Appendix F: The Grant Impact for SBIR Mills

Transcription

1 Appendx F: The Grant Impact for SBIR Mlls Asmallsubsetofthefrmsnmydataapplymorethanonce.Ofthe7,436applcant frms, 71% appled only once, and a further 14% appled twce. Wthn my data, seven companes each submtted more than 50 applcatons. Fgure 1 shows the frequency of frms by ther number of awards, omttng frms wth less than four awards. These companes who wn many SBIR awards, sometmes termed SBIR mlls, have rased concerns snce the early years of the SBIR program (GAO 1992, Wallsten 2000). Appendx G Table 1 shows the relatonshp between applcant rank and prevous non-doe SBIR wns. DOE program offcals appear to be rankng frms hgher that have more prevous wns from other agences. Frms wth more prevous wns are lkely more sklled applers. They have more applcaton experence and may dedcate more resources to accessng government fundng. Implcatons for frms wth a few awards may be more ambguous. For example, Osclla Power (ntroduced n the man text) had two Phase 1 SBIRs from other agences pror to applyng for ts DOE SBIR. For Osclla, all three Phase 1 s funded useful testng work. These frms, often employng specalzed grant applcaton staff, seem unlkely canddates for venture fnance. Ineed, I fnd evdence of decreasng returns to prevous non-doe SBIR awards. Table 1 fnds that among frms wth no prevous SBIRs, an award ncreases a frm s probablty of subsequent VC nvestment by 14.8 pp, sgnfcant at the 1% level. For frms wth at least one prevous SBIR, the effect s halved to 7.5 pp, also sgnfcant at the 1% level. However, the dfference betwen these coeffcents s nsgnfcant. The left panel of Table 2 nteracts treatment wth prevous awards and fnds negatve coeffcents, although n two of the three models they are sgnfcant only at the 10% level. The mprecson could be due to opposng forces: addtonal SBIRs may produce valuable prototypng, but a sgnfcant porton of frms wth prevous SBIRs are mlls and not seekng prvate fnance. When a frm has just one prevous non-doe SBIR award, the Phase 1 mpact on reachng revenue drops precptously - even more so than wth fnancng. Table 2 (rght panel) nteracts treatment wth prevous awards and fnds strong and hghly sgnfcant negatve effects. Table 3 shows that among frms wth no prevous SBIR wns, a grantee s 19 pp more lkely to reach revenue than a loser (column I), sgnfcant at the 1% level. When regressons usng zero and postve SBIR wns are estmated jontly, the dfference n the coeffcents s 14.7 pp, sgnfcant at the 1% level (column III). The effect declnes further along the ntensve margn. Table 4 shows that there may be a smlar precptous Appendx F 1

2 drop for patents by prevous SBIR wns, but the coeffcents are much more mprecse. Ths SBIR mll effect accords wth Lnk and Scott s (2010) concluson that mlls are less lkely to commercalze ther projects, and wth Lerner (1999) s fndng that multple awards are not assocated wth ncreased performance for SBIR awardees. Fgure 1: Appendx F 2

3 Table 1: Impact of Grant on Subsequent VC by Number of Frm s Prevous SBIR Awards Dependent Varable: VC Post ; I. =0 II. > 0 III. Dff I &II IV. < 5 V. 5 VI. Dff IV & V 1 R > *** *** *** *** (0.0380) (0.0260) (0.0263) (0.0247) (0.0385) (0.0371) VC Prev 0.298*** 0.359*** 0.358*** 0.360*** 0.333*** 0.333*** (0.0630) (0.0506) (0.0512) (0.0473) (0.0626) (0.0603) * *** ( ) ( ) ( ) ( ) ( ) 1 R > apple X VC Prev 1 #SBIR Prev 1 1 apple X (0.0452) (0.0449) apple X (0.0823) (0.0777) *** apple X ( ) *** 0.136*** (0.0175) (0.0151) Topc f.e. Y Y Y Y Y Y Topc f.e. 1 apple X N N Y N N Y N R Note: Ths table s an RD estmatng va OLS the mpact of the Phase 1 grant (1 R > 0) onsubsequent VC by number of prevous non-doe SBIR awards (from other gov t agences, e.g. DOD, NSF), usng BW=3. Each column ncludes only data from frms wth the relevant number of wns. In the dfference regressons (columns V-VI), all covarates are nteracted wth a dummy for low SBIRs. Column VI only ncludes frms wth 0 or at least 5 SBIRs. The coeffcents on (1 R > 0 )donotprecselymatchcolumns III-IV because SBIRs are not controlled for n column I (there are none). Topc dummes permt suffcent wthn-group observatons. Standard errors robust and clustered at topc-year level. *** p<.01. Year 1995 Appendx F 3

4 Table 2: Impact of Grant on VC & Reachng Revenue Interacted wth Number of Frm s Prevous SBIR Awards Dependent Varable: VC Post Revenue I. BW=2 II. BW=3 III. BW=all IV. BW=2 V. BW=3 VI. BW=all (1 R > 0) * * ** *** *** *** #SBIR_d Prev (0.0232) (0.0202) (0.0170) (0.0234) (0.0219) (0.0179) 1 R > *** 0.131*** 0.146*** 0.120*** 0.128*** 0.158*** (0.0187) (0.0177) (0.0160) (0.0204) (0.0189) (0.0161) #SBIR_d Prev *** *** *** (0.0179) (0.0163) (0.0125) (0.0199) (0.0187) (0.0153) Competton f.e. Y Y Y Y Y Y N R Note: Ths table s an RD estmatng va OLS the mpact of the Phase 1 grant (1 R > 0) onreachng revenue by number of prevous non-doe SBIR awards (from other government agences, e.g. DOD, NSF) usng BW=1. Here the varable has been demeaned and dvded by 100 for clarty. Standard errors robust and clustered at topc-year level. *** p<.01. Year 1995 Appendx F 4

5 Table 3: Impact of Grant on Reachng Revenue by Number of Frm s Prevous SBIR Awards Dependent Varable: Revenue I. =0 II. > 0 III. Dff I & II 1 R > *** (0.0445) (0.0272) (0.0275) VC Prev 0.289*** ** ** (0.0571) (0.0428) (0.0432) *** *** ( ) ( ) 1 R > *** apple X (0.0535) Topc f.e. Y Y Y Topc f.e. 1 N N Y apple X N R Note: Ths table s an RD estmatng va OLS the mpact of the Phase 1 grant (1 R > 0) onreachng revenue by number of prevous non-doe SBIR awards (from other government agences, e.g. DOD, NSF) usng BW=1. Each column ncludes only data from frms wth the relevant number of wns. To estmate the dfference regressons, all covarates are nteracted wth a dummy that, for example n Column VIII, takes a value of 1 f the frm has 0 SBIR wns, and 0 f at least 5. The coeffcents on treatment (1 R > 0 )n columns VII and VIII do not precsely match because I do not control for prevous SBIRs when there are none (column I). Coeffcents on VC Prev 1 apple X, 1 apple X and 1 apple X not reported for space concerns. Standard errors robust and clustered at topc-year level. *** p<.01. Year 1995 Appendx F 5

6 Table 4: Impact of Phase 1 Grant on Subsequent Patentng wthn 3 Years by Number of Frm s Prevous SBIR Awards (All Gov t) (Negatve Bnomal) Dependent Varable: #Patent 3yrsPost #SBIR < 2 < 5 > I. II. III. IV. V. 1 R > * 0.805* (0.531) (0.481) (0.369) (0.444) (0.576) #Patent Prev 0.479*** 0.451*** 0.183*** 0.158*** 0.145*** (0.0506) (0.0452) (0.0201) (0.0196) (0.0204) VC Prev 1.210*** 1.062*** 0.544*** 0.647*** 0.737*** (0.251) (0.238) (0.170) (0.170) (0.191) *** *** *** *** (0.0242) (0.0118) ( ) ( ) ( ) R, 0.176*** 0.157*** ** (0.0608) (0.0567) (0.0476) (0.0493) (0.0519) R, * 0.854* 0.827*** (0.483) (0.441) (0.298) (0.385) (0.569) R, ** * ( ) ( ) ( ) ( ) ( ) R, ** (0.0922) (0.0835) (0.0372) (0.0490) (0.0733) Year f.e. Y Y Y Y Y N Pseudo-R Note: Ths table s an RD estmatng va a negatve bnomal model the mpact of the Phase 1 grant (1 R > 0) on the frm s patent count wthn three years after grant award by number of prevous non-doe SBIR awards (from other government agences, e.g. DOD, NSF), usng BW=all. Each column ncludes only data from frms wth the relevant number of wns. Unfortunately I could not estmate dfference equatons due to non-convergence of the Posson maxmum lkelhood. Standard errors robust and clustered at topc-year level. *** p<.01. Year 1995 Appendx F 6