shocks have on GDP (from Figure 1.1.1) yields the
contribution of credit supply shocks to GDP for a given
period. Figure 1.1.3 shows the cumulative contribu-
tion of these shocks relative to GDP in the first quarter
of 2008.2 The confidence bands confirm that the tight-
ening of credit supply had a statistically significant nega-
tive effect on GDP, but they also highlight that there is
considerable uncertainty about the precise effects. When
the point estimates are examined, the results reveal
2In the absence of any shocks (including nonfinancial shocks),
GDP would have grown at its estimated trend, which varies from
country to country.
Box 1.1 (continued)
Source: IMF staff calculations.
–5
–4
–3
–2
–1
0
–5
–4
–3
–2
–1
0
1
1 4 8 12 16
1. France
–5
–4
–3
–2
–1
–5
–4
–3
–2
–1
–5
–4
–3
–2
–1
–5
–4
–3
–2
–1
0
1
1 4 8 12 16
2. Germany
1
1 4 8 12 16
3. Ireland
0
1
1 4 8 12 16
4. Italy
0
1
1 4 8 12 16
5. Spain
0
1
1 4 8 12 16
6. United States
Figure 1.1.1. Cumulative Responses of GDP
to a 10 Percentage Point Tightening of
Lending Standards
(Percent of GDP; point estimates and 2 standard
deviation bootstrapped conﬁdence bands; quarters
on x-axis)

that in France, Germany, and the United States, credit
supply shocks led to very similar GDP contractions of
about 3 percent by the beginning of 2009 (Figure 1.1.3,
panels 1, 2, and 6). The negative contribution of credit
supply shocks has subsequently moderated, especially
in Germany and the United States. The improvement
has been considerably weaker in France. As of the third
quarter of 2013, the total cumulative impact of credit
supply shocks in France, Germany, and the United
States had generated a reduction in GDP relative to
the beginning of 2008 of 2.2 percent, 0.9 percent, and
0.4 percent, respectively.
The impact of credit supply shocks on GDP is esti-
mated to have been considerably stronger in Ireland and
Spain, and to a certain extent in Italy, with differences
Box 1.1 (continued)
–30
–20
–10
0
10
20
30
40
2008 09 10 11 12 13:
Q3
Figure 1.1.2. Credit Supply Shocks
(Percentage point changes in lending standards)
Note: LTROs = longer-term reﬁnancing operations; OMTs =
Outright Monetary Transactions.
–30
–20
–10
0
10
20
30
40
2008 09 10 11 12 13:
Q3
1. France, Germany, and the United States
2. Ireland, Italy, and Spain
France
Germany
U.S.
Ireland
Italy
Spain
Lehman
bankruptcy
Greece
bailout
LTROs OMTs
Lehman
bankruptcy
Greece
bailout
LTROs OMTs
0
3
2008 10 12 13:
Q3
0
3
2008 10 12 13:
Q3
–15
–12
–9
–6
–3
–15
–12
–9
–6
–3
–15
–12
–9
–6
–3
–15
–12
–9
–6
–3
–15
–12
–9
–6
–3
–15
–12
–9
–6
–3
0
3
2008 10 12 13:
Q3
1. France 2. Germany
3. Ireland
0
3
2008 10 12 13:
Q3
4. Italy
0
3
2008 10 12 13:
Q3
5. Spain
0
3
2008 10 12 13:
Q3
6. United States
Figure 1.1.3. Contribution of Credit Supply
Shocks to GDP
(Cumulative contribution with respect to 2008:Q1
GDP; point estimates and 2 standard deviation
bootstrapped conﬁdence bands)

that are consistent with the prevalent narratives of
country-specific crises (Figure 1.1.3, panels 3, 4, and
5). Confronted with a severe banking crisis, Ireland
suffered the most from the contraction in credit supply.
According to the point estimates, the impact has been
dramatic, leading to a total reduction of about 10 per-
cent of GDP by the middle of 2010, with GDP losses
starting to reverse at the end of 2010.3 An important
caveat to these findings is the width of the confidence
bands. This suggests that the VAR may fail to capture
other important factors that may have affected the
relationship between credit and GDP growth in Ireland.
For example, Laeven (2012) uses micro data and finds a
more important role for credit demand factors after tak-
ing into account the structural shift from nontradables
to tradables production that occurred during the crisis.
In Italy in 2008, credit supply contracted less than
in France and Germany, consistent with the much
lower exposure to U.S. assets, and recovered tem-
porarily until the middle of 2011. However, credit
conditions severely deteriorated at the end of 2011,
when Italian sovereign yields increased sharply, leading
to a contraction in GDP of about 2 percent. Credit
conditions subsequently stabilized with a stronger
recovery in the middle of 2013. In Spain, credit sup-
3This impact is close to the reduction in GDP actually
experienced by Ireland between 2008 and 2010. However, this
should not be interpreted as suggesting that the severe recession
in Ireland was due entirely to a tightening of credit supply for
two reasons. First, explaining the crisis requires accounting not
only for the fall in GDP, but also for the lack of trend growth.
Second, there may have been other important contractionary
forces, possibly compensated for by other positive shocks, which
the VAR is unable to disentangle.
ply conditions exercised a delayed but continuous
negative effect on GDP from the beginning of 2008
through the first quarter of 2012. Some stabilization is
observed afterward, possibly thanks to the three-year
longer-term refinancing operation, Outright Monetary
Transactions, and the program supported by the Euro-
pean Stability Mechanism to recapitalize the banking
sector. Overall, supply shocks have led to contractions
in GDP in Ireland, Italy, and Spain of 3.9 percent,
2.5 percent, and 4.7 percent, respectively, with signifi-
cant confidence bands around these estimates as noted
earlier.
The historical contribution of credit supply shocks
shown in Figure 1.1.3 can also shed light on the
possible impact of policies to strengthen the bank-
ing sector, such as measures to boost bank capital or
further progress toward banking union in the euro
area. Indeed, the cumulative impact of credit supply
shocks can also be interpreted as the potential gains to
be realized from implementing financial sector poli-
cies that can undo the negative credit supply shocks
experienced since the beginning of 2008. Germany
and the United States have essentially already reversed
the negative effects of credit supply shocks, but con-
siderable payoffs remain for France, Ireland, Italy, and
Spain. In these countries, restoring the credit supply
to precrisis levels could lead to an increase in GDP,
relative to the first quarter of 2008, of 2.2 percent,
2.5 percent, 3.9 percent, and 4.7 percent, respectively.
As a caveat, policies to return credit supply to 2008
levels might not be desirable from a financial stability
perspective given the possibility that precrisis credit
conditions reflected excessive banking sector leverage
and imprudent risk taking.
Box 1.1 (continued)

Following three decades of rapid growth in China of
about 10 percent a year on average, the recent slowdown
has raised many concerns. Among them are the implica-
tions for global commodity markets: China’s demand
rebalancing may lead to lower commodity consumption
and prices and thus create adverse spillovers to commod-
ity exporters (Figure 1.2.1). This box delves into China’s
commodity consumption and its relationship to demand
rebalancing. The analysis finds that China’s commod-
ity consumption is unlikely to have peaked at current
levels of income per capita. Moreover, the pattern of its
commodity consumption closely follows the earlier paths
of other rapidly growing Asian economies.1 However,
recent shifts in the composition of China’s commodity
consumption are consistent with nascent signs of demand
rebalancing—private durable consumption has started
to pick up, while infrastructure investment has slowed.
Global (and Chinese) commodity consumption has been
rising and is predicted to continue to do so, but at a
slower pace for low-grade commodities and an accelerat-
ing one for higher-grade commodities—implying positive
spillovers for exporters of commodities, particularly of
higher-value commodities.
Growth in global commodity demand has moder-
ated somewhat, but China’s commodity consumption is
still rising. Since the global financial crisis, the growth
rate of global commodity consumption appears to
be slowing, relative to the boom in the middle of the
2000s, except in the case of food (Figure 1.2.2). This
slowdown has been accompanied by a compositional
shift in global commodity consumption. Specifically,
within primary energy, the growth rate of natural gas
consumption has risen faster than that of other fuels,
very basic food staples such as rice are giving way to
proteins (the sum of data for edible oils, meat, and
soybeans; excludes seafood and dairy, for which data are
incomplete), and base metal consumption has generally
shifted away from low-grade metals (copper and iron
ore) toward higher-grade ones (aluminum and zinc). In
China, the growth rate of commodity consumption has
also moderated, but is still robust. Within commodity
categories, patterns in energy, metal, and food con-
sumption per capita appear to be broadly in line with
The author of this box is Samya Beidas-Strom, with assistance
from Angela Espiritu, Marina Rousset, and Li Tang. For details
on the methodology and results summarized in this box, see
Beidas-Strom (forthcoming).
1As in Guo and N’Diaye (2010) and Dollar (2013), these
benchmarks are Japan, Korea, and Taiwan Province of China.
those recorded in other fast-growing Asian economies
(namely, Japan, Korea, and Taiwan Province of China)
a few decades earlier. Some idiosyncrasies are evident;
most notable are China’s considerably higher per capita
consumption of coal and high-protein foods. However,
recent shifts in composition commodity categories at
the global level are also evident in China. In particular,
rice has given way to higher-quality foods (edible oils
and soybeans, and to a lesser extent, meat); copper and
iron ore have recently been giving way to aluminum,
tin, and zinc; and coal has started to give way to cleaner
primary energy fuels. Chinese (and other emerging
market) demand for thermal coal softened in 2013 and
early 2014, consistent with the baseline forecast of the
International Energy Agency (2013).
The relationship between commodity consumption and
income can help gauge prospects for future commodity con-
sumption in China. The predicted relationship between
commodity consumption per capita and income per
capita and other determinants is based on cross-country
panel regressions estimated over the period 1980–2013
with country fixed effects for 41 economies (26
advanced: Australia, Austria, Belgium, Canada, Czech
Republic, Denmark, Estonia, Finland, France, Ger-
Box 1.2. Is China’s Spending Pattern Shifting (away from Commodities)?
Figure 1.2.1. China: Real GDP Growth and
Commodity Prices
40
60
80
100
120
140
160
180
200
6
7
8
9
10
11
12
13
14
15
1992 95 98 2001 04 07 10 13 16 19
Sources: IMF, Primary Commodity Price System; and IMF
staff estimates.
Commodity price index (2005 = 100; left scale)
Real GDP (annual rate, percent; right scale)

many, Iceland, Ireland, Israel, Italy, Japan, Korea, Lux-
embourg, Netherlands, New Zealand, Norway, Slovak
Republic, Slovenia, Spain, Sweden, United Kingdom,
United States; and 15 emerging or developing: Chile,
China, Croatia, Hungary, India, Iraq, Mexico, Malaysia,
Pakistan, Poland, Russia, South Africa, Taiwan Province
of China, United Arab Emirates, Vietnam). For pri-
mary energy, the nonlinear relationship with per capita
income predicted earlier (April 2011 World Economic
Outlook) still holds. The estimated regression is
eit = ai + P(yit) + uit, (1.2.1)
in which i denotes the country, t denotes years, e is
primary energy per capita, y is real per capita GDP,
P(y) is a third-order polynomial, and fixed effects
are captured by ai. Specifically, income elasticity of
energy consumption is close to one at current levels of
income per capita in China (as it was earlier in other
fast-growing Asian economies). In contrast, advanced
economies can sustain GDP growth with little if any
increase in energy consumption (Figure 1.2.3, panel
1). This relationship is flat for higher incomes—except
in the United States, where consumption has been
increasing with income per capita. What is new is the
analysis for base metals. The estimated regressions for
average metals and their components are the same as
that for energy but with added arguments: the share
of investment in GDP, the share of durables in private
consumption,2 and the growth rates for both. In
particular, the nonlinear relationship with per capita
income is a good predictor of metal consumption
at the early stages of income convergence,3 with an
income elasticity greater than one in China (and its
Asian comparators). The predicted metal consump-
tion curve reaches an inflection point at a much earlier
income threshold relative to energy, first slowing at the
threshold of $8,000 per capita, then reaching a plateau
at about $18,000 per capita, and thereafter falling
gradually (Figure 1.2.3, panel 2). Moreover, pre-
2Private consumption (durables, nondurables, and services)
for emerging markets is obtained by splicing the full data set
with data from CEIC Data, the Bureau of Economic Analysis,
the Economist Intelligence Unit, Euromonitor, Global Insight,
and the World Bank’s World Development Indicators household
surveys. Measurement error could be present for the “level,” but
here the interest is in “growth” effects. Hence, for the shares
of durables, nondurables, and services, private consumption is
reconstructed.
3Thereafter, the predicted curve falls rapidly to zero when
income per capita is the only determinant.
Box 1.2 (continued)
–20
–15
–10
–5
0
5
10
15
20
25
1981 86 91 96 2001 06 11 13
–30
–20
–10
0
10
20
30
40
50
60
70
1996 99 2002 05 08 11 13
Figure 1.2.2. Growth Rate of Global
Commodity Consumption
–10
–5
0
5
10
15
20
25
1986 89 92 95 98 2001 04 07 10 12
Advanced economies China
EMDE excluding China
1. Primary Energy, 1986–20121
(percent)
2. Metal, 1996–20132
(percent)
3. Food, 1981–20133
(percent)
Sources: British Petroleum Statistical Review; International
Energy Agency; U.S. Department of Agriculture; U.S. Energy
Information Administration; World Bureau of Metal Statistics;
World Steel Association; and IMF staff calculations.
Note: EMDE = emerging market and developing economies.
1
Coal, gas, and oil.
2
Aluminum, cadmium, iron ore, copper, lead, nickel, tin, and
zinc.
3
Barley, beef, corn, milk, palm oil, peanut oil, pork, poultry,
rapeseed oil, rice, soybean oil, soybeans, sunﬂower oil, and
wheat.

dicted consumption is rising in the growth rate of the
investment-to-GDP ratio (unlike for primary energy).
Since the growth rate of investment appears to be slowing
and consumption is beginning to rise in China, a further
disaggregation of base metal consumption could be war-
ranted to assess which metals are more sensitive to these
recent developments in investment and consumption. For a
few high-grade metals, such as aluminum and zinc, the
relationship is found also to be rising significantly in both
the share of durable consumption in private consump-
tion and its growth rate, with the consumption elasticity
significantly larger than one (and larger than that for
the average metal). Hence, the predicted consumption
per capita of high-grade metals grows briskly at levels
of income per capita below about $20,000 (relative to
the growth rate and the plateau predicted for average
metals). However, it falls more rapidly thereafter (relative
to average metals) (Figure 1.2.3, panel 3). This result
implies that investment, durables, and GDP growth more
broadly will come with higher consumption (with an
increasing growth rate) of these metals in the future—this
is likely also to hold true for some precious metals used in
high-end durable manufacturing, such as palladium—at
least until China’s income per capita is double the current
level. This is not the case for low-grade metals, for which
investment and GDP growth will soon be sustained
with lower consumption growth rates for these metals,
implying a slowing in future demand growth. Estimation
results confirm that copper and iron ore consumption
will continue to rise, but at a slowing rate as income rises,
similar to the experiences of China’s Asian benchmarks
earlier. At incomes of $15,000 per capita and higher, con-
sumption of copper and iron ore is predicted to fall more
rapidly than consumption of aluminum. Among base
metals, only copper futures are in backwardation. What
are the broader implications of this analysis, however, for
global commodity demand, and what are the links to
China’s demand rebalancing?
The predicted paths for metal consumption per capita are
consistent with slowing investment in infrastructure and
accelerating consumption of durables in China. Relative
to that in other emerging market economies, China’s
commodity consumption per capita is indeed high and
rising, as established. However, this is not unusual for
its early stage of income convergence given its growth
model, which broadly follows that of Korea and Taiwan
Province of China in the 1970s and 1980s and of Japan
some decades earlier. These benchmark economies relied
on a growth model led by exports, factor accumulation,
low private consumption, and high investment (Figure
Box 1.2 (continued)
0
5
10
15
20
25
30
0 10 20 30 40 50
Per capita income (thousands of PPP-adjusted U.S. dollars)
0
1
2
3
4
5
0 5 10 15 20 25 30
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50
AE China
EMDE G20AE
G20EM Japan
Korea Taiwan Province of China
Predicted
1. Energy
(Mtoe)
2. Metal
(thousand tons)
3. Aluminum
(thousand tons)
Figure 1.2.3. Actual and Predicted Per Capita
Commodity Consumption
Note: AE = advanced economies; EMDE = emerging market
and developing economies; G20AE = G20 advanced
economies; G20EM = G20 emerging market economies;
Mtoe = million tons of oil equivalent; PPP = purchasing
power parity.

1.2.4, panels 1 and 2). Differences between China and
these benchmark economies—studied in IMF (2011,
2013a); Hubbard, Hurley, and Sharma (2012); and
Dollar (2013)—are largely related to somewhat higher
investment-to-GDP and lower household-consumption-
to-GDP ratios, linked to China-specific social and
institutional factors. Private consumption in benchmark
economies also initially declined and later grew as income
began to converge, and their infrastructure investment
slowed concomitantly. China’s high investment (Ahuja
and Nabar, 2012; Roache, 2012) appears to be level-
ing off. This is particularly notable in the growth rate
of infrastructure, as some provinces near a threshold of
industrialization and infrastructure building (McKinsey
Global Institute, 2013).4 Thus, the observed slowing
in metals used heavily in infrastructure seems natural.
Meanwhile, private durables consumption is catching up
following a long delay (Figure 1.2.4, panel 3), perhaps
linked to the acceleration observed in the growth rate of
consumption of aluminum and other high-grade metals
(Deutsche Bank, 2013; Goldman Sachs, 2013a).5
Demand rebalancing should follow. Regression
results suggest that the growth rate of GDP and the
investment-to-GDP ratio drive private consumption
at the early stages of income convergence (before the
$10,000 per capita threshold), when low-grade com-
modities are intensively consumed.6 Thereafter, invok-
ing Eichengreen, Park, and Shin (2013), (higher) levels
of income and other domestic social and institutional
factors largely drive the share of durable consumption
(and services) when demand shifts toward high-grade
4The slowdown is observed for total real fixed-asset investment
during the second half of 2013, with a notable deceleration in
the growth rate during the fourth quarter of the year for invest-
ment directed toward the nontradable real estate, construction,
and infrastructure sectors.
5Industry analysis (Goldman Sachs, 2013b) corroborates this
finding: demand has been rising for high-grade metal-intensive
durables (for example, cars and dishwashers) and higher-end non-
durables (protein foods) and services (tourism and insurance).
6Same period and panel of economies; based on two separate
generalized least-squares panel regressions with fixed effects and
robust standard errors: one for the determinants of the ratio of
private consumption to GDP, the other for the share of durables
in consumption. The following domestic factors are found to
be statistically significant: financial repression or liberalization,
credit to state-owned enterprises, out-of-pocket health and
education private spending (Barnett and Brooks, 2010), and
demographics. Interestingly, foreign financing conditions and
household wealth (for example, house prices) are not found to be
statistically significant.
Box 1.2 (continued)
0.0
0.1
0.2
0.3
0.4
0 10 20 30 40 50
0.3
0.4
0.5
0.6
0.7
0.8
0 10 20 30 40 50
0.1
0.2
0.3
0.4
0.5
0 10 20 30 40 50
Figure 1.2.4. Spending Patterns
AE China EMDE
G20AE G20EM Japan
Korea
1. Total Investment as a Percent of GDP
2. Private Consumption as a Percent of GDP
3. Percent of Durables in Private Consumption
Note: AE = advanced economies; EMDE = emerging market
and developing economies; G20AE = G20 advanced
economies; G20EM = G20 emerging market economies;
PPP = purchasing power parity.

commodities. Such predictions of the determinants of
domestic demand components appear to be consistent
with the shifting commodity composition and spend-
ing pattern observed in China: toward high-grade
commodities and durables since 2012 and soften-
ing demand for low-grade commodities and slower
infrastructure investment during 2013, thus suggestive
of nascent demand rebalancing. Implementation of
the envisaged reforms outlined in the Third Plenum of
the 18th Central Committee, particularly the removal
of factor subsidies and administered credit, should lift
private labor income and foster further rebalancing.
Positive spillovers to both low- and high-grade com-
modity exporters should occur as commodity consump-
tion follows predicted relationships. Rebalancing does
not indicate that the level of China’s consumption
of commodities will peak—at least not until the
country’s per capita income doubles from current
levels. Rather, commodity consumption (glob-
ally and for China) is predicted to increase and to
continue to shift gradually toward high-grade foods
and metals as well as cleaner primary energy fuels.
However, exporters of basic and low-grade com-
modities (such as rice, copper, iron ore, and later,
coal) should expect Chinese demand to grow more
slowly as it shifts toward other commodities, with
increasing, positive spillovers to the exporters of these
commodities.
Box 1.2 (continued)

Could financial conditions unexpectedly tighten in
the world’s largest advanced economies? The ques-
tion arises because underlying inflation has been
running below objective in the euro area, Japan, and
the United States. In Japan, where the undershoot-
ing has persisted the longest, deflation has become
entrenched. Meanwhile, in the euro area and the
United States, the undershooting has already pulled
down shorter-term inflation expectations. If longer-
term inflation expectations start drifting down as a
result, there could be serious implications. Central
banks might find it difficult to ease monetary condi-
tions, because nominal interest rates are effectively at
the zero floor. In this case, real interest rates (based
on long-term expected inflation) would rise, tighten-
ing financial conditions and threatening the still-
fragile recoveries.
This box considers the ways in which central
banks can prevent longer-term expectations from
becoming unanchored. It does this by reviewing
the experiences of three seasoned inflation-targeting
countries (Canada, Czech Republic, Norway), as
well as the three largest advanced economies that
have adopted numerical inflation objectives (euro
area, Japan, United States), to see what lessons can
be drawn.1 Before proceeding, it is worth recall-
ing that keeping long-term inflation expectations
anchored at positive levels is not sufficient to rule
out the risk of undesirably low inflation: in Japan’s
case, inflation expectations remained positive for
many years, even as the economy slid into deflation
(Figure 1.3.1).
Inflation performance and short-term expectations
Low inflation is already putting downward pressure
on shorter-term inflation expectations. The Consensus
Economics survey of professional forecasters shows the
problem: inflation projections for 2014–15 are effec-
tively below objective in the six economies mentioned
The authors of this box are Ali Alichi, Joshua Felman, Emilio
Fernandez Corugedo, Douglas Laxton, and Jean-Marc Natal.
1Canada and Norway are useful to illustrate the difficulties of
balancing competing objectives; the Czech Republic highlights the
importance of having alternative instruments available to lift infla-
tion expectations when the policy interest rate is at the zero floor.
Box 1.3. Anchoring Inflation Expectations When Inflation Is Undershooting
–2
0
2
4
6
1999 2001 03 05 07 09 11 Dec.
13
Inflation objective
Actual inflation (year-over-year percent change)
Six- to ten-year-ahead expectations
One-year-ahead expectations
1. Euro Area
–2
0
2
4
6
1990 94 98 2002 06 10 Dec.
13
Adoption of numerical
objective (Jan. 2012)
2. United States1
–2
0
2
4
1990 94 98 2002 06 10 Dec.
13
Adoption of numerical
objective (Jan. 2013)
3. Japan
2,3
–2
0
2
4
6
1990 94 98 2002 06 10 Dec.
13
Adoption of numerical objective (March 2001)
4. Norway
Sources: Consensus Economics; and IMF staff calculations.
1
The implicit consumer price index (CPI) inflation objective
is estimated at about 0.3 percentage point above the
Federal Reserve’s official personal consumption
expenditures (PCE) inflation objective of 2.0 percent. This
is based on the difference in long-term CPI and PCE
inflation forecasts from the Federal Reserve Bank of
Philadelphia’s Survey of Professional Forecasters.
2
The announcement of the numerical inflation objective
was made in December 2012; implementation occurred in
January 2013.
3
In October 2013, the Japanese government announced
that the value-added tax rate would be increased by 3
percentage points, effective April 2014. This led to a sharp
rise in short-term inflation expectations.
Figure 1.3.1. Inflation Expectations in Euro
Area, United States, Japan, and Norway

above (Table 1.3.1).2 They rise over time, but even
by 2016 they are still projected to be below objective
in the euro area, Japan, and Norway.
Policy frameworks and long-term expectations
What are the risks that these decreases in shorter-
term expectations will feed into longer-term expecta-
tions? Evidence suggests the answer depends on the
policy framework. Figure 1.3.1 provides estimates
of longer-term inflation expectations (6 to 10 years
ahead) for the euro area, Japan, Norway, and the
United States. In the period before Japan and the
United States adopted numerical inflation objectives,
long-term expectations tended to move with short-
term expectations and actual inflation (in the United
States, mainly because it was still disinflating to levels
consistent with its long-term inflation objective).
In contrast, Canada established its constant 2 per-
cent inflation objective much earlier, and long-term
inflation expectations became firmly anchored to the
2Consensus Economics conducts a monthly survey of expected
consumer price inflation for the current year (2014) and the
next year (2015), and a semiannual survey (April and October)
of longer-term expected inflation. The inflation expectations for
Japan in 2014 embody a large transitory effect from a value-
added tax increase expected in April. Measures of underlying
inflation excluding value-added tax effects would be significantly
lower than the 2 percent objective.
target, notwithstanding short-term fluctuations (see
Table 1.3.1).3
This is not an accident. Once central banks
adopt numerical objectives, they devote consider-
able resources to ensuring that long-term inflation
expectations are well anchored. They use their inflation
forecasts to guide monetary policy actions, estimat-
ing the endogenous policy interest rate path that
should return inflation to the target. Most also publish
information about their forecasts to provide forward
guidance to the public.4 Thus, they can ensure their
monetary policy actions are consistent—and are seen
to be consistent—with bringing inflation back to its
objective over time.
Policy since the global financial crisis
In the immediate aftermath of the global financial
crisis, the largest advanced economies faced a dilemma.
They needed to provide massive stimulus to support
3Similarly, Capistrán and Ramos-Francia (2010) find that the
dispersion in short- and medium-term inflation expectations is
lower in inflation-targeting countries.
4The Czech National Bank and the Norges Bank publish the
path of the policy rate consistent with returning inflation to tar-
get, whereas the Bank of Canada simply uses words to describe
the policy assumptions in its baseline forecast. The Czech
National Bank and Norges Bank make it clear that the forecast is
an important input into policymaking, but not the only input.
Box 1.3 (continued)
Table 1.3.1. Consensus Consumer Price Index Inflation Expectations1
(Percent)
2014 2015 2016 Inflation Objective
Publish Policy-Consistent
Interest Rate Path?
Euro Area 1.1 (–0.3) 1.4 (–0.2) 1.8 2.02 No
Spain 0.7 (–0.6) 1.3 (–0.3) 1.7 . . . . . .
Italy 1.1 (–0.5) 1.3 (–0.4) 1.6 . . . . . .
France 1.2 (–0.3) 1.4 (–0.2) 1.7 . . . . . .
Germany 1.6 (–0.3) 2.0 (–0.1) 2.1 . . . . . .
Japan 2.3 (0.0) 1.6 (+0.3) 1.4 2.0 No
United States 1.6 (–0.2) 1.9 (–0.2) 2.3 2.33 Yes4
Canada 1.5 (–0.3) 1.9 (–0.1) 2.0 2.0 No, only use words
Sweden 0.9 (–0.4) 2.0 (–0.1) 2.2 2.0 Yes
Norway 2.0 (+0.1) 2.1 (0.0) 2.0 2.5 Yes
Czech Republic 1.3 (–0.3) 2.2 (+0.4) 2.0 2.0 Yes
New Zealand 2.0 (0.0) 2.3 (–0.1) 2.4 1.0–3.0 Yes
United Kingdom 2.3 (–0.2) 2.3 (–0.3) 2.8 2.0 No
Sources: Bank of England (2012); Consensus Economics; central bank websites; and IMF staff compilation.
1Data for 2014–15 are from a January 2014 Consensus Economics survey (deviations from the October 2013 benchmark survey in parentheses). Data
for 2016 are from an October 2013 benchmark Consensus Economics survey.
2Official European Central Bank objective is “below, but close to 2.0 percent.”
3The implicit consumer price index (CPI) inflation objective is estimated by the IMF staff at about 0.3 percentage point above the Federal Reserve’s
official personal consumption expenditures (PCE) inflation objective of 2.0 percent. This is based on the difference in long-term CPI and PCE inflation
forecasts from the Philadelphia Federal Reserve’s Survey of Professional Forecasters.
4In the United States, interest rate paths are from individual participants in the Federal Open Market Committee meeting.

the real economy in the near term, while keeping
long-term inflation expectations anchored. They also
realized that these objectives could be achieved with
a more transparent monetary policy framework that
focused on longer-term expectations, notwithstanding
short-term inflation fluctuations.5 Accordingly, the
Federal Reserve and Bank of Japan adopted numerical
inflation goals in 2012.
The postcrisis task of keeping long-term expecta-
tions anchored has proved difficult, however. Canada,
the Czech Republic, and Norway were early adopters
of inflation targeting and have relatively long histories
of communicating monetary policy under inflation
targeting.6 Yet in Norway long-term inflation expecta-
tions have actually been drifting downward.
Why is this happening? In part, it is because Norges
Bank has needed to strike a balance between its infla-
tion and financial stability objectives. For some time,
the bank has been concerned that credit (especially
to households) is growing too rapidly, building up
financial imbalances. Accordingly, it has maintained—
and is expected to maintain—policy rates above the
levels needed to bring inflation back to its objective.
Consequently, long-term inflation expectations have
fallen below target.
The Bank of Canada also has concerns about grow-
ing household debt, which may be why inflation is
expected to return to target only by 2016. Yet longer-
term expectations remain well anchored. Why the dif-
ference? One explanation may be the Bank of Canada’s
long track record in controlling inflation. It was one of
the first inflation targeters, implementing an inflation-
targeting framework a decade before Norges Bank. So
it has built considerable credibility.
The experience of the Czech Republic, meanwhile,
illustrates the advantages of having additional policy
instruments available when the policy rate has hit the
zero bound. Because the Czech Republic is a small and
open economy, the exchange rate is a powerful tool for
affecting prices, and given that the koruna’s exchange
5Based on data from before the global financial crisis, Levin,
Natalucci, and Piger (2004) and Box 4.2 of the September 2005
World Economic Outlook show that long-term inflation expecta-
tions were much better anchored in inflation-targeting countries
than in non-inflation-targeting countries.
6Canada was the first Group of Seven country to adopt
inflation targeting, in 1991, and now has more than 20 years
of experience with an inflation-targeting regime. The Czech
Republic and Norway adopted inflation targeting in 1997 and
2001, respectively.
rate was overvalued, foreign exchange intervention
was considered appropriate.7 So the central bank
intervened, accompanied by strong communications,
thereby lifting short-term inflation expectations while
keeping longer-term inflation expectations on target.
Conclusions
What can we conclude from these experiences? One
important lesson is that monetary policy frameworks
supported by numerical inflation objectives (such
as inflation targeting) can help prevent declines in
short-term inflation expectations from translating into
declines in longer-term expectations.
Frameworks can only help so much, however. A sec-
ond lesson is that implementation is also critical—and
difficult when central banks face conflicting objectives.
One strategy may be to assign macroprudential tools
to achieve financial stability goals. When these tools
need to be reinforced with a monetary stance that is
tighter than it would otherwise be, central banks will
need to explain how this will stabilize the economy
over the longer term, thereby ultimately helping to
achieve the inflation objective.
A third critical lesson is that central banks need
adequate tools. With policy rates near zero in many
countries, this is also problematic. There are few cases
in which foreign exchange intervention, as in the
Czech Republic, would be appropriate; a widespread
use of this tool could generate large spillovers, harming
the international system. That leaves other unconven-
tional monetary policies. Although these measures can
have longer-term costs, they have also helped avert
another Great Depression since the global financial
crisis.
Finally, to utilize these tools, central banks will need
operational independence, a key pillar of inflation con-
trol over the past two decades. Recent developments
in this area are not reassuring. The scope for extraor-
dinary interventions––including purchases of a broad
range of private or public sector assets––must not be
circumscribed by political considerations.
In the end, to keep expectations anchored, central
banks not only must talk the talk. They must also be
able to walk the walk.
7For an analysis of the Czech Republic’s exchange rate level,
see Box 3.1 of the April 2013 World Economic Outlook.
Box 1.3 (continued)

The choice of exchange rate regime is a perennial
issue faced by emerging markets. Conventional wis-
dom, especially after the emerging market crises of the
late 1990s, was a bipolar prescription: countries should
choose between floats (the soft end of the prescrip-
tion) and hard pegs (monetary union, dollarization,
currency board). The thinking was that intermediate
regimes (conventional pegs, horizontal bands, crawling
arrangements, managed floats) left countries more
susceptible to crises. The experience of some European
emerging market economies as well as some euro area
economies during the global financial crisis, however,
suggests that hard pegs may make countries more
prone to growth declines and painful current account
reversals, in which case the safety of the hard end of
the prescription may be largely illusory.
The soft end of the prescription is also a bit murky.
An often-overlooked question is what constitutes a
“safe” float—that is, where to draw the line between
floats and riskier intermediate exchange rate regimes.
Although occasional intervention during periods of
market turbulence or extreme events does not turn a
float into an intermediate regime, there remains the
question of how much management of the exchange
rate is too much.
Evolving regimes
These issues are clearly relevant to policy, given that
an increasing number of emerging market central banks
have switched from free floats to de facto managed
floating, conventionally defined as regimes in which
the central bank influences exchange rate movement
through its policies without (at least explicitly) target-
ing a particular parity.1 In fact, based on the IMF’s de
facto exchange rate regime classification, the trend of
“hollowing out of the middle”—countries abandoning
intermediate regimes mostly in favor of free floats—that
started in the immediate aftermath of the Asian crisis
The author of this box is Mahvash Qureshi, based on Ghosh,
Ostry, and Qureshi (2014).
1This is in contrast to free (or independent) floating, in which
the exchange rate is largely market determined. Different de
facto exchange rate regime classifications generally use different
identification criteria. For example, the IMF’s de facto classifica-
tion combines information about actual exchange rate volatility
and a central bank’s intervention policy with qualitative judg-
ment based on IMF country team analysis; Reinhart and Rogoff’s
(2004) classification takes into account exchange rate volatility
and the existence of parallel market exchange rates; Levy-Yeyati
and Sturzenegger (2005) consider the volatility of the nominal
exchange rate and that of international reserves.
of the late 1990s reversed around 2004 (Figure 1.4.1).
Since then, the proportion of intermediate regimes in
emerging market economies has increased (of which
managed floats is the most important category).
What explains this shift toward greater manage-
ment of the exchange rate? In the run-up to the global
financial crisis, the trend was likely motivated by the
surge in capital inflows to emerging market economies,
which raised concern about export competitiveness
and prompted efforts to limit currency appreciation.
During the crisis, however, as these economies faced
sharp declines in capital inflows (and in some cases
even large capital outflows), the purpose of interven-
tion was to support their currencies. Thereafter, the
ebbs and flows of capital to emerging market econo-
Box 1.4. Exchange Rate Regimes and Crisis Susceptibility in Emerging Markets
0
20
40
60
80
100
1980 83 86 89 92 95 98 2001 04 07 10
Hard peg Peg to single currency
Basket peg Horizontal band
Crawling peg Managed float
Free float
Figure 1.4.1. Distribution of Exchange Rate
Regimes in Emerging Markets, 1980–2011
(Percent)
Note: Based on the IMF’s de facto exchange rate regime
classification obtained from the IMF's Annual Report on
Exchange Arrangements and Exchange Restrictions. Hard
pegs include dollarization, currency unions, and currency
boards.

mies have led to alternating concern about currency
appreciation and depreciation—but in either case,
concern about exchange rate volatility, hence the desire
to manage exchange rates.
Regimes, vulnerabilities, and crisis susceptibility
Empirical analysis of the vulnerabilities and risks of
crises under different exchange rate regimes in a sam-
ple of 50 emerging market economies for 1980–2011
suggests that macroeconomic and financial vulnerabili-
ties (such as currency overvaluation, delayed external
adjustment, rapid credit expansion, excessive foreign
borrowing, and foreign-exchange-denominated domes-
tic currency lending) are generally significantly greater
under less flexible exchange rate regimes—including
hard pegs—compared with those under both managed
and free floats. Although not especially susceptible to
banking or currency crises, hard pegs are significantly
more prone to growth collapses than are floats.
Overall, intermediate regimes as a class are the most
susceptible to crisis, but managed floats behave much
more like pure floats, with significantly lower risks and
fewer crises (Figure 1.4.2). Among other factors, exces-
sive credit expansion, real exchange rate overvaluation,
bank foreign liabilities, and large current account defi-
cits are associated with a significantly higher likelihood
of banking and currency crises, whereas more foreign
exchange reserves lower the likelihood. Higher external
debt also significantly raises the probability of banking
and sovereign debt crises, though the association weak-
ens when bank foreign liabilities and the fiscal balance
are included in the model.
Where to draw the line?
Less flexible exchange rate regimes are more prone
to various types of crisis, but what differentiates “safe”
managed floats from “risky” intermediate regimes?2
To delve deeper into what constitutes more risky
management of the exchange rate, a methodology is
adopted that characterizes the crisis susceptibility of
intermediate exchange rate regimes according to vari-
ous factors (such as exchange rate flexibility, degree
of foreign exchange intervention, overvaluation of
the real exchange rate, and financial stability risks)
while allowing for arbitrary thresholds and interactive
2This is a pertinent question, because existing exchange rate
regime classifications often give different information about the
exchange rate regime in a country, and the differences are the
most pronounced within the intermediate regime category.
effects among these factors.3 The results suggest that
there is no simple dividing line (for example, based on
exchange rate flexibility) between safe and risky inter-
mediate exchange rate regimes. Rather, what deter-
mines whether an intermediate regime is safe or risky
is a complex confluence of factors, including financial
vulnerabilities, exchange rate flexibility, degree of inter-
vention, and most important, whether the currency
3This is done through binary recursive tree analysis. A binary
recursive tree is a sequence of rules for predicting a binary vari-
able (for example, crisis versus noncrisis) on the basis of several
explanatory variables such that at each level, the sample is split
into two groups according to some threshold value of one of the
explanatory variables. The threshold value, in turn, is that which
best discriminates between crisis and noncrisis observations based
on a specific criterion (for example, minimizing the sum of type
I and type II errors).
Box 1.4 (continued)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
Bank Currency
Sovereign debt Growth
Figure 1.4.2. Predicted Crisis Probability in
Emerging Markets, 1980–2011
Note: Predicted probabilities are obtained from a probit
model of crisis likelihood evaluated at mean values of control
variables. See Ghosh, Ostry, and Qureshi (2014) for details of
the control variables included in each crisis likelihood
estimation and for definitions of crisis variables.
Single
curren-
cy peg
Basket
peg
Hori-
zontal
band
Crawl-
ing peg
Man-
aged
float
Free
float
Hard
peg

is overvalued. Thus, for example, among intermedi-
ate regimes, although the probability of a banking or
currency crisis is about seven times as high when the
real exchange rate is overvalued than when it is not,
the likelihood of a crisis in both cases is much greater
if domestic private sector credit has grown rapidly
(Figure 1.4.3). Furthermore, if the real exchange rate
is overvalued, intervention to prevent greater overvalu-
ation can reduce the risk of crisis, whereas interven-
tion to defend an overvalued exchange rate makes the
regime more vulnerable.
The upshot of the analysis is threefold. First,
although countries with hard pegs have fewer bank-
ing and currency crises than those using most other
regimes, they are more prone to growth collapses
because hard pegs impede external adjustment and
make it more difficult to regain competitiveness fol-
lowing a negative shock. Second, although countries
with pure floats are the least susceptible to crisis, most
emerging market central banks prefer at least some
management of their exchange rates, presumably
because of concerns about competitiveness or the bal-
ance sheet effects of sharp depreciations. Third, once
a central bank has chosen to manage the currency,
simply counseling that the exchange rate should be as
flexible as possible and that the central bank should
minimize its interventions may not be sufficient to
prevent crisis; rather, what differentiates safe from risky
managed floats is a complex set of factors, including
whether the central bank is defending an overvalued
currency or intervening to prevent further overvalu-
ation, and whether it has other instruments (such as
macroprudential measures or capital controls) that can
be deployed to mitigate financial stability risks.
Box 1.4 (continued)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Overvaluation No overvaluation
Overall Low credit expansion
High credit expansion
Figure 1.4.3. Probability of Banking or
Currency Crisis
Note: Results are obtained from binary recursive tree
analysis. Overvaluation is deﬁned as deviation of the real
effective exchange rate from trend in excess of 5 percent.
High (low) credit expansion is a cumulative change in the
domestic private-credit-to-GDP ratio of more (less) than
30 percentage points over three years.

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1
CHAPTER
2
CHAPTER
COUNTRY AND REGIONAL PERSPECTIVES
The global recovery is expected to strengthen, led
by advanced economies. Growth in emerging mar-
ket and developing economies is expected to pick up
only modestly. The balance of risks to global growth
has improved, largely reflecting better prospects in
advanced economies. However, important downside
risks remain—notably a yet-greater general slowdown in
emerging market economies; risks to activity from lower-
than-expected inflation rates in advanced economies;
incomplete reforms; and rising geopolitical tensions.
D
uring the second half of 2013, growth
in advanced economies rebounded by
1.3 percentage point and is expected to
strengthen further in 2014–15. Growth
is supported by monetary policy, reduced fiscal drag
(except in Japan), and easing crisis legacies amid
improving financial conditions in affected economies.
In the stressed euro area economies, growth is pro-
jected to remain weak and fragile as high debt and
financial fragmentation hold back domestic demand.
In Japan, fiscal consolidation in 2014–15 is projected
to result in some growth moderation. Still-large output
gaps in advanced economies highlight the continued
fragilities in the recovery.
Growth picked up only modestly in emerging
market and developing economies in the second half
of 2013—from 4.6 percent in the first half of 2013 to
5.2 percent in the second—although they continue to
contribute much of global growth. However, robust or
increasing growth was limited to the Asia and sub-
Saharan Africa regions, with most other regions expe-
riencing moderating or modest real growth rates. This
comes despite the broadly positive lift from exports
due to currency depreciation and the firming recovery
in advanced economies in many regions, along with
robust consumption supporting domestic demand.
A worrying development is the downgrade of growth
rates in a few large emerging market economies (e.g.,
Brazil, Russia, South Africa, Turkey) owing to domestic
policy weaknesses, tighter domestic and external finan-
cial conditions, or investment and supply constraints.
Hence only a modest pickup in growth in emerging
market and developing economies is expected this year
(Figure 2.1, panel 1).
Downside risks to global growth remain. Chief
among them is a renewed increase in financial market
volatility, especially in emerging market economies.
If this risk materializes, capital inflows to emerging
market and developing economies will likely decline,
and growth in these economies will be lower compared
with the baseline—with spillovers to advanced econo-
mies, as discussed in this chapter’s Spillover Feature.
The impact of a more prolonged slowdown in major
emerging market economies because of lower invest-
ment—a scenario described in detail in Chapter 1—is
shown in panel 2 of Figure 2.1. In advanced econo-
mies, downside risks to activity stem mainly from pros-
pects of low inflation and the possibility of protracted
stagnation, especially in the euro area and Japan. Other
downside risks include adjustment fatigue and insuffi-
cient policy action in a still financially fragmented euro
area and risks related to the exit from unconventional
monetary policy. On the upside, the stronger-than-
expected growth momentum during the second half of
2013 could buoy confidence in Germany, the United
Kingdom, and the United States.
The United States and Canada: Firming
Momentum
The U.S. economy grew at a faster-than-anticipated
pace in the second half of 2013, led by buoyant domes-
tic demand, robust inventory accumulation, and strong
export growth. Although the harsher-than-usual winter
weather may have slowed activity in early 2014, the
underlying fundamentals of private demand remain
strong, and growth is expected to advance at an above-
potential rate for the rest of this year. In Canada, annual
growth is expected to accelerate in 2014 thanks to stronger
external demand and rising business investment.
Growth in the United States was 1.9 percent in
2013, with the continued recovery of private domestic

Less than 0
Between 0 and 1
Between 1 and 2
Between 2 and 4
Between 4 and 6
Greater than or equal to 6
Insufficient data
1. 2014 GDP Growth Forecasts1
(percent)
2. Effects of a Plausible Downside Scenario
(peak growth deviation from 2014 baseline projections; percentage points)
Very large (greater than
0.75)
Large (between 0.60 and
0.75)
Moderate (between 0.40
and 0.60)
Small (between 0.20
and 0.40)
Minimal (less than or
equal to 0.20)
Insufficient data
Decrease in growth:
Figure 2.1. 2014 GDP Growth Forecasts and the Effects of a Plausible Downside Scenario
Note: Simulations are conducted using the IMF’s Flexible System of Global Models, with 29 individual countries and eight regions (other European Union, other
advanced economies, emerging Asia, newly industrialized Asia, Latin America, Middle East and North Africa, sub-Saharan Africa, oil exporters group). Countries not
included in the model are allocated to the regions based on the WEO classification of fuel exporters, followed by geographical regional classifications. Syria is
excluded due to the uncertain political situation. Ukraine is excluded due to the ongoing crisis.
1
The data for Argentina are officially reported data. The IMF has, however, issued a declaration of censure and called on Argentina to adopt remedial measures to
address the quality of the official GDP data. Alternative data sources have shown significantly lower real growth than the official data since 2008. In this context, the
Fund is also using alternative estimates of GDP growth for the surveillance of macroeconomic developments in Argentina. The Zimbabwe dollar ceased circulating in
early 2009. Data are based on IMF staff estimates of price and exchange rate developments in U.S. dollars. IMF staff estimates of U.S. dollar values may differ from
authorities’ estimates. Real GDP is in constant 2009 prices.

CHAPTER 2 COUNTRY AND REGIONAL PERSPECTIVES
demand partly offset by the hefty fiscal consolidation
effort, which subtracted between 1¼ and 1½ percent-
age points from GDP growth. Economic momentum
picked up during 2013; GDP grew at an average annu-
alized rate of 3.3 percent in the second half compared
with 1.2 percent in the first half. Consumer spending
also picked up, boosted by higher house and stock
prices and a further decline in household debt relative
to disposable income, which raised household net
worth above its long-term average (Figure 2.2). A faster
pace of inventory accumulation and strong export
growth (particularly in regard to petroleum products)
also contributed to sustained activity in the second half
of 2013. Mainly reflecting the October government
shutdown, government spending contracted signifi-
cantly at the end of the year, but financial conditions
remained highly accommodative, with long-term rates
declining after the sharp increase in mid-2013. The
unemployment rate continued to fall in 2013, reaching
6.7 percent in February 2014. However, a major fac-
tor behind the decline was a further drop in the labor
force participation rate, which stood at 63 percent
in February of this year (see Chapter 1). Still-ample
slack in the economy was manifest in subdued price
pressures, with headline consumer price index inflation
standing at 1.6 percent in February 2014. Largely on
account of increases in domestic energy production
and the associated drop in oil imports, the current
account deficit narrowed further to 2.3 percent of
GDP in 2013—the lowest in 15 years (Table 2.1).
The unusually harsh winter weather weighed on
activity in early 2014, but growth is expected to
rebound over the rest of the year—driven by strong
growth in residential investment (bouncing back from
very low levels and given substantial pent-up demand
for housing), solid personal consumption, and a
pickup in nonresidential fixed-investment growth as
consumer and business confidence improves. Growth
will also be supported by less fiscal drag, which is
declining to ¼ to ½ percentage point of GDP this
year, thanks in part to the Bipartisan Budget Act,
which replaced some of the automatic spending cuts
in fiscal years 2014 and 2015 with back-loaded sav-
ings. The debt limit has been suspended until March
2015, reducing the uncertainty that has characterized
fiscal policy in the past few years. Overall, growth is
projected to accelerate to 2.8 percent in 2014 and to
3.0 percent in 2015.
–2
–1
0
1
2
3
4
5
2010–11
12–13
14–15
2010–11
12–13
14–15
300
400
500
600
700
800
40
70
100
130
160
190
220
250
2006 08 10 12 13:
Q4
4. Household Net Worth
and Debt (percent of
disposable income)
–15
–10
–5
0
5
10
15
20
40
60
80
100
120
140
160
180
200
2006 08 10 12 Jan.
14
61
62
63
64
65
66
67
68
69
4
5
6
7
8
9
10
11
12
13
2008 09 10 11 12 Feb.
14
Figure 2.2. United States and Canada: Recovery Firming Up
200
600
1,000
1,400
1,800
2,200
2,600
3,000
2005 07 09 11 Dec.
13
1. Real Activity Indicators
(percent change)
3. House and Equity Prices1
5. U.S. Household
Formation
(thousand units; annu-
alized; four-quarter
moving average)
2. U.S. Labor Market
(percent)
Priv. cons. Net exports
U.S. CAN
–3
–2
–1
0
1
2
3
4
2007 09 11 13 15
GDP growth
6. U.S. Fiscal Impulse2
(percent of GDP)
U.S. net worth
CAN net worth
Labor force participation
rate
Unemployment rate
(right scale)
U.S. FHFA HPI
CAN MLS HPI
SP 500
SP/TSXRight scale:
U.S. household debt
CAN household debt
Right scale:
Household formation
precrisis average
Priv. nonres. inv.
Priv. res. inv.
Sources: Bloomberg, L.P.; Canadian Real Estate Association; Congressional
Budget Office; Haver Analytics; and IMF staff estimates.
Note: CAN = Canada; cons. = consumption; FHFA HPI = Federal Housing Finance
Agency Housing Price Index; inv. = investment; MLS HPI = Multiple Listing
Service Housing Price Index; nonres. = nonresidential; priv. = private; res. =
residential; SP = Standard Poor’s; TSX = Toronto Stock Exchange.
1
Year-over-year percent change for house prices and index; January 2005 = 100
for SP and TSX.
2
The fiscal impulse is the negative of the change in the primary structural
balance.
In the United States, growth in 2013 was higher than expected, and recent data
remain consistent with a further pickup in 2014 as improvement in the labor and
housing markets continues and the fiscal drag wanes. In Canada, growth
strengthened in 2013 and is expected to accelerate in 2014 as a result of rising
business investment and firming external demand.

The balance of risks is tilted slightly to the down-
side. On the external front, protracted sluggishness in
the euro area would weigh on growth, particularly if
deflation dynamics take hold. A slowdown in emerg-
ing market economies could also pose a risk, with
output growth declining by 0.2 percentage point in
response to a 1 percent reduction in those economies’
GDP (see this chapter’s Spillover Feature). On the
domestic front, private domestic demand could also
lose momentum if long-term yields rise more quickly
than expected without an associated improvement in
the outlook. In the medium term, heightened fiscal
sustainability concerns could pose additional downside
risks, while a continuation of the downward trend in
the labor force participation rate would further dent
potential output and, by reducing the slack in the
economy, lead to an earlier-than-expected tightening of
monetary policy. On the upside, a more buoyant hous-
ing market recovery, with feedback to and from lend-
ing conditions, balance sheets, and private demand,
remains a possibility. Moreover, greater confidence in
the economy’s prospects (resulting from a relatively
healthy financial sector and low energy costs) could
induce businesses to shift more aggressively from cash
hoarding toward real investment.
A balanced, gradual, and credible fiscal plan that
puts public debt firmly on a downward path contin-
ues to be the main policy priority. Such a plan would
involve measures to gradually rein in entitlement
spending, a revenue-raising tax reform, and replace-
ment of the sequester cuts with back-loaded new rev-
enues and mandatory savings. (The Bipartisan Budget
Act is a modest step in this direction.) Although the
continued economic momentum justifies the mea-
sured reductions in the Federal Reserve’s asset purchase
program, the overall monetary policy stance should
remain accommodative, considering the sizable slack
and steady inflation expectations (see Chapter 1). The
return to qualitative forward guidance in March 2014
can provide the Federal Reserve with greater flexibility
to achieve its employment and inflation goals. As the
date of the liftoff draws nearer, the Federal Reserve will
have to clearly convey to the market how it will assess
progress toward achieving those objectives, in order to
avoid an increase in policy uncertainty.
Canada’s economy strengthened in 2013, but the
much-needed rebalancing from household consump-
tion and residential construction toward exports and
business investment has not fully materialized. Growth
is expected to rise to 2.3 percent in 2014, up from
2 percent in 2013, with the projected pickup in the
U.S. economy boosting Canada’s export and business
investment growth (Table 2.1, Figure 2.2). Although
external demand could surprise on the upside,
downside risks to the outlook still dominate, includ-
ing from weaker-than-expected exports resulting from
competitiveness challenges, lower commodity prices,
and a more abrupt unwinding of domestic imbalances.
Indeed, despite the recent moderation in the housing
market, elevated household leverage and house prices
remain a key vulnerability (Figure 2.2). With infla-
tion low and downside risks looming, monetary policy
Table 2.1. Selected Advanced Economies: Real GDP, Consumer Prices, Current Account Balance, and
Unemployment
(Annual percent change unless noted otherwise)
Real GDP Consumer Prices1 Current Account Balance2 Unemployment3
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
Advanced Economies 1.3 2.2 2.3 1.4 1.5 1.6 0.4 0.5 0.4 7.9 7.5 7.3
United States 1.9 2.8 3.0 1.5 1.4 1.6 –2.3 –2.2 –2.6 7.4 6.4 6.2
Euro Area4,5 –0.5 1.2 1.5 1.3 0.9 1.2 2.3 2.4 2.5 12.1 11.9 11.6
Japan 1.5 1.4 1.0 0.4 2.8 1.7 0.7 1.2 1.3 4.0 3.9 3.9
United Kingdom4 1.8 2.9 2.5 2.6 1.9 1.9 –3.3 –2.7 –2.2 7.6 6.9 6.6
Canada 2.0 2.3 2.4 1.0 1.5 1.9 –3.2 –2.6 –2.5 7.1 7.0 6.9
Other Advanced Economies6 2.3 3.0 3.2 1.5 1.8 2.4 4.8 4.7 4.3 4.6 4.6 4.5
Note: Data for some countries are based on fiscal years. Please refer to Table F in the Statistical Appendix for a complete list of the reference periods for each country.
1Movements in consumer prices are shown as annual averages. Year-end to year-end changes can be found in Table A6 in the Statistical Appendix.
2Percent of GDP.
3Percent. National definitions of unemployment may differ.
4Based on Eurostat’s harmonized index of consumer prices.
5Excludes Latvia. Current account position corrected for reporting discrepancies in intra-area transactions.
6Excludes the G7 (Canada, France, Germany, Italy, Japan, United Kingdom, United States) and euro area countries but includes Latvia.

should remain accommodative until growth gains
further traction. Fiscal policy needs to strike the right
balance between supporting growth and rebuilding
fiscal buffers, especially at the federal government level,
with less room to maneuver at the provincial level.
Europe
Advanced Europe: From Recession to Recovery
Advanced European economies are expected to resume
growth in 2014, but inflation remains very low. Domestic
demand in the euro area has finally stabilized and turned
toward positive territory, with net exports also contrib-
uting to ending the recession. But high unemployment
and debt, low investment, persistent output gaps, tight
credit, and financial fragmentation in the euro area will
weigh on the recovery. Downside risks stem from incom-
plete reforms, external factors, and even lower inflation.
Accommodative monetary policy, completion of finan-
cial sector reforms, and structural reforms are critical.
The euro area has finally emerged from recession.
Activity shrank by about ½ percent in 2013, but
growth has been positive since the second quarter
after a long period of output decline (Table 2.2). The
turnaround—attributable, in part, to less fiscal drag
and some impetus from private domestic demand
for the first time since 2010—is materializing largely
as anticipated. Budding growth and greatly reduced
tail risks have buoyed financial markets, with marked
compression in sovereign spreads in stressed econo-
mies, although these spreads have increased modestly
with recent financial market volatility (see Chapter 1).
National and collective policy actions have contributed
to this positive turn of events.
Nevertheless, the legacy of the crisis—high unem-
ployment, weak private and public balance sheets,
contracting credit, and a large debt burden—and
longer-term impediments to growth must still be fully
addressed, raising concern about the strength and
durability of the recovery.
•• The recovery is uneven across countries and sectors.
Pockets of stronger growth, such as Germany, are
interspersed with stagnant or declining output else-
where. Growth remains largely export led, although
there has been an incipient revival in domestic
demand (for example, in France, Spain, and particu-
larly Germany). Private investment, however, has yet
to revive strongly across the euro area. Despite some
rebalancing (within the euro area), current account
balances have improved asymmetrically, with persis-
tent surpluses in some core economies and shrinking
external balances in deficit economies.
•• Substantial and persistent slack has led to a general
softening in inflation rates, which were already well
below the European Central Bank’s (ECB’s) objec-
tive (Figure 2.3).
•• Pending bank reform and private sector deleverag-
ing, financial fragmentation, though lessening, con-
tinues to impair monetary transmission. In countries
under stress, the private sector faces high lending
rates and contracting private sector credit.
•• Longer-term concerns about productivity and
competitiveness linger, despite important reforms in
several countries.
The euro area recovery is expected to continue in
2014 (Table 2.2), with growth forecast to be 1.2 per-
cent, reflecting a smaller fiscal drag, expectations of
improving credit conditions, and stronger external
demand. Euro area growth is projected to be about
1½ percent in the medium term. Persistently large
output gaps—except in the case of Germany—are
expected to moderate inflation to under 1¼ percent in
2014–15, well below the ECB’s objective of close to
2 percent for the foreseeable future.
Other advanced economies recorded stronger
growth, but durability is far from assured. Growth
has rebounded more strongly than anticipated in
the United Kingdom on easier credit conditions and
increased confidence. However, the recovery has been
unbalanced, with business investment and exports still
disappointing. Switzerland regained momentum driven
by domestic demand, and the exchange rate floor has
stemmed deflation. Sweden was held back by continu-
ing high unemployment, a strong krona, and structural
labor market weaknesses, although activity is forecast
to pick up this year on stronger external demand.
Notwithstanding a pickup in growth, downside risks
dominate. The euro area recovery could be derailed
should financial stress reemerge from stalled policy
initiatives. High unemployment could foster reform
fatigue, political uncertainty, and policy reversal,
jeopardizing hard-won gains. External shocks—tighter
financial conditions in the United States, financial
contagion and trade disruptions from geopolitical
events, and slower-than-expected emerging market
growth—could hurt growth and stability. For instance,
an external shock involving further growth disappoint-

ment in emerging market economies, if it materializes,
could spill over to the euro area given nonnegligible
trade linkages, and to the United Kingdom through
financial linkages (see this chapter’s Spillover Feature).
More positively, stronger-than-expected business senti-
ment could jump-start investment and growth.
A key risk to activity stems from very low infla-
tion in advanced economies. In the euro area, below-
target inflation for an extended period could deanchor
longer-term inflation expectations and complicate the
task of recovery in the stressed economies, where the
real burden of debt and real interest rates would rise.
Table 2.2. Selected European Economies: Real GDP, Consumer Prices, Current Account Balance, and
Unemployment
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
Europe 0.5 1.7 1.9 1.9 1.6 1.8 1.9 2.1 2.2 . . . . . . . . .
Advanced Europe 0.1 1.5 1.7 1.5 1.1 1.3 2.6 2.6 2.8 10.8 10.6 10.2
Euro Area4,5 –0.5 1.2 1.5 1.3 0.9 1.2 2.3 2.4 2.5 12.1 11.9 11.6
Germany 0.5 1.7 1.6 1.6 1.4 1.4 7.5 7.3 7.1 5.3 5.2 5.2
France 0.3 1.0 1.5 1.0 1.0 1.2 –1.6 –1.7 –1.0 10.8 11.0 10.7
Italy –1.9 0.6 1.1 1.3 0.7 1.0 0.8 1.1 1.1 12.2 12.4 11.9
Spain –1.2 0.9 1.0 1.5 0.3 0.8 0.7 0.8 1.4 26.4 25.5 24.9
Netherlands –0.8 0.8 1.6 2.6 0.8 1.0 10.4 10.1 10.1 6.9 7.3 7.1
Belgium 0.2 1.2 1.2 1.2 1.0 1.1 –1.7 –1.3 –1.0 8.4 9.1 8.9
Austria 0.4 1.7 1.7 2.1 1.8 1.7 3.0 3.5 3.5 4.9 5.0 4.9
Greece –3.9 0.6 2.9 –0.9 –0.4 0.3 0.7 0.9 0.3 27.3 26.3 24.4
Portugal –1.4 1.2 1.5 0.4 0.7 1.2 0.5 0.8 1.2 16.3 15.7 15.0
Finland –1.4 0.3 1.1 2.2 1.7 1.5 –0.8 –0.3 0.2 8.1 8.1 7.9
Ireland –0.3 1.7 2.5 0.5 0.6 1.1 6.6 6.4 6.5 13.0 11.2 10.5
Slovak Republic 0.9 2.3 3.0 1.5 0.7 1.6 2.4 2.7 2.9 14.2 13.9 13.6
Slovenia –1.1 0.3 0.9 1.6 1.2 1.6 6.5 6.1 5.8 10.1 10.4 10.0
Luxembourg 2.0 2.1 1.9 1.7 1.6 1.8 6.7 6.7 5.5 6.8 7.1 6.9
Latvia 4.1 3.8 4.4 0.0 1.5 2.5 –0.8 –1.6 –1.9 11.9 10.7 10.1
Estonia 0.8 2.4 3.2 3.5 3.2 2.8 –1.0 –1.3 –1.5 8.6 8.5 8.4
Cyprus6 –6.0 –4.8 0.9 0.4 0.4 1.4 –1.5 0.1 0.3 16.0 19.2 18.4
Malta 2.4 1.8 1.8 1.0 1.2 2.6 0.9 1.4 1.4 6.5 6.3 6.2
United Kingdom5 1.8 2.9 2.5 2.6 1.9 1.9 –3.3 –2.7 –2.2 7.6 6.9 6.6
Sweden 1.5 2.8 2.6 0.0 0.4 1.6 5.9 6.1 6.2 8.0 8.0 7.7
Switzerland 2.0 2.1 2.2 –0.2 0.2 0.5 9.6 9.9 9.8 3.2 3.2 3.0
Czech Republic –0.9 1.9 2.0 1.4 1.0 1.9 –1.0 –0.5 –0.5 7.0 6.7 6.3
Norway 0.8 1.8 1.9 2.1 2.0 2.0 10.6 10.2 9.2 3.5 3.5 3.5
Denmark 0.4 1.5 1.7 0.8 1.5 1.8 6.6 6.3 6.3 7.0 6.8 6.7
Iceland 2.9 2.7 3.1 3.9 2.9 3.4 0.4 0.8 –0.2 4.4 3.7 3.7
San Marino –3.2 0.0 2.2 1.3 1.0 1.2 . . . . . . . . . 8.0 8.2 7.8
Emerging and Developing
Europe7 2.8 2.4 2.9 4.1 4.0 4.1 –3.9 –3.6 –3.8 . . . . . . . . .
Turkey 4.3 2.3 3.1 7.5 7.8 6.5 –7.9 –6.3 –6.0 9.7 10.2 10.6
Poland 1.6 3.1 3.3 0.9 1.5 2.4 –1.8 –2.5 –3.0 10.3 10.2 10.0
Romania 3.5 2.2 2.5 4.0 2.2 3.1 –1.1 –1.7 –2.2 7.3 7.2 7.0
Hungary 1.1 2.0 1.7 1.7 0.9 3.0 3.1 2.7 2.2 10.2 9.4 9.2
Bulgaria5 0.9 1.6 2.5 0.4 –0.4 0.9 2.1 –0.4 –2.1 13.0 12.5 11.9
Serbia 2.5 1.0 1.5 7.7 4.0 4.0 –5.0 –4.8 –4.6 21.0 21.6 22.0
Croatia –1.0 –0.6 0.4 2.2 0.5 1.1 1.2 1.5 1.1 16.5 16.8 17.1
Lithuania5 3.3 3.3 3.5 1.2 1.0 1.8 0.8 –0.2 –0.6 11.8 10.8 10.5
1Movements in consumer prices are shown as annual averages. Year-end to year-end changes can be found in Tables A6 and A7 in the Statistical Appendix.
2Percent of GDP.
4Excludes Latvia. Current account position corrected for reporting discrepancies in intra-area transactions.
6Real GDP growth and the current account balance for 2013 refer to staff estimates at the time of the third review of the program and are subject to revision.
7Includes Albania, Bosnia and Herzegovina, Kosovo, FYR Macedonia, and Montenegro.

The priority is to set the stage for stronger and more
durable growth and tackle low inflation while ensur-
ing financial stability. The policy mix is complex and
interdependent, comprising fiscal and monetary policy,
financial sector restructuring and reform, and struc-
tural reforms.
•• Macroeconomic policies should stay accommoda-
tive. In the euro area, additional demand support is
necessary. More monetary easing is needed both to
increase the prospects that the ECB’s price stability
objective of keeping inflation below, but close to,
2 percent will be achieved and to support demand.
These measures could include further rate cuts and
longer-term targeted bank funding (possibly to
small and medium-sized enterprises). The neutral
fiscal stance for 2014 is broadly appropriate, but
fiscal support may be warranted in countries with
policy space if low growth persists and monetary
policy options are depleted. In the United Kingdom,
monetary policy should stay accommodative, and
recent modifications by the Bank of England to the
forward-guidance framework are therefore welcome.
Similarly, the government’s efforts to raise capital
spending while staying within the medium-term fis-
cal envelope should help bolster recovery and long-
term growth. Sweden’s supportive monetary policy
and broadly neutral fiscal stance remain adequate.
•• Repairing bank balance sheets and completing the
banking union are critical to restoring confidence
and credit in the euro area (see Chapter 1). To this
end, a sound execution of the bank asset qual-
ity review and stress tests are essential, supported
by strong common backstops to delink sovereigns
and banks, and an independent Single Resolu-
tion Mechanism to ensure timely, least-cost bank
restructuring. The United Kingdom should continue
to restore financial sector soundness, ensure that
stress tests are well coordinated with those of the
European Banking Authority, and guard against
any buildup of financial vulnerabilities, including
from surging house prices. Sweden should continue
to improve bank capitalization and liquidity and
introduce demand-side measures to curb household
credit growth. Switzerland should ensure that its
systemically important banks reduce leverage.
•• Despite progress, there is still need to increase
potential output and reduce intra-euro-area imbal-
ances through improved productivity and invest-
ment. Structural reforms to create flexible labor
–15
–10
–5
0
5
10
15
20
0
10
20
30
40
50
2009 10 11 12 Feb.
14
3. EA: Headline Inflation
(seasonally adjusted;
year-over-year percent
change)
Overall HICP
–6
–4
–2
0
2
4
6
8
EA
Germany
France
Italy
Spain
United
Kingdom
2. WEO Growth Projections
and Revisions (percent;
cumulative, 2013–14)
1
2
3
4
5
6
7
8
2007 08 09 10 11 12 Jan.
14
0
200
400
600
800
1,000
2010 11 12 Mar.
14
60
120
180
240
300
360
6
8
10
12
14
16
18
20
2005 06 07 08 09 10 11 12 13
–5
–4
–3
–2
–1
0
1
2
3
4
5
2002 04 06 08 10 12
5. SME Real Corporate Lending
Rates2
(percent)
4. EA: Debt and
Unemployment
(percent of GDP, un-
less noted otherwise)
6. EA: Current Account
Balances (percent of
EA GDP)
1. Stressed Euro Area:
Bank and Sovereign
CDS Spreads1
Sovereign
Bank
Jan. 2014 Latest
Germany
Italy
Spain
Germany
Italy
Spain
Min Max
General government debt
Total private debt
Unemployment rate
(percent; right scale)
Other surplus EA
Other deficit EA
Number of countries
in deflation (right
scale)
Output gap
Figure 2.3. Advanced Europe: From Recession to Recovery
Financial markets in advanced Europe have been buoyant because of receding tail
risks and the resumption of growth. Output gaps, however, remain large, reflected
in low inflation, which lies well below the ECB’s medium-term objective.
Unemployment rates are stubbornly high, and debt levels are on an upward
trajectory. Financial fragmentation persists. Current account balances have
improved asymmetrically, with persistent surpluses in some core economies.
Sources: Bloomberg, L.P.; European Central Bank (ECB); Eurostat; Haver Analytics;
and IMF staff estimates.
Note: Euro area (EA) = Austria, Belgium, Cyprus, Estonia, Finland, France,
Germany, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak
Republic, Slovenia, Spain. Stressed euro area = Greece, Ireland, Italy, Portugal,
Spain. CDS = credit default swap; HICP = harmonized index of consumer prices;
SME = small and medium-sized enterprises.
1
Bank and sovereign five-year CDS spreads in basis points are weighted by total
assets and general government gross debt, respectively. Data are through March
24, 2014. All stressed euro area countries are included, except Greece.
2
Monetary and financial institutions’ lending to corporations under €1 million, 1–5
years.

markets and competitive product and service
markets, ease entry and exit of firms, and sim-
plify tax systems would be necessary. Reducing
persistently large current account surpluses would
bring beneficial spillovers across the euro area; for
example, more public investment could lower the
current account surplus in Germany while also
raising growth in both Germany and the region. A
targeted implementation of the European Union
(EU) Services Directive would open up protected
professions. A more flexible wage formation process
would help address high unemployment in Sweden,
especially among vulnerable groups.
Emerging and Developing Europe: Recovery
Strengthening but Vulnerabilities Remain
Growth decelerated in emerging and developing Europe in
the second half of 2013 as the region contended with large
capital outflows. Despite positive spillovers from advanced
Europe, the recovery is expected to weaken slightly
in 2014. Fragilities in the euro area, some domestic
policy tightening, rising financial market volatility, and
increased geopolitical risks stemming from developments in
Ukraine pose appreciable downside risks. Policies aimed at
raising potential output remain a priority for the region.
During 2013 economic recovery in emerging
Europe continued to be driven by external demand,
except in the cases of Turkey and the Baltic countries,
where growth was led by private consumption. In con-
trast, the rise in private consumption reflected mostly
procyclical macroeconomic policies in Turkey, and in
the Baltic countries it reflected better labor market
conditions. After an initial improvement, financial
market volatility has increased since early fall in most
countries. As a result, the region, excluding Turkey,
experienced capital outflows (Figure 2.4).
Stronger growth in the euro area is expected to lift
activity in most of emerging and developing Europe.
However, the region as a whole will see slightly
weaker growth in 2014 than it did in 2013, mainly
on account of Turkey, whose economy is much more
cyclically advanced than those of other countries in the
region (Table 2.2).
•• Despite a projected improvement in net exports,
growth in Turkey is expected to weaken in 2014 to
2.3 percent from 4.3 percent in 2013, mainly as a
result of a sharp slowdown in private consumption
–4
0
4
8
12
16
20
24
2008 09 10 11 12 Feb. 14
–20–20
–28
0
20
40
60
80
2009 10 11 12 13:Q3
0
20
40
60
80
2009 10 11 12 13:Q3
75
100
125
150
175
200
225
250
275
Jan.
2013
May
13
Sep.
13
Jan.
14
Mar.
14
–21
–14
–7
0
7
14
21
28
2005 07 09 11 13 15 17
0
10
20
30
40
2009 10 11 12 13:Q3
–30
–20
–10
–30
–20
–10
0
10
20
30
40
2009 10 11 12 13:Q3
–20
0
20
40
60
80
100
120
2009 10 11 12 13
3. Core CPI Inflation1
change)
6. EMBIG Spreads4
(index,
May 21, 2013 = 100;
simple average)
5. Trade Linkages with Euro
Area (year-over-year
percent change)
8. Turkey: Capital Flows
(billions of U.S. dollars)
1. CEE and SEE: Real GDP
Growth (year-over-year
percent change)
2. Turkey: Real GDP Growth
change)
4. Nominal Credit to
Nonfinancial Firms
change; exchange rate
adjusted) CEE and SEE2
Turkey
Consumption
Investment
Net exports
Consumption
Investment
Net exports
Bulgaria Croatia
Hungary Poland
Romania Turkey
Euro area: Real
imports3
Croatia, Serbia, Turkey
Bulgaria, Hungary, Poland,
Romania
Total FDI Total FDI
Real GDP
growthReal GDP growth
CEE and SEE: Real GDP
Turkey: Real GDP
7. CEE and SEE: Capital
Flows (billions of U.S.
dollars)
Portfolio investment
Other investment
Portfolio investment
Other investment
Growth decelerated in emerging and developing Europe in 2013, as the region
contended with large capital outflows, tighter monetary conditions, and rising
financial market volatility.
Figure 2.4. Emerging and Developing Europe: Recovery
Strengthening, but with Vulnerabilities
Sources: Bloomberg, L.P.; CEIC Data Management; European Bank for
Reconstruction and Development; Haver Analytics; and IMF staff estimates.
Note: Central and eastern Europe (CEE) and southeastern Europe (SEE) include
Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Hungary, Kosovo, FYR
Macedonia, Montenegro, Poland, Romania, and Serbia, wherever the data are
available. All country group aggregates are weighted by GDP valued at purchasing
power parity as a share of group GDP unless noted otherwise. CPI = consumer
price index; EMBIG = J.P. Morgan Emerging Markets Bond Index Global; FDI =
foreign direct investment.
1
Data through February 2014 except in the case of Croatia (January 2014).
2
Data through third quarter of 2013.
3
Excludes Latvia.
4
Data through March 25, 2014.

driven by macroprudential measures, the sizable
exchange rate adjustment, and interest rate hikes.
Public investment will likely hold up in line with
the 2014 budget targets.
•• Growth in Hungary and Poland is forecast to
strengthen in 2014 to 2.0 and 3.1 percent, from
1.1 and 1.6 percent in 2013, respectively. In both
economies the strengthening is being driven by a
pickup in domestic demand, supported by monetary
easing, improvements in the labor market, and higher
EU funds, which are expected to boost public invest-
ment. In Hungary, still-high external vulnerabilities,
although declining, could weigh on growth.
•• As was the case last year, the growth pickup in
southeastern Europe will be moderate in 2014 at
about 1.9 percent, mostly on account of improv-
ing external demand. Domestic demand in a few
countries will benefit from EU spending. However,
demand will remain constrained because of slow
progress in resolving nonperforming loans, persistent
unemployment, and the need for fiscal consolidation
in some countries.
Inflation is expected to decline or remain moder-
ate in most countries in the region. Core inflation is
low in several countries and has been decreasing in
Bulgaria, Croatia, and Romania, reflecting a still-
negative output gap, depressed domestic demand, weak
bank credit, and negative external price developments,
among other factors (Figure 2.4). Deflation risks, how-
ever, are low for emerging Europe as domestic demand
takes hold and the effects of one-off factors dissipate.
Delayed recovery in the euro area and renewed
volatility in financial markets resulting from geopoliti-
cal events or the onset of Federal Reserve tapering are
the main downside risks across the region. Regional
growth is highly correlated with euro area growth,
and with strong financial links, the euro area remains
the main source of shocks for emerging and develop-
ing Europe. With large declines in portfolio invest-
ment, gross capital inflows to central and southeastern
Europe turned sharply negative in the third quarter
of 2013 and dropped substantially for Turkey (Figure
2.4). Accelerated outflows become a risk if financial
market volatility spikes again, with negative conse-
quences for financing still-sizable fiscal deficits in many
countries and external deficits in some. In addition,
a further escalation of geopolitical risks related to
Ukraine could have significant negative spillovers for
the region through both financial and trade channels.
Finally, uncertainties associated with the resolution of
foreign-currency-denominated mortgages in Hungary,
financial sector and corporate restructuring in Slovenia,
and achieving the needed fiscal discipline in Serbia also
weigh negatively on the outlooks for these countries.
Policies aimed at raising potential growth, including
by addressing high structural unemployment, making
progress in resolving the large stock of nonperforming
loans, and enhancing the role of the tradables sector,
remain a priority. Low growth largely reflects structural
rigidities in many countries, although negative output
gaps in most countries in the region also point to cycli-
cal weaknesses. However, room for policy maneuvering
is available only to a few: already-low policy rates and
the risk of renewed financial turmoil reduce the scope
for further monetary easing in most countries. At the
same time, elevated public debt and high headline fis-
cal deficits highlight the need for consolidation, largely
relying on expenditure cuts, in several countries.
Asia: Steady Recovery
Except in the case of Japan, growth in Asia picked
up in the second half of 2013 on recovering exports
and robust domestic demand. Global downside risks
are still significant and are particularly relevant for
economies already weakened by domestic and external
vulnerabilities. In addition, homegrown vulnerabilities
in China continue to rise, especially those stemming
from growth in credit. Policy priorities vary across the
region, with some economies tightening, whereas oth-
ers are still able to support growth. Supply-side reforms
would improve resilience and growth prospects.
Economic activity in Asia picked up speed in the
second half of 2013, as exports to advanced econo-
mies accelerated. Domestic demand has been solid,
and retail sales across much of Asia have been brisk.
Exports, particularly to the United States and the euro
area, have gained momentum. In Japan, while private
consumption and public spending remained robust,
GDP growth slowed in the second half of 2013 on
slow recovery of exports and a surge in import demand
due to sustained high energy imports and strong
domestic demand (see Chapter 1). Countries with
strong fundamentals and policies managed to navigate
the pressures seen in mid-2013 and early 2014 from
slowing capital flows, with many in emerging Asia
unscathed and looking more positive. Despite increas-

ing volatility, financial conditions remain accommoda-
tive, partly because weaker currencies are providing
some offset (Figure 2.5).
For Asia as a whole, growth is expected to accel-
erate modestly, from 5.2 percent in 2013 to about
5.5 percent in both 2014 and 2015 (Table 2.3). The
improved outlook in advanced economies, alongside
more competitive exchange rates in some cases, will
help boost exports. Domestic demand will continue to
be supported by strong labor markets and still-buoyant
credit growth. Policies are expected to remain accom-
modative, although in a few cases (India, Indonesia)
interest rate hikes on the one hand will attenuate
vulnerabilities, but on the other hand could weigh
on growth. In Japan, fiscal consolidation will be a
headwind. Inflation is expected to increase slightly,
albeit remaining generally low across the region, as
output gaps close. The main exceptions are India and
Indonesia, whose high inflation rates should continue
to moderate further.
•• In Japan, GDP growth is expected to moderate to
about 1.4 percent in 2014 as fiscal policy weighs on
activity. The positive effect of the recently approved
stimulus measures is expected to be more than offset
by the negative impact of the consumption tax hike
and the waning of reconstruction spending and past
stimulus measures. Monetary support will ensure
that financial conditions remain accommodative,
and inflation will rise temporarily to 2¾ percent this
year as a result of the consumption tax increase (see
Chapter 1).
•• In Korea, the economy should continue its recovery,
with growth accelerating to 3.7 percent in 2014.
Stronger growth will be driven mostly by exports,
which will be lifted by improving trading partner
demand. Domestic demand should also pick up,
benefiting from past fiscal stimulus and monetary
accommodation as well as continued robust labor
market conditions.
•• In Australia, growth is expected to remain broadly
stable at 2.6 percent in 2014 as the slowdown in
mining-related investment continues. In New Zea-
land, growth should pick up to 3.3 percent, helped
by reconstruction spending.
•• In China, growth recovered somewhat in the
second half of 2013 and should remain robust this
year, moderating only marginally to 7.5 percent,
as accommodative policies remain in place. The
announcement of the government’s reform blueprint
–20
–10
0
10
20
30
40
VNM
AUS
NZL
KOR
IND
JPN
IDN
PHL
TWN
CHN
MYS
THA
SGP
HKG
–20
–10
0
10
20
30
40
2010 11 12 13 Feb.
14
5. Change in Credit to GDP,
20145
(percentage points)
–6
–2
1
5
8
12
2011 12 13 Mar.
14
1. Asia (excl. JPN): Net Equity
and Bond Fund Flows1
–21
–19
–17
–15
–13
–11
–9
–7
–5
–2
–1
0
1
2
3
4
2010 12 Feb.
14
–30
–20
–10
0
10
20
30
IDN
THA
PHL
MYS
IND
AUS
TWN
CHN
SGP
HKG
JPN
KOR
NZL
–20
0
20
40
60
80
2010 11 12 Feb.
14
2. Changes in Bilateral
Exchange Rates and
Foreign Reserves2
(percent change since
May 2013)
6. Selected Asia: Retail Sales
Volumes6
(year-over-year
percent change)
JPN CHN AUSChange from
2012
Deviation from
trend
IND
JPN
–8
–6
–4
–2
0
2
4
6
8
2005 09 13:
Q4
3. Exports by Economies3
change)
4. India and Indonesia4
Trade Current
account
IND
IDNIND
IDN
(right
scale)
ASEAN (excl. PHL)
East Asia (excl. CHN)
Change in exchange rate;
US$ per national currency
Change in foreign reserves
ASEAN
CHN
East Asia (excl.
CHN)
Activity in Asia picked up in the second half of 2013 as exports recovered owing
to stronger demand from advanced economies. With domestic demand still
robust, growth is projected to rise to 5.5 percent in 2014 as external demand
recovers further.
Figure 2.5. Asia: Steady Recovery
Sources: Bloomberg, L.P.; CEIC; Haver Analytics; IMF, International Financial
Statistics database; and IMF staff calculations.
Note: Asia = Australia (AUS), China (CHN), Hong Kong SAR (HKG), India (IND),
Indonesia (IDN), Korea (KOR), Malaysia (MYS), New Zealand (NZL), Philippines
(PHL), Singapore (SGP), Thailand (THA), Taiwan Province of China (TWN), Vietnam
(VNM). ASEAN = Association of Southeast Asian Nations (IDN, MYS, PHL, SGP,
THA). East Asia = CHN, HKG, KOR, TWN. JPN = Japan. Country group aggregates
are weighted by purchasing-power-parity GDP as a share of group GDP.
1
Data include exchange-traded fund flows and mutual fund flows; data are through
Mar. 19, 2014.
2
Exchange rate data are for Mar. 2014; reserves data are for Feb. 2014 except in
the case of NZL (Jan. 2014) and CHN (Dec. 2013).
3
ASEAN data are through Jan. 2013.
4
Trade balance data are in three-month moving averages and are through Jan.
2014 for IDN. Current account balance data are in percent of GDP.
5
Latest monthly availability. Trend calculated using Hodrick-Prescott filter over the
period 2000–12.
6
AUS, CHN, JPN, and ASEAN (excluding PHL). Data are through Dec. 2013 for AUS;
Jan. 2014 for JPN, east Asia (excluding CHN), and ASEAN (excluding PHL). Linear
interpolation is applied on quarterly data for AUS.
Bond funds Equity funds
4-week moving
average
Peak 2006–07

has improved sentiment, but progress on rebalanc-
ing the economy remains tentative (see Box 1.2).
Fiscal reforms are expected to increase the efficiency
of the tax system, and ongoing financial reforms
should improve the allocation of capital and effi-
ciency of investment, although they could also create
some near-term volatility in China’s capital markets
(see Chapter 1). Although the inflation outlook is
expected to remain benign, concerns about over
investment and credit quality should mean a continu-
ation of the withdrawal of monetary support for the
economy through slower credit growth and higher
real borrowing costs.
•• India’s growth is expected to recover from 4.4 percent
in 2013 to 5.4 percent in 2014, supported by slightly
stronger global growth, improving export competitive-
ness, and implementation of recently approved invest-
ment projects. A pickup in exports in recent months
and measures to curb gold imports have contributed to
lowering the current account deficit. Policy measures to
bolster capital flows have further helped reduce external
vulnerabilities. Overall growth is expected to firm up
on policies supporting investment and a confidence
boost from recent policy actions, but will remain below
trend. Consumer price inflation is expected to remain
an important challenge, but should continue to move
onto a downward trajectory.
•• Developments in the Association of Southeast Asian
Nations (ASEAN) economies will remain uneven.
Indonesia’s growth is projected to slow this year as sub-
dued investor sentiment and higher borrowing costs
weigh on the domestic economy, although the cur-
rency depreciation since mid-2013 should give exports
a lift. In Thailand, the near-term outlook remains
Table 2.3. Selected Asian Economies: Real GDP, Consumer Prices, Current Account Balance, and Unemployment
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
Asia 5.2 5.4 5.6 3.5 3.9 3.7 1.4 1.6 1.6 . . . . . . . . .
Advanced Asia 2.1 2.3 2.2 1.1 2.4 2.2 2.0 2.1 2.0 4.0 4.0 4.0
Japan 1.5 1.4 1.0 0.4 2.8 1.7 0.7 1.2 1.3 4.0 3.9 3.9
Korea4 2.8 3.7 3.8 1.3 1.8 3.0 5.8 4.4 3.5 3.1 3.1 3.1
Australia 2.4 2.6 2.7 2.4 2.3 2.4 –2.9 –2.6 –2.8 5.7 6.2 6.2
Taiwan Province of China 2.1 3.1 3.9 0.8 1.4 2.0 11.7 11.7 10.9 4.2 4.2 4.1
Hong Kong SAR 2.9 3.7 3.8 4.3 4.0 3.8 3.1 3.3 3.9 3.1 3.1 3.1
Singapore 4.1 3.6 3.6 2.4 2.3 2.6 18.4 17.7 17.1 1.9 2.0 2.1
New Zealand 2.4 3.3 3.0 1.1 2.2 2.2 –4.2 –4.9 –5.4 6.1 5.2 4.7
Emerging and Developing Asia 6.5 6.7 6.8 4.5 4.5 4.3 1.1 1.2 1.4 . . . . . . . . .
China 7.7 7.5 7.3 2.6 3.0 3.0 2.1 2.2 2.4 4.1 4.1 4.1
India 4.4 5.4 6.4 9.5 8.0 7.5 –2.0 –2.4 –2.5 . . . . . . . . .
ASEAN-5 5.2 4.9 5.4 4.4 4.7 4.4 0.1 0.3 0.3 . . . . . . . . .
Indonesia 5.8 5.4 5.8 6.4 6.3 5.5 –3.3 –3.0 –2.7 6.3 6.1 5.8
Thailand 2.9 2.5 3.8 2.2 2.3 2.1 –0.7 0.2 0.3 0.7 0.7 0.8
Malaysia 4.7 5.2 5.0 2.1 3.3 3.9 3.8 4.0 4.0 3.1 3.0 3.0
Philippines 7.2 6.5 6.5 2.9 4.4 3.6 3.5 3.2 2.6 7.1 6.9 6.8
Vietnam 5.4 5.6 5.7 6.6 6.3 6.2 6.6 4.3 3.5 4.4 4.4 4.4
Other Emerging and
Developing Asia5
6.2 6.7 7.1 6.8 6.6 6.4 –2.1 –1.4 –1.2 . . . . . . . . .
Memorandum
Emerging Asia6 6.5 6.7 6.8 4.5 4.4 4.2 1.2 1.3 1.4 . . . . . . . . .
2Percent of GDP.
4Korea’s real GDP series is based on the reference year 2005. This does not reflect the revised national accounts released on March 26, 2014, after the WEO was finalized for
publication. These comprehensive revisions include implementing the 2008 System of National Accounts and updating the reference year to 2010. As a result of these revisions,
real GDP growth in 2013 was revised up to 3 percent from 2.8 percent.
5Other Emerging and Developing Asia comprises Bangladesh, Bhutan, Brunei Darussalam, Cambodia, Fiji, Kiribati, Lao P.D.R., Maldives, Marshall Islands, Micronesia, Mongolia,
Myanmar, Nepal, Palau, Papua New Guinea, Samoa, Solomon Islands, Sri Lanka, Timor-Leste, Tonga, Tuvalu, and Vanuatu.
6Emerging Asia comprises the ASEAN-5 economies, China, and India.

clouded by the political situation; the economy is
slowing as private demand weakens and public invest-
ment plans are delayed. Malaysia and the Philippines,
however, are on a more positive trajectory, and growth
is expected to remain robust in both countries.
•• For developing Asia, the economic outlook is largely
for continued solid growth with some additional
benefit from the ongoing recovery in world trade.
However, in Bangladesh, domestic demand is
expected to recover in 2014 as activity normalizes
following a year of political unrest. In addition,
macroeconomic imbalances related to rapid credit
growth and high current account deficits in Lao
P.D.R. and Mongolia are an ongoing risk.
Concerns linked to the external environment
remain, but Asia is also facing various idiosyncratic
domestic risks. Overall, there are three broad concerns
confronting the region in the coming year (see Chapter
1)—over and above more idiosyncratic risks stemming
from political tensions and uncertainties in several
countries (for example, Thailand):
•• Tightening global financial conditions: As growth in
the United States improves, Asia will have to adapt
to a steady increase in the global term premium.
Economies with weaker fundamentals and greater
reliance on global finance and trade would be most
affected. In some cases, the impact could be ampli-
fied by domestic financial vulnerabilities arising
from leverage in firms or households, thus negatively
affecting the balance sheets of banks.
•• Less effective Abenomics: In Japan, policy measures could
prove less effective at boosting growth than envisaged if
they fail to raise inflation expectations, nominal wages,
exports, and private investment. Slower growth could
have significant negative spillovers for economies with
strong trade and foreign direct investment linkages
with Japan, such as Indonesia and Thailand—especially
if the risk of deflation returns.
•• A sharper-than-envisaged slowdown and financial
sector vulnerabilities in China: A sharper-than-
envisaged slowdown in China—for instance, from
the implementation of structural reforms—would
have significant spillovers for the rest of the region,
especially in economies linked to the regional sup-
ply chain and commodity exporters. A near-term
financial crisis is unlikely, but given recent rapid
credit growth and the growth of shadow banking,
there could be continued news of credit problems
among the trusts or potential debt-servicing prob-
lems among local governments. These could spark
adverse financial market reaction both in China and
globally, but they might also improve the pricing of
risk and thus would be welcome.
In addition to tackling near-term vulnerabilities,
Asia should also continue to push ahead with struc-
tural reforms to enhance medium-term prospects.
Generally, reforms should focus on removing struc-
tural impediments to growth in India and across the
ASEAN economies through higher public and private
investment (particularly in infrastructure). In China,
reforms that liberalize the financial system and raise the
cost of capital will be key to improving the allocation
of credit and boosting productivity growth. In Japan,
structural reforms are needed to achieve a sustainable
pickup in growth and a durable exit from deflation.
Latin America and the Caribbean: Subdued
Growth
Economic activity in Latin America and the Caribbean
is expected to remain in relatively low gear in 2014. The
recovery in advanced economies should generate positive
trade spillovers, but these are likely to be offset by lower
commodity prices, tighter financial conditions, and supply
bottlenecks in some countries. Growth in the Carib-
bean remains constrained by high debt levels and weak
competitiveness. Policymakers need to focus on strength-
ening fiscal positions, addressing potential financial
fragilities, and pressing ahead with growth-enhancing
structural reforms to ease supply-side constraints.
Economic activity across Latin America and the
Caribbean stayed in relatively low gear last year.
Full-year growth for 2013 is estimated to have been
2¾ percent, significantly less than the growth rates
observed during previous years (Figure 2.6). Weak
investment and subdued demand for the region’s
exports held back activity, as did increasingly binding
supply bottlenecks in a number of economies. Coun-
tries with stronger fundamentals were generally affected
less by the market pressures in mid-2013 and early
2014 (see Chapter 1). Nonetheless, most currency,
equity, and bond markets across Latin America and the
Caribbean continue to trade well below the levels of
12 months ago, reflecting tighter external conditions
and a reassessment of medium-term growth prospects.
Looking ahead, regional growth is projected to
remain subdued in 2014, at 2½ percent. The recovery
in the advanced economies is expected to generate pos-
itive trade spillovers, but these are likely to be offset by

the impact of lower commodity prices, tighter financial
conditions, and supply-side constraints in some econo-
mies. However, there is considerable variation in the
outlook for different parts of the region (Table 2.4):
•• Growth in Mexico is expected to rebound to 3 percent
this year, after an unexpectedly weak growth rate of
1.1 percent in 2013. Several of the earlier headwinds to
activity have eased, with fiscal policy shifting to a more
accommodative stance and U.S. demand picking up.
Headline inflation is forecast to stay close to the upper
end of the inflation target range in the near term, as a
result of one-time effects of certain tax measures. How-
ever, core inflation and inflation expectations remain
well anchored. Looking further ahead, Mexico’s ongo-
ing economic reforms, especially in the energy and
telecommunications sectors, herald higher potential
growth for the medium term.
•• Brazil’s economy is expected to remain in low gear,
with growth slowing to 1.8 percent in 2014. Weighing
on activity are domestic supply constraints, especially
in infrastructure, and continued weak private invest-
ment growth, reflecting loss of competitiveness and low
business confidence. Inflation is expected to remain in
the upper part of the official target range, as limited
spare capacity and the recent depreciation of the real
keep up price pressures. The policy mix has been
skewed toward monetary tightening over the past year,
with fiscal policy (including policy lending) expected to
maintain a broadly neutral stance in 2014.
•• Among the other financially integrated economies,
Colombia and Peru are forecast to continue expanding
at fairly rapid rates. Activity in Chile is projected to
moderate somewhat because private investment growth
is decelerating markedly, including in the mining
sector. In all three countries, domestic consumption
remains brisk, supported by record-low unemployment
rates and solid growth in real wages. Nonetheless, price
pressures are projected to remain contained.
•• Activity in Argentina and Venezuela is expected to slow
markedly during 2014, though the outlook is subject to
high uncertainty. Persistently loose macroeconomic poli-
cies have generated high inflation and a drain on official
foreign exchange reserves. The gap between official and
market exchange rates remains large in both countries,
and has continued to widen in Venezuela. Administra-
tive measures taken to manage domestic and external
imbalances, including controls on prices, exchange
rates, and trade, are weighing further on confidence
and activity. Recently, both countries adjusted their
exchange rates, and Argentina raised interest rates, but
–200
–160
–120
–80
–40
0
40
80
–10
–8
–6
–4
–2
0
2
4
2007 09 11 13 14
Percent of GDP:
LA54
(right scale)
LAC5
(right scale)
3. LA5: Change in
Financial Market
Indicators since
End-April 20132
(percent, unless
noted otherwise)
–70
–50
–30
–10
10
30
50
–210
–150
–90
–30
30
90
150
Brazil
Chile
Colombia
Mexico
Peru
EMBI spread (basis
points, right scale)
US$ exchange rate
Equity market
–2
–1
0
1
2
3
4
5
6
Brazil
Chile
Colombia
Mexico
Peru
–40
–20
0
20
40
60
2007 08 09 10 11 12 13:
Q4
2. LAC: Nominal versus Real
Growth of Goods Exports
change)
4. LA5: Current Account
Balance (billions of U.S.
dollars, unless noted
otherwise)
6. LA5: Change in Interest
Rates since End-20122
(percentage points)
Brazil Mexico
–40
–30
–20
–10
0
10
20
30
40
50
2008 09 10 11 12 13:
Q3
1. Selected Latin American
Countries: Contributions
to Quarterly Real GDP
Growth1
(percentage points)
–6
–4
–2
0
2
4
6
8
2010 11 12 13 Feb.
14
5. LA6: 12-month CPI
Inflation Minus Inflation
Target (percentage points)
Brazil
Mexico
Uruguay
Real GDP
Consumption
Investment
Net exports
Nominal
Real
Policy rate
Ten-year bond rate
Rest of LA53
Average: Chile,
Colombia, Peru
Growth in Latin America and the Caribbean eased further in 2013, amid subdued
export performance and a continued slowdown in investment. Activity is expected
to remain in low gear this year, and renewed turbulence in financial markets
represents a downside risk, especially for economies with sizable external
funding needs or domestic policy weaknesses.
Figure 2.6. Latin America and the Caribbean: Subdued
Growth
Sources: Bloomberg, L.P.; Haver Analytics; IMF, International Financial Statistics
database; national authorities; and IMF staff estimates.
Note: CPI = consumer price index; EMBI = J.P. Morgan Emerging Markets Bond
Index; LAC = Latin America and the Caribbean. LA6 = Brazil, Chile, Colombia,
Mexico, Peru, Uruguay. LA5 = LA6 excluding Uruguay.
1
Weighted by GDP valued at purchasing power parity as a share of group GDP for
Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Paraguay, and Peru.
2
Data as of March 24, 2014.
3
Simple average for Chile, Colombia, and Peru.
4
Simple average.
5
Weighted by GDP valued at purchasing power parity as a share of group GDP.

more significant policy changes are needed to stave off a
disorderly adjustment.
•• Bolivia’s economy expanded strongly last year and is
expected to remain above potential in 2014, driven
by a sharp increase in hydrocarbon exports and
accommodative macroeconomic policies. Growth in
Paraguay also rebounded in 2013 as the agricultural
sector recovered from a severe drought.
•• Growth in Central America is expected to remain
broadly unchanged, at 4.0 percent, as the boost
from the pickup in economic activity in the United
States is offset by fiscal policy tightening in some
countries, the effects of a disease on coffee produc-
tion, reduced financing from Venezuela, and other
country-specific factors.
•• The Caribbean continues to face a challenging economic
environment, marked by low growth, high indebtedness,
and financial fragilities. Nonetheless, activity is expected
to recover modestly this year in the tourism-dependent
economies as tourism flows firm up.
Risks to the outlook remain considerable. On
the upside, a stronger-than-expected pickup in U.S.
Table 2.4. Selected Western Hemisphere Economies: Real GDP, Consumer Prices, Current Account Balance,
and Unemployment
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
North America 1.8 2.8 3.0 1.6 1.6 1.8 –2.3 –2.2 –2.5 . . . . . . . . .
United States 1.9 2.8 3.0 1.5 1.4 1.6 –2.3 –2.2 –2.6 7.4 6.4 6.2
Canada 2.0 2.3 2.4 1.0 1.5 1.9 –3.2 –2.6 –2.5 7.1 7.0 6.9
Mexico 1.1 3.0 3.5 3.8 4.0 3.5 –1.8 –1.9 –2.0 4.9 4.5 4.3
South America4 3.2 2.3 2.7 8.1 . . . . . . –2.7 –2.8 –2.9 . . . . . . . . .
Brazil 2.3 1.8 2.7 6.2 5.9 5.5 –3.6 –3.6 –3.7 5.4 5.6 5.8
Argentina5,6 4.3 0.5 1.0 10.6 . . . . . . –0.9 –0.5 –0.5 7.1 7.6 7.6
Colombia 4.3 4.5 4.5 2.0 1.9 2.9 –3.3 –3.3 –3.2 9.7 9.3 9.0
Venezuela 1.0 –0.5 –1.0 40.7 50.7 38.0 2.7 2.4 1.8 9.2 11.2 13.3
Peru 5.0 5.5 5.8 2.8 2.5 2.1 –4.9 –4.8 –4.4 7.5 6.0 6.0
Chile 4.2 3.6 4.1 1.8 3.5 2.9 –3.4 –3.3 –2.8 5.9 6.1 6.2
Ecuador 4.2 4.2 3.5 2.7 2.8 2.6 –1.5 –2.4 –3.1 4.7 5.0 5.0
Bolivia 6.8 5.1 5.0 5.7 6.8 5.3 3.7 3.7 2.4 6.4 6.3 6.2
Uruguay 4.2 2.8 3.0 8.6 8.3 8.0 –5.9 –5.5 –5.2 6.3 6.8 6.9
Paraguay 13.0 4.8 4.5 2.7 4.7 5.0 0.9 –0.9 –1.6 5.4 5.5 5.5
Central America7 4.0 4.0 4.0 4.2 3.8 4.4 –6.9 –6.5 –6.2 . . . . . . . . .
Caribbean8 2.8 3.3 3.3 5.0 4.4 4.5 –3.7 –3.2 –3.2 . . . . . . . . .
Memorandum
Latin America and the Caribbean9 2.7 2.5 3.0 6.8 . . . . . . –2.7 –2.7 –2.8 . . . . . . . . .
Excluding Argentina 2.5 2.8 3.2 6.4 6.8 5.9 –2.8 –2.9 –3.0 . . . . . . . . .
Eastern Caribbean Currency Union10 0.5 1.4 1.8 1.0 1.2 1.8 –17.6 –17.1 –16.7 . . . . . . . . .
2Percent of GDP.
4Includes Guyana and Suriname. See note 6 regarding consumer prices.
5The data for Argentina are officially reported data. The IMF has, however, issued a declaration of censure and called on Argentina to adopt remedial measures to address the quality of
the official GDP data. Alternative data sources have shown significantly lower real growth than the official data since 2008. In this context, the Fund is also using alternative estimates
of GDP growth for the surveillance of macroeconomic developments in Argentina.
6The data for Argentina are officially reported data. Consumer price data from January 2014 onwards reflect the new national CPI (IPCNu), which differs substantively from the preced-
ing CPI (the CPI for the Greater Buenos Aires Area, CPI-GBA). Because of the differences in geographical coverage, weights, sampling, and methodology, the IPCNu data cannot
be directly compared to the earlier CPI-GBA data. Because of this structural break in the data, staff forecasts for CPI inflation are not reported in the Spring 2014 World Economic
Outlook. Following a declaration of censure by the IMF on February 1, 2013, the public release of a new national CPI by end-March 2014 was one of the specified actions in the IMF
Executive Board’s December 2013 decision calling on Argentina to address the quality of its official CPI data. The Executive Board will review this issue again as per the calendar
specified in December 2013 and in line with the procedures set forth in the Fund’s legal framework.
7Central America comprises Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama.
8The Caribbean comprises Antigua and Barbuda, The Bahamas, Barbados, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the
Grenadines, and Trinidad and Tobago.
9Latin America and the Caribbean comprises Mexico and economies from the Caribbean, Central America, and South America. See note 6.
10Eastern Caribbean Currency Union comprises Antigua and Barbuda, Dominica, Grenada, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines, as well as Anguilla and
Montserrat, which are not IMF members.

growth could lift the region’s exports, although positive
trade spillovers would be concentrated in Mexico and
a few Central American and Caribbean countries. On
the downside, a faster-than-anticipated rise in U.S.
interest rates could cause fresh financial headwinds,
especially if capital flows were to reverse abruptly. In
addition, further downward pressure on commodity
prices caused by a sharper-than-expected investment
slowdown in China or other factors would be a drag
on the commodity exporters in the region.
Against this backdrop, policymakers across Latin
America and the Caribbean should focus on improv-
ing domestic fundamentals to reduce their economies’
vulnerability to external shocks. A gradual reduction in
fiscal deficits and public debt levels remains appropri-
ate for countries with large fiscal imbalances, as well as
those with limited spare capacity and elevated external
current account deficits. Further improvements in the
transparency and credibility of fiscal frameworks would
also help strengthen investor confidence. In the same
vein, it is critical to ensure strong prudential oversight
of the financial sector and preemptively address fragili-
ties that could come to the fore if interest rates were to
rise sharply or growth to slow further.
Exchange rate flexibility has already helped coun-
tries adjust to last year’s financial market turmoil and
should remain an important buffer in the event of
renewed volatility. Meanwhile, monetary policy eas-
ing remains the first line of defense against a further
growth slowdown in economies with low inflation and
anchored inflation expectations. In countries with per-
sistent inflation pressures, which could be exacerbated
by further exchange rate depreciation, both monetary
and fiscal policy should focus on anchoring inflation
expectations.
Structural reforms to raise productivity and
strengthen competitiveness are also crucial. Above all,
the region needs to invest more, and more effectively,
in infrastructure and human capital; address obstacles
to greater labor force participation in the formal sector;
and improve the business and regulatory environment.
Commonwealth of Independent States:
Subdued Prospects
Growth in the Commonwealth of Independent States
(CIS) remains subdued despite robust consumption,
reflecting weak investment, political tensions, and policy
uncertainty in some cases. Geopolitical tensions are cast-
ing a pall on part of this region. By contrast, growth is
brisk in the Caucasus and Central Asia (CCA). Poli-
cies should focus on implementing reforms and increas-
ing investment to raise growth potential, and for some
countries, correcting serious imbalances is another priority.
Growth in the European CIS economies continued
to soften in the second half of 2013 and was further
slowed by geopolitical tensions in early 2014 (Figure
2.7). Russia’s growth remained subdued during 2013.
Despite strong consumption, activity was constrained
by weak investment and the slow global recovery. A
bumper harvest and resilient private consumption lifted
Ukraine from recession in the fourth quarter of 2013,
but large domestic and external imbalances have per-
sisted. Volatility in capital flows increased sharply from
the summer onward as concerns over Federal Reserve
tapering intensified. In early 2014 domestic political tur-
moil and the takeover of the Crimea by Russia adversely
affected Ukraine’s economy and sent spillover waves
across the region. The near-term growth outlook for
Russia, already weakened, has been further affected by
these geopolitical tensions. As the ruble faced downward
pressures, with capital outflows intensifying, the central
bank temporarily reverted to discretion and increased
its foreign exchange intervention. Growth in the CCA
region increased by about 1 percentage point to about
6½ percent in 2013, despite the slowdown in Russia,
one of the region’s main trading partners.
Growth in the European CIS economies will remain
weak, while the near-term outlook for the CCA is
expected to soften to 6.2 percent in 2014 (Table 2.5).
•• Russia’s GDP growth is projected to be subdued
at 1.3 percent in 2014. The fallout from emerging
market financial turbulence and geopolitical tensions
relating to Ukraine are headwinds on the back of
already weak activity.
•• In Ukraine, output will likely drop significantly as
the acute economic and political shocks take their
toll on investment and consumption. Toward the
end of 2014, net exports and investment recovery
should bring back moderate growth.
•• Belarus’s growth will remain lackluster at 1.6 percent
in 2014. In Moldova, GDP growth will moderate to
3½ percent in 2014, mainly reflecting the expected
slowdown in agriculture.
•• Strengthening external demand as well as recovery
of domestic demand in Armenia and Georgia owing
to fiscal easing, and increased hydrocarbon exports
from Turkmenistan on past expansions in productive
capacity, will support economic activity in the CCA,

despite a temporary weakening of oil output growth
in Kazakhstan and flat gold exports from the Kyrgyz
Republic.
Inflation will be broadly stable at about 6 percent in
2014, but remains high in some economies (Table 2.5).
In Russia, it exceeded the target range in 2013 partly
because of a temporary uptick in food prices and ruble
depreciation and will likely remain higher than the 2014
midpoint target. In Kazakhstan, the recent devaluation
of the tenge will add to inflation pressure this year. Infla-
tion has declined in Belarus but will remain in double
digits under current policies, whereas it is expected to
remain within central banks’ targets in most of the CCA
countries. In Georgia, inflation is expected to come close
to the 5 percent target in 2015, on a pickup in domes-
tic demand and some recent currency depreciation.
In Uzbekistan, inflation will continue to linger in the
double digits because of increases in administered prices,
currency depreciation, and strong credit growth.
The balance of risks remains to the downside, con-
sidering rising geopolitical uncertainties following the
takeover of the Crimea by Russia, tightening financial
conditions, and volatile capital flows. Intensification
of sanctions and countersanctions could affect trade
flows and financial assets. Contagion could spread
through real (trade, remittances) and financial (asset
valuation, banking) channels. Even in the absence of
sanctions, lower growth in Russia and Ukraine could
have a significant impact on neighboring economies
over the medium term. Softer commodity prices (see
the Commodity Special Feature in Chapter 1) would
delay recovery in Ukraine and hamper growth in Rus-
sia and in the CCA hydrocarbon exporters. However,
countries with large foreign asset buffers would be
less affected. Growth in the CCA oil importers would
also weaken if growth prospects in emerging markets
were to be revised down, with adverse effects on trade,
remittances, and project funding, especially consider-
ing limited external and fiscal buffers. A slowdown in
Russia owing to unsettled conditions would affect the
CCA through both real sector and financial channels,
particularly if energy supply is disrupted and oil and
gas prices rise. On the upside, a stronger recovery in
advanced economies could keep oil and gas prices
high, benefiting both the oil and gas exporters and
the commodity importers through a stronger-than-
expected recovery in Russia.
Policies should aim to preserve macroeconomic stabil-
ity and boost growth potential with ambitious reforms.
To manage the potential effects of emerging market
–9
–6
–3
0
3
6
9
12
15
18
2004 06 08 10 12 14
–30
–20
–10
0
10
20
2009 10 11 12 13:
Q3
0
5
10
15
20
25
2004 06 08 10 12 14
–0.08
–0.06
–0.04
–0.02
0.00
0.02
0.04
2008 09 10 11 12 Mar.
14
–15
–10
–5
0
5
10
15
20
2004 06 08 10 12 14
–10
–8
–6
–4
–2
0
2
4
6
8
2006 08 10 12 14
2. Real GDP Growth
(percent)
5. Inflation
(percent)
4. Bond Country Flows2
(percent of GDP)
1. European CIS: Real GDP
Growth1
(year-over-year
percent change)
Private consumption
Public consumption
Investment
Net exports
Real GDP growth
CIS
Russia
NEI
NEE excluding Russia
Russia
Ukraine
NEE excluding Russia
Russia
Ukraine
CIS Russia
NEI NEE excluding
Russia
CIS Russia
NEI
3. Output Gap
(percent of potential GDP)
6. Fiscal Balance3
(percent of fiscal year GDP)
Growth in the Commonwealth of Independent States (CIS) has continued to soften,
reflecting further deceleration in Russia and weak external demand elsewhere, and
capital flows to the region have declined. Policies should focus on implementing
stronger reforms to raise growth potential, and for some countries, correcting
serious imbalances.
Sources: EPFR Global/Haver Analytics; Haver Analytics; and IMF staff estimates.
Note: Net energy exporters (NEE) = Azerbaijan, Kazakhstan, Russia, Turkmenistan,
Uzbekistan. Net energy importers (NEI) = Armenia, Belarus, Georgia, Kyrgyz
Republic, Moldova, Tajikistan, Ukraine. All country group aggregates are weighted
by GDP valued at purchasing power parity as a share of group GDP. Projections for
Ukraine are excluded due to the ongoing crisis.
1
European CIS includes Belarus, Moldova, Russia, and Ukraine.
2
Data through March 18, 2014.
3
General government net lending/borrowing except in the case of NEI, for which it
is the overall balance.
Figure 2.7. Commonwealth of Independent States: Subdued
Prospects

financial turmoil and geopolitical tensions, Russia
should continue to rely on exchange rate flexibility to
facilitate adjustment while avoiding excessive volatility,
keep monetary policy focused on anchoring inflation,
and maintain a broadly neutral structural fiscal policy
while allowing automatic stabilizers to work. Fiscal
consolidation and tapering of quasi-fiscal losses in the
energy sector are critical for economic stabilization in
Ukraine. Although financial support from Russia could
provide Belarus with some short-term breathing space,
steps to reduce wage and credit growth and to increase
exchange rate flexibility should be taken expeditiously
to narrow imbalances. While remaining committed
to medium-term consolidation, Armenia and Georgia
are planning some fiscal stimulus in 2014. Structural
reforms to improve the business environment, diversify
the economy, and enhance external competitiveness are
also needed across the region for strong growth to last
and become more inclusive in the years ahead.
The Middle East and North Africa: Turning the
Corner?
Growth was tepid across the Middle East and North
Africa, Afghanistan, and Pakistan (MENAP) in 2013,
as declines in oil production and weak private invest-
ment growth amid continued political transitions and
conflict offset increases in public spending. Economic
activity will strengthen in 2014–15 as export growth
improves in line with trading partners’ recoveries and
public and private investment accelerates. However,
weak confidence, high unemployment, low competi-
tiveness, and in many cases, large public deficits will
continue to weigh on economic prospects in the region.
Risks are tilted to the downside on slow progress in
reforms during complex political transitions. Reforms
to raise and diversify potential output and improve
competitiveness and resilience are essential for achiev-
ing sustainable and inclusive growth and creating jobs.
Table 2.5. Commonwealth of Independent States: Real GDP, Consumer Prices, Current Account Balance,
and Unemployment
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
Commonwealth of Independent States 2.1 2.3 3.1 6.4 6.6 6.1 0.7 1.9 1.5 . . . . . . . . .
Net Energy Exporters 2.2 2.2 3.1 6.7 6.2 5.7 1.9 2.5 1.9 . . . . . . . . .
Russia 1.3 1.3 2.3 6.8 5.8 5.3 1.6 2.1 1.6 5.5 6.2 6.2
Kazakhstan 6.0 5.7 6.1 5.8 9.2 7.5 0.1 1.9 2.0 5.2 5.2 5.2
Uzbekistan 8.0 7.0 6.5 11.2 11.0 11.0 1.7 2.2 1.9 . . . . . . . . .
Azerbaijan 5.8 5.0 4.6 2.4 3.5 4.0 19.7 15.0 9.9 6.0 6.0 6.0
Turkmenistan 10.2 10.7 12.5 6.6 5.7 6.0 –3.3 –1.1 1.3 . . . . . . . . .
Net Energy Importers 1.2 2.8 3.5 4.9 12.0 11.4 –8.9 –9.0 –7.5 . . . . . . . . .
Ukraine4 0.0 . . . . . . –0.3 . . . . . . –9.2 . . . . . . 7.4 . . . . . .
Belarus 0.9 1.6 2.5 18.3 16.8 15.8 –9.8 –10.0 –7.8 0.6 0.6 0.6
Georgia5 3.2 5.0 5.0 –0.5 4.0 4.6 –6.1 –7.9 –7.3 . . . . . . . . .
Armenia 3.2 4.3 4.5 5.8 5.0 4.0 –8.4 –7.2 –6.8 18.5 18.0 17.9
Tajikistan 7.4 6.2 5.7 5.0 5.4 5.9 –1.9 –2.1 –2.3 . . . . . . . . .
Kyrgyz Republic 10.5 4.4 4.9 6.6 6.1 6.6 –12.6 –15.5 –14.3 7.6 7.6 7.5
Moldova 8.9 3.5 4.5 4.6 5.5 5.9 –4.8 –5.9 –6.4 5.2 5.6 5.3
Memorandum
Caucasus and Central Asia6 6.6 6.2 6.4 6.0 7.7 7.1 2.6 3.0 2.4 . . . . . . . . .
Low-Income CIS Countries7 7.1 6.0 5.8 7.7 8.3 8.4 –2.2 –2.3 –2.2 . . . . . . . . .
Net Energy Exporters Excluding
Russia 6.8 6.4 6.7 6.4 8.1 7.4 3.6 4.2 3.4 . . . . . . . . .
2Percent of GDP.
4Projections for Ukraine are excluded due to the ongoing crisis.
5Georgia, which is not a member of the Commonwealth of Independent States (CIS), is included in this group for reasons of geography and similarity in economic structure.
6Includes Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan.
7Low-Income CIS countries comprise Armenia, Georgia, Kyrgyz Republic, Moldova, Tajikistan, and Uzbekistan.

Oil-Exporting Economies
For MENAP oil exporters, economic activity moder-
ated in 2013 to about 2 percent, less than half the
growth rate experienced in recent years. Growth in the
non-oil economy was supported by sustained public
investment in infrastructure and private credit expan-
sion. However, tepid global oil demand, increased oil
supply from the United States, and regional oil supply
disruptions—mainly those in Libya, where a wave of
instability caused oil output to fall to about one-third
of capacity—slowed growth in the oil sectors (Figure
2.8; also see the Commodity Special Feature in Chap-
ter 1).
As oil output stabilizes alongside strengthen-
ing global activity and sustained consumption and
investment, total GDP growth is expected to rise to
about 3½ percent in 2014 (Table 2.6). In the United
Arab Emirates, where real estate prices are rising at a
fast pace, the award of World Expo 2020 has further
strengthened growth prospects. Likewise, Qatar has
embarked on a large public investment program to
advance economic diversification and prepare for the
Fédération Internationale de Football Association 2022
World Cup.
Softening food prices are expected to contain
inflation at less than 5 percent in most oil exporters.
A notable exception is the Islamic Republic of Iran,
which is experiencing stagflation despite some recent
improvements in the outlook resulting from temporary
easing of some international sanctions.
Falling oil revenues are already causing fiscal
surpluses to decline, to 2.6 percent in 2014, despite
withdrawal of the fiscal stimulus initiated by many
countries during the global recession and the Arab
Spring. Large current account surpluses are also
expected to decline because of lower oil revenues
(Table 2.6). Although fiscal positions have been weak-
ening across the Gulf Cooperation Council (GCC)
economies over the past several years, most still have
substantial buffers to withstand large shocks to oil
prices, provided the shocks are short lived.
Risks to the near-term outlook for oil exporters have
declined. The recent interim agreement between the
P5+1 and Iran has eased geopolitical tensions, and the
potential for further large oil supply disruptions in other
non-GCC countries now appears more limited. Faster-
than-expected growth in the U.S. oil supply and linger-
ing risks of weaker-than-expected global oil demand
because of a slowdown in either emerging markets or
4
5
6
7
8
9
10
11
12
13
Nov. 10 Nov. 11 Nov. 12 Feb. 14
0
50
100
150
200
250
0 50 100 150 200 250
YEM
ARE
SAU
QAT
OMN
LBY
KWT
IRQ
IRN
BHR
DZA
Externalbreak-evenprice
Fiscal break-even price
5. MENAPOE: Break-Even
Oil Prices, 20142
–4
0
4
8
12
16
–4 0 4 8 12 16
TUN
SDN
PAK
MAR
MRT
LBNJOR
EGY
DJI
AFG
Averagefiscaldeficit,2010–13
(percentofGDP)
Reserves, 2013
(months of imports)
30
60
90
120
150
180
2010 11 12 13:Q3
6. MENAPOI: Fiscal
Deficits vs. Reserves3
1.4
1.6
1.8
2.0
2.2
2.4
50
52
54
56
58
60
62
64
66
2010 11 12 Feb. 14
–4
–2
0
2
4
6
8
10
12
2011 12 13 14 15
Figure 2.8. Middle East, North Africa, Afghanistan, and
Pakistan: Turning a Corner?
2. MENAPOI: Political
Environment
1
4. MENAPOI: Exports and FDI
(index, 2009 = 100; four-
quarter moving average)
3. MENAPOE: Crude Oil
Production
(million barrels a day)
1. Real GDP Growth
(percent)
MENAPOE: Oil GDP
MENAPOE: Non-oil GDP
MENAPOI: Overall GDP
Exports of goods
FDI
WEO oil
price
Saudi Arabia
Non-GCC
Other GCC
Consumer
confidence
Political
stability
Sources: Haver Analytics; IMF, Direction of Trade Statistics database; International
Energy Agency; national authorities; PRS Group, Inc., International Country Risk
Guide; and IMF staff estimates.
Note: MENAP oil exporters (MENAPOE) = Algeria (DZA), Bahrain (BHR), Iran (IRN),
Iraq (IRQ), Kuwait (KWT), Libya (LBY), Oman (OMN), Qatar (QAT), Saudi Arabia
(SAU), United Arab Emirates (ARE), and Yemen (YEM); MENAP oil importers
(MENAPOI) = Afghanistan (AFG), Djibouti (DJI), Egypt (EGY), Jordan (JOR), Lebanon
(LBN), Mauritania (MRT), Morocco (MAR), Pakistan (PAK), Sudan (SDN), Syria (SYR),
and Tunisia (TUN). FDI = foreign direct investment; GCC = Gulf Cooperation Council.
Data from 2011 onward exclude SYR. Country group aggregates for panel 1 and
exports of goods in panel 4 are weighted by purchasing-power-parity GDP as a
share of group GDP; panel 2 shows simple averages (excludes AFG, DJI, and MRT);
panel 3 and FDI (for EGY, MAR, PAK, and TUN) in panel 4 show sums.
1
Consumer confidence on the left scale and political stability on the right scale.
Higher values of the consumer confidence measure (political stability rating) signify
greater consumer confidence (political stability).
2
Prices at which the government budget and current account are balanced,
respectively. YEM data are for 2013.
3
Bubble size is relative to each country’s 2013 purchasing-power-parity GDP.
Growth was tepid across the Middle East, North Africa, Afghanistan, and Pakistan
(MENAP) in 2013, as high public spending was offset by declines in oil supply and
weak non-oil exports amid continued sociopolitical upheaval. Robust non-oil
activity on high public spending and recovery in oil production, however, should
accelerate activity this year.

advanced economies present downside risks to oil prices
and GCC production. Policy priorities continue to
be centered on diversifying these economies to reduce
dependence on oil, increase employment opportunities
in the private sector for nationals, and enhance resilience
to shocks. Reforms to foster entrepreneurship, along
with public wage and employment restraint, are key. Fis-
cal policy needs to manage demand pressures, preserve
wealth for future generations, and ensure efficient public
capital spending. Reduction of energy subsidies, cur-
rently ranging from 4 percent to 12½ percent of GDP,
would curtail energy consumption and free up resources
for targeted social spending and to help finance public
investment. Eliminating subsidies should be gradual and
would require an effective communications strategy to
broaden public support and reduce the risk of policy
reversals.
Oil-Importing Economies
In 2013, three years after the Arab Spring, recovery in
the MENAP oil importers remained sluggish. Uncer-
tainties arising from political transitions and social
unrest and drag from unresolved structural problems
continued to weigh on confidence and economic
activity. Despite supportive fiscal and monetary
policies, growth has hovered around 3 percent since
2011—half the rate needed to reduce the region’s
high and persistent unemployment and improve living
standards.
The outlook is for continued slow recovery, with
growth lingering around 3 percent in 2014 before rising
to 4 percent in 2015. Export growth will strengthen
gradually as internal demand in trading partner coun-
tries, particularly those in Europe, recovers. Recent
Table 2.6. Selected Middle East and North African Economies: Real GDP, Consumer Prices, Current Account Balance,
and Unemployment
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
Middle East and North Africa 2.2 3.2 4.5 10.5 8.4 8.3 10.3 8.7 6.6 . . . . . . . . .
Oil Exporters4 2.0 3.4 4.6 11.3 8.4 8.3 14.1 11.9 9.7 . . . . . . . . .
Iran –1.7 1.5 2.3 35.2 23.0 22.0 8.1 5.2 2.8 12.9 14.0 14.6
Saudi Arabia 3.8 4.1 4.2 3.5 3.0 3.2 17.4 15.8 13.3 5.5 . . . . . .
Algeria 2.7 4.3 4.1 3.3 4.0 4.0 0.4 0.5 –1.3 9.8 9.4 9.0
United Arab Emirates 4.8 4.4 4.2 1.1 2.2 2.5 14.9 13.3 12.4 . . . . . . . . .
Qatar 6.1 5.9 7.1 3.1 3.6 3.5 29.2 25.4 20.5 . . . . . . . . .
Kuwait 0.8 2.6 3.0 2.7 3.4 4.0 38.8 37.4 34.2 2.1 2.1 2.1
Iraq 4.2 5.9 6.7 1.9 1.9 3.0 0.0 1.0 1.2 . . . . . . . . .
Oil Importers5 2.7 2.7 4.2 7.9 8.5 8.2 –6.4 –5.5 –6.4 . . . . . . . . .
Egypt 2.1 2.3 4.1 6.9 10.7 11.2 –2.1 –1.3 –4.6 13.0 13.0 13.1
Morocco 4.5 3.9 4.9 1.9 2.5 2.5 –7.4 –6.6 –5.8 9.2 9.1 9.0
Tunisia 2.7 3.0 4.5 6.1 5.5 5.0 –8.4 –6.7 –5.7 16.7 16.0 15.0
Sudan 3.4 2.7 4.6 36.5 20.4 14.3 –10.6 –8.2 –7.1 9.6 8.4 8.0
Lebanon 1.0 1.0 2.5 3.2 2.0 2.0 –16.2 –15.8 –13.9 . . . . . . . . .
Jordan 3.3 3.5 4.0 5.5 3.0 2.4 –11.1 –12.9 –9.3 12.2 12.2 12.2
Memorandum
Middle East, North Africa, Afghanistan,
and Pakistan 2.4 3.2 4.4 10.1 8.5 8.3 9.5 8.0 6.1 . . . . . . . . .
Pakistan 3.6 3.1 3.7 7.4 8.8 9.0 –1.0 –0.9 –1.0 6.7 6.9 7.2
Afghanistan 3.6 3.2 4.5 7.4 6.1 5.5 2.8 3.3 –0.3 . . . . . . . . .
Israel6 3.3 3.2 3.4 1.5 1.6 2.0 2.5 1.4 1.7 6.4 6.7 6.5
Maghreb7 2.0 2.9 7.5 3.3 3.9 4.0 –3.2 –6.1 –5.8 . . . . . . . . .
Mashreq8 2.1 2.2 3.9 6.4 9.3 9.7 –4.7 –4.3 –6.1 . . . . . . . . .
Note: Data for some countries are based on fiscal years. Please refer to Table F in the Statistical Appendix for a complete list of reference periods for each country.
2Percent of GDP.
4Includes Bahrain, Libya, Oman, and Yemen.
5Includes Djibouti and Mauritania. Excludes Syria due to the uncertain political situation.
6Israel, which is not a member of the region, is included for reasons of geography. Note that Israel is not included in the regional aggregates.
7The Maghreb comprises Algeria, Libya, Mauritania, Morocco, and Tunisia.
8The Mashreq comprises Egypt, Jordan, and Lebanon. Excludes Syria due to the uncertain political situation.

reforms set in motion to relax supply-side constraints
and enhance competitiveness should also help improve
confidence, spurring economic activity and foreign
direct investment. However, domestic demand will
remain subdued because of lingering policy uncertainty.
In some countries, fiscal stimulus will turn into a slight
fiscal drag, because consolidation is necessary to arrest
erosion of fiscal and external buffers. Inflation will
rise slightly to 8.5 percent, with upward pressure from
energy subsidy phase-outs partly offset by declining
global commodity prices (Table 2.6).
Beyond these broad trends, country-specific out-
looks are as follows:
•• In Egypt, growth in 2014 is expected to be broadly the
same as in 2013, as political uncertainty will continue
to weigh on tourism and foreign direct investment,
notwithstanding the fiscal stimulus supported by GCC
financing. Large imbalances will persist unless struc-
tural reforms and fiscal consolidation are initiated.
•• The Syrian conflict continues to weigh heavily on
Lebanon, with intensification of sectarian violence,
hampered confidence, and added pressures to a dete-
riorating fiscal position—leaving growth flat in 2014.
The conflict has also significantly increased the fiscal
adjustment and financing burden in Jordan.
•• In Pakistan, faster-than-expected manufacturing
sector recovery, reflecting improved electricity sup-
ply and recent exchange rate depreciation, is being
partly offset by weak cotton production.
•• Tunisian growth is expected to strengthen, spurred
by improved confidence from a new constitution,
reduced security tensions, and preelection reforms.
•• Economic activity in Morocco will slow, albeit increas-
ingly driven by the nonagricultural sectors, owing to
reforms supporting economic diversification.
The recovery remains fragile, and risks are to the
downside. Political transitions, intensification of social
and security tensions, and spillovers from regional
conflicts could damage confidence and threaten
macroeconomic stability. Lower-than-expected growth
in emerging market economies, Europe, or the GCC
could slow exports. Domestic interest rates may rise in
countries with limited exchange rate flexibility if global
financial conditions tighten sharply, although reliance
on official external financing and bond guarantees
should limit these effects. On the upside, faster prog-
ress in political transitions and economic reforms could
boost confidence and growth.
A lasting improvement in economic prospects will
require structural reforms, from lowering the cost of
doing business to deepening trade integration with
international and regional markets. Many of these
reforms are difficult to implement during political
transitions. However, some measures can be pursued
immediately and should help improve confidence:
streamlining business regulations, training the unem-
ployed and unskilled, and improving customs proce-
dures, for example.
Macroeconomic policies need to balance the dual
goals of bolstering growth and ensuring economic sta-
bility. Broadening the tax base in some countries as a
means of mobilizing resources to finance higher social
spending and public investment would help. Increases
in public investment and social support to the poor
can also help boost domestic demand. Given large
fiscal deficits and debt, these public expenditures have
to be financed by reorienting spending away from gen-
eralized subsidies that benefit the rich. Fiscal consolida-
tion can proceed at a gradual pace, if financing allows,
anchored in credible medium-term plans to ensure
continued willingness of investors to provide adequate
financing. Accommodative monetary policy, and in
some cases greater exchange rate flexibility, can soften
the near-term adverse impact of fiscal consolidation on
growth, while strengthening external buffers.
Sub-Saharan Africa: Accelerating Growth
Growth in sub-Saharan Africa remains robust and is
expected to accelerate in 2014. Tight global financing
conditions or a slowdown in emerging market economies
could generate some external headwinds, especially for
middle-income countries with large external linkages,
producers of natural resources, and frontier economies.1
However, some of the most salient risks are domestic, stem-
ming from policy missteps in various countries, security
threats, and domestic political uncertainties ahead of
elections. Policymakers should avoid a procyclical fiscal
stance in fast-growing countries, tackle emerging risks in
countries facing major fiscal imbalances, address vul-
nerabilities in those countries more exposed to external
shocks, and foster sustainable and inclusive growth.
Growth in sub-Saharan Africa remained strong in
2013 at 4.8 percent, virtually unchanged from 2012,
underpinned by improved agricultural production and
1Frontier market economies in sub-Saharan Africa include Ghana,
Kenya, Mauritius, Nigeria, Rwanda, Senegal, Tanzania, Uganda, and
Zambia.

investment in natural resources and infrastructure.
Growth was robust throughout the region, especially in
low-income and fragile states.2 Outside these groups,
in Nigeria growth remained strong owing to relatively
high oil prices, despite security problems in the north
and large-scale oil theft in the first half of 2013. In
contrast, growth in South Africa continued to deceler-
ate, constrained by tense industrial relations in the
mining sector, tight electricity supply, anemic private
investment, and weak consumer and investor confi-
dence (Table 2.7).
2Fragile states include Burundi, the Central African Republic, the
Comoros, the Democratic Republic of the Congo, Côte d’Ivoire,
Eritrea, Guinea, Guinea-Bissau, Liberia, São Tomé and Príncipe,
Togo, and Zimbabwe. This list does not include some fragile
countries where oil sales account for a major share of exports and
government revenue, which are classified as oil exporters.
Inflation continued to abate, with a few excep-
tions (Figure 2.9). The currencies of South Africa and
some frontier market economies weakened, reflecting
tightening global monetary conditions and, in some
instances, weak external or fiscal balances (Ghana,
Nigeria, South Africa, Zambia). Because of high fiscal
deficits, a few countries’ credit ratings were down-
graded, putting additional pressure on yields, and some
countries postponed sovereign bond issuance.
Growth is projected to accelerate to about 5½ per-
cent in 2014, reflecting positive domestic supply-side
developments and the strengthening global recovery:
•• In South Africa, growth is forecast to rise moderately,
driven by improvements in external demand, but risks
are to the downside. (See Chapter 1 for details.)
•• Nigerian growth is projected to rebound by 0.8 per-
centage point, as major oil pipelines are repaired
Table 2.7. Selected Sub-Saharan African Economies: Real GDP, Consumer Prices, Current Account Balance,
and Unemployment
2013
Projections
2013
Projections
2013
Projections
2013
Projections
2014 2015 2014 2015 2014 2015 2014 2015
Sub-Saharan Africa 4.9 5.4 5.5 6.3 6.1 5.9 –3.6 –3.6 –3.9 . . . . . . . . .
Oil Exporters4 5.8 6.7 6.7 7.4 6.9 6.6 3.9 3.3 2.1 . . . . . . . . .
Nigeria 6.3 7.1 7.0 8.5 7.3 7.0 4.7 4.9 4.0 . . . . . . . . .
Angola 4.1 5.3 5.5 8.8 7.7 7.7 5.0 2.2 –0.4 . . . . . . . . .
Equatorial Guinea –4.9 –2.4 –8.3 3.2 3.9 3.7 –12.0 –10.2 –10.9 . . . . . . . . .
Gabon 5.9 5.7 6.3 0.5 5.6 2.5 10.6 6.9 4.5 . . . . . . . . .
Republic of Congo 4.5 8.1 5.8 4.6 2.4 2.4 –1.2 2.0 0.1 . . . . . . . . .
Middle-Income Countries5 3.0 3.4 3.7 5.8 5.9 5.5 –5.7 –5.1 –4.9 . . . . . . . . .
South Africa 1.9 2.3 2.7 5.8 6.0 5.6 –5.8 –5.4 –5.3 24.7 24.7 24.7
Ghana 5.4 4.8 5.4 11.7 13.0 11.1 –13.2 –10.6 –7.8 . . . . . . . . .
Cameroon 4.6 4.8 5.1 2.1 2.5 2.5 –4.4 –3.5 –3.6 . . . . . . . . .
Côte d’Ivoire 8.1 8.2 7.7 2.6 1.2 2.5 –1.2 –2.2 –2.0 . . . . . . . . .
Botswana 3.9 4.1 4.4 5.8 3.8 3.4 –0.4 0.4 0.2 . . . . . . . . .
Senegal 4.0 4.6 4.8 0.8 1.4 1.7 –9.3 –7.5 –6.6 . . . . . . . . .
Low-Income Countries6 6.5 6.8 6.8 6.0 5.5 5.5 –11.8 –11.8 –11.7 . . . . . . . . .
Ethiopia 9.7 7.5 7.5 8.0 6.2 7.8 –6.1 –5.4 –6.0 . . . . . . . . .
Kenya 5.6 6.3 6.3 5.7 6.6 5.5 –8.3 –9.6 –7.8 . . . . . . . . .
Tanzania 7.0 7.2 7.0 7.9 5.2 5.0 –14.3 –13.9 –12.9 . . . . . . . . .
Uganda 6.0 6.4 6.8 5.4 6.3 6.3 –11.7 –12.6 –12.1 . . . . . . . . .
Democratic Republic of the
Congo
8.5 8.7 8.5 0.8 2.4 4.1 –9.9 –7.9 –7.2 . . . . . . . . .
Mozambique 7.1 8.3 7.9 4.2 5.6 5.6 –41.9 –42.8 –43.2 . . . . . . . . .
Memorandum
Sub-Saharan Africa Excluding
South Sudan 4.7 5.4 5.4 6.4 6.1 5.9 –3.6 –3.6 –4.0 . . . . . . . . .
2Percent of GDP.
4Includes Chad and South Sudan.
5Includes Cabo Verde, Lesotho, Mauritius, Namibia, Seychelles, Swaziland, and Zambia.
6Includes Benin, Burkina Faso, Burundi, Central African Republic, Comoros, Eritrea, The Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Niger, Rwanda, São
Tomé and Príncipe, Sierra Leone, Togo, and Zimbabwe.

and production in the non-oil sectors continues to
expand. Other oil producers are also expected to see
a significant growth pickup.
•• Growth is also expected to accelerate in other
countries, including several fragile states, in the
wake of an improved domestic political and security
situation (Mali), massive investments in infrastruc-
ture and mining (Democratic Republic of the Congo,
Mozambique, Niger), and maturing investments
(Mozambique).
Moderate food prices and prudent monetary poli-
cies should facilitate further declines in inflation in
much of the region, and fiscal balances are projected
to improve by about ½ percent of GDP on average.
Nevertheless, the average current account deficit is not
expected to narrow, owing to relatively tepid prospects
for commodity prices (see the Commodity Special Fea-
ture in Chapter 1) and demand from emerging market
economies, and to continuing high levels of foreign-
direct-investment-related imports.
In several countries, the largest downside risks are
domestic, including policy uncertainty, deteriorating
security conditions, and industrial tensions. External
risks are particularly important for natural resource
exporters, which could suffer from a slowdown in
emerging markets and a shifting pattern in China from
investment- to consumption-led growth. In addition,
they are important for countries with external mar-
ket access, such as South Africa and frontier markets,
which are most exposed to a reversal of portfolio flows
if global financial conditions tighten further.
To avoid a procyclical fiscal stance and increase their
resilience to shocks, fast-growing economies in the
region should take advantage of the growth momen-
tum to strengthen their fiscal balances. In a few cases
in which deficits have become large or public debt is at
high levels, fiscal consolidation needs to be pursued to
ensure continued macroeconomic stability, and in many
countries mobilizing resources for high-value spend-
ing remains a priority. Throughout the region, urgent
requirements include improving the efficiency of public
expenditure; investing in strategic and carefully selected
projects to develop energy supply and critical infrastruc-
ture; and implementing structural reforms aimed at
promoting economic diversification, private investment,
and competitiveness. Monetary policies should remain
focused on consolidating the gains on the inflation
front. In some countries, sustained exchange rate depre-
ciations may pose risks to the inflation outlook.
Private
consumption
Public
consumption
Investment Net exports
Discrepancy GDP growth
80
100
120
140
160
180
200
2004 06 08 10 12 14
2
6
10
14
18
22
26
30
2004 06 08 10 12 14
Figure 2.9. Sub-Saharan Africa: Accelerating Growth
–2
–2
–6
0
2
4
6
8
10
12
14
2004 06 08 10 12 14
0
5
10
15
20
25
30
35
2007 09 11 13 15
–10
–5
0
5
10
15
20
2004 06 08 10 12 14
2. Real Output Growth
(percent)
4. Terms of Trade
(index; 2004 = 100)
5. Inflation2
(year-over-year
percent change)
6. General Government
Fiscal Balance3
(percent of GDP)
1. SSA: Contributions to
Output Growth1
(percent)
–15
–10
–5
0
5
10
15
20
25
30
2004 06 08 10 12 14
3. Current Account Balance
(percent of GDP)
SSA
Oil exporters
MICs
LICs
SSA
Oil exporters
MICs
Oil exporters
MICs
LICs
SSA
Oil exporters
MICs
LICs
SSA
Oil exporters
MICs
LICs
LICs
In 2013, investments in natural resources and infrastructure and good harvests
sustained robust growth in sub-Saharan Africa. Inflation continued to abate, but
fiscal deficits widened, driven by increased expenditure on investment and wages,
contributing to a worsening of current account balances. Growth is projected to
accelerate in 2014, helped by improved domestic supply and a favorable global
environment. In the face of significant domestic and external downside risks,
countries in the region should improve their resilience to shocks by strengthening
their fiscal balances and increasing their budget flexibility.
Sources: Haver Analytics; IMF, International Financial Statistics database; and IMF
staff estimates.
Note: LIC = low-income country (SSA); MIC = middle-income country (SSA). SSA =
sub-Saharan Africa. See Table 2.7 for country groupings and the Statistical
Appendix for country group aggregation methodology.
1
Liberia, South Sudan, and Zimbabwe are excluded because of data limitations.
2
Because of data limitations, the following are excluded: South Sudan from oil
exporters; Eritrea and Zimbabwe from LICs.
3
General government includes the central government, state governments, local
governments, and social security funds.

South Africa and the group of frontier market econ-
omies should prepare to weather further tightening of
global financing conditions by preserving their budget
flexibility and, where vulnerabilities are of particular
importance, by tightening policies. These countries
should be ready to adjust their financing plans in a
scenario of greatly reduced access to external fund-
ing, while allowing their exchange rates to respond to
changes in capital flows. Consideration should also be
given to prefinancing rollovers when reasonable condi-
tions arise. Countries should also bolster macropruden-
tial supervision to address potential areas of strain and
step up international cooperation to supervise cross-
border banks and subsidiaries.

Economic activity in emerging market economies
weakened during the past few months, raising concern
in some quarters about the implications of a further
synchronized downturn in these economies for the
global economy as a whole and for the still-fragile
recovery in advanced economies. Although spillovers
to advanced economies from previous episodes of weak
growth in emerging market economies were limited, an
across-the-board negative growth shock to these econo-
mies in the present climate would likely have some
effect on advanced economies, given stronger economic
links between these two groups.1
A common growth shock in emerging market
economies can spill over into advanced economies
through several channels. A negative growth shock will
affect demand for advanced economies’ exports, which
tend to be capital-intensive goods. Shocks capable of
disrupting global supply chains would also adversely
affect advanced economies with an upstream position
in global trading networks. A growth shock in emerg-
ing market economies could influence their asset prices
and currencies, which would hurt advanced economies
with substantial financial exposure to these markets.
Financial stresses in emerging market economies could
also raise global risk aversion and lead to sharp correc-
tions in advanced economy financial markets.
This Spillover Feature analyzes the impact on
advanced economies of growth shocks emanating from
emerging markets. Specifically, it addresses the follow-
ing questions: What are the spillover channels and how
have they changed over time? What were the spillover
effects on the advanced economies from previous
broad-based growth downturns in emerging market
economies? How much would a widespread growth
shock in emerging market economies today affect
advanced economies’ output growth?
The analysis in this feature suggests that a negative
growth shock to emerging market economies, akin to
The author of this spillover feature is Juan Yépez, with research
assistance from Angela Espiritu. Ben Hunt and Keiko Honjo pre-
pared the model simulations.
1For this feature, advanced economies comprise four euro area
countries (France, Germany, Italy, Spain), Japan, the United King-
dom, and the United States. Emerging market economies included
are Argentina, Brazil, Chile, China, Colombia, India, Indonesia,
Malaysia, Mexico, the Philippines, Poland, Russia, South Africa,
Thailand, Turkey, and Venezuela.
those experienced in the mid- to late 1990s but not
necessarily crisis driven, would have moderate effects
on all advanced economies, with Japan affected the
most. Trade has been the most prominent spillover
channel. There is evidence to suggest, however, that
the financial channel could play a bigger role in future
transmission of growth shocks in emerging markets.
The Evolution of Trade and Financial Links
between Advanced Economies and Emerging
Market Economies
The growing role of emerging markets in the global
economy is good reason for concern about a possible
downturn. During the past half century, emerging
market economies have moved from peripheral players
to systemically important trade and financial centers
(IMF, 2011a). In the new global economic landscape,
economic linkages among advanced and emerging
market economies are stronger, and advanced econo-
mies are more exposed to economic developments in
the latter group.
Trade linkages between the two groups have
increased sharply (Figure 2.SF.1).2 Exports of goods
to emerging market economies represent, on average,
3 percent of GDP in advanced economies (compared
with 1.6 percent in 1992–2002). During the past
decade, emerging market economies absorbed close to
20 percent of total exports of goods from advanced
economies, and China absorbed a quarter of those
exports (compared with 13 percent in the 1990s). The
ratios presented in the figure are calculated using the
IMF’s Direction of Trade Statistics database, which
measures trade in gross terms and includes both
intermediate and final goods, and the IMF’s World
Economic Outlook (WEO) database. As discussed in
IMF (2011a) and Koopman and others (2010), gross
exports tend to overstate the exposure of advanced
economies to emerging market economies. The reason
2Trade linkages among emerging market economies have markedly
increased as well, with exports to other emerging market economies
representing, on average, 10 percent of GDP, concentrated in the
largest such economies. These links, in turn, make larger emerging
market economies more systemically important, particularly to com-
modity exporters with relatively less-diversified economies (Roache,
2012; Ahuja and Nabar, 2012).
Spillover Feature: Should Advanced Economies Worry about Growth Shocks in
Emerging Market Economies?

Food and fuel Manufacturing Chemicals and others
Machinery and transportation equipment
0
1
2
3
4
5
6
7
0
10
20
30
40
50
60
70
0
20
40
60
80
100
3. Structure of AEs’ Exports
to EMEs
2. AEs’ Real Imports of
Goods from EMEs
0
1
2
3
4
5
6
7
0
5
10
15
20
25
30
35
40
45
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–13
1992–2002
2003–12
1992–2002
2003–12
1992–2002
2003–12
1992–2002
2003–12
1992–2002
2003–12
1992–2002
2003–12
1992–2002
2003–12
1992–2002
2003–12
1. AEs’ Real Exports of
Goods to EMEs
Share of GDP (left
scale)
Share of total
exports (right
scale)
Euro
area1
United
Kingdom
Japan United
States
Share of GDP (left scale)
Share of total imports
(right scale)
0
20
40
60
80
100
4. Structure of AEs’ Imports
from EMEs
Euro
area1
United
Kingdom
Japan United
States
Euro
area1
United
Kingdom
Japan United
States
Euro
area1
United
Kingdom
Japan United
States
Sources: IMF, Direction of Trade Statistics database; and U.N. Commodity Trade
Statistics Database.
1
Euro area = France, Germany, Italy, and Spain. Unweighted average.
Trade linkages between advanced economies (AEs) and emerging market
economies (EMEs) have increased sharply in recent years. Exports from advanced
economies to emerging market economies are concentrated in capital-related
goods (namely, machinery and transportation equipment), whereas imports from
emerging market economies continue to be dominated by commodity and
low-technology manufacturing goods.
Figure 2.SF.1. Real Trade Linkages between Advanced
Economies and Emerging Market Economies
(Percent)
is that exports’ gross value is much larger than the
value added in exports to economies that engage heav-
ily in assembly and processing trade, such as those
in east Asia, because gross exports incorporate inputs
from these economies. This implies that only a part of
gross exports to emerging market economies depends
on domestic demand in those economies. This appears
to be particularly true for large manufacturing export-
ers such as Japan (Table 2.SF.1).
Exports from advanced economies to emerging
markets are concentrated in capital goods and related
products (for example, machinery and transportation
equipment), although the share of capital goods in
total exports has declined considerably since 2000 as
high-technology exports have shifted toward the most
dynamic emerging markets (IMF, 2011a).3 Despite
their marked reduction as a share of total exports in
advanced economies, capital goods still represent,
on average, 50 percent of total imports in emerging
market economies. An abrupt downturn in the largest
of these economies, accompanied by a sharp drop in
investment, could hurt advanced economies that have
large trade exposures to emerging market economies,
particularly in capital goods. For example, capital
goods constitute the bulk of exports to emerging mar-
ket economies for Japan (58 percent) and the euro area
(53 percent).
Advanced economies’ imports from emerging mar-
ket economies have also increased markedly. Imports
from these economies represent, on average, 30 percent
of advanced economies’ total imports, and the ratio of
imports to GDP has doubled as well. The composi-
tion of imports from these economies continues to be
dominated by commodities (fuels and food products)
and low-technology manufactured goods (food and
textiles). Since 2000, however, there has been a sizable
increase in the share of machinery and transporta-
tion equipment in advanced economies’ imports from
emerging markets—evidence of the larger role of
emerging markets in global supply chains. As a result,
large manufacturing exporters (namely, Japan and Ger-
many) are particularly susceptible to any disruption in
trade flows. These exporters are vulnerable because of
their upstream position in regional and global supply
3This is particularly important in the United States, where
machinery and transportation equipment in 2012 accounted for
roughly 30 percent of total exports to emerging market economies,
compared with close to 50 percent in the 1990s.
SPILLOVER FEATURE SHOULD ADVANCED ECONOMIES WORRY ABOUT GROWTH SHOCKS IN EMERGING MARKET ECONOMIES?

chains and as trade networks continue to expand and
become more dispersed.
Financial links have also strengthened in recent
years. The median exposure of advanced economies
to emerging market economies, measured as gross
external asset holdings, reached 8.7 percent of GDP in
2012—an increase of almost 3.5 percentage points of
GDP from the median value in 1997 (Figure 2.SF.2).
Although financial exposure remains concentrated in
bank claims, exposure through portfolio investment
has increased, particularly in equity investment. Not
surprisingly, advanced economies that are financial
centers have seen the largest increase in exposures to
emerging market economies. In the United Kingdom,
bank claims on these economies currently represent
14 percent of total foreign bank claims, up from
just 4 percent a decade ago. It is important to note
that because the United Kingdom is a major finan-
cial center, gross financial exposures could overstate
actual financial linkages between the United Kingdom
and emerging markets.4 Advanced economies with
large exposures to emerging market economies could
be susceptible to significant valuation and wealth
effects resulting from sharp movements in asset prices
and currencies in these economies. Given that large
output drops in emerging market economies have
often preceded past default episodes (Levy-Yeyati and
Panizza, 2011), increased economic turbulence in those
economies, coupled with bad memories of past crises,
could sour investors’ risk sentiment and result in sharp
corrections in global financial centers.
Advanced economies could also be vulnerable to a
sudden reduction in demand from emerging market
economies for their debt instruments. China is the
second-largest exporter of capital in the world, after
the United States, and China’s central bank is the
4In addition, most of these claims are held by two banks that,
although notionally British, have very limited banking presence in
the United Kingdom. This could overstate the financial exposure of
the United Kingdom to emerging market economies.
Table 2.SF.1. Exports to Emerging Market Economies, 1995 versus 2008
(1)
Ratio of Gross Exports in
2008 to Gross Exports in
1995
(2)
Ratio of Value-Added
Exports in 2008 to Value-
Added Exports in 1995
(1)/(2)
Ratio of Gross Exports
to Ratio of Value-Added
Exports
Euro Area 1.71 1.54 1.11
United Kingdom 1.20 1.27 0.95
Japan 2.45 1.99 1.23
United States 1.30 1.23 1.06
Source: Organization for Economic Cooperation and Development–World Trade Organization Trade in Value-Added database.
0
5
10
15
20
25
30
35
40
1997 2012 1997 2012 1997 2012 1997 2012
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2004 2012 2004 2012 2004 2012 2004 2012
Bank loans
Debt
Equity
Sources: Bank for International Settlements; and IMF, Coordinated Portfolio
Investment Survey database.
1
Median value for France, Germany, Italy, and Spain.
2
Excluding China.
1. Structure of Financial Exposure of AEs to EMEs by Asset Class
2. Structure of Financial Exposure of EMEs to AEs by Asset Class2
Euro area1
United Kingdom Japan United States
Debt
Equity
Euro area1
United Kingdom Japan United States
Financial exposure of advanced economies (AEs) to emerging market economies
(EMEs) remains concentrated in foreign bank claims, although exposure through
portfolio investment has recently surged. Advanced economies that are ﬁnancial
centers have seen the largest increase in exposures to emerging market
economies. Except in the case of China, risks from a reduction in the demand of
emerging market economies for advanced economies’ securities appear limited.
Figure 2.SF.2. Financial Exposure of Advanced Economies
to Emerging Market Economies
(Percent of GDP)

largest purchaser of U.S. financial assets. (See the April
2013 Global Financial Stability Report.) A shock to
emerging market economies capable of slowing the
pace of reserves accumulation in China or causing
a sell-off of its reserves in an attempt to defend its
currency could affect advanced economies by raising
their long-term yields. Long-term yields in the United
States and other advanced economies could also rise if
China gradually changes its portfolio away from U.S.
to emerging market treasuries (IMF, 2011b).
Spillover Effects on Advanced Economies during
Previous Episodes of Financial Turbulence in
Emerging Market Economies
To obtain some order of magnitude of the effects from
past spillovers, an event study is conducted around
past episodes with synchronized growth slowdowns in
emerging market economies: the Mexican Tequila crisis
in 1995, the east Asian crisis in 1997, and the Russian
crisis in 1998.5 The analysis focuses on the dynamics
of trade and financial variables during a four-quarter
window after the realization of each event.6
Results suggest that during episodes of financial tur-
moil, import demand in emerging market economies
was an important spillover channel, particularly during
the east Asian and Russian crises (Figure 2.SF.3). Dur-
ing these events, bilateral real exports contracted by at
least one standard deviation from their 15-year average.
Japanese exports have been particularly vulnerable to
shocks stemming from emerging market economies,
which could be explained by Japan’s high trade inter-
connectedness with emerging market economies in east
Asia and the high share of capital goods in its export
structure.
Although imports from emerging market economies
have also tended to decline during these episodes,
partly as a result of supply-chain disruptions, reduc-
tions have been more moderate. The behavior of
exports around these events could be explained by the
dynamics of bilateral nominal exchange rates, with
5The analysis starts in 1990 because of data limitations for emerg-
ing market economies. The 1995 Mexican Tequila crisis, the 1997
east Asian crisis, and the 1998 Russian crisis could be characterized
as events in emerging market economies that, to a certain extent,
were unrelated to developments in advanced economies. The dates of
the events are obtained from the chronology in Laeven and Valencia
(2012).
6With the exception of the analysis of the dynamics of stock mar-
ket indexes, in which the behavior of these indexes is examined three
months after the realization of each event.
–12
–8
–4
0
4
8
12
16
Euro
area
United
Kingdom
Japan United
States
Tequila crisis East Asian crisis Russian crisis
Sources: Haver Analytics; IMF, Direction of Trade Statistics database; and IMF staff
calculations.
1
Standard Poor’s 500 for United States, Nikkei 225 for Japan, FTSE 100 for
United Kingdom, and average of Deutscher Aktien Index and Société des Bourses
Françaises 120 for the euro area.
2. Dynamics of Real Imports
of AEs from EMEs
Following Crisis Events in
EMEs (percent)
–20
–15
–10
–5
0
5
10
15
Euro
area
United
Kingdom
Japan United
States
1. Dynamics of Real Exports
of AEs to EMEs Following
Crisis Events in EMEs
(percent)
Greater than 1 standard deviation but less than 1.5 standard deviations
Greater than 1.5 standard deviations
–30
–20
–10
0
10
20
30
Euro
area
United
Kingdom
Japan United
States
–30
0
30
60
90
120
150
180
Euro
area
United
Kingdom
Japan United
States
4. Dynamics of Net Portfolio
Inflows Following Crisis
Events in EMEs
–30
–20
–10
0
10
20
30
Euro
area
United
Kingdom
Japan United
States
5. Dynamics of Stock Market
Indexes in AEs Following
(percent)
1
–1.0
–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
Euro
area
United
Kingdom
Japan United
States
6. Impact of a Reduction in
Exports to EMEs on AEs’
GDP, East Asian Crisis
(percentage points)
1997
2012
3. Dynamics of Bilateral
Nominal Exchange
Rates Following
(percent; negative value
represents appreciation)
Event studies built around major episodes of financial turmoil in emerging market
economies (EMEs) point to the sensitivity of import demand in those economies
during these events. The sharp reduction in exports from advanced economies
(AEs) to emerging market economies during these episodes came hand in hand
with substantial appreciation of their currencies, in part explained by a spike in
capital inflows. The dynamics of stock markets during these episodes also shed
light on the importance of financial markets in transmitting these shocks to
emerging market economies. Given that trade and financial linkages are now
stronger, similar growth downturn events are likely to have sizable effects on
most exposed advanced economies.
Figure 2.SF.3. Event Studies around Downturn Episodes in
(Peak effect in four quarters)

currencies in advanced economies appreciating, on
average, more than 20 percent, 1½ standard deviations
above their mean. The strengthening of advanced econ-
omies’ currencies also points to a flight-to-safety sce-
nario, as evidenced by large spikes in portfolio inflows.
In addition, dynamics of stock market price indexes in
advanced economies show that shocks from emerg-
ing market economies can be transmitted via financial
markets, most notably in Japan and the euro area.
The east Asian crisis stands out in the brief event
analysis because it was triggered by a common shock
whose effect on regional comovements was almost as
large as that of the global financial crisis (Chapter 3
of the October 2013 WEO). What was the spillover
effect of a shock of the magnitude of the east Asian
crisis on Japan’s output growth?7 An informal estimate
suggests that the 15 percent drop in exports in Japan
during the east Asian crisis could have represented
a 0.3 percentage point decline in Japan’s real GDP
growth, given that Japanese exports to emerging mar-
kets were 2 percent of GDP in 1997. A similar shock
in 2012 would have implied a much larger decline in
output growth (that is, 0.8 percentage point), because
the share of exports to emerging market economies
in Japan’s GDP has more than doubled since the east
Asian crisis.
Quantifying the Spillover Effects of Emerging
Market Economy Growth Shocks on Advanced
Economies’GDP
The impact of a growth shock in emerging market
economies on advanced economies is estimated using
a standard vector-autoregression-based (VAR-based)
approach and through simulations from a dynamic
stochastic general equilibrium model. These estimates
are much more informative than the simple informal
calculations reported earlier.
The first element of the empirical analysis involves
estimating country-wise VARs for each advanced
economy with the following recursive specification:
the growth rate of output of all advanced economies
excluding the advanced economy for which the VAR is
estimated, the growth rate of output in the advanced
economy of interest, the growth rate of output in
emerging market economies, and the growth rate of
7Japan experienced its own banking crisis in 1997–98; therefore
the large growth spillover impact on Japan during the east Asian
crisis should be interpreted cautiously.
real bilateral exports from the advanced economy of
interest to emerging market economies. Because the
global financial crisis was an exceptional event with
unusual effects, a modified version of the VAR model
is also estimated. In this modified version, the regres-
sors are also allowed to interact with a dummy variable
that equals one from the last quarter in 2007 to the
first quarter in 2009 and zero otherwise.8
The spillover effects on advanced economies of a 1
percentage point drop in the GDP growth of emerg-
ing market economies range from a 0.15 percentage
point drop in output growth in the United Kingdom
to a 0.5 percentage point decline in Japan (Figure
2.SF.4). In line with the findings discussed in the
event study analysis, results from the empirical exercise
suggest that the impact of shocks to emerging market
economies’ output on advanced economies’ output
is significant (both economically and statistically) in
Japan and the euro area.9 Based on the decomposi-
tion of the responses of advanced economies’ GDP
growth, it appears that the trade channel is particularly
important for the transmission of shocks to Japan,
whereas nontrade effects seem to dominate in other
advanced economies.10 Results from the interaction
VAR estimation show that, when the global financial
crisis is controlled for—that is, when the dummy is
equal to zero—elasticities are reduced by half (except
in the case of the United Kingdom) and spillovers are
neither statistically nor economically significant across
advanced economies.
The results from the simple VAR analysis illustrate
the magnitude of possible spillover effects; how-
ever, they do not identify the sources of the growth
slowdown, which matter for the spillovers. Differ-
ent spillover transmission channels may be involved,
depending on the nature of the shock.
8The country-wise VARs are estimated using seasonally adjusted
quarterly data from 1996 through 2013, with two lags based on the
Akaike information criterion. The second specification implements
an interaction VAR framework introduced by Towbin and Weber
(2013).
9The large effect observed in Japan could reflect a banking crisis
experienced at the same time as the east Asian crisis and the use of
gross instead of value-added real bilateral exports in the VAR analy-
sis. As discussed earlier, gross trade linkages tend to overstate direct
trade exposures to emerging market economies in countries with an
upstream position in global trade networks.
10The nontrade transmission channel corresponds to the estimated
responses of GDP growth in advanced economies using the full VAR
dynamics, but with real bilateral exports treated as an exogenous
variable (that is, the GDP growth equation coefficients on real bilat-
eral exports set to zero).

To illustrate the potential impact of emerging mar-
ket economy shocks on advanced economies under a
more structural simulation, the IMF’s Flexible Sys-
tem of Global Models is used.11 The baseline model
is calibrated such that a 1 percentage point drop in
emerging market economy GDP growth reduces the
growth rate of total exports of advanced economies,
on average, by 1.3 percentage points (a value of similar
magnitude to the average response observed in the
baseline VAR estimations). In a second specification,
the baseline model is modified to incorporate a capital
flight scenario by assuming that turbulence in emerg-
ing market economies is accompanied by an increase
in the sovereign risk premium of 200 basis points and
an increase in the corporate risk premium of 400 basis
points.12 Both scenarios show a slight real currency
appreciation in advanced economies, whereas emerging
market economy currencies depreciate, on average, by
0.2 percent from baseline. In addition, import demand
in emerging market economies softens by 4 percent
in both scenarios. In line with the VAR estimations
presented earlier, Japan is most susceptible to an
emerging market economy growth shock, with output
growth declining by 0.32 percentage point in response
to a 1 percent reduction in emerging market economy
GDP (Figure 2.SF.5). The United Kingdom is the least
affected by the shock. Estimations from this model
are likely to be on the high side, given that monetary
policy responses across advanced economies to a slow-
down in emerging market economies are constrained
by the zero bound on nominal interest rates.
It is important to note that in both scenarios, the
trade channel is the main transmitter of the shock in
the emerging market economies to advanced econo-
mies. This result hinges, however, on the assump-
tion that there are no direct financial spillovers from
emerging market to advanced economies. Depending
on the origin of the slowdown in the emerging market
economies, this assumption could be too restrictive.
For example, if risk premiums in advanced economies
react to the growth shock in emerging market econo-
mies—possibly because of concern about balance sheet
11The Flexible System of Global Models is an annual, multi
regional general equilibrium model, combining both micro-founded
and reduced-form formulations of various economic sectors. It has a
fully articulated demand side and some supply-side features. Inter-
national linkages are modeled in aggregate for each region. It does
not model intermediate goods; therefore, supply chain effects are not
captured in these simulations.
12Shocks last for one year.
–1.00
–0.75
–0.50
–0.25
0.00
0.25
0.50
0.75
1.00
Baseline Alternative
2. Effect of a 1 Percentage
Point Decline in EME
Growth on the
United Kingdom
–1.00
–0.75
–0.50
–0.25
0.00
0.25
0.50
0.75
1.00
Growth on Euro Area
–1.00
–0.75
–0.50
–0.25
0.00
0.25
0.50
0.75
1.00
Growth on the
United States
–1.00
–0.75
–0.50
–0.25
0.00
0.25
0.50
0.75
1.00
Growth on Japan
Transmitted through trade channel
Transmitted through nontrade channels
Statistically signiﬁcant at 10 percent level
Note: “Baseline” refers to the model in which advanced economies’ GDP growth is
contemporaneously exogenous to emerging market economies’ GDP growth.
“Alternative” refers to elasticities obtained from the interaction vector
autoregression model, when the dummy variable denoting global economic crisis
is equal to zero.
The impact of shocks to emerging market economies’ (EMEs’) output on advanced
economies’ (AEs’) output is signiﬁcant (both statistically and economically) only for
Japan and the euro area. The trade channel is particularly important for the
transmission of shocks to Japan, whereas nontrade effects appear to dominate in
other advanced economies. The impact of growth shocks in emerging market
economies on advanced economies’ output tends to be attenuated, and become
negligible, when the effects of the global economic crisis are controlled for.
Figure 2.SF.4. Peak Effect of a Growth Shock to Emerging
Market Economies on Advanced Economies’ Output Growth
(Four quarters after impact; percentage points)

exposure of financial intermediaries—the spillover
could be larger and financial channels come into play.
Similarly, once cross-border asset linkages are incorpo-
rated, shocks to asset prices in emerging market econo-
mies could also have wealth and other direct effects on
aggregate demand of advanced economies.
Conclusions
Macroeconomic fundamentals in many emerging
market economies are generally stronger today than in
the 1990s and early 2000s, and a simultaneous shock
to all emerging market economies similar to those two
decades ago is unlikely. Nevertheless, a recurrence of
similar events could now have different outcomes for
advanced economies, given that the global economic
landscape and economic linkages between these two
groups have changed. Emerging market economies
are now much larger and more integrated into global
trade and financial markets, which has increased the
exposure of advanced economies to these economies.
Spillovers from a synchronized downturn in emerging
market economy output, operating primarily through
trade channels, could be sizable for some advanced
economies, but would likely remain manageable and
probably short lived. At the same time, financial links
between advanced economies and emerging market
economies have strengthened recently, and although
the magnitudes are much more challenging to quan-
tify, financial spillovers in the case of a slowdown
in emerging market economies and their effects on
advanced economies could be important. The recovery
of advanced economies from the global financial crisis
is still fragile, and policymakers in these economies
should closely monitor growth in emerging markets
and be prepared to take action to mitigate the impact
of external disturbances.
–0.5
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
Baseline
Alternative
Baseline
Alternative
Baseline
Alternative
Baseline
Alternative
Output growth Exports Other
Euro area United Kingdom Japan United States
Change in
Note: “Baseline” refers to the baseline simulation. “Alternative” refers to results
from simulation in which a negative growth shock to emerging market economies
is accompanied by a rise in the sovereign risk premium of 200 basis points and a
rise in the corporate risk premium of 400 basis points.
A synchronous shock has nonnegligible effects across the advanced economies.
Japan is particularly susceptible to emerging market economies’ growth shock,
and the United Kingdom is the least affected by the shock. Spillovers are
transmitted mainly through the trade channel, given the assumption that risk
premiums in advanced economies are not affected by the growth downturn in
emerging market economies. However, simulation-based estimates from this
model are likely to be on the high side, because monetary policy response across
advanced economies to a slowdown in emerging market economies is constrained
by the zero bound on nominal interest rates.
Figure 2.SF.5. Model Simulations of Potential Growth
Spillover Effects from Emerging Market Economies on
Advanced Economies
(Contribution to change in output growth; percentage points)

References
International Monetary Fund (IMF), 2011a, “Changing Patterns
of Global Trade,” prepared by the Strategy, Policy, and Review
Department (Washington).
———, 2011b, People’s Republic of China: Spillover Report for the
2011 Article IV Consultation and Selected Issues, IMF Country
Report No. 11/193 (Washington).
Koopman, Robert, William Powers, Zhi Wang, and Shang-Jin
Wei, 2010, “Give Credit Where Credit Is Due: Tracing Value
Added in Global Production Chains,” NBER Working Paper
No. 16426 (Cambridge, Massachusetts: National Bureau of
Economic Research).
Laeven, Luc, and Fabián Valencia, 2012, “Systemic Banking Cri-
ses Database: An Update,” IMF Working Paper No. 12/163
Levy-Yeyati, Eduardo, and Ugo Panizza, 2011, “The Elusive
Costs of Sovereign Defaults,” Journal of Development Econom-
ics, Vol. 94, No. 1, pp. 95–105.
Roache, Shaun, 2012, “China’s Impact on World Commodity
Markets,” IMF Working Paper No. 12/115 (Washington:
Towbin, Pascal, and Sebastian Weber, 2013, “Limits of Floating
Exchange Rates: The Role of Foreign Currency Import Structure,”
Journal of Development Economics, Vol. 101 (March), pp. 179–94.

1
CHAPTER
3
CHAPTER
PERSPECTIVES ON GLOBAL REAL INTEREST RATES
Real interest rates worldwide have declined substantially
since the 1980s and are now in slightly negative territory.
Common factors account for much of these movements,
highlighting the relevance of global patterns in saving and
investment. Since the late 1990s, three factors appear to
account for most of the decline. First, a steady increase
in income growth in emerging market economies during
2000–07 led to substantially higher saving rates in these
economies. Second, the demand for safe assets increased,
largely reflecting the rapid reserve accumulation in some
emerging market economies and increases in the riskiness
of equity relative to bonds. Third, there has been a sharp
and persistent decline in investment rates in advanced
economies since the global financial crisis. This chapter
argues that global real interest rates can be expected to rise
in the medium term, but only moderately, since these three
factors are unlikely to reverse substantially. The zero lower
bound on nominal interest rates will remain a concern
for some time: real interest rates will likely remain low
enough for the zero lower bound to reemerge should risks
of very low growth in advanced economies materialize.
I
n the past few years, many borrowers with good
credit ratings have enjoyed a cost of debt close
to zero or even negative when it is adjusted for
inflation. This is not just a consequence of the
global financial crisis. Since the early 1980s, yields of
all maturities have declined worldwide well beyond the
decline in inflation (Figure 3.1).
However, because the recent interest rate declines
reflect, to a large extent, weak economic conditions
in advanced economies after the crisis, some reversal
is likely as these economies return to a more normal
state. But how much of a reversal? Certain factors
suggest a substantial increase in interest rates in the
medium term: high and rising debt levels in advanced
economies; population aging; lower growth in emerg-
ing market economies, which might lower their saving
rates; and further financial deepening in emerging
market economies, which would reduce borrowing
constraints and thereby net saving.1 Other factors,
however, would work in the opposite direction: long-
lasting negative effects of the global financial crisis on
economic activity (Cerra and Saxena, 2008; Reinhart
and Rogoff, 2008), persistence of the “saving glut” in
key emerging market economies, and renewed declines
in the relative price of investment goods.
This chapter constructs global real interest rates at
short and long maturities and reviews their evolution
since 1980. It also traces the evolution of the cost of
1For example, McKinsey Global Institute (2010) argues that
worldwide real interest rates are set to increase substantially in the
medium to long term, putting an end to cheap capital.
The main authors of this chapter are Davide Furceri and Andrea
Pescatori (team leader), with support from Sinem Kilic Celik and
Katherine Pan, and with contributions from the Economic Modeling
Division of the IMF’s Research Department.
0
2
4
6
8
10
12
14
16
1970 75 80 85 90 95 2000 05 10 13
Ten-year nominal interest rate Inflation rate
Figure 3.1. Ten-Year Interest Rate on Government Bonds
and Inflation
(Simple average across France, Germany, United Kingdom, and
United States; percent a year)
Sources: Bloomberg, L.P.; Haver Analytics; Organization for Economic
Cooperation and Development; World Bank, World Development Indicators
database; and IMF staff calculations.
Note: Inflation is calculated as the percent changes in the consumer price
index.

capital—a weighted average of the cost of debt and the
cost of equity. It then analyzes key factors that could
explain the observed patterns: shifts in private saving,
changes to fiscal policy, shifts in investment demand,
changes in the relative price of investment, monetary
policy, and portfolio shifts between bonds and equity.
It closes by considering how the main factors behind
the decline in real rates might play out in the medium
term. The analysis is largely qualitative. The effects of
each factor are discussed in a general equilibrium con-
text, but the quantitative effects may not be identified
precisely.
The following questions arise:
•• Is there a global trend in interest rates, or do
country-specific dynamics dominate?
•• What have been the main factors contributing to
the decline in real interest rates since the 1980s?
•• What have been the effects of the global financial
crisis on real rates, and how long are these effects
likely to last?
•• What should we expect in the medium term?
•• What are the implications for fiscal authorities in
advanced economies and for fund and asset manag-
ers? What are the implications for monetary policy?
These are the main findings:
•• Economic and financial integration has increased
sufficiently during the past three decades or so for
real rates to be determined largely by common fac-
tors. Thus, using a global measure of real interest
rates and exploring global patterns of saving and
investment are appropriate.
•• Since the early 1980s, global real interest rates have
strongly declined. The cost of capital has also fallen,
but to a lesser extent because the required return on
equity has increased since 2000.
•• Monetary policy dominated the evolution of real
rates and the cost of capital in the 1980s and early
1990s. Fiscal policy improvement in advanced econ-
omies was the main factor underlying the decline
in real interest rates during the rest of the 1990s. In
addition, the decline in the relative price of invest-
ment may have reduced the demand for loanable
funds in both the 1980s and 1990s.
•• Since the late 1990s, the following factors have
largely driven the decline in real rates and the cost
of capital:
oo A large increase in the emerging market economy
saving rate between 2000 and 2007 more than
offset a reduction in advanced economy pub-
lic saving rates. Strikingly, increases in income
growth seem to be the most relevant proxi-
mate cause behind the rise in emerging market
economy saving rates during the same period.
oo Portfolio shifts in the 2000s in favor of bonds were
due to higher demand for safe assets, mostly from
the official sector in emerging market econo-
mies, and to an increase in the riskiness of equity
relative to that of bonds. These shifts led to an
increase in the real required return on equity and
a decline in real rates—that is, an increase in the
equity premium.2
oo Scars from the global financial crisis have resulted
in a sharp and persistent decline in investment in
advanced economies. Their effects on saving have
been more muted.
Real interest rates and the cost of capital are likely
to rise moderately in the medium term from current
levels. Part of the reason is cyclical: the extremely low
real rates of recent years reflect large negative output
gaps in advanced economies—indeed, real rates might
have declined even further in the absence of the zero
lower bound on nominal interest rates. The analysis in
this chapter suggests, however, that real rates and the
cost of capital are likely to remain relatively low in the
medium term, even when output gaps are eventually
closed. The main reasons are as follows:
•• The effects of the global financial crisis will per-
sist. The findings of the chapter suggest that the
investment-to-GDP ratios in many advanced econo-
mies are unlikely to recover to precrisis levels in the
next five years.
•• The portfolio shift in favor of bonds that started in
the early 2000s is unlikely to be reversed. Although
bond rates may rise again on account of a rising
term premium when unconventional monetary
policy is wound down, this will probably have a
smaller effect on bond rates than will other forces.
In particular, stronger financial regulation will
further increase demand for safe assets. A reduction
in emerging market economy saving and thus in the
pace of official reserve accumulation would work the
2Between 2008 and 2012, quantitative easing, mainly in the
United States and United Kingdom, may also have contributed to a
portfolio shift by compressing term premiums on long-term bonds.
There is, however, uncertainty about the magnitude of estimates of
these premiums, and even upper-end estimates suggest that the long-
term impact of quantitative easing over the period 2008–13 on the
equity premium has probably been modest.

CHAPTER 3 PERSPECTIVES ON GLOBAL REAL INTEREST RATES
opposite way, and the net effect is therefore likely to
be small.3
•• Lower growth in emerging market economies com-
pared with growth during the precrisis boom years
is expected to result in somewhat lower saving rates.
Based on the evidence of previous saving shifts, the
magnitude of the effect on real rates is likely to be
modest.
In summary, real rates are expected to rise. However,
there are no compelling reasons to believe in a quick
return to the average level observed during the mid-
2000s (that is, about 2 percent). Within this global
picture, however, there may well be some countries
that will see higher real rates than in the early 2000s
because of higher sovereign risk premiums. The con-
clusions here apply to the risk-free rate.
An important concern is the possibility of a pro-
longed period of very low growth (“secular stagnation”)
in advanced economies, especially if new shocks were
to hit demand in these economies or if policies do not
address crisis legacy issues as expected (see Chapter 1
of the October 2013 World Economic Outlook, WEO).
As discussed in Chapter 1, with current low inflation,
real interest rates will likely be low enough for the zero
lower bound issue to reemerge if such risks of very low
growth in advanced economies materialize. Real inter-
est rates may then be unable to decline to the negative
levels required to restore full employment.
The prospect that real interest rates could increase to
relatively low levels in the medium term has important
implications:
•• Pension funds, insurance companies that provide
defined benefits, and savers in general may suf-
fer from a prolonged period of continued low real
interest rates. An environment of continued low
real (and nominal) interest rates may also induce
financial institutions to search for higher real (and
nominal) yields by taking on more risk.4 This, in
turn, may increase systemic financial sector risks,
and appropriate macro- and microprudential
3Withdrawal from quantitative easing may also induce a modest
reversal of the portfolio shifts observed between 2008 and 2013 by
raising real term premiums to precrisis levels. Its effect on the global
cost of capital, however, will probably be small.
4Maddaloni and Peydró (2011) find that periods of low short-
term rates are associated with softening of bank lending standards
in the euro area and the United States. Altunbas, Gambacorta, and
Marqués-Ibañez (2012) also find that low interest rates over pro-
tracted periods lead to an increase in bank risk.
oversight will be critical for maintaining financial
stability.
•• Symmetrically, borrowers would enjoy the benefits
of low rates, all else equal.5 For one thing, achiev-
ing fiscal sustainability would be less difficult. As an
example, a 1 percentage point reduction in real rates
in the next five years relative to the rate currently
projected (October 2013 WEO) would reduce the
average advanced economy debt-to-GDP ratio by
about 4 percentage points. If real rates are expected
to be close to or lower than real GDP growth rates
for a long time, some increases in debt-financed
government spending, especially public investment,
may not lead to increases in public debt in the
medium term.6
•• With respect to monetary policy, a period of con-
tinued low real interest rates could mean that the
neutral policy rate will be lower than it was in the
1990s or the early 2000s. It could also increase the
probability that the nominal interest rate will hit the
zero lower bound in the event of adverse shocks to
demand with inflation targets of about 2 percent.
This, in turn, could have implications for the appro-
priate monetary policy framework.
The rest of the chapter is structured as follows. The
second section constructs the global real rate and cost
of capital; the third section introduces the conceptual
framework to analyze observed patterns in the global real
rate and the cost of capital; the fourth section tests the
hypotheses laid out in the third; the fifth section summa-
rizes the findings and draws implications for fiscal policy
in the medium term; and the final section concludes.
Stylized Facts: Measuring Real Rates and the
Cost of Capital
Real interest rates are directly observable only from
the yields on inflation-indexed bonds. Such bonds,
however, are typically not issued at short maturities
5To the extent that rates are lower than expected because of lower-
than-expected activity, however, borrowers may well be worse off
than under a scenario of higher growth and higher interest rates.
6If the real rate is permanently lower than real GDP growth, then
a temporary debt-financed increase in government spending will lead
to only a temporary increase in the public debt ratio. More generally,
the debt-to-GDP ratio may not increase in the medium term if the
increased spending permanently raises GDP (for example, by raising
the productivity of private capital), generating an increase in annual
tax revenue large enough to cover the increase in annual debt service,
as argued by Delong and Summers (2012).

(that is, less than one year), and even at longer maturi-
ties few countries have good data coverage (King and
Low, 2014).7 In the absence of inflation-protected
securities, real rates can be approximated by the differ-
ence between the nominal interest rate and inflation
expectations over the relevant time horizon:
rt
[n] = it
[n] – Etpt,t+n, (3.1)
in which it
[n] is the nominal yield of a zero cou-
pon bond of maturity n at time t, and Etpt,t+n is
the expected consumer price inflation over the life
7Markets for indexed bonds are not deep and are susceptible to
changes in the liquidity premium and to technical factors. Following
Blanchard (1993), because of tax considerations, for the United
Kingdom, the real rate is adjusted by adding τ/(1 − τ) × π, in which
τ denotes the income tax rate on coupon payments and is set at 20
percent (see Blanchard, 1993) and π denotes the expected inflation
rate over the life of the security.
of the bond. Bond yields are observable, but infla-
tion expectations are not (at least not directly). For
estimates of expected inflation, the analysis relies on
survey information and on forecasts from an estimated
autoregressive process. Because the parameters of this
autoregressive process are likely to change over time,
rolling windows are used. To maximize sample cover-
age, three-month and ten-year maturities are used to
represent short- and long-term real rates, respectively.8
Estimated three-month real rates for the United States
and ten-year real rates for the United States and the
United Kingdom are shown in Figure 3.2. The model-
and survey-based approaches give very similar estimates.
The figure suggests that real rates in the two countries
have declined sharply since the early 1980s. Moreover,
the rate decline has been global (Figure 3.3). The aver-
age global ten-year real rate declined from a high of
6 percent in 1983 to approximately zero in 2012.9
The relevance of common forces driving the worldwide
decline in real rates is confirmed by a principal component
analysis. The results show that the contribution of the first
common factor to the variation in real rates increased from
about 55 percent in 1980–95 to almost 75 percent in
1995–2012 (Figure 3.4, panel 1).10 The greater relevance
of common factors can also be seen in the evolution of the
cross-country dispersion in real rates over time.
Figure 3.4 (panel 2) shows that the cross-sectional
standard deviation of ten-year real rates declined from
about 400 basis points in the early 1980s to 100 basis
points in the most recent years.11 This decline is consis-
tent with the view that within-country factors driving
rates away from the common global mean have become
8See Appendix 3.1 for details. The sample comprises 40 countries:
25 advanced economies and 15 emerging market economies. The
interest rates used are those on government securities, where avail-
able; otherwise interbank rates are used.
9These are GDP-weighted averages. A similar pattern emerges from
simple averages for Group of Seven (G7) countries (Canada, France,
Germany, Italy, Japan, United Kingdom, United States) and for GDP-
weighted averages excluding the United States (see Appendix 3.7).
10Similar results are obtained when changes in real interest rates
are used.
11Similar results can be found for short-term emerging market
economy securities using a sample starting in 1990 (the data for
long-term rates are scant for emerging market economies). These
results show that the contribution of emerging market economies
to overall real rate dispersion has declined markedly. The analysis
excludes those countries that have experienced a significant increase
in default risk in the aftermath of the global financial crisis (that is,
some noncore euro area countries), because analyzing the deter-
minants of default risks goes beyond the scope of the chapter. It is
possible to observe, in regard to the euro area, that whereas the
–4
–2
0
2
4
6
8
1967 72 77 82 87 92 97 2002 07 13
Figure 3.2. Real Interest Rate Comparison
(Percent a year)
Model
Philadelphia FRB
Cleveland FRB
–4
–2
0
2
4
6
8
10
1967 77 87 97 2007 13
–4
–2
0
2
4
6
8
10
1967 77 87 97 2007 13
Model
IPS
CF
1. Three-Month Real Interest Rate Comparison
(United States)
Ten-Year Real Interest Rate Comparison
2. United States 3. United Kingdom
Sources: Consensus Economics; Federal Reserve Bank of Cleveland; Federal
Reserve Bank of Philadelphia, Livingston Survey; Federal Reserve Bank of
Philadelphia, Survey of Professional Forecasters; Haver Analytics; and IMF staff
calculations.
Note: CF = Consensus Forecasts; FRB = Federal Reserve Bank; IPS =
inﬂation-protected securities.
Model
IPS
Cleveland FRB
Livingston

less important. However, even though the fraction of the
total variance explained by the first factor has increased for
both three-month and ten-year real rates, it remains sig-
nificantly lower at the shorter maturity. This is consistent
with continued scope for monetary policy in individual
countries to play an important countercyclical role in
smoothing domestic output fluctuations.
The greater weight of the common factors may be
attributable to a variety of reasons. Because inflation risk
affects the term premium, a common decline in long-
term real rates may be due to simultaneous adoption of
–8
–6
–4
–2
0
2
4
6
8
10
1970 75 80 85 90 95 2000 05 10 12
Figure 3.3. Real Interest Rates, Real Returns on Equity, and
Cost of Capital
(Percent a year)
Three-month real rate
Ten-year real rate
Term spread
1. Short- and Long-Term Global Real Interest Rates
0
1
2
3
4
5
6
7
8
9
1973 78 83 88 93 98 2003 08 13
2. Expected Real Returns on Equity
United States United Kingdom
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
1991–2000 2001–07 2008–13
3. Global Real Interest Rates and Cost of Capital
Global real interest rate
Global cost of capital
Sources: Bloomberg, L.P.; Haver Analytics; IMF, International Financial Statistics
database; Organization for Economic Cooperation and Development; World
Bank, World Development Indicators database; and IMF staff calculations.
Note: Term spread is defined as the difference between short- and long-term
real rates.
0
10
20
30
40
50
60
70
80
90
100
1980–95 1996–2012
Contribution of first
factor
Contribution of
second factor
Contribution of
third factor
Figure 3.4. Common Factors in Real Interest Rates
0
2
4
6
8
0
2
4
6
8
10
12
1970 75 80 85 90 95 2000 05 10 13
2. Convergence of Real Interest Rates and Financial Integration
(percent)
Standard deviation of real rates (left scale)
Financial integration (right scale)
Sources: Bank for International Settlements; Bloomberg, L.P.; Haver
Analytics; IMF, International Financial Statistics database; Organization for
Economic Cooperation and Development; World Bank, World Development
Indicators database; and IMF staff calculations.
Note: Financial integration is constructed as banks’ bilateral assets and
liabilities as a share of countries’ GDP.
1. Principal Component Analysis of Long-Term Real Interest Rates
(percent, share of real-rate variation explained by the first three
common factors)
standard deviation of long-term real rates has steadily declined for
core euro area countries, it has recently increased for noncore euro
area countries (see Appendix 3.7). In contrast, the standard deviation
of short-term real rates has decreased for both core and noncore
countries.

monetary policy frameworks that ensure low and stable
inflation. However, such simultaneous adoption would
not explain the trend decline in short-term real rates,
because such rates are little affected by inflation risk. In
other words, a worldwide decline in the inflation risk
premium would have caused a similar decline in the
term spread, which has not happened (Figure 3.3, panel
1).12 An alternative hypothesis for the increased rel-
evance of common factors is increased financial market
integration. Figure 3.4 (panel 2) shows the evolution of
cross-holdings of banks’ assets and liabilities (a measure
of financial market integration). According to this mea-
sure, financial integration has steadily and substantially
increased during the past three decades. The correlation
between the financial integration and real-rate dispersion
variables is −0.74, supporting the hypothesis.
Financing decisions are not limited to short-term
borrowing or the fixed-income market. A firm’s evalu-
ation of whether it is worthwhile to undertake a given
investment project requires that the expected return on
the project be greater than the overall cost of capital,
which includes the cost of equity finance as well as that
of borrowing.
For the cost of equity, a measure of expected real
return on major stock markets is constructed.13 Stated
roughly, the expected return on equity is equal to the
dividend yield plus the expected long-term growth
rate of real dividends. Expected dividend growth is
estimated through a vector autoregressive process of
dividend and GDP growth. Figure 3.3 (panel 2) shows
the expected long-term real return on equity con-
structed for the U.S. and U.K. stock markets.
The estimated cost of capital is a weighted average
of the estimates for the real long-term interest rate
and the required return on equity.14 The ex ante real
12The average real term spread (the difference between long- and
short-term real rates) for the entire period is about 100 basis points. The
absence of a trend suggests a stable term premium (at short and medium
frequency, the term spread varies because of the business cycle). More
recently, default risk has been a factor in the euro area. The evolution of
default risk, however, is beyond the scope of this chapter.
13The real required (internal) rate of return on equity in period t
for a horizon n, Re,t
[n], is estimated from the following equation:
St/Dt = Sn
j=0(1 + Re,t
[n])–jEt gt,t+1+j,
in which S is a stock price index, D denotes dividends consistent
with the stock index chosen, and Et gt,t+j = Dt+j/Dt is the expected
cumulated dividend growth.
14Equal weights for the two variables are assumed for the United
States, and two-thirds (cost of debt) and one-third (cost of equity)
for all the other countries. Weights are chosen based on average val-
ues of corporate bond and stock market capitalization in the United
returns on both bonds and equity declined between
the 1980s and the late 1990s, but after the dot-com
bubble burst in 2000–01, the expected return on
equity increased. The decline in the overall cost of
capital was therefore less than the decline in the real
interest rate.15 Thus, although the estimated global real
interest rate in the first part of the 2000s was 1.15 per-
centage points lower than in the 1990s, the estimated
global cost of capital was only 0.62 percentage point
lower (Figure 3.3, panel 3).
Determinants of Real Rates: A Saving-
Investment Framework
The equilibrium real interest rate is the price that
equilibrates the desired demand for and supply of
funds. Factors affecting the equilibrium real rate shift
or tilt the demand or supply schedules (Figure 3.5).
A reduction in the equilibrium real rate would be
produced by an outward shift in the supply schedule of
funds or an inward shift in the demand schedule. The
supply of funds may come from private saving, public
saving (the budget surplus), or monetary policy
actions.
Changes in expected investment profitability and
in the relative price of investment goods (for example,
machinery, equipment, information technology) may
shift the demand for funds. A decrease in the profit-
ability of investment reduces investment and real rates,
and the economy converges to a smaller capital stock.
A reduction in the relative price of investment, for a
given investment volume, reduces the value of loan
demand. At the same time, it is likely to increase the
volume of investment. Thus, in theory, the net effect
on the value of global investment, and on real interest
rates, depends on the elasticity of the volume of invest-
ment to its relative price.
Shifts in private saving can be induced by several
factors: changes in current and expected income, social
safety nets, and demographics, as well as financial
innovations, among others. For example, the permanent
income hypothesis predicts a decrease in the saving rate
whenever a new development increases expected future
income growth. A different result may arise, however, in
the presence of consumption habits: an increase in GDP
States and in other countries, and tax corrections are not included.
Nevertheless, since 2000, for any possible choice of weights, the cost
of capital has declined less than the real rate.
15Similar results are obtained when the cost of debt is measured
using real corporate yields.

growth can raise the saving rate (see Appendix 3.6). All
else equal, such a shift in the saving schedule would
reduce real interest rates, increasing the equilibrium level
of global investment. Population aging reduces saving
under the life cycle model, which predicts that saving
rates are the highest for age groups in the middle. Over-
all, aging should increase real interest rates and reduce
global investment.
Changes in public saving (that is, fiscal policy) affect
the aggregate saving schedule similarly to those in
private saving. Because long-term rates are a weighted
average of expected future short-term rates, expecta-
tions of future deficits will tend to increase today’s
long-term real bond rate. In addition, the overall effect
of fiscal policy on real rates includes an effect from
the stock of public debt. Given that saving decisions
depend partly on wealth, of which public debt is a
part, a high level of debt tends to depress private sav-
ing and, in turn, increase real interest rates.16
A neutral monetary policy (that is, keeping output
at its potential) does not contribute to the determi-
nation of the real interest rate, which is then at its
natural level. However, deviations of monetary policy
from a neutral stance should lead the real rate to move
away from its natural level. Loosely speaking, monetary
policy easing (tightening) can be represented as an
outward (inward) shift in the supply of funds.17
In the absence of portfolio shifts, the equity pre-
mium is constant, implying that movements in the
16Appendix 3.3 shows the negative effect of the stock of public
debt on private saving in an overlapping-generations model in which
Ricardian equivalence does not hold.
17In the standard Investment Saving–Liquidity Preference Money
Supply (IS-LM) model, a decrease in money supply (a leftward shift
in the LM curve) increases the real rate, which, in turn, reduces
output and investment. The decline in output would shift the saving
curve until saving and investment are in equilibrium.
cost of capital can be summarized by movements in
real rates. The equity premium, however, varies over
time. Specifically, two factors can affect the equity
premium: (1) a shift in the relative supply of (demand
for) bonds and equities and (2) a change in the relative
risks of holding bonds and equities.18
The hypotheses outlined above, and their implications
for real rates, returns on equities, and global investment
and saving schedules, are summarized in Table 3.1.
18More technically, a change in the relative risk of holding bonds
and equities is a change in the covariance of long-term bonds or
equity with households’ marginal utility of consumption, making
one of the two asset classes relatively riskier (or safer) as a financial
investment.
Figure 3.5. Real Interest Rate and Shifts in Demand for
and Supply of Funds
Source: IMF staff illustration.
Real
rate
(percent)
Funds
(U.S. real dollars, bond market)
Supply
Supply'
Demand
Demand'
Table 3.1. Alternative Hypotheses Explaining a Decline in Real Interest Rates
Hypothesis
Predicted Effect
Real
Interest
Rates
Expected
Return on
Equity
Global
Investment
Ratio
Investment Shift Decrease in the Relative Price of Investment ? ? ?
Decrease in Investment Profitability ↓ ↓ ↓
Saving Shift Tight Fiscal Policy ↓ ↓ ?
GDP Growth Increase (habit) ↓ ↓ ↑
Demographics (aging) ↑ ↑ ↓
Monetary Policy Easing ↓ ↓ ↑
Portfolio Shift Increase in Relative Risk of Equities ↓ ↑ ?
Increase in Relative Demand for Bonds ↓ ↑ =
Source: IMF staff illustration.

Which Factors Contributed to the Decline in
Real Interest Rates?
This section assesses various hypotheses for explaining
the observed decline in real interest rates.
Shifts in the Demand for Funds
The investment-to-GDP ratio in advanced economies
shows a marked decline since 1980, particularly since
2000 (Figure 3.6). This decline may reflect two factors:
a lower price of investment and a reduction in the
profitability of investment.
Decline in the relative price of investment
Figure 3.7 (panel 1) shows the evolution of the rela-
tive price of investment and of the value and volume
of investment as a share of GDP. The figure shows
that although the relative price of investment did not
decline meaningfully after 2002, it fell steadily from
1980 to the beginning of the 2000s.19 This reduction,
in turn, led to a decline in the value of investment as a
share of GDP.20
Reduced investment profitability
Figure 3.7 also presents the evolution of real corporate
profit growth (panel 2) and of corporate profit rates
(panel 3). It shows that although no negative shifts in
investment profitability are observable up to the early
to mid-2000s, investment profitability has markedly
declined in the aftermath of the global financial crisis,
particularly in the euro area, Japan, and the United
Kingdom. Therefore, the hypothesis that a decline
in investment profitability in advanced economies
has contributed to the decline in real rates does not
find empirical support up to the crisis, after which it
becomes a key factor.21
Another way to examine the evolution of the
attractiveness of investment is to look at the dynamic
of Tobin’s q (Hayashi, 1982). A q value greater than
one for a company means that the market value of
the company is greater than the value of its recorded
assets and that firms have an incentive to invest in
it. Likewise, a decline in the value of q implies that
investment becomes less attractive. Using Thomson
Reuters Worldscope data for a sample of more than
30,000 firms for 74 countries for 1990–2013 (Brooks
and Ueda, 2011), the analysis finds that the dynamic
of q seems to follow the evolution of investment
profitability presented above (Figure 3.7, panel 4).22
In particular, no negative shifts in the attractiveness
of investment are observable in the 1990s and early
to mid-2000s, but q slumped in the aftermath of the
global financial crisis.
19The decline in the relative price of investment has been exten-
sively documented in previous studies (for example, Gordon, 1990).
These studies typically associate the decline in investment price with
better research and development, embodied in new, more efficient
investment goods (for example, Fisher, 2006). In addition, falling
commodity prices (such as that for steel) also may have contributed
to the decline in the relative price of investment in the 1980s and
1990s.
20Although the volume of investment increased during this period,
it could not compensate for the reduction in the relative price of the
value of investment.
21The decline in investment profitability in advanced economies
is confirmed by an estimated measure of profitability (see Appendix
3.2). Furthermore, it coincides with the decline in productivity
growth observed in many advanced economies in the aftermath of
the crisis.
22The calculations in this analysis assume that the marginal q value
is equal to the average q value.
18
20
22
24
26
28
30
32
34
1980 85 90 95 2000 05 10 13
Figure 3.6. Investment-to-GDP Ratios
(Percent of GDP)
Global nominal investment (saving)-to-GDP ratio
Advanced economy nominal investment-to-GDP ratio
Emerging market economy nominal investment-to-GDP ratio
Sources: Haver Analytics; Organization for Economic Cooperation and

In summary, both of these factors contributed to the
decline in advanced economy investment ratios, but
during different periods: (1) from 1980 to early in the
first decade of the 2000s, the substantial decline in the
relative price of investment was important, and (2) in
the aftermath of the global financial crisis, the negative
shift in investment profitability was important.
Shifts in Saving: The Role of Emerging Market
Economies
The saving-to-GDP ratio in emerging market econo-
mies increased markedly after 2000 (Figure 3.8, panel
1). As a result, the global saving rate between 2000
and 2007 increased by 1.7 percentage points (of which
1.5 percentage points can be attributed to increased
saving rates in emerging market economies and a
further 0.8 percentage point to the increased weight
of emerging market economies in world GDP, with a
subtraction of 0.6 percentage point resulting from the
decline of advanced economy saving rates). Within the
emerging market economies, China’s saving accounted
for an ever-increasing share—approaching 18 percent
of total emerging market economy GDP by 2013,
about half of total emerging market economy saving
(Figure 3.8, panel 2). The increased supply of saving
from emerging market economies, in particular from
China, must have contributed significantly to the
decline in real interest rates.
What factors explain this increase in emerging
market economy saving? Higher oil prices contributed
to the increase in saving in the oil exporters in this
group between 2004 and 2008 (Figure 3.8, panel 2).
In addition to rising oil prices, various causes have been
proposed, including the erosion of the social safety net
in China, financial constraints, demographic factors, and
the desire to accumulate a substantial buffer in official
reserves (see next section).23 However, in many emerging
market economies, financial constraints have decreased
(Abiad, Detragiache, and Tressel, 2010), and safety nets
have generally been strengthened, which would result in
lower saving rates.24 For China, Wu (2011) finds that
developments in demographics, safety nets, and financial
23See, for example, Chamon and Prasad (2010), Song and Yang
(2010), Curtis, Lugauer, and Mark (2011), Wei and Zhang (2011),
and G20 (2011, 2012).
24For example, between 2000 and 2007, the ratio of public health
expenditure to GDP increased to 3.0 percent from 2.7 percent in
emerging market economies and to 0.75 percent from 0.49 percent
in China.
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
18
20
22
24
26
28
1980 85 90 95 2000 05 10 13
Figure 3.7. Investment Shifts in Advanced Economies
Relative price of investment (left scale)
Investment value (percent of GDP; right scale)
Investment volume (percent of GDP; right scale)
1. Relative Price of Investment, 1980–2013
–6
–4
–2
0
2
4
6
8
AEs EA JPN UK US
0
5
10
15
20
AEs EA JPN UK US
Investment Profitability, 1980–2013
1981–90 1991–2000 2001–07 2008–13
2. Real Profit Growth
(percent)
3. Profit Rates
(percentage points)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
EA AEs Japan UK US
4. Tobin’s q, 1991–2013
1991–2000 2001–07 2008–13
Sources: Brooks and Ueda (2011); Haver Analytics; Organization for Economic
Cooperation and Development; World Bank, World Development Indicators
Note: Real profit growth is the rate of growth of real corporate gross
operational surplus. Profit rate is the ratio of corporate gross operational
surplus to the capital stock. AEs = advanced economies, EA = euro area,
JPN = Japan, UK = United Kingdom, US = United States.

constraints have contributed only modestly to the
increase in saving rates. Empirical research performed
for this chapter confirms this result (Box 3.1).
Demographic factors and financial constraints seem
important in explaining long-term saving trends and
sustained cross-country differences (IMF, 2013). As
discussed in Box 3.1, however, they cannot explain the
rapid increase in emerging market economy saving rates
during 2000–07. A more relevant explanation is that
saving rates increased because growth steadily increased
(see also Carroll and Weil, 1994). This hypothesis is
investigated in Box 3.1. A time-series model, in which
saving rates are a function of lagged saving rates and
contemporaneous real GDP growth, explains most of
the time-series variation in emerging market economy
saving rates (Figure 3.8, panels 3 and 4).25 The model
suggests that the steady increase in emerging market
economy growth in the past decade contributed to
a shift in saving rates of about 10 percentage points
between 2000 and 2007 (panel 3 of the figure), mainly
accounted for by the effect of the acceleration in China
(panel 4). These results strongly support the hypothesis
that increased emerging market economy growth in
the first decade of the 2000s contributed to the rise
in emerging market economy saving rates above and
beyond the increase in investment rates (that is, net
saving increased).26
Shifts in Saving: The Role of Fiscal Policy
Theory suggests three main channels through which
fiscal policy may affect long-term real rates. The first
is by reducing public sector saving, thereby raising
contemporaneous short-term real rates. The second
is through anticipated future deficits, which affect
expected short-term real rates. The third is via the
stock of public debt and future taxes, which can affect
private wealth and thus current saving and consump-
tion decisions. Each of these is examined in turn.
25The model also fits the evolution of saving rates in advanced
economies remarkably well, explaining about 90 percent of the
variation.
26The relationship between growth and saving is complex and
difficult to pin down with great confidence. To the extent Box 3.1
can do so, it finds that the positive relationship between growth
and saving in the short to medium term is determined by the effect
of growth on saving, rather than the effect of saving on growth.
Similarly, strong evidence is found that a steady reduction in growth
in many advanced economies (notably Japan) has contributed signifi-
cantly to the decline in their saving rates.
0
5
10
15
20
25
30
35
40
1980 83 86 89 92 95 98 2001 04 07 10 13
10
15
20
25
30
35
40
1980 85 90 95 2000 05 10 13
Figure 3.8. Saving Shifts in Emerging Markets
Advanced economies
EMEs
1. Nominal Saving-to-GDP Ratios
(percent of GDP)
2. Saving in Total GDP for Emerging Markets
(1980–2013, percent)
EMEs
China
Oil exporters
Other EMEs
20
25
30
35
40
2001 03 05 07 09 11 13
30
35
40
45
50
55
60
2001 03 05 07 09 11 13
Actual Predicted Counterfactual
3. Emerging Markets 4. China
Contribution of Higher Growth to Increased Saving
(percent of GDP, 2001–13)
Sources: Organization for Economic Cooperation and Development; World
Note: EMEs = emerging market economies; Actual = actual saving-to-GDP
ratio; Predicted = predicted saving-to-GDP ratio obtained by regressing the
EME saving rate on its lagged value and EME real GDP growth; Counterfactual
= conditional forecast of the saving rate assuming real GDP growth is constant
at the average value of the late 1990s.

•• Panel 1 of Figure 3.9 shows the historical evolu-
tion of world public sector saving as a percentage
of world GDP. The global public saving ratio rose
during the mid- to late 1980s and mid- to late
1990s, broadly reflecting the profile of the advanced
economy ratio (Figure 3.9, panels 2 and 3).
•• Figure 3.9 (panel 4) shows expected fiscal posi-
tions, as represented by WEO forecasts. These, too,
improved considerably in the second part of the
1990s.27
•• Finally, following Blanchard and Summers (1984)
and Blanchard (1985), a forward-looking index is
constructed that depends on the current level of debt
and ten-year forecasts of primary deficits. A decrease
in the index over time indicates a reduction in private
wealth due to fiscal policy and, thus, a positive shift in
total saving.28 The evolution of the aggregate index for
advanced economies shows a decline of 2.1 percentage
points from 1994 to 2000 (Figure 3.9, panel 5).29
Thus, the evidence regarding all three channels indi-
cates that advanced economy fiscal policies contributed
significantly to the decline in real interest rates in the
1990s. Outside of that decade, however, they had the
opposite effect. The fact that real rates nevertheless
continued to decline during the 2000s means that
other factors more than offset the effect of fiscal policy.
Monetary Policy
To the extent that monetary policy is neutral (that is,
keeping output at its potential), it does not contribute
to the determination of the real interest rate, which
is then anchored at its natural level. In practice, it is
reasonable to assume that whenever a central bank
does not deviate from the systematic behavior implied
by its long-standing monetary policy rule, its stance
is approximately neutral across business cycles.30 In
27These forecasts are available beginning in 1990, but unfortu-
nately only for advanced economies.
28The index is constructed as xt = 0.1[bt + ∑∞
i=0(1.1)–ipdt,t+i], in
which pdt,t+i is the WEO forecast for the primary-deficit-to-GDP
ratio in year t + i, and bt is the debt-to-GDP ratio at time t. See
Appendix 3.3 for details.
29This suggests an arc elasticity of about 0.21. In all other periods,
the index has increased, putting upward pressure on real rates.
30This is clearly an approximation. For example, over the business
cycle, whenever there is a trade-off between output gap and inflation
stabilization, the monetary authority has too few instruments to
achieve the first-best allocation. This, in turn, implies that over the
cycle, the actual real rate cannot be equal to the natural (Wicksell-
ian) rate.
Sources: Organization for Economic Cooperation and Development; World
Figure 3.9. Effect of Fiscal Policy on Real Interest Rates
(Percent of GDP)
Public-saving-to-GDP ratio
Public saving net of interest as percent of GDP
2
4
6
8
10
12
14
16
1990 96 2002 08 13
5. Advanced Economies,
Fiscal Index Based
on Debt and
Expected Deficits
–9
–6
–3
0
3
1990 94 98 02 06 10 13
4. Advanced Economies,
Expected Deficits
Five-year-ahead
forecasts
Average of one- to
five-year-ahead
forecasts
–3
–2
–1
0
1
2
3
4
5
6
1980–84 1990–94 2000–04 2010–12
0
2
4
6
8
10
12
1980–84 1990–94 2000–04 2010–12
–2
–1
0
1
2
3
4
5
6
1980–84 1985–89 1990–94 1995–99 2000–04 2005–09 2010–12
1. World
2. Advanced Economies 3. Emerging Market
Economies

contrast, monetary policy shocks, defined as deviations
from the policy rule, should lead to deviations from
the neutral stance. For example, a series of tightening
shocks should lead to a real rate above the natural rate
for some time.
To assess the role played by monetary policy, the
analysis uses a measure of U.S. monetary policy
shocks. The United States is interesting in itself because
of its prominent role in the global financial system.
Moreover, it is the only country for which a reliable
measure of monetary policy shocks that dates back to
the 1980s is available (Coibion, 2012).31 In essence,
the estimated shocks are exogenous innovations in the
policy rate—that is, changes in the rate that are not
related to current or expected inflation and economic
conditions. Following the approach proposed by
Romer and Romer (2004), the effect of monetary
policy is estimated as follows:
Drt = a + b(l)mpst + et, (3.2)
in which r is a real rate, and mps is a monetary policy
shock.
The results, depicted in Figure 3.10 (panel 1), show
that monetary policy shocks have significant and long-
lasting effects on short-term real interest rates.32 To
what extent does monetary policy explain the actual
decline in real interest rates? Panel 2 of Figure 3.10
plots the actual evolution of short-term real rates as
well as the evolution that can be explained by mone-
tary policy shocks. Until 1992, about 88 percent of the
variance in short-term real rates is explained by mon-
etary policy shocks alone; afterward, the percentage
of the variance explained is much lower. The story is
similar for long-term real rates (panel 3 of the figure),
although, as one would expect, monetary policy shocks
explain less of the variation.
Large tightening policy shocks mostly occurred in
the 1980s: between 1980 and 1989, the average policy
shock was positive at about 24 basis points a quarter.
These positive shocks are consistent with the dra-
matic change in the conduct of U.S. monetary policy
31The estimated monetary policy shocks are the residuals from an
estimated monetary rule based on the Federal Reserve’s Greenbook
forecasts. The approach is similar to the one originally proposed
by Romer and Romer (2004), but by introducing time-varying
parameters, Coibion (2012) allows a distinction to be made between
innovations to the central bank’s rule and changes in the rule itself.
This distinction is particularly useful for an analysis of a long time
span.
32This finding is not novel, and it is consistent with the hypothesis
of price rigidities (Christiano, Eichenbaum, and Evans, 1999).
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Quarters
1. Effect on Short-Term Real Rate, 1980:Q1–2008:Q4
(percentage points)
–2
–1
0
1
2
3
4
5
6
7
1983 89 95 2001 07
2. Short-Term Real Rate
(percent)
–2
0
2
4
6
8
10
1981 85 89 93 97 2001 05 08
3. Long-Term Real Rate
(percent)
Actual
Predicted
Actual
Predicted
–4
–3
–2
–1
0
1
2
3
4
1980 87 94 2001 08
4. U.S. Monetary Policy
Shocks, 1980:Q1–2008:Q4
(percent)
1981 86 91 96 2001 06 09
5. Global Real Interest Rate
(percent a year)
Actual
Predicted
Figure 3.10. Effect of U.S. Monetary Policy Shocks on
Real Interest Rates
Sources: Bloomberg, L.P.; Coibion (2012); Organization for Economic
Cooperation and Development; and IMF staff calculations.
Note: In the ﬁrst panel, the solid line denotes estimated effect; dashed lines
denote 90 percent conﬁdence bands. t = 0 is the year of the monetary
policy shock. In panel 5, global real rates exclude U.S. real rates.
0
2
4
6
8

inaugurated at the Federal Reserve by Chairman Paul
Volcker on October 6, 1979, which eventually led to
successful disinflation (Bernanke and Mishkin, 1992).
After 1990 the size of monetary policy shocks declined
markedly because the low-inflation regime was by then
solidly established (Figure 3.10, panel 4).33
If there is little doubt that the fluctuations in U.S.
real interest rates in the 1980s were driven mainly by
U.S. monetary policy, it is also clear that U.S. mon-
etary policy shocks explained a substantial part of the
fluctuations in the global rate (excluding the U.S. real
rate) in that decade (Figure 3.10, panel 5). There are
two economic explanations for this result. First, U.S.
monetary shocks have substantial spillover effects on
other countries’ short-term interest rates, especially for
those countries that attempt to stabilize their exchange
rates with the U.S. dollar (October 2013 WEO).34
Second, during the 1980s and early 1990s, central
banks around the world adopted inflation reduction
policies that initially required tighter monetary policy
stances, similar to the U.S. Federal Reserve’s.35
Portfolio Shifts
The hypotheses evaluated so far predict a decline in
the real return on a wide spectrum of assets. How-
ever, although trends in the returns on bonds and
equity were both declining between the 1980s and the
late 1990s, after the bursting of the dot-com bubble
in 2000–01, the equity premium increased sharply
(Figure 3.11).36 There are three explanations for the
divergent trend.
First, the surge in excess saving (that is, current
account surpluses) in emerging market economies led
to a steep increase in their foreign exchange reserves in
the 2000s (Figure 3.12, panel 1), which were invested
33Various authors have attributed a prominent role to better
monetary policy in explaining the reduction in output volatility
(see, among others, Galí and Gambetti, 2009; Nakov and Pescatori,
2010).
34In the 1980s, various inflation-prone countries adopted
exchange rate targeting as a way of finding a nominal anchor.
35Many advanced economies had reduced inflation and inflation
volatility substantially by the early 1990s. Most emerging market
economies substantially reduced inflation between the second half of
the 1990s and the beginning of the 2000s. In an increasing number
of countries, the policy shift was embodied in the adoption of infla-
tion targeting.
36Although the analysis focuses on the United States because of
the availability of longer time series for the equity premium, most
advanced and emerging market economies follow a similar pattern.
U.S. stock market capitalization accounts for more than 35 percent
of global stock market capitalization.
mainly in government or government-guaranteed
fixed-income liabilities. Indeed, foreign holdings of U.S.
Treasury securities increased considerably after 2000,
and foreign official holdings in China and other emerg-
ing market economies accounted for the largest part of
this increase (Figure 3.12, panels 2 and 3). Conversely,
the share of foreign private holdings of U.S. equities and
other assets remained relatively stable (Figure 3.12, panel
4). Empirical evidence suggests that these foreign official
purchases of U.S. Treasuries significantly contributed to
the decline in real interest rates in the first decade of the
2000s (Warnock and Warnock, 2009; Bernanke, Rein-
hart, and Sack, 2004; Beltran and others, 2013).37
37A comparison of previous studies’ estimates of the effects of
purchases on Treasury yields suggests that if foreign official inflows
into U.S. Treasuries were to decrease in a given month by $100
billion, Treasury rates would rise by 46 to 100 basis points in the
short term and by 4 to 20 basis points in the long term (Beltran and
others, 2013).
0
1
2
3
4
5
6
7
8
9
1983 85 87 89 91 93 95 97 99 2001
Figure 3.11. Real Long-Term Interest Rates and Real Returns
on Equity
(Percent a year)
1. 1983–2001
–2
–1
0
1
2
3
4
5
2001 02 03 04 05 06 07 08 09 10 11 12 13
2. 2001–13
Real returns on equity Real long-term interest rates
Sources: Bloomberg, L.P.; Organization for Economic Cooperation and

Second, a change in the relative riskiness of bonds
and equities has made bonds relatively more attractive.
In particular, the evidence summarized in Figure 3.13
(panel 1) shows that the correlation between bond and
equity returns has steadily declined (similar results have
been found in Campbell, Sunderam, and Viceira, 2013),
whereas the correlation between consumption growth and
equity returns has dramatically increased since 2000.38
Panel 2 of Figure 3.13 shows that the volatility of
equity holdings markedly increased in the aftermaths
of the bursting of the dot-com bubble and of the
global financial crisis.39
Finally, between 2008 and 2013 some central banks
in advanced economies embarked on unconventional
monetary policies aimed at stimulating the economy. In
38The correlation between annual consumption growth and equity
returns increased from −0.27 in the 1970–99 sample to more than
0.50 in the period 2000–13. An asset with high returns when con-
sumption is low provides a hedge and therefore yields a low expected
return, a negative risk premium. In general, the more procyclical an
asset’s return, the higher the risk premium associated with that asset.
39Figure 3.13 also suggests that the increase in the variance of bond
returns relative to those of equities may explain the short-lived increase
in U.S. real interest rates in the early 1980s (Blanchard, 1993).
particular, some empirical studies (D’Amico and others,
2012; Joyce and others, 2011) provide evidence that quan-
titative easing, in the form of long-term asset purchases,
may have compressed real term premiums on long-term
government bonds in the United States and United King-
dom between 2008 and 2012. A reduction in the real term
premium, in turn, may explain part of the increase in the
equity premium.40 Even though the estimates of the effect
of quantitative easing on the term premium are surrounded
by wide uncertainty, it is possible that quantitative easing
contributed moderately to the observed increase in the
equity premium between 2008 and 2013.41
40D’Amico and others (2012) estimate a cumulated effect of
Federal Reserve long-term asset purchases on ten-year U.S. govern-
ment bond yields of about 80 basis points (a similar result is found
by Joyce and others, 2011, for the United Kingdom). They claim
that most of this effect is attributable to the compression of the real
term premium. There is substantial uncertainty, however, about the
persistence of the effect.
41It is possible, however, that in the absence of quantitative easing,
the increase in the expected real return on equity would have been
greater.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0
5
10
15
20
1990 96 2002 08 14
Figure 3.12. Portfolio Shifts and Relative Demand for Bonds
versus Equity
Sources: Beltran and others (2013); and IMF staff calculations.
Note: EMEs = emerging market economies.
Change in foreign
exchange reserves
(left scale)
Gross saving
(right scale)
1. Percent of Global GDP
0
1
2
3
4
5
6
1984 90 96 2002 08 11
China
Other EMEs
Total
2. Foreign Holdings of U.S.
Government Securities
(trillions of U.S. dollars)
0
1
2
3
4
5
6
1984 90 96 2002 08 11
Ofﬁcial
Total
3. Foreign Holdings of U.S.
Government Securities
0
1
2
3
4
5
1984 90 96 2002 08 11
Government securities
Private securities
Total
4. Foreign Ofﬁcial Holdings of
U.S. Securities
–0.08
–0.04
0.00
0.04
0.08
0.12
0.16
–0.8
–0.4
0.0
0.4
0.8
1.2
1.6
1980 83 86 89 92 95 98 2001 04 07 10 13
Figure 3.13. Portfolio Shifts and Relative Riskiness of Bonds
versus Equity, 1980–2013
(Percent)
Difference in volatility between bond and stock returns
(left scale)
Correlation between bond and stock returns (right scale)
1. Difference in Variances and Correlations between Bonds
and Equity
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
1980 83 86 89 92 95 98 2001 04 07 10 13
2. Variance of Bonds and Equity
Variance of stock returns
Variance of bond returns
Sources: Bloomberg, L.P.; and IMF staff calculations.
Note: Based on autoregressive (ARCH(1)) and generalized autoregressive
(GARCH(1)) conditional heteroscedasticity models of bond and stock returns.

Scars from the Global Financial Crisis
Investment-to-GDP ratios in many advanced economies
have not yet recovered to precrisis levels. What should
we expect in the medium term? A look at previous
financial crises helps answer this question. Two sets of
episodes provide the basis for the examination: (1) the
entire sample of advanced economy financial crises
between 1970 and 2007 identified by Laeven and Valen-
cia (2012) and (2) the “Big 5” financial crises (Spain,
1977; Norway, 1987; Finland, 1991; Sweden, 1991; and
Japan, 1992) identified by Reinhart and Rogoff (2008)
as the most comparable in severity to the recent one.
Looking at financial crises in individual countries allows
investment and saving to be analyzed separately.42
The econometric estimates imply that financial
crises cause significant and long-lasting declines in the
investment-to-GDP ratio (Figure 3.14, panels 1 and
2).43 Financial crises have typically reduced this ratio
by about 1 percentage point in the short term (one
year after the occurrence of the crisis), with a peak
effect of 3 to 3½ percentage points three years after the
crisis. The estimated effect matches the 2½ percentage
point decline in the investment-to-GDP ratio between
2008 and 2013 remarkably well. Moreover, it is in line
with the effect, found in previous studies (Furceri and
Mourougane, 2012; Chapter 4 of the October 2009
WEO), of financial crises on the capital-to-labor ratio.
With respect to saving, previous financial crises have
typically reduced the saving-to-GDP ratio by about
2 percentage points over a two-year horizon. This
reduction tapers off to nothing in the medium term
(Figure 3.14, panels 3 and 4). The reason financial cri-
ses do not have a persistent impact on the total saving
rate is that the decline in public saving rates—which
typically occurs in the aftermath of financial crises
(Reinhart and Rogoff, 2011; Furceri and Zdzienicka,
2012)—is offset by a persistent increase in private sav-
ing rates (Figure 3.14, panels 5 and 6).
Based on this evidence, the global financial crisis can
be expected to leave significant scars in the medium
term on investment but not on saving, which will
contribute to continued low real interest rates for some
time.
42A similar exercise cannot be performed for a global crisis, since
investment and saving are equal at the global level.
43See Appendix 3.4 for a description of the methodology used
to assess the impact of financial crises on investment and saving as
shares of GDP.
–6
–5
–4
–3
–2
–1
0
–6
–5
–4
–3
–2
–1
0
1
–1 0 1 2 3 4 5 6 7 8 9 10
Figure 3.14. Effect of Financial Crises on Saving- and
Investment-to-GDP Ratios
(Percent of GDP)
1. Effect of Crises on
Investment (all crises)
1
–1 0 1 2 3 4 5 6 7 8 9 10
Investment (Big 5 crises)
Investment-to-GDP ratio
Actual nominal investment to GDP, 2007–13 (index, 2007 = 0)
–8
–6
–4
–2
0
2
4
6
8
10
–1 0 1 2 3 4 5 6 7 8 9 10
Saving (all crises)
–6
–4
–2
0
2
4
6
8
10
–1 0 1 2 3 4 5 6 7 8 9 10
Saving (Big 5 crises)
0
4
8
12
16
–1 0 1 2 3 4 5 6 7 8 9 10
Public and Private Saving
(all crises)
–12
–8
–4
–12
–8
–4
0
4
8
12
16
–1 0 1 2 3 4 5 6 7 8 9 10
Public and Private Saving
(Big 5 crises)
Saving-to-GDP ratio
Actual nominal saving to GDP, 2007–13 (index, 2007 = 0)
Public-saving-to-GDP ratio Private-saving-to-GDP ratio
Sources: Organization for Economic Cooperation and Development; and IMF
staff calculations.
Note: Big 5 financial crises are those in Spain, 1977; Norway, 1987; Finland,
1991; Sweden, 1991; and Japan, 1992. Solid blue (red) line denotes
estimated effect; dashed blue (red) lines denote 90 percent confidence
bands; and black line denotes the actual evolution of the investment-to-GDP
ratio in advanced economies from 2007 to 2013. X-axis units are years; t = 0
denotes the year of the financial crisis.

Should We Expect a Large Reversal in Real
Rates?
The past 15-year period is divided by the global finan-
cial crisis. Before the crisis real interest rates declined
even as the global investment-to-GDP ratio increased,
suggesting that a shift in the global saving schedule
took place. However, if the outward shift in global sav-
ing was the only factor driving the decline in the real
rate, a similar decline in the cost of capital should have
been observed, but it was not. More precisely, whereas
real interest rates declined by about 1.2 percentage
points, the cost of capital decreased only by 0.6 per-
centage point. This difference in declines suggests that
portfolio shifts contributed about 0.6 percentage point
to decreases in real bond yields (Table 3.2).44
In the aftermath of the global financial crisis, real
rates have continued to decline, but equilibrium sav-
ing and investment have decreased. The analysis above
suggests that an inward shift in the global investment
schedule (of about 2 percentage points) was the primary
factor—while saving responded to the change in yield.
Again, there was a difference in declines between the
real rate and the cost of capital. The former declined by
about 1½ percentage points, whereas the latter declined
only by 0.7 percentage point, suggesting that portfo-
lio shifts contributed about 0.8 percentage point to
decreases in real bond yields. Quantitative easing (in the
form of long-term asset purchases), by compressing the
term premium on long-term government bonds, may
explain part of the observed portfolio shift.45 Moreover,
44It is possible that looser fiscal policy in advanced economies
moderated the real-rate decline.
45An upper-bound estimate of the cumulated effect of quantita-
tive easing between 2009 and 2012 in the United States and United
Kingdom on the term premium of ten-year government bonds is
80 basis points (D’Amico and others, 2012; Joyce and others, 2011).
Since the fixed-income market in those countries is about the same
size as the equity market, the impact of quantitative easing would be
at most 40 basis points on both the U.S. and U.K. cost of capital.
Because these countries contribute to the global cost of capital by no
high elasticity of real rates to investment shifts (that is,
of about 1.5) implies that real rates would have declined
considerably more (that is, by about 3 percentage
points) in the absence of the zero lower bound on nomi-
nal interest rates.46 Unconventional monetary policy in
the advanced economies has only mitigated the effects of
the zero lower bound, suggesting that natural real rates
likely are negative now.
Should an increase in real rates be expected in the
medium term? Answering this question requires some
conjecture about the future evolution of the main
determinants of the real rates since 2000:
•• Investment shifts: The evidence on the effect of
severe financial crises suggests that a full reversal
of the downward investment shift in advanced
economies is unlikely. In emerging market econo-
mies, growth is expected to be about 1 percentage
point a year less than that in the first decade of the
2000s. Such a deceleration would reduce machinery
and equipment investment in the medium term. In
the case of China, the reduction would be amplified
by the rebalancing of growth away from investment
and toward consumption.
•• Saving shifts: The empirical evidence suggests that
the lower projected growth would lead to a medium-
term negative shift in emerging market economy
saving rates of about 3.5 percentage points.47 Such a
reduction would be significantly smaller in absolute
terms than the upward shift during the first decade of
the 2000s. In advanced economies, the effect of high
more than half, the contribution of unconventional monetary policy
to portfolio shifts was 0.2 at most.
46A 1 percentage point shift in investment is estimated in this analy-
sis to reduce the real interest rate (the cost of capital) by about 1.5
percentage points (see Appendix 3.5). This estimate implies that the
investment shift that took place (of about 2 percentage points) may
have reduced the equilibrium real rate by about 3 percentage points.
47Simulations based on the IMF’s Global Integrated Monetary and
Fiscal model suggest that the impact of a 3.5 percentage point reduc-
tion in emerging market economy saving rates on the global real rate
is between 0.25 and 1.25 percentage points in the long term.
Table 3.2. Factors Affecting Real Interest Rates
Real Interest Rate
(percent)
Cost of Capital
(percent) Saving Shifts
Investment
Shifts Portfolio Shifts
1996–2000 3.3 3.5
2001–07 2.1 2.9 ↓↓ — ↓↓
2008–12 0.6 2.2 — ↓↓ ↓↓
Future, Medium Term 2.1 2.9 ↑ — —
Note: Arrows denote the impact of saving, investment, and portfolio shifts on the real interest rate and the cost of capital. ↑(↓) denotes positive (negative)
effects. Multiple arrows indicate larger effects. Dash equals no effect.

stocks of public debt on real rates would probably be
more than offset by projected improvements in those
economies’ fiscal positions.48
•• Portfolio shifts: To the extent that the high demand for
safe assets continues in the medium term—as a result
of strengthened financial regulation—a reversal of the
portfolio shift out of equities is unlikely to occur.49
•• Monetary policy: While output is below potential in
advanced economies, monetary policy will prob-
ably not contribute to increasing real rates.50 In the
medium term, once output gaps are closed, mon-
etary policy is expected to be neutral.
In summary, although real interest rates are likely to
increase in the medium term, there are no compelling
reasons to believe that rates will return to the levels of
the early 2000s.
Implications of Persistent Low Real Interest Rates for
Debt Sustainability
Given the high levels of public debt in advanced
economies, even small differences in real interest rates
during the coming decades will have major implica-
tions for fiscal policy. For a given level of economic
activity, if interest rates are higher than expected, cur-
rent fiscal consolidation targets may not be sufficient
to ensure debt sustainability. If they are lower, the debt
decline could be faster.
The results presented in Figure 3.15 show that if real
rates were to remain, for example, about 1.5 percent,
which is about 1 percentage point lower than the Octo-
ber 2013 WEO projection, all else equal, this would
reduce the advanced economy debt-to-GDP ratio five
years ahead by about 4 percentage points. The impact
would be larger for countries with higher initial stocks
48The projected evolution of the fiscal index derived in the previ-
ous section suggests that fiscal policy in advanced economies may
contribute to maintaining low real rates in the medium term. In
particular, the fiscal index is projected to decline from about 1.3 in
2013 to about 1.1 in 2018.
49Withdrawal from quantitative easing may induce a modest
reversal of the portfolio shifts observed between 2008 and 2013 by
raising real term premiums to precrisis levels.
50To the extent that the zero lower bound constrains the reduction
of nominal rates and thus prevents real rates from being reduced as
desired, actual real rates are likely to be higher than the natural rate.
The monetary policy stance is thus involuntarily tight—although
unconventional monetary policy can partly mitigate this problem.
Once the recovery is sufficiently strong, the natural rate will start
rising. Monetary policy, however, is expected to be accommoda-
tive until output gaps are closed, by keeping policy rates below the
natural level.
of debt (notably Japan). To achieve the same reduction
in the debt path with fiscal policy, the primary-surplus-
to-GDP ratio would have to be higher by about 0.8 per-
centage point a year.51
Summary and Policy Conclusions
Movements in domestic real interest rates have a major
common, global component. Therefore, examining
shifts in the global supply of and demand for funds is
necessary to understand the behavior of interest rates
within any region.
51These figures are illustrative examples. They do not take into
account all the details (for example, the maturity structure of debt)
needed for a precise calculation. In addition, the exercise assumes
that GDP growth is the same in the two scenarios.
0.0
0.4
0.8
1.2
1.6
2.0
–12
–10
–8
–6
–4
–2
0
2
United
States
United
Kingdom
Japan Euro area Advanced
economies
United
States
United
Kingdom
Japan Euro area Advanced
economies
Figure 3.15. Implications of Lower Real Interest Rates for
Debt Sustainability
(Percent of GDP)
1. Debt Differences
2. Primary Deficit Differences
Note: Panel 1 shows the differences in the five-year-ahead debt-to-GDP ratio
implied by lower real rates. Panel 2 shows the increase in the primary deficit
that would need to be sustained each year from 2014 to 2018 to reach the
same debt-to-GDP ratio, under the same lower real rates as in panel 1.

Global real interest rates have declined substantially
since the 1980s. The cost of capital has fallen to a lesser
extent, because the return on equity has increased since
2000. Since the early 2000s, three factors have contrib-
uted to the declines in real rates and in the cost of capital:
•• Saving shifts: The substantial increase in saving in
emerging market economies, especially China, in
the middle of the first decade of the 2000s con-
tributed to a modest decline in the cost of capital.
High income growth in emerging market economies
during this period seems to have been the most
important factor behind the saving shift.
•• Portfolio shifts: About half of the reduction in
real rates in the first decade of the 2000s can be
attributed to an increase in the relative demand for
bonds, which, in turn, reflected an increase in the
riskiness of equity and the resulting higher demand
for safe assets among emerging market economies
to increase official foreign reserves accumulation.52
In the aftermath of the global financial crisis, these
factors, though more moderate, have continued to
contribute to the decline in real rates.
•• Investment shifts: The postcrisis reduction in the
cost of capital has been driven mainly by a collapse
in the demand for funds for investment in advanced
economies.
The evidence presented here does not suggest a
quick recovery in the investment-to-output ratio
for advanced economies in the medium term. The
monetary policy stance is expected to be neutral in
the medium term once output gaps are closed. A full
reversal of the portfolio shift favoring bonds observed
in the 2000s is unlikely: although a reduction in
surplus emerging market economy saving, and thus in
the pace of official reserves accumulation, might reduce
the demand for safe assets, strengthened financial
regulation will have the opposite effect. The net effect
on real interest rates is likely to be small, unless there
is a major unexpected change in policies. In advanced
economies the effect of high stocks of public debt
on real rates is likely to be more than offset by the
projected improvements in fiscal balances. The pro-
jected reduction in GDP growth in emerging market
economies would probably reduce their net saving
52Higher demand for safe assets was only partly satisfied by the
deterioration in advanced economies’ public finances. The 2000s also
saw a vast expansion in holdings of government-guaranteed debt,
in particular, mortgage-backed securities. The securitization boom
preceding the global financial crisis can be seen as a market response
to higher demand for safe assets.
rate—and this could be amplified by the rebalancing
of growth away from investment in China.53 In sum-
mary, it is likely that real interest rates will rise, but no
compelling reasons suggest a return to the average level
observed during the mid-2000s (that is, about 2 per-
cent). Within this global picture, however, there may
be some countries that will see higher real rates because
of higher sovereign risk premiums. The conclusions
here apply to the risk-free rate.
A protracted period of low real interest rates would
have negative implications for pension funds and
insurance companies with defined-benefit obligations.
An environment of continued low real (and nominal)
interest rates might also induce investors and financial
institutions more broadly to search for higher real (and
nominal) yields by taking on more risk. Increased risk
taking, in turn, might increase systemic financial sector
risks, and appropriate macro- and microprudential
oversight would therefore be critical for maintaining
financial stability.
If real interest rates were to be lower than currently
projected in the WEO, achieving fiscal sustainability
would be somewhat easier. For example, with real
interest rates 1 percentage point lower than pro-
jected, the average medium-term debt-to-GDP ratio
in advanced economies would be about 4 percentage
points lower. Moreover, if real rates are expected to be
close to or below the real GDP growth rate for some
time, some increases in debt-financed government
spending, especially public investment, may not lead to
increases in public debt in the medium term.
Lower natural real rates also have important implica-
tions for monetary policy. For example, with an inflation
target of 2 percent, if the equilibrium real interest rate is
substantially less than 2 percent as anticipated, the typical
neutral policy rate would be significantly less than 4 per-
cent.54 A lower natural rate does not reduce the effective-
ness of monetary policy during normal times. However,
for a given inflation target, it raises the probability that
nominal interest rates will hit the zero lower bound. The
higher risk of potential monetary policy ineffectiveness in
times of recessions, in turn, may be an important consid-
eration in the choice of an appropriate monetary policy
framework.
53The effect would be reduced by a composition effect. The
countries with the highest GDP growth rates are the ones with the
highest saving rates. Their rapid growth would continue to raise the
global saving rate even if their own rate were to decline slightly.
54In the United States, the average policy rate between 1990 and
2007 was 4.4 percent.

Appendix 3.1. Model-Based Inflation and
Dividend Growth Expectations
This appendix describes the empirical methodology
used to construct real interest rates and real returns
on equity for an unbalanced sample of 25 advanced
economies and 15 emerging market economies from
1970 through 2013.
Real Interest Rates
Real rates can be approximated by computing the
difference between the nominal bond yield and the
relevant inflation expectations. Survey information
and forecasts from an estimated autoregressive process
for inflation are used to obtain inflation expectations
(model-based inflation expectations).
In particular, model-based inflation expectations
over any horizon j are estimated using a monthly
autoregressive process AR(p) for the variable gt =
lnPt − lnPt–12, in which P is the consumer price index
and p = 12 is the order of the process. The AR(p)
process is estimated on a rolling window of 60 months
to minimize the effect of parameter instability. Using
out-of-sample forecasts of gt, Et lnPt+j – lnPt, which is
the inflation expectation at time t for the period t + j,
is calculated.55
Real rates are then constructed as
(1 – g)
rt
[n] = it
[n] – ——— Sn
i=1 giEtpt,t+i, (3.3)
(1 – gn)
with g = (1 + I
–
)–i, in which rt
[n] and it
[n] are the real and
nominal rates, respectively, on a bond of maturity n;
Etpt,t+i is the inflation expectation at time t for period
t + i; and I
–
is the average nominal rate for the period
examined. In sum, the real rate is defined as the nomi-
nal rate minus the weighted average inflation expecta-
tion over the entire life of the bond.
Real Returns on Equity
The real required internal rate of return on equity in
period t for horizon n is estimated as
St/Dt = Sn
j=0(1 + Re,t
[n])–j Et gt,t+1+j, (3.4)
55This methodology produces smaller forecast errors, and matches
survey expectations better, than an autoregressive process with con-
sumer price index log differences over the previous month, a vector
autoregression (VAR) with commodity prices, and a VAR with GDP
growth.
in which S is an equity price index and gt,t+j = Dt+j/Dt
is cumulated dividend growth, consistent with the
equity index chosen. Stated roughly, the expected
return on equity (Re,t
[n]) is equal to the dividend
yield plus the expected long-term growth rate of
real dividends. Expected dividend growth rates
are constructed by estimating a quarterly bivariate
VAR(p) of dividend and GDP growth, with p = 4.
The VAR(p) process is estimated on a rolling window
of 60 months to minimize the effect of parameter
instability.
Appendix 3.2. Investment Profitability
One possible explanation for the decrease in invest-
ment-to-GDP ratios in many advanced economies
is that investment profitability has declined. Various
factors can explain shifts in investment profitability
(including changes in business taxation, factor prices,
productivity, and uncertainty), and quantifying them
is difficult. As an alternative, the analysis assesses
whether the reduction in the investment-to-GDP
ratio can be attributed to the unexpected strengthen-
ing of GDP or instead to an anticipated decline in
profitability. To discriminate between these two fac-
tors, following Blanchard and Summers (1984), the
following regression is estimated for each country in
the sample:
ln It = a + S2
i=0 bilnYt–i + ut, (3.5)
in which
ut = rut–1 + et, (3.6)
with I denoting real private investment and Y real
GDP. Under the hypothesis that there has been
a negative shift in expected profitability, invest-
ment should have declined more than predicted by
the evolution in output, thus implying a negative
forecast error eˆt. Panel 1 of Figure 3.16 presents the
aggregated forecast errors for advanced economies.
The figure suggests that the hypothesis that a decline
in investment profitability has contributed to the
decline in real interest rates does not find empirical
support up to the global financial crisis, after which
it becomes a key factor. A similar conclusion can be
reached by looking at the evolution of total factor
productivity (Figure 3.16, panel 2).

Appendix 3.3. Fiscal Indicator
This appendix describes the framework for assessing
the impact of debt on total saving and real interest
rates. As noted in the chapter text, measuring the
impact of fiscal policy on real rates requires looking
not only at current and future anticipated deficits, but
also at the level of the stock of public debt. Following
Blanchard and Summers (1984) and Blanchard (1985),
a fiscal index is derived.
In a standard life cycle model, consumption is
related to wealth. Formally, this can be formulated as
C = ω[K + B + p(W − T; r + p)], (3.7)
in which C denotes consumption, K + B financial
wealth, ω the marginal propensity to consume out of
wealth, and p(W − T; r + p) the present value of after-
tax labor income discounted at rate r + p. The term r
is the real interest rate, and p is a myopia coefficient,
reflecting the mortality of current consumers or their
myopia about the future. Focusing on the share of
aggregate demand (X ) that depends directly on fiscal
policy and subtracting the present value of government
spending yields
X = ω[B + p(D; r + p)] + [G – ωp(G; r + p)], (3.8)
in which G is government spending, and D denotes
primary deficits. The first term of equation (3.8)
represents the effect of debt and government finance
on demand; the second term represents the effect of
government spending financed by current taxes.
If consumers are not myopic (p = 0), the first term
of equation (3.8) is equal to zero, because consumers
fully anticipate the fiscal implications of the govern-
ment’s budget constraint: if consumers discount future
taxes at the interest rate, the timing of a change in
taxes does not affect their level of spending (Ricardian
equivalence). If consumers are myopic, however, the
first term is positive, because they do not fully antici-
pate that taxes will go up to finance higher interest
payments on the stock of public debt.
To construct an empirical counterpart of X, given
the more limited reliability of forecasts for G, the
focus is on the first term of equation (3.8). Dividing
each term of equation (3.8) by GDP and focusing on
the first term of the equation, equation (3.8) can be
rewritten as
x = ω[b + p(d; r + p – g)], (3.9)
in which lowercase letters indicate shares of GDP, and
g is the rate of GDP growth. Assuming a value for ω
equal to 0.1, and a value of r + p – g equal to 10 per-
cent a year,56 the empirical index is determined as
xt = 0.1[bt + S∞
i=0(1.1)–ipdt,t+i], (3.10)
in which bt is the stock of public debt at time t, and
pdt,t+i is the forecast of primary deficits at time t for
the period t + i. In particular, anticipated deficits are
constructed using WEO forecasts. These forecasts are
available beginning only in 1990, and they should, in
principle, incorporate changes in current policies, as
well as forecasts of output growth and the evolution
of debt and interest payments over time. However,
because the forecasts are available only for a time hori-
zon of five years, the ratio of deficits to GDP for year
56The value is chosen as in Blanchard and Summers (1984) and
is based on Hayashi’s (1982) estimates. Although choosing a differ-
ent value would affect the level of the index, it would not affect its
evolution, which is the main interest in this analysis.
–0.06
–0.04
–0.02
0.00
0.02
0.04
0.06
United States United
Kingdom
Japan Advanced
economies
Euro area
Figure 3.16. Investment Shifts in Advanced Economies
1981–90 1991–2000 2001–07 2008–13
1. Estimated Investment Proﬁtability Forecast Errors, 1980–2013
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
OECD United
Kingdom
Japan United
States
France Germany Italy
1991–2000 2001–07 2008–13
2. Productivity Growth, 1991–2013
(percent)
Sources: Haver Analytics; Organization for Economic Cooperation and
Development (OECD); World Bank, World Development Indicators database;
and IMF staff calculations.
Note: Investment proﬁtability is computed as described in the appendix text.

t + i 5 is assumed to be equal to the ratio forecast
for year t + 5.
Appendix 3.4. The Effect of Financial Crises on
Investment and Saving
This appendix describes the statistical technique used
to assess the impact of financial crises on investment
and saving as shares of GDP. The statistical method
follows the approach proposed by Jordà (2005) to esti-
mate robust impulse response functions. This approach
has been advocated by, among others, Stock and Wat-
son (2007) and Auerbach and Gorodnichenko (2013)
as a flexible alternative that does not impose dynamic
restrictions embedded in vector autoregression (autore-
gressive distributed lag) specifications. The model is
particularly suitable when the dependent variable is
highly persistent, as in the analysis in this chapter.
More formally, the following econometric specifica-
tion is estimated:
yi,t+k – yi,t–1 = ak
i + gk
t + Sl
j=2 gk
j Dyi,t–j + bkDi,t + ek
i,t,
(3.11)
in which y denotes the investment- (saving-)to-GDP
ratio, D is a dummy that takes the value one for the
starting date of the occurrence of the crisis and zero
otherwise, and ai and gt are country and time fixed
effects, respectively.
The sample consists of an unbalanced panel of 35
advanced economies from 1970 through 2007. Crisis
episodes are taken from Laeven and Valencia (2012).
Two sets of crisis episodes are of particular interest:
(1) the entire sample of financial crisis episodes in
advanced economies (1970–2007) and (2) the “Big 5”
financial crises (Spain, 1977; Norway, 1987; Finland,
1991; Sweden, 1991; and Japan, 1992) identified by
Reinhart and Rogoff (2008) as the most comparable in
severity to the recent one.
The model is estimated for each k = 0, . . . , 10.
Impulse response functions are computed using the
estimated coefficients bk. The confidence bands associ-
ated with the estimated impulse response functions
are obtained using the estimated standard deviations
of the coefficients bk. The number of lags (l) has been
tested, and the results suggest that inclusion of two
lags produces the best specification. Corrections for
heteroscedasticity, when appropriate, are applied using
robust standard errors; the problem of autocorrelation
is solved using the two lags of the change in the invest-
ment- (saving-)to-GDP ratio as control variables.57
Although the presence of a lagged dependent vari-
able and country fixed effects may, in principle, bias
the estimation of gk
j and bk in small samples (Nickell,
1981), the length of the time dimension mitigates this
concern.58 In theory, another potential concern could
be reverse causality, because changes in the investment-
(saving-)to-GDP ratio may affect the probability of
occurrence of financial crises. However, this empirical
strategy addresses the issue by estimating changes in
the investment- (saving-)to-GDP ratio in the years that
follow a crisis.59
Appendix 3.5. Sensitivity of Saving and
Investment to Real Rates
This appendix presents a framework for assessing the
sensitivity of global saving and investment to the real
interest rate. The demand for funds (that is, the elastic-
ity of investment to the real rate) is identified using
changes in safety nets (proxied by social expenditure)
that give rise to exogenous shifts in the supply of funds
(saving); the supply of funds is identified using changes
in the relative price of investment, which shifts the
demand for funds.
In particular, the following system of equations is
estimated on annual data from 1980 through 2013:
st = a0 + a1rt + a2nt + et, (3.12)
it = b0 + b1rt + b2pt + et, (3.13)
st = it, (3.14)
in which s denotes global saving as a percent of
GDP, i is global investment as a percent of GDP, n is
advanced economy social expenditure as a percent of
GDP, and p is the advanced economy relative price of
investment.
The inclusion of the variables n and p allows the
exercise to identify the coefficients of the structural
equations (3.12 and 3.13) from a linear combination
of the reduced-form coefficients. In particular, the esti-
mates of reduced-form coefficients presented in Table
3.3 give an elasticity of investment to the real rate of
57Tests for autocorrelation of the residuals have been performed and
have rejected the hypothesis of serial correlation.
58The finite sample bias is on the order of 1/T, where T in the
sample is 38.
59In addition, robustness checks for endogeneity confirm the
validity of the results.

about −0.5, and an elasticity of saving to the real rate
of about 0.15.60 This also implies that the impact of
exogenous shifts in saving and investment on the real
rate can be quantified as Dr = 1.5(Saving shifts – Invest-
ment shifts).
Appendix 3.6. Saving and Growth with
Consumption Habit
This appendix derives a simple closed-form solution
for both consumption and the saving rate in a rational-
expectations permanent income model.
Assume households in each period t enjoy a utility
flow from u(ct*) in which ct* = ct – gct–1 and the utility
function is quadratic. The role of habit formation is
captured by the parameter g; when g = 0, there is no
habit. Denote household income as yt and financial
wealth as At–1. Households discount the future at a rate
r, which is also the return on wealth. Saving is defined
as St = rAt–1 + yt – ct. It is then possible to derive the
following relationship (Alessie and Lusardi, 1997):
g
St = gSt+1 + Dyt – 1 – ——– Et S∞
j=0(1 + r)–jDyt+j.
1 + r
(3.15)
Dividing both sides of equation (3.15) by yt, we get
g
st(1 + gt) = gst–1 + gt – 1 – ——– 1 + r
× Et S∞
j=0(1 + r)–jDyt+j/yt–1, (3.16)
in which st = St/yt and gt = Dyt/yt–1. When gt is suf-
ficiently small, equation (3.16) can be approximated
as
60The estimated elasticity of investment to the real rate is similar
to that found in previous studies. For example, Gilchrist and
Zakrajsek (2007), using a panel of 926 publicly traded U.S. nonfarm
firms from 1973 to 2005, find that a 1 percentage point increase in
the cost of capital implies a reduction in the rate of investment of
½ percentage point.
g
st ≅ const + gst–1 + gt – 1 – ——Et S∞
j=0(1 + r)–jgt+j.
1 + r
(3.17)
Assume that output growth follows a stochastic process
Et gt+j = rjgt, with |r| 1; then equation (3.17) can be
written as
g − r
st ≅ const + gst–1 + ———— gt. (3.18)
1 + r – r
If the habit parameter is higher than the persistence
parameter of the growth process, an increase in GDP
growth leads to a rise in the saving rate.
Appendix 3.7. Sample of Countries Used in
Tables and Figures
This appendix describes the sample used to estimate
global real interest rates, global investment, global
saving, the standard deviation of the real interest rates,
and the financial integration indicator. In general,
the sample was chosen based on the availability of the
data. The coverage period and the full list of countries
used to estimate short- and long-term global real inter-
est rates, global nominal investment, and the nominal
saving-to-GDP ratio are presented in Table 3.4. The
countries in the samples used for some specific figures
are also presented in the following paragraphs.
Figure 3.3, panel 1, uses a balanced sample of
countries for which real interest rates are available since
1970. The global short-term real rate includes data for
Australia, Austria, Belgium, Canada, Finland, France,
Germany, Greece, Japan, Luxembourg, the Nether-
lands, Norway, Portugal, South Africa, Spain, Sweden,
the United Kingdom, and the United States. The
global long-term real rate includes data for Australia,
Austria, Belgium, Canada, Finland, France, Germany,
Greece, Italy, Japan, the Netherlands, New Zealand,
Norway, Portugal, Spain, Sweden, Switzerland, the
Table 3.3. Investment (Saving) and the Real Interest Rate, Reduced-Form Equations
Investment (Saving) Equation Real Interest Rate Equation
Safety Nets −0.553***
(0.016)
0.106***
(0.042)
Relative Price of Investment 3.334***
(1.121)
21.369***
(2.978)
R Squared 0.400 0.660
Note: Robust standard errors are in parentheses. *** denotes significance at the 1 percent level.

Table 3.4. Data Coverage for Global Interest Rates, Investment, and Saving
Country
Period
Short-Term
Interest Rate
Long-Term
Interest Rate Investment Saving
Albania n.a. n.a. 1960–2013 1960–2013
Algeria n.a. n.a. 1963–2013 1966–2013
Angola n.a. n.a. 1980–2013 1970–2013
Antigua and Barbuda n.a. n.a. 1977–2013 1977–2013
Argentina 2000–13 2003–13 1960–2013 1967–2013
Australia 1968–2013 1967–2013 1960–2013 1960–2013
Austria 1967–2013 1967–2013 1960–2013 1965–2013
The Bahamas n.a. n.a. 1962–2013 1968–2013
Bahrain n.a. n.a. 1969–2013 1969–2013
Bangladesh n.a. n.a. 1963–2013 1968–2013
Barbados n.a. n.a. 1965–2013 1967–2013
Belgium 1967–2013 1967–2013 1960–2013 1980–2013
Belize n.a. n.a. 1963–2013 1968–2013
Benin n.a. n.a. 1969–2013 1969–2013
Bhutan n.a. n.a. 1979–2013 1980–2013
Bolivia n.a. n.a. 1970–2013 1967–2013
Botswana n.a. n.a. 1963–2013 1968–2013
Brazil 2001–13 2001–13 1963–2013 1967–2013
Bulgaria n.a. n.a. 1969–2013 1969–2013
Burkina Faso n.a. n.a. 1963–2013 1968–2013
Burundi n.a. n.a. 1960–2013 1968–2013
Cabo Verde n.a. n.a. 1963–2013 n.a.
Cameroon n.a. n.a. 1963–2013 1963–2013
Canada 1967–2013 1967–2013 1960–2013 1960–2013
Central African Republic n.a. n.a. 1969–2013 1969–2013
Chad n.a. n.a. 1969–2013 n.a.
Chile 1990–2012 2004–13 1960–2013 1960–2013
China 1991–2013 2002–13 1963–2013 1968–2013
Colombia n.a. 2009–12 1960–2013 1968–2013
Comoros n.a. n.a. 1969–2013 1969–2013
Democratic Rep. of the Congo n.a. n.a. 1960–2013 1978–2013
Republic of Congo n.a. n.a. 1963–2013 1968–2013
Costa Rica n.a. n.a. 1960–2013 1967–2013
Côte d’Ivoire n.a. n.a. 1963–2013 1968–2013
Cuba n.a. n.a. 1970–2010 n.a.
Cyprus n.a. n.a. 1963–2013 1967–2013
Czech Republic 1998–2013 2000–13 n.a. n.a.
Denmark 1974–2013 1974–2013 1966–2013 1969–2013
Dominica n.a. n.a. 1963–2013 1968–2013
Dominican Republic n.a. n.a. 1960–2013 1967–2013
Ecuador n.a. n.a. 1965–2013 1976–2013
Egypt n.a. n.a. 1963–2013 1967–2013
Equatorial Guinea n.a. n.a. 1969–2013 n.a.
Estonia 1999–2012 n.a. n.a. n.a.
Ethiopia n.a. n.a. 1963–2013 1967–2013
Fiji n.a. n.a. 1963–2013 1979–2008
Finland 1970–2013 1967–2013 1960–2013 1969–2013
France 1970–2013 1967–2013 1960–2013 1965–2013
Gabon n.a. n.a. 1963–2013 1968–2013
The Gambia n.a. n.a. 1963–2013 1968–2013
Germany 1967–2013 1967–2013 1960–2013 1960–2013
Ghana n.a. n.a. 1963–2013 1967–2013
Greece 1967–2013 1967–2013 1960–2013 1960–2013
Grenada n.a. n.a. 1977–2013 1980–2013
Guatemala n.a. n.a. 1960–2013 1967–2013
Guinea n.a. n.a. 1969–2013 1969–2013
Guinea-Bissau n.a. n.a. 1979–2013 n.a.
Guyana n.a. n.a. 1960–2013 1967–2013
Haiti n.a. n.a. 1963–2013 n.a.
Honduras n.a. n.a. 1963–2013 1967–2013

Table 3.4. Data Coverage for Global Interest Rates, Investment, and Saving (continued)
Country
Period
Short-Term
Interest Rate
Long-Term
Hong Kong SAR 1987–2013 1991–2013 1961–2013 1961–2013
Hungary 1988–2013 1999–2013 1960–2013 1968–2013
Iceland 1983–2013 1983–2013 1960–2013 1960–2013
India 1996–2012 1990–2013 1960–2013 1967–2013
Indonesia 1990–2013 2003–13 1963–2013 1967–2013
Iran n.a. n.a. 1963–2013 1963–2013
Ireland 1983–2013 1982–2013 1960–2013 1960–2013
Israel 1992–2013 1997–2013 1963–2013 1963–2013
Italy 1971–2013 1967–2013 1960–2013 1965–2013
Jamaica n.a. n.a. 1963–2013 1967–2013
Japan 1967–2013 1967–2013 1960–2013 1960–2013
Jordan n.a. n.a. 1963–2013 n.a.
Kenya n.a. n.a. 1963–2013 1963–2013
Kiribati n.a. n.a. 1977–1992 1979–1992
Korea 1980–2013 1982–2013 1960–2013 1965–2013
Kuwait n.a. n.a. 1963–2013 n.a.
Latvia n.a. n.a. 1980–2013 n.a.
Lebanon n.a. n.a. 1963–2013 1967–2013
Lesotho n.a. n.a. 1963–2013 1968–2013
Libya n.a. n.a. 1976–2013 1969–2013
Luxembourg 1967–2013 1985–2013 1960–2013 1970–2013
Madagascar n.a. n.a. 1963–2013 1968–2013
Malawi n.a. n.a. 1963–2013 1967–2013
Malaysia 1976–2013 1992–2013 1960–2013 1966–2013
Maldives n.a. n.a. 1980–2013 1968–2013
Mali n.a. n.a. 1967–2013 1969–2013
Malta n.a. n.a. 1970–2013 1971–2013
Mauritania n.a. n.a. 1960–2013 n.a.
Mauritius n.a. n.a. 1963–2013 1967–2013
Mexico 1978–2013 2002–13 1960–2013 1967–2013
Mongolia n.a. n.a. 1969–2013 1969–2013
Morocco n.a. n.a. 1963–2013 1968–2013
Mozambique n.a. n.a. 1963–2013 1968–2013
Myanmar n.a. n.a. 1960–2013 n.a.
Namibia n.a. n.a. 1980–2013 n.a.
Nepal n.a. n.a. 1963–2013 1968–2013
Netherlands 1967–2013 1967–2013 1960–2013 1970–2013
New Zealand 1974–2013 1967–2013 1960–2013 1969–2013
Nicaragua n.a. n.a. 1960–2013 1969–2013
Niger n.a. n.a. 1963–2013 1963–2013
Nigeria n.a. n.a. 1963–2013 n.a.
Norway 1970–2013 1967–2013 1960–2013 1969–2013
Oman n.a. n.a. 1967–2013 1969–2013
Pakistan 1991–2013 2002–12 1960–2013 1967–2013
Panama n.a. n.a. 1963–2013 1967–2013
Papua New Guinea n.a. n.a. 1960–2013 1968–2013
Paraguay n.a. n.a. 1963–2013 1967–2013
Peru n.a. 2007–12 1960–2013 1968–2013
Philippines 1976–2013 1998–2013 1960–2013 1968–2013
Poland n.a. n.a. n.a. 1963–2013
Portugal 1967–2013 1967–2013 1960–2013 1969–2013
Puerto Rico n.a. n.a. 1960–2011 n.a.
Qatar n.a. n.a. 1963–2013 1968–2013
Romania 1997–2013 2011–12 1963–2013 1979–2013
Rwanda n.a. n.a. 1963–2013 n.a.
St. Kitts and Nevis n.a. n.a. 1963–2013 n.a.
St. Lucia n.a. n.a. 1963–2013 1968–2013
St. Vincent and the Grenadines n.a. n.a. 1963–2013 1968–2013
Saudi Arabia n.a. n.a. 1963–2013 1967–2013
Senegal n.a. n.a. 1963–2013 1968–2013

United Kingdom, and the United States. Figure 3.3,
panel 3, includes countries with data available starting
in 1991. The global real interest rate includes data for
Australia, Austria, Belgium, Canada, Denmark, Fin-
land, France, Germany, Greece, Hong Kong SAR, Ice-
land, India, Ireland, Italy, Japan, Korea, Luxembourg,
the Netherlands, New Zealand, Norway, Portugal, Sin-
gapore, South Africa, Spain, Sweden, Switzerland, the
United Kingdom, and the United States. The global
cost of capital includes data for Austria, Belgium,
Canada, Denmark, France, Germany, Hong Kong
SAR, the Netherlands, Spain, Switzerland, the United
Kingdom, and the United States.
The principal component analysis in Figure 3.4,
panel 1, includes data for Australia, Austria, Belgium,
Canada, Finland, France, Italy, Japan, the Netherlands,
New Zealand, Norway, Portugal, Spain, Sweden, Swit-
zerland, the United Kingdom, and the United States.
The standard deviation of the real interest rate in
Figure 3.4, panel 2, employs data for the same sample
as the short-term global real rate in Figure 3.3, panel
1. The financial integration in Figure 3.4, panel 2, is
constructed using data for Australia, Austria, Belgium,
Canada, Finland, France, Germany, Italy, Japan, the
Netherlands, New Zealand, Norway, Portugal, Spain,
Sweden, Switzerland, the United Kingdom, and the
United States.
The global long-term real interest rate in Figure
3.17 is estimated using data for the same sample as in
Figure 3.3, panel 1.
Table 3.4. Data Coverage for Global Interest Rates, Investment, and Saving (continued)
Country
Period
Short-Term
Interest Rate
Long-Term
Seychelles n.a. n.a. 1976–2013 1969–2013
Sierra Leone n.a. n.a. 1963–2013 1967–2013
Singapore 1981–2013 1986–2013 1965–2013 1965–2013
Solomon Islands n.a. n.a. 1963–2013 1968–2013
South Africa 1967–2013 1980–2013 1960–2013 1960–2013
Spain 1967–2013 1967–2013 1960–2013 1969–2013
Sri Lanka n.a. n.a. 1963–2013 1967–2013
Sudan n.a. n.a. 1976–2013 n.a.
Suriname n.a. n.a. 1977–2005 n.a.
Swaziland n.a. n.a. 1963–2013 1968–2013
Sweden 1967–2013 1967–2013 1960–2013 1960–2013
Switzerland 1974–2013 1967–2013 1965–2013 1980–2011
Syria n.a. n.a. 1965–2010 1969–2010
Taiwan Province of China 1983–2013 1992–2013 1963–2013 1963–2013
Tanzania n.a. n.a. 1963–2013 1967–2013
Thailand 1977–2013 1996–2012 1960–2013 1968–2013
Togo n.a. n.a. 1963–2013 1968–2013
Tonga n.a. n.a. 1975–2013 n.a.
Trinidad and Tobago n.a. n.a. 1960–2013 1967–2013
Tunisia n.a. n.a. 1963–2013 1968–2013
Turkey n.a. n.a. 1960–2013 1963–2013
Uganda n.a. n.a. 1963–2013 1963–2013
Ukraine 2007–13 2007–13 n.a. n.a.
United Arab Emirates n.a. n.a. 1964–2013 1968–2013
United Kingdom 1967–2013 1967–2013 1960–2013 1960–2013
United States 1967–2013 1967–2013 1960–2013 1960–2013
Uruguay n.a. n.a. 1960–2013 1967–2013
Venezuela n.a. n.a. 1963–2013 1966–2013
Vietnam n.a. n.a. 1963–2013 1967–2013
Zambia n.a. n.a. 1963–2013 1967–2013
Zimbabwe n.a. n.a. 1960–2013 n.a.

Finally, the construction of global long-term real
rates excludes those countries that have experienced a
significant increase in default risk in the aftermath of
the global financial crisis (that is, some noncore euro
area countries), because analyzing the determinants
of default risks goes beyond the scope of the chapter.
It is possible to observe, in regard to the euro area,
that whereas global long-term real rates have steadily
declined for core euro area countries, they have
recently increased for noncore euro area countries. In
contrast, short-term real rates have decreased for both
core and noncore countries (Figure 3.18).
–8
–6
–4
–2
0
2
4
6
8
1970 74 78 82 86 90 94 98 2002 06 10 13
Figure 3.17. Global Long-Term Real Interest Rates
(Percent a year)
Global long-term real interest rate (weighted by U.S. dollar GDP)
Global excluding U.S. long-term real interest rate (weighted by
U.S. dollar GDP)
G7 long-term real interest rate (equal weights)
Sources: Bloomberg, L.P.; Haver Analytics; IMF, International Financial
Statistics database; Organization for Economic Cooperation and Development;
World Bank, World Development Indicators database; and IMF staff
calculations.
Note: G7 comprises Canada, France, Germany, Italy, Japan, United Kingdom,
and United States.
–2
0
2
4
6
8
10
1990 92 94 96 98 2000 02 04 06 08 10 12 13
Figure 3.18. Convergence of Real Interest Rates in the Euro
Area
(Percent)
1. Noncore Euro Area Countries
–2
–1
0
1
2
3
4
5
6
7
1990 92 94 96 98 2000 02 04 06 08 10 12 13
2. Core Euro Area Countries
Long-term real interest rates Short-term real interest rates
Note: Noncore euro area countries comprise Greece, Ireland, Italy,
Portugal, and Spain.

The study of private saving behavior has long been
central to economics because private national saving
is the main source for the financing of investment.
Within this research, the causal nexus between the sav-
ing rate and economic growth has been the subject of
long-standing debate. This box argues that this issue is
critical to the understanding of recent saving develop-
ments in the global economy. It presents evidence that
the increased growth acceleration in emerging market
economies during the early years of the 2000s contrib-
uted to the increase in their saving rates.
In principle the causality between saving and growth
may run in both directions. For example, it may be
reasonable to consider high saving a precondition for
high growth, especially if domestic investment cannot
be easily financed with foreign capital (Solow, 1956;
Romer, 1986; Rebelo, 1992). In contrast, Modigli-
ani and Brumberg (1954, 1980) predict that higher
income growth causes the household saving rate to
rise. The crucial assumption behind their argument
is that over the life cycle, young, working generations
save, whereas the old spend what they accumulated
when they were young. In the presence of productiv-
ity growth, the young generation is richer than its
parents were at the same age. If incomes are growing,
the young will be saving on a larger scale than the old
are dissaving, so that higher economic growth causes
higher saving rates.
This prediction has been challenged on both theo-
retical and empirical grounds. Kotlikoff and Summers
(1980, 1988) argue that life cycle saving (that is, sav-
ing for retirement) is only a small fraction of national
saving.1 Others argue that with more realistic demo-
graphic structures, the effects of productivity growth
on aggregate saving could go either way.2
Recent studies of consumption behavior have
revived the idea that higher growth may lead to higher
medium-term saving. In the presence of consumption
habits, households whose incomes rise (fall) will adjust
their consumption only slowly to the new higher
The authors of this box are Davide Furceri, Andrea Pescatori,
and Boqun Wang.
1It is also possible that uncertainty about life span, health, and
health costs makes older people cautious about spending their
assets (Deaton, 1992).
2The presence of liquidity constraints or prudential saving in a
life cycle model can, however, induce young generations to save
even in the presence of income growth (see Kimball, 1990; Jap-
pelli and Pagano, 1994) and may be another explanation for the
positive correlation between growth and the saving rate.
(lower) level—that is, the saving rate will temporarily
rise (fall) (Carroll and Weil, 1994).3
This box revisits the saving-growth nexus from an
empirical point of view, paying particular attention to
the ability of growth to predict saving in the short to
medium term.
First, the analysis addresses the direction of causality
between saving rates and output growth in the short
to medium term by looking at whether past real GDP
growth and private-saving-to-GDP ratios help predict
one another.4 The results of this analysis suggest that
increases in saving rates seem to predict lower (not
higher) GDP growth in the short to medium term.5
In contrast, increases in GDP growth seem to predict
higher saving rates (Table 3.1.1).6 Overall, the results
imply that even though the causality between saving
and growth runs in both directions, the observed posi-
tive correlation between growth and saving must be
driven by the effects of changes in growth on saving
rates, not the other way around.7
Next, the growth-saving nexus in light of recent
experience in advanced economies and emerging mar-
ket economies, and in Japan and China, is reviewed
(Figure 3.1.1). The experiences of Japan and China
are relevant because they have contributed signifi-
cantly to the recent changes in saving behavior in
3Technically, the introduction of consumption habits means
that households want to smooth not only the level of their
consumption but also its change.
4Technically, a Granger causality test, which is a test of predic-
tive causality, is being performed. The specification used is the
following:
sit = ai1 + r1sit–1 + b1git–1 + εit1,
git = ai2 + r2git–1 + b2sit–1 + eit2,
in which st and gt denote the five-year (nonoverlapping) averages
of the private-saving-to-GDP ratio and real GDP growth, respec-
tively. The inclusion of country fixed effects makes it possible
to analyze deviations from countries’ averages. The analysis is
performed for an unbalanced sample of 45 advanced and emerg-
ing market economies from 1970 to 2013.
5The sign of the effect, however, turns positive when country
fixed effects are excluded, corroborating the growth theories’
prediction that higher saving rates lead to higher output (growth)
in the long term.
6These results are in line with those obtained by Carroll and
Weil (1994).
7Similar results are also obtained using a two-step generalized-
method-of-moments system estimator.
Box 3.1. Saving and Economic Growth

advanced economies and emerging market economies,
respectively.
Beginning with emerging market economies, panel
1 of Figure 3.1.1 shows that increases (decreases) in
saving rates followed increases (decreases) in growth.
In China, the increase in growth early in the first
decade of the 2000s was followed by an increase in
the saving rate of about 12 percentage points during
2000–07 (panel 2 of the figure). Conversely, the recent
growth slowdown was followed by a decline in the
saving rate.
In advanced economies, the decline in the saving
rate was preceded by declines in growth rates (panel
3 of the figure). This trend is particularly evident for
Japan (panel 4 of the figure), where lower growth after
1990 was followed by a reduction in the saving rate
of about 10 percentage points. These experiences also
suggest that the effect of growth on saving has been
broadly symmetric (that is, it has been present both
when growth increases and when growth decreases).
The results suggest that current saving rates are well
explained by lagged saving rates and real GDP growth
(Table 3.1.1, columns 1 and 2). This holds not only
for a panel of countries at medium-term frequencies,
but also at the country level at annual frequencies (the
estimated equations typically explain about 90 percent
of the variation in saving rates).8
8It can be shown that this specification is equivalent to a
reduced-form life cycle model with habit in which st = a0 + a1ht*
+ ut, and ht* = bgt + (1 – b)h*t–1. In this equation, st is the saving-
to-GDP ratio at time t, gt is the growth rate of income at time t,
and ht* is the unobservable stock of habit at time t. The reduced-
form equation is then estimated using instrumental variables. See
Furceri, Pescatori, and Wang (forthcoming).
This model is used to assess the extent to which per-
fect foresight about GDP growth would help predict
saving rates. To this end, the evolution of saving rates
since 2001 is predicted, conditional on observed GDP
growth for the same period and the initial saving-to-
GDP ratio in 2000. The results, presented in Figure
3.1.2, show that the predicted values closely follow the
actual evolution of the saving rate.9 For example, in
the case of China, the saving rate between 2001 and
2007 increased by about 13 percentage points. The
results suggest that about 11 percentage points (that is,
85 percent) of the actual increase can be attributed to
the increase in GDP growth.
Finally, the analysis turns to some other possible
determinants of saving in the short to medium term.
In addition to growth, other factors may affect saving
rates, including safety nets, financial constraints, and
demographic structures. For example, these factors
have been found to contribute to an explanation of
long-term trends and cross-country differences in sav-
ing rates (IMF, 2013). Here, the exercise tests whether
they also explain short- and medium-term movements
in saving rates. For this purpose, the saving rate is
regressed against its lagged value, GDP growth, and a
vector of controls, including (1) the private-credit-to-
GDP ratio (as a proxy for financial deepening), (2) the
age-dependency ratio (defined as the ratio of the popu-
lation ages 0–14 and 65 and older to the population
9In particular, the average absolute ten-year-ahead forecast
error of saving rates is only about 1.1 percentage points of GDP
(that is, about 4½ percent of the saving-to-GDP ratio). Figure
3.1.2 presents the results only for selected countries. Similar
results (available on request) are obtained for most of the coun-
tries in the sample.
Box 3.1 (continued)
Table 3.1.1. Saving and Growth: Granger Causality Tests
Variable
Saving Growth
(1) (2) (3) (4)
Lagged Five-Year Saving 0.534*** 0.556*** −0.0748*** −0.0846***
(0.034) (0.033) (0.020) (0.020)
Lagged Five-Year Growth 0.269*** 0.187** 0.0965** 0.128***
(0.080) (0.073) (0.046) (0.045)
Constant 0.0970*** 0.101*** 0.0317*** 0.0263***
(0.016) (0.015) (0.009) (0.009)
Number of Observations 502 502 502 502
R Squared 0.902 0.899 0.432 0.333
Country Fixed Effects Yes Yes Yes Yes
Year Fixed Effects Yes No Yes No
Note: Standard errors are in parentheses. *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively.

in the 15- to 64-year-old age bracket), and (3) public
health expenditure as a share of GDP (as a proxy for
safety nets).10
The results show that even though the signs of the
coefficients are as expected—increases in safety nets,
financial deepening, and aging reduce saving—none
of the control variables is statistically significant (Table
10In particular, the following specification is estimated:
Sit = ai + r1Sit–1 + b1git + d′Zit + eit.
Country fixed effects are included so that the effect of the
explanatory variables on deviations of the saving rates from
countries’ averages can be analyzed.
Box 3.1 (continued)
4
8
12
16
35
40
45
50
55
60
1990 2000 12
Figure 3.1.1. Saving Rate and Accelerations
(Decelerations) in GDP
2. China
GDP growth rate (percent; left scale)
Saving rate (percent of GDP; right scale)
–2
–1
0
1
2
3
4
5
6
7
15
20
25
30
35
40
1990 2000 12
4. Japan
2
4
6
8
10
20
24
28
32
36
40
1990 2000 12
1. Emerging
Market
Economies
–1.0
0.0
1.0
2.0
3.0
4.0
5.0
15
17
19
21
23
25
1990 2000 12
3. Advanced
Economies
Sources: Haver Analytics; Organization for Economic
Cooperation and Development; World Bank, World
Development Indicators database; and IMF staff
calculations.
20
22
24
26
28
2001 04 07 10 12
Figure 3.1.2. Total Saving: Actual versus
Conditional Forecasts
(Percent of GDP)
2. Japan
Forecast Actual
16
18
20
22
2001 04 07 10 12
4. Italy
14
16
18
20
2001 04 07 10 12
1. United States
16
18
20
22
2001 04 07 10 12
3. France
22
26
30
34
38
2001 04 07 10 12
6. India
36
40
44
48
52
56
2001 04 07 10 12
5. China
Sources: World Bank, World Development Indicators
Note: Forecast is conditional on observed GDP growth
and the initial saving-to-GDP ratio observed in 2000.

3.1.2, column 1).11 A possible explanation for this
result is that these variables differ significantly across
countries and they move only gradually. Therefore,
whereas they are important in explaining cross-country
differences in saving rates, as shown in IMF (2013),
they do not seem significant in explaining short- to
medium-term movements within countries.
Another way through which some of these factors
(namely, financial constraints and safety nets) may
affect saving rates is by strengthening the response of
saving to changes in income (for example, Jappelli and
Pagano, 1994; Sandri, 2010; Furceri, Pescatori, and
11These results are robust to the inclusion of time fixed
effects, using a two-step generalized-method-of-moments system
estimator and alternative specifications of the variables, such as
(1) using both old and youth age-dependency ratios; (2) using
a low-order polynomial to represent 15 population brackets:
0–4, 5–9, . . . , 65–69, 70+ (Higgins, 1998); and (3) using de
jure measures of financial constraints (Abiad, Detragiache, and
Tressel, 2010).
Wang, forthcoming). To test this hypothesis, interac-
tion terms between growth and the set of control vari-
ables are included in the previous specification.12 The
results suggest that interaction effects are not statisti-
cally significant (Table 3.1.2, columns 2–4). Moreover,
the inclusion of these variables (both as controls and
as interaction terms) does not improve the fit of the
regression and does not significantly affect the overall
impact of growth on saving.13
In summary, the analysis performed confirms a
strong relationship between the saving rate and growth
at the country level in the short to medium term.
Overall, life cycle motives coupled with consumption
habits (and possibly prudential saving behavior) are
plausible explanations for the observed saving patterns.
12In particular, the following specification is estimated:
Sit = ai + r1Sit–1 + b1git + d′Zit + ϑ′git Zit + eit.
13When the interaction terms are included, the average impact
of growth on saving is given by b1 + ϑZ
–
.
Box 3.1 (continued)
Table 3.1.2. Determinants of the Evolution in Saving-to-GDP Ratios
(1) (2) (3) (4)
Lagged Saving Ratio 0.756***
(0.029)
0.763***
(0.028)
0.756***
(0.028)
0.756***
(0.028)
GDP Growth 0.282***
(0.045)
0.302***
(0.074)
0.202*
(1.78)
0.203*
(0.115)
Financial Deepening –0.003
(0.006)
–0.005
(0.004)
–0.001
(0.006)
Safety Nets –0.161
(0.145)
–0.245*
(0.125)
–0.223
(0.165)
Age-Dependency Ratio –0.748
(2.772)
GDP Growth × Financial Deepening –0.001
(0.001)
–0.001
(0.001)
GDP Growth × Safety Nets 0.003
(0.002)
0.002
(0.002)
Average Short-Term Impact of Growth on Saving 0.282*** 0.290*** 0.350*** 0.289***
Adjusted R Squared 0.890 0.890 0.890 0.890
Note: Country fixed effects are included but not reported. Clustered robust standard errors are in parentheses. The average (short-term) impact
of growth on saving is computed as b1 + ϑZ
–
, in which Z
–
is the simple average of the control variable interacted with GDP growth. *, **, and ***
denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively.

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1
CHAPTER
4
CHAPTER ON THE RECEIVING END? EXTERNAL CONDITIONS AND EMERGING
MARKET GROWTH BEFORE, DURING, AND AFTER THE GLOBAL
FINANCIAL CRISIS
This chapter finds that external factors induce signifi-
cant fluctuations in emerging market economies’ growth,
explaining about half the variance in their growth rates.
Higher growth in advanced economies benefits emerging
markets even though it is accompanied by higher global
interest rates. A tighter external financing environment,
stemming from a higher risk premium on emerging
markets’ sovereign debt, reduces their growth. The payoffs
from positive demand shocks are greater for economies that
have strong trade ties with advanced economies and lesser
for economies that are financially open. Adverse exter-
nal financing shocks hit economies that are financially
open, as well as those with limited policy space. China
itself has become a key external factor for other emerging
markets in the past 15 years—its strong growth provided
a buffer during the global financial crisis. China’s recent
slowdown has, however, weighed on emerging markets’
growth. Despite the importance of external factors, how
much emerging markets are affected also depends on
their internal policy responses. The influence of these
internal factors has risen in the past two years, although
they appear to be reducing rather than spurring growth
in some key economies, including China. The persistent
dampening effect from internal factors in recent years
suggests that trend growth could be affected as well.
T
he recent slowdown in emerging market
and developing economies has caused much
angst in policy circles. These economies grew
at a remarkable pace from the late 1990s
until the onset of the global financial crisis in 2008–09
(Figure 4.1, panel 1). With a few exceptions—nota-
bly in emerging and developing Europe—activity in
these economies also rebounded much more strongly
in 2009–10 than in advanced economies (panel 2 of
the figure). However, economic growth decelerated
after this initial rebound, and growth in some major
emerging market economies is now significantly below
levels recorded before the global financial crisis. Thus,
policymakers worry that this slowdown could be a sign
of the lasting effects of the crisis—temporarily offset by
policy stimulus—and the beginning of worse to come.
Two polar views have been offered to explain
emerging markets’ growth experience, with quite dif-
ferent implications for their future prospects. Some
have argued that the slowdown in these economies
is inevitable following years of rapid growth, helped
by a favorable—but ultimately transitory—external
environment characterized by high commodity prices
and cheap external credit (Aslund, 2013; Eichengreen,
Park, and Shin, 2011). In contrast, others have argued
that their improved performance was underpinned by
structural reforms and strong macroeconomic policies
(de la Torre, Levy Yeyati, and Pienknagura, 2014; Sub-
ramanian, 2013; Abiad and others, 2012). The reality
could indeed lie somewhere between these competing
views, wherein positive external conditions provided
emerging market economies with the opportunity to
strengthen their economic policies and reforms, and
although growth may soften with the unwinding of
these conditions, it will remain strong.
In this light, it is useful to understand how external
conditions have typically affected emerging market
economies’ growth, so as to get a picture of how they
will cope with the impending changes in these condi-
tions. Historically, different external factors have prob-
ably affected these economies in different ways: for
example, recent weak growth in advanced economies
was likely unfavorable for emerging market economies’
exports and growth, whereas ultralow global interest
rates (see Chapter 3), set to support the recovery in
advanced economies, may have helped sustain growth
by fueling domestic demand. As shown by the black
squares in panel 3 of Figure 4.1, domestic demand
in some emerging market economies has been grow-
ing at a stronger pace than before the global financial
crisis. Looking ahead, these global conditions are set to
shift: growth in advanced economies should gain speed
and support emerging markets’ external demand, but
global interest rates will also rise as advanced econo-
The authors of this chapter are Aseel Almansour, Aqib Aslam,
John Bluedorn, and Rupa Duttagupta (team leader), with support
from Gavin Asdorian and Shan Chen. Alexander Culiuc also con-
tributed. Luis Cubeddu provided many helpful suggestions.

mies’ monetary policies normalize (see Chapter 1).
Similarly, many emerging market economies, especially
commodity exporters, will face weaker terms of trade
as commodity price increases are reversed. How these
economies perform will depend not only on their
exposures to these external factors, but also on whether
and how they use policies to respond to the changes.
This chapter analyzes the effect of external factors
on emerging market economies’ growth in the period
before, during, and after the global financial crisis and
more recently.1 Specifically, it addresses the following
questions:
•• How have external conditions (such as growth in
advanced economies, global financing conditions, and
terms of trade) typically affected emerging market
economies’ growth over the past decade and a half?
•• Are the effects of external factors similar or differ-
ent across time? Are all emerging markets equally
exposed to external shocks, or are some economies
more vulnerable?
•• Within emerging market economies, how has
China’s growth influenced growth in other emerging
markets?
•• How has the relationship between emerging market
economies’ growth and the underlying external and
internal factors changed since the onset of the global
financial crisis?
•• What are the prospects for emerging market
economies’ growth—given the expected changes in
the global environment—and what are the policy
implications?
The chapter’s main findings and conclusions are the
following: changes in external conditions have important
effects on emerging market economies’ growth. Specifi-
cally, an unexpected 1 percentage point increase in U.S.
growth raises emerging markets’ growth by 0.3 percent-
age point on impact, and the cumulated effects remain
positive beyond the short term (more than one to two
years). These positive effects incorporate the fact that
the 1 percentage point U.S. growth increase also raises
the 10-year U.S. Treasury bond rate by close to 10 basis
points on impact and 25 basis points after one year.
1A related literature analyzes to what extent recent growth changes
in emerging market economies are explained by structural versus
cyclical factors (see Box 1.2 of the October 2013 World Economic
Outlook). Although this chapter does not distinguish between struc-
tural growth and cyclical growth, it relates to this issue by addressing
whether the growth effects of changes in external conditions are
persistent or transitory.
–10
–8
–6
–4
–2
0
2
4
6
8
RUS
IND
POL
CHN
ZAF
THA
VEN
MEX
MYS
TUR
BRA
CHL
COL
IDN
ARG
PHL
–4
–2
0
2
4
6
8
10
12
1998 2000 02 04 06 08 10 12
Figure 4.1. Growth Developments in Advanced and Emerging
Market and Developing Economies
1. Real GDP Growth Rates
(percent)
3. Emerging Market GDP Growth and Domestic Demand Growth
Deviation, 2013
(percentage point difference from trend based on 1999–2006
growth)
Advanced economies
70
80
90
100
110
120
130
140
150
2004 06 08 10 12 14
2. GDP since the Global Financial Crisis Relative to
Precrisis Trend
(2008 = 100; dashed lines indicate precrisis trends)
Advanced economies
Emerging market and
Note: X-axis in panel 3 uses International Organization for Standardization (ISO)
country codes.
2013 domestic demand growth
deviation from 1999–2006 average
Emerging market economies grew at a remarkable pace from the late 1990s until
the onset of the global financial crisis in 2008–09. With some exceptions, activity
in emerging market and developing economies rebounded much more strongly in
2009–10 than in advanced economies. However, economic growth has recently
decelerated, with growth in some major emerging markets now significantly below
levels recorded prior to the global financial crisis.

CHAPTER 4 ON THE RECEIVING END?
Similarly, stronger euro area growth boosts emerging
market economies’ growth. Conversely, growth is hurt
by tighter external financing conditions: a 100 basis
point increase in the composite emerging market global
sovereign yield reduces growth by ¼ percentage point
on impact. On average, in the medium term, external
shocks—stemming from external demand, financing
costs, and terms of trade—explain about half of the vari-
ance in emerging market economies’ growth rates.
The incidence of external shocks varies across econo-
mies, with stronger growth in advanced economies
having a stronger growth effect on emerging market
economies that are relatively more exposed to advanced
economies in trade and a weaker effect on economies
that are more financially open. Similarly, the adverse
effects of global financing shocks are higher for emerg-
ing market economies that are typically more prone to
capital flow volatility or have relatively higher current
account deficits and public debt.
External factors have contributed as much as or
more than other, mostly internal, factors in explaining
emerging markets’ growth deviations from the estimated
average growth over the past 15 years—although there is
considerable heterogeneity across time and across econo-
mies. The sharp dip in these economies’ growth during
the global financial crisis was almost fully accounted for
by external factors. Conversely, the pullback in growth
for some emerging market economies since 2012 is
mostly attributable to internal factors. External factors
have generally been much less important compared with
internal factors for some relatively large or closed econo-
mies, such as China, India, and Indonesia.
China is, in fact, an important contributor to
growth for other emerging market economies. China’s
strong expansion provided emerging markets with an
important buffer during the global financial crisis.
However, China’s recent slowdown has also softened
emerging market economies’ growth. Specifically, of
the 2 percentage point decline in average emerging
market economy growth since 2012 compared with
2010–11, China has accounted for close to ½ per-
centage point, other external factors for 1¼ percent-
age points, and other, mostly internal, factors for the
remaining ¼ percentage point.
Finally, although emerging markets’ output and
growth outturns since the crisis have been stronger
than those observed after most previous global reces-
sions, dynamic forecasts from the empirical model
in the analysis, conditional on the path of external
factors, show that in some economies—such as China
and a few large emerging market economies—growth
since 2012 has been systematically lower than expected
given external developments. The persistent dampen-
ing effects from these factors suggest that growth could
remain lower for some time, affecting growth in the
rest of the world as well.
Should emerging markets therefore be concerned
about their growth prospects as the external environ-
ment changes? This chapter’s findings suggest that
these economies are likely to face a more complex and
challenging growth environment than in the period
before the global financial crisis, when most external
factors were supportive of growth. On the one hand, if
external changes are dominated by a strong recovery in
advanced economies, this will, overall, benefit emerg-
ing markets despite the accompanying higher U.S.
interest rates. However, if external financing conditions
tighten by more than can be explained by the recovery
in advanced economies, as observed for some emerg-
ing market economies during the bouts of market
turbulence in the summer of 2013 and the beginning
of 2014, emerging markets will suffer. Moreover, as the
Chinese economy transitions to a more sustainable but
slower pace of growth, this will temporarily weigh on
growth in other emerging market economies. Finally,
growth will decline further if the drag from internal
factors, as observed in some emerging market econo-
mies since 2012, continues. In this light, the prior-
ity is to better understand the role of these internal
factors and assess whether there is scope for policies to
improve emerging market growth prospects, without
generating macroeconomic imbalances.
The rest of the chapter is structured as follows. The
next section presents the empirical framework for ana-
lyzing the effects of external factors on emerging market
economies’ growth and maps those factors’ contributions
over the past decade and a half. It also highlights the
heterogeneity across emerging markets in the incidence
of shocks. The subsequent section discusses the role of
China as an independent external factor, followed by an
assessment of the relationship between external factors
and medium-term growth. The penultimate section
discusses how the relationship between emerging market
economies’ growth and its underlying external and
internal drivers has evolved since the onset of the global
financial crisis. The final section draws on the chapter’s
findings to discuss emerging market economies’ growth
prospects and the implications for policy.

Effects of External Factors on Emerging Market
Growth
Analytical Framework
The analysis draws on a simple organizing framework
to consider the relationship between emerging market
economies’ growth and external conditions. It assumes
that most emerging markets are small open economies
and that global economic conditions are exogenous to
their growth, at least on impact. Thus, the impact of
external shocks on a particular economy depends on
how exposed the economy is to these shocks via cross-
border linkages and on how domestic policy stabilizers
are allowed to work. Over time, the cumulated effect
on domestic growth may be amplified or dampened as
domestic policies respond further to external shocks.
However, such a framework does not fully consider
the potential implications of the rising importance of
emerging market economies. Emerging market and
developing economies now account for more than one-
third of world output at market exchange rates—up
from less than 20 percent in the 1990s. Thus, global
economic conditions could be treated as endogenous
to shocks emanating from emerging market economies
as a group. Emerging market and advanced economies
could also be driven by common shocks. The analysis
in this chapter assumes that any such contemporane-
ous feedback effects from emerging market economies’
domestic conditions within a quarter are small enough
to be ignored, but allows for these domestic conditions
to affect global conditions with a lag.2 The chapter also
considers the effects of China’s growth—as an external
factor distinct from other traditional external factors—
on growth in other emerging market economies. With
this in mind, this chapter adds to the related literature
in three ways:3
2Given these restrictions, one caveat is that the analysis could
overstate the effects of external shocks. It is, however, reassuring that
the chapter’s estimates for the magnitude of the effects of external
conditions are similar to estimates from other recent studies. See
note 21 for details.
3Other studies analyzing the role of external conditions in emerg-
ing markets’ growth include Calvo, Leiderman, and Reinhart (1993),
Canova (2005), Swiston and Bayoumi (2008), and Österholm and
Zettelmeyer (2007) for Latin America; Utlaut and van Roye (2010)
for Asia; and Adler and Tovar (2012), Erten (2012), and Mackowiak
(2007) for a more diverse group of emerging market economies.
Most, if not all, find that external shocks—however identified—are
important for emerging markets’ growth, explaining about half of its
variance.
•• First, by focusing on the past decade and a half, dur-
ing which emerging market economies’ performance
and policies improved remarkably, as evidenced by
their resilience to the deepest global recession in recent
history, it analyzes whether the role of external condi-
tions in determining emerging market economies’
growth has fundamentally changed in recent years.
•• Second, it documents how the heterogeneity in the
incidence of external shocks across emerging market
economies relates to differences in their structural
characteristics and policies.
•• Third, it addresses whether and how the emergence
of China as a systemically important component of
the global economy has reshaped the impact of exter-
nal factors on emerging market economies’ growth.4
The analysis uses a standard structural vector autore-
gression (VAR) model to quantify the growth effects
of external shocks. The baseline model comprises nine
variables, each placed into either an external or an
internal block. The external variables (the “external
block”) include U.S. real GDP growth, U.S. inflation
as measured by the consumer price index, the 10-year
U.S. Treasury bond rate, the composite emerging
market economy bond yield (from the J.P. Morgan
Emerging Market Bond Index (EMBI) Global), and
economy-specific terms-of-trade growth. In expanded
versions of the baseline specification, the external block
is augmented by additional proxies for global financing
conditions, such as the U.S. high-yield spread, as well
as proxies for global demand, such as growth in China
and the euro area. The domestic variables (the “internal
block”) include domestic real GDP growth, domestic
consumer price inflation, the rate of appreciation of the
economy’s real exchange rate against the U.S. dollar,
and the domestic short-term interest rate. The external
block is assumed to be contemporaneously exogenous
to the internal block—that is, external variables are not
affected by internal variables within a quarter.
Within the external block, the structural shocks are
identified using a recursive scheme, based on the above
order. In other words, U.S. growth shocks are able
to affect all other variables within a quarter, whereas
shocks to other variables can affect U.S. growth only
with a lag of at least one quarter. U.S. inflation shocks
are able to affect all the variables ordered below U.S.
inflation within a quarter, whereas shocks to the
4Utlaut and van Roye (2010) ask a similar question for emerging
Asia, as do Cesa-Bianchi and others (2011) for Latin America.

variables below U.S. inflation can affect it only with a
lag. A similar logic then applies to variables lower in
the external block. Within the internal block, struc-
tural shocks are not explicitly ordered and therefore are
not identified.5
Taken together, the U.S. variables in the external
block proxy for advanced economy economic con-
ditions: U.S. growth captures advanced economy
demand shocks; after U.S. growth is controlled for,
U.S. inflation captures advanced economy supply
shocks; and the 10-year U.S. Treasury bond rate
captures the stance of advanced economy monetary
policy.6 Changes in emerging market financing condi-
tions arising from factors other than external demand
conditions are incorporated through the EMBI Global
yield. Similarly, changes in terms-of-trade growth rep-
resent factors other than changes in external demand
or financing conditions.
The model is estimated individually for each econ-
omy in the sample using quarterly data from the first
quarter of 1998 through the latest available quarter in
2013. The focus is on the period after the 1990s, given
the significant shifts in policies in these economies dur-
ing this time (Abiad and others, 2012). These include,
for example, the adoption of flexible exchange rate
regimes, inflation targeting, and the reduction of debt
levels. Furthermore, the first quarter of 1998 was the
earliest common starting point for all the economies
based on data availability at a quarterly frequency. The
number of variables and lags chosen for the specifica-
tion results in a generous parameterization relative to
the short sample length. As a result, degrees of freedom
are limited such that standard VAR techniques may
yield imprecisely estimated relationships that closely
fit the data—a problem referred to as “overfitting.” A
Bayesian approach, as advocated by Litterman (1986),
is adopted to overcome this problem. It allows previ-
ous information about the model’s parameters to be
combined with information contained within the data
to provide more accurate estimates. Given the observed
persistence in emerging market economy growth (see
5See Appendix 4.1 for a description of the data and Appendix 4.2
for additional details regarding the recursive identification.
6With the federal funds rate constant at near zero since 2008 and
the Federal Reserve’s focus on lowering U.S. interest rates at the long
end, the 10-year Treasury bond rate is likely a better proxy for U.S.
monetary policy for the analysis. That said, none of the main results
of the analysis would be affected if the federal funds rate were used
instead (see Appendix 4.2 for details).
Chapter 4 of the October 2012 World Economic Out-
look, WEO), it is assumed that all variables follow a
first-order autoregressive (AR(1)) process, with the AR
coefficient of 0.8 in the priors.7
In view of the short sample length, and given the
need to focus on a select few measures for external
conditions, a number of robustness checks on the main
analysis have been performed, as reported in Appendix
4.2.8 Overall, the main results are found to be largely
unaffected by changes in the underlying specification
of the model, addition of new variables, changes in the
assumptions about the priors (for example, white noise
around the unconditional means instead of AR(1) pro-
cesses), or even changes in the statistical methodology
(for example, pooling across economies in a panel VAR
and discarding the Bayesian approach).
The sample comprises 16 of the largest emerging
market economies, spanning a broad spectrum of
economic and structural characteristics (Figure 4.2).9
Together, they account for three-quarters of the output
of all emerging market and developing economies in
purchasing-power-parity terms. Malaysia, the Philip-
pines, and Thailand are relatively more integrated
with global trade and financial markets (panels 1 and
3 of Figure 4.2). Malaysia, Mexico, and Poland are
relatively more exposed to advanced economies in
goods trade (panel 2). Chile is also financially highly
integrated but not that vulnerable to capital flow
volatility (panels 3 and 4). Brazil and India have low
levels of goods trade exposure to advanced economies
7A more persistent growth process in the prior in part recognizes
that growth could in fact be drifting away from its mean for a
prolonged period during the sample period. This is possible for a
number of the economies in the sample, as observed in their actual
growth movements in the past 15 years (see Appendix 4.1).
8The Bayesian methodology is particularly helpful given the rela-
tively short estimation period. With 60 to 62 observations for each
economy-specific regression and 37 coefficients to estimate, the prior
gets a weight of slightly less than 25 percent in the baseline specifica-
tion. The weight does increase with the alternative specifications,
rising to 50 percent for the short sample regressions in the penulti-
mate section. However, alternative methodologies that do not rely
on Bayesian techniques yield broadly similar results: Box 4.1 sheds
light on the medium-term relationship between growth and external
factors, whereby growth is averaged over a five-year period to remove
any effects from business cycles. Appendix 4.2 also discusses the
results of the main analysis for a smaller sample of economies for
which data are available back to the mid-1990s, which, therefore,
does not use Bayesian methods. Finally, it also outlines additional
robustness checks using panel VARs.
9The sample is Argentina, Brazil, Chile, China, Colombia, India,
Indonesia, Malaysia, Mexico, Philippines, Poland, Russia, South
Africa, Thailand, Turkey, Venezuela.

and are relatively less open among the sample econo-
mies. Argentina and Venezuela experience large output
fluctuations—likely reflecting their narrow export bases
(panel 5), but also domestic policies—as do Russia and
Turkey (panel 6).
The discussion of the results focuses on the findings
for emerging market economies that enjoyed strong
macroeconomic performance during the past 15 years
but are now slowing. Although the impulse responses
to alternative shocks show the mean group estimates
based on all the economies in the sample, the average
response for a smaller subsample of emerging market
economies, excluding economies that experienced high
macroeconomic volatility or recent crises (specifically,
Argentina, Russia, and Venezuela), is also presented.
Key Findings
Stronger external demand has a lasting positive effect
on emerging market economies’ growth despite the
attendant rise in the 10-year U.S. Treasury bond rate
(Table 4.1, Figure 4.3). A 1 percentage point increase
in U.S. growth typically raises emerging markets’
growth by 0.3 percentage point on impact; the incre-
mental effects remain positive for six quarters (panels 1
and 2 of the figure), and the cumulative effects remain
positive beyond the short term (more than one to two
years), as shown by the black squares in panel 2 of the
figure. Positive spillovers are also transmitted through
a small boost to emerging market economies’ terms-of-
trade growth (Table 4.1). The impact effect tends to be
stronger for economies that are relatively more exposed
to advanced economies in trade (for example, Malaysia
and Mexico), but also stands out for some others (for
example, India and Turkey).10 As shown in Table 4.1,
the increase in U.S. growth induces an increase in the
10-year U.S. Treasury bond rate by close to 10 basis
points on impact and further through the first two
years (see the estimates in the third grouping within
the first data column of the table).11
10The relatively high impact elasticity of India’s growth to U.S.
growth could reflect the fact that the Indian economy is more
closely integrated with that of the United States than is implied by
a measure of integration based on the share of India’s goods trade to
advanced economies, as in Figure 4.2, panel 2, notably through its
sizable service sector exports (for example, outsourcing). Even the
data suggest a relatively strong correlation between India’s growth
and advanced economy growth in the past 15 years (see Appendix
4.1).
11The effects of the increase in U.S. growth remain strong at
about the same level even after growth in other advanced economies is
0
10
20
30
40
0
50
100
150
200
250
BRA COL ARG IND TUR VEN RUS MEX CHN IDN ZAF CHL POL PHL THA MYS
IND BRA ARG IDN TUR COL ZAF CHL CHN RUS PHL THA POL VEN MEX MYS
IND BRA ARGIDN TURCOL ZAFCHLCHN RUSPHL THAPOL VENMEX MYS
IND BRA ARGIDNTUR COLZAF CHLCHN RUSPHLTHA POL VENMEX MYS
INDBRA ARGIDN TURCOLZAF CHLCHN RUSPHL THAPOL VENMEXMYS
2. Trade Exposure to Advanced Economies
(goods exports to United States and euro area; percent of GDP)
0
2
4
6
8
10
Sources: IMF, Balance of Payments Statistics database; IMF, Direction of Trade
Statistics database; IMF, International Financial Statistics database; IMF, April 2012
World Economic Outlook, Chapter 4; and IMF staff calculations.
Note: X-axis in panels uses International Organization for Standardization (ISO)
country codes.
Figure 4.2. Average Country Rankings, 2000–12
1. Trade Openness
(exports plus imports; percent of GDP)
0
2
4
6
84. Exposure to Capital Flow Volatility
(standard deviation of net nonofﬁcial inﬂows; percent of GDP)
6. Output Volatility
(standard deviation of real GDP per capita growth)
–10
–5
0
5
10
15
20
255. Commodity Concentration
(net commodity exports; percent of GDP)
The sample of 16 of the largest emerging market economies covers a broad
spectrum of economic and structural characteristics.
3. Financial Openness
(international investment assets plus liabilities;
percent of GDP)
0
50
100
150
200
250
MYSCHLZAFARGTHARUSVENPOLPHLCHNMEXTURBRAIDNCOLIND

Growth boosts from other advanced econo-
mies—proxied by euro area growth in addition to
U.S. growth in an alternative specification—are also
substantial on impact for emerging market growth
(panel 3 in Figure 4.3), even though the positive effects
do not endure for as long as those from the U.S.
growth shock. This emphasizes the broader sensitivity
of growth in emerging market economies to external
demand shocks from advanced economies beyond sim-
ply the United States. Given the prevailing downside
risks to growth prospects in the euro area (see Chap-
ter 1), the risk of adverse spillovers to emerging market
growth from Europe also remains strong.
Tighter external financing conditions result in a
decline in emerging market economies’ growth within
the same quarter (Figures 4.4 and 4.5). A 100 basis
point increase in the composite EMBI yield (a risk
premium shock) reduces emerging market economies’
growth by ¼ percentage point on impact, and the
cumulated effects remain negative even after two years
controlled for. These findings are in line with the related literature (see
Österholm and Zettelmeyer, 2007). See Appendix 4.2 for details.
for a majority of the economies. The real exchange
rate tends to depreciate, and domestic short-term rates
are typically raised in response, possibly reflecting the
capital outflows associated with such shocks. The net
effect partly depends on the extent to which a weaker
currency is able to support export growth.
Shocks to other proxies for emerging markets’ exter-
nal financing conditions yield results similar to those
for shocks to the EMBI yield. Since EMBI yields also
fluctuate with domestic developments within emerging
markets, the composite index, rather than the country-
specific yields, is used as the proxy for external financ-
ing conditions. In this index, country-specific factors
should be less important. That said, it is possible
that changes in the composite EMBI yield could still
reflect changes in market sentiment toward underlying
domestic developments in emerging markets. There-
fore, in an alternative specification, the U.S. corporate
high-yield spread is used as an additional proxy for
external financing conditions.12 An increase in the U.S.
12The U.S. high-yield spread is placed before the EMBI yield, and
after all other U.S. variables, in the external block.
Table 4.1. Impulse Responses to Shocks within the External Block: Baseline Model
(Percentage points)
Response1
Shock
U.S. Real GDP
Growth U.S. Inflation
Ten-Year U.S.
Treasury Bond
Rate EMBI Yield
Terms-of-Trade
Growth2
U.S. Real GDP
Growth
On Impact 1.00 0.00 0.00 0.00 0.00
End of First Year 3.20 –0.63 0.10 –0.09 0.02
End of Second Year 3.86 –2.44 –0.72 0.72 0.06
End of Third Year 3.28 –2.04 –2.72 1.61 0.09
U.S. Inflation On Impact 0.11 1.00 0.00 0.00 0.00
End of First Year 0.66 1.96 0.21 –0.31 0.01
End of Second Year 1.50 0.66 1.21 –0.42 0.02
End of Third Year 1.56 0.70 0.91 –0.18 0.05
Ten-Year U.S.
Treasury Bond
Rate
On Impact 0.07 0.07 1.00 0.00 0.00
End of First Year 0.26 –0.07 3.08 –0.01 0.01
End of Second Year 0.65 –0.07 4.96 0.21 0.01
End of Third Year 1.00 –0.14 6.21 0.49 0.02
EMBI Yield On Impact –0.31 –0.17 0.22 1.00 0.00
End of First Year –0.85 0.14 0.96 2.83 0.00
End of Second Year –1.00 0.51 2.56 4.13 –0.02
End of Third Year –0.67 0.44 4.76 4.98 –0.04
Terms-of-Trade
Growth2
On Impact 0.09 1.43 0.29 –0.28 1.00
End of First Year 1.22 0.45 1.86 –1.47 2.23
End of Second Year 1.10 –2.79 1.89 –0.76 1.88
End of Third Year –0.39 –0.83 –0.44 –0.35 2.04
Note: EMBI = J.P. Morgan Emerging Markets Bond Index.
1All responses are cumulated for the end of the period and normalized for a 1 percentage point shock.
2Averaged across country-specific shocks and responses.

–2
0
2
4
6
8
10
12
–0.6
0.0
0.6
1.2
1.8
2.4
3.0
3.6
BRA
IDN
IND
CHN
POL
PHL
THA
CHL
COL
ARG
ZAF
TUR
MYS
MEX
VEN
RUS
AVG
1
Cumulated response of U.S. real GDP growth to its
own shock at end of second year (left scale)
Stronger external demand, proxied by a rise in real GDP growth in advanced
economies, has a lasting positive effect on emerging market economies’ growth.
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 4.3. Impulse Responses of Domestic Real GDP Growth
to External Demand Shocks
(Percentage points)
1. Response to Real GDP Growth Shock in the United States
(1 standard deviation = 0.55 percentage point)
Average response
25th–75th percentile range
2. Response to Real GDP Growth Shock in the United States
(normalized to a 1 percentage point rise in U.S. growth)
Growth effect on impact (right scale)
Cumulated effect on output at end of
second year (left scale)
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
3. Response to Real GDP Growth Shock in the Euro Area
Average response
Note: X-axis units in panels 1 and 3 are quarters; t = 0 denotes the quarter of the
shock. X-axis in panel 2 uses International Organization for Standardization (ISO)
country codes.
1
Average for all sample economies except Argentina, Russia, and Venezuela.
–10
–8
–6
–4
–2
0
2
4
6
8
0.0
0.5
1.0
1.5
2.0
ARG
VEN
BRA
COL
PHL
IDN
CHL
POL
CHN
MEX
ZAF
RUS
MYS
THA
TUR
IND
AVG
1
Cumulated response of EMBI yield to its own shock at
the end of second year (left scale)
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 4.4. Impulse Responses to External Financing Shock
(Percentage points)
1. Domestic Real GDP Growth Response
Average response
2. Domestic Short-Term Interest Rate Response
(1 standard deviation = 0.54
percentage point)
Cumulated effect on output at end of second year
(left scale)
–2.0
–1.5
–1.0
–0.5
–2.0
–2.5
–1.5
–1.0
–0.5
0.0
0.5
1.0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Average response
3. Domestic Real Exchange Rate Response
(normalized to a 1 percentage point rise in the EMBI yield)
Average response
Sources: Federal Reserve Economic Data; Haver Analytics; IMF, International
Financial Statistics database; Thomson Reuters Datastream; and IMF staff
calculations.
Note: X-axis units in panels 1–3 are quarters; t = 0 denotes the quarter of the
country codes. EMBI = J.P. Morgan Emerging Markets Bond Index.
1
A higher risk premium on emerging market economies’ sovereign debt reduces
their growth.

high-yield spread has an even stronger negative growth
effect, with a 100 basis point increase in the spread
reducing emerging markets’ growth by 0.4 percentage
point on impact (Figure 4.5).
Effects of changes in U.S. monetary policy, as
proxied by the 10-year U.S. Treasury bond rate in the
baseline specification, are also considered. The rise in
the U.S. 10-year rate has a negative effect on emerg-
ing market growth after a lag of five to six quarters.
This may reflect the fact that changes in the U.S.
10-year rates (that are unrelated to U.S. GDP growth
and inflation) can still embody many other factors
unrelated to the U.S. monetary policy stance, such as
expectations about the path of the U.S. economy, or
even changes to risk appetite in international investors
because of non-U.S. factors as observed through safe
haven flows to U.S. Treasury bonds during crises. The
details are discussed in Appendix 4.2. Similar results—
a lagged negative growth response to a U.S. interest
rate increase after the early 1990s—have also been
found by others (Mackowiak, 2007; Österholm and
Zettelmeyer, 2007; Ilzetzki and Jin, 2013).13
Simple associations linking economies’ growth
responses to external shocks with their structural and
macroeconomic characteristics are examined by way of
bivariate scatter plots (Figure 4.6). With 16 observa-
tions for each correlation in this figure, the statistical
relationships are suggestive at best. Notable observa-
tions include the following:
•• Higher advanced economy growth imparts stronger
growth spillovers for emerging markets that trade
relatively more with advanced economies (for example,
Mexico; see panel 1 of the figure) but weaker spillovers
for those that are financially more open (for example,
Chile; see panel 2). Countries exposed to greater capital
flow volatility in general (for example, Thailand; see
panel 3) also benefit less. It is possible that stronger
growth in advanced economies (and the attendant rise
in their interest rates) results in greater capital outflows
13Other proxies for U.S. monetary policy (besides the 10-year
U.S. Treasury bond rate in the baseline specification) that are
considered include the effective federal funds or policy rate, the ex
ante real federal funds rate, the change in the policy rate, the term
spread (the 10-year Treasury bond rate minus the effective federal
funds rate), and measures of pure monetary policy shocks (such as
those in Kuttner, 2001, and Romer and Romer, 2004). For each of
these proxies, the 10-year rate is replaced with the proxy in alterna-
tive specifications. Shocks to most of these proxies result in a lagged
negative effect on emerging markets’ growth. Only increases in the
term spread have an immediate negative effect (see Appendix 4.2 for
details).
–12
–10
–8
–6
–4
–2
0
2
4
6
–6
–5
–4
–3
–2
–1
0
1
2
3
VEN
ARG
RUS
COL
BRA
MEX
ZAF
POL
CHL
PHL
MYS
IDN
CHN
THA
IND
TUR
AVG
1
Cumulated response of U.S. high-yield spread to its
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
A rise in the U.S. high-yield spread also has a strong negative effect on emerging
market economies’ growth.
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 4.5. Impulse Responses to U.S. High-Yield Spread
Shock
(Percentage points)
Average response
2. Domestic Short-Term Interest Rate Response
Cumulated effect on output at end of second
year (left scale)
–2.0
–1.5
–1.0
–0.5
0.0
0.5
1.0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Average response
3. Domestic Real Exchange Rate Response
(normalized to a 1 percentage point rise in the U.S. high-yield
spread)
Average response
calculations.
Note: X-axis units in panels 1–3 are quarters; t = 0 denotes the quarter of the
country codes.
1

from financially integrated economies, partly or fully
offsetting the beneficial effects of the external demand
increase, especially for economies that do not have
strong trade ties with advanced economies.
•• Adverse external financing shocks hurt economies
more when they tend to be more exposed to capital
flow volatility (for example, Thailand and Turkey; see
panel 4) or when they have relatively higher external
current account deficits and public debt (see panels
5 and 6). The effects are less acute for some econo-
mies despite their financial openness, which could
be attributable to relatively strong macroeconomic
positions (for example, Malaysia). Chile and Malaysia
are among the few economies in the sample that have
tended to hold their domestic interest rates steady
or have even cut them in response to higher EMBI
yields. For some others, inadequate policy space may
have limited the scope for countercyclical policies to
cushion the growth effects of higher EMBI yields.
These results resonate well with policies observed in
the second half of 2013 and so far in 2014 in response
to financial market volatility. Many emerging market
economies have resorted to raising domestic interest
rates as external financing conditions have tightened and
have allowed their exchange rates to adjust. The findings
in this chapter suggest that how these economies will be
affected will depend on whether their external financial
conditions tighten by more than what can be explained
by a growth recovery in advanced economies, as well as
on their domestic policy response. If financing condi-
tions are tighter, and emerging market economies are
forced to limit capital outflows by raising domestic rates,
growth will decline, with the decline offset, in part,
by exchange rate depreciation. Growth will be further
hit in economies that are more exposed to capital flow
volatility or those with limited policy space to respond
countercyclically to these shocks.
Increases in emerging market economies’ terms-of-
trade growth that are not accounted for by external
demand have a small positive effect on growth that
lasts about one year (Figure 4.7). The relatively muted
response (compared with responses to other shocks)
may reflect the fact that these terms-of-trade changes
are driven by supply shocks.14
14As shown in Appendix 4.2, an alternative specification that con-
siders the global commodity price index, as an additional proxy for
emerging market economies’ terms of trade, yields broadly similar
results for the effects of shocks from global commodity price growth
on emerging market economies’ real GDP growth.
0.0
0.2
0.4
–15 –10 –5 0 5
VEN
TURTHA
ZAF
RUS
POL
PHL
MEX
MYS IDN
IND
COL
CHN
CHL
BRA
ARG
Average current account deficit,
2000–12, percent of GDP
–1.0
–0.8
–0.6
–0.4
–0.2
–1.0
–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
0 15 30 45 60 75 90
VEN
TUR
THA
ZAF
RUS
POL
PHL
MEX
MYS IDN
IND
COL
CHN
CHL
BRA
ARG
Average public debt, 2000–12,
percent of GDP
6. Impact Effect of a 1 Percent
EMBI Yield Shock
Financial openness (international
investment assets plus liabilities in
percent of GDP)
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 5 10 15 20 25 30
VEN
TUR
THA
ZAF
RUS
POL
PHL
MEX
MYS
IDN
IND
COL
CHN
CHL
BRA
ARG
Trade exposure to advanced
economies (goods exports to the United
States and euro area in percent of
domestic GDP)
EMBI Yield Shock
–1.0
–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
1 2 3 4 5 6 7
VEN
TUR
THA
ZAF
RUS
POL
PHL
MEX
MYS
IDN
IND
COL
CHN
CHL
BRA
ARG
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1 2 3 4 5 6 7
VEN
TUR
THA
ZAF
RUS
POL
PHL
MEX
MYS
IDN
IND
COL
CHN
CHL
BRA
ARG
Capital flow volatility (standard
deviation of net capital flows to GDP
during 2000–12)
Capital flow volatility (standard
deviation of net capital flows to GDP
during 2000–12)
U.S. Growth Shock
EMBI Yield Shock
U.S. Growth Shock
Stronger external demand is more beneficial to economies that have stronger trade
links with advanced economies and less beneficial to economies that are financially
very open. External financing shocks more severely affect economies that are more
exposed to capital flow volatility and those with relatively less policy space.
Sources: IMF, Balance of Payments Statistics database; IMF, Direction of Trade
Statistics database; IMF, International Financial Statistics database; IMF, April
2012 World Economic Outlook, Chapter 4; and IMF staff calculations.
Note: EMBI = J.P. Morgan Emerging Markets Bond Index. Data labels in the figure
use International Organization for Standardization (ISO) country codes.
Figure 4.6. Correlations between Growth Responses to
External Shocks and Country-Specific Characteristics
(Percentage points)
U.S. Growth Shock
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 40 80 120 160 200 240
VEN
TUR
THA
ZAF
RUS
POL
PHL
MEX MYS
IDN
IND
COL
CHN
CHL
BRA
ARG

External versus Internal Factors’Contributions in
Historical Growth Dynamics
The analysis so far has confirmed that shocks stemming
from external demand and financing conditions have
significant repercussions for emerging markets’ growth.
However, the combination of domestic structures and
policies has helped offset the shocks in some cases,
whereas it has amplified them in others. In this light,
this section looks back historically to assess the extent
to which emerging market economies’ growth perfor-
mance relative to their estimated average growth over
the sample period is explained by external factors.
External factors tended to explain one-half or more
of the deviation in emerging market economies’ growth
from the estimated sample mean during 1998–2013
(Figure 4.8, panel 1).15 The higher contribution of
external factors is particularly noticeable during the last
two recessions originating in advanced economies—in
the early 2000s and during the global financial crisis.
However, the other, mostly internal factors contributed
more during the onset of emerging markets’ rapid
expansion in the period before the global financial cri-
sis, as well as during the slowdown beginning in 2012.
Internal factors played a more important role,
however, in relatively closed or large economies for
the entire sample period (Figure 4.8, panels 2–7).
Note that in Figure 4.8, the increase or decline in the
contribution of a factor is measured by the change
in its level relative to the previous quarter. In China,
internal factors started contributing less to deviations
from average growth beginning in early 2007. The
negative contribution of internal factors increased at
the onset of the crisis, peaking in the first quarter of
2009, after which a large-scale fiscal stimulus pack-
age was deployed (see Dreger and Zhang, 2011).
The contribution of internal factors started rising in
mid-2009, turning positive in the fourth quarter of
2009 and peaking in 2010. Similarly, in India, internal
factors began dampening growth in early 2008, likely
as the result of tensions from growing bottlenecks in
15Given the estimates from the reduced-form VAR, growth for
each economy at any point in history can be expressed as the sum
of initial conditions and all the structural shocks in the model. The
sum of the shocks from the identified external factors—advanced
economy indicators, EMBI yield, and terms-of-trade growth—pro-
vides the contribution of all external factors. The remaining shocks
likely stem from domestic variables (such as domestic inflation, real
exchange rates, and short-term interest rates in the model) and are
termed internal. That said, these unidentified residual shocks could
also partly embody other factors, such as common or exogenous
shocks (for example, natural disasters).
infrastructure after a period of rapid growth (see IMF,
2008a). Their negative incidence continued until mid-
2009, when internal factors started contributing more
to growth again. In contrast, the sharp dip in growth
in Brazil and Indonesia during the global financial
crisis was almost fully driven by external factors. In
Russia and South Africa, external factors dominated
growth dynamics during the global financial crisis, but
internal factors also played a role, possibly reflecting
problems related to domestic overheating (in Russia;
see IMF, 2008b) or supply-side constraints (in South
Africa; see IMF, 2008c).
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
ARG
MEX
CHN
RUS
TUR
IND
COL
BRA
PHL
IDN
MYS
VEN
ZAF
CHL
POL
THA
AVG
1
Cumulated response of terms-of-trade growth to its
–0.2
–0.1
0.0
0.1
0.2
0.3
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 4.7. Impulse Responses of Domestic Real GDP Growth
to Terms-of-Trade Growth Shock
(Percentage points)
1. Terms-of-Trade Growth Shock
(1 standard deviation = 2.96 percentage points)
Average response
2. Terms-of-Trade Growth Shock
(normalized to a 1 percentage point rise in terms-of-trade
growth)
Cumulated effect on output at end of second
year (left scale)
Increases in emerging market economies’ terms-of-trade growth that are not
accounted for by external demand have a small positive effect on growth that lasts
for about one year.
Sources: Haver Analytics; IMF, International Financial Statistics database;
Organization for Economic Cooperation and Development; and IMF staff
calculations.
Note: X-axis units in panel 1 are quarters; t = 0 denotes the quarter of the shock.
X-axis in panel 2 uses International Organization for Standardization (ISO) country
codes. Average response to terms-of-trade growth shock is calculated as the
average of the responses of emerging market economies’ growth to their country-
speciﬁc terms-of-trade growth shock.
1

Internal factors appear to have been pulling down
growth in some economies in recent years, although
their contribution to growth changes over time has
differed across countries. In China, these factors were
largely depressing growth after late 2010, but there is
a small uptick in their contribution in the last quarter
of 2012. A similar picture emerges for India, wherein
internal factors reduced growth from 2011 until the
third quarter of 2012, but there is an increase in their
contribution since late 2012. A more nuanced picture
emerges for Brazil and South Africa, but in both
economies, after a drag from internal factors in the
second half of 2012, these factors contributed more to
growth in the first half of 2013.
Global Chain or Global China? Quantifying
China’s Impact
China’s dramatic expansion during the past several
decades has garnered much policy attention. The
economy’s rising weight in international trade has
offered many emerging market economies the scope
to diversify their exports away from advanced econo-
mies toward China. A number of recent studies have
found significant implications of changes in China’s
real activity for growth in the rest of the world (Arora
and Vamvakidis, 2010; Ahuja and Nabar, 2012; Cesa-
Bianchi and others, 2011; IMF, 2012, 2013a; and
the Spillover Feature in Chapter 2). Moreover, China
itself has become more resilient to changes in advanced
economies’ economic developments, as documented in
the previous section.
Accordingly, this section analyzes the implications
of China as a distinct external factor for other emerg-
ing markets’ growth since the late 1990s. How China
influences growth beyond its borders will, of course,
depend on the nature of its cross-country linkages.
One prominent channel is the global supply chain,
through which China imports intermediate inputs
from elsewhere—especially emerging Asia—to produce
final goods for advanced economy markets. In this
role, changes in China’s growth are largely endog-
enous to changes in demand conditions in advanced
economies. Another channel arises from China’s own
demand. China’s investment-oriented growth can boost
commodity-exporting emerging market economies
via higher commodity demand and prices. Further
demand rebalancing toward private consumption will
also benefit those exporting final goods to China (see
–8
–6
–4
–2
0
2
4
1999 2001 03 05 07 09 11
1. Emerging Market Economy Average1
Internal factors External factors Deviation
2. Brazil 3. China
4. India 5. Indonesia
6. Russia 7. South Africa
–6
–4
–2
0
2
4
1999 2003 07 11
–16
–12
–8
–4
0
4
8
1999 2003 07 11
0
4
8
1999 2003 07 11
–3
–2
–1
0
1
2
1999 2003 07 10 12
–8
–4
–8
–4
0
4
8
1999 2003 07 11
–4
–2
0
2
4
6
1999 2003 07 10 12
Figure 4.8. Historical Decompositions of Real GDP Growth
into Internal and External Factors
(Percentage points)
External factors tended to explain one-half or more of emerging market
economies’ growth deviation relative to the estimated sample mean during
1998–2013. The roles of external versus internal factors, however, varied
across economies, with internal factors playing a more important role in
relatively closed or large economies throughout the sample period.
Sources: Haver Analytics; Thomson Reuters Datastream; and IMF staff
calculations.
Note: The underlying vector autoregression model includes U.S. real GDP
growth, U.S. inﬂation, 10-year U.S. Treasury bond rate, J.P. Morgan Emerging
Markets Bond Index yield, and terms-of-trade growth in the external block.
1
13:
Q2
13:
Q2
13:
Q2
13:
Q2
13:
Q2

Box 1.2). Finally, China can also support growth else-
where through higher foreign direct investment flows
into those economies (Dabla-Norris, Espinoza, and
Jahan, 2012). To identify China’s economic impact on
others, its growth is placed in the external block for the
other 15 emerging market economies in the sample.16
The results confirm China’s systemic importance in
emerging markets’ growth (Figure 4.9). A 1 percentage
point rise in China’s growth—which is not explained
by U.S. growth—increases other emerging market
economies’ growth by about 0.1 percentage point on
impact. The positive effect tends to build over time as
emerging markets’ terms of trade get a further boost,
highlighting China’s relevance for global commodity
markets (see Table 4.2).17 The impact elasticity is high
for some economies in Asia, such as Thailand, but also
for commodity exporters such as Russia.18 Growth
shocks from China also feed back into the global
economy. A 1 percentage point growth shock in China
boosts U.S. growth with a lag, the cumulative effect
rising to 0.4 percentage point for a cumulative rise in
China’s growth to 4.6 percent after two years (see Table
4.2 and panel 2 of Figure 4.9). However, the effect
reverses fully within three years.
Emerging markets’ economic integration with China
has provided an offset to other external factors at key
moments (Figure 4.10). Note once again that the
increase or decline in the contribution of a factor is
measured by the change in its level relative to the pre-
vious quarter. China’s growth contributed positively to
other emerging markets’ growth from mid-2001 until
early 2002, helping to ameliorate the negative effects of
other external factors in the aftermath of the advanced
economy recession. Also, after the onset of the global
financial crisis, recovering Chinese growth—boosted by
16In this specification, the U.S.-specific variables control for
advanced economy growth influences on emerging market econo-
mies through the global supply chain and are placed before China’s
growth in the recursive ordering. In an alternative specification with
both China and euro area growth, the euro area’s growth is placed
after U.S. growth in the recursive ordering, whereas China’s growth
still comes after all advanced economy indicators. However, switch-
ing the place of China’s growth in the external block (either after
U.S. or euro area growth or after all advanced economy indicators)
does not materially affect the main results.
17The effects of changes in China’s real investment growth on
domestic growth follow a similar pattern but are smaller in magni-
tude (see Appendix 4.2 for details).
18For some commodity exporters, the positive effects build over
time and peak at the end of the second year (for example, Brazil and
Chile).
–6
–4
–2
0
2
4
6
8
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
TUR
CHL
PHL
IDN
MYS
IND
POL
BRA
MEX
COL
THA
ZAF
RUS
ARG
VEN
AVG
1
Cumulated response of China real GDP growth to
its own shock at end of second year (left scale)
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 4.9. Impulse Responses to Real GDP Growth Shock in
China
(Percentage points)
Average response
(normalized to a 1 percentage point rise in China growth)
Cumulated effect on output at end of second year
(left scale)
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
3. U.S. Real GDP Growth Response
Average response
Note: X-axis units in panels 1 and 3 are quarters; t = 0 denotes the quarter of the
country codes.
1
A 1 percentage point rise in China’s growth increases emerging market economies’
growth by 0.1 percentage point on impact, on average. The positive effect builds
over time as emerging market economies’ terms-of-trade growth gets a further
boost, highlighting China’s relevance for global commodity markets.

China’s large fiscal stimulus—increased its contribution
to emerging market economies’ growth from the third
quarter of 2009 until 2010.19 Of the 3¾ percentage
point improvement in emerging market economies’
quarterly (year-over-year) growth in 2010–11 relative
to 2008–09, China accounted for ½ percentage point,
other external factors 2¼ percentage points, and inter-
nal factors the remaining 1 percentage point.
However, emerging market economies’ diversification
toward China has also exposed them to adverse shocks
from China’s growth. Specifically, China’s recent slow-
down provided an additional setback to their growth:
of the 2 percentage point shortfall in emerging market
economies’ quarterly (year-over-year) growth in 2012–13
relative to 2010–11, China accounted for ½ percentage
19China’s fiscal stimulus packages during the global financial crisis
are estimated to have been on the order of 3 percent of GDP in
2009 and 2¾ percent of GDP in 2010 (Dreger and Zhang, 2011).
point, other external factors for 1¼ percentage points,
and internal factors for the remaining ¼ percentage
point.20
Growth Effects: The Long and the Short of It
Besides growth concerns relating to the ongoing cycli-
cal transitions in the global economy, another issue
on the minds of policymakers in emerging markets is
the trend growth rate of their economies. Many worry
that the observed deceleration is due to declining trend
growth compared with the levels recorded in the early
2000s and are concerned about the role of external fac-
tors in this trend growth. Although this chapter focuses
primarily on understanding the links between emerg-
20Note that to the extent domestic policies were adopted in
response to the global financial crisis and subsequently unwound,
they would still be accounted for by external factors rather than
independent internal factors.
Table 4.2. Impulse Responses to Shocks within the External Block: Modified Baseline Model with China Real GDP
Growth
(Percentage points)
Response1
Shock
U.S. Real GDP
Growth U.S. Inflation
Ten-Year U.S.
Treasury Bond
Rate
China Real
GDP Growth EMBI Yield
Terms-of-
Trade Growth2
U.S. Real GDP
Growth
On Impact 1.00 0.00 0.00 0.00 0.00 0.00
End of First Year 3.18 –0.55 0.28 0.32 –0.04 0.01
End of Second Year 3.88 –2.31 –0.35 0.39 0.56 0.06
End of Third Year 3.40 –1.99 –2.47 –0.50 1.04 0.08
U.S. Inflation On Impact 0.12 1.00 0.00 0.00 0.00 0.00
End of First Year 0.66 2.08 0.28 0.19 –0.20 0.01
End of Second Year 1.42 0.91 1.46 0.68 –0.16 0.01
End of Third Year 1.51 0.89 1.46 0.67 0.01 0.05
Ten-Year U.S.
Treasury
Bond Rate
On Impact 0.07 0.07 1.00 0.00 0.00 0.00
End of First Year 0.25 –0.08 3.11 0.08 0.03 0.01
End of Second Year 0.64 –0.12 5.02 0.29 0.31 0.02
End of Third Year 1.00 –0.18 6.31 0.45 0.62 0.03
China Real GDP
Growth
On Impact 0.27 0.28 0.94 1.00 0.00 0.00
End of First Year 0.70 –0.19 3.44 3.24 –0.27 0.04
End of Second Year 0.83 –0.15 6.33 4.59 –0.60 0.11
End of Third Year 1.11 0.23 8.00 5.13 –0.88 0.16
EMBI Yield On Impact –0.30 –0.15 0.22 –0.02 1.00 0.00
End of First Year –0.81 0.12 0.87 –0.21 2.84 0.00
End of Second Year –0.91 0.51 2.27 –0.42 4.13 –0.01
End of Third Year –0.57 0.42 4.22 –0.34 5.02 –0.03
Terms-of-Trade
Growth2
On Impact 0.22 1.63 0.48 0.69 –0.24 1.00
End of First Year 1.50 1.05 2.36 2.10 –1.11 2.28
End of Second Year 1.43 –2.47 3.20 2.67 –0.38 1.97
End of Third Year –0.20 –0.35 1.20 1.64 –0.22 2.03
1All responses are cumulated for the end of the period and normalized for a 1 percentage point shock.
2Averaged across country-specific shocks and responses.

ing market economies’ growth and external factors at
shorter horizons, this section considers the potential
implications for the medium term.
The analysis in the previous section suggests that the
cumulated growth effects from external shocks—espe-
cially from external demand and financing condi-
tions—linger well beyond the short term (see Figures
4.3–4.5 and 4.9). Although trend growth is likely
determined by a myriad of factors, including domestic
macroeconomic and structural policies, external condi-
tions also have a persistent effect. Thus, a stronger
recovery in advanced economies will likely influence
emerging market economies’ trend growth, as will
tighter global financing conditions relative to today.
Moreover, external shocks explain about half the
variance in emerging market economies’ growth in
the medium term (Table 4.3). For Malaysia, which
is generally more integrated with trade and financial
markets, and Mexico, which is integrated with the
U.S. economy, these shares are in the range of 60 to
70 percent. Even for the Indian and Indonesian econo-
mies, in which variance in growth is predominantly
domestically driven, the share of external factors is still
in the range of 25 to 30 percent. Given the sizable
share of external shocks in explaining the variation
in growth over the medium term, it is reasonable to
expect these shocks to have persistent effects on trend
growth as well.21
In this context, Box 4.1 revisits the relationship
between external conditions and growth from a
medium-term perspective. It estimates growth regres-
sions for a broader group of emerging market economies
from 1997 through 2011 to correlate five-year averages
of GDP growth per capita with alternative external
21These findings compare well with those in the literature,
although the estimated effects from this analysis are somewhat lower
compared with those in some of the other studies, reflecting differ-
ences in the sample, estimation period, and methodology. Österholm
and Zettelmeyer (2007) find that external shocks explain 50 to
60 percent of the volatility in growth for Latin American economies
over the medium term, and the overall impact of a global or U.S.
growth shock on Latin America’s growth is roughly one for one
over time. In comparison, the findings of this chapter show that a
1 percentage point U.S growth shock is associated with a cumulated
4 percentage point rise in U.S. growth and a corresponding 2 per-
centage point rise in emerging markets’ average growth after two
years (see panel 2 of Figure 4.3). This suggests a proportional but
less than one-for-one increase in emerging market growth with the
increase in U.S. growth over time. The results with regard to shocks
to the EMBI yield and the U.S. high-yield spread are very similar to
those of Österholm and Zettelmeyer, however. Utlaut and van Roye
(2010) and Erten (2012) also find somewhat larger growth effects of
real shocks from China, the euro area, and the United States.
–3
–2
–1
0
1
2
1999 2003 07 10 12
–8
–6
–4
–2
0
2
4
1999 2003 07 11 13:
Q2
Internal factors China real GDP growth
Other external factors Deviation
1. Emerging Market Economies’ Average1
0
2
4
6
8
1999 2003 07 11 13:
Q2
–8
–6
–4
–8
–4
–16
–12
–2
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0
2
4
6
8
1999 2003 07 11 13:
Q2
2. Brazil 3. India
4. Indonesia 5. Russia
–20
–15
–10
–5
0
5
10
1999 2003 07 10 13:
Q1
6. South Africa 7. Turkey
–6
–4
–2
0
2
4
1999 2003 07 10 13:
Q2
0
4
8
1999 2003 07 11 13:
Q2
Figure 4.10. Historical Decomposition of Real GDP Growth
with China as an Explicit External Factor
(Percentage points)
China has been an important offset to other external factors in explaining
changes in emerging market growth. During the global ﬁnancial crisis, China’s
expansion provided a buffer for emerging market growth. China’s recent
slowdown, however, has reduced growth in these economies.
calculations.
Note: The underlying vector autoregression model includes U.S. real GDP
growth, U.S. inﬂation, 10-year U.S. Treasury bond rate, China real GDP growth,
J.P. Morgan Emerging Market Bond Index yield, and terms-of-trade growth in
the external block.
1
Average for all sample economies except Argentina, China, Russia, and
Venezuela.

conditions and provide a sense of average responses of
the group to changes in these conditions. It finds that
growth in emerging market economies is significantly
associated with growth in their trading partners, includ-
ing that in other large emerging markets such as the
BRICS (Brazil, Russia, India, China, South Africa),
and with global financing conditions. It highlights the
increasing sensitivity of emerging market economies’
growth to changes in these external conditions as these
economies have rapidly integrated into the global
economy.
In essence, although domestic economic and
structural policies remain important determinants of
growth over short and long horizons, the analysis in
this chapter demonstrates that external conditions also
deserve attention. In this regard, if impending changes
in the external environment are dominated by an
improvement in advanced economies’ growth, emerg-
ing market economies will benefit in both the short
and medium term. Conversely, if external financing
conditions tighten by more than what is accounted
for by an improving outlook in advanced economies,
growth in emerging markets will suffer a relatively
lasting effect. However, even if external conditions
deteriorate, emerging markets’ ability to weather such
shocks will be influenced by the domestic policies they
deploy to offset those shocks. The priority, now, for
policymakers in some of these economies is to assess
why these internal factors, cyclical or structural, are
currently reducing growth to less than the averages of
the past 15 years and what, if anything, can be done to
reverse the situation.
Shifting Gears: Have Emerging Markets’Growth
Dynamics Changed since the Global Financial
Crisis?
This section assesses in what ways, if any, the behavior of
growth in emerging market economies and its relation-
ship with its underlying external and internal drivers
have shifted since the onset of the global financial crisis.
With the recovery in many advanced economies still
anemic, it is possible that emerging markets’ output and
growth have also suffered in an enduring way and that
their growth today responds differently to external and
internal factors than it did before the crisis. This assess-
Table 4.3. Share of Output Variance Due to External Factors
(Horizon = five years)
ARG BRA CHL CHN COL IDN IND MEX MYS PHL POL RUS THL TUR VEN ZAF Avg.1
Baseline Model2
Total Contribution from External Factors 0.55 0.60 0.37 0.27 0.35 0.25 0.28 0.69 0.61 0.37 0.36 0.72 0.31 0.46 0.34 0.56 0.42
U.S. Factors3 0.37 0.43 0.23 0.22 0.25 0.15 0.19 0.61 0.53 0.26 0.21 0.57 0.19 0.37 0.28 0.42 0.31
EMBI Yield 0.12 0.12 0.07 0.04 0.06 0.07 0.06 0.02 0.01 0.09 0.02 0.05 0.05 0.08 0.02 0.03 0.06
Terms-of-Trade Growth 0.06 0.05 0.07 0.02 0.05 0.03 0.03 0.06 0.07 0.02 0.13 0.10 0.07 0.01 0.05 0.11 0.06
Modified Baseline Model4
Total Contribution from External Factors 0.55 0.61 0.38 . . . 0.33 0.26 0.30 0.69 0.57 0.43 0.48 0.73 0.31 0.44 0.37 0.67 0.46
U.S. Factors3 0.35 0.45 0.19 . . . 0.22 0.13 0.20 0.58 0.45 0.29 0.21 0.57 0.17 0.34 0.24 0.35 0.30
China Real GDP Growth 0.06 0.07 0.07 . . . 0.08 0.06 0.02 0.05 0.02 0.09 0.10 0.06 0.06 0.02 0.06 0.23 0.07
EMBI Yield 0.09 0.05 0.04 . . . 0.01 0.05 0.07 0.01 0.01 0.04 0.02 0.02 0.03 0.06 0.01 0.02 0.04
Terms-of-Trade Growth 0.04 0.04 0.09 . . . 0.01 0.02 0.01 0.04 0.09 0.01 0.15 0.08 0.05 0.02 0.06 0.08 0.05
Alternative Model5
Total Contribution from External Factors 0.50 0.60 0.40 . . . 0.30 0.24 0.34 0.73 0.57 0.41 0.49 0.75 0.27 0.46 0.36 0.68 0.46
U.S. Factors3 0.30 0.40 0.14 . . . 0.15 0.10 0.20 0.53 0.40 0.24 0.18 0.52 0.14 0.24 0.18 0.31 0.25
Euro Area Real GDP Growth 0.02 0.07 0.09 . . . 0.06 0.01 0.05 0.09 0.07 0.05 0.06 0.10 0.01 0.13 0.05 0.10 0.07
China Real GDP Growth 0.07 0.07 0.06 . . . 0.06 0.06 0.02 0.03 0.01 0.08 0.09 0.04 0.05 0.02 0.05 0.17 0.06
EMBI Yield 0.07 0.04 0.04 . . . 0.01 0.04 0.06 0.01 0.01 0.03 0.02 0.02 0.03 0.06 0.01 0.02 0.03
Terms-of-Trade Growth 0.03 0.02 0.08 . . . 0.01 0.02 0.01 0.07 0.07 0.01 0.13 0.06 0.04 0.01 0.06 0.08 0.05
Note: EMBI = J.P. Morgan Emerging Markets Bond Index. Column heads use International Organization for Standardization (ISO) country codes.
1The numbers are the average for all sample economies except Argentina, Russia, and Venezuela.
2Recursive ordering of external factors is as follows: U.S. real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate, EMBI yield, and terms-of-trade growth.
3U.S. factors include U.S. real GDP growth, U.S. inflation, and 10-year U.S. Treasury bond rate.
4Recursive ordering of external factors is as follows: U.S. real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate, China real GDP growth, EMBI yield, and terms-of-trade
growth.
5Recursive ordering of external factors is as follows: U.S. real GDP growth, euro area real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate, China real GDP growth, EMBI
yield, and terms-of-trade growth.

ment is an important part of understanding to what
extent the past can be a guide for the future relationship
between growth and its external drivers.
A number of studies have highlighted the serious
real effects of financial crises for both advanced and
emerging market economies.22 Among the economies
considered in this chapter, a few (for example, Rus-
sia and Venezuela) suffered serious growth setbacks as
they experienced financial distress of their own (Figure
4.11, panel 3; see Laeven and Valencia, 2013). Some
others experienced sharp downturns as well, likely
reflecting their financial linkages to advanced econo-
mies that experienced the financial crisis (for example,
South Africa). In contrast, a few weathered the crisis
reasonably well (for example, Indonesia and the Philip-
pines). What was the overall growth impact on these
economies that were not at the epicenter of the global
financial crisis? A starting point is an assessment of the
severity of the global financial crisis for emerging mar-
ket economies’ growth compared with that of previous
global recessions.
The post-global-financial-crisis output dynamics in
emerging markets—relative to the precrisis average lev-
els—compare favorably with those following the global
recessions in 1975, 1982, and 1991.23 Panels 1 and 2
of Figure 4.11 show that whereas the global financial
crisis inflicted a sharp decline in output for advanced
economies in its first year, the average output loss for
noncrisis emerging market economies in the sample
was less than 1½ percent. Also, unlike in advanced
economies, whose four- to five-year output loss wid-
ened even more sharply to nearly 9 percent, losses for
emerging markets have remained low.
This strong performance after the global financial
crisis was surpassed only by emerging markets’ experi-
ence during the 1991 global recession, when econo-
mies in both emerging Asia and Latin America enjoyed
rapid growth relative to the pre-1991 growth trends
(the black squares in panel 2 of the figure). As for the
recent crisis, countercyclical policies, undertaken by
both emerging market economies and their advanced
22Most of these studies highlight how the path of output tends to
be depressed substantially and persistently following crises, for both
advanced and emerging market economies undergoing crises, with
no rebound, on average, to the precrisis trend in the medium term
(Abiad and others, 2014; Cerra and Saxena, 2008; Reinhart and
Rogoff, 2009).
23The dating of global recessions draws on recent work by Kose,
Loungani, and Terrones (2013), whereas the metric to compute
precrisis trends draws on Abiad and others (2014).
+3
–12
–10
–8
–6
–4
–2
0
2
4
6
8
10
1975 1982 1991 2009
GDP growth deviation
2. Emerging Market Economies’ GDP Deviation from Pre–
Global Recession Trend1
(percent)
Figure 4.11. Emerging Markets’ Output and Growth
Performance after Global Recessions
–12
–10
–8
–6
–4
–2
0
2
4
6
8
10
1975 1982 1991 2009
1. Advanced Economies’ GDP Deviation from Pre–Global
Recession Trend
(percent)
0 +3 +5 0 +5 0 +3 +5 0 +3 +4 (est.)
0 +3 +5 0 +3 +5 0 +3 +5 0 +3 +4 (est.)
–30
–25
–20
–15
–10
–5
0
5
10
15
–6
–5
–4
–3
–2
–1
0
1
2
3
RUS
VEN
ZAF
THA
TUR
MYS
CHN
POL
MEX
BRA
CHL
IND
COL
PHL
IDN
ARG
3. Emerging Market Economies’ GDP Deviation, 2013
(percent difference from trend based on
1999–2006 growth; left scale)
2013 GDP growth deviation from
1999–2006 average (right scale)
GDP growth deviation
The output and growth dynamics in emerging market economies after the
recent global ﬁnancial crisis compare favorably relative to those following
the global recessions in 1975, 1982, and 1991.
Note: X-axis in panel 3 uses International Organization for Standardization
(ISO) country codes.
1
Average for all sample economies except Argentina, Russia, and
Venezuela.

economy trading partners, likely helped maintain their
growth rates very close to the precrisis trends. This
is remarkable given that precrisis growth was excep-
tionally strong for these economies (see Figure 4.1,
panel 1).
The hypothesis that the relationship between emerg-
ing market growth and external and internal factors
may have changed substantially in the aftermath of the
global financial crisis is examined next. To do this, the
conditional out-of-sample growth forecasts of domes-
tic growth are evaluated using the model estimated
through the fourth quarter of 2007, taking as given
all external variables not specific to emerging market
economies.24 The deviation of the conditional forecast
from actual growth is interpreted as reflecting other,
mostly internal, factors that have driven growth in
these economies since 2008.
On average, the conditional forecasts track actual
growth since 2008 reasonably well, suggesting that there
were no major aftershocks from the global financial cri-
sis to the relationship between emerging market growth
and its underlying external factors (Figures 4.12 and
4.13). The conditional forecasts based on one of the two
specifications are able to project a sharp dip during the
global financial crisis, the subsequent rebound, and the
slowdown since 2012. Also, as Figure 4.13 shows, the
forecast errors (actual growth minus conditional forecast
growth) for most economies are within 1 to 2 percent
of the standard deviation of the economies’ growth over
the sample period. The notable exceptions are Russia
and Venezuela, for which the forecast errors are signifi-
cantly larger, reflecting in part the lesser suitability of the
estimation method—with an underlying assumption of
a linear VAR model with stable coefficients—for econo-
mies that experienced significant volatility, or many
structural shocks, or both, during the sample period.
That said, forecast performances differ across the
economies, and two specific periods reveal larger
forecast errors for many. First, at the peak of the global
financial crisis, actual growth fell more sharply than
forecast growth—based on either of the two alterna-
tive models—for 7 of the 16 economies: Chile, China,
Malaysia, the Philippines, Russia, South Africa, and
24Two alternative models for the conditional forecasts are con-
sidered. The first is based on the modified baseline model that adds
China’s growth in the external block. An alternative model adds
growth in both China and the euro area in the external block. For
China, the conditional forecasts are based on the baseline model and
an alternative model that includes growth in the euro area in the
external block.
Thailand (Figure 4.12). This possibly reflects the
unusual shock embodied in the global financial crisis,
which affected emerging markets’ growth more deeply
than is captured by the traditional external channels
and identified within the linear VAR framework.
Growth since 2012 has also undershot the level
predicted given current global economic conditions
for 9 of the 16 economies, suggesting again the role
of internal factors. This group comprises Brazil, Chile,
China, Colombia, India, Russia, South Africa, Turkey,
and Venezuela. In fact, for most of these economies,
the forecast errors since 2012 are larger than even
those for 2008–09 (see Figure 4.13). In some econo-
mies, however (for example, Indonesia, Mexico, and
the Philippines), actual growth since 2012 has mostly
outpaced conditional forecasts, pointing instead to the
role of internal factors in boosting growth.
Note that although the forecast underperformance is
interpreted here as reflecting the role of internal factors
in moderating growth, other possibilities include other
unidentified factors, such as common or intra-emerg-
ing-market shocks (beyond those related to China),
or exogenous factors unrelated to domestic policy
shocks, such as natural disasters (for example, see, in
Figure 4.12, panel 14, the sharp negative deviation of
Thailand’s growth from its conditional forecast in the
last quarter of 2011, when the country was buffeted by
floods of unprecedented magnitude). In economies in
which such other unidentified factors may have played
a larger role, the analysis could overstate the effects
of internal factors. That said, the findings do resonate
with recent related work that has also underscored
constraints from domestic structural factors as becom-
ing increasingly binding for growth in many of these
economies (see IMF, 2013b and 2014, for India; IMF,
2013c, for South Africa; and IMF, 2013d, for Turkey).
China is prominent among emerging markets for
which growth outturns have systematically been below
the level indicated by conditional forecasts in recent
years. In fact, the widening of the forecast errors for
China since 2011 (see Figure 4.13) suggests that the
drag from internal factors has remained persistent.
Indeed, China’s medium-term growth forecast, as pro-
jected in the WEO (dashed line in Figure 4.12), is lower
than both actual growth and the conditional forecast,
reflecting the transition of the economy toward a more
moderate pace of growth over the medium term.
In summary, the recent systematic divergence
between actual and forecast growth for a few major
emerging markets suggests that internal factors may

0
2
4
6
8
2003 06 09 12:
Q4
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0
4
8
12
2003 09 13:
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0
5
10
15
20
2003 06 09 13:
Q1
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2003 06 09 13:
Q3
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2003 09 13:
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3
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2003 06 09 13:
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15
20
2003 06 09 13:
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0
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2003 06 09 13:
Q3
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0
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16
2003 06 09 13:
Q3
–6
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0
3
6
9
12
2003 06 09 13:
Q3
–4
0
4
8
12
2003 06 09 13:
Q3
–30
0
30
60
2003 06 09 13:
Q1
–10
–5
0
5
10
15
2003 06 09 13:
Q3
–30
–20
–10
0
10
20
30
2003 06 09 13:
Q1
0
4
8
12
16
2003 06 09 12:
Q4
16. Venezuela
–12
–8
–4
0
4
8
12
2003 09 13:
Q2
15. Turkey14. Thailand13. South Africa
12. Russia11. Poland10. Philippines9. Mexico
8. Malaysia7. Indonesia6. India
4. China3. Chile2. Brazil
5. Colombia
1. Argentina
Figure 4.12. Out-of-Sample Growth Forecasts Conditional on External Factors, by Country
(Percent)
Actual GDP growth Conditional GDP growth forecast (modified baseline)
Conditional GDP growth forecast (alternative specification) 2018 GDP growth forecast (WEO)
06 06 06
Sources: Haver Analytics; Thomson Reuters Datastream; and IMF staff calculations.
Note: For all economies except China, the modified baseline vector autoregression model includes U.S. real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate,
China real GDP growth, J.P. Morgan Emerging Markets Bond Index (EMBI) yield, and terms-of-trade growth in the external block; the alternative specification includes
U.S. real GDP growth, euro area real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate, China real GDP growth, EMBI yield, and terms-of-trade growth in the
external block. For China, the modified baseline vector autoregression model includes U.S. real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate, EMBI yield,
and terms-of-trade growth in the external block; the alternative specification includes U.S. real GDP growth, euro area real GDP growth, U.S. inflation, 10-year U.S.
Treasury bond rate, EMBI yield, and terms-of-trade growth in the external block.
Although forecast performances differ across emerging market economies, two specific periods reveal larger forecast errors for many economies: first, during the peak
of the global financial crisis, from the final quarter of 2008 until mid-2009; and second, since 2012.

–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2008–09 2008–09 2008–092010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
Figure 4.13. Conditional Forecast and Actual Growth since the Global Financial Crisis, by Country
(Percentage points)
6. India
9. Mexico 10. Philippines
13. South Africa 14. Thailand
1. Argentina 2. Brazil
7. Indonesia 8. Malaysia
11. Poland 12. Russia
15. Turkey 16. Venezuela
3. Chile 4. China
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
5. Colombia
Actual GDP growth minus conditional GDP growth forecast (modified baseline)
Actual GDP growth minus conditional GDP growth forecast (alternative specification)
Average of actual GDP growth minus conditional GDP growth forecasts from the modified baseline and alternative specifications
Differences between actual growth and forecast growth conditional on external conditions are not that large for most sample economies.
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
–7
–6
–5
–4
–3
–2
–1
0
1
2
3
2008–09 2010–11 2012–
present
Sources: Haver Analytics; Thomson Reuters Datastream; and IMF staff calculations.
Note: For all economies except China, the modified baseline vector autoregression model includes U.S. real GDP growth, U.S. inflation, 10-year U.S. Treasury bond
rate, China real GDP growth, J.P. Morgan Emerging Markets Bond Index (EMBI) yield, and terms-of-trade growth in the external block; the alternative specification
includes U.S. real GDP growth, euro area real GDP growth, U.S. inflation, 10-year U.S. Treasury bond rate, China real GDP growth, EMBI yield, and terms-of-trade
growth in the external block. For China, the modified baseline vector autoregression model includes U.S. real GDP growth, U.S. inflation, 10-year U.S. Treasury bond
rate, EMBI yield, and terms-of-trade growth in the external block; the alternative specification includes U.S. real GDP growth, euro area real GDP growth, U.S.
inflation, 10-year U.S. Treasury bond rate, EMBI yield, and terms-of-trade growth in the external block. All values have been normalized using the standard deviation
of country-specific real GDP growth between the first quarter of 1998 and the fourth quarter of 2007.

have become more important in determining growth
for these economies. In many cases, these factors have
pulled growth below the level expected under current
global economic conditions. Given their persistence,
these factors are likely to affect trend growth as well.
Even for emerging market economies in which growth
is still broadly tracking the path determined by global
economic conditions, what happens to their growth
will depend in large part on how growth evolves in
larger economies, particularly China.
Policy Implications and Conclusions
The deceleration of emerging markets’ growth in the
past two years following a prolonged period of rapid
growth has raised many concerns about these econo-
mies’ future prospects: for instance, will growth suffer
as advanced economies gain momentum and begin to
raise their interest rates? What are the likely effects of
a slower pace of expansion in China? Are emerging
markets helplessly on the receiving end of these shocks?
Has the global financial crisis changed the relationship
between growth and its drivers, and has trend growth
shifted to a lower plane?
This chapter sheds light on some of these concerns
by analyzing the external drivers of emerging market
economies’ growth and assessing how this relationship
has endured both before and since the global financial
crisis. The findings suggest that emerging markets are
facing a more complex growth environment than in
the period before the crisis and provide the following
broad lessons.
First, if growth in advanced economies strengthens
as expected in the current WEO baseline forecasts,
this, by itself, should entail net gains for emerging
markets, despite the attendant higher global inter-
est rates. Stronger growth in advanced economies
will improve emerging market economies’ external
demand both directly and by boosting their terms
of trade. Conversely, if downside risks to growth
prospects in some major advanced economies were
to materialize, the adverse spillovers to emerging
market growth would be large. The payoffs from
higher growth in advanced economies will be relatively
higher for economies that are more open to advanced
economies in trade and lower for economies that are
financially very open.
Second, if external financing conditions tighten
by more than what advanced economy growth can
account for, as seen in recent bouts of sharp increases
in sovereign bond yields for some emerging market
economies, their growth will decline. Mounting exter-
nal financing pressure without any improvement in
global economic growth will harm emerging markets’
growth as they attempt to stem capital outflows with
higher domestic interest rates, although exchange rate
flexibility will provide a buffer. Economies that are
naturally prone to greater capital flow volatility and
those with relatively limited policy space are likely to
be affected most.
Third, China’s transition into a slower, if more sus-
tainable, pace of growth will also reduce growth in many
other emerging market economies, at least temporar-
ily. The analysis also suggests that external shocks have
relatively lasting effects on emerging market economies,
implying that their trend growth can be affected by the
ongoing external developments as well.
Finally, although external factors have typically
played an important role in emerging markets’ growth,
the extent to which growth has been affected has also
depended on their domestic policy responses and
internal factors. More recently, the influence of these
internal factors in determining changes in growth has
risen. However, these factors are currently more of a
challenge than a boon for a number of economies. The
persistence of the dampening effects of these internal
factors suggests that trend growth is affected as well.
Therefore, policymakers in these economies need to bet-
ter understand why these factors are suppressing growth
and whether growth can be strengthened without induc-
ing imbalances. At the same time, the global economy
will need to be prepared for the ripple effects from the
medium-term growth transitions in these emerging
markets.
Appendix 4.1. Data Definitions, Sources, and
Descriptions
The chapter primarily uses the World Economic Out-
look (WEO) database from October 2013. Additional
data sources are listed in Table 4.4. Data are collected
for all variables on a quarterly basis from the first quar-
ter of 1998 to the latest available quarter.
Economy Characteristics
Table 4.5 lists the 16 emerging market economies
included in the data set. These economies represent

Table 4.4. Data Sources
Variable Sources Calculations and Transformations
Ten-Year U.S. Treasury Bond Rate Haver Analytics
Thirty-Day Federal Funds Futures CME Group, Thomson Reuters Datastream
Capital Flow Volatility IMF, Balance of Payments and International
Investment Position (IIP) Statistics Database and
IMF Staff Calculations
Standard deviation of net nonofficial inflows in
percent of GDP, 2000–12. See Appendix 4.1 of
the April 2011 World Economic Outlook for the
methodology
China Real Investment Growth IMF Staff Calculations
CPI Inflation World Economic Outlook Database
EMBI Global Bond Spread Thomson Reuters Datastream
EMBI Global Bond Yield Thomson Reuters Datastream
Financial Openness IMF Staff Calculations Sum of international investment position assets
and international investment position liabilities in
percent of GDP (U.S. dollars), 2000–12
Global Commodity Price Index IMF Staff Calculations
IIP Assets and Liabilities IMF, Balance of Payments and IIP Statistics
Database
Nominal Exchange Rate versus U.S. Dollar IMF, International Financial Statistics Database
Nominal Exports World Economic Outlook Database, Direction of
Trade Statistics Database
Nominal GDP World Economic Outlook Database
Nominal GDP in U.S. Dollars World Economic Outlook Database
Nominal Imports World Economic Outlook Database
Nominal Short-Term Interest Rate Thomson Reuters Datastream, Haver Analytics,
Federal Reserve Economic Data (FRED, Federal
Reserve Bank of St. Louis)
Nonfuel Commodity Terms of Trade IMF Staff Calculations
Per Capita Output Volatility IMF, World Economic Outlook Database Standard deviation of per capita real GDP growth,
2000–12
Real Exchange Rate versus U.S. Dollar IMF Staff Calculations Nominal exchange rate versus U.S. dollar divided
by the ratio of local consumer price index (CPI)
inflation to U.S. CPI inflation
Real GDP IMF, World Economic Outlook Database
Share of Net Commodity Exports in GDP IMF Staff Calculations See Appendix 4.2 of the April 2012 World
Economic Outlook for the methodology
Terms-of-Trade Growth Haver Analytics; IMF, International Financial
Statistics Database; Organization for Economic
Cooperation and Development; World Bank,
World Development Indicators database; and
IMF Staff Calculations
China terms of trade: quarterly terms of trade
for China are interpolated using a Chow-Lin
procedure applied to annual terms-of-trade data
(from the World Bank’s World Development
Indicators database) and three quarterly
explanatory variables: Hong Kong import unit
value, Hong Kong export unit value, and China
producer price index; Venezuela terms of trade:
quarterly terms of trade for Venezuela are
estimated using the commodity oil price (as a
proxy for export prices) and unit import values
(from the IMF’s International Financial Statistics
database)
Trade Exposure to Advanced Economies IMF, Direction of Trade Statistics Database and
World Economic Outlook Database
Sum of exports of goods to the United States and
the euro area expressed as a percent of GDP,
2000–12
Trade Openness IMF, World Economic Outlook Database Nominal exports plus nominal imports in percent of
GDP, 2000–12
U.S. Effective Federal Funds Rate Haver Analytics
U.S. High-Yield Spread Bank of America Merrill Lynch and Haver Analytics U.S. investment grade corporate yield minus U.S.
(junk bond) high yield
U.S. Inflation Expectations Federal Reserve Bank of Philadelphia, Survey of
Professional Forecasters
U.S. Real Short-Term Interest Rate Haver Analytics, Federal Reserve Bank of
Philadelphia, and IMF Staff Calculations
U.S. effective federal funds rate minus U.S.
inflation expectations
U.S. Term Spread Haver Analytics and IMF Staff Calculations Ten-year U.S. Treasury bond rate minus U.S.
effective federal funds rate
Source: IMF staff compilation.

75 percent of 2013 GDP (in purchasing-power-parity
terms) for the group of emerging market and develop-
ing economies. China alone accounts for 31 percent,
and the other 15 economies close to 45 percent.
Among these, 10 economies—that is, all except China,
India, the Philippines, Poland, Thailand, and Tur-
key—were net commodity exporters during the sample
period. However, only four economies in the sample
are heavily concentrated in commodities, with net
commodity exports as a percentage of GDP—averaged
over 2000–10—greater than or equal to 10 percent
(Argentina, Chile, Russia, Venezuela). The share for
Indonesia is also high, at 8.5 percent.
Real GDP growth has varied significantly over
the sample period for the 16 economies. Figure 4.14
shows that year-over-year quarterly real GDP growth
in China outperforms growth in nine of the sample
economies since 2000. Only Argentina, India, Thai-
land, Turkey, and Venezuela are exceptions, typically
because of very high output volatility rather than con-
tinuing outperformance. In addition, some emerging
market economies were unable to post higher growth
than the United States until the mid-2000s: these were
largely economies in Latin America; economies in East
Asia generally grew at rates above those of the United
States, although below the level of China’s growth.
Figure 4.15 presents regional growth averages based
on the economies in the sample and compares those
averages with the evolution of growth in advanced
economies and China. Once again, it is clear that
China’s growth rate dominates those of almost all other
economies in the sample. In fact, with China excluded,
the surge in the sample economies’ average growth
before the global financial crisis is much less spectacu-
lar. Among the three regional groups (emerging Asia
excluding China, emerging Europe and South Africa,
Latin America), emerging Asia’s growth performance
was the strongest both before and during the global
financial crisis. Growth in the LA4 (Brazil, Chile,
Colombia, Mexico) tended to trail that in other econo-
mies. Growth in emerging Europe and South Africa
was close to the levels for emerging Asia before the
crisis, but then fell the most during the global financial
crisis. Since then, the recovery in emerging Europe
and South Africa has tended to be weaker than that in
emerging Asia.
Table 4.6 provides information on simple pairwise
correlations between domestic real GDP growth for
the sample economies and the key variables used in the
statistical analysis over the sample period. There are a
few items of note:
•• Domestic output growth is positively correlated
with output growth in China for all economies in
the sample. For Argentina, Brazil, Colombia, India,
Indonesia, Thailand, and Venezuela, the growth
correlation with China’s growth is stronger than that
with the euro area or the United States. In contrast,
output growth in Chile, Malaysia, Mexico, Russia,
and Turkey is more correlated with growth in the
United States than with growth in China. Among
the economies examined, those in emerging Europe
and South Africa (Poland, Russia, South Africa, Tur-
key) generally tend to have the highest growth corre-
lations with growth in the advanced economies and
China. Furthermore, growth in China, Colombia,
and Indonesia is negatively correlated with growth
in the euro area, the United States, or both.
•• Interestingly, terms-of-trade growth is not always
positively correlated with domestic GDP growth. In
fact, for six economies (China, Indonesia, Philip-
pines, Poland, South Africa, Turkey), the correla-
tion is negative, whereas for two, the correlation is
numerically insignificant (India, Venezuela). This
may reflect the fact that increases in the terms of
trade do not always reflect improvement in global
demand, and to the extent that it is actually associ-
ated with supply shocks, the effect may not be posi-
tive for growth.
Table 4.5. Sample of Emerging Market Economies and International Organization for
Standardization Country Codes
Africa Asia Europe Latin America
South Africa (ZAF) China (CHN) Poland (POL) Argentina (ARG)
India (IND) Russia (RUS) Brazil (BRA)
Indonesia (IDN) Turkey (TUR) Chile (CHL)
Malaysia (MYS) Colombia (COL)
Philippines (PHL) Mexico (MEX)
Thailand (THA) Venezuela (VEN)
Source: IMF staff compilation.

–8
–4
0
4
8
12
16
1998 2002 06 10 13:
Q3
–20
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13:
Q3
–8
–4
0
4
8
12
16
1998 2002 06 10 13:
Q3
–10
–5
0
5
10
15
1998 2002 06 10 13:
Q3
–10
–5
0
5
10
15
1998 2002 06 10 13:
Q3
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13:
Q3
0
4
8
12
16
1998 2002 06 10
–8
–4
0
4
8
12
16
1998 2002 06 10
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13:
Q3
–20
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13:
Q3
–8
–4
0
4
8
12
16
1998 2002 06 10 13:
Q3
Figure 4.14. Domestic Real GDP Growth across Emerging Markets versus United States and China
(Percent)
Domestic real GDP growth U.S. real GDP growth China real GDP growth
6. India
13. South Africa
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13:
Q3
14. Thailand
–8
–4
0
4
8
12
16
1998 2002 06 10 13:
Q3
15. Turkey
–30
–20
–10
0
10
20
30
40
1998 2002 06 10 13:
Q3
16. Venezuela
3. Chile
5. Colombia
–20
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13:
Q3
13:
Q3
13:
Q3
4. China
–8
–4
–8
–4
0
4
8
12
16
1998 2002 06 10 13:
Q3

•• All economies demonstrate a strong negative cor-
relation between domestic growth and proxies for
global financial conditions, such as the J.P. Morgan
Emerging Markets Bond Index (EMBI) spread and
yield. There is much more cross-economy hetero-
geneity in the correlation between domestic growth
and the U.S. federal funds rate and the 10-year U.S.
Treasury bond rate. On average, only half of the
sample shows a negative correlation between domes-
tic growth and U.S. interest rates.
Appendix 4.2. Estimation Approach and
Robustness Checks
This appendix provides further details regarding the
identification and Bayesian estimation of the structural
vector autoregression (SVAR) model used in the chap-
ter and presents alternative specifications that assess the
robustness of the main results.
Model Identification
The analysis uses a standard SVAR model to estimate
the growth effects of external factors. The model is
estimated separately for each economy using quarterly
data from the first quarter of 1998 to the latest avail-
able quarter in 2013.
The baseline model takes the following form:
A(L)yt = et = A0ut, (4.1)
in which yt is a k × 1 vector, where k is the total
number of endogenous variables; A(L) is a k × k matrix
polynomial of lag operator L with lag length p; and
et is a k × 1 vector of contemporaneously correlated,
mean-zero reduced-form errors. The contemporane-
ous relationships across variables are disentangled by
mapping et to a k × 1 vector of mutually orthogonal,
mean-zero, structural shocks, ut, through the k × k
matrix A0.
Each economy’s baseline vector autoregression
(VAR) consists of nine variables in the vector yt (k =
9) ordered as follows: U.S. real GDP growth (Dy*),
U.S. inflation (p*), the nominal 10-year U.S. govern-
ment bond rate (r*), the EMBI Global yield (rEMBI*),
the economy-specific terms-of-trade growth (Dtot),
domestic real GDP growth (Dy), domestic inflation
(p), the rate of appreciation of the economy’s real
exchange rate vis-à-vis the U.S. dollar (e), and the
domestic monetary policy rate or short-term interest
rate (r). Note that all growth rates are calculated as
–10
–5
0
5
10
1998 2002 06 10 13
–8
–6
–4
–2
0
2
4
6
8
10
1998 2002 06 10 13
Figure 4.15. Average Growth for Regional Groups of
(Percent)
4. LA4: Brazil, Chile,
Colombia, and Mexico
6. EEA: Poland, Russia,
South Africa, and Turkey
1. EME16 versus
Advanced
2. EMEs by Region
–10
–5
0
5
10
15
1998 2002 06 10 13
EME16
China
BRICS excl. China
Advanced economies
EME16
LA4
Advanced economies
EME16
China
East Asia excl. China
Advanced economies
EME16
EEA
Advanced economies
5. East Asia: India,
Indonesia, Malaysia,
Philippines, and
Thailand
3. BRICS: Brazil, Russia,
India, China, and South
Africa
–15
–10
–5
0
5
10
15
20
1998 2002 06 10 13
Source : IMF staff calculations.
Note: EME = emerging market economy. EME16 denotes the 16 emerging market
economies within the sample. LA4 denotes the Latin American economies within
the sample, excluding Argentina and Venezuela. EEA denotes economies from
emerging and developing Europe and Africa within the sample.
–20
–15
–10
–5
0
5
10
15
1998 2002 06 10 13
China
EME16 excl. China
Advanced economies
LA4
EEA
China
East Asia excl. China
Advanced economies
–10
–5
0
5
10
15
1998 2002 06 10 13

log differences of the relevant level’s time series. The
first five variables constitute the “external” or for-
eign block, and the remaining variables make up the
“internal” or domestic block.
Identification (the mapping to the structural shocks)
uses contemporaneous restrictions on the structure of
the matrix A0. The key restriction is that shocks to the
external block are assumed to be exogenous to shocks
to the internal block; in other words, the external
variables do not respond to the internal variables con-
temporaneously. Within the external block, structural
shocks are further identified using a recursive (Cho-
lesky) scheme, defined by the ordering of the variables
in the vector yt. Therefore, U.S. real GDP growth
is assumed to respond to other shocks only with a
lag. U.S. inflation is affected by U.S. growth shocks
contemporaneously, but by other shocks with a lag.
The U.S. interest rate responds contemporaneously to
U.S. real GDP growth and inflation shocks, but not
to the EMBI Global yield or to any emerging market
economy’s terms-of-trade growth. The EMBI Global
yield is placed ahead of economy-specific terms-of-
trade growth, but behind all the U.S. variables. Finally,
terms-of-trade growth is placed last in the recursive
ordering, implying that it responds contemporaneously
to all other external variables, but not to the domestic
variables. Structural shocks within the internal block
are unidentified.
All variables enter the model with four lags. Other
than the contemporaneous restrictions on the matrix A0,
there are no restrictions on the coefficients for the lagged
variables; that is, the lags of the internal block variables
are allowed to affect the external block variables.
Estimation by Bayesian Methods
The number of sample observations relative to the
number of parameters to be estimated in each equation
of each economy’s SVAR is not very large. This means
that there is a danger of overfitting if the model esti-
mation is left unrestricted. Overfitting leads to good
performance of the estimated model within the sample
(as it tends to follow the noise in the sample more
closely), but to poor out-of-sample performance.
There are a number of ways to address this overfit-
ting problem. One is to impose hard restrictions on
the parameters, by fixing some of them to specific
values. However, by taking a hard stance before the
fact, such restrictions rule out potentially interest-
ing dynamics. An alternative to such restrictions is to
estimate the model using Bayesian methods, which is
the approach followed in this chapter. This involves
specifying restrictions on estimated parameters that
are softer, such as constraining them to be more likely
at some values than at others. Operationally, a prior
probability distribution is imposed on the estimated
parameters, pulling in additional information from
outside the sample observations, to avoid overfitting.
This is combined with the information in the sample
to generate estimates for the parameters.
Table 4.6. Correlations of Domestic Real GDP Growth with Key Variables, 1998–2013
U.S. Real
GDP Growth
U.S. Federal
Funds Rate
Ten-Year
U.S. Treasury
Bond Rate
Euro Area
Real GDP
Growth
China Real
GDP Growth EMBI Spread EMBI Yield
Terms-of-
Trade Growth
Argentina 0.12 –0.13 –0.28 0.15 0.56 –0.68 –0.64 0.33
Brazil 0.15 0.03 0.03 0.42 0.51 –0.51 –0.37 0.63
Chile 0.31 –0.01 –0.11 0.44 0.25 –0.62 –0.52 0.33
China –0.10 0.05 –0.05 0.16 1.00 –0.64 –0.50 –0.27
Colombia –0.08 –0.18 –0.28 0.15 0.53 –0.82 –0.71 0.29
India 0.27 0.10 0.19 0.42 0.66 –0.44 –0.29 0.03
Indonesia –0.32 –0.38 –0.35 –0.15 0.27 –0.56 –0.52 –0.26
Malaysia 0.26 –0.07 0.00 0.33 0.21 –0.37 –0.26 0.29
Mexico 0.76 0.35 0.18 0.77 0.16 –0.26 –0.16 0.52
Philippines 0.18 –0.27 –0.32 0.16 0.32 –0.61 –0.58 –0.40
Poland 0.40 0.44 0.36 0.61 0.49 –0.32 –0.13 –0.20
Russia 0.45 0.30 0.31 0.66 0.21 –0.23 –0.04 0.77
South Africa 0.39 0.32 0.23 0.67 0.42 –0.38 –0.18 –0.14
Thailand 0.17 –0.15 –0.07 0.18 0.26 –0.31 –0.24 0.15
Turkey 0.44 –0.06 –0.04 0.45 0.38 –0.51 –0.41 –0.14
Venezuela 0.17 0.12 –0.02 0.24 0.26 –0.48 –0.38 0.09
Note: Period is 1998:Q1–2013:Q2. EMBI = J.P. Morgan Emerging Markets Bond Index.

The prior used in this chapter is a so-called Min-
nesota prior, inspired by Litterman (1986), in which
each variable is assumed to follow a first-order autore-
gressive (AR(1)) process with independent, normally
distributed errors. Given that the variables have already
been transformed to induce stationarity, a random
walk, with a unit AR(1) coefficient for the prior, would
not be appropriate. Simple AR(1) regressions, however,
do suggest estimated AR(1) coefficients of about 0.8,
which is the AR(1) coefficient used in the prior for the
baseline estimation. Some of this persistence reflects
the fact that all growth rates are calculated as year-
over-year differences.
The weight of the prior versus the sample in the
estimation is determined according to the Bayesian
approach presented in Sims and Zha (1998). If twice
the number of parameters to be estimated in an equa-
tion is greater than the estimation sample size, the
chapter applies a rule of thumb that gives the prior a
(T – p)
relative weight of 1 – ————∈ [0,1], in which
2(kp + 1)
T is the number of available sample observations and k
and p are defined as above.25
Figure 4.16 compares the average baseline SVAR
results using the AR(1) priors with those from an
alternative white-noise prior. As expected, with a
white-noise prior, the impulse responses show lower
persistence and amplitude. The conditional out-of-
sample forecasts from these specifications are largely
similar to those shown in Figures 4.12 and 4.13,
although the forecast performance improves with a
less persistent prior for some economies (for example,
Malaysia, Mexico, and the Philippines).
Robustness of the Baseline Results
A variety of alternative specifications are used to assess
the robustness of the main results. In particular, a
number of additional variables are introduced as prox-
ies for external demand, U.S. monetary policy, external
financing conditions, and the terms of trade. The
results are described in the following.
25In the case of China, there are 60 observations for the reduced-
form VAR. With 37 coefficients to estimate, the priors receive
a weight (importance) of slightly less than 0.25 in the baseline
specification (and a maximum weight of 0.50 in the specification for
out-of-sample forecasting reported in the chapter text).
Alternative U.S. monetary policy measures
As described in the chapter, alternative proxies for global
financing conditions are tried to assess the robustness
of the findings: the 10-year U.S. Treasury bond rate,
which is in the baseline specification (see Figure 4.16);
and alternative specifications in which the 10-year U.S.
Treasury bond rate is replaced by (1) the U.S. effec-
tive federal funds rate; (2) the ex ante U.S. real federal
funds rate; (3) the change in the U.S. federal funds rate;
(4) the U.S. term spread (defined as the 10-year U.S.
Treasury bond rate minus the U.S. federal funds rate);
(5) Kuttner (2001)–style unanticipated monetary policy
shocks, inferred from the behavior of federal funds
futures; and (6) an extension of the Romer and Romer
(2004) exogenous monetary policy shock series, based
on Coibion (2012).
–0.01
0.00
0.01
0.02
0.03
0.04
0.05
0 5 10 15 20
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0 5 10 15 20
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20
Figure 4.16. Impact of Prior Choice on Average Impulse
Responses
(Percentage points)
Baseline specification: AR(1) prior, ρ = 0.8
Alternative specification: white-noise prior
4. Terms-of-Trade
Growth Shock
1. U.S. GDP Growth
Shock
2. U.S. Treasury Bond
Rate Shock
3. EMBI Spread Shock
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
Source : IMF staff calculations.
Note: AR(1) = first-order autoregression; EMBI = J.P. Morgan Emerging Markets
Bond Index. Shocks are normalized to a 1 percentage point increase. X-axis units are
quarters; t = 0 denotes the quarter of the shock.

Note that an increase in the U.S. federal funds
or policy rate—nominal or real—negatively affects
emerging market economies’ growth only after a lag
of six quarters just as the 10-year U.S. Treasury bond
rate does (Figures 4.17 and 4.18). The impact effect
is negative for very few economies (Chile, Malaysia,
Thailand, Venezuela). These puzzling results may indi-
cate that the U.S. rate increase embodies expectations
of an improvement in future U.S. growth. Indeed,
even U.S. growth is adversely affected with a delay
(see Table 4.1). Emerging market economies’ growth
declines only as domestic interest rates gradually rise in
response to the U.S. rate increase.
The alternative proxy using the term spread pro-
duces a more immediate negative effect (Figure 4.17).
It is possible that the Federal Reserve’s heavy reliance
on unconventional policies to lower long-term rates
over the past few years means that long-term rates
are now a better measure of its stance than short-
term rates. With the short-term rate at the zero lower
bound, positive shocks to the term spread would indi-
cate a tighter U.S. monetary policy (see also Ahmed
and Zlate, 2013). With the exception of the U.S.
term spread, emerging markets’ growth responses to
shocks to the alternative measures are similar to their
responses to shocks to the 10-year U.S. Treasury bond
rate or the U.S. policy rate.26
It is important to note that shocks to the 10-year
U.S. Treasury bond rate may not correspond closely to
unanticipated U.S. monetary policy changes unrelated
to U.S. GDP growth and inflation. Because it is a
long-term rate, it is much more likely that shocks to
the 10-year rate reflect expectations in regard to the
U.S. economy. Furthermore, since the global financial
crisis, the 10-year U.S. Treasury bond rate has been
suppressed by safe haven flows into U.S. Treasuries,
reflecting not just the U.S. growth outlook, but also
uncertainty over the global recovery. Therefore, shocks
to the 10-year U.S. Treasury bond rate could occur in
response to a wide range of external (non-U.S.) factors.
The impulse responses from specifications (5) and
(6) use monetary policy measures to represent more
accurately true U.S. monetary policy shocks. As shown
in Figure 4.19, the sign and shape of the responses are
broadly the same as for the other proxies discussed ear-
lier. Growth in emerging market economies responds
to U.S. monetary policy shocks only after one year.
The reason for such responses could be that monetary
policy shocks have been fairly limited and muted over
the sample period. As Figure 4.20 shows, the largest
shocks are shown to have occurred in the 1980s, when
calculated using the technique set out in Romer and
Romer (2004), and to have occurred with much less
frequency, when calculated using the information con-
tained in federal funds futures contracts, as described
in Kuttner (2001).
External financing conditions
Robustness checks are also conducted for different
types of external financing shocks besides the EMBI
Global yield used in the baseline specification. The
26Another alternative specification is also tried in which the
10-year U.S. Treasury bond rate is added after the policy rate in the
external block. Shocks to either the policy rate or the 10-year rate
in this expanded specification still elicit a lagged negative growth
response for most emerging markets.
–3
–2
–1
0
1
2
3
0 5 10 15 20
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
0 5 10 15 20
Figure 4.17. Average Impulse Responses to Shocks from
Alternative U.S. Monetary Policy Variables
(Percentage points)
U.S. federal funds rate U.S. real short-term rate
U.S. term spread Change in U.S. federal funds rate
4. Domestic Real
Exchange Rate
1. Domestic GDP Growth 2. U.S. GDP Growth
3. Domestic Short-Term
Interest Rate
–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
Note: Shocks are normalized to a 1 percentage point increase. X-axis units are
quarters; t = 0 denotes the quarter of the shock.

–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
0 5 10 15 20
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
0 5 10 15 20
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
0 5 10 15 20
–0.8
–0.4
0.0
0.4
0.8
0 5 10 15 20
–0.8
–0.4
0.0
0.4
0.8
1.2
0 5 10 15 20
–0.6
–0.3
0.0
0.3
0.6
0.9
1.2
0 5 10 15 20
–0.3
0.0
0.3
0.6
0 5 10 15 20
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
0 5 10 15 20
–3
–2
–1
0
1
2
3
0 5 10 15 20
–0.8
–0.4
0.0
0.4
0.8
0 5 10 15 20
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
–0.6
–0.3
0.0
0.3
0.6
0.9
1.2
0 5 10 15 20
–0.6
–0.3
0.0
0.3
0.6
0.9
0 5 10 15 20
–0.6
–0.3
0.0
0.3
0.6
0.9
1.2
0 5 10 15 20
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
0 5 10 15 20
Note: Shocks are normalized to a 1 percentage point increase. X-axis units are quarters; t = 0 denotes the quarter of the shock.
U.S. federal funds rate Ten-year U.S. Treasury bond rate
6. India
13. South Africa 14. Thailand
15. Turkey 16. Venezuela
3. Chile 4. China
5. Colombia
Figure 4.18. Domestic Real GDP Growth Response to U.S. Federal Funds Rate and 10-Year U.S. Treasury Bond Rate under
Alternative Speciﬁcations
(Percentage points)

variables used across the alternative specifications
are (1) the EMBI Global spread and (2) the U.S.
high-yield spread. As Figure 4.21 shows, the average
response of domestic GDP growth in the 16 emerging
market economies to all three identified shocks is very
similar.
External demand conditions
The analysis assesses whether and how the effects of
U.S. real GDP growth on domestic growth are affected
by controlling for real GDP growth in the euro area.
The euro area growth indicator enters the external
block of the SVAR after U.S. real GDP growth in the
recursive identification, but before the other U.S. vari-
ables. However, placing euro area growth after all the
U.S. variables does not change the main results.
As shown in panel 1 of Figure 4.22, the average
response of domestic growth to U.S. real GDP growth
is largely unaffected by the introduction of this addi-
tional variable. Moreover, the response of domestic real
GDP growth to euro area growth is also as strong as
the response to U.S. real GDP growth, confirming that
it is reasonable to use U.S. real GDP growth as a proxy
for general advanced economy real growth shocks
(Figure 4.22, panel 2). Some economy-specific differ-
ences appear in the results: for instance, economies
with deeper external trade ties with the euro area (for
example, Poland and South Africa) show larger growth
effects with respect to euro area real GDP growth
changes than with respect to U.S. real GDP growth
changes, whereas growth in Mexico shows the reverse
(that is, larger effects with respect to U.S. real GDP
growth changes).
The analysis also considers China’s real investment
growth as an alternative proxy (instead of China’s real
GDP growth) for external demand shocks emanat-
ing from China (Figure 4.22, panel 3). Although the
pattern of domestic growth responses to changes in
China’s investment growth is very similar to responses
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
–2
–1
0
1
2
3
4
0 5 10 15 20
–0.6
–0.3
0.0
0.3
0.6
0.9
1.2
1.5
1.8
–2
–1
0
1
2
3
4
5
6
0 5 10 15 20
–0.6
–0.3
0.0
0.3
0.6
0.9
1.2
1.5
–2
–1
0
1
2
3
4
5
0 5 10 15 20
1. Domestic Real GDP
Growth
Figure 4.19. Average Impulse Responses to Alternative
Measures of U.S. Monetary Policy Shock
(Percentage points)
2. U.S. Real GDP Growth
Based on Romer and Romer (2004)1
(left scale)
Based on Kuttner (2001) (right scale)
–4
–2
0
2
4
–15
–10
–5
0
5
10
15
0 5 10 15 20
3. Domestic Short-Term
Interest Rate
4. Domestic Real
Exchange Rate
calculations.
Note: Shocks are normalized to a 1 percentage point increase. X-axis units in
panels are quarters; t = 0 denotes the quarter of the shock.
1
See Coibion (2012).
–5
–4
–3
–2
–1
0
1
2
3
4
5
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
1969:
Q1
75 80 85 90 95 2000 05 08 13:
Q4
Note: X-axis units in panels are quarters; t = 0 denotes the quarter of the shock.
1
See Coibion (2012).
Figure 4.20. Alternative Monetary Policy Shocks
(Percentage points)
Approach based on Romer and Romer (2004)1
(left scale)
Approach based on Kuttner (2001) (right scale)

to China’s real GDP growth, the elasticity is negligible
on impact, building up slightly over time.
Terms-of-trade growth alternatives
As a potentially more exogenous proxy for emerging
market economies’ terms of trade, the exercise includes
the global commodity price index in the external
block, placing it in the second position within the
recursive ordering for the identification of external
structural shocks. Panel 4 of Figure 4.22 shows a simi-
lar pattern of response to that resulting from a positive
shock to terms-of-trade growth.
Longer time period
The economy-specific SVARs are also estimated using
the longest available quarterly data. Only three econo-
mies have all baseline variables available from the first
quarter of 1995: Brazil, Mexico, and South Africa.
The results for those economies with additional data
are not affected by the longer-sample SVAR. Figure
4.23 presents, for Brazil, a comparison of the impulse
responses of domestic GDP growth to shocks from
four of the key external factors. Similar results are
obtained for Mexico and South Africa.
Robustness checks with panel vector autoregressions
The final section of this appendix assesses how the
estimated relationship between emerging market
economies’ growth and external conditions is affected
by an alternative estimation technique in a panel setup.
A panel VAR allows for many more degrees of freedom
relative to the SVAR because all the economy-specific
observations are pooled. As such, it provides a sense of
the average behavior among the sample of economies
to the alternative external shocks.
–0.6
–0.5
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
0 2 4 6 8 10 12 14 16 18 20
Figure 4.21. Impulse Response of Domestic Real GDP Growth
to External Financing Shocks
(Percentage points)
Response to EMBI yield
Response to EMBI spread
Response to U.S. high-yield spread
Sources: Bank of America Merrill Lynch; Haver Analytics; Thomson Reuters
Datastream; and IMF staff calculations.
panel are quarters; t = 0 denotes the quarter of the shock. EMBI = J.P. Morgan
Emerging Markets Bond Index.
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 5 10 15 20
1. Response to 1 percent U.S.
Real GDP Growth Shock
2. Responses from Alternative
VAR Specification with
Euro Area Real GDP
GrowthBaseline
specification
Alternative
specification with
euro area real GDP
growth
Response to 1 percent
U.S. GDP growth shock
euro area GDP growth
shock
–0.06
–0.04
–0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0 5 10 15 20
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
3. 4.Response to 1
percent China real
GDP growth shock
(baseline)
Response to 1
percent China real
investment growth
shock (alternative)
terms-of-trade growth
shock (baseline)
global commodity price
growth shock (alternative)
Responses from Baseline and Alternative VAR Specifications
Figure 4.22. Average Impulse Responses of Domestic Real
GDP Growth to Shocks under Alternative Vector
Autoregression Specifications
(Percentage points)
Organization for Economic Cooperation and Development; and IMF staff
calculations.
Note: Average for all sample economies. Shocks are normalized to a 1 percentage
point increase. X-axis units in panels are quarters; t= 0 denotes the quarter of the
shock. VAR = vector autoregression.

As Figure 4.24 illustrates, the responses of emerging
market economy growth to changes in external condi-
tions in the panel VAR are broadly similar to the average
responses from the country-specific SVARs used in the
chapter text. The panel VAR typically produces somewhat
larger amplitudes, however, such that the cumulated
effects are greater. A 1 percent rise in the U.S. growth rate
results in a 0.4 percent rise in emerging market economy
growth, whereas a 100 basis point rise in the EMBI yield
reduces growth by 0.3 percentage point. However, an
increase in China’s growth has a small negative effect on
impact, although the effects build up over time.
–1.0
–0.5
0.0
0.5
1.0
1.5
0 5 10 15 20
–0.8
–0.6
–0.4
–0.2
–0.8
–0.6
–0.4
–0.08
–0.04
–0.2
0.0
0.2
0.4
0.6
0.8
0 5 10 15 20
1. Shock to U.S. Real GDP
Growth
2. Shock to 10-Year U.S.
Treasury Bond Rate
Long sample from 1995:Q1 Baseline sample from 1998:Q1
0.00
0.04
0.08
0.12
0 5 10 15 20
0.0
0.2
0.4
0 5 10 15 20
3. Shock to EMBI Global
Yield
4. Shock to Terms-of-Trade
Growth
Organization for Economic Cooperation and Development; Thomson Reuters
Datastream; and IMF staff calculations.
panels are quarters; t = 0 denotes the quarter of the shock.
Figure 4.23. Brazil: Comparison of Responses under the
Baseline Model with Responses from Model with Sample
Beginning in the First Quarter of 1995
(Percentage points)
–0.5
–0.4
–0.3
–0.2
–0.1
0.0
0.1
0.2
–1.5
–1.2
–0.9
–0.6
–0.3
0.0
0.3
0.6
0 5 10 15 20
–0.4
–0.2
0.0
0.2
0.4
0.6
–1.4
–0.7
0.0
0.7
1.4
2.1
0 5 10 15 20
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
–2.1
–1.4
–0.7
0.0
0.7
1.4
2.1
2.8
0 5 10 15 20
–0.2
–0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
–0.6
–0.3
0.0
0.3
0.6
0.9
1.2
1.5
1.8
0 5 10 15 20
1. U.S. Real GDP Growth
Shock
2. Ten-Year U.S.
Treasury Bond Rate
Shock
Baseline specification (VAR, AR(1) prior; left scale)
Alternative specification (VAR, white-noise priors; left scale)
Alternative specification (panel VAR; right scale)
3. EMBI Global Yield
Shock
4. China Real GDP
Growth Shock
Figure 4.24. Comparison of Impulse Responses from Panel
Vector Autoregression with Responses from the Baseline
Model
(Percentage points)
calculations.
panels are quarters; t = 0 denotes the quarter of the shock. AR(1) = first-order
autoregression; EMBI = J.P. Morgan Emerging Markets Bond Index; VAR = vector
autoregression.

This box uses panel growth regressions to estimate the
impact of external demand and global financial condi-
tions on medium-term growth in emerging market
economies. Thus, it complements the analysis in the
chapter, which is more focused on the shorter-term
growth implications of changes in external conditions.
Growth regressions, which abstract from the business
cycle by aggregating data over five-year periods, natu-
rally lend themselves to addressing questions relating
to the medium-term impact of a protracted period
of adverse external conditions on emerging market
economies’ growth. Also, given wider availability of
data at an annual frequency, the findings of the box
are applicable to a broader group of emerging markets.
Economic theory suggests several channels through
which external conditions affect long-term growth.
The standard growth model is the obvious starting
point. Real external shocks, such as an increase in
external demand or a change in the terms of trade,
directly affect the productivity of capital and therefore
capital accumulation.
Financial linkages
As for financial linkages, arbitrage ensures that a small
open economy with an open capital account will be in
a steady state when the productivity of domestic capital
is equal to the global interest rate. Although there
are many reasons why this equalization may never be
achieved (for example, country risk, investment costs),
an increase in global real interest rates will necessarily
reduce funding for marginal investment projects and
negatively affect growth. This process can progress in a
dramatic fashion, with an increase in international rates
precipitating banking crises and the ensuing decrease in
output (Eichengreen and Rose, 2004).
This box analyzes the impact of both trade and
financial linkages in a single regression. The two chan-
nels operate in opposite directions: whereas a recession
in advanced economies may adversely affect emerging
market economies’ growth (through a combination
of lower external demand and weaker terms of trade),
relatively lower interest rates in advanced economy
downturns can boost domestic demand growth in
emerging markets. Analyzing all external factors
simultaneously reduces omitted-variable bias, even if it
does not allow identification of the exogenous impact
of each separately.
Specification and methodology
The empirical approach estimates fixed-effects panel
growth regressions—for growth averaged over consecu-
tive five-year periods—of the following general form:
DlnGDPPCi,t = b1'(External Conditions)i,t + b2' Xi,t +
gi + ht + ei,t, (4.1.1)
in which
DlnGDPPCi,t = first difference in the log of real per
capita GDP;
External Conditions = variables measuring external
conditions, which include
Trading partner growth, computed following Arora
and Vamvakidis (2005),1
Change in the log of the terms of trade, and
International financing conditions (for example, the
real interest rate on the 10-year U.S. Treasury bond)
interacted with the degree of financial openness;
Xi,t = standard growth regressors, such as initial level
of income, population growth, and investment ratio;
gi = country fixed effect; and
ht = time fixed effect to control for changes in
global conditions not captured by the model.
For most specifications, the panel is estimated for
the period 1997–20112 and includes 62 emerging
market economies with populations of more than two
million, of which 14 are classified as mineral commod-
ity exporters. The emerging market economy universe
is larger than the one considered in the chapter, cover-
ing a number of countries (mostly in eastern Europe)
only recently reclassified as advanced economies.3
1A similar approach is also used by Drummond and Ramirez
(2009) and Dabla-Norris, Espinoza, and Jahan (2012).
2The period is chosen to coincide roughly with the period
covered in the chapter. Results, especially those concerning trade
linkages, remain broadly unchanged if the period is stretched
back to the mid-1980s and even the 1970s.
3The panel is constructed using data from IMF sources (World
Economic Outlook, International Financial Statistics, Direction
of Trade Statistics, Annual Report on Exchange Arrangements and
Exchange Restrictions), as well as from the World Development
Indicators (World Bank), Lane and Milesi-Ferretti (2007), Klein
and Shambaugh (2008), and the Armed Conflict Dataset (Peace
Research Institute Oslo).
Box 4.1. The Impact of External Conditions on Medium-Term Growth in Emerging Market Economies
The author of this box is Alexander Culiuc.

Trade linkages
The growth regressions are estimated separately for
all emerging market economies in the sample and
for non–mineral commodity exporters. The regres-
sions confirm that emerging markets’ per capita
GDP growth is subject to conditional convergence
(negative coefficient on lagged GDP per capita), and
both investment and the terms of trade have positive
growth effects (Table 4.1.1, columns 1 and 2 for the
full sample, and columns 3 and 4 for non-commodity-
exporting emerging markets). Medium-term growth
exhibits a correlation close to one vis-à-vis growth
in export partner economies. This elasticity tends to
increase with trade openness (column 2 of the table
and Figure 4.1.1), particularly for the non-commod-
ity-exporting economies (column 4 of the table and
Figure 4.1.1). The results also suggest that the terms of
trade have a limited role in determining medium-term
growth, especially for non–commodity exporters.
The analysis also tracks the relationship between
partner growth elasticity and trade openness over time
by introducing interaction effects with time dum-
mies (Figure 4.1.2). As panel 1 of Figure 4.1.2 shows,
partner growth elasticity has been increasing since the
mid-1980s in line with the median export-to-GDP
ratio. However, although advanced economy partner
growth elasticity has been rising over time, emerg-
ing market economy partner growth elasticity started
rapidly picking up (from zero) only in the early 1990s
(panel 2 of Figure 4.1.2).
The increase in the growth elasticity of emerging
markets with respect to growth in their emerging
market partners coincides with—and is likely driven
by—the growing prominence of Brazil, Russia, India,
China, and South Africa (BRICS) and, particularly,
the proliferation of supply chains with China. To
assess this supposition, the growth regressions are
reestimated for all non-BRICS emerging markets
(Table 4.1.2 and panels 3 and 4 of Figure 4.1.2).4
Panel 3 of the figure appears to corroborate the
hypothesis: for the average emerging market economy,
correlation with BRICS growth is fairly high (0.3)
4All partner growth elasticities are weighted by the share of
partner countries in the export basket of each emerging market.
This means, among other things, that the BRICS partner growth
elasticity is heavily weighted toward China, which, for the aver-
age emerging market economy, accounts for more than one-third
of exports to the BRICS.
Box 4.1 (continued)
Table 4.1.1. Growth Regressions for Emerging Markets, 1997–2011
All Emerging Market Economies
Non-Commodity-Exporting Emerging
Market Economies
(1) (2) (3) (4)
Lagged GDP per Capita (log) –0.053** –0.051** –0.083*** –0.082***
(0.025) (0.025) (0.020) (0.020)
Population Growth 1.473** 1.432** 0.128 0.235
(0.571) (0.542) (0.311) (0.305)
Gross Capital Formation/GDP 0.052 0.062 0.183*** 0.178***
(0.054) (0.058) (0.032) (0.032)
War –0.006 –0.001 0.000 0.000
(0.005) (0.003) (0.003) (0.003)
Terms-of-Trade Growth 0.121* 0.114* 0.066 0.060
(0.068) (0.060) (0.070) (0.068)
Trading Partner GDP Growth 0.910*** 0.692 0.847*** 0.541**
(0.255) (0.466) (0.177) (0.262)
Exports/GDP –0.054 –0.025
(0.043) (0.037)
Trading Partner GDP Growth × Exports/
GDP
0.685
(1.085)
1.072
(1.078)
Time Fixed Effects Yes Yes Yes Yes
Country Fixed Effects Yes Yes Yes Yes
Number of Countries 57 57 42 42
R Squared 0.505 0.486 0.685 0.668
Note: Standard errors (in parentheses) are clustered at the country level. *, **, *** indicate that coefficients are significant at the 10, 5, and 1
percent levels, respectively.

and statistically significant. This result, however, hides
heterogeneity across country groups. Panel 4 presents
results estimated separately for commodity exporters
and non–commodity exporters. For non–commodity
exporters, BRICS partner growth elasticity is border-
line statistically significant. Growth in commodity
exporters, on the other hand, exhibits a very strong
correlation with both BRICS and other emerging
market economy partners, confirming the growing
importance of the BRICS, and China in particular, in
the global demand for mineral commodities.
Financial linkages
The role of external financial conditions in emerging
markets’ growth is considered next. Although for a
small open economy, an increase in the global interest
rate is expected to increase the opportunity cost of
capital and, correspondingly, depress growth in the
short term, the effect in the medium term remains an
open question.
Regressions presented in Table 4.1.3 augment the
model with global financing conditions proxied by the
Box 4.1 (continued)
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
–10
0
10
20
30
40
50
0 10 20 30 40 50 60
Exports (percent of GDP)
Figure 4.1.1. Export Partner Growth
Elasticity
Share of emerging market economy GDP
(percent of GDP; right scale)
Partner growth elasticity (left scale)
95 percent confidence interval (left scale)
Note: On the x-axis, 0 denotes 0–10 percent of GDP;
10 denotes 10–20 percent of GDP; and so on.
Median of
exports (percent
of GDP; right
scale)
Partner growth
elasticity1
(left
scale)
–0.5–0.5
0.0
0.5
1.0
1.5
2.0
2.5
Non–commodity
Commodity
0.0
0.5
1.0
1.5
2.0
2.5
All emerging markets
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1977–81
1982–86
1987–91
1992–96
1997–2001
2002–06
2007–11
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
2.5
5
10
15
20
25
30
35
40
1977–81
1982–86
1987–91
1992–96
1997–2001
2002–06
2007–11
Figure 4.1.2. Export Partner Growth
1. All Export Partner
Growth
BRICS versus Other Emerging
Market Trading Partners
Advanced
economy
partners
Emerging
market
economy
partners
BRICS partners Non-BRICS emerging
market economy partners
4. Commodity
versus Non–
Commodity
3. All Emerging
Markets
2. Advanced Economy
versus Emerging
Market Economy
Partner Growth
Note: BRICS = Brazil, Russia, India, China, South Africa. In
panels 3 and 4, the upper and lower points of each line show
the top and bottom of the 95 percent confidence interval.
The estimation period is 1997–2011. “Non-commodity”
and “Commodity” refer to non–commodity exporters and
commodity exporters, respectively, among the emerging
market economies in the sample.
1
Dashed lines denote 95 percent confidence interval for
partner growth elasticity.

real interest rate on the 10-year U.S. Treasury bond
interacted with the degree of financial integration.5
Results confirm the negative effect of high global inter-
est rates on medium-term growth—a 100 basis point
increase in the former is associated with a 0.5 percent-
age point decrease in the latter for the median emerging
market economy, with a degree of financial integration
of 115 percent of GDP (columns 1 and 2 of the table).
However, the relationship is not statistically significant
for the sample since the mid-1990s. To make the results
comparable to those of previous studies (Frankel and
Roubini, 2001; Reinhart and others, 2001; Reinhart
and Reinhart, 2001), the model is reestimated for
1997–2011 using annual data (column 3). The nega-
tive impact of the foreign interest rate is statistically
significant. This suggests that the effect of international
borrowing conditions on emerging market economies’
growth may be shorter term in nature and cannot be
5The degree of financial integration is computed from the
updated and extended version of the data set constructed by Lane
and Milesi-Ferretti (2007) as the sum of gross foreign assets and
liabilities net of international reserves as a percentage of GDP.
reliably captured when five-year averages are considered.
In a similar manner, the terms of trade also gain statisti-
cal significance in the regression using annual data.
Conclusion
The main messages of the analysis in this box are the
following. First, the importance of partner country
growth has increased dramatically as emerging market
economies have integrated into the world economy.
Second, as some emerging markets have gained a
prominent role in the global economy, their impact
on smaller peers has also increased. BRICS’ growth,
in particular, has become an important factor driving
growth in other emerging market economies, espe-
cially those dependent on mineral commodity exports.
Third, international financing conditions, which tend
to affect the cyclical component of growth in emerging
market economies (as also shown in the main analy-
sis), also exercise a longer-lasting effect, especially for
financially integrated countries. Although the analysis
has shown that external factors are important for long-
term growth, it should be noted that this finding does
not diminish the critical role of appropriate domestic
Box 4.1 (continued)
Table 4.1.2. Growth Regressions for Emerging Markets: Brazil, China, India, Russia, and South Africa
versus Other Emerging Market Partner Growth, 1997–2011
All EMEs Non–Commodity Exporter Commodity Exporter
(1) (2) (3) (4) (5) (6)
Lagged GDP per Capita (log) –0.056*
(–0.030)
–0.054*
(–0.030)
–0.102***
(–0.021)
–0.098***
(–0.021)
0.130**
(–0.053)
0.114**
(–0.048)
Population Growth 1.645***
(–0.515)
1.732***
(–0.562)
0.465
(–0.359)
0.459
(–0.383)
–0.911
(–1.066)
–0.363
(–1.433)
Gross Capital Formation/GDP 0.055
(–0.049)
0.060
(–0.049)
0.163***
(–0.037)
0.166***
(–0.037)
0.178**
(–0.071)
0.164*
(–0.078)
War 0.001
(–0.006)
0.000
(–0.006)
0.005
(–0.004)
0.006
(–0.004)
0.010
(–0.013)
0.008
(–0.013)
Terms-of-Trade Growth 0.145*
(–0.074)
0.152**
(–0.075)
0.104
(–0.073)
0.126*
(–0.074)
0.192*
(–0.099)
0.127
(–0.132)
AE Partner GDP Growth –1.210
(–0.931)
–1.395
(–0.956)
0.859
(–0.715)
0.738
(–0.729)
–5.666***
(–1.257)
–6.116***
(–1.653)
EME Partner GDP Growth 0.666***
(–0.184)
0.545***
(–0.126)
1.718***
(–0.382)
BRICS Partner GDP Growth 0.295*
(–0.149)
0.175*
(–0.098)
0.718**
(–0.260)
Non-BRICS EME Partner GDP
Growth
0.527***
(–0.167)
0.500***
(–0.141)
1.259**
(–0.427)
Time Fixed Effects Yes Yes Yes Yes Yes Yes
Country Fixed Effects Yes Yes Yes Yes Yes Yes
Number of Observations 164 164 121 121 43 43
Number of Countries 57 57 42 42 15 15
R Squared 0.505 0.486 0.685 0.668 0.818 0.790
Note: AE = advanced economy; BRICS = Brazil, Russia, India, China, and South Africa; EME = emerging market economy. Standard errors (in
parentheses) are clustered at the country level. *, **, *** indicate that coefficients are significant at the 10, 5, and 1 percent levels, respectively.

economic and structural policies in this area. Indeed,
recent work (see Chapter 4 of the October 2012 World
Economic Outlook) has established how improvements
in domestic policy frameworks have contributed to
the increased resilience of emerging market economies
since the 1990s.
Box 4.1 (continued)
Table 4.1.3. Growth Regressions for Emerging Markets
1987–2011 1997–2011
1997–2011
(annual data)
(1) (2) (3)
Lagged GDP per Capita (log) –0.040**
(0.017)
–0.043*
(0.025)
–0.061**
(0.025)
Population Growth 0.270
(0.443)
1.498**
(0.629)
–0.356
(0.349)
Gross Capital Formation/GDP 0.087**
(0.039)
0.054
(0.045)
0.193***
(0.050)
War –0.010***
(0.003)
0.000
(0.004)
0.002
(0.008)
Terms-of-Trade Growth –0.008
(0.053)
0.092
(0.085)
0.061**
(0.026)
Terms-of-Trade Growth × Commodity Exporter 0.105
(0.075)
0.051
(0.125)
–0.038
(0.038)
Trading Partner GDP Growth 0.970***
(0.239)
0.891***
(0.263)
0.693***
(0.206)
Financial Integration –0.016***
(0.006)
–0.016***
(0.005)
–0.023***
(0.005)
Financial Integration × Real 10-Year U.S. Treasury Bond –0.494**
(0.226)
–0.409
(0.377)
–0.237**
(0.109)
Country Fixed Effects Yes Yes Yes
Year Fixed Effects Yes Yes Yes
Number of Observations 248 178 874
Number of Countries 62 62 62
R Squared 0.510 0.508 0.428
Note: Standard errors (in parentheses) are clustered at the country level. *, **, *** indicate that coefficients are significant at the 10, 5, and 1 percent
levels, respectively.

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E
xecutive Directors welcomed the strengthen-
ing of global activity in the second half 2013.
They observed that much of the impetus has
come from advanced economies, but infla-
tion in these economies continues to undershoot
projections, reflecting still-large output gaps. While
remaining fairly robust, growth activity in emerging
market and developing economies slowed in 2013, in
an environment of increased capital flow volatility and
worsening external financing conditions. Directors
underscored that, despite improved growth prospects,
the global recovery is still fragile and significant down-
side risks, including geopolitical, remain.
Directors agreed that global growth will continue
to improve this year and next, on the back of slower
fiscal tightening and still highly accommodative mon-
etary conditions in advanced economies. In emerging
market and developing economies, growth will pick up
gradually, with stronger external demand being partly
offset by the dampening impact of tighter financial
conditions.
Directors acknowledged that successfully transition-
ing from liquidity-driven to growth-driven markets
will require overcoming key challenges, including
strengthening policy coordination. In advanced econo-
mies, a sustained rise in corporate investment and con-
tinued efforts to strengthen bank balance sheets will be
necessary, especially in the euro area. Risks to emerging
market economies have increased with rising public
and corporate sector leverage and greater foreign bor-
rowing. Directors noted that the recent increase in
financial volatility likely reflected renewed market con-
cern about fundamentals, against the backdrop of early
steps toward monetary policy normalization in some
advanced economies. In view of possible capital flow
reversals from emerging markets, Directors considered
the risks related to sizable external funding needs and
disorderly currency depreciations and welcomed the
recent tightening of macroeconomic policies, which
appears to have shored up confidence. Regarding
the financial sector, Directors noted that, despite the
progress made in reducing global financial vulnerabili-
ties, the too-important-to-fail issue still remains largely
unresolved.
Most Directors recommended closer monitoring
of the risks to activity associated with low inflation
in advanced economies, especially in the euro area.
Longer-term inflation expectations could drift down,
leading to higher real interest rates, an increase in pri-
vate and public debt burdens, and a further slowdown
in demand and output. Directors noted, however,
that continued low nominal interest rates in advanced
economies could also pose financial stability risks
and have already led to pockets of increased leverage,
sometimes accompanied by a weakening of underwrit-
ing standards.
Against this backdrop, Directors called for more
policy efforts to fully restore confidence, lower down-
side risks, and ensure robust and sustainable global
growth. In an environment of continued fiscal consoli-
dation, still-large output gaps, and very low inflation,
monetary policy should remain accommodative. Many
Directors argued that in the euro area, further mone-
tary easing, including unconventional measures, would
help to sustain activity and limit the risk of very low
inflation or deflation. A number of Directors thought
that current monetary conditions in the euro area are
already accommodative and further easing would not
be justified. Some Directors also called for a more
comprehensive analysis of exchange rates and global
imbalances in the World Economic Outlook.
Directors recommended designing and implement-
ing clear and credible medium-term fiscal consolida-
tion plans to help mitigate fiscal risks and address the
debt overhang in advanced economies, including the
United States and Japan. They welcomed the expected
shift from tax to expenditure consolidation measures,
particularly in those advanced economies where rais-
ANNEX
The following remarks were made by the Acting Chair at the conclusion of the Executive Board’s discussion of the World
Economic Outlook, Global Financial Stability Report, and Fiscal Monitor on March 21, 2014.
IMF EXECUTIVE BOARD DISCUSSION OF THE OUTLOOK,
MARCH 2014

ing tax burdens could hamper growth. Moreover,
they agreed that a new impulse to structural reforms
is needed to lift investment and growth prospects in
advanced economies.
Directors welcomed the progress made in strength-
ening the banking sector in the euro area, but noted
that more needs to be done to address financial frag-
mentation, repair bank and corporate sector balance
sheets following a credible comprehensive assessment,
and recapitalize weak banks in order to enhance
confidence and revive credit. While acknowledging the
EU Council’s recent agreement on a Single Resolu-
tion Mechanism (SRM), Directors underscored the
importance of completing the banking union, includ-
ing through functional independence of the SRM with
the capacity to undertake timely bank resolution and
common backstops to sever the link between sover-
eigns and banks.
Directors noted that the appropriate policy mea-
sures will differ across emerging market economies,
but observed that there are some common priorities.
Exchange rates should be allowed to respond to chang-
ing fundamentals and facilitate external adjustment.
Where international reserves are adequate, foreign
exchange interventions can be used to smooth volatil-
ity and avoid financial disruption. In economies where
inflationary pressures are still high, further monetary
policy tightening may be necessary. If warranted,
macroprudential measures can help contain the growth
of corporate leverage, particularly in foreign currency.
Strengthening the transparency and consistency of
policy frameworks would contribute to building policy
credibility.
Directors underscored the need for emerging market
and low-income economies to rebuild fiscal buffers and
rein in fiscal deficits (including by containing public
sector contingent liabilities), particularly in the context
of elevated public debt and financing vulnerabilities.
Fiscal consolidation plans should be country specific
and properly calibrated between tax and expenditure
measures to support equitable, sustained growth.
Priority social spending should be safeguarded, and the
efficiency of public spending improved, through better
targeting of social expenditures, rationalizing the pub-
lic sector wage bill, and enhancing public investment
project appraisal, selection, and audit processes.
Directors agreed that emerging market economies
could enhance their resilience to global financial shocks
through a deepening of their domestic financial mar-
kets and the development of a local investor base. They
supported tightening prudential and regulatory over-
sight, including over nonbank institutions in China,
removing implicit guarantees, and enhancing the role
of market forces in the nonbank sector in order to
mitigate financial stability risks and any negative cross-
border spillovers.
Directors concurred that many emerging market
and developing economies should implement other key
structural reforms, designed to boost employment and
prospects for diversified and sustained growth, while also
promoting global rebalancing. Reforms should, among
other things, encompass the removal of barriers to entry
in product and services markets, improve the business
climate and address key supply-side bottlenecks, and in
China, support sustainable and balanced growth, includ-
ing the shift from investment toward consumption.

STATISTICAL APPENDIX
T
he Statistical Appendix presents historical
data as well as projections. It comprises six
sections: Assumptions, What’s New, Data
and Conventions, Classification of Coun-
tries, General Features and Composition of Groups in
the World Economic Outlook Classification, and Statisti-
cal Tables.
The assumptions underlying the estimates and pro-
jections for 2014–15 and the medium-term scenario
for 2016–19 are summarized in the first section. The
second section presents a brief description of the
changes to the database and statistical tables since the
October 2013 issue of the World Economic Outlook
(WEO). The third section provides a general descrip-
tion of the data and the conventions used for calculat-
ing country group composites. The classification of
countries in the various groups presented in the WEO
is summarized in the fourth section. The fifth section
provides information on methods and reporting stan-
dards for the member countries’ national account and
government finance indicators included in the report.
The last, and main, section comprises the statistical
tables. (Statistical Appendix A is included here; Sta-
tistical Appendix B is available online.) Data in these
tables have been compiled on the basis of informa-
tion available generally through March 24, 2014. The
figures for 2014 and beyond are shown with the same
degree of precision as the historical figures solely for
convenience; because they are projections, the same
degree of accuracy is not to be inferred.
Assumptions
Real effective exchange rates for the advanced econo-
mies are assumed to remain constant at their average
levels during the period January 31 to February 28,
2014. For 2014 and 2015, these assumptions imply
average U.S. dollar/special drawing right (SDR)
conversion rates of 1.542 and 1.557, U.S. dollar/euro
conversion rates of 1.369 and 1.393, and yen/U.S. dol-
lar conversion rates of 101.6 and 100.0, respectively.
It is assumed that the price of oil will average
$104.17 a barrel in 2014 and $97.92 a barrel in 2015.
Established policies of national authorities are
assumed to be maintained. The more specific policy
assumptions underlying the projections for selected
economies are described in Box A1.
With regard to interest rates, it is assumed that the
London interbank offered rate (LIBOR) on six-month
U.S. dollar deposits will average 0.4 percent in 2014
and 0.8 percent in 2015, that three-month euro depos-
its will average 0.3 percent in 2014 and 0.4 percent
in 2015, and that six-month yen deposits will average
0.2 percent in 2014 and 2015.
With respect to introduction of the euro, on Decem-
ber 31, 1998, the Council of the European Union
decided that, effective January 1, 1999, the irrevocably
fixed conversion rates between the euro and curren-
cies of the member countries adopting the euro are as
follows.
See Box 5.4 of the October 1998 WEO for details on
how the conversion rates were established.
1 euro = 13.7603 Austrian schillings
= 40.3399 Belgian francs
= 0.585274 Cyprus pound1
= 1.95583 Deutsche mark
= 15.6466 Estonian krooni2
= 5.94573 Finnish markkaa
= 6.55957 French francs
= 340.750 Greek drachmas3
= 0.787564 Irish pound
= 1,936.27 Italian lire
= 0.702804 Latvian lats4
= 40.3399 Luxembourg francs
= 0.42930 Maltese lira1
= 2.20371 Netherlands guilders
= 200.482 Portuguese escudos
= 30.1260 Slovak koruna5
= 239.640 Slovenian tolars6
= 166.386 Spanish pesetas
1Established on January 1, 2008.
3Established on January 1, 2001.

What’s New
•• On January 1, 2014, Latvia became the 18th
country to join the euro area. Data for Latvia are
not included in the euro area aggregates, because
the database has not yet been converted to euros,
but are included in data aggregated for advanced
economies.
•• Starting with the April 2014 WEO, the Central and
Eastern Europe and Emerging Europe regions have
been renamed Emerging and Developing Europe.
The Developing Asia region has been renamed
Emerging and Developing Asia.
•• Projections for Ukraine are excluded due to the
ongoing crisis.
•• The consumer price projections for Argentina are
excluded because of a structural break in the data.
Please refer to note 6 in Table A7 for further details.
•• Korea’s real GDP series is based on the reference
year 2005. This does not reflect the revised national
accounts released on March 26, 2014, after the WEO
was finalized for publication. These comprehensive
revisions include implementing the 2008 System of
National Accounts and updating the reference year to
2010. As a result of these revisions, real GDP growth
in 2013 was revised up to 3 percent from 2.8 percent
(which is the figure included in Tables 2.3 and A2).
•• Cape Verde is now called Cabo Verde.
•• As in the October 2013 WEO, data for Syria are
excluded for 2011 onward because of the uncertain
political situation.
Data and Conventions
Data and projections for 189 economies form the sta-
tistical basis of the World Economic Outlook (the WEO
database). The data are maintained jointly by the IMF’s
Research Department and regional departments, with
the latter regularly updating country projections based
on consistent global assumptions.
Although national statistical agencies are the
ultimate providers of historical data and definitions,
international organizations are also involved in statisti-
cal issues, with the objective of harmonizing meth-
odologies for the compilation of national statistics,
including analytical frameworks, concepts, definitions,
classifications, and valuation procedures used in the
production of economic statistics. The WEO database
reflects information from both national source agencies
and international organizations.
Most countries’ macroeconomic data presented in
the WEO conform broadly to the 1993 version of the
System of National Accounts (SNA). The IMF’s sec-
tor statistical standards—the Balance of Payments and
International Investment Position Manual, Sixth Edition
(BPM6), the Monetary and Financial Statistics Manual
(MFSM 2000), and the Government Finance Statistics
Manual 2001 (GFSM 2001)—have been or are being
aligned with the 2008 SNA.1 These standards reflect
the IMF’s special interest in countries’ external posi-
tions, financial sector stability, and public sector fiscal
positions. The process of adapting country data to the
new standards begins in earnest when the manuals are
released. However, full concordance with the manuals
is ultimately dependent on the provision by national
statistical compilers of revised country data; hence,
the WEO estimates are only partially adapted to these
manuals. Nonetheless, for many countries the impact,
on major balances and aggregates, of conversion to the
updated standards will be small. Many other countries
have partially adopted the latest standards and will
continue implementation over a period of years.
Consistent with the recommendations of the 1993
SNA, several countries have phased out their tradi-
tional fixed-base-year method of calculating real macro-
economic variable levels and growth by switching to a
chain-weighted method of computing aggregate growth.
The chain-weighted method frequently updates the
weights of price and volume indicators. It allows
countries to measure GDP growth more accurately by
reducing or eliminating the downward biases in vol-
ume series built on index numbers that average volume
components using weights from a year in the moder-
ately distant past. Table F indicates which countries use
a chain-weighted method.
Composite data for country groups in the WEO are
either sums or weighted averages of data for individual
countries. Unless noted otherwise, multiyear averages
of growth rates are expressed as compound annual rates
of change.2 Arithmetically weighted averages are used
for all data for the emerging market and developing
1Many other countries are implementing the 2008 SNA and will
release national accounts data based on the new standard in 2014.
A few countries use versions of the SNA older than 1993. A similar
adoption pattern is expected for the BPM6. Although the conceptual
standards use the BPM6, the WEO will continue to use the BPM5
presentation until a representative number of countries have moved
their balance of payments accounts into the BPM6 framework.
2Averages for real GDP and its components, employment, GDP
per capita, inflation, factor productivity, trade, and commodity
prices are calculated based on the compound annual rate of change,

economies group except inflation and money growth,
for which geometric averages are used. The following
conventions apply.
•• Country group composites for exchange rates, inter-
est rates, and growth rates of monetary aggregates
are weighted by GDP converted to U.S. dollars at
market exchange rates (averaged over the preceding
three years) as a share of group GDP.
•• Composites for other data relating to the domes-
tic economy, whether growth rates or ratios, are
weighted by GDP valued at purchasing power parity
(PPP) as a share of total world or group GDP.3
•• Composites for data relating to the domestic
economy for the euro area (18 member countries
throughout the entire period, unless noted other-
wise) are aggregates of national source data using
GDP weights. Annual data are not adjusted for
calendar-day effects. For data prior to 1999, data
aggregations apply 1995 European currency unit
exchange rates.
•• Composites for fiscal data are sums of individual
country data after conversion to U.S. dollars at the
average market exchange rates in the years indicated.
•• Composite unemployment rates and employment
growth are weighted by labor force as a share of
group labor force.
•• Composites relating to external sector statistics are
sums of individual country data after conversion to
U.S. dollars at the average market exchange rates
in the years indicated for balance of payments data
and at end-of-year market exchange rates for debt
denominated in currencies other than U.S. dollars.
•• Composites of changes in foreign trade volumes and
prices, however, are arithmetic averages of percent
changes for individual countries weighted by the
U.S. dollar value of exports or imports as a share
of total world or group exports or imports (in the
preceding year).
•• Unless noted otherwise, group composites are com-
puted if 90 percent or more of the share of group
weights is represented.
except in the case of the unemployment rate, which is based on the
simple arithmetic average.
3See Box A2 of the April 2004 WEO for a summary of the revised
PPP-based weights and Annex IV of the May 1993 WEO. See also
Anne-Marie Gulde and Marianne Schulze-Ghattas, “Purchasing
Power Parity Based Weights for the World Economic Outlook,” in Staff
Studies for the World Economic Outlook (Washington: International
Monetary Fund, December 1993), pp. 106–23.
Data refer to calendar years, except in the case of a few
countries that use fiscal years. Please refer to Table F,
which lists the reporting period for each country.
Classification of Countries
Summary of the Country Classification
The country classification in the WEO divides the
world into two major groups: advanced economies
and emerging market and developing economies.4 This
classification is not based on strict criteria, economic
or otherwise, and it has evolved over time. The objec-
tive is to facilitate analysis by providing a reasonably
meaningful method of organizing data. Table A pro-
vides an overview of the country classification, showing
the number of countries in each group by region and
summarizing some key indicators of their relative
size (GDP valued by PPP, total exports of goods and
services, and population).
Some countries remain outside the country classifi-
cation and therefore are not included in the analysis.
Anguilla, Cuba, the Democratic People’s Republic of
Korea, and Montserrat are examples of countries that
are not IMF members, and their economies therefore
are not monitored by the IMF. Somalia is omitted
from the emerging market and developing economies
group composites because of data limitations.
General Features and Composition of
Groups in the World Economic Outlook
Classification
Advanced Economies
The 36 advanced economies are listed in Table B. The
seven largest in terms of GDP—the United States,
Japan, Germany, France, Italy, the United Kingdom,
and Canada—constitute the subgroup of major
advanced economies often referred to as the Group of
Seven (G7). The members of the euro area are also
distinguished as a subgroup. Composite data shown in
the tables for the euro area cover the current mem-
bers for all years, even though the membership has
increased over time.
4As used here, the terms “country” and “economy” do not always
refer to a territorial entity that is a state as understood by interna-
tional law and practice. Some territorial entities included here are
not states, although their statistical data are maintained on a separate
and independent basis.

Table C lists the member countries of the European
Union, not all of which are classified as advanced
economies in the World Economic Outlook.
The group of emerging market and developing econo-
mies (153) includes all those that are not classified as
advanced economies.
The regional breakdowns of emerging market and
developing economies are Commonwealth of Indepen-
dent States (CIS); emerging and developing Asia; emerg-
ing and developing Europe (sometimes also referred to
as central and eastern Europe); Latin America and the
Caribbean (LAC); Middle East, North Africa, Afghani-
stan, and Pakistan (MENAP); and sub-Saharan Africa
(SSA).
Emerging market and developing economies are also
classified according to analytical criteria. The analytical
criteria reflect the composition of export earnings and
other income from abroad; a distinction between net
creditor and net debtor economies; and, for the net
debtors, financial criteria based on external financing
sources and experience with external debt servicing.
The detailed composition of emerging market and
developing economies in the regional and analytical
groups is shown in Tables D and E.
The analytical criterion by source of export earnings
distinguishes between categories: fuel (Standard Inter-
national Trade Classification—SITC 3) and nonfuel
and then focuses on nonfuel primary products (SITCs 0,
1, 2, 4, and 68). Economies are categorized into one of
these groups when their main source of export earnings
exceeds 50 percent of total exports on average between
2008 and 2012.
The financial criteria focus on net creditor economies, net
debtor economies, heavily indebted poor countries (HIPCs),
and low-income developing countries (LIDCs). Economies
are categorized as net debtors when their current account
balance accumulations from 1972 (or earliest data avail-
able) to 2012 are negative. Net debtor economies are fur-
ther differentiated on the basis of two additional financial
criteria: official external financing and experience with debt
servicing.5 Net debtors are placed in the official external
financing category when 66 percent or more of their total
debt, on average, between 2008 and 2012 was financed
by official creditors.
The HIPC group comprises the countries that are
or have been considered by the IMF and the World
Bank for participation in their debt initiative known as
the HIPC Initiative, which aims to reduce the external
debt burdens of all the eligible HIPCs to a “sustain-
able” level in a reasonably short period of time.6 Many
of these countries have already benefited from debt
relief and have graduated from the initiative.
The LIDCs are countries that were designated
Poverty Reduction and Growth Trust (PRGT)–eligible
in the 2013 PRGT eligibility review and had a level of
per capita gross national income less than the PRGT
income graduation threshold for non–small states (that
is, twice the IDA operational threshold, or US$2,390 in
2011 as measured by the World Bank’s Atlas method);
and Zimbabwe.
5During 2008–12, 34 economies incurred external payments
arrears or entered into official or commercial bank debt-rescheduling
agreements. This group is referred to as economies with arrears and/or
rescheduling during 2008–12.
6 See David Andrews, Anthony R. Boote, Syed S. Rizavi, and Suk-
winder Singh, Debt Relief for Low-Income Countries: The Enhanced
HIPC Initiative, IMF Pamphlet Series No. 51 (Washington: Interna-
tional Monetary Fund, November 1999).

Table A. Classification by World Economic Outlook Groups and Their Shares in Aggregate GDP, Exports of
Goods and Services, and Population, 20131
(Percent of total for group or world)
GDP
Exports of Goods
and Services Population
Number of
Economies
Advanced
Economies World
Advanced
Economies World
Advanced
Economies World
Advanced Economies 36 100.0 49.6 100.0 61.1 100.0 14.7
United States 38.9 19.3 16.1 9.8 30.5 4.5
Euro Area2 17 26.4 13.1 41.5 25.3 31.8 4.7
Germany 7.5 3.7 13.1 8.0 7.8 1.1
France 5.3 2.6 5.7 3.5 6.1 0.9
Italy 4.2 2.1 4.4 2.7 5.8 0.8
Spain 3.2 1.6 3.3 2.0 4.5 0.7
Japan 10.9 5.4 5.9 3.6 12.3 1.8
United Kingdom 5.5 2.7 5.6 3.4 6.2 0.9
Canada 3.5 1.8 3.9 2.4 3.4 0.5
Other Advanced Economies 15 14.7 7.3 27.1 16.6 15.7 2.3
Memorandum
Major Advanced Economies 7 75.9 37.6 54.7 33.4 72.1 10.6
Emerging
Market and
Developing
Economies World
Emerging
Market and
Developing
Economies World
Emerging
Market and
Developing
Economies World
Emerging Market and Developing Economies 153 100.0 50.4 100.0 38.9 100.0 85.3
Regional Groups
Commonwealth of Independent States3 12 8.3 4.2 10.0 3.9 4.8 4.0
Russia 5.8 2.9 6.6 2.6 2.4 2.0
Emerging and Developing Asia 29 51.4 25.9 44.1 17.2 57.4 49.0
China 30.5 15.4 26.9 10.5 22.7 19.3
India 11.6 5.8 5.3 2.0 20.7 17.7
Excluding China and India 27 9.3 4.7 11.9 4.6 14.0 11.9
Emerging and Developing Europe 13 6.6 3.3 8.6 3.4 3.0 2.5
Latin America and the Caribbean 32 17.1 8.6 14.0 5.4 9.9 8.4
Brazil 5.5 2.8 3.1 1.2 3.3 2.8
Mexico 4.2 2.1 4.4 1.7 2.0 1.7
Middle East, North Africa, Afghanistan, and
Pakistan 22 11.4 5.7 18.1 7.1 10.4 8.9
Middle East and North Africa 20 10.0 5.0 17.7 6.9 6.8 5.8
Sub-Saharan Africa 45 5.1 2.6 5.2 2.0 14.6 12.5
Excluding Nigeria and South Africa 43 2.7 1.3 2.9 1.1 10.9 9.3
Analytical Groups4
By Source of Export Earnings
Fuel 28 17.6 8.9 28.4 11.0 11.4 9.7
Nonfuel 125 82.4 41.6 71.6 27.9 88.6 75.5
Of Which, Primary Products 28 3.6 1.8 3.5 1.4 7.1 6.1
By External Financing Source
Net Debtor Economies 123 49.9 25.1 41.4 16.1 63.7 54.3
Of Which, Official Financing 27 4.0 2.0 3.0 1.2 9.7 8.3
Net Debtor Economies by Debt-
Servicing Experience
Economies with Arrears and/or
Rescheduling during 2008–12 34 6.4 3.2 4.1 1.6 10.3 8.8
Other Net Debtor Economies 89 43.4 21.9 37.4 14.5 53.3 45.5
Other Groups
Heavily Indebted Poor Countries 38 2.5 1.2 1.9 0.7 11.0 9.4
Low-Income Developing Countries 59 6.5 3.3 5.9 2.3 22.4 19.1
1The GDP shares are based on the purchasing-power-parity valuation of economies’ GDP. The number of economies comprising each group reflects those for
which data are included in the group aggregates.
2Data for Latvia are not included in the euro area aggregates because the database has not yet been converted to euros.
3Georgia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarity in economic
structure.
4South Sudan is omitted from the net external position groups composite for lack of a fully developed database.

Table B. Advanced Economies by Subgroup
Major Currency Areas
United States
Euro Area
Japan
Euro Area1
Austria Germany Netherlands
Belgium Greece Portugal
Cyprus Ireland Slovak Republic
Estonia Italy Slovenia
Finland Luxembourg Spain
France Malta
Major Advanced Economies
Canada Italy United States
France Japan
Germany United Kingdom
Other Advanced Economies
Australia Israel San Marino
Czech Republic Korea Singapore
Denmark Latvia Sweden
Hong Kong SAR2 New Zealand Switzerland
Iceland Norway Taiwan Province of China
1Data for Latvia are not included in the euro area aggregates because the database has not yet been
converted to euros.
2On July 1, 1997, Hong Kong was returned to the People’s Republic of China and became a Special
Administrative Region of China.
Table C. European Union
Austria Germany Poland
Belgium Greece Portugal
Bulgaria Hungary Romania
Croatia Ireland Slovak Republic
Cyprus Italy Slovenia
Czech Republic Latvia Spain
Denmark Lithuania Sweden
Estonia Luxembourg United Kingdom
Finland Malta
France Netherlands

Table D. Emerging Market and Developing Economies by Region and Main Source of Export Earnings
Fuel Nonfuel Primary Products
Commonwealth of Independent States
Azerbaijan Uzbekistan
Kazakhstan
Russia
Turkmenistan
Emerging and Developing Asia
Brunei Darussalam Mongolia
Timor-Leste Papua New Guinea
Solomon Islands
Tuvalu
Bolivia Chile
Ecuador Guyana
Trinidad and Tobago Paraguay
Venezuela Suriname
Uruguay
Middle East, North Africa, Afghanistan, and Pakistan
Algeria Afghanistan
Bahrain Mauritania
Iran Sudan
Iraq
Kuwait
Libya
Oman
Qatar
Saudi Arabia
United Arab Emirates
Yemen
Sub-Saharan Africa
Angola Burkina Faso
Chad Burundi
Republic of Congo Central African Republic
Equatorial Guinea Democratic Republic of the Congo
Gabon Eritrea
Nigeria Guinea
South Sudan Guinea-Bissau
Malawi
Mali
Mozambique
Niger
Sierra Leone
South Africa
Zambia
Zimbabwe

Net External Position Heavily
Indebted Poor
Countries2
Low-Income
Developing
Countries
Net
Creditor
Net
Debtor1
Commonwealth of Independent States3
Armenia *
Azerbaijan *
Belarus *
Georgia *
Kazakhstan *
Kyrgyz Republic • *
Moldova * *
Russia *
Tajikistan * *
Turkmenistan *
Ukraine *
Uzbekistan * *
Bangladesh • *
Bhutan • *
Brunei Darussalam *
Cambodia * *
China *
Fiji *
India *
Indonesia *
Kiribati * *
Lao P.D.R. * *
Malaysia *
Maldives *
Marshall Islands •
Micronesia •
Mongolia * *
Myanmar * *
Nepal * *
Palau *
Papua New Guinea * *
Philippines *
Samoa *
Solomon Islands * *
Sri Lanka •
Thailand *
Timor-Leste *
Tonga •
Tuvalu •
Vanuatu *
Vietnam * *
Emerging and Developing Europe
Albania *
Bosnia and Herzegovina *
Indebted Poor
Countries2
Low-Income
Developing
Countries
Net
Creditor
Net
Debtor1
Bulgaria *
Croatia *
Hungary •
Kosovo *
Lithuania *
FYR Macedonia *
Montenegro *
Poland *
Romania *
Serbia *
Turkey *
Antigua and Barbuda *
Argentina *
The Bahamas *
Barbados *
Belize *
Bolivia * • *
Brazil *
Chile *
Colombia *
Costa Rica *
Dominica *
Dominican Republic *
Ecuador •
El Salvador *
Grenada *
Guatemala *
Guyana * •
Haiti • • *
Honduras * • *
Jamaica *
Mexico *
Nicaragua • • *
Panama *
Paraguay *
Peru *
St. Kitts and Nevis *
St. Lucia *
St. Vincent and the
Grenadines *
Suriname •
Trinidad and Tobago *
Uruguay *
Venezuela *
Table E. Emerging Market and Developing Economies by Region, Net External Position, Status as Heavily Indebted Poor
Countries, and Low-Income Developing Countries

Indebted Poor
Countries2
Low-Income
Developing
Countries
Net
Creditor
Net
Debtor1
Middle East, North Africa, Afghanistan, and Pakistan
Afghanistan * • *
Algeria *
Bahrain *
Djibouti * *
Egypt *
Iran *
Iraq *
Jordan *
Kuwait *
Lebanon *
Libya *
Mauritania * • *
Morocco *
Oman *
Pakistan •
Qatar *
Saudi Arabia *
Sudan • * *
Syria •
Tunisia *
United Arab Emirates *
Yemen * *
Sub-Saharan Africa
Angola *
Benin * • *
Botswana *
Burkina Faso • • *
Burundi • • *
Cabo Verde *
Cameroon * • *
Central African Republic • • *
Chad * * *
Comoros • • *
Democratic Republic of
the Congo * • *
Indebted Poor
Countries2
Low-Income
Developing
Countries
Net
Creditor
Net
Debtor1
Republic of Congo • • *
Côte d’Ivoire * • *
Equatorial Guinea *
Eritrea • * *
Ethiopia • • *
Gabon *
The Gambia * • *
Ghana * • *
Guinea * • *
Guinea-Bissau • • *
Kenya * *
Lesotho • *
Liberia * • *
Madagascar * • *
Malawi * • *
Mali * • *
Mauritius *
Mozambique * • *
Namibia *
Niger * • *
Nigeria * *
Rwanda * • *
São Tomé and Príncipe • • *
Senegal * • *
Seychelles *
Sierra Leone * • *
South Africa *
South Sudan4 … *
Swaziland *
Tanzania * • *
Togo • • *
Uganda * • *
Zambia * • *
Zimbabwe * *
Table E. (concluded)
1Dot instead of star indicates that the net debtor’s main external finance source is official financing.
2Dot instead of star indicates that the country has reached the completion point.
3Georgia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarity in economic structure.
4South Sudan is omitted from the net external position groups composite for lack of a fully developed database.

Table F. Key Data Documentation
Country Currency
National Accounts
Historical Data
Source1
Latest Actual
Data Base Year2
Reporting
Period3
Use of Chain-
Weighted
Methodology4
Afghanistan Afghan Afghani NSO 2011/12 2002/03
Albania Albanian lek IMF staff 2012 1996 From 1996
Algeria Algerian dinar NSO 2011 2001 From 2005
Angola Angolan kwanza NSO 2011 2002
Antigua and Barbuda Eastern Caribbean dollar CB 2013 20065
Argentina Argentine peso MEP 2012 1993
Armenia Armenian dram NSO 2012 2005
Australia Australian dollar NSO 2013 2011/12 From 1980
Austria Euro NSO 2013 2005 From 1988
Azerbaijan Azerbaijan manat NSO 2013 2003 From 1994
The Bahamas Bahamian dollar NSO 2012 2006
Bahrain Bahrain dinar MoF 2012 2010
Bangladesh Bangladesh taka NSO 2012 2005
Barbados Barbados dollar NSO and CB 2012 19745
Belarus Belarusian rubel NSO 2012 2009 From 2005
Belgium Euro CB 2013 2011 From 1995
Belize Belize dollar NSO 2012 2000
Benin CFA franc NSO 2011 2000
Bhutan Bhutanese ngultrum NSO 2006/07 20005 Jul/Jun
Bolivia Bolivian boliviano NSO 2012 1990
Bosnia and Herzegovina Convertible marka NSO 2012 2010 From 2000
Botswana Botswana pula NSO 2010 2006
Brazil Brazilian real NSO 2013 1995
Brunei Darussalam Brunei dollar NSO 2012 2000
Bulgaria Bulgarian lev NSO 2013 2005 From 2005
Burkina Faso CFA franc NSO and MEP 2011 1999
Burundi Burundi franc NSO 2010 2005
Cabo Verde Cabo Verde escudo NSO 2011 2007 From 2011
Cambodia Cambodian riel NSO 2012 2000
Cameroon CFA franc NSO 2010 2000
Canada Canadian dollar NSO 2013 2007 From 1980
Central African Republic CFA franc NSO 2012 2005
Chad CFA franc CB 2010 2005
Chile Chilean peso CB 2013 2008 From 2003
China Chinese yuan NSO 2012 19905
Colombia Colombian peso NSO 2012 2005 From 2000
Comoros Comorian franc NSO 2012 2000
Congo
Congo franc NSO 2006 2005
Republic of Congo CFA franc NSO 2009 1990
Costa Rica Costa Rican colón CB 2012 1991
Côte d'Ivoire CFA franc MEP 2011 2000
Croatia Croatian kuna NSO 2012 2005
Cyprus Euro Eurostat 2012 2005 From 1995
Czech Republic Czech koruna NSO 2013 2005 From 1995
Denmark Danish krone NSO 2013 2005 From 1980
Djibouti Djibouti franc NSO 1999 1990

Country
Government Finance Prices (CPI) Balance of Payments
Historical Data
Source1
Latest
Actual
Data
Reporting
Period3
Historical Data
Source1
Latest
Actual
Data
Historical Data
Source1
Latest
Actual
Data
Afghanistan MoF 2012/13 Solar year6 NSO 2013 NSO 2012
Albania IMF staff 2012 NSO 2013 CB 2012
Algeria CB 2012 NSO 2012 CB 2012
Angola MoF 2012 CB 2013 CB 2012
Antigua and Barbuda MoF 2013 NSO 2013 CB 2013
Argentina MEP 2012 NSO 2012 MEP 2012
Armenia MoF 2012 NSO 2013 CB 2012
Australia MoF 2012/13 NSO 2013 NSO 2013
Austria NSO 2013 NSO 2013 NSO 2013
Azerbaijan MoF 2012 NSO 2013 CB 2012
The Bahamas MoF 2012/13 Jul/Jun NSO 2012 CB 2012
Bahrain MoF 2012 NSO 2012 CB 2012
Bangladesh MoF 2011/12 Jul/Jun NSO 2013 CB 2011
Barbados MoF 2012/13 Apr/Mar CB 2012 CB 2012
Belarus MoF 2013 NSO 2013 CB 2012
Belgium CB 2012 CB 2013 CB 2012
Belize MoF 2012/13 Apr/Mar NSO 2012 CB 2012
Benin MoF 2011 NSO 2011 CB 2010
Bhutan MoF 2010/11 Jul/Jun CB 2008 CB 2007/08
Bolivia MoF 2013 NSO 2013 CB 2012
Bosnia and Herzegovina MoF 2013 NSO 2013 CB 2012
Botswana MoF 2008/09 Apr/Mar NSO 2010 CB 2009
Brazil MoF 2013 NSO 2013 CB 2013
Brunei Darussalam MoF 2013 NSO 2013 MEP 2011
Bulgaria MoF 2012 NSO 2013 CB 2013
Burkina Faso MoF 2013 NSO 2013 CB 2011
Burundi MoF 2012 NSO 2012 CB 2011
Cabo Verde MoF 2013 NSO 2013 CB 2013
Cambodia MoF 2012 NSO 2013 CB 2012
Cameroon MoF 2012 NSO 2012 MoF 2010
Canada NSO and OECD 2013 NSO 2013 NSO 2013
Central African Republic MoF 2012 NSO 2012 CB 2012
Chad MoF 2012 NSO 2013 CB 2010
Chile MoF 2013 NSO 2013 CB 2013
China MoF 2013 NSO 2013 State Admin. of Foreign
Exchange
2012
Colombia MoF 2012 NSO 2012 CB and NSO 2012
Comoros MoF 2012 NSO 2012 CB and IMF staff 2012
Congo
MoF 2013 CB 2013 CB 2013
Republic of Congo MoF 2012 NSO 2013 CB 2008
Costa Rica MoF and CB 2012 CB 2013 CB 2012
Côte d'Ivoire MoF 2011 MoF 2011 CB 2009
Croatia MoF 2013 NSO 2012 CB 2013
Cyprus Eurostat 2013 Eurostat 2013 Eurostat 2012
Czech Republic MoF 2013 NSO 2013 NSO 2013
Denmark NSO 2013 NSO 2013 NSO 2013
Djibouti MoF 2012 NSO 2012 CB 2012

Table F. Key Data Documentation (continued)
Country Currency
National Accounts
Historical Data
Source1
Latest Actual
Data Base Year2
Reporting
Period3
Use of Chain-
Weighted
Methodology4
Dominica Eastern Caribbean dollar NSO 2013 2006
Dominican Republic Dominican peso CB 2013 1991
Ecuador U.S. dollar CB 2012 2007
Egypt Egyptian pound Other 2012/13 2001/02 Jul/Jun
El Salvador U.S. dollar CB 2012 1990
Equatorial Guinea CFA franc MEP and CB 2006 2006
Eritrea Eritrean nakfa IMF staff 2006 2000
Estonia Euro NSO 2013 2005 From 1995
Ethiopia Ethiopian birr NSO 2012/13 2010/11 Jul/Jun
Fiji Fiji dollar NSO 2012 20085
Finland Euro NSO 2013 2000 From 1980
France Euro NSO 2013 2005 From 1980
Gabon CFA franc MoF 2010 2001
The Gambia Gambian dalasi NSO 2012 2004
Georgia Georgian lari NSO 2012 2000 From 1996
Germany Euro NSO 2013 2005 From 1991
Ghana Ghanaian cedi NSO 2011 2006
Greece Euro NSO 2013 2005 From 2000
Grenada Eastern Caribbean dollar NSO 2013 2006
Guatemala Guatemalan quetzal CB 2012 2001 From 2001
Guinea Guinean franc NSO 2009 2003
Guinea-Bissau CFA franc NSO 2011 2005
Guyana Guyana dollar NSO 2012 20065
Haiti Haitian gourde NSO 2012/13 1986/87 Oct/Sep
Honduras Honduran lempira CB 2012 2000
Hong Kong SAR Hong Kong dollar NSO 2013 2011 From 1980
Hungary Hungarian forint NSO 2012 2005 From 2005
Iceland Icelandic króna NSO 2013 2000 From 1990
India Indian rupee NSO 2012/13 2004/05 Apr/Mar
Indonesia Indonesian rupiah NSO 2013 2000
Iran Iranian rial CB 2011/12 1997/98 Apr/Mar
Iraq Iraqi dinar NSO 2013 1988
Ireland Euro NSO 2012 2011 From 2011
Israel Israeli shekel NSO 2012 2010 From 1995
Italy Euro NSO 2012 2005 From 1980
Jamaica Jamaica dollar NSO 2012 2007
Japan Japanese yen NSO and Nomura 2013 2005 From 1980
Jordan Jordanian dinar NSO 2013 1994
Kazakhstan Kazakhstani tenge NSO 2012 2007 From 1994
Kenya Kenya shilling NSO 2013 2000
Kiribati Australian dollar NSO 2009 2006
Korea Korean won CB 2012 2005 From 1980
Kosovo Euro NSO 2012 2012
Kuwait Kuwaiti dinar MEP and NSO 2012 2000

Country
Historical Data
Source1
Latest
Actual
Data
Reporting
Period3
Historical Data
Source1
Latest
Actual
Data
Historical Data
Source1
Latest
Actual
Data
Dominica MoF 2012/13 Jul/Jun NSO 2013 CB 2013
Dominican Republic MoF 2013 CB 2013 CB 2013
Ecuador CB and MoF 2012 NSO and CB 2012 CB 2012
Egypt MoF 2012/13 Jul/Jun NSO 2012/13 CB 2012/13
El Salvador MoF 2013 NSO 2013 CB 2012
Equatorial Guinea MoF 2012 MEP 2012 CB 2006
Eritrea MoF 2008 NSO 2009 CB 2008
Estonia MoF 2013 NSO 2013 CB 2013
Ethiopia MoF 2012/13 Jul/Jun NSO 2012 CB 2012/13
Fiji MoF 2011 NSO 2013 CB 2012
Finland MoF 2012 NSO and
Eurostat
2013 CB 2012
France NSO 2012 NSO 2013 CB 2013
Gabon IMF staff 2013 MoF 2013 CB 2006
The Gambia MoF 2013 NSO 2013 CB and IMF staff 2012
Georgia MoF 2013 NSO 2013 NSO and CB 2012
Germany NSO and Eurostat 2013 NSO 2013 CB 2013
Ghana MoF 2011 NSO 2011 CB 2011
Greece MoF 2012 NSO 2013 CB 2013
Grenada MoF 2013 NSO 2013 CB 2013
Guatemala MoF 2012 NSO 2013 CB 2012
Guinea MoF 2012 NSO 2013 CB and MEP IMF staff
estimates
Guinea-Bissau MoF 2011 NSO 2011 CB 2011
Guyana MoF 2012 NSO 2012 CB 2012
Haiti MoF 2012/13 Oct/Sep NSO 2013 CB 2013
Honduras MoF 2012 CB 2013 CB 2012
Hong Kong SAR NSO 2012/13 Apr/Mar NSO 2013 NSO 2011
Hungary MEP and Eurostat 2012 NSO 2013 CB 2012
Iceland NSO 2013 NSO 2013 CB 2013
India MoF 2012/13 Apr/Mar NSO 2012/13 CB 2012/13
Indonesia MoF 2013 CEIC 2013 CEIC 2013
Iran MoF 2011/12 Apr/Mar CB 2013 CB 2012
Iraq MoF 2013 NSO 2013 CB 2012
Ireland MoF 2012 NSO 2012 NSO 2012
Israel MoF 2012 Haver
Analytics
2013 Haver Analytics 2012
Italy NSO 2012 NSO 2012 NSO 2012
Jamaica MoF 2012/13 Apr/Mar NSO 2013 CB 2012
Japan Cabinet Office of
Japan
2012 NSO and
Nomura
2013 NSO and Nomura 2013
Jordan MoF 2013 NSO 2013 CB 2012
Kazakhstan IMF staff 2012 CB 2012 CB 2012
Kenya MoF 2013 NSO 2013 CB 2013
Kiribati MoF 2010 NSO 2010 NSO 2009
Korea MoF 2012 CB 2013 CB 2013
Kosovo MoF 2012 NSO 2012 CB 2011
Kuwait MoF 2012 MEP and NSO 2012 CB 2012

Country Currency
National Accounts
Historical Data
Source1
Latest Actual
Data Base Year2
Reporting
Period3
Use of Chain-
Weighted
Methodology4
Kyrgyz Republic Kyrgyz som NSO 2013 1995
Lao P.D.R. Lao kip NSO 2011 2002
Latvia Latvian lats NSO 2013 2010 From 1995
Lebanon Lebanese pound NSO 2011 2000 From 2010
Lesotho Lesotho loti NSO 2012 2004
Liberia U.S. dollar CB 2011 1992
Libya Libyan dinar MEP 2009 2003
Lithuania Lithuanian litas NSO 2013 2005 From 2005
Luxembourg Euro NSO 2012 2005 From 1995
FYR Macedonia Macedonian denar NSO 2013 2005
Madagascar Malagasy ariary NSO 2012 2000
Malawi Malawi kwacha NSO 2009 2007
Malaysia Malaysian ringgit NSO 2013 2005
Maldives Maldivian rufiyaa MEP 2012 2003
Mali CFA franc MoF 2011 1987
Malta Euro Eurostat 2012 2005 From 2000
Marshall Islands U.S. dollar NSO 2011/12 2003/04 Oct/Sep
Mauritania Mauritanian ouguiya NSO 2009 1998
Mauritius Mauritian rupee NSO 2013 2000 From 1999
Mexico Mexican peso NSO 2013 2008
Micronesia U.S. dollar NSO 2012 2004 Oct/Sept
Moldova Moldovan leu NSO 2013 1995
Mongolia Mongolian togrog NSO 2012 2005
Montenegro Euro NSO 2011 2006
Morocco Moroccan dirham NSO 2013 1998 From 1998
Mozambique Mozambican metical NSO 2012 2000
Myanmar Myanmar kyat MEP 2010/11 2010/11 Apr/Mar
Namibia Namibia dollar NSO 2009 2000
Nepal Nepalese rupee NSO 2011/12 2000/01 Aug/Jul
Netherlands Euro NSO 2013 2005 From 1980
New Zealand New Zealand dollar NSO 2011/12 1995/96 From 1987
Nicaragua Nicaraguan córdoba IMF staff 2012 2006 From 1994
Niger CFA franc NSO 2010 2000
Nigeria Nigerian naira NSO 2012 2000
Norway Norwegian krone NSO 2013 2011 From 1980
Oman Omani rial NSO 2012 2000
Pakistan Pakistan rupee MoF 2012/13 2005/06 Jul/Jun
Palau U.S. dollar MoF 2012 2005 Oct/Sep
Panama U.S. dollar NSO 2012 1996
Papua New Guinea Papua New Guinea kina NSO and MOF 2012 1998
Paraguay Paraguayan guaraní CB 2012 1994
Peru Peruvian nuevo sol CB 2013 1994
Philippines Philippine peso NSO 2013 2000
Poland Polish zloty NSO 2013 2005 From 1995
Portugal Euro NSO 2012 2006 From 1980
Qatar Qatari riyal NSO and MEP 2012 2004
Romania Romanian leu NSO and Eurostat 2013 2005 From 2000

Country
Historical Data
Source1
Latest
Actual
Data
Reporting
Period3
Historical Data
Source1
Latest
Actual
Data
Historical Data
Source1
Latest
Actual
Data
Kyrgyz Republic MoF 2013 NSO 2013 MoF 2012
Lao P.D.R. MoF 2012/13 Oct/Sep NSO 2013 CB 2011
Latvia MoF 2013 Eurostat 2013 CB 2013
Lebanon MoF 2013 NSO 2013 CB 2012
Lesotho MoF 2012/13 Apr/Mar NSO 2013 CB 2012
Liberia MoF 2012 CB 2013 CB 2012
Libya MoF 2011 NSO 2009 CB 2010
Lithuania MoF 2013 NSO 2013 CB 2013
Luxembourg MoF 2012 NSO 2013 NSO 2012
FYR Macedonia MoF 2012 NSO 2013 CB 2013
Madagascar MoF 2012 NSO 2012 CB 2011
Malawi MoF 2012/13 Jul/Jun NSO 2013 NSO 2012
Malaysia MoF 2012 NSO 2013 NSO 2013
Maldives MoF and Treasury 2011 CB 2010 CB 2009
Mali MoF 2012 MoF 2012 CB 2011
Malta Eurostat 2012 Eurostat 2012 NSO 2012
Marshall Islands MoF 2011/12 Oct/Sep NSO 2013 NSO 2012
Mauritania MoF 2012 NSO 2012 CB 2009
Mauritius MoF 2013 NSO 2013 CB 2013
Mexico MoF 2013 NSO 2013 CB 2013
Micronesia MoF 2011/12 Oct/Sep NSO 2012 NSO 2012
Moldova MoF 2013 NSO 2013 CB 2012
Mongolia MoF 2013 NSO 2013 CB 2013
Montenegro MoF 2013 NSO 2013 CB 2012
Morocco MEP 2013 NSO 2013 Foreign Exchange Office 2013
Mozambique MoF 2012 NSO 2012 CB 2011
Myanmar MoF 2011/12 Apr/Mar NSO 2012 IMF staff 2012
Namibia MoF 2008/09 Apr/Mar NSO 2009 CB 2009
Nepal MoF 2011/12 Aug/Jul CB 2011/12 CB 2010/11
Netherlands MoF 2013 NSO 2013 CB 2012
New Zealand MoF 2012/13 NSO 2013 NSO 2012
Nicaragua MoF 2012 CB 2012 IMF staff 2012
Niger MoF 2011 NSO 2011 CB 2010
Nigeria MoF 2012 NSO 2013 CB 2012
Norway NSO and MoF 2012 NSO 2013 NSO 2012
Oman MoF 2011 NSO 2012 CB 2011
Pakistan MoF 2012/13 Jul/Jun MoF 2012/13 CB 2012/13
Palau MoF 2012 Oct/Sep MoF 2011/12 MoF 2012
Panama MEP 2012 NSO 2012 NSO 2012
Papua New Guinea MoF 2012 NSO 2012 CB 2012
Paraguay MoF 2012 CB 2012 CB 2012
Peru MoF 2012 CB 2013 CB 2013
Philippines MoF 2013 NSO 2013 CB 2012
Poland Eurostat 2013 NSO 2013 CB 2013
Portugal NSO 2012 NSO 2012 CB 2012
Qatar MoF 2012/13 Apr/Mar NSO 2013 CB and IMF staff 2012
Romania MoF 2013 NSO 2013 CB 2013

Country Currency
National Accounts
Historical Data
Source1
Latest Actual
Data Base Year2
Reporting
Period3
Use of Chain-
Weighted
Methodology4
Russia Russian ruble NSO 2013 2008 From 1995
Rwanda Rwanda franc MoF 2012 2006
Samoa Samoa tala NSO 2012/13 2002 Jul/Jun
San Marino Euro NSO 2011 2007
São Tomé and Príncipe São Tomé and Príncipe
dobra
NSO 2010 2000
Saudi Arabia Saudi Arabian riyal NSO and MEP 2013 1999
Senegal CFA franc NSO 2011 2000
Serbia Serbian dinar NSO 2012 2010 From 2010
Seychelles Seychelles rupee NSO 2011 2006
Sierra Leone Sierra Leonean leone NSO 2012 2006 From 2010
Singapore Singapore dollar NSO 2013 2005 From 2005
Slovak Republic Euro Haver Analytics 2013 2005 From 1993
Slovenia Euro NSO 2013 2000 From 2000
Solomon Islands Solomon Islands dollar CB 2011 2004
South Africa South African rand CB 2012 2005
South Sudan South Sudanese pound NSO 2011 2010
Spain Euro NSO 2013 2008 From 1995
Sri Lanka Sri Lanka rupee CB 2012 2002
St. Kitts and Nevis Eastern Caribbean dollar NSO 2013 20065
St. Lucia Eastern Caribbean dollar NSO 2013 2006
St. Vincent and the Grenadines Eastern Caribbean dollar NSO 2013 20065
Sudan Sudanese pound NSO 2010 2008
Suriname Surinamese dollar NSO 2011 2007
Swaziland Swaziland lilangeni NSO 2009 2000
Sweden Swedish krona NSO 2012 2012 From 1993
Switzerland Swiss franc NSO 2013 2005 From 1980
Syria Syrian pound NSO 2010 2000
Taiwan Province of China New Taiwan dollar NSO 2013 2006
Tajikistan Tajik somoni NSO 2012 1995
Tanzania Tanzania shilling NSO 2012 2001
Thailand Thai baht NSO 2013 1988
Timor-Leste U.S. dollar MoF 2011 20105
Togo CFA franc NSO 2012 2000
Tonga Tongan pa’anga CB 2012 2010/11 Jul/Jun
Trinidad and Tobago Trinidad and Tobago dollar NSO 2011 2000
Tunisia Tunisian dinar NSO 2012 2005 From 2009
Turkey Turkish lira NSO 2012 1998
Turkmenistan New Turkmen manat NSO and IMF staff 2012 2005 From 2000
Tuvalu Australian dollar PFTAC advisors 2012 2005
Uganda Uganda shilling NSO 2013 2002
Ukraine Ukrainian hryvnia State Statistics
Committee
2013 2007 From 2005
United Arab Emirates U.A.E. dirham NSO 2012 2007
United Kingdom Pound sterling NSO 2013 2010 From 1980

Country
Historical Data
Source1
Latest
Actual
Data
Reporting
Period3
Historical Data
Source1
Latest
Actual
Data
Historical Data
Source1
Latest
Actual
Data
Russia MoF 2013 NSO 2013 CB 2013
Rwanda MoF 2012 MoF 2012 CB 2012
Samoa MoF 2010/11 Jul/Jun NSO 2013 CB 2011/12
San Marino MoF 2012 NSO 2012 . . . . . .
São Tomé and Príncipe MoF and Customs 2012 NSO 2013 CB 2012
Saudi Arabia MoF 2013 NSO 2013 CB 2012
Senegal MoF 2011 NSO 2011 CB and IMF staff 2011
Serbia MoF 2013 NSO 2013 CB 2012
Seychelles MoF 2012 NSO 2012 CB 2012
Sierra Leone MoF 2012 NSO 2012 CB 2012
Singapore MoF 2011/12 Apr/Mar NSO 2013 NSO 2013
Slovak Republic Haver Analytics 2013 Haver
Analytics
2013 IFS 2013
Slovenia MoF 2013 NSO 2013 NSO 2013
Solomon Islands MoF 2012 NSO 2012 CB 2012
South Africa MoF 2012/13 NSO 2013 CB 2012
South Sudan MoF 2012 NSO 2013 Other 2011
Spain MoF and Eurostat 2012 NSO 2013 CB 2013
Sri Lanka MoF 2011 NSO 2012 CB 2011
St. Kitts and Nevis MoF 2013 NSO 2013 CB 2013
St. Lucia MoF 2012/13 Apr/Mar NSO 2013 CB 2013
St. Vincent and the
Grenadines
MoF 2013 NSO 2013 CB 2013
Sudan MoF 2011 NSO 2010 CB 2011
Suriname MoF 2012 NSO 2013 CB 2012
Swaziland MoF 2011/12 Apr/Mar NSO 2012 CB 2010
Sweden MoF 2012 NSO 2013 NSO 2012
Switzerland MoF 2011 NSO 2013 CB 2012
Syria MoF 2009 NSO 2011 CB 2009
Taiwan Province of China MoF 2012 NSO 2013 CB 2013
Tajikistan MoF 2012 NSO 2012 CB 2011
Tanzania MoF 2012/13 Jul/Jun NSO 2013 CB 2011
Thailand MoF 2012/13 Oct/Sep NSO 2013 CB 2013
Timor-Leste MoF 2012 NSO 2012 CB 2012
Togo MoF 2013 NSO 2013 CB 2012
Tonga CB and MoF 2012 Jul/Jun CB 2012 CB and NSO 2012
Trinidad and Tobago MoF 2012/13 Oct/Sep NSO 2013 CB and NSO 2011
Tunisia MoF 2012 NSO 2012 CB 2012
Turkey MoF 2013 NSO 2013 CB 2013
Turkmenistan MoF 2012 NSO 2012 NSO and IMF staff 2012
Tuvalu IMF staff 2012 NSO 2012 PFTAC advisors 2012
Uganda MoF 2013 CB 2013/14 CB 2013
Ukraine MoF 2013 NSO 2013 CB 2013
United Arab Emirates MoF 2012 NSO 2012 CB 2012
United Kingdom NSO 2012 NSO 2013 NSO 2013

Table F. Key Data Documentation (concluded)
Country Currency
National Accounts
Historical Data
Source1
Latest Actual
Data Base Year2
Reporting
Period3
Use of Chain-
Weighted
Methodology4
United States U.S. dollar NSO 2013 2009 From 1980
Uruguay Uruguayan peso CB 2012 2005
Uzbekistan Uzbek sum NSO 2012 1995
Vanuatu Vanuatu vatu NSO 2012 2006
Venezuela Venezuelan bolívar fuerte CB 2010 1997
Vietnam Vietnamese dong NSO 2013 2010
Yemen Yemeni rial IMF staff 2008 1990
Zambia Zambian kwacha NSO 2013 2000
Zimbabwe U.S. dollar NSO 2012 2009
Source: IMF staff.
Note: CPI = consumer price index.
1BEA = U.S. Bureau of Economic Analysis; CB = Central Bank; IFS = IMF, International Financial Statistics; MEP = Ministry of Economy and/or Planning; MoC
= Ministry of Commerce; MoF = Ministry of Finance; NSO = National Statistics Office; OECD = Organization for Economic Cooperation and Development;
PFTAC = Pacific Financial Technical Assistance Centre.
2National accounts base year is the period with which other periods are compared and the period for which prices appear in the denominators of the price
relationships used to calculate the index.
3Reporting period is calendar year unless a fiscal year is indicated.
4Use of chain-weighted methodology allows countries to measure GDP growth more accurately by reducing or eliminating the downward biases in volume
series built on index numbers that average volume component using weights from a year in the moderately distant past.
5Nominal GDP is not measured in the same way as real GDP.
6Before 2012, based on March 21 to March 20; therafter, from December 21 to December 20.

Country
Historical Data
Source1
Latest
Actual
Data
Reporting
Period3
Historical Data
Source1
Latest
Actual
Data
Historical Data
Source1
Latest
Actual
Data
United States BEA 2013 NSO 2013 NSO 2013
Uruguay MoF 2012 NSO 2013 CB 2012
Uzbekistan MoF 2012 NSO 2012 MEP 2012
Vanuatu MoF 2012 NSO 2012 CB 2012
Venezuela MoF 2010 CB 2010 CB 2012
Vietnam MoF 2013 NSO 2013 CB 2012
Yemen MoF 2009 NSO and CB 2009 IMF staff 2009
Zambia MoF 2013 NSO 2013 CB 2013
Zimbabwe MoF 2012 NSO 2013 CB and MoF 2012

Fiscal Policy Assumptions
The short-term fiscal policy assumptions used in the
World Economic Outlook (WEO) are based on officially
announced budgets, adjusted for differences between
the national authorities and the IMF staff regarding
macroeconomic assumptions and projected fiscal out-
turns. The medium-term fiscal projections incorporate
policy measures that are judged likely to be imple-
mented. For cases in which the IMF staff has insuf-
ficient information to assess the authorities’ budget
intentions and prospects for policy implementation,
an unchanged structural primary balance is assumed
unless indicated otherwise. Specific assumptions used
in regard to some of the advanced economies follow.
(See also Tables B5 to B9 in the online section of the
Statistical Appendix for data on fiscal net lending/bor-
rowing and structural balances.1)
Argentina: The 2012 estimates are based on actual
data on outturns and IMF staff estimates. For the
outer years, the fiscal balance is projected to remain
roughly at the current level.
Australia: Fiscal projections are based on the 2013–
14 Mid-Year Economic and Fiscal Outlook, Australian
Bureau of Statistics, and IMF staff projections.
Austria: Projections take into account the authori-
ties’ medium-term fiscal framework, as well as associ-
ated further implementation needs and risks. For
2014, the creation of a defeasance structure for Hypo
Alpe Adria is assumed to increase the general govern-
ment debt-to-GDP ratio by 5½ percentage points and
the deficit by 1.2 percentage points.
Belgium: IMF staff projections for 2014 and beyond
are based on unchanged policies.
1 The output gap is actual minus potential output, as a percent
of potential output. Structural balances are expressed as a percent
of potential output. The structural balance is the actual net
lending/borrowing minus the effects of cyclical output from
potential output, corrected for one-time and other factors, such
as asset and commodity prices and output composition effects.
Changes in the structural balance consequently include effects of
temporary fiscal measures, the impact of fluctuations in interest
rates and debt service costs, and other noncyclical fluctuations in
net lending/borrowing. The computations of structural balances
are based on IMF staff estimates of potential GDP and revenue
and expenditure elasticities. (See Annex I of the October 1993
WEO.) Net debt is calculated as gross debt minus financial assets
corresponding to debt instruments. Estimates of the output gap
and of the structural balance are subject to significant margins of
uncertainty.
Brazil: For 2013, preliminary outturn estimates
are based on the information available as of Janu-
ary 2014. Projections for 2014 take into account the
latest adjustments to the original budget, as per the
Presidential Decree of February 2014. In outer years,
the IMF staff assumes adherence to the announced
primary target.
Canada: Projections use the baseline forecasts in the
Economic Action Plan 2014 (the fiscal year 2014/15
budget) and 2014 provincial budgets as available. The
IMF staff makes some adjustments to this forecast for
differences in macroeconomic projections. The IMF
staff forecast also incorporates the most recent data
releases from Statistics Canada’s Canadian System
of National Economic Accounts, including federal,
provincial, and territorial budgetary outturns through
the end of the fourth quarter of 2013.
Chile: Projections are based on the authorities’
budget projections, adjusted to reflect the IMF staff’s
projections for GDP and copper prices.
China: The pace of fiscal consolidation is likely to
be more gradual, reflecting reforms to strengthen social
safety nets and the social security system announced as
part of the Third Plenum reform agenda.
Denmark: Projections for 2013–15 are aligned with
the latest official budget estimates and the underly-
ing economic projections, adjusted where appropriate
for the IMF staff’s macroeconomic assumptions. For
2016–19, the projections incorporate key features
of the medium-term fiscal plan as embodied in the
authorities’ 2013 Convergence Program submitted to
the European Union (EU).
France: Projections for 2014 reflect the budget
law. For 2015–17, they are based on the 2013–17
multiyear budget, the April 2013 stability plan, and
the medium-term projection annexed to the 2014
budget adjusted for differences in assumptions on
macro and financial variables, and revenue projections.
The fiscal data for 2011 were revised following a May
15, 2013, revision by the statistical institute of both
national accounts and fiscal accounts. Fiscal data for
2012 reflect the preliminary outturn published by the
statistical institute in May 2013. Projections for 2013
reflect discussion with the authorities on monthly
developments on spending and revenue.
Germany: The estimates for 2013 are prelimi-
nary estimates from the Federal Statistical Office of
Germany. The IMF staff’s projections for 2014 and
Box A1. Economic Policy Assumptions Underlying the Projections for Selected Economies

beyond reflect the authorities’ adopted core federal
government budget plan, adjusted for the differences
in the IMF staff’s macroeconomic framework and
assumptions about fiscal developments in state and
local governments, the social insurance system, and
special funds. The estimate of gross debt includes
portfolios of impaired assets and noncore business
transferred to institutions that are winding up, as well
as other financial sector and EU support operations.
Greece: Fiscal projections for 2013 and the medium
term are consistent with the policies discussed between
the IMF staff and the authorities in the context of the
Extended Fund Facility.
Hong Kong SAR: Projections are based on the author-
ities’ medium-term fiscal projections on expenditures.
The fiscal year 2015/16 balance is adjusted to include
HK$50 billion for health care reform expenditure.
Hungary: Fiscal projections include IMF staff pro-
jections of the macroeconomic framework and of the
impact of recent legislative measures, as well as fiscal
policy plans announced in the 2014 budget.
India: Historical data are based on budgetary execu-
tion data. Projections are based on available informa-
tion on the authorities’ fiscal plans, with adjustments
for IMF staff assumptions. Subnational data are
incorporated with a lag of up to two years; general
government data are thus finalized well after central
government data. IMF and Indian presentations differ,
particularly regarding divestment and license auction
proceeds, net versus gross recording of revenues in cer-
tain minor categories, and some public sector lending.
Indonesia: IMF projections for 2013–18 are based on
a gradual increase in administrative fuel prices, the intro-
duction beginning in 2014 of new social protections, and
moderate tax policy and administration reforms.
Ireland: Fiscal projections are based on the 2014
budget. The fiscal projections are adjusted for differ-
ences between the IMF staff’s macroeconomic projec-
tions and those of the Irish authorities.
Italy: Fiscal projections incorporate the government’s
announced fiscal policy, as outlined in the 2014 Bud-
getary Plan, adjusted for different growth outlooks and
estimated impact of measures. Estimates of the cyclically
adjusted balance include the expenditure to clear capital
arrears in 2013, which are excluded from the structural
balance. After 2014, the IMF staff projects convergence
to a structural balance in line with Italy’s fiscal rule,
which implies corrective measures in some years, as yet
unidentified. Fiscal proposals by the new government
were announced after the finalization of the WEO
projections and are not included in the figures.
Japan: The projections include fiscal measures
already announced by the government, including
consumption tax increases, earthquake reconstruction
spending, and the stimulus package.
Korea: The medium-term forecast incorporates the
government’s announced medium-term consolidation
path.
Mexico: Fiscal projections for 2014 are broadly in
line with the approved budget; projections for 2014
onward assume compliance with rules established in
the Fiscal Responsibility Law.
Netherlands: Fiscal projections for the period 2012–
18 are based on the authorities’ Bureau for Economic
Policy Analysis budget projections, after adjusting for
differences in macroeconomic assumptions.
New Zealand: Fiscal projections are based on the
authorities’ 2013 Half Year Economic and Fiscal
Update and on IMF staff estimates.
Portugal: Projections for 2013–14 reflect the
authorities’ commitments under the EU- and IMF-
supported program; projections thereafter are based on
IMF staff estimates.
Russia: Projections for 2013–19 are based on the
oil-price-based fiscal price rule introduced in Decem-
ber 2012, with adjustments by the IMF staff.
Saudi Arabia: The authorities base their budget on
a conservative assumption for oil prices, with adjust-
ments to expenditure allocations considered in the
event that revenues exceed budgeted amounts. IMF
staff projections of oil revenues are based on WEO
baseline oil prices. On the expenditure side, wage bill
estimates incorporate 13th-month pay awards every
three years in accordance with the lunar calendar;
capital spending estimates over the medium term are
in line with the authorities’ priorities established in the
National Development Plans.
Singapore: For fiscal year 2013/14, projections are
based on budget numbers. For the remainder of the
projection period, the IMF staff assumes unchanged
policies.
South Africa: Fiscal projections are based on the
authorities’ Medium Term Budget Policy Statement,
released on October 23, 2013.
Spain: For 2013 and beyond, fiscal projections are
based on the measures specified in the Stability Pro-
Box A1. (continued)

gram Update 2013–16; the revised fiscal policy recom-
mendations by the European Council in June 2013;
the 2014 budget plan issued in October 2013; and the
2014 budget, approved in December 2013.
Sweden: Fiscal projections are broadly in line with
the authorities’ projections based on the 2014 Budget
Bill. The impact of cyclical developments on the
fiscal accounts is calculated using the Organization
for Economic Cooperation and Development’s latest
semi-elasticity.
Switzerland: Projections for 2012–18 are based on
IMF staff calculations, which incorporate measures to
restore balance in the federal accounts and strengthen
social security finances.
Turkey: Fiscal projections assume that both current
and capital spending will be in line with the authori-
ties’ 2013–15 Medium-Term Program based on cur-
rent trends and policies.
United Kingdom: Fiscal projections are based on
the U.K. Treasury’s 2014 budget, published in March
2014. However, on the revenue side, the authori-
ties’ projections are adjusted for differences between
IMF staff forecasts of macroeconomic variables (such
as GDP growth) and the forecasts of these variables
assumed in the authorities’ fiscal projections. In addi-
tion, IMF staff projections exclude the temporary
effects of financial sector interventions and the effect
on public sector net investment during 2012–13 of
transferring assets from the Royal Mail Pension Plan
to the public sector. Real government consumption
and investment are part of the real GDP path, which,
according to the IMF staff, may or may not be the
same as that projected by the U.K. Office for Budget
Responsibility. Transfers of profits from the Bank
of England’s Asset Purchases Facility affect general
government net interest payments. The timing of these
payments can create differences between fiscal year
primary balances published by the authorities and
calendar year balances shown in the WEO.
United States: Fiscal projections are based on the
February 2014 Congressional Budget Office baseline
adjusted for the IMF staff’s policy and macroeco-
nomic assumptions. The baseline incorporates the
key provisions of the Bipartisan Budget Act of 2013,
including a partial rollback of the sequester spend-
ing cuts in fiscal years 2014 and 2015. The rollback
is fully offset by savings elsewhere in the budget. In
fiscal years 2016 through 2021, the IMF staff assumes
that the sequester cuts will continue to be partially
replaced, in portions similar to the case in fiscal years
2014 and 2015, with back-loaded measures generat-
ing savings in mandatory programs and additional
revenues. Over the medium term, the IMF staff
assumes that Congress will continue to make regular
adjustments to Medicare payments (“DocFix”) and
will extend certain traditional programs (such as the
research and development tax credit). The fiscal pro-
jections are adjusted to reflect the IMF staff’s forecasts
of key macroeconomic and financial variables and
different accounting treatment of financial sector sup-
port and are converted to a general government basis.
Historical data start at 2001 for most series because
data compiled according to the 2001 Government
Finance Statistics Manual (GFSM2001) may not be
available for earlier years.
Monetary Policy Assumptions
Monetary policy assumptions are based on the
established policy framework in each country. In most
cases, this implies a nonaccommodative stance over
the business cycle: official interest rates will increase
when economic indicators suggest that inflation
will rise above its acceptable rate or range; they will
decrease when indicators suggest that inflation will
not exceed the acceptable rate or range, that out-
put growth is below its potential rate, and that the
margin of slack in the economy is significant. On this
basis, the London interbank offered rate (LIBOR) on
six-month U.S. dollar deposits is assumed to aver-
age 0.4 percent in 2014 and 0.8 percent in 2015 (see
Table 1.1). The rate on three-month euro deposits is
assumed to average 0.3 percent in 2014 and 0.4 per-
cent in 2015. The interest rate on six-month Japanese
yen deposits is assumed to average 0.2 percent in 2014
and 2015.
Australia: Monetary policy assumptions are in line
with market expectations.
Brazil: Monetary policy assumptions are consistent
with gradual convergence of inflation toward the
middle of the target range over the relevant horizon.
Canada: Monetary policy assumptions are in line
with market expectations.
China: Monetary policy will remain broadly
unchanged from its current status, consistent with
the authorities’ announcement of maintaining stable
economic growth.
Box A1. (continued)

Denmark: The monetary policy is to maintain the
peg to the euro.
Euro area: Monetary policy assumptions for euro area
member countries are in line with market expectations.
Hong Kong SAR: The IMF staff assumes that the
currency board system remains intact.
India: The policy (interest) rate assumption is based
on the average of market forecasts.
Indonesia: Monetary policy assumptions are in line
with market expectations and reduction of inflation by
2014 to within the central bank’s targeted band.
Japan: The current monetary policy conditions are
maintained for the projection period, and no further
tightening or loosening is assumed.
Korea: Normalization is assumed to commence
in the second half of 2014, with policy rates rising
through 2015.
Mexico: Monetary assumptions are consistent with
attaining the inflation target.
Russia: Monetary projections assume increasing
exchange rate flexibility as part of the transition to
the new full-fledged inflation-targeting regime, as
indicated in recent statements by the Central Bank of
Russia. Specifically, policy rates are assumed to remain
at the current levels, gradually reducing the number of
interventions in the foreign exchange markets.
Saudi Arabia: Monetary policy projections are based
on the continuation of the exchange rate peg to the
U.S. dollar.
Singapore: Broad money is projected to grow in line
with the projected growth in nominal GDP.
South Africa: Monetary projections are consistent
with South Africa’s 3–6 percent inflation target range.
Sweden: Monetary projections are in line with Riks-
bank projections.
Switzerland: Monetary policy variables reflect
historical data from the national authorities and the
market.
Turkey: Broad money and the long-term bond yield
are based on IMF staff projections. The short-term
deposit rate is projected to evolve with a constant
spread against the interest rate of a similar U.S.
instrument.
United Kingdom: On monetary policy, the projec-
tions assume no changes to the policy rate or the level
of asset purchases through 2014.
United States: Given the outlook for sluggish
growth and inflation, the IMF staff expects the federal
funds target to remain near zero until late 2014.
This assumption is consistent with the Federal Open
Market Committee’s statement following its January
2013 meeting (and reaffirmed in subsequent meet-
ings) that economic conditions are likely to warrant
an exceptionally low federal funds rate at least through
late 2014.
Box A1. (concluded)

List of Tables
Output
A1. Summary of World Output
A2. Advanced Economies: Real GDP and Total Domestic Demand
A3. Advanced Economies: Components of Real GDP
A4. Emerging Market and Developing Economies: Real GDP
Inflation
A5. Summary of Inflation
A6. Advanced Economies: Consumer Prices
A7. Emerging Market and Developing Economies: Consumer Prices
Financial Policies
A8. Major Advanced Economies: General Government Fiscal Balances and Debt
Foreign Trade
A9. Summary of World Trade Volumes and Prices
Current Account Transactions
A10. Summary of Balances on Current Account
A11. Advanced Economies: Balance on Current Account
A12. Emerging Market and Developing Economies: Balance on Current Account
Balance of Payments and External Financing
A13. Emerging Market and Developing Economies: Net Financial Flows
A14. Emerging Market and Developing Economies: Private Financial Flows
Flow of Funds
A15. Summary of Sources and Uses of World Savings
Medium-Term Baseline Scenario
A16. Summary of World Medium-Term Baseline Scenario

Table A1. Summary of World Output1
(Annual percent change)
Average Projections
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
World 3.7 5.2 5.3 2.7 –0.4 5.2 3.9 3.2 3.0 3.6 3.9 3.9
Advanced Economies 2.8 3.0 2.7 0.1 –3.4 3.0 1.7 1.4 1.3 2.2 2.3 2.1
United States 3.4 2.7 1.8 –0.3 –2.8 2.5 1.8 2.8 1.9 2.8 3.0 2.2
Euro Area2 2.1 3.3 3.0 0.4 –4.4 2.0 1.6 –0.7 –0.5 1.2 1.5 1.5
Japan 1.0 1.7 2.2 –1.0 –5.5 4.7 –0.5 1.4 1.5 1.4 1.0 1.1
Other Advanced Economies3 3.6 4.0 4.2 1.0 –2.4 4.5 2.7 1.5 2.1 2.9 2.9 3.0
Emerging Market and Developing Economies 5.2 8.2 8.7 5.9 3.1 7.5 6.3 5.0 4.7 4.9 5.3 5.3
Regional Groups
Commonwealth of Independent States4 4.2 8.8 8.9 5.3 –6.4 4.9 4.8 3.4 2.1 2.3 3.1 3.2
Emerging and Developing Asia 7.1 10.3 11.5 7.3 7.7 9.7 7.9 6.7 6.5 6.7 6.8 6.5
Emerging and Developing Europe 4.0 6.4 5.3 3.3 –3.4 4.7 5.4 1.4 2.8 2.4 2.9 3.4
Latin America and the Caribbean 2.9 5.6 5.8 4.3 –1.3 6.0 4.6 3.1 2.7 2.5 3.0 3.6
Middle East, North Africa, Afghanistan, and
Pakistan 4.9 6.7 6.0 5.1 2.8 5.2 3.9 4.2 2.4 3.2 4.4 4.5
Middle East and North Africa 4.9 6.8 6.0 5.1 3.0 5.5 3.9 4.1 2.2 3.2 4.5 4.4
Sub-Saharan Africa 4.7 6.3 7.1 5.7 2.6 5.6 5.5 4.9 4.9 5.4 5.5 5.4
Memorandum
European Union 2.5 3.6 3.4 0.6 –4.4 2.0 1.7 –0.3 0.2 1.6 1.8 1.9
Analytical Groups
Fuel 4.6 7.9 7.5 5.3 –1.2 5.1 4.8 4.4 2.4 3.0 3.9 3.9
Nonfuel 5.3 8.3 9.0 6.0 4.1 8.1 6.6 5.2 5.2 5.3 5.6 5.6
Of Which, Primary Products 4.0 5.8 6.0 4.3 1.0 5.2 4.8 4.2 4.1 4.0 4.5 4.5
Net Debtor Economies 4.1 6.5 6.6 4.3 1.6 6.8 5.1 3.7 3.6 3.8 4.5 5.0
Of Which, Official Financing 4.7 5.9 5.0 4.9 1.9 4.1 5.0 4.1 4.6 4.4 4.7 5.2
Net Debtor Economies by Debt-Servicing
Experience
Rescheduling during 2008–12 4.2 6.9 6.7 6.1 1.9 5.7 5.0 3.0 3.8 2.7 3.4 4.1
Memorandum
Median Growth Rate
Advanced Economies 3.4 4.0 4.0 0.8 –3.7 2.3 1.9 0.9 0.9 1.9 2.2 2.2
Output per Capita
Advanced Economies 2.1 2.3 2.0 –0.6 –4.1 2.5 1.2 0.9 0.8 1.7 1.8 1.6
World Growth Rate Based on Market Exchange 3.0 4.0 3.9 1.5 –2.1 4.1 3.0 2.5 2.4 3.1 3.3 3.3
Value of World Output (billions of U.S. dollars)
At Market Exchange Rates 35,002 50,059 56,440 61,848 58,623 64,020 70,896 72,106 73,982 76,776 81,009 100,847
At Purchasing Power Parities 44,472 62,474 67,466 70,558 70,627 75,099 79,381 83,258 86,995 91,093 96,256 121,265
1Real GDP.
2Excludes Latvia.
3In this table, Other Advanced Economies means advanced economies excluding the United States, Euro Area countries, and Japan but including Latvia.

Table A2. Advanced Economies: Real GDP and Total Domestic Demand1
Fourth Quarter2
Average Projections Projections
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019 2013:Q4 2014:Q4 2015:Q4
Real GDP
Advanced Economies 2.8 3.0 2.7 0.1 –3.4 3.0 1.7 1.4 1.3 2.2 2.3 2.1 2.1 2.1 2.4
United States 3.4 2.7 1.8 –0.3 –2.8 2.5 1.8 2.8 1.9 2.8 3.0 2.2 2.6 2.7 3.0
Euro Area3 2.1 3.3 3.0 0.4 –4.4 2.0 1.6 –0.7 –0.5 1.2 1.5 1.5 0.5 1.3 1.5
Germany 1.2 3.9 3.4 0.8 –5.1 3.9 3.4 0.9 0.5 1.7 1.6 1.3 1.4 1.6 1.7
France 2.2 2.5 2.3 –0.1 –3.1 1.7 2.0 0.0 0.3 1.0 1.5 1.9 0.8 1.2 1.6
Italy 1.4 2.2 1.7 –1.2 –5.5 1.7 0.4 –2.4 –1.9 0.6 1.1 0.9 –0.9 0.7 1.4
Spain 3.7 4.1 3.5 0.9 –3.8 –0.2 0.1 –1.6 –1.2 0.9 1.0 1.3 –0.2 1.1 0.9
Netherlands 2.7 3.4 3.9 1.8 –3.7 1.5 0.9 –1.2 –0.8 0.8 1.6 2.1 0.8 0.6 1.7
Belgium 2.2 2.7 2.9 1.0 –2.8 2.3 1.8 –0.1 0.2 1.2 1.2 1.5 1.0 1.1 1.3
Austria 2.4 3.7 3.7 1.4 –3.8 1.8 2.8 0.9 0.4 1.7 1.7 1.4 0.5 2.3 1.3
Greece 3.7 5.5 3.5 –0.2 –3.1 –4.9 –7.1 –7.0 –3.9 0.6 2.9 2.8 –2.5 2.3 3.2
Portugal 2.5 1.4 2.4 0.0 –2.9 1.9 –1.3 –3.2 –1.4 1.2 1.5 1.8 1.6 0.7 2.0
Finland 3.7 4.4 5.3 0.3 –8.5 3.4 2.8 –1.0 –1.4 0.3 1.1 1.8 –0.5 2.1 0.0
Ireland 7.6 5.5 5.0 –2.2 –6.4 –1.1 2.2 0.2 –0.3 1.7 2.5 2.5 –0.6 –1.3 0.5
Slovak Republic 4.2 8.3 10.5 5.8 –4.9 4.4 3.0 1.8 0.9 2.3 3.0 3.6 1.4 2.9 3.0
Slovenia 4.0 5.8 7.0 3.4 –7.9 1.3 0.7 –2.5 –1.1 0.3 0.9 1.9 1.9 –0.9 1.5
Luxembourg 4.8 4.9 6.6 –0.7 –5.6 3.1 1.9 –0.2 2.0 2.1 1.9 2.2 1.8 2.1 1.7
Latvia 6.9 11.0 10.0 –2.8 –17.7 –1.3 5.3 5.2 4.1 3.8 4.4 4.0 3.9 4.2 4.0
Estonia 6.9 10.1 7.5 –4.2 –14.1 2.6 9.6 3.9 0.8 2.4 3.2 3.7 0.9 6.1 3.3
Cyprus4 3.5 4.1 5.1 3.6 –1.9 1.3 0.4 –2.4 –6.0 –4.8 0.9 1.9 . . . . . . . . .
Malta . . . 2.6 4.1 3.9 –2.8 3.3 1.7 0.9 2.4 1.8 1.8 1.7 2.9 2.0 1.1
Japan 1.0 1.7 2.2 –1.0 –5.5 4.7 –0.5 1.4 1.5 1.4 1.0 1.1 2.5 1.2 0.5
United Kingdom 3.4 2.8 3.4 –0.8 –5.2 1.7 1.1 0.3 1.8 2.9 2.5 2.4 2.7 3.0 1.9
Canada 3.3 2.6 2.0 1.2 –2.7 3.4 2.5 1.7 2.0 2.3 2.4 2.0 2.7 2.1 2.4
Korea5 4.8 5.2 5.1 2.3 0.3 6.3 3.7 2.0 2.8 3.7 3.8 3.8 4.0 3.3 4.1
Australia 3.7 2.7 4.5 2.7 1.5 2.2 2.6 3.6 2.4 2.6 2.7 3.0 2.8 2.4 3.1
Taiwan Province of China 4.4 5.4 6.0 0.7 –1.8 10.8 4.2 1.5 2.1 3.1 3.9 4.5 2.3 2.2 5.9
Sweden 3.1 4.3 3.3 –0.6 –5.0 6.6 2.9 0.9 1.5 2.8 2.6 2.4 3.1 2.1 2.6
Hong Kong SAR 3.4 7.0 6.5 2.1 –2.5 6.8 4.8 1.5 2.9 3.7 3.8 4.0 2.9 3.9 3.8
Switzerland 1.7 3.8 3.8 2.2 –1.9 3.0 1.8 1.0 2.0 2.1 2.2 1.7 1.9 2.6 2.0
Singapore 5.3 8.9 9.0 1.9 –0.6 15.1 6.0 1.9 4.1 3.6 3.6 3.8 5.5 2.6 4.2
Czech Republic 3.0 7.0 5.7 3.1 –4.5 2.5 1.8 –1.0 –0.9 1.9 2.0 2.4 1.3 1.1 2.0
Norway 2.9 2.3 2.7 0.0 –1.4 0.6 1.1 2.8 0.8 1.8 1.9 2.1 1.3 2.0 1.7
Israel 3.6 5.8 6.9 4.5 1.2 5.7 4.6 3.4 3.3 3.2 3.4 3.5 3.2 3.3 3.3
Denmark 2.1 3.4 1.6 –0.8 –5.7 1.4 1.1 –0.4 0.4 1.5 1.7 1.8 0.6 2.0 1.8
New Zealand 3.5 2.8 3.4 –0.8 –1.4 2.1 1.9 2.6 2.4 3.3 3.0 2.5 1.6 4.7 1.9
Iceland 4.6 4.7 6.0 1.2 –6.6 –4.1 2.7 1.4 2.9 2.7 3.1 2.3 2.3 3.2 1.9
San Marino . . . 3.8 7.1 3.4 –9.5 –5.0 –8.5 –5.1 –3.2 0.0 2.2 2.9 . . . . . . . . .
Memorandum
Major Advanced Economies 2.6 2.6 2.2 –0.3 –3.8 2.8 1.6 1.7 1.4 2.2 2.3 1.9 2.2 2.1 2.2
Real Total Domestic Demand
Advanced Economies 2.9 2.8 2.3 –0.4 –3.8 2.9 1.4 1.1 1.0 2.0 2.2 2.0 1.9 1.8 2.3
United States 3.9 2.6 1.1 –1.3 –3.8 2.9 1.7 2.6 1.7 2.6 3.1 2.2 2.3 2.8 3.2
Euro Area 2.0 3.1 2.8 0.3 –3.7 1.2 0.7 –2.2 –1.0 0.9 1.0 1.4 0.1 1.0 1.1
Germany 0.6 2.8 2.0 1.0 –2.3 2.3 2.8 –0.2 0.5 1.4 1.3 1.3 0.5 2.1 1.3
France 2.3 2.4 3.2 0.3 –2.6 1.8 2.0 –0.9 0.4 1.0 1.0 1.7 1.2 0.8 1.1
Italy 1.8 2.1 1.4 –1.2 –4.4 2.1 –0.9 –5.1 –3.0 0.5 0.7 0.9 –1.0 0.2 1.1
Spain 4.4 5.2 4.1 –0.5 –6.3 –0.6 –2.0 –4.1 –2.7 0.5 0.3 0.7 –0.6 0.6 0.4
Japan 0.7 0.9 1.1 –1.3 –4.0 2.9 0.4 2.3 1.8 1.5 0.6 1.1 3.0 0.5 0.2
United Kingdom 3.8 2.4 3.4 –1.6 –6.3 2.4 –0.1 1.2 1.9 2.8 2.3 2.3 2.7 2.5 2.0
Canada 3.4 3.9 3.4 2.8 –2.7 5.2 2.9 2.2 1.8 2.0 2.0 1.9 2.3 1.6 2.1
Other Advanced Economies6 3.3 4.2 5.0 1.5 –2.9 5.7 2.9 2.0 1.9 2.5 2.7 3.2 2.6 1.4 3.6
Memorandum
Major Advanced Economies 2.8 2.4 1.7 –0.8 –3.8 2.8 1.4 1.5 1.3 2.1 2.2 1.9 2.0 2.0 2.2
1In this and other tables, when countries are not listed alphabetically, they are ordered on the basis of economic size.
2From the fourth quarter of the preceding year.
3Excludes Latvia.
4Owing to the unusually large macroeconomic uncertainty, projections for this variable are not available. The national accounts data for 2013 refer to staff estimates at the time of the
third review of the program and are subject to revision.
5Korea’s real GDP series is based on the reference year 2005. This does not reflect the revised national accounts released on March 26, 2014, after the WEO was finalized for
publication. These comprehensive revisions include implementing the 2008 System of National Accounts and updating of the reference year to 2010. As a result of these revisions, real
GDP growth in 2013 was revised up to 3 percent from 2.8 percent.
6In this table, Other Advanced Economies means advanced economies excluding the G7 (Canada, France, Germany, Italy, Japan, United Kingdom, United States) and Euro Area
countries but including Latvia.

Table A3. Advanced Economies: Components of Real GDP
Averages Projections
1996–2005 2006–15 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Private Consumer Expenditure
Advanced Economies 3.0 1.4 2.6 2.4 0.1 –1.1 2.0 1.5 1.2 1.3 1.9 2.1
United States 3.9 1.8 3.0 2.2 –0.4 –1.6 2.0 2.5 2.2 2.0 2.7 2.9
Euro Area1 2.0 0.4 2.1 1.7 0.4 –1.0 1.0 0.3 –1.4 –0.7 0.6 1.0
Germany 0.9 0.9 1.6 –0.2 0.7 0.3 1.0 2.3 0.7 1.0 1.0 1.1
France 2.3 0.9 2.2 2.4 0.2 0.3 1.6 0.6 –0.3 0.4 0.9 1.0
Italy 1.6 –0.5 1.4 1.1 –0.8 –1.6 1.5 –0.3 –4.0 –2.6 –0.2 0.5
Spain 3.8 –0.1 4.0 3.5 –0.6 –3.7 0.2 –1.2 –2.8 –2.1 1.2 0.9
Japan 1.0 0.9 1.1 0.9 –0.9 –0.7 2.8 0.3 2.0 1.9 0.7 0.6
United Kingdom 4.1 0.9 1.8 2.7 –1.0 –3.6 1.0 –0.4 1.5 2.3 2.4 2.6
Canada 3.4 2.5 4.1 4.2 2.9 0.3 3.4 2.3 1.9 2.2 2.2 2.1
Memorandum
Major Advanced Economies 2.8 1.3 2.4 1.9 –0.2 –1.2 1.9 1.7 1.4 1.6 1.9 2.1
Public Consumption
Advanced Economies 2.2 1.0 1.7 1.8 2.3 3.1 0.9 –0.7 0.3 –0.1 0.4 0.4
United States 2.0 0.4 1.1 1.4 2.5 3.7 0.1 –2.7 –0.2 –2.0 –0.6 0.1
Euro Area1 1.8 0.9 2.1 2.2 2.3 2.6 0.6 –0.1 –0.5 0.2 0.3 –0.2
Germany 0.9 1.4 0.9 1.4 3.2 3.0 1.3 1.0 1.0 0.7 0.9 0.9
France 1.4 1.2 1.4 1.5 1.3 2.5 1.8 0.4 1.4 1.7 0.4 –0.1
Italy 1.8 –0.3 0.5 1.0 0.6 0.8 –0.4 –1.3 –2.6 –0.8 –0.1 –0.4
Spain 4.2 0.9 4.6 5.6 5.9 3.7 1.5 –0.5 –4.8 –2.3 –1.7 –2.2
Japan 2.4 1.3 0.0 1.1 –0.1 2.3 1.9 1.2 1.7 2.2 1.7 1.0
United Kingdom 2.8 0.9 2.2 0.7 2.1 0.7 0.5 0.0 1.6 0.9 1.2 –0.5
Canada 1.7 2.1 3.1 2.8 4.6 3.3 2.7 0.8 1.1 0.8 1.0 1.0
Memorandum
Major Advanced Economies 2.0 0.7 1.1 1.3 2.1 2.9 0.7 –1.1 0.4 –0.5 0.2 0.4
Gross Fixed Capital Formation
Advanced Economies 3.5 0.5 3.9 2.5 –3.0 –11.9 1.8 2.5 1.9 0.9 3.4 4.0
United States 5.1 0.5 2.2 –1.2 –4.8 –13.1 1.1 3.4 5.5 2.9 4.0 6.3
Euro Area1 2.7 –0.6 5.6 5.2 –1.4 –12.8 –0.4 1.6 –4.1 –3.0 2.2 2.6
Germany 0.0 1.7 8.9 5.1 0.7 –12.2 5.4 7.0 –1.4 –0.6 3.2 2.5
France 3.3 0.5 4.0 6.3 0.4 –10.6 1.5 3.0 –1.2 –2.1 1.9 2.7
Italy 2.6 –2.1 3.4 1.8 –3.7 –11.7 0.6 –2.2 –8.0 –4.7 1.9 2.6
Spain 6.2 –3.5 7.1 4.5 –4.7 –18.0 –5.5 –5.4 –7.0 –5.1 0.6 1.2
Japan –0.8 –0.4 1.5 0.3 –4.1 –10.6 –0.2 1.4 3.4 2.6 2.6 –0.2
United Kingdom 4.5 0.0 5.6 7.5 –6.9 –16.7 2.8 –2.4 0.7 –0.5 7.7 5.2
Canada 5.9 2.2 6.2 3.2 1.6 –12.0 11.3 4.2 4.3 0.0 1.6 3.0
Other Advanced Economies2 3.4 2.6 5.6 6.3 0.1 –6.3 6.6 3.7 1.9 2.2 2.8 3.2
Memorandum

Table A3. Advanced Economies: Components of Real GDP (concluded)
1996–2005 2006–15 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Final Domestic Demand
United States 3.9 1.3 2.6 1.4 –0.9 –3.0 1.5 1.8 2.4 1.6 2.5 3.2
Euro Area1 2.1 0.3 2.8 2.5 0.4 –2.8 0.6 0.4 –1.7 –0.9 0.8 1.0
Germany 0.7 1.2 2.8 1.2 1.1 –1.6 1.8 2.9 0.4 0.6 1.4 1.3
France 2.2 0.9 2.4 3.0 0.5 –1.4 1.6 1.0 –0.1 0.3 0.9 1.0
Italy 1.9 –0.8 1.6 1.2 –1.2 –3.2 0.9 –0.9 –4.5 –2.6 0.2 0.7
Spain 4.5 –0.7 5.0 4.1 –0.7 –6.2 –0.9 –2.0 –4.1 –2.7 0.5 0.3
Japan 0.8 0.7 1.0 0.8 –1.6 –2.3 2.0 0.7 2.2 2.1 1.3 0.5
United Kingdom 3.9 0.8 2.5 3.1 –1.4 –4.8 1.2 –0.6 1.4 1.6 2.9 2.3
Canada 3.6 2.4 4.4 3.7 2.9 –1.9 5.0 2.4 2.3 1.4 1.8 2.1
Other Advanced Economies2 3.3 2.5 4.0 4.9 1.1 –0.9 4.2 2.8 2.0 2.1 2.6 2.7
Memorandum
Stock Building3
Advanced Economies 0.0 0.0 0.1 0.0 –0.2 –1.1 1.1 0.0 –0.1 0.0 0.1 0.0
United States 0.0 0.0 0.0 –0.2 –0.5 –0.8 1.5 –0.2 0.2 0.2 0.1 0.0
Euro Area1 0.0 0.0 0.3 0.3 –0.1 –1.0 0.6 0.3 –0.5 –0.1 0.1 0.0
Germany –0.1 0.0 0.1 0.8 –0.1 –0.6 0.5 0.0 –0.5 –0.1 0.0 0.0
France 0.1 –0.1 0.1 0.2 –0.2 –1.2 0.2 1.1 –0.9 0.1 0.0 0.0
Italy –0.1 0.0 0.5 0.2 0.0 –1.2 1.1 –0.1 –0.7 –0.4 0.3 0.0
Spain 0.0 0.0 0.3 –0.1 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.0
Japan 0.0 0.0 –0.1 0.3 0.2 –1.5 0.9 –0.2 0.1 –0.3 0.1 0.1
United Kingdom 0.0 0.0 –0.1 0.3 –0.2 –1.5 1.2 0.4 –0.2 0.3 0.0 0.0
Canada 0.0 0.0 –0.1 –0.1 0.0 –0.8 0.2 0.5 0.0 0.4 0.0 –0.1
Other Advanced Economies2 0.0 0.0 0.1 0.1 0.3 –1.9 1.4 0.1 0.0 –0.2 –0.1 0.0
Memorandum
Major Advanced Economies 0.0 0.0 0.0 0.1 –0.3 –1.0 1.1 0.0 –0.1 0.1 0.1 0.0
Foreign Balance3
Advanced Economies –0.1 0.3 0.2 0.4 0.5 0.3 0.2 0.4 0.4 0.3 0.3 0.2
United States –0.6 0.2 –0.1 0.6 1.1 1.1 –0.5 0.1 0.1 0.1 0.1 –0.3
Euro Area1 0.1 0.4 0.2 0.2 0.1 –0.7 0.7 0.9 1.5 0.5 0.4 0.4
Germany 0.5 0.4 1.2 1.5 –0.1 –3.0 1.7 0.7 1.1 0.0 0.4 0.3
France –0.1 0.0 0.0 –0.9 –0.3 –0.5 –0.1 –0.1 1.0 –0.1 0.0 0.5
Italy –0.3 0.5 0.1 0.3 0.0 –1.2 –0.4 1.5 2.6 0.8 0.6 0.4
Spain –0.7 1.0 –1.4 –0.8 1.5 2.9 0.4 2.1 2.5 1.5 0.4 0.6
Japan 0.2 0.0 0.8 1.0 0.2 –2.0 2.0 –0.8 –0.7 –0.2 –0.2 0.3
United Kingdom –0.6 0.2 0.2 –0.1 0.9 0.9 –0.5 1.2 –0.7 0.1 0.0 0.1
Canada –0.2 –0.7 –1.4 –1.5 –1.9 0.0 –2.0 –0.4 –0.6 0.3 0.4 0.4
Memorandum
Major Advanced Economies –0.3 0.2 0.2 0.5 0.5 0.0 0.0 0.2 0.2 0.1 0.1 0.0
1Excludes Latvia.
2In this table, Other Advanced Economies means advanced economies excluding the G7 (Canada, France, Germany, Italy, Japan, United Kingdom, United States) and Euro Area countries
but including Latvia.
3Changes expressed as percent of GDP in the preceding period.

Table A4. Emerging Market and Developing Economies: Real GDP
Average Projections
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Commonwealth of Independent States1,2 4.2 8.8 8.9 5.3 –6.4 4.9 4.8 3.4 2.1 2.3 3.1 3.2
Russia 3.8 8.2 8.5 5.2 –7.8 4.5 4.3 3.4 1.3 1.3 2.3 2.5
Excluding Russia 5.0 10.6 9.9 5.6 –3.1 6.0 6.1 3.3 3.9 5.3 5.7 5.0
Armenia 8.6 13.2 13.7 6.9 –14.1 2.2 4.7 7.1 3.2 4.3 4.5 5.0
Azerbaijan 9.5 34.5 25.0 10.8 9.3 5.0 0.1 2.2 5.8 5.0 4.6 4.2
Belarus 6.9 10.0 8.7 10.3 0.1 7.7 5.5 1.7 0.9 1.6 2.5 2.8
Georgia 6.5 9.4 12.3 2.3 –3.8 6.3 7.2 6.2 3.2 5.0 5.0 5.0
Kazakhstan 6.4 10.7 8.9 3.3 1.2 7.3 7.5 5.0 6.0 5.7 6.1 5.4
Kyrgyz Republic 4.7 3.1 8.5 7.6 2.9 –0.5 6.0 –0.9 10.5 4.4 4.9 5.2
Moldova 2.2 4.8 3.0 7.8 –6.0 7.1 6.8 –0.7 8.9 3.5 4.5 4.0
Tajikistan 6.0 7.0 7.8 7.9 3.9 6.5 7.4 7.5 7.4 6.2 5.7 5.8
Turkmenistan 9.9 11.0 11.1 14.7 6.1 9.2 14.7 11.1 10.2 10.7 12.5 8.3
Ukraine3 2.8 7.4 7.6 2.3 –14.8 4.1 5.2 0.2 0.0 . . . . . . . . .
Uzbekistan 4.6 7.5 9.5 9.0 8.1 8.5 8.3 8.2 8.0 7.0 6.5 5.5
Bangladesh 5.4 6.5 6.3 6.0 5.9 6.4 6.5 6.1 5.8 6.0 6.5 7.0
Bhutan 6.9 7.0 12.6 10.8 5.7 9.3 10.1 6.5 5.0 6.4 7.6 8.0
Brunei Darussalam 1.7 4.4 0.2 –1.9 –1.8 2.6 3.4 0.9 –1.2 5.4 3.0 3.5
Cambodia 8.3 10.8 10.2 6.7 0.1 6.1 7.1 7.3 7.0 7.2 7.3 7.5
China 9.2 12.7 14.2 9.6 9.2 10.4 9.3 7.7 7.7 7.5 7.3 6.5
Fiji 2.5 1.9 –0.9 1.0 –1.4 3.0 2.7 1.7 3.0 2.3 2.3 2.4
India 6.4 9.3 9.8 3.9 8.5 10.3 6.6 4.7 4.4 5.4 6.4 6.8
Indonesia 2.6 5.5 6.3 6.0 4.6 6.2 6.5 6.3 5.8 5.4 5.8 6.0
Kiribati 2.3 –4.5 7.5 2.8 –0.7 –0.5 2.7 2.8 2.9 2.7 2.0 2.0
Lao P.D.R. 6.0 8.6 7.8 7.8 7.5 8.1 8.0 7.9 8.2 7.5 7.8 7.5
Malaysia 4.7 5.6 6.3 4.8 –1.5 7.4 5.1 5.6 4.7 5.2 5.0 5.0
Maldives 6.7 19.6 10.6 12.2 –3.6 7.1 6.5 0.9 3.7 4.2 4.5 4.8
Marshall Islands . . . 1.9 3.8 –2.0 –1.8 5.9 0.6 3.2 0.8 3.2 1.7 1.5
Micronesia 0.2 –0.2 –2.1 –2.6 1.0 2.5 2.1 0.4 0.6 0.6 0.6 0.7
Mongolia 4.6 8.6 10.2 8.9 –1.3 6.4 17.5 12.4 11.7 12.9 7.7 8.8
Myanmar . . . 13.1 12.0 3.6 5.1 5.3 5.9 7.3 7.5 7.8 7.8 7.7
Nepal 4.2 3.4 3.4 6.1 4.5 4.8 3.4 4.9 3.6 4.5 4.5 5.0
Palau . . . –1.4 1.7 –5.5 –10.7 3.2 5.2 5.5 –0.2 1.8 2.2 2.2
Papua New Guinea 1.5 2.3 7.2 6.6 6.1 7.7 10.7 8.1 4.6 6.0 21.6 3.7
Philippines 4.1 5.2 6.6 4.2 1.1 7.6 3.6 6.8 7.2 6.5 6.5 6.0
Samoa 4.2 2.1 1.8 4.3 –5.1 0.5 1.4 2.9 –0.3 1.6 1.9 2.0
Solomon Islands 0.1 4.0 6.4 7.1 –4.7 7.8 10.7 4.9 2.9 4.0 3.6 3.6
Sri Lanka 4.3 7.7 6.8 6.0 3.5 8.0 8.2 6.3 7.3 7.0 6.5 6.5
Thailand 2.7 5.1 5.0 2.5 –2.3 7.8 0.1 6.5 2.9 2.5 3.8 4.5
Timor-Leste4 . . . –3.2 11.6 14.6 12.8 9.5 12.0 9.3 8.4 9.0 8.8 9.1
Tonga 1.2 –2.8 –1.4 2.6 3.3 3.1 1.9 0.7 1.0 1.6 1.7 1.7
Tuvalu . . . 2.1 6.4 8.0 –4.4 –2.7 8.5 0.2 1.1 1.6 1.9 1.9
Vanuatu 1.9 8.5 5.2 6.5 3.3 1.6 1.2 1.8 2.8 3.5 4.5 4.0
Vietnam 7.1 7.0 7.1 5.7 5.4 6.4 6.2 5.2 5.4 5.6 5.7 6.0
Emerging and Developing Europe 4.0 6.4 5.3 3.3 –3.4 4.7 5.4 1.4 2.8 2.4 2.9 3.4
Albania 5.7 5.4 5.9 7.5 3.3 3.8 3.1 1.3 0.7 2.1 3.3 4.7
Bosnia and Herzegovina . . . 5.7 6.0 5.6 –2.7 0.8 1.0 –1.2 1.2 2.0 3.2 4.0
Bulgaria 2.4 6.5 6.4 6.2 –5.5 0.4 1.8 0.6 0.9 1.6 2.5 3.0
Croatia 3.9 4.9 5.1 2.1 –6.9 –2.3 –0.2 –1.9 –1.0 –0.6 0.4 2.0
Hungary 3.6 3.9 0.1 0.9 –6.8 1.1 1.6 –1.7 1.1 2.0 1.7 1.7
Kosovo . . . 3.4 8.3 7.2 3.5 3.2 4.4 2.5 2.5 3.9 4.5 4.5
Lithuania 6.2 7.8 9.8 2.9 –14.8 1.6 6.0 3.7 3.3 3.3 3.5 3.8
FYR Macedonia 2.3 5.0 6.1 5.0 –0.9 2.9 2.8 –0.4 3.1 3.2 3.4 4.0
Montenegro . . . 8.6 10.7 6.9 –5.7 2.5 3.2 –2.5 3.4 2.8 2.9 3.1
Poland 4.2 6.2 6.8 5.1 1.6 3.9 4.5 1.9 1.6 3.1 3.3 3.6
Romania 2.2 7.9 6.3 7.3 –6.6 –1.1 2.2 0.7 3.5 2.2 2.5 3.5
Serbia . . . 3.6 5.4 3.8 –3.5 1.0 1.6 –1.5 2.5 1.0 1.5 4.0
Turkey 4.3 6.9 4.7 0.7 –4.8 9.2 8.8 2.2 4.3 2.3 3.1 3.5

Table A4. Emerging Market and Developing Economies: Real GDP (continued)
Average Projections
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Latin America and the Caribbean 2.9 5.6 5.8 4.3 –1.3 6.0 4.6 3.1 2.7 2.5 3.0 3.6
Antigua and Barbuda 3.9 12.7 7.1 1.5 –10.7 –8.6 –2.1 2.8 0.5 1.6 1.9 2.2
Argentina5 2.3 8.5 8.7 6.8 0.9 9.2 8.9 1.9 4.3 0.5 1.0 2.0
The Bahamas 4.0 2.5 1.4 –2.3 –4.2 1.0 1.7 1.8 1.9 2.3 2.8 2.3
Barbados 2.0 5.7 1.7 0.3 –4.1 0.2 0.8 0.0 –0.7 –1.2 0.9 2.3
Belize 5.7 4.7 1.2 3.8 0.3 3.1 2.1 4.0 1.6 2.5 2.5 2.5
Bolivia 3.3 4.8 4.6 6.1 3.4 4.1 5.2 5.2 6.8 5.1 5.0 5.0
Brazil 2.4 4.0 6.1 5.2 –0.3 7.5 2.7 1.0 2.3 1.8 2.7 3.5
Chile 4.3 5.8 5.2 3.2 –0.9 5.7 5.7 5.4 4.2 3.6 4.1 4.5
Colombia 2.3 6.7 6.9 3.5 1.7 4.0 6.6 4.2 4.3 4.5 4.5 4.5
Costa Rica 4.5 8.8 7.9 2.7 –1.0 5.0 4.5 5.1 3.5 3.8 4.1 4.5
Dominica 1.9 4.6 6.0 7.8 –1.1 1.2 0.2 –1.1 0.8 1.7 1.7 1.9
Dominican Republic 5.2 10.7 8.5 5.3 3.5 7.8 4.5 3.9 4.1 4.5 4.1 4.0
Ecuador 3.0 4.4 2.2 6.4 0.6 3.5 7.8 5.1 4.2 4.2 3.5 3.5
El Salvador 2.7 3.9 3.8 1.3 –3.1 1.4 2.2 1.9 1.6 1.6 1.7 2.0
Grenada 5.9 –4.0 6.1 0.9 –6.6 –0.5 0.8 –1.8 1.5 1.1 1.2 2.5
Guatemala 3.3 5.4 6.3 3.3 0.5 2.9 4.2 3.0 3.5 3.5 3.5 3.5
Guyana 1.6 5.1 7.0 2.0 3.3 4.4 5.4 4.8 4.8 4.3 4.0 3.3
Haiti 1.0 2.2 3.3 0.8 3.1 –5.5 5.5 2.9 4.3 4.0 4.0 4.0
Honduras 3.8 6.6 6.2 4.2 –2.4 3.7 3.8 3.9 2.6 3.0 3.1 3.0
Jamaica 0.6 2.9 1.4 –0.8 –3.4 –1.4 1.4 –0.5 0.5 1.3 1.7 2.7
Mexico 3.4 5.0 3.1 1.4 –4.7 5.1 4.0 3.9 1.1 3.0 3.5 3.8
Nicaragua 4.1 4.2 5.0 4.0 –2.2 3.6 5.4 5.2 4.2 4.0 4.0 4.0
Panama 4.9 8.5 12.1 10.1 3.9 7.5 10.9 10.8 8.0 7.2 6.9 5.8
Paraguay 1.2 4.8 5.4 6.4 –4.0 13.1 4.3 –1.2 13.0 4.8 4.5 4.5
Peru 3.3 7.7 8.9 9.8 0.9 8.8 6.9 6.3 5.0 5.5 5.8 5.8
St. Kitts and Nevis 3.9 4.6 4.8 3.4 –3.8 –3.8 –1.9 –0.9 1.7 2.7 3.0 3.1
St. Lucia 2.0 7.2 1.4 4.7 –0.1 –0.7 1.4 –1.3 –1.5 0.3 1.0 2.2
St. Vincent and the Grenadines 3.8 6.0 3.0 –0.5 –2.0 –2.3 0.3 1.5 2.1 2.3 2.9 3.3
Suriname 3.4 5.8 5.1 4.1 3.0 4.2 5.3 4.8 4.7 4.0 4.0 4.3
Trinidad and Tobago 7.9 13.2 4.8 3.4 –4.4 0.2 –2.6 1.2 1.6 2.2 2.2 1.6
Uruguay 1.2 4.1 6.5 7.2 2.2 8.9 6.5 3.9 4.2 2.8 3.0 3.8
Venezuela 1.6 9.9 8.8 5.3 –3.2 –1.5 4.2 5.6 1.0 –0.5 –1.0 1.0
and Pakistan 4.9 6.7 6.0 5.1 2.8 5.2 3.9 4.2 2.4 3.2 4.4 4.5
Afghanistan . . . 5.4 13.3 3.9 20.6 8.4 6.5 14.0 3.6 3.2 4.5 5.6
Algeria 4.3 1.7 3.4 2.4 1.6 3.6 2.8 3.3 2.7 4.3 4.1 4.3
Bahrain 4.9 6.5 8.3 6.2 2.5 4.3 2.1 3.4 4.9 4.7 3.3 3.5
Djibouti 1.2 4.8 5.1 5.8 5.0 3.5 4.5 4.8 5.0 6.0 6.5 5.8
Egypt 4.8 6.8 7.1 7.2 4.7 5.1 1.8 2.2 2.1 2.3 4.1 4.0
Iran 5.1 6.2 6.4 0.6 3.9 5.9 2.7 –5.6 –1.7 1.5 2.3 2.4
Iraq . . . 10.2 1.4 6.6 5.8 5.5 10.2 10.3 4.2 5.9 6.7 9.2
Jordan 4.8 8.1 8.2 7.2 5.5 2.3 2.6 2.7 3.3 3.5 4.0 4.5
Kuwait 5.0 7.5 6.0 2.5 –7.1 –2.4 6.3 6.2 0.8 2.6 3.0 3.9
Lebanon 3.5 1.6 9.4 9.1 10.3 8.0 2.0 1.5 1.0 1.0 2.5 4.0
Libya 3.1 6.5 6.4 2.7 –0.8 5.0 –62.1 104.5 –9.4 –7.8 29.8 3.5
Mauritania 3.3 11.4 1.0 3.5 –1.2 4.3 4.0 7.0 6.7 6.8 6.5 10.7
Morocco 4.4 7.8 2.7 5.6 4.8 3.6 5.0 2.7 4.5 3.9 4.9 5.6
Oman 3.1 5.5 6.7 13.2 3.3 5.6 4.5 5.0 5.1 3.4 3.4 3.7
Pakistan 4.6 5.8 5.5 5.0 0.4 2.6 3.7 4.4 3.6 3.1 3.7 5.0
Qatar 9.7 26.2 18.0 17.7 12.0 16.7 13.0 6.2 6.1 5.9 7.1 6.4
Saudi Arabia 3.3 5.6 6.0 8.4 1.8 7.4 8.6 5.8 3.8 4.1 4.2 4.3
Sudan6 15.5 8.9 8.5 3.0 4.7 3.0 –1.2 –3.0 3.4 2.7 4.6 4.3
Syria7 2.7 5.0 5.7 4.5 5.9 3.4 . . . . . . . . . . . . . . . . . .
Tunisia 5.0 5.7 6.3 4.5 3.1 2.9 –1.9 3.6 2.7 3.0 4.5 4.5
United Arab Emirates 5.8 9.8 3.2 3.2 –4.8 1.7 3.9 4.4 4.8 4.4 4.2 4.2
Yemen 4.7 3.2 3.3 3.6 3.9 7.7 –12.7 2.4 4.4 5.1 4.4 4.7

Table A4. Emerging Market and Developing Economies: Real GDP (concluded)
Average Projections
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Angola 8.2 20.7 22.6 13.8 2.4 3.4 3.9 5.2 4.1 5.3 5.5 6.7
Benin 4.5 3.8 4.6 5.0 2.7 2.6 3.3 5.4 5.6 5.5 5.2 4.8
Botswana 5.8 8.0 8.7 3.9 –7.8 8.6 6.1 4.2 3.9 4.1 4.4 3.8
Burkina Faso 6.6 6.3 4.1 5.8 3.0 8.4 5.0 9.0 6.8 6.0 7.0 7.0
Burundi 0.9 5.4 3.4 4.9 3.8 5.1 4.2 4.0 4.5 4.7 4.8 5.4
Cabo Verde 7.1 9.1 9.2 6.7 –1.3 1.5 4.0 1.0 0.5 3.0 3.5 4.0
Cameroon 4.2 3.2 2.8 3.6 1.9 3.3 4.1 4.6 4.6 4.8 5.1 5.4
Central African Republic 0.7 4.8 4.6 2.1 1.7 3.0 3.3 4.1 –36.0 1.5 5.3 5.7
Chad 8.6 0.6 3.3 3.1 4.2 13.6 0.1 8.9 3.6 10.8 7.3 3.5
Comoros 2.1 1.2 0.5 1.0 1.8 2.1 2.2 3.0 3.5 4.0 4.0 4.0
Democratic Republic of the Congo –0.1 5.3 6.3 6.2 2.9 7.1 6.9 7.2 8.5 8.7 8.5 5.6
Republic of Congo 3.2 6.2 –1.6 5.6 7.5 8.7 3.4 3.8 4.5 8.1 5.8 2.6
Côte d’Ivoire 1.5 0.7 1.6 2.3 3.7 2.4 –4.7 9.8 8.1 8.2 7.7 5.7
Equatorial Guinea 38.4 1.3 13.1 12.3 –8.1 –1.3 5.0 3.2 –4.9 –2.4 –8.3 –9.4
Eritrea 1.8 –1.0 1.4 –9.8 3.9 2.2 8.7 7.0 1.3 2.3 1.9 3.6
Ethiopia 5.4 11.5 11.8 11.2 10.0 10.6 11.4 8.5 9.7 7.5 7.5 6.5
Gabon 0.5 –1.9 6.3 1.7 –2.3 6.2 6.9 5.5 5.9 5.7 6.3 5.8
The Gambia 4.4 1.1 3.6 5.7 6.4 6.5 –4.3 5.3 6.3 7.4 7.0 5.5
Ghana 4.9 6.1 6.5 8.4 4.0 8.0 15.0 7.9 5.4 4.8 5.4 3.8
Guinea 3.7 2.5 1.8 4.9 –0.3 1.9 3.9 3.8 2.5 4.5 5.0 17.6
Guinea-Bissau 0.2 2.1 3.2 3.2 3.0 3.5 5.3 –1.5 0.3 3.0 3.9 4.3
Kenya 2.9 6.3 7.0 1.5 2.7 5.8 4.4 4.6 5.6 6.3 6.3 6.5
Lesotho 3.4 4.1 4.9 5.1 4.5 5.6 4.3 6.0 5.8 5.6 5.5 5.1
Liberia . . . 8.4 12.9 6.0 5.1 6.1 7.9 8.3 8.0 7.0 8.7 7.4
Madagascar 3.1 5.4 6.5 7.2 –3.5 0.1 1.5 2.5 2.4 3.0 4.0 5.1
Malawi 3.2 2.1 9.5 8.3 9.0 6.5 4.3 1.9 5.0 6.1 6.5 5.9
Mali 5.1 5.3 4.3 5.0 4.5 5.8 2.7 0.0 1.7 6.5 5.0 4.4
Mauritius 4.1 4.5 5.9 5.5 3.0 4.1 3.8 3.3 3.1 3.7 4.0 4.0
Mozambique 9.1 8.7 7.3 6.8 6.3 7.1 7.3 7.2 7.1 8.3 7.9 7.8
Namibia 4.2 7.1 5.4 3.4 –1.1 6.3 5.7 5.0 4.3 4.3 4.5 4.7
Niger 4.4 5.8 3.2 9.6 –0.7 8.4 2.3 11.1 3.6 6.5 5.9 8.3
Nigeria 7.1 6.2 7.0 6.0 7.0 8.0 7.4 6.6 6.3 7.1 7.0 6.7
Rwanda 8.7 9.2 7.6 11.2 6.2 7.2 8.2 8.0 5.0 7.5 7.5 7.5
São Tomé and Príncipe 2.6 12.6 2.0 9.1 4.0 4.5 4.9 4.0 4.0 5.0 5.5 6.0
Senegal 4.4 2.5 4.9 3.7 2.4 4.3 2.1 3.5 4.0 4.6 4.8 5.2
Seychelles 2.8 9.4 10.4 –2.1 –1.1 5.9 7.9 2.8 3.6 3.7 3.8 3.4
Sierra Leone 0.7 4.2 8.0 5.2 3.2 5.3 6.0 15.2 16.3 13.9 10.8 5.0
South Africa 3.3 5.6 5.5 3.6 –1.5 3.1 3.6 2.5 1.9 2.3 2.7 3.0
South Sudan . . . . . . . . . . . . . . . . . . . . . –47.6 24.4 7.1 17.6 5.8
Swaziland 2.5 3.3 3.5 2.4 1.2 1.9 –0.6 1.9 2.8 2.1 2.1 2.1
Tanzania 5.5 6.7 7.1 7.4 6.0 7.0 6.4 6.9 7.0 7.2 7.0 6.9
Togo 1.6 4.1 2.3 2.4 3.5 4.1 4.8 5.9 5.6 6.0 6.0 5.2
Uganda 7.0 7.0 8.1 10.4 4.1 6.2 6.2 2.8 6.0 6.4 6.8 7.4
Zambia 3.8 6.2 6.2 5.7 6.4 7.6 6.8 7.2 6.0 7.3 7.1 6.0
Zimbabwe8 . . . –3.6 –3.3 –16.4 8.2 11.4 11.9 10.6 3.0 4.2 4.5 4.0
1Data for some countries refer to real net material product (NMP) or are estimates based on NMP. The figures should be interpreted only as indicative of broad orders of magnitude
because reliable, comparable data are not generally available. In particular, the growth of output of new private enterprises of the informal economy is not fully reflected in the recent
figures.
4In this table only, the data for Timor-Leste are based on non-oil GDP.
5The data for Argentina are officially reported data. The IMF has, however, issued a declaration of censure and called on Argentina to adopt remedial measures to address the quality of the
official GDP data. Alternative data sources have shown significantly lower real growth than the official data since 2008. In this context, the Fund is also using alternative estimates of GDP
growth for the surveillance of macroeconomic developments in Argentina.
6Data for 2011 exclude South Sudan after July 9. Data for 2012 and onward pertain to the current Sudan.
7Data for Syria are excluded for 2011 onward due to the uncertain political situation.
8The Zimbabwe dollar ceased circulating in early 2009. Data are based on IMF staff estimates of price and exchange rate developments in U.S. dollars. IMF staff estimates of U.S. dollar
values may differ from authorities’ estimates. Real GDP is in constant 2009 prices.

Table A5. Summary of Inflation
(Percent)
Average Projections
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
GDP Deflators
United States 2.0 3.1 2.7 2.0 0.8 1.2 2.0 1.7 1.5 1.5 1.8 2.0
Euro Area1 1.7 1.8 2.4 2.0 1.0 0.8 1.2 1.3 1.4 1.2 1.4 1.6
Japan –1.0 –1.1 –0.9 –1.3 –0.5 –2.2 –1.9 –0.9 –0.6 1.6 1.0 1.3
Consumer Prices
United States 2.5 3.2 2.9 3.8 –0.3 1.6 3.1 2.1 1.5 1.4 1.6 2.0
Euro Area1,3 1.9 2.2 2.2 3.3 0.3 1.6 2.7 2.5 1.3 0.9 1.2 1.6
Japan –0.1 0.2 0.1 1.4 –1.3 –0.7 –0.3 0.0 0.4 2.8 1.7 2.0
Regional Groups
Commonwealth of Independent States4 24.8 9.5 9.7 15.6 11.2 7.2 10.1 6.5 6.4 6.6 6.1 5.8
Emerging and Developing Europe 27.0 5.9 6.0 7.9 4.7 5.4 5.4 5.8 4.1 4.0 4.1 4.0
Latin America and the Caribbean5 10.1 5.3 5.4 7.9 5.9 6.0 6.6 5.9 6.8 . . . . . . . . .
and Pakistan 6.0 8.2 10.2 12.2 7.4 6.9 9.8 10.6 10.1 8.5 8.3 7.4
Middle East and North Africa 5.9 8.2 10.6 12.3 6.3 6.5 9.3 10.5 10.5 8.4 8.3 7.6
Memorandum
European Union 3.5 2.3 2.4 3.7 0.9 2.0 3.1 2.6 1.5 1.1 1.4 1.8
Analytical Groups
Fuel 17.0 9.4 10.4 14.3 9.0 7.8 9.8 9.0 10.2 9.0 8.1 7.2
Nonfuel 8.4 4.9 5.5 8.0 4.5 5.5 6.7 5.3 4.8 4.7 4.6 4.1
Of Which, Primary Products 10.4 6.2 6.2 12.1 7.0 5.4 7.0 7.2 6.8 6.5 5.9 5.1
Net Debtor Economies 10.9 6.4 6.0 9.1 7.4 6.7 7.6 7.1 6.3 5.9 5.7 5.0
Of Which, Official Financing 8.9 7.2 8.1 12.5 9.1 7.5 11.3 10.2 7.5 6.8 6.9 5.3
Experience
Rescheduling during 2008–125 8.8 7.5 7.6 11.2 10.9 9.2 12.6 12.0 8.8 . . . . . . . . .
Memorandum
Median Inflation Rate
1Excludes Latvia.
5See note 6 to Table A7.

Table A6. Advanced Economies: Consumer Prices1
End of Period2
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019 2013 2014 2015
Advanced Economies 2.0 2.4 2.2 3.4 0.1 1.5 2.7 2.0 1.4 1.5 1.6 2.0 1.2 1.6 1.7
United States 2.5 3.2 2.9 3.8 –0.3 1.6 3.1 2.1 1.5 1.4 1.6 2.0 1.2 1.5 1.7
Euro Area3,4 1.9 2.2 2.2 3.3 0.3 1.6 2.7 2.5 1.3 0.9 1.2 1.6 0.8 1.0 1.1
Germany 1.3 1.8 2.3 2.7 0.2 1.2 2.5 2.1 1.6 1.4 1.4 1.7 1.2 1.4 1.4
France 1.7 1.9 1.6 3.2 0.1 1.7 2.3 2.2 1.0 1.0 1.2 1.6 0.0 1.0 1.2
Italy 2.4 2.2 2.0 3.5 0.8 1.6 2.9 3.3 1.3 0.7 1.0 1.6 0.7 0.7 1.0
Spain 2.9 3.6 2.8 4.1 –0.2 2.0 3.1 2.4 1.5 0.3 0.8 1.1 0.3 0.5 0.8
Netherlands 2.3 1.7 1.6 2.2 1.0 0.9 2.5 2.8 2.6 0.8 1.0 1.5 1.7 0.9 1.1
Belgium 1.8 2.3 1.8 4.5 0.0 2.3 3.4 2.6 1.2 1.0 1.1 1.4 1.2 0.8 1.1
Austria 1.6 1.7 2.2 3.2 0.4 1.7 3.6 2.6 2.1 1.8 1.7 1.7 2.0 1.8 1.7
Greece 4.1 3.2 2.9 4.2 1.2 4.7 3.3 1.5 –0.9 –0.4 0.3 1.6 –1.7 0.0 0.7
Portugal 2.8 3.0 2.4 2.7 –0.9 1.4 3.6 2.8 0.4 0.7 1.2 1.5 0.2 2.5 –1.9
Finland 1.5 1.3 1.6 3.9 1.6 1.7 3.3 3.2 2.2 1.7 1.5 2.0 1.9 1.4 1.5
Ireland 3.0 2.7 2.9 3.1 –1.7 –1.6 1.2 1.9 0.5 0.6 1.1 1.7 1.8 0.2 0.9
Slovak Republic 7.0 4.3 1.9 3.9 0.9 0.7 4.1 3.7 1.5 0.7 1.6 2.2 0.4 1.6 1.6
Slovenia 6.8 2.5 3.6 5.7 0.9 1.8 1.8 2.6 1.6 1.2 1.6 2.0 0.7 1.3 1.8
Luxembourg 2.2 3.0 2.7 4.1 0.0 2.8 3.7 2.9 1.7 1.6 1.8 1.9 1.5 1.7 1.8
Latvia 5.4 6.6 10.1 15.3 3.3 –1.2 4.2 2.3 0.0 1.5 2.5 2.3 –0.4 2.4 2.5
Estonia 6.6 4.4 6.7 10.6 0.2 2.7 5.1 4.2 3.5 3.2 2.8 2.2 3.2 2.8 2.5
Cyprus4 2.7 2.3 2.2 4.4 0.2 2.6 3.5 3.1 0.4 0.4 1.4 1.9 –1.2 0.4 1.4
Malta 2.7 2.6 0.7 4.7 1.8 2.0 2.5 3.2 1.0 1.2 2.6 1.8 1.0 4.1 1.2
Japan –0.1 0.2 0.1 1.4 –1.3 –0.7 –0.3 0.0 0.4 2.8 1.7 2.0 1.4 2.9 1.9
United Kingdom4 1.5 2.3 2.3 3.6 2.2 3.3 4.5 2.8 2.6 1.9 1.9 2.0 2.1 1.9 1.9
Canada 2.0 2.0 2.1 2.4 0.3 1.8 2.9 1.5 1.0 1.5 1.9 2.0 1.0 1.8 2.0
Korea 3.6 2.2 2.5 4.7 2.8 2.9 4.0 2.2 1.3 1.8 3.0 3.0 1.1 2.5 3.0
Australia 2.5 3.6 2.3 4.4 1.8 2.9 3.3 1.8 2.4 2.3 2.4 2.5 2.7 1.8 2.5
Taiwan Province of China 1.0 0.6 1.8 3.5 –0.9 1.0 1.4 1.9 0.8 1.4 2.0 2.0 0.3 1.7 2.0
Sweden 1.0 1.4 2.2 3.4 –0.5 1.2 3.0 0.9 0.0 0.4 1.6 2.0 0.1 0.8 2.0
Hong Kong SAR 0.0 2.0 2.0 4.3 0.6 2.3 5.3 4.1 4.3 4.0 3.8 3.5 4.3 4.0 3.8
Switzerland 0.8 1.1 0.7 2.4 –0.5 0.7 0.2 –0.7 –0.2 0.2 0.5 1.0 0.0 1.0 1.0
Singapore 0.8 1.0 2.1 6.6 0.6 2.8 5.2 4.6 2.4 2.3 2.6 2.4 2.0 2.3 2.7
Czech Republic 4.5 2.5 2.9 6.3 1.0 1.5 1.9 3.3 1.4 1.0 1.9 2.0 1.4 1.2 2.0
Norway 2.0 2.3 0.7 3.8 2.2 2.4 1.3 0.7 2.1 2.0 2.0 2.5 2.0 2.0 2.0
Israel 4.0 2.1 0.5 4.6 3.3 2.7 3.5 1.7 1.5 1.6 2.0 2.0 1.8 1.7 2.0
Denmark 2.1 1.9 1.7 3.4 1.3 2.3 2.8 2.4 0.8 1.5 1.8 2.2 0.8 1.6 2.2
New Zealand 2.0 3.4 2.4 4.0 2.1 2.3 4.0 1.1 1.1 2.2 2.2 2.0 1.6 2.5 2.1
Iceland 3.5 6.7 5.1 12.7 12.0 5.4 4.0 5.2 3.9 2.9 3.4 2.5 3.3 3.3 3.1
San Marino . . . 2.1 2.5 4.1 2.4 2.6 2.0 2.8 1.3 1.0 1.2 1.7 1.3 1.0 1.2
Memorandum
Major Advanced Economies 1.8 2.4 2.2 3.2 –0.1 1.4 2.6 1.9 1.3 1.6 1.6 1.9 1.2 1.7 1.6
1Movements in consumer prices are shown as annual averages.
2Monthly year-over-year changes and, for several countries, on a quarterly basis.
3Excludes Latvia.

Table A7. Emerging Market and Developing Economies: Consumer Prices1
End of Period2
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019 2013 2014 2015
Commonwealth of Independent
States3,4 24.8 9.5 9.7 15.6 11.2 7.2 10.1 6.5 6.4 6.6 6.1 5.8 6.2 6.3 6.1
Russia 25.5 9.7 9.0 14.1 11.7 6.9 8.4 5.1 6.8 5.8 5.3 5.0 6.5 5.3 5.3
Excluding Russia 22.9 8.9 11.6 19.4 10.2 7.9 14.1 9.9 5.6 9.3 8.6 8.0 5.4 9.5 8.8
Armenia 5.6 3.0 4.6 9.0 3.5 7.3 7.7 2.5 5.8 5.0 4.0 4.0 5.6 4.0 4.0
Azerbaijan 3.7 8.4 16.6 20.8 1.6 5.7 7.9 1.0 2.4 3.5 4.0 4.9 3.6 3.4 4.5
Belarus 67.7 7.0 8.4 14.8 13.0 7.7 53.2 59.2 18.3 16.8 15.8 16.5 16.5 16.3 15.4
Georgia 9.7 9.2 9.2 10.0 1.7 7.1 8.5 –0.9 –0.5 4.0 4.6 5.0 2.3 4.0 5.0
Kazakhstan 11.7 8.6 10.8 17.1 7.3 7.1 8.3 5.1 5.8 9.2 7.5 5.4 4.8 10.1 7.5
Kyrgyz Republic 13.5 5.6 10.2 24.5 6.8 7.8 16.6 2.8 6.6 6.1 6.6 5.5 4.0 7.0 6.0
Moldova 16.0 12.7 12.4 12.7 0.0 7.4 7.6 4.6 4.6 5.5 5.9 5.0 5.2 5.2 6.5
Tajikistan 47.6 10.0 13.2 20.4 6.5 6.5 12.4 5.8 5.0 5.4 5.9 6.0 3.7 5.3 6.5
Turkmenistan 47.0 8.2 6.3 14.5 –2.7 4.4 5.3 5.3 6.6 5.7 6.0 6.0 5.5 6.0 6.0
Ukraine5 18.2 9.1 12.8 25.2 15.9 9.4 8.0 0.6 –0.3 . . . . . . . . . 0.5 . . . . . .
Uzbekistan 27.8 14.2 12.3 12.7 14.1 9.4 12.8 12.1 11.2 11.0 11.0 11.0 10.2 11.5 11.6
Emerging and Developing Asia 4.1 4.3 5.3 7.4 3.2 5.3 6.5 4.6 4.5 4.5 4.3 3.9 4.3 4.4 4.3
Bangladesh 4.9 6.8 9.1 8.9 5.4 8.1 10.7 6.2 7.5 7.3 6.7 5.7 7.3 7.0 6.4
Bhutan 5.7 4.9 5.2 6.3 7.1 4.8 8.6 10.1 8.7 10.2 8.8 6.7 10.0 9.6 8.4
Brunei Darussalam 0.5 0.2 1.0 2.1 1.0 0.2 0.1 0.1 0.4 0.5 0.5 0.6 0.1 0.5 0.5
Cambodia 4.2 6.1 7.7 25.0 –0.7 4.0 5.5 2.9 3.0 3.8 3.2 3.0 4.6 3.0 3.0
China 1.6 1.5 4.8 5.9 –0.7 3.3 5.4 2.6 2.6 3.0 3.0 3.0 2.5 3.0 3.0
Fiji 2.9 2.5 4.8 7.7 3.7 3.7 7.3 3.4 2.9 3.0 3.0 2.9 3.4 3.0 3.0
India 5.7 7.3 6.1 8.9 13.0 10.5 9.6 10.2 9.5 8.0 7.5 6.1 8.1 8.0 7.4
Indonesia 13.5 13.1 6.7 9.8 5.0 5.1 5.3 4.0 6.4 6.3 5.5 5.0 8.1 5.5 5.4
Kiribati 1.6 –1.0 3.6 13.7 9.8 –3.9 1.5 –3.0 2.0 2.5 2.5 2.5 2.0 2.5 2.5
Lao P.D.R. 28.7 6.8 4.5 7.6 0.0 6.0 7.6 4.3 6.4 7.5 7.5 5.7 6.6 7.7 7.3
Malaysia 2.4 3.6 2.0 5.4 0.6 1.7 3.2 1.7 2.1 3.3 3.9 2.7 3.2 3.3 3.9
Maldives 2.1 3.5 6.8 12.0 4.5 6.1 11.3 10.9 4.0 3.3 4.4 4.4 3.1 4.4 4.4
Marshall Islands . . . 5.3 2.6 14.7 0.5 2.2 4.9 4.5 1.4 1.6 1.8 2.2 1.4 1.6 1.8
Micronesia . . . 4.6 3.3 8.3 6.2 3.9 5.4 4.6 4.0 3.3 2.7 2.0 4.5 3.3 2.7
Mongolia 13.7 4.5 8.2 26.8 6.3 10.2 7.7 15.0 9.6 12.0 11.0 6.5 12.3 13.3 8.1
Myanmar . . . 26.3 30.9 11.5 2.2 8.2 2.8 2.8 5.8 6.6 6.9 4.7 6.7 7.0 6.7
Nepal 5.7 8.0 6.2 6.7 12.6 9.5 9.6 8.3 9.9 9.8 7.0 5.5 7.7 9.3 7.3
Palau . . . 4.8 3.0 10.0 4.7 1.1 2.6 5.4 2.8 3.0 3.5 2.0 3.0 3.5 3.0
Papua New Guinea 9.8 2.4 0.9 10.8 6.9 6.0 8.4 2.2 3.8 6.0 5.0 5.0 5.5 6.0 5.0
Philippines 5.8 5.5 2.9 8.2 4.2 3.8 4.7 3.2 2.9 4.4 3.6 3.5 4.1 4.0 3.5
Samoa 4.7 3.5 4.7 6.3 14.6 –0.2 2.9 6.2 –0.2 –1.0 3.0 2.5 –1.7 1.0 3.5
Solomon Islands 8.8 11.2 7.7 17.3 7.1 0.9 7.4 5.9 6.1 5.9 5.6 5.5 6.3 6.0 5.6
Sri Lanka 9.8 10.0 15.8 22.4 3.5 6.2 6.7 7.5 6.9 4.7 6.4 5.5 4.7 6.0 6.2
Thailand 3.2 4.6 2.2 5.5 –0.9 3.3 3.8 3.0 2.2 2.3 2.1 2.0 1.7 2.4 2.3
Timor-Leste . . . 4.1 9.0 7.6 0.1 4.5 11.7 13.1 10.6 9.5 8.1 6.0 10.4 8.5 7.6
Tonga 6.7 6.1 7.4 7.5 3.5 3.9 4.6 3.1 3.2 3.9 4.6 5.9 3.5 4.4 4.9
Tuvalu . . . 4.2 2.3 10.4 –0.3 –1.9 0.5 1.4 2.6 2.6 2.8 2.6 2.7 2.7 2.7
Vanuatu 2.3 2.0 3.8 4.2 5.2 2.7 0.7 1.4 1.3 1.8 2.4 2.7 1.5 2.0 2.7
Vietnam 4.2 7.5 8.3 23.1 6.7 9.2 18.7 9.1 6.6 6.3 6.2 5.1 6.0 6.3 6.1
Emerging and Developing Europe 27.0 5.9 6.0 7.9 4.7 5.4 5.4 5.8 4.1 4.0 4.1 4.0 3.4 4.6 3.9
Albania 7.8 2.4 2.9 3.4 2.3 3.5 3.4 2.0 1.9 2.7 2.8 3.0 1.9 2.6 3.0
Bosnia and Herzegovina . . . 6.1 1.5 7.4 –0.4 2.1 3.7 2.0 –0.1 1.1 1.5 2.1 –0.1 1.1 1.5
Bulgaria 46.5 7.4 7.6 12.0 2.5 3.0 3.4 2.4 0.4 –0.4 0.9 2.2 –0.9 0.5 1.3
Croatia 3.5 3.2 2.9 6.1 2.4 1.0 2.3 3.4 2.2 0.5 1.1 2.5 0.3 1.0 1.4
Hungary 10.4 3.9 7.9 6.1 4.2 4.9 4.0 5.7 1.7 0.9 3.0 3.0 0.4 2.9 3.0
Kosovo . . . 0.6 4.4 9.4 –2.4 3.5 7.3 2.5 1.9 1.8 1.5 1.5 1.5 1.5 1.5
Lithuania . . . 3.8 5.8 11.1 4.2 1.2 4.1 3.2 1.2 1.0 1.8 2.2 0.5 1.7 1.8
FYR Macedonia 2.1 3.2 2.3 8.4 –0.8 1.5 3.9 3.3 2.8 1.8 2.3 2.3 1.4 2.3 2.3
Montenegro . . . 2.1 3.5 9.0 3.6 0.7 3.1 3.6 2.2 0.2 1.1 1.4 0.3 0.9 1.1
Poland 7.6 1.0 2.5 4.2 3.4 2.6 4.3 3.7 0.9 1.5 2.4 2.5 0.7 2.1 2.5
Romania 39.3 6.6 4.8 7.8 5.6 6.1 5.8 3.3 4.0 2.2 3.1 2.7 1.6 3.5 3.1
Serbia . . . 10.7 6.9 12.4 8.1 6.2 11.1 7.3 7.7 4.0 4.0 4.0 2.2 5.3 4.0
Turkey 48.5 9.6 8.8 10.4 6.3 8.6 6.5 8.9 7.5 7.8 6.5 6.0 7.4 8.0 6.0

Table A7. Emerging Market and Developing Economies: Consumer Prices1 (continued)
End of Period2
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019 2013 2014 2015
Latin America and the Caribbean6 10.1 5.3 5.4 7.9 5.9 6.0 6.6 5.9 6.8 . . . . . . . . . 7.4 . . . . . .
Antigua and Barbuda 1.8 1.8 1.4 5.3 –0.6 3.4 3.5 3.4 1.1 1.0 1.7 2.5 1.1 1.1 2.0
Argentina6 4.9 10.9 8.8 8.6 6.3 10.5 9.8 10.0 10.6 . . . . . . . . . 10.9 . . . . . .
The Bahamas 1.6 2.1 2.5 4.7 1.9 1.3 3.2 2.0 0.3 2.0 2.5 1.3 0.3 5.5 2.5
Barbados 2.3 7.3 4.0 8.1 3.7 5.8 9.4 4.5 2.3 2.0 1.7 2.6 2.2 1.8 1.6
Belize 1.8 4.2 2.3 6.4 –1.1 0.9 1.5 1.4 0.5 1.2 2.0 2.0 0.4 2.0 2.0
Bolivia 4.7 4.3 6.7 14.0 3.3 2.5 9.9 4.5 5.7 6.8 5.3 5.0 6.5 5.5 5.2
Brazil 8.1 4.2 3.6 5.7 4.9 5.0 6.6 5.4 6.2 5.9 5.5 4.7 5.9 5.8 5.4
Chile 3.9 3.4 4.4 8.7 1.5 1.4 3.3 3.0 1.8 3.5 2.9 3.0 3.0 3.0 3.0
Colombia 10.9 4.3 5.5 7.0 4.2 2.3 3.4 3.2 2.0 1.9 2.9 3.0 1.9 2.7 3.0
Costa Rica 11.9 11.5 9.4 13.4 7.8 5.7 4.9 4.5 5.2 2.9 4.5 4.5 3.7 4.5 4.5
Dominica 1.4 2.6 3.2 6.4 0.0 2.8 1.3 1.5 –0.4 1.8 1.8 1.8 –0.9 2.3 1.7
Dominican Republic 12.2 7.6 6.1 10.6 1.4 6.3 8.5 3.7 4.8 3.9 4.2 4.0 3.9 4.5 4.0
Ecuador 27.8 3.3 2.3 8.4 5.2 3.6 4.5 5.1 2.7 2.8 2.6 2.5 2.7 2.7 2.5
El Salvador 3.6 4.0 4.6 7.3 0.5 1.2 5.1 1.7 0.8 1.8 2.6 2.6 0.8 2.0 2.6
Grenada 1.6 4.3 3.9 8.0 –0.3 3.4 3.0 2.4 0.0 1.6 1.7 2.3 –1.2 1.7 1.6
Guatemala 7.6 6.6 6.8 11.4 1.9 3.9 6.2 3.8 4.3 4.0 4.1 4.0 4.4 4.3 4.2
Guyana 5.4 6.7 12.2 8.1 3.0 3.7 5.0 2.4 3.5 3.9 4.3 3.8 3.5 4.3 4.3
Haiti 16.5 14.2 9.0 14.4 3.4 4.1 7.4 6.8 6.8 4.1 5.8 5.0 4.5 5.7 5.0
Honduras 12.1 5.6 6.9 11.4 5.5 4.7 6.8 5.2 5.2 5.5 6.5 5.5 4.9 7.0 6.0
Jamaica 11.0 8.9 9.2 22.0 9.6 12.6 7.5 6.9 9.4 9.1 8.2 6.9 9.7 8.5 8.0
Mexico 11.8 3.6 4.0 5.1 5.3 4.2 3.4 4.1 3.8 4.0 3.5 3.0 4.0 4.0 3.7
Nicaragua 8.5 9.1 11.1 19.8 3.7 5.5 8.1 7.2 7.4 7.0 7.0 7.0 6.9 7.0 7.0
Panama 1.1 2.5 4.2 8.8 2.4 3.5 5.9 5.7 4.0 3.8 3.6 2.5 3.7 3.6 3.5
Paraguay 8.7 9.6 8.1 10.2 2.6 4.7 8.3 3.7 2.7 4.7 5.0 5.0 3.7 5.0 5.0
Peru 4.4 2.0 1.8 5.8 2.9 1.5 3.4 3.7 2.8 2.5 2.1 2.0 2.9 2.3 2.0
St. Kitts and Nevis 3.2 8.5 4.5 5.3 2.1 0.6 7.1 1.4 0.7 0.7 1.8 2.5 0.4 1.5 2.0
St. Lucia 2.3 3.6 2.8 5.5 –0.2 3.3 2.8 4.2 1.5 1.1 2.4 3.1 –1.4 2.4 1.8
St. Vincent and the Grenadines 1.6 3.0 7.0 10.1 0.4 0.8 3.2 2.6 0.9 0.9 1.1 2.0 0.2 1.7 1.7
Suriname 25.2 11.1 6.6 15.0 0.0 6.9 17.7 5.0 1.9 1.7 3.1 3.7 0.6 2.2 3.3
Trinidad and Tobago 4.4 8.3 7.9 12.0 7.6 10.5 5.1 9.3 5.2 4.8 4.0 4.0 5.6 4.0 4.0
Uruguay 11.8 6.4 8.1 7.9 7.1 6.7 8.1 8.1 8.6 8.3 8.0 6.5 8.5 8.5 7.6
Venezuela 31.0 13.7 18.7 30.4 27.1 28.2 26.1 21.1 40.7 50.7 38.0 30.0 56.1 75.0 75.0
Middle East, North Africa,
Afghanistan, and Pakistan 6.0 8.2 10.2 12.2 7.4 6.9 9.8 10.6 10.1 8.5 8.3 7.4 7.9 9.0 7.9
Afghanistan . . . 6.8 8.7 26.4 –6.8 2.2 11.8 6.4 7.4 6.1 5.5 5.0 7.2 4.0 6.4
Algeria 4.6 2.3 3.7 4.9 5.7 3.9 4.5 8.9 3.3 4.0 4.0 4.0 1.1 5.3 4.0
Bahrain 0.7 2.0 3.3 3.5 2.8 2.0 –0.4 2.8 3.3 2.5 2.4 2.6 3.9 2.6 2.2
Djibouti 2.0 3.5 5.0 12.0 1.7 4.0 5.1 3.7 2.5 2.5 2.5 2.5 1.1 2.3 2.3
Egypt 4.7 4.2 11.0 11.7 16.2 11.7 11.1 8.6 6.9 10.7 11.2 12.2 9.8 11.3 11.5
Iran 15.9 11.9 18.4 25.3 10.8 12.4 21.5 30.5 35.2 23.0 22.0 20.0 22.0 24.0 20.0
Iraq . . . 53.2 30.8 2.7 –2.2 2.4 5.6 6.1 1.9 1.9 3.0 3.0 3.1 2.3 3.0
Jordan 2.6 6.3 4.7 13.9 –0.7 5.0 4.4 4.6 5.5 3.0 2.4 1.8 3.0 2.4 2.2
Kuwait 1.8 3.1 5.5 6.3 4.6 4.5 4.9 3.2 2.7 3.4 4.0 4.0 2.7 3.4 4.0
Lebanon 2.4 5.6 4.1 10.8 1.2 5.1 7.2 5.9 3.2 2.0 2.0 2.5 1.3 2.0 2.0
Libya –0.7 1.5 6.2 10.4 2.4 2.5 15.9 6.1 2.6 4.8 6.3 2.5 1.7 7.5 5.4
Mauritania 6.1 6.2 7.3 7.5 2.1 6.3 5.7 4.9 4.1 4.7 5.2 5.5 4.5 5.0 5.5
Morocco 1.6 3.3 2.0 3.9 1.0 1.0 0.9 1.3 1.9 2.5 2.5 2.5 0.4 2.5 2.5
Oman 0.1 3.4 5.9 12.6 3.5 3.3 4.0 2.9 1.3 2.7 3.1 3.4 1.3 2.7 3.1
Pakistan 6.3 8.0 7.8 10.8 17.6 10.1 13.7 11.0 7.4 8.8 9.0 6.0 5.9 10.0 8.0
Qatar 3.6 11.9 13.6 15.2 –4.9 –2.4 1.9 1.9 3.1 3.6 3.5 3.4 3.1 3.6 3.5
Saudi Arabia –0.3 1.9 5.0 6.1 4.1 3.8 3.7 2.9 3.5 3.0 3.2 3.5 3.0 3.3 3.4
Sudan7 21.8 7.2 8.0 14.3 11.3 13.0 18.1 35.5 36.5 20.4 14.3 5.5 41.9 18.1 12.0
Syria8 2.2 10.4 4.7 15.2 2.8 4.4 . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tunisia 2.8 4.1 3.4 4.9 3.5 4.4 3.5 5.6 6.1 5.5 5.0 4.0 6.0 5.3 4.5
United Arab Emirates 3.1 9.3 11.1 12.3 1.6 0.9 0.9 0.7 1.1 2.2 2.5 3.9 1.7 2.4 2.7
Yemen 12.8 10.8 7.9 19.0 3.7 11.2 19.5 9.9 11.1 10.4 9.8 7.7 9.8 10.0 9.5

Table A7. Emerging Market and Developing Economies: Consumer Prices1 (concluded)
End of Period2
1996–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019 2013 2014 2015
Sub-Saharan Africa 14.2 7.2 6.2 13.0 9.7 7.5 9.4 9.0 6.3 6.1 5.9 5.5 5.9 6.2 5.8
Angola 208.2 13.3 12.2 12.5 13.7 14.5 13.5 10.3 8.8 7.7 7.7 6.5 7.7 8.0 7.5
Benin 3.3 3.8 1.3 7.4 0.9 2.2 2.7 6.7 1.0 1.7 2.8 2.8 –1.8 4.0 2.8
Botswana 8.1 11.6 7.1 12.6 8.1 6.9 8.5 7.5 5.8 3.8 3.4 3.2 4.1 3.5 3.3
Burkina Faso 2.7 2.4 –0.2 10.7 2.6 –0.6 2.8 3.8 2.0 2.0 2.0 2.0 2.0 2.0 2.0
Burundi 12.4 9.1 14.4 26.0 4.6 4.1 14.9 12.0 8.8 5.9 6.0 4.5 8.8 5.9 6.0
Cabo Verde 2.6 4.8 4.4 6.8 1.0 2.1 4.5 2.5 1.5 1.7 2.0 2.0 0.1 2.0 2.0
Cameroon 2.5 4.9 1.1 5.3 3.0 1.3 2.9 2.4 2.1 2.5 2.5 2.5 1.7 2.5 2.5
Central African Republic 1.6 6.7 0.9 9.3 3.5 1.5 1.2 5.9 6.6 4.5 4.2 2.0 5.9 3.9 2.3
Chad 2.9 7.7 –7.4 8.3 10.1 –2.1 1.9 7.7 0.2 2.4 3.0 3.0 0.9 3.2 3.0
Comoros 3.2 3.4 4.5 4.8 4.8 3.9 6.8 6.3 2.3 3.2 3.2 3.1 3.2 3.2 3.2
Democratic Republic of the Congo 137.3 13.2 16.7 18.0 46.2 23.5 15.5 2.1 0.8 2.4 4.1 5.5 1.0 3.7 4.5
Republic of Congo 3.7 4.7 2.6 6.0 4.3 5.0 1.8 5.0 4.6 2.4 2.4 2.2 2.1 2.7 2.3
Côte d'Ivoire 3.1 2.5 1.9 6.3 1.0 1.4 4.9 1.3 2.6 1.2 2.5 2.5 0.4 0.0 2.5
Equatorial Guinea 5.4 4.5 2.8 4.7 5.7 5.3 4.8 3.4 3.2 3.9 3.7 3.0 4.9 3.7 3.4
Eritrea 14.2 15.1 9.3 19.9 33.0 12.7 13.3 12.3 12.3 12.3 12.3 12.3 12.3 12.3 12.3
Ethiopia 3.3 13.6 17.2 44.4 8.5 8.1 33.2 24.1 8.0 6.2 7.8 8.0 7.7 7.0 8.0
Gabon 1.1 –1.4 –1.0 5.3 1.9 1.4 1.3 2.7 0.5 5.6 2.5 2.5 3.3 2.5 2.5
The Gambia 5.8 2.1 5.4 4.5 4.6 5.0 4.8 4.6 5.2 5.3 5.0 5.0 5.6 5.0 5.0
Ghana 22.4 10.2 10.7 16.5 20.6 11.7 8.7 9.2 11.7 13.0 11.1 8.1 13.5 12.3 9.8
Guinea 8.6 34.7 22.9 18.4 4.7 15.5 21.4 15.2 12.0 10.2 8.5 6.0 11.0 8.5 7.8
Guinea-Bissau 10.7 0.7 4.6 10.4 –1.6 1.1 5.1 2.1 0.6 2.5 2.0 2.0 1.7 2.8 2.0
Kenya 7.3 6.0 4.3 15.1 10.6 4.3 14.0 9.4 5.7 6.6 5.5 5.0 7.1 6.6 5.1
Lesotho 7.5 6.1 8.0 10.7 7.4 3.6 5.0 6.2 5.3 4.7 4.6 4.0 4.6 4.6 4.6
Liberia . . . 9.5 11.4 17.5 7.4 7.3 8.5 6.8 7.6 8.1 7.5 5.8 8.5 7.9 7.0
Madagascar 10.2 10.8 10.4 9.2 9.0 9.3 10.0 5.8 5.8 6.2 6.0 5.0 6.3 6.5 6.0
Malawi 21.9 13.9 8.0 8.7 8.4 7.4 7.6 21.3 27.7 15.1 6.9 5.2 20.1 9.7 5.8
Mali 2.0 1.5 1.5 9.1 2.2 1.3 3.1 5.3 –0.6 3.9 2.5 2.2 0.0 8.1 3.3
Mauritius 5.5 8.9 8.8 9.7 2.5 2.9 6.5 3.9 3.5 3.8 4.5 5.0 3.5 4.5 5.0
Mozambique 12.5 13.2 8.2 10.3 3.3 12.7 10.4 2.1 4.2 5.6 5.6 5.6 3.0 6.0 5.6
Namibia 7.5 5.1 6.7 10.4 8.8 4.5 5.0 6.5 6.2 5.9 5.7 5.5 6.0 5.8 5.7
Niger 2.6 0.1 0.1 11.3 4.3 –2.8 2.9 0.5 2.3 2.5 2.1 –0.8 1.1 2.6 1.2
Nigeria 13.8 8.2 5.4 11.6 12.5 13.7 10.8 12.2 8.5 7.3 7.0 7.0 7.9 7.0 7.0
Rwanda 6.6 8.8 9.1 15.4 10.3 2.3 5.7 6.3 4.2 4.1 4.8 5.0 3.6 4.5 5.0
São Tomé and Príncipe 22.1 23.1 18.6 32.0 17.0 13.3 14.3 10.6 8.1 6.6 4.9 3.0 7.1 6.0 4.0
Senegal 1.5 2.1 5.9 5.8 –1.7 1.2 3.4 1.4 0.8 1.4 1.7 1.9 1.2 1.7 1.7
Seychelles 2.9 –1.9 –8.6 37.0 31.7 –2.4 2.6 7.1 4.3 3.5 3.3 3.0 3.4 3.5 3.2
Sierra Leone 13.2 9.5 11.6 14.8 9.2 17.8 18.5 13.8 9.8 7.8 6.7 5.4 8.5 7.5 6.0
South Africa 5.9 4.7 7.1 11.5 7.1 4.3 5.0 5.7 5.8 6.0 5.6 5.2 5.4 6.3 5.6
South Sudan . . . . . . . . . . . . . . . . . . . . . 45.1 0.0 11.2 9.0 5.0 –8.8 14.2 5.0
Swaziland 6.5 5.2 8.1 12.7 7.4 4.5 6.1 8.9 5.6 5.5 5.2 5.2 4.4 5.6 5.2
Tanzania 8.4 7.3 7.0 10.3 12.1 7.2 12.7 16.0 7.9 5.2 5.0 5.0 5.6 5.0 5.0
Togo 2.6 2.2 0.9 8.7 3.7 1.4 3.6 2.6 2.0 3.0 2.7 2.5 2.2 2.8 2.7
Uganda 4.8 7.2 6.1 12.0 13.1 4.0 18.7 14.0 5.4 6.3 6.3 5.0 5.6 7.0 5.6
Zambia 24.4 9.0 10.7 12.4 13.4 8.5 8.7 6.6 7.0 7.0 6.0 5.0 7.1 6.5 5.5
Zimbabwe9 . . . 33.0 –72.7 157.0 6.2 3.0 3.5 3.7 1.6 1.5 1.7 2.5 0.3 2.0 2.0
1Movements in consumer prices are shown as annual averages.
2Monthly year-over-year changes and, for several countries, on a quarterly basis.
3For many countries, inflation for the earlier years is measured on the basis of a retail price index. Consumer price index (CPI) inflation data with broader and more up-to-date coverage
are typically used for more recent years.
6The data for Argentina are officially reported data. Consumer price data from January 2014 onwards reflect the new national CPI (IPCNu), which differs substantively from the preceding
CPI (the CPI for the Greater Buenos Aires Area, CPI-GBA). Because of the differences in geographical coverage, weights, sampling, and methodology, the IPCNu data cannot be directly
compared to the earlier CPI-GBA data. Because of this structural break in the data, staff forecasts for CPI inflation are not reported in the Spring 2014 World Economic Outlook. Following
a declaration of censure by the IMF on February 1, 2013, the public release of a new national CPI by end-March 2014 was one of the specified actions in the IMF Executive Board’s
December 2013 decision calling on Argentina to address the quality of its official CPI data. The Executive Board will review this issue again as per the calendar specified in December
2013 and in line with the procedures set forth in the Fund’s legal framework.
values may differ from authorities’ estimates.

Table A8. Major Advanced Economies: General Government Fiscal Balances and Debt1
(Percent of GDP unless noted otherwise)
Average Projections
1998–2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Major Advanced Economies
Net Lending/Borrowing –3.9 –5.1 –10.8 –9.6 –8.2 –7.3 –5.9 –5.1 –4.4 –3.5
Output Gap2 0.0 –1.2 –5.7 –3.9 –3.5 –3.2 –3.1 –2.4 –1.7 0.0
Structural Balance2 –4.0 –4.5 –7.0 –7.8 –6.7 –5.8 –4.3 –3.9 –3.6 –3.5
United States
Net Lending/Borrowing3 –4.4 –7.8 –14.7 –12.5 –11.0 –9.7 –7.3 –6.4 –5.6 –5.7
Output Gap2,3 –0.5 –3.1 –7.1 –5.6 –5.2 –4.3 –4.1 –3.3 –2.2 0.0
Net Debt 41.7 50.4 62.1 69.7 76.2 80.1 81.3 82.3 82.7 84.5
Gross Debt 60.7 72.8 86.1 94.8 99.0 102.4 104.5 105.7 105.7 106.7
Euro Area4
Output Gap2 0.9 2.3 –2.8 –1.6 –0.6 –1.7 –2.6 –2.2 –1.7 –0.2
Net Debt 54.4 54.1 60.2 64.3 66.5 70.2 72.4 73.2 72.6 65.5
Gross Debt 69.4 70.3 80.1 85.7 88.1 92.8 95.2 95.6 94.5 85.5
Germany5
Net Lending/Borrowing –2.2 –0.1 –3.1 –4.2 –0.8 0.1 0.0 0.0 –0.1 0.4
Output Gap2 0.0 2.3 –3.7 –1.4 0.8 0.5 –0.4 –0.1 0.0 –0.1
Structural Balance2,6 –2.4 –1.0 –1.1 –2.6 –1.1 –0.1 0.3 0.2 –0.1 0.4
Net Debt 46.8 50.0 56.5 58.2 56.5 58.1 55.7 52.9 49.9 40.2
Gross Debt 63.4 66.8 74.5 82.5 80.0 81.0 78.1 74.6 70.8 58.7
France
Net Lending/Borrowing –2.7 –3.3 –7.6 –7.1 –5.3 –4.8 –4.2 –3.7 –3.0 0.0
Output Gap2 1.4 1.1 –3.0 –2.2 –1.0 –1.8 –2.4 –2.4 –2.0 0.1
Structural Balance2,6 –3.6 –4.1 –5.7 –5.7 –4.6 –3.5 –2.4 –1.9 –1.5 0.0
Net Debt 55.5 62.3 72.0 76.1 78.6 84.0 87.6 89.5 89.8 81.4
Gross Debt 61.5 68.2 79.2 82.4 85.8 90.2 93.9 95.8 96.1 87.7
Italy
Output Gap2 1.7 1.9 –3.4 –1.6 –1.3 –2.8 –4.2 –3.5 –2.4 –0.4
Structural Balance2,7 –4.4 –4.0 –4.2 –3.8 –3.8 –1.6 –0.3 –0.8 –0.3 0.0
Net Debt 91.6 89.3 97.9 100.0 102.5 106.1 110.7 112.4 111.2 101.7
Gross Debt 107.3 106.1 116.4 119.3 120.7 127.0 132.5 134.5 133.1 121.7
Japan
Output Gap2 –1.1 –1.4 –7.1 –3.1 –3.9 –3.1 –2.1 –1.4 –1.0 0.0
Net Debt 70.0 95.3 106.2 113.1 127.3 129.5 134.1 137.1 140.0 143.8
Gross Debt8 162.4 191.8 210.2 216.0 229.8 237.3 243.2 243.5 245.1 245.0
United Kingdom
Output Gap2 1.9 1.7 –2.2 –1.9 –2.5 –3.0 –2.7 –1.7 –1.1 0.0
Net Debt 36.4 48.0 62.4 72.2 76.8 81.4 83.1 84.4 85.7 77.6
Gross Debt 41.1 51.9 67.1 78.5 84.3 88.6 90.1 91.5 92.7 84.6
Canada
Net Lending/Borrowing 1.2 –0.3 –4.5 –4.9 –3.7 –3.4 –3.0 –2.5 –2.0 –0.6
Output Gap2 1.3 0.7 –3.5 –2.0 –1.3 –1.5 –1.3 –0.9 –0.6 0.0
Structural Balance2 0.4 –0.8 –2.3 –3.7 –2.9 –2.5 –2.2 –1.9 –1.6 –0.6
Net Debt 40.4 22.4 27.6 29.7 32.4 36.7 38.5 39.5 39.9 37.6
Gross Debt 78.9 71.3 81.3 83.1 83.5 88.1 89.1 87.4 86.6 81.9
Note: The methodology and specific assumptions for each country are discussed in Box A1. The country group composites for fiscal data are calculated as the sum of the U.S. dollar
values for the relevant individual countries.
1Debt data refer to the end of the year and are not always comparable across countries. Gross and net debt levels reported by national statistical agencies for countries that have adopted the
System of National Accounts (SNA) 2008 (Australia, Canada, United States) are adjusted to exclude unfunded pension liabilities of government employees’ defined-benefit pension plans.
Fiscal data for the aggregated Major Advanced Economies and the United States start in 2001, and the average for the aggregate and the United States is therefore for the period 2001–07.
2Percent of potential GDP.
3Data have been revised as a result of the Bureau of Economic Analysis’s recent comprehensive revision of the National Income and Product Accounts (NIPA).
4Excludes Latvia.
5Beginning in 1995, the debt and debt-services obligations of the Treuhandanstalt (and of various other agencies) were taken over by the general government. This debt is equivalent to 8
percent of GDP, and the associated debt service to 0.5 to 1 percent of GDP.
6Excludes sizable one-time receipts from the sale of assets, including licenses.
7Excludes one-time measures based on the authorities’ data and, in the absence of the latter, receipts from the sale of assets.
8Includes equity shares.

Table A9. Summary of World Trade Volumes and Prices
1996–2005 2006–15 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Trade in Goods and Services
World Trade1
Volume 6.7 4.2 9.3 7.9 2.8 –10.6 12.8 6.2 2.8 3.0 4.3 5.3
Price Deflator
In U.S. Dollars 0.7 2.5 5.0 7.7 11.4 –10.3 5.6 11.1 –1.8 –0.8 –0.2 –0.4
In SDRs 0.9 2.0 5.5 3.5 7.9 –8.1 6.8 7.4 1.2 0.0 –1.6 –1.3
Volume of Trade
Exports
Emerging Market and Developing Economies 8.7 5.6 11.2 9.4 4.3 –7.9 13.9 7.0 4.2 4.4 5.0 6.2
Imports
Terms of Trade
Advanced Economies –0.1 –0.3 –1.2 0.3 –2.1 2.5 –1.0 –1.5 –0.7 0.7 0.0 –0.2
Emerging Market and Developing Economies 1.3 0.8 3.0 1.7 3.3 –4.9 2.1 3.4 0.6 –0.3 –0.2 –0.7
Trade in Goods
World Trade1
Volume 6.8 4.0 9.3 7.1 2.2 –11.7 14.0 6.6 2.6 2.7 4.3 5.3
Price Deflator
In U.S. Dollars 0.5 2.7 5.6 7.9 12.4 –11.6 6.7 12.2 –1.9 –1.1 –0.3 –0.6
In SDRs 0.8 2.2 6.0 3.7 8.9 –9.4 7.8 8.4 1.1 –0.3 –1.8 –1.5
World Trade Prices in U.S. Dollars2
Manufactures –0.3 1.4 2.4 5.4 6.3 –6.5 2.5 6.1 0.2 –1.1 –0.3 –0.4
Oil 12.0 6.3 20.5 10.7 36.4 –36.3 27.9 31.6 1.0 –0.9 0.1 –6.0
Nonfuel Primary Commodities 0.0 4.6 23.1 13.9 7.9 –15.8 26.5 17.9 –10.0 –1.2 –3.5 –3.9
Food –0.4 4.7 10.2 14.8 24.5 –14.8 11.9 19.9 –2.4 1.1 –5.3 –5.9
Beverages –2.3 5.5 8.4 13.8 23.3 1.6 14.1 16.6 –18.6 –11.9 15.1 0.8
Agricultural Raw Materials –1.8 3.2 8.7 5.0 –0.7 –17.1 33.2 22.7 –12.7 1.5 0.5 –0.3
Metal 2.8 5.2 56.2 17.4 –7.8 –19.2 48.2 13.5 –16.8 –4.3 –5.4 –3.9
World Trade Prices in SDRs2
Manufactures –0.1 0.9 2.8 1.3 3.0 –4.1 3.7 2.5 3.3 –0.3 –1.7 –1.4
Oil 12.3 5.7 21.0 6.4 32.1 –34.8 29.3 27.2 4.1 –0.1 –1.3 –6.9
Food –0.1 4.2 10.7 10.3 20.5 –12.7 13.1 15.8 0.6 1.9 –6.6 –6.8
Beverages –2.1 5.0 8.8 9.4 19.4 4.1 15.4 12.7 –16.1 –11.2 13.5 –0.2
Agricultural Raw Materials –1.6 2.6 9.2 0.9 –3.8 –15.1 34.6 18.6 –10.0 2.3 –0.9 –1.3
Metal 3.1 4.7 56.9 12.8 –10.7 –17.2 49.8 9.7 –14.3 –3.5 –6.8 –4.8
World Trade Prices in Euros2
Manufactures 0.2 0.3 1.6 –3.4 –1.0 –1.2 7.6 1.2 8.4 –4.3 –3.2 –2.2
Oil 12.5 5.1 19.5 1.4 27.1 –32.7 34.3 25.5 9.3 –4.1 –2.9 –7.7
Food 0.1 3.5 9.3 5.1 15.9 –9.9 17.4 14.3 5.7 –2.1 –8.1 –7.5
Beverages –1.8 4.3 7.5 4.2 14.8 7.3 19.8 11.2 –11.9 –14.8 11.7 –1.0
Agricultural Raw Materials –1.3 2.0 7.9 –3.8 –7.5 –12.5 39.8 17.0 –5.5 –1.7 –2.5 –2.1
Metal 3.3 4.0 55.0 7.5 –14.1 –14.6 55.5 8.3 –10.0 –7.3 –8.2 –5.5

Table A9. Summary of World Trade Volumes and Prices (concluded)
1996–2005 2006–15 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Trade in Goods
Volume of Trade
Exports
Fuel Exporters 4.9 2.5 4.3 4.2 3.1 –7.3 3.6 5.0 6.0 1.1 1.4 4.2
Nonfuel Exporters 10.3 6.6 13.4 10.6 3.5 –8.5 17.7 7.6 4.3 5.4 6.7 7.0
Imports
Fuel Exporters 8.0 8.0 12.4 23.8 14.0 –12.7 6.2 10.2 10.8 7.0 5.1 6.5
Nonfuel Exporters 8.4 6.8 11.6 12.4 6.4 –8.9 17.1 10.0 4.3 4.9 5.5 6.5
Price Deflators in SDRs
Exports
Advanced Economies 0.1 1.4 3.9 3.4 5.7 –6.7 4.5 6.0 –0.2 0.4 –1.4 –0.8
Fuel Exporters 8.8 5.6 18.4 8.0 25.8 –25.9 24.5 23.9 3.2 –1.8 –2.6 –4.9
Nonfuel Exporters 1.7 2.8 7.8 4.7 9.6 –7.5 10.2 8.7 2.0 –0.4 –2.7 –2.3
Imports
Advanced Economies 0.2 1.8 5.4 3.0 8.4 –10.1 5.7 7.9 1.0 –0.2 –1.1 –0.8
Fuel Exporters 1.3 2.9 8.8 4.0 8.8 –4.8 9.3 6.3 1.9 0.1 –2.4 –1.8
Nonfuel Exporters 2.3 2.8 6.8 4.0 10.5 –8.9 11.9 9.0 2.1 –0.9 –2.3 –2.3
Terms of Trade
Advanced Economies –0.2 –0.4 –1.4 0.4 –2.5 3.8 –1.1 –1.8 –1.2 0.6 –0.3 0.0
Regional Groups
Commonwealth of Independent States3 5.0 2.6 7.9 1.9 15.9 –17.4 12.7 11.2 1.8 –1.2 –0.4 –2.1
Emerging and Developing Asia –1.5 –0.3 –0.6 0.3 –1.4 3.2 –6.2 –2.4 1.3 1.4 0.6 0.6
Emerging and Developing Europe 0.0 –0.8 –1.0 1.7 –2.7 3.5 –4.0 –1.9 –0.1 0.4 –2.9 –0.5
Latin America and the Caribbean 1.5 1.4 7.1 2.3 3.0 –8.9 11.1 9.0 –3.1 –1.5 –1.7 –1.6
and Pakistan 6.8 2.2 6.8 3.2 12.7 –18.2 11.6 14.4 –0.1 –1.6 0.2 –3.1
Middle East and North Africa 7.2 2.3 7.0 3.2 13.4 –18.6 11.5 14.7 0.4 –1.7 0.4 –3.1
Sub-Saharan Africa . . . 2.0 7.1 4.7 8.9 –13.0 12.7 8.9 –1.4 –1.8 –1.2 –2.3
Analytical Groups
Fuel Exporters 7.4 2.6 8.9 3.9 15.6 –22.2 13.8 16.6 1.2 –1.9 –0.2 –3.2
Nonfuel Exporters –0.5 0.1 0.9 0.7 –0.8 1.5 –1.5 –0.3 –0.1 0.5 –0.4 0.0
Memorandum
World Exports in Billions of U.S. Dollars
Goods and Services 8,482 20,390 14,891 17,336 19,830 15,880 18,916 22,317 22,535 23,083 23,990 25,123
Goods 6,835 16,396 12,035 13,920 15,984 12,469 15,167 18,123 18,260 18,591 19,281 20,132
Average Oil Price4 12.0 6.3 20.5 10.7 36.4 –36.3 27.9 31.6 1.0 –0.9 0.1 –6.0
In U.S. Dollars a Barrel 26.82 88.84 64.27 71.13 97.04 61.78 79.03 104.01 105.01 104.07 104.17 97.92
Export Unit Value of Manufactures5 –0.3 1.4 2.4 5.4 6.3 –6.5 2.5 6.1 0.2 –1.1 –0.3 –0.4
Note: SDR = special drawing right.
1Average of annual percent change for world exports and imports.
2As represented, respectively, by the export unit value index for manufactures of the advanced economies and accounting for 83 percent of the advanced economies’ trade (export of goods)
weights; the average of U.K. Brent, Dubai Fateh, and West Texas Intermediate crude oil prices; and the average of world market prices for nonfuel primary commodities weighted by their
2002–04 shares in world commodity exports.
4Percent change of average of U.K. Brent, Dubai Fateh, and West Texas Intermediate crude oil prices.
5Percent change for manufactures exported by the advanced economies.

Table A10. Summary of Balances on Current Account
(Billions of U.S. dollars)
Projections
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Advanced Economies –429.2 –327.4 –490.5 –57.7 –19.9 –43.5 –26.6 193.3 247.7 217.6 222.5
United States –798.5 –713.4 –681.3 –381.6 –449.5 –457.7 –440.4 –379.3 –391.1 –472.0 –627.1
Euro Area1,2 53.9 46.4 –96.5 33.1 72.7 109.2 246.0 366.0 391.6 432.6 498.7
Japan 170.9 212.1 159.9 146.6 204.0 119.3 60.4 34.3 57.2 65.0 84.8
Other Advanced Economies3 144.5 127.5 127.5 144.3 152.9 185.8 107.5 172.3 190.0 192.0 266.1
Emerging Market and Developing Economies 632.1 604.4 674.4 248.8 325.3 414.0 368.4 210.0 239.1 175.0 98.5
Regional Groups
Commonwealth of Independent States4 94.0 65.5 108.6 43.0 69.1 108.1 67.7 20.5 50.2 39.2 29.0
Emerging and Developing Asia 271.1 394.8 429.3 275.9 238.7 97.4 104.1 145.2 177.5 213.9 335.9
Emerging and Developing Europe –84.1 –129.7 –154.5 –50.3 –84.4 –118.8 –80.9 –75.6 –68.3 –76.6 –109.6
Latin America and the Caribbean 46.2 6.2 –39.5 –30.0 –62.1 –79.4 –107.1 –153.3 –154.1 –167.7 –208.7
and Pakistan 275.4 255.7 332.3 39.1 175.0 418.7 418.8 320.5 283.6 225.5 125.2
Sub-Saharan Africa 29.5 11.8 –1.9 –28.8 –11.0 –11.9 –34.2 –47.2 –49.9 –59.3 –73.3
Memorandum
European Union –28.2 –62.9 –172.1 4.7 19.1 83.6 174.5 328.9 357.4 404.9 505.4
Analytical Groups
Fuel 475.5 419.8 586.2 140.5 319.0 635.6 607.5 445.2 414.0 344.6 223.2
Nonfuel 156.7 184.6 88.2 108.3 6.3 –221.5 –239.0 –235.2 –174.9 –169.6 –124.7
Of Which, Primary Products –12.1 –17.1 –34.9 –23.3 –13.5 –29.4 –65.8 –65.6 –58.4 –60.0 –65.0
Net Debtor Economies –107.4 –207.9 –376.0 –179.9 –273.7 –402.4 –461.0 –451.7 –429.2 –466.3 –604.1
Of Which, Official Financing –17.7 –21.6 –32.9 –17.6 –12.1 –8.6 –20.4 –16.5 –17.1 –22.1 –32.3
Net Debtor Economies by
Debt-Servicing Experience
Rescheduling during 2008–12 –5.8 –13.2 –27.1 –30.6 –32.6 –33.5 –53.4 –55.9 –55.8 –68.8 –89.6
World1 203.0 277.0 183.9 191.1 305.4 370.6 341.9 403.3 486.8 392.6 321.1

Table A10. Summary of Balances on Current Account (concluded)
(Percent of GDP)
Projections
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Advanced Economies –1.2 –0.8 –1.2 –0.1 0.0 –0.1 –0.1 0.4 0.5 0.4 0.4
United States –5.8 –4.9 –4.6 –2.6 –3.0 –2.9 –2.7 –2.3 –2.2 –2.6 –2.8
Euro Area1,2 0.5 0.4 –0.7 0.3 0.6 0.8 2.0 2.9 2.9 3.1 3.0
Japan 3.9 4.9 3.3 2.9 3.7 2.0 1.0 0.7 1.2 1.3 1.5
Other Advanced Economies3 1.8 1.4 1.3 1.7 1.6 1.8 1.0 1.6 1.7 1.6 1.8
Emerging Market and Developing Economies 4.9 3.8 3.5 1.4 1.5 1.6 1.4 0.7 0.8 0.6 0.2
Regional Groups
and Pakistan 15.5 12.2 12.8 1.7 6.5 13.1 12.6 9.5 8.0 6.1 2.6
Middle East and North Africa 17.2 13.6 14.3 2.2 7.1 14.1 13.7 10.3 8.7 6.6 2.9
Memorandum
European Union –0.2 –0.4 –0.9 0.0 0.1 0.5 1.0 1.9 1.9 2.1 2.2
Analytical Groups
Fuel 16.3 11.6 12.7 3.7 7.1 11.5 10.4 7.4 6.7 5.4 2.8
Nonfuel 1.6 1.5 0.6 0.7 0.0 –1.1 –1.1 –1.0 –0.7 –0.7 –0.4
Of Which, Primary Products –2.0 –2.6 –4.9 –3.3 –1.5 –2.9 –6.4 –6.3 –5.6 –5.4 –4.4
Net Debtor Economies –1.5 –2.4 –3.9 –1.9 –2.5 –3.2 –3.7 –3.5 –3.3 –3.4 –3.3
Of Which, Official Financing –3.4 –3.6 –4.7 –2.6 –1.6 –1.1 –2.6 –1.9 –1.9 –2.3 –2.5
Experience
Rescheduling during 2008–12 –0.8 –1.5 –2.6 –3.0 –2.8 –2.5 –3.7 –3.7 –3.7 –4.4 –4.3
World1 0.4 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.6 0.5 0.3
Memorandum
In Percent of Total World Current Account
Transactions 0.7 0.8 0.5 0.6 0.8 0.8 0.8 0.9 1.0 0.8 0.5
In Percent of World GDP 0.4 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.6 0.5 0.3
1Reflects errors, omissions, and asymmetries in balance of payments statistics on current account, as well as the exclusion of data for international organizations and a limited number of
countries. See “Classification of Countries” in the introduction to this Statistical Appendix.
2Calculated as the sum of the balances of individual Euro Area countries excluding Latvia.

Table A11. Advanced Economies: Balance on Current Account
(Percent of GDP)
Projections
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Advanced Economies –1.2 –0.8 –1.2 –0.1 0.0 –0.1 –0.1 0.4 0.5 0.4 0.4
United States –5.8 –4.9 –4.6 –2.6 –3.0 –2.9 –2.7 –2.3 –2.2 –2.6 –2.8
Euro Area1 0.5 0.4 –0.7 0.3 0.6 0.8 2.0 2.9 2.9 3.1 3.0
Germany 6.3 7.4 6.2 5.9 6.4 6.8 7.4 7.5 7.3 7.1 5.7
France –0.6 –1.0 –1.7 –1.3 –1.3 –1.8 –2.2 –1.6 –1.7 –1.0 0.4
Italy –1.5 –1.3 –2.9 –2.0 –3.5 –3.1 –0.4 0.8 1.1 1.1 –0.4
Spain –9.0 –10.0 –9.6 –4.8 –4.5 –3.8 –1.1 0.7 0.8 1.4 3.4
Netherlands 9.3 6.7 4.3 5.2 7.4 9.5 9.4 10.4 10.1 10.1 9.2
Belgium 1.9 1.9 –1.3 –0.6 1.9 –1.1 –2.0 –1.7 –1.3 –1.0 0.3
Austria 2.8 3.5 4.9 2.7 3.4 1.4 1.8 3.0 3.5 3.5 3.6
Greece –11.4 –14.6 –14.9 –11.2 –10.1 –9.9 –2.4 0.7 0.9 0.3 1.4
Portugal –10.7 –10.1 –12.6 –10.9 –10.6 –7.0 –2.0 0.5 0.8 1.2 2.6
Finland 4.2 4.3 2.6 1.8 1.5 –1.5 –1.7 –0.8 –0.3 0.2 0.5
Ireland –3.6 –5.3 –5.6 –2.3 1.1 1.2 4.4 6.6 6.4 6.5 6.2
Slovak Republic –7.8 –5.3 –6.6 –2.6 –3.7 –3.8 2.2 2.4 2.7 2.9 2.5
Slovenia –1.8 –4.2 –5.4 –0.5 –0.1 0.4 3.3 6.5 6.1 5.8 1.6
Luxembourg 10.4 10.1 5.4 7.3 7.7 6.6 6.6 6.7 6.7 5.5 5.0
Latvia –22.6 –22.4 –13.2 8.7 2.9 –2.1 –2.5 –0.8 –1.6 –1.9 –2.0
Estonia –15.3 –15.9 –9.2 2.7 2.8 1.8 –1.8 –1.0 –1.3 –1.5 0.1
Cyprus2 –7.0 –11.8 –15.6 –10.7 –9.8 –3.3 –6.8 –1.5 0.1 0.3 –0.2
Malta –9.7 –4.0 –4.8 –8.3 –6.9 –0.6 2.1 0.9 1.4 1.4 1.5
Japan 3.9 4.9 3.3 2.9 3.7 2.0 1.0 0.7 1.2 1.3 1.5
United Kingdom –2.8 –2.2 –0.9 –1.4 –2.7 –1.5 –3.7 –3.3 –2.7 –2.2 –0.6
Canada 1.4 0.8 0.1 –2.9 –3.5 –2.8 –3.4 –3.2 –2.6 –2.5 –2.2
Korea 1.5 2.1 0.3 3.9 2.9 2.3 4.3 5.8 4.4 3.5 3.0
Australia –5.8 –6.7 –4.9 –4.6 –3.5 –2.8 –4.1 –2.9 –2.6 –2.8 –3.3
Taiwan Province of China 7.0 8.9 6.9 11.4 9.3 9.0 10.7 11.7 11.7 10.9 9.6
Sweden 8.7 9.3 9.0 6.3 6.3 6.0 6.1 5.9 6.1 6.2 5.8
Hong Kong SAR 11.9 12.1 13.4 8.4 5.4 5.2 2.8 3.1 3.3 3.9 5.0
Switzerland 14.4 8.6 2.1 10.5 14.8 9.0 9.6 9.6 9.9 9.8 9.8
Singapore 24.1 25.6 13.9 17.2 25.3 23.2 17.4 18.4 17.7 17.1 15.0
Czech Republic –2.1 –4.4 –2.1 –2.5 –3.8 –2.9 –2.4 –1.0 –0.5 –0.5 –0.9
Norway 16.4 12.5 16.0 11.7 11.9 13.5 14.3 10.6 10.2 9.2 7.8
Israel 4.7 3.2 1.4 3.8 3.1 1.3 0.3 2.5 1.4 1.7 1.7
Denmark 3.0 1.4 2.9 3.4 5.8 5.9 6.0 6.6 6.3 6.3 6.6
New Zealand –7.2 –6.9 –7.8 –2.3 –2.3 –2.9 –4.1 –4.2 –4.9 –5.4 –6.3
Iceland –25.6 –15.7 –28.4 –11.6 –8.5 –5.6 –5.0 0.4 0.8 –0.2 2.5
San Marino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Memorandum
Major Advanced Economies –1.9 –1.1 –1.3 –0.6 –0.8 –0.8 –1.0 –0.7 –0.6 –0.6 –0.7
Euro Area3 –0.1 0.1 –1.5 –0.1 0.1 0.1 1.3 2.3 2.4 2.5 2.4
1Calculated as the sum of the balances of individual Euro Area countries excluding Latvia.
2The balance on the current account for 2013 is a staff estimate at the time of the third review of the program and is subject to revision.
3Corrected for reporting discrepancies in intra-area transactions excluding Latvia.

Table A12. Emerging Market and Developing Economies: Balance on Current Account
(Percent of GDP)
Projections
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Russia 9.3 5.5 6.3 4.1 4.4 5.1 3.6 1.6 2.1 1.6 1.0
Excluding Russia 0.6 –1.4 0.9 –1.8 0.3 1.8 –0.7 –1.8 1.0 0.8 0.5
Armenia –1.8 –6.4 –11.8 –15.8 –14.8 –10.9 –11.2 –8.4 –7.2 –6.8 –6.3
Azerbaijan 17.6 27.3 35.5 23.0 28.0 26.5 21.8 19.7 15.0 9.9 4.6
Belarus –3.9 –6.7 –8.2 –12.6 –15.0 –8.5 –2.7 –9.8 –10.0 –7.8 –5.5
Georgia –15.2 –19.8 –22.0 –10.5 –10.2 –12.7 –11.7 –6.1 –7.9 –7.3 –5.5
Kazakhstan –2.5 –8.0 4.7 –3.6 0.9 5.4 0.3 0.1 1.9 2.0 1.4
Kyrgyz Republic –3.1 –6.2 –15.5 –2.5 –6.4 –6.5 –15.0 –12.6 –15.5 –14.3 –6.8
Moldova –11.3 –15.2 –16.1 –6.9 –7.0 –11.3 –6.0 –4.8 –5.9 –6.4 –6.4
Tajikistan –2.8 –8.6 –7.6 –5.9 –1.2 –4.8 –2.0 –1.9 –2.1 –2.3 –2.5
Turkmenistan 15.7 15.5 16.5 –14.7 –10.6 2.0 0.0 –3.3 –1.1 1.3 3.2
Ukraine2 –1.5 –3.7 –7.1 –1.5 –2.2 –6.3 –8.1 –9.2 . . . . . . . . .
Uzbekistan 9.2 7.3 8.7 2.2 6.2 5.8 1.2 1.7 2.2 1.9 0.8
Bangladesh 1.2 0.8 1.4 2.8 0.5 –1.2 0.8 1.8 0.5 –0.7 –0.9
Bhutan –4.4 14.6 –2.2 –2.0 –10.3 –23.7 –17.6 –22.2 –22.6 –24.7 –6.6
Brunei Darussalam 50.1 47.8 48.9 40.3 45.5 43.1 46.9 39.0 39.3 37.9 38.8
Cambodia –0.6 –1.9 –5.7 –4.5 –3.9 –8.1 –8.7 –8.6 –8.4 –7.4 –5.8
China 8.5 10.1 9.3 4.9 4.0 1.9 2.3 2.1 2.2 2.4 3.0
Fiji –15.4 –10.4 –15.9 –4.2 –4.5 –5.7 –1.5 –18.5 –6.3 –7.1 –10.1
India –1.0 –1.3 –2.3 –2.8 –2.7 –4.2 –4.7 –2.0 –2.4 –2.5 –2.6
Indonesia 2.6 1.6 0.0 2.0 0.7 0.2 –2.8 –3.3 –3.0 –2.7 –2.6
Kiribati –23.6 –19.4 –20.4 –23.3 –16.9 –32.6 –29.0 –15.7 –36.2 –30.5 –31.0
Lao P.D.R. –9.9 –15.7 –18.5 –21.0 –18.2 –15.2 –28.4 –29.5 –27.3 –23.7 –17.0
Malaysia 16.1 15.4 17.1 15.5 10.9 11.6 6.1 3.8 4.0 4.0 3.7
Maldives –23.2 –17.2 –32.3 –11.1 –8.9 –20.0 –22.9 –20.6 –22.7 –22.1 –20.1
Marshall Islands –4.3 –5.4 –3.5 –17.4 –28.8 –9.0 –8.1 –9.3 –20.6 –10.8 –11.2
Micronesia –13.7 –9.2 –16.2 –18.3 –14.9 –17.4 –12.0 –9.6 –9.5 –9.0 –8.0
Mongolia 6.5 6.3 –12.9 –8.9 –15.0 –31.5 –32.6 –27.9 –22.1 –19.7 –15.9
Myanmar 6.8 –0.7 –4.2 –1.3 –1.5 –2.1 –4.4 –4.9 –5.3 –5.2 –5.4
Nepal 2.1 –0.1 2.7 4.2 –2.4 –0.9 4.8 3.3 2.4 0.8 –1.0
Palau –24.7 –16.7 –16.8 –4.7 –7.2 –4.1 –5.0 –6.5 –5.5 –5.3 –5.6
Papua New Guinea –1.7 3.9 8.5 –15.2 –21.4 –23.5 –51.0 –27.9 –3.7 11.0 4.6
Philippines 4.4 4.8 2.1 5.6 4.5 3.2 2.9 3.5 3.2 2.6 0.5
Samoa –10.2 –15.5 –6.4 –6.2 –7.6 –4.1 –9.2 –2.3 –6.1 –5.6 –4.9
Solomon Islands –9.1 –15.7 –20.5 –21.4 –30.8 –6.7 0.2 –4.2 –13.0 –12.4 –10.1
Sri Lanka –5.3 –4.3 –9.5 –0.5 –2.2 –7.8 –6.6 –4.1 –3.8 –3.6 –2.9
Thailand 1.1 6.3 0.8 8.3 3.1 1.2 –0.4 –0.7 0.2 0.3 0.5
Timor-Leste 19.2 39.7 45.6 39.0 39.8 40.4 43.4 34.2 31.9 26.7 23.7
Tonga –5.6 –5.6 –8.1 –6.7 –3.7 –4.8 –6.2 –5.3 –4.2 –3.4 –2.7
Tuvalu 21.1 –21.7 0.3 5.4 –4.7 –29.0 32.3 37.1 25.3 24.2 24.4
Vanuatu –6.2 –7.3 –10.8 –7.9 –5.4 –8.1 –6.4 –4.4 –5.6 –5.7 –5.4
Vietnam –0.2 –9.0 –11.0 –6.5 –3.8 0.2 5.8 6.6 4.3 3.5 –3.3
Albania –5.6 –10.4 –15.2 –14.1 –10.0 –9.6 –9.3 –9.1 –10.3 –12.4 –8.2
Bosnia and Herzegovina –7.9 –9.1 –14.1 –6.6 –6.2 –9.8 –9.7 –5.6 –7.5 –7.0 –4.6
Bulgaria –17.6 –25.2 –23.0 –8.9 –1.5 0.1 –0.9 2.1 –0.4 –2.1 –3.2
Croatia –6.7 –7.3 –9.0 –5.2 –1.2 –0.9 0.0 1.2 1.5 1.1 –2.0
Hungary –7.4 –7.3 –7.4 –0.2 0.2 0.5 1.0 3.1 2.7 2.2 –1.5
Kosovo –7.2 –10.2 –16.0 –9.4 –12.0 –13.8 –7.7 –6.8 –7.7 –6.9 –7.6
Lithuania –10.6 –14.5 –13.3 3.9 0.0 –3.7 –0.2 0.8 –0.2 –0.6 –1.8
FYR Macedonia –0.4 –7.1 –12.8 –6.8 –2.0 –2.5 –3.0 –1.8 –3.9 –5.5 –4.3
Montenegro –31.3 –39.5 –49.8 –27.9 –22.9 –17.7 –18.7 –15.0 –17.9 –21.9 –16.7
Poland –3.8 –6.2 –6.6 –4.0 –5.1 –4.9 –3.5 –1.8 –2.5 –3.0 –3.4
Romania –10.4 –13.4 –11.6 –4.1 –4.4 –4.5 –4.4 –1.1 –1.7 –2.2 –3.3
Serbia –10.1 –17.8 –21.7 –6.6 –6.8 –9.1 –10.7 –5.0 –4.8 –4.6 –7.2
Turkey –6.0 –5.8 –5.5 –2.0 –6.2 –9.7 –6.2 –7.9 –6.3 –6.0 –5.4

Table A12. Emerging Market and Developing Economies: Balance on Current Account (continued)
(Percent of GDP)
Projections
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Antigua and Barbuda –25.7 –29.9 –26.7 –14.0 –14.7 –10.4 –14.0 –13.8 –12.3 –11.4 –10.0
Argentina3 3.4 2.6 1.8 2.5 0.3 –0.6 –0.1 –0.9 –0.5 –0.5 –0.5
The Bahamas –17.7 –11.5 –10.6 –10.3 –10.1 –15.3 –18.4 –19.6 –14.7 –10.4 –6.3
Barbados –8.2 –5.4 –10.7 –6.8 –5.8 –11.4 –10.1 –11.4 –7.8 –7.3 –6.3
Belize –2.1 –4.0 –10.6 –4.9 –2.4 –1.1 –2.2 –4.2 –4.5 –4.8 –6.3
Bolivia 11.2 11.4 11.9 4.3 3.9 0.3 7.8 3.7 3.7 2.4 1.1
Brazil 1.3 0.1 –1.7 –1.5 –2.2 –2.1 –2.4 –3.6 –3.6 –3.7 –3.5
Chile 4.6 4.1 –3.2 2.0 1.6 –1.2 –3.4 –3.4 –3.3 –2.8 –2.5
Colombia –1.9 –2.9 –2.8 –2.1 –3.0 –2.9 –3.2 –3.3 –3.3 –3.2 –2.8
Costa Rica –4.5 –6.3 –9.3 –2.0 –3.5 –5.3 –5.2 –5.0 –5.1 –5.1 –5.3
Dominica –13.0 –21.1 –28.7 –22.7 –17.4 –14.5 –18.9 –17.0 –17.7 –16.7 –15.4
Dominican Republic –3.6 –5.3 –9.9 –5.0 –8.4 –7.9 –6.8 –4.2 –4.5 –5.2 –3.7
Ecuador 3.7 3.7 2.8 0.5 –2.3 –0.3 –0.3 –1.5 –2.4 –3.1 –6.0
El Salvador –4.1 –6.1 –7.1 –1.5 –2.7 –4.9 –5.4 –6.7 –6.3 –5.9 –4.9
Grenada –30.8 –29.7 –28.0 –22.2 –22.1 –21.8 –19.2 –27.2 –22.6 –21.0 –17.4
Guatemala –5.0 –5.2 –3.6 0.7 –1.4 –3.4 –2.6 –3.0 –2.6 –2.3 –2.1
Guyana –13.4 –9.5 –13.7 –9.1 –9.6 –13.1 –13.3 –17.9 –18.3 –19.9 –12.0
Haiti –1.5 –1.5 –3.1 –1.9 –1.5 –4.3 –5.4 –6.5 –5.8 –5.7 –5.2
Honduras –3.7 –9.1 –15.4 –3.8 –4.3 –8.0 –8.6 –8.8 –7.4 –6.0 –5.5
Jamaica –10.0 –15.3 –17.7 –11.0 –8.7 –13.4 –13.0 –10.4 –8.6 –7.4 –5.1
Mexico –0.8 –1.4 –1.8 –0.9 –0.3 –1.1 –1.2 –1.8 –1.9 –2.0 –1.6
Nicaragua –10.4 –13.5 –18.4 –8.6 –9.7 –13.2 –12.9 –13.2 –12.7 –12.2 –11.1
Panama –3.2 –8.0 –10.9 –0.7 –11.4 –15.9 –10.6 –11.9 –11.5 –11.2 –7.1
Paraguay 1.6 5.7 1.0 3.0 –0.3 0.5 –1.0 0.9 –0.9 –1.6 –1.1
Peru 3.2 1.4 –4.2 –0.6 –2.5 –1.9 –3.4 –4.9 –4.8 –4.4 –3.5
St. Kitts and Nevis –13.6 –16.1 –27.3 –27.3 –21.5 –15.7 –11.9 –8.5 –17.4 –17.1 –15.1
St. Lucia –29.3 –30.1 –28.7 –11.6 –16.2 –18.8 –12.8 –11.8 –11.4 –11.4 –12.1
St. Vincent and the Grenadines –19.5 –28.0 –33.1 –29.2 –30.6 –29.4 –27.8 –28.9 –30.7 –24.4 –18.1
Suriname 8.4 11.1 9.2 0.3 6.4 5.8 0.6 –4.7 –4.5 –6.7 2.8
Trinidad and Tobago 39.6 23.9 30.5 8.5 20.3 12.4 4.9 10.2 10.1 8.9 6.2
Uruguay –2.0 –0.9 –5.7 –1.3 –1.9 –3.0 –5.4 –5.9 –5.5 –5.2 –3.7
Venezuela 14.4 6.9 10.2 0.7 3.0 7.7 2.9 2.7 2.4 1.8 –2.8
and Pakistan 15.5 12.2 12.8 1.7 6.5 13.1 12.6 9.5 8.0 6.1 2.6
Afghanistan –1.1 6.0 5.2 1.9 3.1 3.1 3.9 2.8 3.3 –0.3 –3.6
Algeria 24.7 22.7 20.1 0.3 7.5 9.9 6.0 0.4 0.5 –1.3 –3.3
Bahrain 11.8 13.4 8.8 2.4 3.0 11.2 7.3 12.0 10.4 9.4 4.5
Djibouti –11.5 –21.4 –24.3 –9.3 –5.4 –14.1 –12.3 –13.2 –16.3 –17.5 –16.5
Egypt 1.6 2.1 0.5 –2.3 –2.0 –2.6 –3.9 –2.1 –1.3 –4.6 –6.1
Iran 8.5 10.6 6.5 2.6 6.5 11.0 6.6 8.1 5.2 2.8 0.4
Iraq 12.9 7.7 12.8 –8.0 3.0 12.0 6.7 0.0 1.0 1.2 4.0
Jordan –11.5 –16.8 –9.3 –3.3 –5.3 –12.0 –18.1 –11.1 –12.9 –9.3 –6.1
Kuwait 44.6 36.8 40.9 26.7 30.8 41.8 43.2 38.8 37.4 34.2 25.1
Lebanon –7.3 –7.2 –11.1 –12.6 –13.3 –15.7 –15.7 –16.2 –15.8 –13.9 –12.1
Libya 51.1 44.1 42.5 14.9 19.5 9.1 35.4 –2.8 –27.7 –16.7 –15.4
Mauritania –1.3 –17.2 –14.9 –16.2 –9.4 –7.5 –32.5 –25.8 –26.3 –38.0 –14.8
Morocco 2.2 –0.1 –5.2 –5.4 –4.1 –8.0 –9.7 –7.4 –6.6 –5.8 –4.2
Oman 15.4 5.9 8.3 –1.3 10.0 15.3 11.6 9.7 7.8 2.5 –2.1
Pakistan –3.6 –4.5 –8.1 –5.5 –2.2 0.1 –2.1 –1.0 –0.9 –1.0 –0.8
Qatar 15.5 14.4 23.1 6.5 19.0 30.3 32.4 29.2 25.4 20.5 6.5
Saudi Arabia 26.3 22.5 25.5 4.9 12.7 23.7 22.4 17.4 15.8 13.3 9.9
Sudan4 –8.8 –6.0 –1.6 –9.6 –2.1 –0.4 –10.4 –10.6 –8.2 –7.1 –3.1
Syria5 1.4 –0.2 –1.3 –2.9 –2.8 . . . . . . . . . . . . . . . . . .
Tunisia –1.8 –2.4 –3.8 –2.8 –4.7 –7.4 –8.2 –8.4 –6.7 –5.7 –3.7
United Arab Emirates 16.3 6.9 7.1 3.1 2.5 14.6 17.3 14.9 13.3 12.4 6.9
Yemen 1.1 –7.0 –4.6 –10.1 –3.4 –4.0 –1.3 –2.7 –1.5 –2.7 –4.4

Table A12. Emerging Market and Developing Economies: Balance on Current Account (concluded)
(Percent of GDP)
Projections
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2019
Angola 25.6 19.9 10.3 –9.9 8.1 12.6 9.2 5.0 2.2 –0.4 –1.0
Benin –4.9 –10.2 –8.1 –8.9 –8.7 –7.8 –7.9 –14.5 –9.2 –7.2 –6.8
Botswana 19.2 15.1 0.4 –10.2 –5.4 –0.2 –4.9 –0.4 0.4 0.2 –3.7
Burkina Faso –9.5 –8.3 –11.5 –4.7 –2.2 –1.2 –0.8 –7.2 –7.3 –8.4 –7.8
Burundi –21.5 –5.4 –1.0 1.7 –12.2 –13.6 –17.3 –23.2 –21.5 –21.3 –16.8
Cabo Verde –4.8 –12.9 –13.7 –14.6 –12.4 –16.3 –11.2 –1.9 –10.0 –10.1 –6.2
Cameroon 1.6 1.4 –1.2 –3.3 –3.0 –2.9 –4.0 –4.4 –3.5 –3.6 –4.2
Central African Republic –3.0 –6.2 –10.0 –9.2 –10.2 –7.6 –5.6 –10.4 –13.9 –13.4 –11.9
Chad 4.6 8.2 3.7 –9.2 –9.0 –5.6 –8.3 –8.1 –6.0 –6.4 –6.2
Comoros –6.0 –5.8 –12.1 –7.8 –5.7 –9.4 –3.8 –6.1 –11.5 –11.1 –8.6
Democratic Republic of the Congo –2.3 –0.7 –10.6 –7.8 –4.9 –5.9 –8.0 –9.9 –7.9 –7.2 –6.2
Republic of Congo 2.8 –6.5 –0.5 –6.0 3.8 5.8 –1.3 –1.2 2.0 0.1 –0.2
Côte d’Ivoire 2.8 –0.2 2.3 7.6 2.5 12.9 –1.3 –1.2 –2.2 –2.0 –4.5
Equatorial Guinea 16.9 15.9 12.2 –7.5 –9.6 –0.6 –4.6 –12.0 –10.2 –10.9 –11.1
Eritrea –3.6 –6.1 –5.5 –7.6 –5.6 0.6 2.3 0.3 0.2 –1.2 –2.9
Ethiopia –9.2 –4.5 –5.7 –5.1 –4.1 –0.7 –6.5 –6.1 –5.4 –6.0 –4.4
Gabon 14.1 15.3 23.4 7.5 8.7 13.2 14.0 10.6 6.9 4.5 0.5
The Gambia –6.9 –8.3 –12.3 –12.3 –16.0 –15.6 –17.0 –17.0 –14.3 –14.9 –14.9
Ghana –8.2 –8.7 –11.9 –5.4 –8.6 –9.1 –12.2 –13.2 –10.6 –7.8 –6.7
Guinea –4.6 –11.6 –10.6 –8.6 –11.5 –20.5 –33.0 –20.1 –18.0 –48.1 –23.3
Guinea-Bissau –5.6 –3.4 –4.9 –6.6 –8.6 –1.2 –6.5 –8.7 –4.6 –4.4 –1.7
Kenya –2.3 –4.0 –6.5 –5.5 –7.3 –11.2 –10.4 –8.3 –9.6 –7.8 –5.6
Lesotho 26.3 24.6 23.4 8.9 –4.7 –8.6 –4.2 –1.3 –0.8 –5.4 –11.5
Liberia –18.2 –12.1 –54.8 –28.5 –37.4 –34.0 –31.9 –31.4 –48.3 –30.7 –20.7
Madagascar –3.8 –8.9 –17.8 –19.5 –8.8 –5.6 –6.2 –4.6 –1.9 –2.2 –0.5
Malawi –11.3 1.0 –9.7 –4.8 –1.3 –5.8 –4.0 –3.4 –2.2 –2.2 –0.9
Mali –3.7 –6.3 –12.2 –7.3 –12.6 –6.0 –3.3 –3.3 –6.7 –5.7 –5.6
Mauritius –9.1 –5.4 –10.1 –7.4 –10.3 –13.3 –7.9 –9.1 –8.7 –8.4 –5.6
Mozambique –8.6 –10.9 –12.9 –12.2 –11.7 –24.4 –45.6 –41.9 –42.8 –43.2 –37.1
Namibia 13.8 9.1 2.8 –1.1 –1.8 –3.5 –2.6 –4.6 –5.1 –6.9 5.6
Niger –8.6 –8.2 –12.9 –24.4 –19.8 –22.3 –15.4 –17.2 –21.8 –17.7 –11.7
Nigeria 25.3 16.5 14.0 8.2 5.8 3.5 7.7 4.7 4.9 4.0 2.5
Rwanda –4.3 –2.2 –4.9 –7.3 –5.4 –7.2 –11.4 –7.3 –11.5 –10.3 –6.5
São Tomé and Príncipe –34.5 –31.9 –35.0 –23.7 –23.0 –26.6 –20.5 –20.3 –15.3 –13.9 –9.6
Senegal –9.2 –11.6 –14.1 –6.7 –4.4 –7.9 –10.3 –9.3 –7.5 –6.6 –6.2
Seychelles –16.1 –18.8 –27.2 –22.4 –22.3 –26.6 –24.8 –17.7 –14.5 –13.2 –9.0
Sierra Leone –4.2 –4.2 –8.9 –6.3 –19.7 –44.9 –36.7 –14.2 –9.4 –7.6 –7.1
South Africa –5.3 –7.0 –7.2 –4.0 –2.0 –2.3 –5.2 –5.8 –5.4 –5.3 –4.5
South Sudan . . . . . . . . . . . . . . . 18.4 –27.7 2.2 –2.3 2.2 –2.3
Swaziland –6.7 –2.1 –7.7 –13.1 –10.0 –8.6 4.1 5.5 1.9 –1.2 –3.5
Tanzania –9.6 –11.0 –10.2 –9.8 –9.3 –14.5 –15.9 –14.3 –13.9 –12.9 –10.7
Togo –8.4 –8.7 –6.8 –6.6 –6.3 –9.1 –11.8 –12.0 –10.9 –9.8 –6.9
Uganda –4.2 –5.5 –8.7 –7.3 –11.1 –12.5 –10.5 –11.7 –12.6 –12.1 –10.2
Zambia –0.4 –6.5 –7.1 4.6 7.4 3.7 3.8 1.2 0.9 1.1 1.9
Zimbabwe6 –6.5 –5.4 –16.7 –39.6 –20.3 –28.8 –20.1 –19.7 –18.3 –17.1 –14.3
3Calculations are based on Argentina’s official GDP data. See note 5 to Table A4.
values may differ from authorities’ estimates.

Table A13. Emerging Market and Developing Economies: Net Financial Flows1
Average Projections
2003–05 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Private Financial Flows, Net 253.1 321.3 714.5 182.6 263.8 557.8 479.6 228.7 419.9 362.1 385.2
Private Direct Investment, Net 208.6 301.6 442.9 468.8 332.2 409.9 520.1 471.4 475.6 439.6 447.4
Private Portfolio Flows, Net 44.5 –37.2 108.2 –81.6 57.6 193.4 86.8 234.8 186.5 162.9 164.6
Other Private Financial Flows, Net 0.0 56.9 163.4 –204.5 –126.0 –45.5 –127.4 –477.6 –242.1 –240.3 –226.7
Official Financial Flows, Net2 –76.1 –177.2 –58.8 –79.2 166.7 98.1 –10.6 10.3 –45.3 –76.2 –15.0
Change in Reserves3 –392.6 –721.8 –1,186.6 –654.9 –496.1 –816.3 –720.9 –404.2 –509.3 –550.0 –525.2
Memorandum
Current Account4 255.2 632.1 604.4 674.4 248.8 325.3 414.0 368.4 210.0 239.1 175.0
Private Financial Flows, Net 18.6 51.2 129.3 –98.0 –62.7 –25.4 –63.3 –41.4 –43.7 –60.5 –29.1
Private Portfolio Flows, Net 3.5 4.8 18.8 –31.3 –8.8 8.7 –27.5 –4.9 5.1 5.0 9.7
Other Private Financial Flows, Net 5.1 25.3 82.5 –116.3 –69.6 –43.8 –49.2 –53.7 –60.6 –79.0 –58.1
Official Flows, Net2 –13.3 –25.1 –5.7 –19.0 41.6 1.3 –17.9 1.9 –2.2 –6.6 –7.0
Change in Reserves3 –54.9 –127.5 –167.7 26.7 –7.2 –52.1 –23.9 –29.9 31.7 17.6 –2.4
Private Portfolio Flows, Net 24.8 –53.4 52.2 –0.4 48.5 82.0 56.7 109.0 64.8 88.9 79.5
Other Private Financial Flows, Net 11.9 16.3 –21.9 –117.6 42.8 84.6 25.2 –231.1 23.6 0.9 –30.3
Official Flows, Net2 –8.3 7.1 7.2 –4.1 31.8 31.4 10.8 19.0 17.6 29.5 26.2
Change in Reserves3 –228.3 –368.3 –621.2 –479.6 –461.4 –570.2 –437.5 –131.8 –441.0 –490.9 –450.8
Private Portfolio Flows, Net 13.8 0.7 –3.3 –10.8 8.5 27.2 34.3 46.3 28.0 24.8 23.4
Other Private Financial Flows, Net 21.5 47.3 107.8 97.7 –2.3 32.7 23.8 –6.4 20.1 2.8 6.1
Official Flows, Net2 5.2 4.5 –6.4 19.5 45.4 33.7 22.1 16.2 –9.8 –1.2 1.0
Change in Reserves3 –22.1 –28.8 –34.6 –8.3 –32.7 –35.8 –13.8 –22.7 –3.8 –2.4 –4.2
Private Portfolio Flows, Net –8.3 8.2 45.8 –13.2 29.2 65.7 54.1 34.1 53.0 18.4 22.0
Other Private Financial Flows, Net –18.4 4.9 –24.2 –15.2 –64.8 –28.5 –4.6 –39.7 –69.8 –32.3 –27.4
Official Flows, Net2 –8.7 –44.9 –0.9 3.5 44.7 48.1 24.7 62.7 47.9 32.6 38.0
Change in Reserves3 –1.0 –10.0 –98.1 10.3 –26.3 –64.9 –81.1 –29.3 9.0 6.8 4.3
and Pakistan
Private Financial Flows, Net 19.0 15.5 72.5 4.2 30.6 9.6 –101.3 –48.0 –72.9 –75.0 –57.5
Private Portfolio Flows, Net 10.7 –3.5 –5.5 1.9 –16.8 10.6 –22.3 40.2 36.2 24.6 29.5
Other Private Financial Flows, Net –16.8 –29.5 26.9 –59.3 –18.7 –51.0 –99.4 –119.3 –135.1 –120.1 –113.0
Official Flows, Net2 –50.0 –84.9 –61.6 –89.7 –16.1 –49.7 –79.1 –124.5 –125.7 –158.6 –97.8
Change in Reserves3 –72.3 –156.3 –236.6 –187.0 23.4 –92.7 –141.1 –171.2 –99.3 –75.5 –62.9
Sub-Saharan Africa
Private Financial Flows, Net 10.9 7.0 14.7 14.5 16.1 –18.1 0.6 14.6 14.5 26.6 43.9
Private Portfolio Flows, Net 0.0 6.0 0.2 –27.8 –3.0 –0.9 –8.4 10.1 –0.7 1.2 0.6
Other Private Financial Flows, Net –3.4 –7.4 –7.6 6.1 –13.4 –39.5 –23.2 –27.4 –20.3 –12.8 –4.0
Official Flows, Net2 –1.1 –33.9 8.6 10.6 19.4 33.1 28.8 35.0 26.9 28.1 24.6
Change in Reserves3 –13.9 –30.9 –28.2 –16.9 8.1 –0.7 –23.6 –19.3 –5.9 –5.7 –9.3
Memorandum
Fuel Exporting Countries
Other Countries
1Net financial flows comprise net direct investment, net portfolio investment, other net official and private financial flows, and changes in reserves.
2Excludes grants and includes transactions in external assets and liabilities of official agencies.
3A minus sign indicates an increase.
4The sum of the current account balance, net private financial flows, net official flows, and the change in reserves equals, with the opposite sign, the sum of the capital account and errors
and omissions.

Table A14. Emerging Market and Developing Economies: Private Financial Flows1
Average Projections
2003–05 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Assets –226.3 –618.5 –821.6 –579.0 –302.6 –645.5 –709.8 –805.0 –665.1 –669.7 –741.6
Liabilities 478.1 940.4 1,536.9 768.6 567.4 1,200.9 1,189.4 1,029.0 1,078.7 1,029.7 1,124.5
Assets –52.5 –100.4 –161.4 –264.9 –74.9 –104.9 –164.7 –161.1 –164.6 –173.0 –168.8
Liabilities 71.0 151.6 290.7 167.0 12.2 79.3 101.3 119.6 120.8 112.6 139.8
Assets –54.7 –219.3 –260.4 –169.3 –96.6 –256.5 –296.1 –397.6 –257.0 –290.3 –353.5
Liabilities 172.2 304.8 459.6 209.7 301.7 640.4 661.6 505.7 565.1 576.6 572.2
Assets –18.1 –54.6 –39.7 –31.0 –8.9 –8.0 12.4 –2.3 13.0 –1.3 –10.3
Liabilities 80.4 164.8 215.6 183.7 46.6 92.6 84.2 66.3 56.3 54.5 71.0
Assets –43.1 –92.5 –109.7 –81.2 –99.8 –167.4 –115.3 –140.1 –122.1 –77.8 –76.8
Liabilities 66.6 144.8 233.4 157.3 137.3 288.4 297.6 266.8 261.4 207.5 225.6
and Pakistan
Private Financial Flows, Net 19.0 15.5 72.5 4.2 30.6 9.6 –101.3 –48.0 –72.9 –75.0 –57.5
Assets –45.1 –118.7 –216.3 –14.4 –9.5 –81.6 –118.7 –83.3 –113.1 –115.0 –120.7
Liabilities 64.1 134.1 288.7 18.6 40.4 91.3 17.5 35.9 40.5 40.8 63.1
Sub-Saharan Africa
Private Financial Flows, Net 10.9 7.0 14.7 14.5 16.1 –18.1 0.6 14.6 14.5 26.6 43.9
Assets –12.8 –32.9 –34.0 –18.3 –13.0 –27.2 –27.3 –20.6 –21.3 –12.4 –11.4
Liabilities 23.8 40.2 48.9 32.3 29.2 8.9 27.1 34.8 34.7 37.7 52.7
1Private financial flows comprise direct investment, portfolio investment, and other long- and short-term investment flows.

Table A15. Summary of Sources and Uses of World Savings
(Percent of GDP)
Projections
Averages Average
1992–99 2000–07 2008 2009 2010 2011 2012 2013 2014 2015 2016–19
World
Savings 22.7 23.1 24.7 22.7 23.9 24.7 24.8 25.0 25.5 25.6 26.2
Investment 23.3 23.1 24.5 22.4 23.6 24.1 24.4 24.5 24.8 25.1 25.9
Advanced Economies
Savings 22.5 21.3 20.6 18.3 19.2 19.7 19.6 19.9 20.4 20.6 21.3
Investment 22.8 22.1 22.0 18.7 19.5 19.9 19.9 19.7 20.0 20.3 21.0
Net Lending –0.3 –0.8 –1.4 –0.5 –0.4 –0.1 –0.2 0.3 0.4 0.3 0.3
Current Transfers –0.5 –0.6 –0.8 –0.8 –0.9 –0.8 –0.8 –0.9 –0.9 –0.9 –0.9
Factor Income –0.3 0.5 0.3 0.4 0.6 1.1 0.9 0.9 0.9 0.8 0.7
Resource Balance 0.5 –0.6 –0.8 0.1 0.0 –0.2 –0.2 0.3 0.5 0.6 0.6
United States
Savings 19.1 18.4 15.5 14.4 15.1 15.8 16.3 17.2 17.6 17.9 18.9
Investment 21.6 22.5 20.8 17.5 18.4 18.4 19.0 19.5 19.9 20.5 21.7
Net Lending –2.5 –4.1 –5.3 –3.1 –3.3 –2.6 –2.7 –2.3 –2.2 –2.6 –2.8
Factor Income –0.5 1.0 0.3 0.4 0.9 1.8 1.4 1.4 1.2 1.1 0.8
Resource Balance –1.4 –4.5 –4.8 –2.7 –3.3 –3.6 –3.3 –2.8 –2.6 –2.7 –2.9
Euro Area1
Savings 21.4 21.7 21.5 19.1 19.8 20.5 20.5 20.6 21.2 21.5 22.0
Investment 21.3 21.3 22.2 18.8 19.2 19.6 18.4 17.7 18.1 18.3 18.8
Net Lending 0.1 0.5 –0.7 0.3 0.6 0.8 2.1 2.9 3.0 3.2 3.1
Current Transfers2 –0.6 –0.9 –1.1 –1.2 –1.2 –1.2 –1.2 –1.3 –1.3 –1.3 –1.3
Factor Income2 –0.5 –0.3 –0.6 –0.1 0.3 0.4 0.4 0.5 0.5 0.4 0.3
Resource Balance2 1.5 1.6 1.0 1.5 1.6 1.6 2.8 3.6 3.8 4.1 4.2
Germany
Savings 21.1 22.1 25.5 22.3 23.7 25.1 24.7 24.3 24.8 24.7 23.8
Investment 22.1 18.9 19.3 16.4 17.3 18.3 17.3 16.7 17.4 17.6 17.6
Net Lending –1.0 3.2 6.2 5.9 6.4 6.8 7.4 7.5 7.3 7.1 6.2
Factor Income 0.0 0.4 1.3 2.5 2.2 2.7 2.9 2.8 2.8 2.8 2.8
Resource Balance 0.5 4.1 6.2 4.8 5.7 5.4 6.0 6.2 6.1 5.8 4.9
France
Savings 19.3 20.3 20.2 17.6 18.0 19.0 17.6 17.7 18.4 19.1 20.4
Investment 17.8 19.8 21.9 18.9 19.3 20.8 19.8 19.4 19.7 19.8 20.1
Net Lending 1.5 0.5 –1.7 –1.3 –1.3 –1.8 –2.2 –1.6 –1.3 –0.7 0.3
Factor Income 0.0 1.3 1.7 1.7 2.0 2.3 1.5 1.7 2.0 2.0 2.0
Resource Balance 2.2 0.3 –2.2 –1.3 –1.7 –2.3 –1.9 –1.4 –1.4 –0.7 0.2
Italy
Savings 21.2 20.6 18.8 16.9 16.5 16.7 17.6 17.8 19.0 19.2 19.5
Investment 20.0 21.2 21.6 18.9 20.1 19.8 18.0 17.1 17.9 18.1 19.3
Net Lending 1.2 –0.6 –2.9 –2.0 –3.5 –3.1 –0.4 0.8 1.1 1.1 0.2
Factor Income –1.4 –0.4 –1.2 –0.7 –0.5 –0.6 –0.5 –0.7 –0.7 –0.8 –1.2
Resource Balance 3.1 0.4 –0.7 –0.5 –1.9 –1.5 1.1 2.5 2.9 3.2 2.6
Japan
Savings 30.4 26.4 26.3 22.6 23.5 22.2 21.8 21.7 22.8 22.8 23.2
Investment 27.9 23.1 23.0 19.7 19.8 20.2 20.8 21.0 21.6 21.5 21.8
Net Lending 2.4 3.3 3.3 2.9 3.7 2.0 1.0 0.7 1.2 1.3 1.4
Factor Income 1.0 2.0 3.2 2.7 2.6 3.0 3.0 3.5 3.6 3.4 3.4
Resource Balance 1.6 1.5 0.4 0.5 1.4 –0.7 –1.8 –2.6 –2.2 –1.9 –1.9
United Kingdom
Savings 16.2 15.3 16.1 12.7 12.3 13.5 10.9 11.0 12.2 13.1 15.4
Investment 17.2 17.5 17.1 14.1 15.0 14.9 14.7 14.4 14.9 15.3 16.5
Net Lending –1.0 –2.2 –0.9 –1.4 –2.7 –1.5 –3.7 –3.3 –2.7 –2.2 –1.1
Factor Income –0.1 1.1 2.2 1.3 0.9 1.5 –0.1 –0.3 –0.1 0.2 0.8

Table A15. Summary of Sources and Uses of World Savings (continued)
(Percent of GDP)
Projections
Averages Average
1992–99 2000–07 2008 2009 2010 2011 2012 2013 2014 2015 2016–19
Canada
Savings 17.8 23.4 24.1 18.9 19.8 21.1 21.2 21.1 21.6 21.8 22.3
Investment 19.8 21.7 24.0 21.8 23.3 23.8 24.7 24.4 24.3 24.3 24.6
Net Lending –2.0 1.7 0.1 –2.9 –3.5 –2.8 –3.4 –3.2 –2.6 –2.5 –2.3
Current Transfers –0.1 0.0 0.0 –0.2 –0.2 –0.2 –0.2 –0.2 –0.2 –0.2 –0.2
Factor Income –3.9 –2.3 –1.6 –1.3 –1.4 –1.3 –1.2 –1.4 –1.3 –1.4 –1.7
Resource Balance 1.9 4.1 1.7 –1.5 –1.9 –1.2 –2.0 –1.7 –1.2 –1.0 –0.5
Emerging Market and Developing
Economies
Savings 23.7 28.8 33.7 32.2 32.9 33.4 33.4 32.9 33.4 33.3 33.4
Investment 25.3 26.2 30.0 30.7 31.4 31.7 32.0 32.2 32.6 32.8 33.1
Net Lending –1.6 2.7 3.6 1.6 1.6 1.7 1.4 0.8 0.9 0.6 0.4
Current Transfers 0.8 1.5 1.4 1.3 1.2 1.1 0.9 0.8 0.9 0.8 0.8
Factor Income –1.6 –1.8 –1.4 –1.4 –1.7 –1.9 –1.8 –1.8 –1.7 –1.6 –1.4
Resource Balance –0.8 3.0 3.6 1.6 2.1 2.6 2.3 1.8 1.7 1.4 1.0
Memorandum
Acquisition of Foreign Assets 2.2 7.0 6.4 4.6 6.9 5.9 4.9 4.2 3.9 3.7 3.2
Change in Reserves 0.9 3.7 3.4 2.7 3.7 2.8 1.5 1.8 1.9 1.7 1.4
Regional Groups
Commonwealth of Independent
States3
Savings 25.5 29.7 30.0 22.0 26.1 28.5 25.9 24.7 26.6 26.6 26.5
Investment 25.1 22.0 25.2 19.2 22.5 24.1 23.3 23.9 24.7 25.2 25.6
Net Lending 0.5 7.6 4.9 2.8 3.6 4.4 2.6 0.8 2.0 1.5 1.0
Factor Income –2.4 –2.7 –3.3 –3.6 –3.6 –3.9 –3.9 –3.9 –3.7 –3.4 –2.4
Memorandum
Change in Reserves 0.2 6.6 –1.2 0.4 2.6 1.0 1.1 –1.1 –0.7 0.1 0.2
Savings 32.7 37.7 44.6 45.3 44.7 43.3 43.8 43.8 43.9 43.8 43.4
Investment 33.4 34.3 38.6 41.8 42.1 42.3 43.0 42.7 42.7 42.4 42.0
Net Lending –0.6 3.3 5.9 3.5 2.5 0.9 0.8 1.0 1.2 1.3 1.4
Factor Income –1.4 –1.2 –0.2 –0.6 –0.9 –1.2 –1.1 –1.1 –1.1 –1.1 –1.2
Memorandum
Emerging and Developing
Europe
Savings 19.3 16.6 16.7 15.7 15.7 16.5 16.2 16.4 16.5 16.5 16.4
Investment 21.6 21.4 24.9 18.9 20.6 22.8 20.6 20.3 20.0 20.2 20.4
Net Lending –2.3 –4.7 –8.1 –3.2 –4.9 –6.4 –4.5 –3.9 –3.5 –3.7 –4.0
Factor Income –1.1 –1.9 –2.4 –2.5 –2.5 –2.8 –2.7 –2.8 –2.9 –3.0 –3.2
Memorandum
Acquisition of Foreign Assets 1.3 3.5 2.1 2.1 2.7 –0.4 0.6 0.2 –0.3 0.1 –0.1

Table A15. Summary of Sources and Uses of World Savings (continued)
(Percent of GDP)
Projections
Averages Average
1992–99 2000–07 2008 2009 2010 2011 2012 2013 2014 2015 2016–19
Savings 18.4 20.0 22.0 19.7 20.0 20.0 19.2 18.5 18.3 18.3 18.8
Investment 21.5 20.3 23.1 20.4 21.4 21.7 21.3 21.3 21.1 21.2 21.7
Net Lending –3.2 –0.3 –1.1 –0.7 –1.4 –1.7 –2.1 –2.8 –2.8 –2.9 –2.9
Factor Income –2.7 –3.1 –2.8 –2.6 –2.6 –2.9 –2.7 –2.8 –2.8 –2.8 –2.7
Resource Balance –1.3 1.1 0.1 0.4 0.0 0.1 –0.5 –1.1 –1.2 –1.3 –1.3
Memorandum
Change in Reserves 0.2 0.1 –0.2 0.6 1.3 1.4 0.5 –0.2 –0.1 –0.1 0.0
Middle East, North Africa,
Afghanistan, and Pakistan
Savings 23.2 33.9 42.2 32.6 36.1 40.4 38.8 35.7 34.7 32.8 31.2
Investment 22.6 23.2 28.0 29.8 28.6 26.4 25.3 25.4 26.0 26.0 26.9
Net Lending 0.5 11.0 14.2 3.6 8.0 14.5 14.2 11.3 9.7 7.5 4.8
Current Transfers –1.0 0.1 0.0 –0.5 –0.6 –0.6 –0.6 –0.9 –0.6 –1.0 –1.0
Factor Income 2.4 1.1 1.5 1.0 0.5 0.6 0.5 0.5 0.7 1.2 2.5
Memorandum
Change in Reserves 1.1 5.5 7.2 –1.0 3.4 4.4 5.1 2.9 2.1 1.7 1.2
Sub-Saharan Africa
Savings 13.7 19.4 22.5 19.8 21.1 20.7 20.1 19.5 19.6 19.2 19.1
Investment 17.3 19.9 22.3 22.9 22.3 21.5 22.7 23.0 23.2 23.2 22.9
Net Lending –3.6 –0.5 0.1 –3.1 –1.1 –0.8 –2.6 –3.6 –3.5 –3.9 –3.8
Factor Income –4.3 –5.0 –5.4 –3.9 –4.6 –4.7 –5.0 –4.9 –4.5 –4.2 –3.7
Resource Balance –0.9 1.5 0.9 –3.8 –0.7 0.4 –1.4 –2.6 –2.9 –3.3 –3.5
Memorandum
Change in Reserves 0.6 2.1 1.8 –0.9 0.1 1.9 1.5 0.4 0.4 0.6 0.6
Analytical Groups
Fuel Exporters
Savings 24.6 34.9 39.5 30.5 34.0 37.6 35.9 33.2 32.7 31.6 30.1
Investment 23.5 23.3 26.1 26.0 26.2 25.5 25.0 25.4 25.6 25.8 26.2
Net Lending 1.2 11.7 13.4 4.9 8.0 12.2 11.1 8.3 7.5 6.2 4.1
Factor Income 0.7 –1.1 –1.5 –1.4 –1.9 –2.1 –2.3 –2.3 –1.9 –1.5 0.0
Memorandum
Change in Reserves –0.5 4.7 2.5 –2.1 1.9 2.9 3.7 1.0 0.5 0.6 0.3
Nonfuel Exporters
Savings 23.5 27.3 31.9 32.6 32.7 32.2 32.7 32.8 33.6 33.8 34.1
Investment 25.7 26.9 31.2 31.8 32.6 33.3 33.8 33.9 34.4 34.5 34.6
Net Lending –2.2 0.5 0.6 0.8 0.0 –1.1 –1.1 –1.1 –0.7 –0.7 –0.5
Factor Income –2.0 –2.0 –1.4 –1.5 –1.7 –1.8 –1.6 –1.7 –1.7 –1.7 –1.7
Resource Balance –1.6 0.3 –0.1 0.2 –0.1 –0.9 –1.0 –0.8 –0.6 –0.4 –0.1
Memorandum

Table A15. Summary of Sources and Uses of World Savings (concluded)
(Percent of GDP)
Projections
Averages Average
1992–99 2000–07 2008 2009 2010 2011 2012 2013 2014 2015 2016–19
Net Debtor Economies
Savings 19.5 20.8 21.8 21.6 22.3 21.8 20.8 20.8 21.2 21.2 21.9
Investment 22.4 22.3 25.6 23.5 24.7 25.0 24.5 24.3 24.5 24.6 25.3
Net Lending –2.9 –1.4 –3.8 –1.9 –2.5 –3.2 –3.7 –3.5 –3.3 –3.4 –3.4
Factor Income –2.2 –2.5 –2.4 –2.2 –2.4 –2.4 –2.5 –2.6 –2.7 –2.7 –2.8
Memorandum
Official Financing
Savings 15.8 19.4 19.2 19.5 20.6 20.8 19.7 20.0 20.7 20.6 21.9
Investment 19.7 21.2 23.2 21.5 21.7 21.3 22.0 21.8 22.6 22.9 24.9
Net Lending –4.0 –1.9 –4.1 –2.1 –1.1 –0.5 –2.3 –1.9 –1.9 –2.3 –3.0
Factor Income –2.8 –2.9 –2.9 –2.7 –2.5 –2.2 –2.5 –2.6 –2.6 –2.6 –3.1
Memorandum
Acquisition of Foreign Assets 1.1 1.9 2.1 1.7 1.7 1.0 –3.4 –1.7 0.2 0.1 0.1
Change in Reserves 1.2 1.5 2.4 2.7 1.6 0.9 –1.3 –0.4 1.2 1.1 0.9
Net Debtor Economies by
Debt-Servicing Experience
Rescheduling during 2008–12
Savings 15.4 19.0 20.8 18.3 18.9 18.6 17.0 17.1 17.8 17.2 17.6
Investment 18.8 18.9 23.8 21.3 22.4 22.4 21.4 21.3 21.8 21.8 22.1
Net Lending –3.5 0.0 –3.0 –3.0 –3.6 –3.8 –4.4 –4.2 –4.1 –4.7 –4.5
Factor Income –2.2 –2.9 –2.6 –2.6 –3.7 –4.0 –3.2 –3.0 –2.9 –2.7 –2.4
Memorandum
Acquisition of Foreign Assets 2.6 3.3 1.7 0.4 2.7 1.6 –1.1 –1.0 –0.7 0.0 0.4
Change in Reserves 1.0 1.2 0.4 0.8 1.3 –0.5 –1.6 –0.8 0.0 0.4 0.5
Note: The estimates in this table are based on individual countries’ national accounts and balance of payments statistics. Country group composites are calculated as the sum of the
U.S. dollar values for the relevant individual countries. This differs from the calculations in the April 2005 and earlier issues of the World Economic Outlook, in which the composites
were weighted by GDP valued at purchasing power parities as a share of total world GDP. For many countries, the estimates of national savings are built up from national accounts
data on gross domestic investment and from balance-of-payments-based data on net foreign investment. The latter, which is equivalent to the current account balance, comprises three
components: current transfers, net factor income, and the resource balance. The mixing of data sources, which is dictated by availability, implies that the estimates for national savings
that are derived incorporate the statistical discrepancies. Furthermore, errors, omissions, and asymmetries in balance of payments statistics affect the estimates for net lending; at the
global level, net lending, which in theory would be zero, equals the world current account discrepancy. Despite these statistical shortcomings, flow-of-funds estimates, such as those
presented in these tables, provide a useful framework for analyzing developments in savings and investment, both over time and across regions and countries.
1Excludes Latvia.
2Calculated from the data of individual Euro Area countries excluding Latvia.

Table A16. Summary of World Medium-Term Baseline Scenario
Projections
Averages Averages
1996–2003 2004–11 2012 2013 2014 2015 2012–15 2016–19
Annual Percent Change
World Real GDP 3.5 4.0 3.2 3.0 3.6 3.9 3.4 3.9
Advanced Economies 2.8 1.6 1.4 1.3 2.2 2.3 1.8 2.3
Emerging Market and Developing Economies 4.6 6.8 5.0 4.7 4.9 5.3 5.0 5.4
Memorandum
Potential Output
Major Advanced Economies 2.5 1.6 1.3 1.3 1.5 1.5 1.4 1.7
World Trade, Volume1 6.1 5.6 2.8 3.0 4.3 5.3 3.9 5.7
Imports
Exports
Terms of Trade
Advanced Economies 0.1 –0.6 –0.7 0.7 0.0 –0.2 –0.1 0.0
Emerging Market and Developing Economies 0.5 2.1 0.6 –0.3 –0.2 –0.7 –0.1 –0.4
World Prices in U.S. Dollars
Manufactures –1.3 2.9 0.2 –1.1 –0.3 –0.4 –0.4 0.5
Oil 6.7 17.4 1.0 –0.9 0.1 –6.0 –1.5 –3.0
Nonfuel Primary Commodities –2.5 11.1 –10.0 –1.2 –3.5 –3.9 –4.7 –0.6
Consumer Prices
Interest Rates Percent
Real Six-Month LIBOR2 2.7 0.5 –1.1 –1.1 –1.1 –1.0 –1.1 1.3
World Real Long-Term Interest Rate3 3.0 1.5 0.1 0.8 1.0 1.5 0.9 2.3
Balances on Current Account Percent of GDP
Advanced Economies –0.4 –0.6 –0.1 0.4 0.5 0.4 0.3 0.4
Total External Debt
Debt Service
1Data refer to trade in goods and services.
2London interbank offered rate on U.S. dollar deposits minus percent change in U.S. GDP deflator.
3GDP-weighted average of 10-year (or nearest maturity) government bond rates for Canada, France, Germany, Italy, Japan, United Kingdom, and United States.

1
CHAPTER
World Economic Outlook Archives
World Economic Outlook: The Global Demographic Transition September 2004
World Economic Outlook: Globalization and External Balances April 2005
World Economic Outlook: Building Institutions September 2005
World Economic Outlook: Globalization and Inflation April 2006
World Economic Outlook: Financial Systems and Economic Cycles September 2006
World Economic Outlook: Spillovers and Cycles in the Global Economy April 2007
World Economic Outlook: Globalization and Inequality October 2007
World Economic Outlook: Housing and the Business Cycle April 2008
World Economic Outlook: Financial Stress, Downturns, and Recoveries October 2008
World Economic Outlook: Crisis and Recovery April 2009
World Economic Outlook: Sustaining the Recovery October 2009
World Economic Outlook: Rebalancing Growth April 2010
World Economic Outlook: Recovery, Risk, and Rebalancing October 2010
World Economic Outlook: Tensions from the Two-Speed Recovery—Unemployment, Commodities,
and Capital Flows April 2011
World Economic Outlook: Slowing Growth, Rising Risks September 2011
World Economic Outlook: Growth Resuming, Dangers Remain April 2012
World Economic Outlook: Coping with High Debt and Sluggish Growth October 2012
World Economic Outlook: Hopes, Realities, Risks April 2013
World Economic Outlook: Transitions and Tensions October 2013
World Economic Outlook: Recovery Strengthens, Remains Uneven April 2014
I. Methodology—Aggregation, Modeling, and Forecasting
How Accurate Are the Forecasts in the World Economic Outlook? April 2006, Box 1.3
Drawing the Line Between Personal and Corporate Savings April 2006, Box 4.1
Measuring Inequality: Conceptual, Methodological, and Measurement Issues October 2007, Box 4.1
New Business Cycle Indices for Latin America: A Historical Reconstruction October 2007, Box 5.3
Implications of New PPP Estimates for Measuring Global Growth April 2008, Appendix 1.1
Measuring Output Gaps October 2008, Box 1.3
Assessing and Communicating Risks to the Global Outlook October 2008, Appendix 1.1
Fan Chart for Global Growth April 2009, Appendix 1.2
Indicators for Tracking Growth October 2010, Appendix 1.2
Inferring Potential Output from Noisy Data: The Global Projection Model View October 2010, Box 1.3
Uncoordinated Rebalancing October 2010, Box 1.4
World Economic Outlook Downside Scenarios April 2011, Box 1.2
WORLD ECONOMIC OUTLOOK
SELECTED TOPICS

II. Historical Surveys
External Imbalances Then and Now April 2005, Box 3.1
Long-Term Interest Rates from a Historical Perspective April 2006, Box 1.1
Recycling Petrodollars in the 1970s April 2006, Box 2.2
Historical Perspective on Growth and the Current Account October 2008, Box 6.3
A Historical Perspective on International Financial Crises October 2009, Box 4.1
The Good, the Bad, and the Ugly: 100 Years of Dealing with Public Debt Overhangs October 2012, Chapter 3
III. Economic Growth—Sources and Patterns
How Will Demographic Change Affect the Global Economy? September 2004, Chapter 3
HIV/AIDS: Demographic, Economic, and Fiscal Consequences September 2004, Box 3.3
Implications of Demographic Change for Health Care Systems September 2004, Box 3.4
Workers’ Remittances and Economic Development April 2005, Chapter 2
Output Volatility in Emerging Market and Developing Countries April 2005, Chapter 2
How Does Macroeconomic Instability Stifle Sub-Saharan African Growth? April 2005, Box 1.5
How Should Middle Eastern and Central Asian Oil Exporters Use Their Oil Revenues? April 2005, Box 1.6
Why Is Volatility Harmful? April 2005, Box 2.3
Building Institutions September 2005, Chapter 3
Return on Investment in Industrial and Developing Countries September 2005, Box 2.2
The Use of Specific Levers to Reduce Corruption September 2005, Box 3.2
Examining the Impact of Unrequited Transfers on Institutions September 2005, Box 3.3
The Impact of Recent Housing Market Adjustments in Industrial Countries April 2006, Box 1.2
Awash with Cash: Why Are Corporate Savings So High? April 2006, Chapter 4
The Global Implications of an Avian Flu Pandemic April 2006, Appendix 1.2
Asia Rising: Patterns of Economic Development and Growth September 2006, Chapter 3
Japan’s Potential Output and Productivity Growth September 2006, Box 3.1
The Evolution and Impact of Corporate Governance Quality in Asia September 2006, Box 3.2
Decoupling the Train? Spillovers and Cycles in the Global Economy April 2007, Chapter 4
Spillovers and International Business Cycle Synchronization: A Broader Perspective April 2007, Box 4.3
The Discounting Debate October 2007, Box 1.7
Taxes versus Quantities under Uncertainty (Weitzman, 1974) October 2007, Box 1.8
Experience with Emissions Trading in the European Union October 2007, Box 1.9
Climate Change: Economic Impact and Policy Responses October 2007, Appendix 1.2
What Risks Do Housing Markets Pose for Global Growth? October 2007, Box 2.1
The Changing Dynamics of the Global Business Cycle October 2007, Chapter 5
Major Economies and Fluctuations in Global Growth October 2007, Box 5.1
Improved Macroeconomic Performance—Good Luck or Good Policies? October 2007, Box 5.2
House Prices: Corrections and Consequences October 2008, Box 1.2
Global Business Cycles April 2009, Box 1.1
How Similar Is the Current Crisis to the Great Depression? April 2009, Box 3.1
Is Credit a Vital Ingredient for Recovery? Evidence from Industry-Level Data April 2009, Box 3.2
From Recession to Recovery: How Soon and How Strong? April 2009, Chapter 3
What’s the Damage? Medium-Term Output Dynamics after Financial Crises October 2009, Chapter 4
Will the Recovery Be Jobless? October 2009, Box 1.3
Unemployment Dynamics during Recessions and Recoveries: Okun’s Law and Beyond April 2010, Chapter 3
Does Slow Growth in Advanced Economies Necessarily Imply Slow Growth in Emerging Economies? October 2010, Box 1.1
The Global Recovery: Where Do We Stand? April 2012, Box 1.2

SELECTED TOPICS
How Does Uncertainty Affect Economic Performance? October 2012, Box 1.3
Resilience in Emerging Market and Developing Economies: Will It Last? October 2012, Chapter 4
Jobs and Growth: Can’t Have One without the Other? October 2012, Box 4.1
Spillovers from Policy Uncertainty in the United States and Europe April 2013, Chapter 2,
Spillover Feature
Breaking through the Frontier: Can Today’s Dynamic Low-Income Countries Make It? April 2013, Chapter 4
What Explains the Slowdown in the BRICS? October 2013, Box 1.2
Dancing Together? Spillovers, Common Shocks, and the Role of Financial and Trade Linkages October 2013, Chapter 3
Output Synchronicity in the Middle East, North Africa, Afghanistan, and Pakistan and in the
Caucasus and Central Asia October 2013, Box 3.1
Spillovers from Changes in U.S. Monetary Policy October 2013, Box 3.2
Saving and Economic Growth April 2014, Box 3.1
On the Receiving End? External Conditions and Emerging Market Growth before, during,
and after the Global Financial Crisis April 2014, Chapter 4
The Impact of External Conditions on Medium-Term Growth in Emerging Market Economies April 2014, Box 4.1
IV. Inflation and Deflation and Commodity Markets
Is Global Inflation Coming Back? September 2004, Box 1.1
What Explains the Recent Run-Up in House Prices? September 2004, Box 2.1
Will the Oil Market Continue to Be Tight? April 2005, Chapter 4
Should Countries Worry about Oil Price Fluctuations? April 2005, Box 4.1
Data Quality in the Oil Market April 2005, Box 4.2
Long-Term Inflation Expectations and Credibility September 2005, Box 4.2
The Boom in Nonfuel Commodity Prices: Can It Last? September 2006, Chapter 5
International Oil Companies and National Oil Companies in a Changing Oil Sector Environment September 2006, Box 1.4
Commodity Price Shocks, Growth, and Financing in Sub-Saharan Africa September 2006, Box 2.2
Has Speculation Contributed to Higher Commodity Prices? September 2006, Box 5.1
Agricultural Trade Liberalization and Commodity Prices September 2006, Box 5.2
Recent Developments in Commodity Markets September 2006,
Appendix 2.1
Who Is Harmed by the Surge in Food Prices? October 2007, Box 1.1
Refinery Bottlenecks October 2007, Box 1.5
Making the Most of Biofuels October 2007, Box 1.6
Commodity Market Developments and Prospects April 2008, Appendix 1.2
Dollar Depreciation and Commodity Prices April 2008, Box 1.4
Why Hasn’t Oil Supply Responded to Higher Prices? April 2008, Box 1.5
Oil Price Benchmarks April 2008, Box 1.6
Globalization, Commodity Prices, and Developing Countries April 2008, Chapter 5
The Current Commodity Price Boom in Perspective April 2008, Box 5.2
Is Inflation Back? Commodity Prices and Inflation October 2008, Chapter 3
Does Financial Investment Affect Commodity Price Behavior? October 2008, Box 3.1
Fiscal Responses to Recent Commodity Price Increases: An Assessment October 2008, Box 3.2
Monetary Policy Regimes and Commodity Prices October 2008, Box 3.3
Assessing Deflation Risks in the G3 Economies April 2009, Box 1.3
Will Commodity Prices Rise Again when the Global Economy Recovers? April 2009, Box 1.5
Commodity Market Developments and Prospects October 2009, Appendix 1.1

What Do Options Markets Tell Us about Commodity Price Prospects? October 2009, Box 1.6
What Explains the Rise in Food Price Volatility? October 2009, Box 1.7
How Unusual Is the Current Commodity Price Recovery? April 2010, Box 1.2
Commodity Futures Price Curves and Cyclical Market Adjustment April 2010, Box 1.3
Commodity Market Developments and Prospects October 2010, Appendix 1.1
Dismal Prospects for the Real Estate Sector October 2010, Box 1.2
Have Metals Become More Scarce and What Does Scarcity Mean for Prices? October 2010, Box 1.5
Oil Scarcity, Growth, and Global Imbalances April 2011, Chapter 3
Life Cycle Constraints on Global Oil Production April 2011, Box 3.1
Unconventional Natural Gas: A Game Changer? April 2011, Box 3.2
Short-Term Effects of Oil Shocks on Economic Activity April 2011, Box 3.3
Low-Frequency Filtering for Extracting Business Cycle Trends April 2011, Appendix 3.1
The Energy and Oil Empirical Models April 2011, Appendix 3.2
Commodity Market Developments and Prospects September 2011, Appendix 1.1
Financial Investment, Speculation, and Commodity Prices September 2011, Box 1.4
Target What You Can Hit: Commodity Price Swings and Monetary Policy September 2011, Chapter 3
Commodity Market Review April 2012, Chapter 1,
Special Feature
Commodity Price Swings and Commodity Exporters April 2012, Chapter 4
Macroeconomic Effects of Commodity Price Shocks on Low-Income Countries April 2012, Box 4.1
Volatile Commodity Prices and the Development Challenge in Low-Income Countries April 2012, Box 4.2
Commodity Market Review October 2012, Chapter 1,
Special Feature
Unconventional Energy in the United States October 2012, Box 1.4
Food Supply Crunch: Who Is Most Vulnerable? October 2012, Box 1.5
Commodity Market Review April 2013, Chapter 1,
Special Feature
The Dog That Didn’t Bark: Has Inflation Been Muzzled or Was It Just Sleeping? April 2013, Chapter 3
Does Inflation Targeting Still Make Sense with a Flatter Phillips Curve? April 2013, Box 3.1
Commodity Market Review October 2013, Chapter 1,
Special Feature
Energy Booms and the Current Account: Cross-Country Experience October 2013, Box 1.SF.1
Oil Price Drivers and the Narrowing WTI-Brent Spread October 2013, Box 1.SF.2
Anchoring Inflation Expectations When Inflation is Undershooting April 2014, Box 1.3
Commodity Prices and Forecasts April 2014, Chapter 1,
Special Feature
V. Fiscal Policy
Has Fiscal Behavior Changed under the European Economic and Monetary Union? September 2004, Chapter 2
Bringing Small Entrepreneurs into the Formal Economy September 2004, Box 1.5
HIV/AIDS: Demographic, Economic, and Fiscal Consequences September 2004, Box 3.3
Implications of Demographic Change for Health Care Systems September 2004, Box 3.4
Impact of Aging on Public Pension Plans September 2004, Box 3.5
How Should Middle Eastern and Central Asian Oil Exporters Use Their Oil Revenues? April 2005, Box 1.6
Financial Globalization and the Conduct of Macroeconomic Policies April 2005, Box 3.3
Is Public Debt in Emerging Markets Still Too High? September 2005, Box 1.1

SELECTED TOPICS
Improved Emerging Market Fiscal Performance: Cyclical or Structural? September 2006, Box 2.1
When Does Fiscal Stimulus Work? April 2008, Box 2.1
Fiscal Policy as a Countercyclical Tool October 2008, Chapter 5
Differences in the Extent of Automatic Stabilizers and Their Relationship with Discretionary Fiscal Policy October 2008, Box 5.1
Why Is It So Hard to Determine the Effects of Fiscal Stimulus? October 2008, Box 5.2
Have the U.S. Tax Cuts Been “TTT” [Timely, Temporary, and Targeted]? October 2008, Box 5.3
Will It Hurt? Macroeconomic Effects of Fiscal Consolidation October 2010, Chapter 3
Separated at Birth? The Twin Budget and Trade Balances September 2011, Chapter 4
Are We Underestimating Short-Term Fiscal Multipliers? October 2012, Box 1.1
The Implications of High Public Debt in Advanced Economies October 2012, Box 1.2
The Good, the Bad, and the Ugly: 100 Years of Dealing with Public Debt Overhangs October 2012, Chapter 3
The Great Divergence of Policies April 2013, Box 1.1
Public Debt Overhang and Private Sector Performance April 2013, Box 1.2
VI. Monetary Policy, Financial Markets, and Flow of Funds
Adjustable- or Fixed-Rate Mortgages: What Influences a Country’s Choices? September 2004, Box 2.2
What Are the Risks from Low U.S. Long-Term Interest Rates? April 2005, Box 1.2
Regulating Remittances April 2005, Box 2.2
Financial Globalization and the Conduct of Macroeconomic Policies April 2005, Box 3.3
Monetary Policy in a Globalized World April 2005, Box 3.4
Does Inflation Targeting Work in Emerging Markets? September 2005, Chapter 4
A Closer Look at Inflation Targeting Alternatives: Money and Exchange Rate Targets September 2005, Box 4.1
How Has Globalization Affected Inflation? April 2006, Chapter 3
The Impact of Petrodollars on U.S. and Emerging Market Bond Yields April 2006, Box 2.3
Globalization and Inflation in Emerging Markets April 2006, Box 3.1
Globalization and Low Inflation in a Historical Perspective April 2006, Box 3.2
Exchange Rate Pass-Through to Import Prices April 2006, Box 3.3
Trends in the Financial Sector’s Profits and Savings April 2006, Box 4.2
How Do Financial Systems Affect Economic Cycles? September 2006, Chapter 4
Financial Leverage and Debt Deflation September 2006, Box 4.1
Financial Linkages and Spillovers April 2007, Box 4.1
Macroeconomic Conditions in Industrial Countries and Financial Flows to Emerging Markets April 2007, Box 4.2
Macroeconomic Implications of Recent Market Turmoil: Patterns from Previous Episodes October 2007, Box 1.2
What Is Global Liquidity? October 2007, Box 1.4
The Changing Housing Cycle and the Implications for Monetary Policy April 2008, Chapter 3
Is There a Credit Crunch? April 2008, Box 1.1
Assessing Vulnerabilities to Housing Market Corrections April 2008, Box 3.1
Financial Stress and Economic Downturns October 2008, Chapter 4
Policies to Resolve Financial System Stress and Restore Sound Financial Intermediation October 2008, Box 4.1
The Latest Bout of Financial Distress: How Does It Change the Global Outlook? October 2008, Box 1.1
How Vulnerable Are Nonfinancial Firms? April 2009, Box 1.2
The Case of Vanishing Household Wealth April 2009, Box 2.1
Impact of Foreign Bank Ownership during Home-Grown Crises April 2009, Box 4.1
A Financial Stress Index for Emerging Economies April 2009, Appendix 4.1
Financial Stress in Emerging Economies: Econometric Analysis April 2009, Appendix 4.2
How Linkages Fuel the Fire April 2009, Chapter 4

Lessons for Monetary Policy from Asset Price Fluctuations October 2009, Chapter 3
Were Financial Markets in Emerging Economies More Resilient than in Past Crises? October 2009, Box 1.2
Risks from Real Estate Markets October 2009, Box 1.4
Financial Conditions Indices April 2011, Appendix 1.1
House Price Busts in Advanced Economies: Repercussions for Global Financial Markets April 2011, Box 1.1
International Spillovers and Macroeconomic Policymaking April 2011, Box 1.3
Credit Boom-Bust Cycles: Their Triggers and Policy Implications September 2011, Box 1.2
Are Equity Price Drops Harbingers of Recession? September 2011, Box 1.3
Cross-Border Spillovers from Euro Area Bank Deleveraging April 2012, Chapter 2,
Spillover Feature
The Financial Transmission of Stress in the Global Economy October 2012, Chapter 2,
Spillover Feature
The Great Divergence of Policies April 2013, Box 1.1
Taper Talks: What to Expect When the United States Is Tightening October 2013, Box 1.1
Credit Supply and Economic Growth April 2014, Box 1.1
Should Advanced Economies Worry about Growth Shocks in Emerging Market Economies? April 2014, Chapter 2,
Spillover Feature
Perspectives on Global Real Interest Rates April 2014, Chapter 3
VII. Labor Markets, Poverty, and Inequality
The Globalization of Labor April 2007, Chapter 5
Emigration and Trade: How Do They Affect Developing Countries? April 2007, Box 5.1
Labor Market Reforms in the Euro Area and the Wage-Unemployment Trade-Off October 2007, Box 2.2
Globalization and Inequality October 2007, Chapter 4
The Dualism between Temporary and Permanent Contracts: Measures, Effects, and Policy Issues April 2010, Box 3.1
Short-Time Work Programs April 2010, Box 3.2
Slow Recovery to Nowhere? A Sectoral View of Labor Markets in Advanced Economies September 2011, Box 1.1
The Labor Share in Europe and the United States during and after the Great Recession April 2012, Box 1.1
Jobs and Growth: Can’t Have One without the Other? October 2012, Box 4.1
VIII. Exchange Rate Issues
Learning to Float: The Experience of Emerging Market Countries since the Early 1990s September 2004, Chapter 2
How Did Chile, India, and Brazil Learn to Float? September 2004, Box 2.3
Foreign Exchange Market Development and Intervention September 2004, Box 2.4
How Emerging Market Countries May Be Affected by External Shocks September 2006, Box 1.3
Exchange Rates and the Adjustment of External Imbalances April 2007, Chapter 3
Exchange Rate Pass-Through to Trade Prices and External Adjustment April 2007, Box 3.3
Depreciation of the U.S. Dollar: Causes and Consequences April 2008, Box 1.2
Lessons from the Crisis: On the Choice of Exchange Rate Regime April 2010, Box 1.1
Exchange Rate Regimes and Crisis Susceptibility in Emerging Markets April 2014, Box 1.4
IX. External Payments, Trade, Capital Movements, and Foreign Debt
Is the Doha Round Back on Track? September 2004, Box 1.3
Regional Trade Agreements and Integration: The Experience with NAFTA September 2004, Box 1.4
Trade and Financial Integration in Europe: Five Years after the Euro’s Introduction September 2004, Box 2.5

SELECTED TOPICS
Globalization and External Imbalances April 2005, Chapter 3
The Ending of Global Textile Trade Quotas April 2005, Box 1.3
What Progress Has Been Made in Implementing Policies to Reduce Global Imbalances? April 2005, Box 1.4
Measuring a Country’s Net External Position April 2005, Box 3.2
Global Imbalances: A Saving and Investment Perspective September 2005, Chapter 2
Impact of Demographic Change on Saving, Investment, and Current Account Balances September 2005, Box 2.3
How Will Global Imbalances Adjust? September 2005,
Appendix 1.2
Oil Prices and Global Imbalances April 2006, Chapter 2
How Much Progress Has Been Made in Addressing Global Imbalances? April 2006, Box 1.4
The Doha Round after the Hong Kong SAR Meetings April 2006, Box 1.5
Capital Flows to Emerging Market Countries: A Long-Term Perspective September 2006, Box 1.1
How Will Global Imbalances Adjust? September 2006, Box 2.1
External Sustainability and Financial Integration April 2007, Box 3.1
Large and Persistent Current Account Imbalances April 2007, Box 3.2
Multilateral Consultation on Global Imbalances October 2007, Box 1.3
Managing the Macroeconomic Consequences of Large and Volatile Aid Flows October 2007, Box 2.3
Managing Large Capital Inflows October 2007, Chapter 3
Can Capital Controls Work? October 2007, Box 3.1
Multilateral Consultation on Global Imbalances: Progress Report April 2008, Box 1.3
How Does the Globalization of Trade and Finance Affect Growth? Theory and Evidence April 2008, Box 5.1
Divergence of Current Account Balances across Emerging Economies October 2008, Chapter 6
Current Account Determinants for Oil-Exporting Countries October 2008, Box 6.1
Sovereign Wealth Funds: Implications for Global Financial Markets October 2008, Box 6.2
Global Imbalances and the Financial Crisis April 2009, Box 1.4
Trade Finance and Global Trade: New Evidence from Bank Surveys October 2009, Box 1.1
From Deficit to Surplus: Recent Shifts in Global Current Accounts October 2009, Box 1.5
Getting the Balance Right: Transitioning out of Sustained Current Account Surpluses April 2010, Chapter 4
Emerging Asia: Responding to Capital Inflows October 2010, Box 2.1
Latin America-5: Riding Another Wave of Capital Inflows October 2010, Box 2.2
Do Financial Crises Have Lasting Effects on Trade? October 2010, Chapter 4
Unwinding External Imbalances in the European Union Periphery April 2011, Box 2.1
International Capital Flows: Reliable or Fickle? April 2011, Chapter 4
External Liabilities and Crisis Tipping Points September 2011, Box 1.5
The Evolution of Current Account Deficits in the Euro Area April 2013, Box 1.3
External Rebalancing in the Euro Area October 2013, Box 1.3
The Yin and Yang of Capital Flow Management: Balancing Capital Inflows with Capital Outflows October 2013, Chapter 4
Simulating Vulnerability to International Capital Market Conditions October 2013, Box 4.1
X. Regional Issues
What Are the Risks of Slower Growth in China? September 2004, Box 1.2
Governance Challenges and Progress in Sub-Saharan Africa September 2004, Box 1.6
The Indian Ocean Tsunami: Impact on South Asian Economies April 2005, Box 1.1
Workers’ Remittances and Emigration in the Caribbean April 2005, Box 2.1
What Explains Divergent External Sector Performance in the Euro Area? September 2005, Box 1.3
Pressures Mount for African Cotton Producers September 2005, Box 1.5
Is Investment in Emerging Asia Too Low? September 2005, Box 2.4

Developing Institutions to Reflect Local Conditions: The Example of
Ownership Transformation in China versus Central and Eastern Europe September 2005, Box 3.1
How Rapidly Are Oil Exporters Spending Their Revenue Gains? April 2006, Box 2.1
EMU: 10 Years On October 2008, Box 2.1
Vulnerabilities in Emerging Economies April 2009, Box 2.2
East-West Linkages and Spillovers in Europe April 2012, Box 2.1
The Evolution of Current Account Deficits in the Euro Area April 2013, Box 1.3
XI. Country-Specific Analyses
Why Is the U.S. International Income Account Still in the Black, and Will This Last? September, 2005, Box 1.2
Is India Becoming an Engine for Global Growth? September, 2005, Box 1.4
Saving and Investment in China September, 2005, Box 2.1
China’s GDP Revision: What Does It Mean for China and the Global Economy? April 2006, Box 1.6
What Do Country Studies of the Impact of Globalization on Inequality Tell Us?
Examples from Mexico, China, and India October 2007, Box 4.2
Japan after the Plaza Accord April 2010, Box 4.1
Taiwan Province of China in the Late 1980s April 2010, Box 4.2
Did the Plaza Accord Cause Japan’s Lost Decades? April 2011, Box 1.4
Where Is China’s External Surplus Headed? April 2012, Box 1.3
The U.S. Home Owners’ Loan Corporation April 2012, Box 3.1
Household Debt Restructuring in Iceland April 2012, Box 3.2
Abenomics: Risks after Early Success? October 2013, Box 1.4
Is China’s Spending Pattern Shifting (away from Commodities)? April 2014, Box 1.2
XII. Special Topics
Climate Change and the Global Economy April 2008, Chapter 4
Rising Car Ownership in Emerging Economies: Implications for Climate Change April 2008, Box 4.1
South Asia: Illustrative Impact of an Abrupt Climate Shock April 2008, Box 4.2
Macroeconomic Policies for Smoother Adjustment to Abrupt Climate Shocks April 2008, Box 4.3
Catastrophe Insurance and Bonds: New Instruments to Hedge Extreme Weather Risks April 2008, Box 4.4
Recent Emission-Reduction Policy Initiatives April 2008, Box 4.5
Complexities in Designing Domestic Mitigation Policies April 2008, Box 4.6

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