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1) Venture capital can catalyze the commercialization of research by redirecting funds towards startups that can commercialize innovations rather than existing firms. 2) Regions that receive more venture capital see greater numbers of patents and startups, even when controlling for federal R&D funding. 3) Venture capital helps address agency problems between investors and entrepreneurs by actively monitoring portfolio companies and providing expertise.

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Venture Capital as a Catalyst to Commercialization Sampsa Samila and Olav Sorenson 1
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$241 billion invested in research by OECD governments in 2007 2
Some regions have benefited, but not all equally... 3
Some regions have benefited, but not all equally... 3
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Atlanta? 4
12 10 Log of Patents 1993-2002 4 6 2 8 5 10 15 20 25 Log of Federal R&D Support 1993-2002 Log of Patents 1993-2002 Fitted values 5
12 Log of Count of Startups 1993-2002 7 8 9 10 11 5 10 15 20 25 Log of Federal R&D Support 1993-2002 Log of Startups 1993-2002 Fitted values 6
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Claim: Venture capital catalyzes commercialization 7
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Start-up vs. existing firms 8
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Redirecting research 9
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Complementary assets/skills 10
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Solution to agency problems 11
INVESTECO PRIVATE EQUITY FUND III Limited partners $ ve ry ity l! tiv a c c Portfolio companies A lo General partners $ 12
12 Log of Patents 1993-2002 4 6 2 8 10 5 10 15 20 25 Log of Federal R&D Support 1993-2002 MSAs with VC MSAs without VC Fitted values Fitted values 13
12 Log of Count of Startups 1993-2002 7 8 9 10 11 5 10 15 20 25 Log of Federal R&D Support 1993-2002 MSAs with VC MSAs without VC Fitted values Fitted values 14
Empirical Setting Metropolitan Metropolitan Areas of the United States and Puerto Rico: 1999 ALASKA INSET 0 50 100 Kilometers 0 300 600 900 Kilometers Manchester United States 0 50 100 Miles 0 300 600 Miles 1:6,000,000 Portsmouth-Rochester 1:45,000,000 Fitchburg- Nashua Leominster Lowell Lawrence Worcester SPRINGFIELD BOSTON-WORCESTER- ANCHORAGE LAWRENCE PITTSFIELD Boston HARTFORD Brockton Waterbury Danbury BARNSTABLE-YARMOUTH Stamford- BELLI Norwalk New Bedford SEATTL E-TACOM NGHAM NEW LONDON- 1993 - 2002 Bremer A-BREME PROVIDENCE- ton New Haven- NORWICH FALL RIVER-WARWICK Seatt RTON Bridgeport Meriden le- Bellevu SPOKANE NEW YORK-NORTHERN NEW JERSEY- Everette- LONG ISLAND (PART) Olym pia YAKIMA RICHLAND MISSOULA BANGOR -KENN Tacom PASCO EWICK- GREAT FALLS a GRAND FORKS GTON BURLIN BISMARCK BURN Portlan ON-AU d-Vanc ME LEWIST PORT ouver UTICA-RO BANY- Y- AL CTAD PORTLA ND LAND-S CORVALL Salem ALEM DULUTH-SUPERIOR SCHENEOY GLENS IS FARGO-MOORHEAD TR FALLS SAGINAW- - EUGENE BILLINGS BAY CITYD SET -SPRIN GFIELD ST. CLOUD EAU CLAIRE WAUSAU GREEN BAY MIDLAN SYRACU SE SEE IN DS- TER GRAND RAPI ROCHES MINNEAPOLIS- MUSKEGON- R-FLINT NN ARBO k ST. PAUL HOLLAND Newar burgh KOSH- APPLETON-OSH AH LANSING- G DETROIT-ADetroit BUFFALO - AMTON New ses ic -Passa NEEN Flint or FALLS BINGH Dutchnty BergenSuffolk ISLAND MEDFOR EAST LANSIN Ann Arb NIAGARA Cou u- Nassa rk -LONG BOISE CITY ROCHESTER SHEBOYGAN ELMIRA D-ASHLAN INE JACKSON D- AKRO N ES- --WILK N New ty Yo JERSEY D RAPID CITY LA CROSSE MILWAUKEE-RAC sha SOUTH CLEVELAN WARREN OWN RANTON ETO JAMEST T SCBARRE--HAZL Ci Jersey-NORTHER rdon N NEW POCATELLO SIOUX FALLS Milw aukee-Wauke BEND YOUNGSTO WN- SPOR unte CASPER MADISON Racine eveland- Cl n- WILLIAM NEex YORK W-Somerset-HSTON - JANESVILLEIT Kenosha Lorai ERIE STATEGE es LEHEM- EA DUBUQUE LLE Middl OWN-BETH RENO WATERLOO- BELO BENTON ElyriaAkron SHARON COGH ROCKFORD HARBOR KALAMAZOO- PITTSBUR N LANCAST ER ALLENT n to CEDAR FALLS BATTLE TrenING -Ocean REDDING Chicago CREEK JOHNSTOW READ outh SIOUX CITY CANTON- Monm lphia C CITY CHICO-PA RADISE Sacra CEDAR RAPIDS DES CHICAGO- ART- TOLEDO MASSILLON Philade ape May INGTON-A TLANTI mento GARY- ELKH LIMA ALTOONA own tic-C M Vallejo- YUBA CHEYENNE MOINES KENOSHA Gary GOSHEN LD LLE- Hagerst Atlan PHIA-WIL ridgeton Fairfiel CITY OMAHA IOWA CITY FORT WAYNE MANSFIE STEUBENVI EL illville-B Napa d- SALT LAKE CITY- DAVEN PORT- Kankakee LAFAYETTE OMO COLUM BUS WEIRTON PHILAD d-M-Newark OGDEN MOLINE- PEORIA- KOK RG- Vinelangton Yolo FORT COLLINS-LOVELAND PEKIN WHEELINGPARKERSBU Wilm in ISLE Santa SACR ROCK ISLAND MARIETTA MBERLAND - ON-CARL Greeley MUNCIE DOVER URG-LEBAN YOLO AMENTO- Ro LINCOLN San Fr sa PROVO-OREM DECATUR TERRE CU GTON BLOOMINGTON- i Cincinnat TI- WASHIN ORE HARRISB DAYTON-FIELD BALTIM ton ancisco HAUTE SA N FRAN Boulder-Longmont NORMAL CINCINNA YORK ore BURG CI DENVER-BOULDER-GREELEY ST. JOSEPH CHAMPAIGN- HAMILTON SPRING Washing Baltim D-PETERS OAKLAN SCO- STOCKTON URBANA RICHM ON WS SAN JO D- -LO MODESTO DI Denver KANSAS CITY SPRINGFIELD TTESVILLE PORT NE SE COLUMBIA INDIANAPOLIS MINGTON CHARLO ACH-NEW MERCED BLOO INIA BE Oaklan K-VIRG d FRESNO EVANSVILLE- RLESTON NORFOL San Jo COLORADO TOPEKA HENDERSON N- CHA ANOKE Santa Cr se GRAND JUNCTION HUNTINGTO RO RO-- RG SPRINGS LAWRENCE ASHLAND GREENSBO M-- LYNCHBU L HILL Watson uz- VISALIA- N-SALE E DANVILLURHAM- CHAPE Y ville LOUISVILLE NGTON WINSTOHIGH POINT TULARE- ST. LOUIS Hamilton- n LEXI ROCK UNT PORTERVIL PUEBLO HICKORY N- RALEIGH-D SALINAS LE LAS VEGAS OWENSBORO Middletow CITY- MO EENVILLE BAKERSFIE WICHITA SPRINGFIELD JOHNSON OL RIST MORGANTO GR SAN LUI S OB LD JOPLIN KINGSPORT-B LENOIR ATASCADE ISPO- Riverside San Bernar - CLARKSVILLE- ORO PASO RO RO- dino FLAGSTAFF HOPK INSVILLE NASHVILLE GOLDSB BLES TTE- CHARLO IA- SANTA BAR KNOXVILLE GASTON L VILLE SANTA MABARA- ENID FAYETTEVILLE- ASHEVILLE ROCK HIL FAYETTEVILLE JACKSON LOMPOCRIA- TULSA SPRINGDALE- JONESBORO JACKSON CHA TTANOOGA - CE TON ALBUQUERQUE ROGERS LITTLE ROCK- GREENVILLE FLOREN WILMING HUNTSVILLE RG- LOS ANGE SANTA FE NORTH LITTLE ROCK FLORENCE SPARTANBU RIVER LES- Ventura ATLA NTA AND ERSON MYRTLE LEGEND ORANGE SIDE- AMARILLO OKLAHOMA FORT MEMPHIS SUMTER BEACH COUNTY CITY GADSDEN AMARILLO Metropolitan Statistical Area (MSA) SMITH ATHENS COLUMBIA DECATUR ON- PORTLAND-SALEM Consolidated Metropolitan Statistical Area (CMSA) LAWTON INGHAM CHARLEST ESTON ARL Yolo Primary Metropolitan Statistical Area (PMSA) YUMA PINE BLUFF BIRM CH Orange PHOENIX-MESA SHERMAN- AUGUSTA- NORTH Los AngelCounty DENISON ANNISTON AIKEN State Long Be es- WICHITA FALLS TEXARKANA-TEXARKANA AUBURN- COLUMBUS Metropolitan county or county equivalent ach OPELICA SAN DIEGO LUBBOCK Dallas SA MACON LONGVIEW- TUSCALOO SAVANNAH DALLAS-FORT WORTH MARSHALL MONROE JACKS ON Y 0 100 200 300 400 Kilometers LAS CRUCES Fort Worth-Arlington MONTGOMER ALBANY TUCSON DOTHAN ABILENE 0 100 200 300 Miles ODESSA-MIDLAND SHREVEPORT- LLE TYLER BOSSIER CITY HATTIESBURG MOBILE JACKSONVI 1:13,500,000 EL PASO WACO ALEXANDRIA PENSACOLA SAN ANGELO FORT WALTON TALLAHASSE E BEACH E BATON ROUGE GAINESVILL KILLEEN-TEMPLE BRYAN-COLLEGE STATION LAKE DAYTON A BEACH CHARLES PANAMA CITY OCALA AUSTIN-SAN MARCOS T- BILOXI-GULFPOR LLE-PALM BAY HAWAII LAFAYETTE PASCAGOULA E-TITUSVI ORLAND O MELBOURN SAN ANTONIO Houston BEAUMONT- NEW ORLEANS RWATER ARTHUR PETERSBURG-CLEA ST. LUCIE PUERTO RICO VICTORIA PORT HOUMA TAMPA-ST. RCE-PORT Galveston-Texas City ND- FORT PIE SAN JUAN-CAGUAS-ARECIBO LAKELAHAVEN - HONOLULU Brazoria HOUSTON-GALVESTON-BRAZORIA WINTER M BEACH WEST PAL ON AGUADILLA Arecibo San Juan-Bayamón RADENTON BOCA RAT uderdale LAREDO CORPUS CHRISTI SARASOTA-B DA Fort La PUNTA GOR I- AL MIAM LAUDERDALE PONCE ERS -CAPE COR FO RT FORT MY NAPLES MAYAGÜEZ Miami Caguas McALLEN- 0 100 200 300 Kilometers EDINBURG-MISSION 0 20 40 Kilometers 0 100 200 Miles Metropolitan Area boundaries and names are those defined by the 0 20 40 Miles 1:15,000,000 Federal Office of Management and Budget on June 30, 1999. All BROWNSVILLE- 1:5,000,000 HARLINGEN-SAN BENITO other boundaries and names are as of June 30, 1999. U.S. DEPARTMENT OF COMMERCE, Economics and Statistics Administration, Bureau of the Census 15
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Measures constructed 17
Measures constructed Idea Measure Innovation Patent applications Entrepreneurship Establishment births, 0-19 VC Activity Investment count R&D Funding All federal funding 17
Identification procedure Innovation/ VC Activity LP Returns E’ship ??? 18
Identification procedure Innovation/ VC Activity LP Returns E’ship ??? 18
INVESTECO PRIVATE EQUITY FUND III Limited partners $ Portfolio companies General partners $ 19
Portfolio Asset Allocation 12% Limited partners 13% 41% 34% Stocks Bonds Cash Alternative 20
Instrumental Variable: Portfolio Rebalancing ¯ IVit = Rt × Ni Nationwide Count of institutional avg returns investors in region Year FE remove macro-level economic effects Use of industry average return and MSA FE remove regional effects 21
Population (thousands) 659.4 1104.9 3270 Patents 246.8 583.0 3270 Patent Citations (5yr) 1228.2 3671.0 2614 Births of Establishments, 0-19 1415.2 2523.6 3270 R&D Support (millions) 49.6 122.6 3270 VC Investment Count 15.9 106.4 3270 Innovation VC Investment Amount (millions) 39.8 340.1 3270 Table 2: Effects on Innovation (1) (2) (3) (4) (5) (6) Patents Patents Citations Patents Patents Citations Population (t-1) 1.427∗∗∗ 1.405∗∗∗ 1.721∗∗∗ 1.453∗∗∗ 1.444∗∗∗ 1.719∗∗∗ (4.81) (4.71) (3.45) (4.90) (4.87) (3.46) R&D Support (t-1) -0.000460 0.0202∗∗ 0.0207∗∗ -0.000410 0.00720 0.0145∗∗ (-0.19) (2.19) (2.00) (-0.17) (1.53) (2.00) VC Count (t) 0.0231∗ 0.0261∗∗ -0.00568 (1.83) (2.05) (-0.37) VC Count (t) x R&D (t-1) 0.00764∗∗ 0.00636∗ (2.27) (1.78) VC Amount (t) 0.000643 0.00143 0.000229 (0.42) (0.89) (0.11) VC Amount (t) x R&D (t-1) 0.000452∗ 0.000654∗ (1.71) (1.73) Time period 1993-2002 1993-2002 1993-2000 1993-2002 1993-2002 1993-2000 Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes 2 R 0.16 0.16 0.15 0.15 0.15 0.15 Clusters 328 328 328 328 328 328 Observations 3270 3270 2624 3270 3270 2624 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 37 22
Entrepreneurship Table 3: Effects on Entrepreneurship (7) (8) (9) (10) Births Births Births Births Population (t-1) 0.820∗∗∗ 0.815∗∗∗ 0.825∗∗∗ 0.821∗∗∗ (10.43) (10.25) (10.55) (10.45) R&D Support (t-1) -0.000679 0.00413 -0.000658 0.00319 (-1.04) (1.54) (-1.01) (1.34) VC Count (t) 0.0100∗∗∗ 0.0107∗∗∗ (2.97) (3.12) VC Count (t) x R&D (t-1) 0.00178∗ (1.89) VC Amount (t) 0.00101∗∗ 0.00141∗∗∗ (2.57) (3.21) VC Amount (t) x R&D (t-1) 0.000229∗ (1.73) Year Dummies Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes 2 R 0.22 0.22 0.22 0.22 Clusters 328 328 328 328 Observations 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 23
Robustness Table 4: Robustness to Removing San Francisco Bay Area and Boston Excluding Bay Area Excluding Boston (11) (12) (13) (14) Patents Births Patents Births Population (t-1) 1.421∗∗∗ 0.813∗∗∗ 1.409∗∗∗ 0.816∗∗∗ (4.75) (10.22) (4.72) (10.27) R&D Support (t-1) 0.0138 0.00398 0.0194∗∗ 0.00388 (1.60) (1.45) (2.10) (1.43) VC Count (t) 0.0190 0.0101∗∗∗ 0.0254∗∗ 0.0104∗∗∗ (1.56) (2.89) (1.99) (3.05) VC Count (t) x R&D (t-1) 0.00529∗ 0.00173∗ 0.00736∗∗ 0.00169∗ (1.68) (1.79) (2.17) (1.77) Year Dummies Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes R2 0.15 0.22 0.16 0.22 Clusters 324 324 327 327 Observations 3230 3230 3260 3260 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. Table 5: Innovation by Agencies Source of R&D Support HHS NSF DoD DoA NASA 24 (15) (16) (17) (18) (19) Patents Patents Patents Patents Patents
VC Count (t) x R&D (t-1) 0.00529 0.00173 0.00736 0.00169 (1.68) (1.79) (2.17) (1.77) Year Dummies Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes R2 0.15 0.22 0.16 0.22 Innovation by Agency Clusters Observations 324 3230 324 3230 327 3260 327 3260 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. Table 5: Innovation by Agencies Source of R&D Support HHS NSF DoD DoA NASA (15) (16) (17) (18) (19) Patents Patents Patents Patents Patents Population (t-1) 1.411∗∗∗ 1.403∗∗∗ 1.397∗∗∗ 1.441∗∗∗ 1.425∗∗∗ (4.77) (4.71) (4.71) (4.85) (4.77) R&D Support (t-1) 0.00997 0.0135∗∗ 0.0119∗∗ -0.00108 0.00888∗ (1.57) (2.30) (2.25) (-0.52) (1.85) VC Count (t) 0.0272∗∗ 0.0267∗∗ 0.0308∗∗ 0.0250∗ 0.0258∗ (2.09) (2.08) (2.28) (1.75) (1.94) VC Count (t) x R&D (t-1) 0.00245 0.00543∗∗ 0.00348∗ 0.000502 0.00142 (1.09) (2.41) (1.82) (0.51) (0.74) Year Dummies Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes R2 0.16 0.16 0.16 0.16 0.16 Clusters 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 39 25
Entrepreneurship by Agency Table 6: Entrepreneurship by Agencies Source of R&D Support HHS NSF DoD DoA NASA (20) (21) (22) (23) (24) Births Births Births Births Births Population (t-1) 0.817∗∗∗ 0.812∗∗∗ 0.814∗∗∗ 0.822∗∗∗ 0.820∗∗∗ (10.33) (10.23) (10.32) (10.34) (10.41) R&D Support (t-1) 0.00146 0.00426∗∗∗ 0.00211 0.000191 -0.000157 (1.07) (2.76) (1.42) (0.31) (-0.10) VC Count (t) 0.0104∗∗∗ 0.0111∗∗∗ 0.0113∗∗∗ 0.0113∗∗∗ 0.00967∗∗∗ (2.94) (3.23) (3.07) (3.11) (2.67) VC Count (t) x R&D (t-1) 0.000295 0.00161∗∗∗ 0.000614 0.000312 -0.000138 (0.64) (2.77) (1.19) (1.24) (-0.25) Year Dummies Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes R2 0.22 0.22 0.22 0.22 0.22 Clusters 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 26
Table 7: Lag Structure of R&D Effects on Innovation (25) (26) (27) (28) (29) (30) Patents Patents Patents Patents Patents Patents Population (t-1) 1.418∗∗∗ 1.405∗∗∗ 1.405∗∗∗ 1.398∗∗∗ 1.415∗∗∗ 1.418∗∗∗ (4.77) (4.71) (4.71) (4.71) (4.77) (4.75) VC Count (t) 0.0239∗ 0.0261∗∗ 0.0263∗∗ 0.0270∗∗ 0.0261∗∗ 0.0253∗∗ (1.88) (2.05) (2.06) (2.11) (2.05) (2.00) R&D Support (t) 0.00803 (0.85) VC Count (t) x R&D (t) 0.00246 (0.71) R&D Support (t-1) 0.0202∗∗ (2.19) VC Count (t) x R&D (t-1) 0.00764∗∗ (2.27) R&D Support (t-2) 0.0190∗∗ (2.23) VC Count (t) x R&D (t-2) 0.00689∗∗ (2.20) R&D Support (t-3) 0.0221∗∗ (2.41) VC Count (t) x R&D (t-3) 0.00809∗∗ (2.38) R&D Support (t-4) 0.0174∗ (1.81) VC Count (t) x R&D (t-4) 0.00733∗∗ (2.11) R&D Support (t-5) 0.0157 (1.60) VC Count (t) x R&D (t-5) 0.00761∗∗ (2.13) Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.16 0.16 0.16 0.16 0.16 0.16 Clusters 328 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 41 27
Table 8: Lag Structure of R&D Effects on Entrepreneurship (31) (32) (33) (34) (35) (36) Births Births Births Births Births Births Population (t-1) 0.821∗∗∗ 0.815∗∗∗ 0.815∗∗∗ 0.813∗∗∗ 0.811∗∗∗ 0.812∗∗∗ (10.49) (10.25) (10.23) (10.18) (10.11) (10.15) VC Count (t) 0.0101∗∗∗ 0.0107∗∗∗ 0.0105∗∗∗ 0.0107∗∗∗ 0.0108∗∗∗ 0.0107∗∗∗ (2.97) (3.12) (3.07) (3.12) (3.15) (3.13) R&D Support (t) -0.000782 (-0.34) VC Count (t) x R&D (t) 0.000329 (0.40) R&D Support (t-1) 0.00413 (1.54) VC Count (t) x R&D (t-1) 0.00178∗ (1.89) R&D Support (t-2) 0.00382∗ (1.67) VC Count (t) x R&D (t-2) 0.00128 (1.56) R&D Support (t-3) 0.00444∗ (1.81) VC Count (t) x R&D (t-3) 0.00154∗ (1.78) R&D Support (t-4) 0.00588∗∗ (2.20) VC Count (t) x R&D (t-4) 0.00188∗ (1.95) R&D Support (t-5) 0.00602∗ (1.94) VC Count (t) x R&D (t-5) 0.00193∗ (1.74) Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.22 0.22 0.22 0.22 0.22 0.22 Clusters 328 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 42 28
Table 9: Lag Structure of Venture Capital Effects on Innovation and Entrepreneurship (37) (38) (39) (40) (41) (42) Patents Patents Patents Births Births Births Population (t-1) 1.405∗∗∗ 1.414∗∗∗ 1.420∗∗∗ 0.815∗∗∗ 0.815∗∗∗ 0.818∗∗∗ (4.71) (4.73) (4.77) (10.25) (10.15) (10.17) R&D Support (t-1) 0.0202∗∗ 0.00413 (2.19) (1.54) VC Count (t) 0.0261∗∗ 0.0107∗∗∗ (2.05) (3.12) VC Count (t) x R&D (t-1) 0.00764∗∗ 0.00178∗ (2.27) (1.89) R&D Support (t-2) 0.0247∗∗∗ 0.00177 (2.80) (0.49) VC Count (t-1) 0.0195 0.0108∗∗∗ (1.60) (2.59) VC Count (t-1) x R&D (t-2) 0.00894∗∗∗ 0.000520 (2.83) (0.40) R&D Support (t-3) 0.0298∗∗∗ 0.00239 (3.67) (0.70) VC Count (t-2) 0.0145 0.00837∗ (1.22) (1.92) VC Count (t-2) x R&D (t-3) 0.0109∗∗∗ 0.000782 (3.73) (0.63) Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.16 0.16 0.16 0.22 0.22 0.22 Clusters 328 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 29
Instrumental Variables Table 10: Instrumental Variables Estimation for Innovation and Entrepreneurship (43) (44) (45) (46) 1st Stage Patents Patents Births Births Population (t-1) 1.116∗∗ 1.330∗∗∗ 1.368∗∗∗ 0.803∗∗∗ 0.813∗∗∗ (2.45) (4.71) (4.85) (9.32) (9.13) LP Returns 0.0112∗∗∗ (10.49) R&D Support (t-1) 0.000791 -0.000617 0.0265∗ -0.000706 0.00664 (0.60) (-0.22) (1.89) (-0.98) (1.60) VC Count (t) 0.0957∗∗∗ 0.0814∗∗ 0.0227∗∗ 0.0189∗ (3.23) (2.57) (2.35) (1.95) VC Count (t) x R&D (t-1) 0.00974∗∗ 0.00264∗ (1.98) (1.83) Year Dummies Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes R2 0.27 Clusters 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 Notes: 2SLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Bootstrapped t-statistics in parentheses. 30
Venture capital as catalyst to commercialization Increased innovation Increased entrepreneurship 31
Further results Non-compete covenants Social capital 32
End note: Key to getting funding 33
End note: Key to getting funding 33
End note: Key to getting funding 33
End note: Key to getting funding Applied Physics Research 33
Thank you 34

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Vce tkk 2010 06-14

  • 1.
    Venture Capital asa Catalyst to Commercialization Sampsa Samila and Olav Sorenson 1
  • 2.
  • 3.
    $241 billion invested in researchby OECD governments in 2007 2
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
    12 10 Log of Patents 1993-2002 4 6 2 8 5 10 15 20 25 Log of Federal R&D Support 1993-2002 Log of Patents 1993-2002 Fitted values 5
  • 9.
    12 Logof Count of Startups 1993-2002 7 8 9 10 11 5 10 15 20 25 Log of Federal R&D Support 1993-2002 Log of Startups 1993-2002 Fitted values 6
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
    Solution to agencyproblems 11
  • 20.
    INVESTECO PRIVATE EQUITY FUND III Limited partners $ ve ry ity l! tiv a c c Portfolio companies A lo General partners $ 12
  • 21.
    12 Log of Patents1993-2002 4 6 2 8 10 5 10 15 20 25 Log of Federal R&D Support 1993-2002 MSAs with VC MSAs without VC Fitted values Fitted values 13
  • 22.
    12 Log ofCount of Startups 1993-2002 7 8 9 10 11 5 10 15 20 25 Log of Federal R&D Support 1993-2002 MSAs with VC MSAs without VC Fitted values Fitted values 14
  • 23.
    Empirical Setting Metropolitan Metropolitan Areas of the United States and Puerto Rico: 1999 ALASKA INSET 0 50 100 Kilometers 0 300 600 900 Kilometers Manchester United States 0 50 100 Miles 0 300 600 Miles 1:6,000,000 Portsmouth-Rochester 1:45,000,000 Fitchburg- Nashua Leominster Lowell Lawrence Worcester SPRINGFIELD BOSTON-WORCESTER- ANCHORAGE LAWRENCE PITTSFIELD Boston HARTFORD Brockton Waterbury Danbury BARNSTABLE-YARMOUTH Stamford- BELLI Norwalk New Bedford SEATTL E-TACOM NGHAM NEW LONDON- 1993 - 2002 Bremer A-BREME PROVIDENCE- ton New Haven- NORWICH FALL RIVER-WARWICK Seatt RTON Bridgeport Meriden le- Bellevu SPOKANE NEW YORK-NORTHERN NEW JERSEY- Everette- LONG ISLAND (PART) Olym pia YAKIMA RICHLAND MISSOULA BANGOR -KENN Tacom PASCO EWICK- GREAT FALLS a GRAND FORKS GTON BURLIN BISMARCK BURN Portlan ON-AU d-Vanc ME LEWIST PORT ouver UTICA-RO BANY- Y- AL CTAD PORTLA ND LAND-S CORVALL Salem ALEM DULUTH-SUPERIOR SCHENEOY GLENS IS FARGO-MOORHEAD TR FALLS SAGINAW- - EUGENE BILLINGS BAY CITYD SET -SPRIN GFIELD ST. CLOUD EAU CLAIRE WAUSAU GREEN BAY MIDLAN SYRACU SE SEE IN DS- TER GRAND RAPI ROCHES MINNEAPOLIS- MUSKEGON- R-FLINT NN ARBO k ST. PAUL HOLLAND Newar burgh KOSH- APPLETON-OSH AH LANSING- G DETROIT-ADetroit BUFFALO - AMTON New ses ic -Passa NEEN Flint or FALLS BINGH Dutchnty BergenSuffolk ISLAND MEDFOR EAST LANSIN Ann Arb NIAGARA Cou u- Nassa rk -LONG BOISE CITY ROCHESTER SHEBOYGAN ELMIRA D-ASHLAN INE JACKSON D- AKRO N ES- --WILK N New ty Yo JERSEY D RAPID CITY LA CROSSE MILWAUKEE-RAC sha SOUTH CLEVELAN WARREN OWN RANTON ETO JAMEST T SCBARRE--HAZL Ci Jersey-NORTHER rdon N NEW POCATELLO SIOUX FALLS Milw aukee-Wauke BEND YOUNGSTO WN- SPOR unte CASPER MADISON Racine eveland- Cl n- WILLIAM NEex YORK W-Somerset-HSTON - JANESVILLEIT Kenosha Lorai ERIE STATEGE es LEHEM- EA DUBUQUE LLE Middl OWN-BETH RENO WATERLOO- BELO BENTON ElyriaAkron SHARON COGH ROCKFORD HARBOR KALAMAZOO- PITTSBUR N LANCAST ER ALLENT n to CEDAR FALLS BATTLE TrenING -Ocean REDDING Chicago CREEK JOHNSTOW READ outh SIOUX CITY CANTON- Monm lphia C CITY CHICO-PA RADISE Sacra CEDAR RAPIDS DES CHICAGO- ART- TOLEDO MASSILLON Philade ape May INGTON-A TLANTI mento GARY- ELKH LIMA ALTOONA own tic-C M Vallejo- YUBA CHEYENNE MOINES KENOSHA Gary GOSHEN LD LLE- Hagerst Atlan PHIA-WIL ridgeton Fairfiel CITY OMAHA IOWA CITY FORT WAYNE MANSFIE STEUBENVI EL illville-B Napa d- SALT LAKE CITY- DAVEN PORT- Kankakee LAFAYETTE OMO COLUM BUS WEIRTON PHILAD d-M-Newark OGDEN MOLINE- PEORIA- KOK RG- Vinelangton Yolo FORT COLLINS-LOVELAND PEKIN WHEELINGPARKERSBU Wilm in ISLE Santa SACR ROCK ISLAND MARIETTA MBERLAND - ON-CARL Greeley MUNCIE DOVER URG-LEBAN YOLO AMENTO- Ro LINCOLN San Fr sa PROVO-OREM DECATUR TERRE CU GTON BLOOMINGTON- i Cincinnat TI- WASHIN ORE HARRISB DAYTON-FIELD BALTIM ton ancisco HAUTE SA N FRAN Boulder-Longmont NORMAL CINCINNA YORK ore BURG CI DENVER-BOULDER-GREELEY ST. JOSEPH CHAMPAIGN- HAMILTON SPRING Washing Baltim D-PETERS OAKLAN SCO- STOCKTON URBANA RICHM ON WS SAN JO D- -LO MODESTO DI Denver KANSAS CITY SPRINGFIELD TTESVILLE PORT NE SE COLUMBIA INDIANAPOLIS MINGTON CHARLO ACH-NEW MERCED BLOO INIA BE Oaklan K-VIRG d FRESNO EVANSVILLE- RLESTON NORFOL San Jo COLORADO TOPEKA HENDERSON N- CHA ANOKE Santa Cr se GRAND JUNCTION HUNTINGTO RO RO-- RG SPRINGS LAWRENCE ASHLAND GREENSBO M-- LYNCHBU L HILL Watson uz- VISALIA- N-SALE E DANVILLURHAM- CHAPE Y ville LOUISVILLE NGTON WINSTOHIGH POINT TULARE- ST. LOUIS Hamilton- n LEXI ROCK UNT PORTERVIL PUEBLO HICKORY N- RALEIGH-D SALINAS LE LAS VEGAS OWENSBORO Middletow CITY- MO EENVILLE BAKERSFIE WICHITA SPRINGFIELD JOHNSON OL RIST MORGANTO GR SAN LUI S OB LD JOPLIN KINGSPORT-B LENOIR ATASCADE ISPO- Riverside San Bernar - CLARKSVILLE- ORO PASO RO RO- dino FLAGSTAFF HOPK INSVILLE NASHVILLE GOLDSB BLES TTE- CHARLO IA- SANTA BAR KNOXVILLE GASTON L VILLE SANTA MABARA- ENID FAYETTEVILLE- ASHEVILLE ROCK HIL FAYETTEVILLE JACKSON LOMPOCRIA- TULSA SPRINGDALE- JONESBORO JACKSON CHA TTANOOGA - CE TON ALBUQUERQUE ROGERS LITTLE ROCK- GREENVILLE FLOREN WILMING HUNTSVILLE RG- LOS ANGE SANTA FE NORTH LITTLE ROCK FLORENCE SPARTANBU RIVER LES- Ventura ATLA NTA AND ERSON MYRTLE LEGEND ORANGE SIDE- AMARILLO OKLAHOMA FORT MEMPHIS SUMTER BEACH COUNTY CITY GADSDEN AMARILLO Metropolitan Statistical Area (MSA) SMITH ATHENS COLUMBIA DECATUR ON- PORTLAND-SALEM Consolidated Metropolitan Statistical Area (CMSA) LAWTON INGHAM CHARLEST ESTON ARL Yolo Primary Metropolitan Statistical Area (PMSA) YUMA PINE BLUFF BIRM CH Orange PHOENIX-MESA SHERMAN- AUGUSTA- NORTH Los AngelCounty DENISON ANNISTON AIKEN State Long Be es- WICHITA FALLS TEXARKANA-TEXARKANA AUBURN- COLUMBUS Metropolitan county or county equivalent ach OPELICA SAN DIEGO LUBBOCK Dallas SA MACON LONGVIEW- TUSCALOO SAVANNAH DALLAS-FORT WORTH MARSHALL MONROE JACKS ON Y 0 100 200 300 400 Kilometers LAS CRUCES Fort Worth-Arlington MONTGOMER ALBANY TUCSON DOTHAN ABILENE 0 100 200 300 Miles ODESSA-MIDLAND SHREVEPORT- LLE TYLER BOSSIER CITY HATTIESBURG MOBILE JACKSONVI 1:13,500,000 EL PASO WACO ALEXANDRIA PENSACOLA SAN ANGELO FORT WALTON TALLAHASSE E BEACH E BATON ROUGE GAINESVILL KILLEEN-TEMPLE BRYAN-COLLEGE STATION LAKE DAYTON A BEACH CHARLES PANAMA CITY OCALA AUSTIN-SAN MARCOS T- BILOXI-GULFPOR LLE-PALM BAY HAWAII LAFAYETTE PASCAGOULA E-TITUSVI ORLAND O MELBOURN SAN ANTONIO Houston BEAUMONT- NEW ORLEANS RWATER ARTHUR PETERSBURG-CLEA ST. LUCIE PUERTO RICO VICTORIA PORT HOUMA TAMPA-ST. RCE-PORT Galveston-Texas City ND- FORT PIE SAN JUAN-CAGUAS-ARECIBO LAKELAHAVEN - HONOLULU Brazoria HOUSTON-GALVESTON-BRAZORIA WINTER M BEACH WEST PAL ON AGUADILLA Arecibo San Juan-Bayamón RADENTON BOCA RAT uderdale LAREDO CORPUS CHRISTI SARASOTA-B DA Fort La PUNTA GOR I- AL MIAM LAUDERDALE PONCE ERS -CAPE COR FO RT FORT MY NAPLES MAYAGÜEZ Miami Caguas McALLEN- 0 100 200 300 Kilometers EDINBURG-MISSION 0 20 40 Kilometers 0 100 200 Miles Metropolitan Area boundaries and names are those defined by the 0 20 40 Miles 1:15,000,000 Federal Office of Management and Budget on June 30, 1999. All BROWNSVILLE- 1:5,000,000 HARLINGEN-SAN BENITO other boundaries and names are as of June 30, 1999. U.S. DEPARTMENT OF COMMERCE, Economics and Statistics Administration, Bureau of the Census 15
  • 24.
  • 25.
  • 26.
    Measures constructed Idea Measure Innovation Patent applications Entrepreneurship Establishment births, 0-19 VC Activity Investment count R&D Funding All federal funding 17
  • 27.
    Identification procedure Innovation/ VC Activity LP Returns E’ship ??? 18
  • 28.
    Identification procedure Innovation/ VC Activity LP Returns E’ship ??? 18
  • 29.
    INVESTECO PRIVATE EQUITY FUND III Limited partners $ Portfolio companies General partners $ 19
  • 30.
    Portfolio Asset Allocation 12% Limited partners 13% 41% 34% Stocks Bonds Cash Alternative 20
  • 31.
    Instrumental Variable: Portfolio Rebalancing ¯ IVit = Rt × Ni Nationwide Count of institutional avg returns investors in region Year FE remove macro-level economic effects Use of industry average return and MSA FE remove regional effects 21
  • 32.
    Population (thousands) 659.4 1104.9 3270 Patents 246.8 583.0 3270 Patent Citations (5yr) 1228.2 3671.0 2614 Births of Establishments, 0-19 1415.2 2523.6 3270 R&D Support (millions) 49.6 122.6 3270 VC Investment Count 15.9 106.4 3270 Innovation VC Investment Amount (millions) 39.8 340.1 3270 Table 2: Effects on Innovation (1) (2) (3) (4) (5) (6) Patents Patents Citations Patents Patents Citations Population (t-1) 1.427∗∗∗ 1.405∗∗∗ 1.721∗∗∗ 1.453∗∗∗ 1.444∗∗∗ 1.719∗∗∗ (4.81) (4.71) (3.45) (4.90) (4.87) (3.46) R&D Support (t-1) -0.000460 0.0202∗∗ 0.0207∗∗ -0.000410 0.00720 0.0145∗∗ (-0.19) (2.19) (2.00) (-0.17) (1.53) (2.00) VC Count (t) 0.0231∗ 0.0261∗∗ -0.00568 (1.83) (2.05) (-0.37) VC Count (t) x R&D (t-1) 0.00764∗∗ 0.00636∗ (2.27) (1.78) VC Amount (t) 0.000643 0.00143 0.000229 (0.42) (0.89) (0.11) VC Amount (t) x R&D (t-1) 0.000452∗ 0.000654∗ (1.71) (1.73) Time period 1993-2002 1993-2002 1993-2000 1993-2002 1993-2002 1993-2000 Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes 2 R 0.16 0.16 0.15 0.15 0.15 0.15 Clusters 328 328 328 328 328 328 Observations 3270 3270 2624 3270 3270 2624 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 37 22
  • 33.
    Entrepreneurship Table 3: Effects on Entrepreneurship (7) (8) (9) (10) Births Births Births Births Population (t-1) 0.820∗∗∗ 0.815∗∗∗ 0.825∗∗∗ 0.821∗∗∗ (10.43) (10.25) (10.55) (10.45) R&D Support (t-1) -0.000679 0.00413 -0.000658 0.00319 (-1.04) (1.54) (-1.01) (1.34) VC Count (t) 0.0100∗∗∗ 0.0107∗∗∗ (2.97) (3.12) VC Count (t) x R&D (t-1) 0.00178∗ (1.89) VC Amount (t) 0.00101∗∗ 0.00141∗∗∗ (2.57) (3.21) VC Amount (t) x R&D (t-1) 0.000229∗ (1.73) Year Dummies Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes 2 R 0.22 0.22 0.22 0.22 Clusters 328 328 328 328 Observations 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 23
  • 34.
    Robustness Table 4: Robustness to Removing San Francisco Bay Area and Boston Excluding Bay Area Excluding Boston (11) (12) (13) (14) Patents Births Patents Births Population (t-1) 1.421∗∗∗ 0.813∗∗∗ 1.409∗∗∗ 0.816∗∗∗ (4.75) (10.22) (4.72) (10.27) R&D Support (t-1) 0.0138 0.00398 0.0194∗∗ 0.00388 (1.60) (1.45) (2.10) (1.43) VC Count (t) 0.0190 0.0101∗∗∗ 0.0254∗∗ 0.0104∗∗∗ (1.56) (2.89) (1.99) (3.05) VC Count (t) x R&D (t-1) 0.00529∗ 0.00173∗ 0.00736∗∗ 0.00169∗ (1.68) (1.79) (2.17) (1.77) Year Dummies Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes R2 0.15 0.22 0.16 0.22 Clusters 324 324 327 327 Observations 3230 3230 3260 3260 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. Table 5: Innovation by Agencies Source of R&D Support HHS NSF DoD DoA NASA 24 (15) (16) (17) (18) (19) Patents Patents Patents Patents Patents
  • 35.
    VC Count (t)x R&D (t-1) 0.00529 0.00173 0.00736 0.00169 (1.68) (1.79) (2.17) (1.77) Year Dummies Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes R2 0.15 0.22 0.16 0.22 Innovation by Agency Clusters Observations 324 3230 324 3230 327 3260 327 3260 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. Table 5: Innovation by Agencies Source of R&D Support HHS NSF DoD DoA NASA (15) (16) (17) (18) (19) Patents Patents Patents Patents Patents Population (t-1) 1.411∗∗∗ 1.403∗∗∗ 1.397∗∗∗ 1.441∗∗∗ 1.425∗∗∗ (4.77) (4.71) (4.71) (4.85) (4.77) R&D Support (t-1) 0.00997 0.0135∗∗ 0.0119∗∗ -0.00108 0.00888∗ (1.57) (2.30) (2.25) (-0.52) (1.85) VC Count (t) 0.0272∗∗ 0.0267∗∗ 0.0308∗∗ 0.0250∗ 0.0258∗ (2.09) (2.08) (2.28) (1.75) (1.94) VC Count (t) x R&D (t-1) 0.00245 0.00543∗∗ 0.00348∗ 0.000502 0.00142 (1.09) (2.41) (1.82) (0.51) (0.74) Year Dummies Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes R2 0.16 0.16 0.16 0.16 0.16 Clusters 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 39 25
  • 36.
    Entrepreneurship by Agency Table 6: Entrepreneurship by Agencies Source of R&D Support HHS NSF DoD DoA NASA (20) (21) (22) (23) (24) Births Births Births Births Births Population (t-1) 0.817∗∗∗ 0.812∗∗∗ 0.814∗∗∗ 0.822∗∗∗ 0.820∗∗∗ (10.33) (10.23) (10.32) (10.34) (10.41) R&D Support (t-1) 0.00146 0.00426∗∗∗ 0.00211 0.000191 -0.000157 (1.07) (2.76) (1.42) (0.31) (-0.10) VC Count (t) 0.0104∗∗∗ 0.0111∗∗∗ 0.0113∗∗∗ 0.0113∗∗∗ 0.00967∗∗∗ (2.94) (3.23) (3.07) (3.11) (2.67) VC Count (t) x R&D (t-1) 0.000295 0.00161∗∗∗ 0.000614 0.000312 -0.000138 (0.64) (2.77) (1.19) (1.24) (-0.25) Year Dummies Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes R2 0.22 0.22 0.22 0.22 0.22 Clusters 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 26
  • 37.
    Table 7: LagStructure of R&D Effects on Innovation (25) (26) (27) (28) (29) (30) Patents Patents Patents Patents Patents Patents Population (t-1) 1.418∗∗∗ 1.405∗∗∗ 1.405∗∗∗ 1.398∗∗∗ 1.415∗∗∗ 1.418∗∗∗ (4.77) (4.71) (4.71) (4.71) (4.77) (4.75) VC Count (t) 0.0239∗ 0.0261∗∗ 0.0263∗∗ 0.0270∗∗ 0.0261∗∗ 0.0253∗∗ (1.88) (2.05) (2.06) (2.11) (2.05) (2.00) R&D Support (t) 0.00803 (0.85) VC Count (t) x R&D (t) 0.00246 (0.71) R&D Support (t-1) 0.0202∗∗ (2.19) VC Count (t) x R&D (t-1) 0.00764∗∗ (2.27) R&D Support (t-2) 0.0190∗∗ (2.23) VC Count (t) x R&D (t-2) 0.00689∗∗ (2.20) R&D Support (t-3) 0.0221∗∗ (2.41) VC Count (t) x R&D (t-3) 0.00809∗∗ (2.38) R&D Support (t-4) 0.0174∗ (1.81) VC Count (t) x R&D (t-4) 0.00733∗∗ (2.11) R&D Support (t-5) 0.0157 (1.60) VC Count (t) x R&D (t-5) 0.00761∗∗ (2.13) Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.16 0.16 0.16 0.16 0.16 0.16 Clusters 328 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 41 27
  • 38.
    Table 8: LagStructure of R&D Effects on Entrepreneurship (31) (32) (33) (34) (35) (36) Births Births Births Births Births Births Population (t-1) 0.821∗∗∗ 0.815∗∗∗ 0.815∗∗∗ 0.813∗∗∗ 0.811∗∗∗ 0.812∗∗∗ (10.49) (10.25) (10.23) (10.18) (10.11) (10.15) VC Count (t) 0.0101∗∗∗ 0.0107∗∗∗ 0.0105∗∗∗ 0.0107∗∗∗ 0.0108∗∗∗ 0.0107∗∗∗ (2.97) (3.12) (3.07) (3.12) (3.15) (3.13) R&D Support (t) -0.000782 (-0.34) VC Count (t) x R&D (t) 0.000329 (0.40) R&D Support (t-1) 0.00413 (1.54) VC Count (t) x R&D (t-1) 0.00178∗ (1.89) R&D Support (t-2) 0.00382∗ (1.67) VC Count (t) x R&D (t-2) 0.00128 (1.56) R&D Support (t-3) 0.00444∗ (1.81) VC Count (t) x R&D (t-3) 0.00154∗ (1.78) R&D Support (t-4) 0.00588∗∗ (2.20) VC Count (t) x R&D (t-4) 0.00188∗ (1.95) R&D Support (t-5) 0.00602∗ (1.94) VC Count (t) x R&D (t-5) 0.00193∗ (1.74) Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.22 0.22 0.22 0.22 0.22 0.22 Clusters 328 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 42 28
  • 39.
    Table 9: LagStructure of Venture Capital Effects on Innovation and Entrepreneurship (37) (38) (39) (40) (41) (42) Patents Patents Patents Births Births Births Population (t-1) 1.405∗∗∗ 1.414∗∗∗ 1.420∗∗∗ 0.815∗∗∗ 0.815∗∗∗ 0.818∗∗∗ (4.71) (4.73) (4.77) (10.25) (10.15) (10.17) R&D Support (t-1) 0.0202∗∗ 0.00413 (2.19) (1.54) VC Count (t) 0.0261∗∗ 0.0107∗∗∗ (2.05) (3.12) VC Count (t) x R&D (t-1) 0.00764∗∗ 0.00178∗ (2.27) (1.89) R&D Support (t-2) 0.0247∗∗∗ 0.00177 (2.80) (0.49) VC Count (t-1) 0.0195 0.0108∗∗∗ (1.60) (2.59) VC Count (t-1) x R&D (t-2) 0.00894∗∗∗ 0.000520 (2.83) (0.40) R&D Support (t-3) 0.0298∗∗∗ 0.00239 (3.67) (0.70) VC Count (t-2) 0.0145 0.00837∗ (1.22) (1.92) VC Count (t-2) x R&D (t-3) 0.0109∗∗∗ 0.000782 (3.73) (0.63) Year Dummies Yes Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.16 0.16 0.16 0.22 0.22 0.22 Clusters 328 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 3270 Notes: OLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Robust t-statistics in parentheses; disturbances clustered by MSA. 29
  • 40.
    Instrumental Variables Table 10: Instrumental Variables Estimation for Innovation and Entrepreneurship (43) (44) (45) (46) 1st Stage Patents Patents Births Births Population (t-1) 1.116∗∗ 1.330∗∗∗ 1.368∗∗∗ 0.803∗∗∗ 0.813∗∗∗ (2.45) (4.71) (4.85) (9.32) (9.13) LP Returns 0.0112∗∗∗ (10.49) R&D Support (t-1) 0.000791 -0.000617 0.0265∗ -0.000706 0.00664 (0.60) (-0.22) (1.89) (-0.98) (1.60) VC Count (t) 0.0957∗∗∗ 0.0814∗∗ 0.0227∗∗ 0.0189∗ (3.23) (2.57) (2.35) (1.95) VC Count (t) x R&D (t-1) 0.00974∗∗ 0.00264∗ (1.98) (1.83) Year Dummies Yes Yes Yes Yes Yes MSA Fixed Effects Yes Yes Yes Yes Yes R2 0.27 Clusters 328 328 328 328 328 Observations 3270 3270 3270 3270 3270 Notes: 2SLS regression results; * p < 0.10, ** p < 0.05, *** p < 0.01. Bootstrapped t-statistics in parentheses. 30
  • 41.
    Venture capital ascatalyst to commercialization Increased innovation Increased entrepreneurship 31
  • 42.
    Further results Non-compete covenants Social capital 32
  • 43.
    End note: Key togetting funding 33
  • 44.
    End note: Key togetting funding 33
  • 45.
    End note: Key togetting funding 33
  • 46.
    End note: Key togetting funding Applied Physics Research 33
  • 47.