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elseviercomlocaterespol Public funds and local biotechnology 64257rm creation Christos Kolympiris Nicholas Kalaitzandonakes Douglas Miller Management Studies Wageningen University and Research Center Hollandseweg 1 6706 KN Wageningen Building 201 De

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Research Policy 43 (2014) 121 137 lists available at ScienceDirect Research Policy funds and local biotechnology rm creationChristos Kolympiris Nicholas Kalaitzandonakes Douglas Miller Management Studies, Wageningen University and Research Center, Hollandseweg 1, 6706 KN, Wageningen (Building 201), De Leeuwenborch, Room 5063,The NetherlandsbDepartment of Agricultural and Economics, University of Missouri, 125 A Mumford Hall, Columbia, MO 65211, USAcNew Analytics LLC and Hutton School of Business, University of the Cumberlands, Williamsburg, KY 40769, USA a r t i c l e i n f o Article history:Received 24 June 2012Received in revised form 3 July 2013Accepted 24 July 2013Available online 24 August 2013 JEL classication:C23Keywords:Biotechnology birthFederal fundingNIH b s t r a c t A long stream of academic literature has established that public funding towards research and devel-opment matters for economic growth because it relates to increases in innovation, productivity andthe like. The impact of public funding on the creation of new rms has received less attention in thisliterature despite theoretical constructs that support such association. In the present paper we studywhether indeed there is a relationship between public research funds and local rm births in the con-text of the U.S. biotechnology industry. In doing so, we introduce a number of changes that strengthenthe robustness of our ndings when compared with existing literature. These changes include a directmeasure of research expenditures and a considerably longitudinal dataset which allows us tocapture a structural relationship and not a chance event. We empirically demonstrate that increases inthe level of research funding from the National Institutes of Health towards biotechnology associate withincreases in the number of biotechnology rm births at the Metropolitan Statistical Area level. Further,we reveal that public funds towards established rms associate with local rm births considerably morestrongly when compared with funds towards universities and research institutes/hospitals. We concludethe paper with academic and policy implications of the present work that highlight the complexity offactors that underlie the creation of local rms in high technology industries. 2013 Elsevier B.V. All rights reserved. 1. Introduction Following the Great Recession and recognizing the robustnessof the U.S. knowledge economy, which sustained high employmentand wages amid broad economic weakness, 2009 the U.S. gov-ernment made a strategic decision to substantially increase federalfunding for research and development (R&D) in high technologyindustries such as biotechnology ( 2009; Mervis, 2009 In2011 Ben Bernanke, the chairman of the Federal Reserve, pub-licly outlined the rationale and the inuential role of government Corresponding author at: Management Studies, University andResearch Center, Hollandseweg 1, 6706 KN, Wageningen (Building 201), DeLeeuwenborch, Room 5063, The Netherlands. Tel.: +31 0317 482372.E-mail addresses: christos.kolympiris@wur.nl (C. Kolympiris), (N. Kalaitzandonakes), (D. Miller).1 subscribe to the denition of the knowledge economy in Powell andSnellman (2004) production and services based on knowledge-intensive activi-ties that contribute to an accelerated pace of technological and scientic advance aswell as equally rapid obsolescence. investments in R&D ( 2011 Not surprisingly, interest inmeasuring the returns to public R&D investments quickly followed,not only in the U.S. ( but also, in Europe, Australia,New Zealand and elsewhere (pg. 137 Stephan, 2012 These recentdevelopments have revitalized a general interest towards the rela-tionship between government funded R&D and economic growth.This relationship has been the focus of a long stream of researchthat has stressed the contribution of public R&D to increased inno-vation and productivity and has concluded that the social rateof return to public R&D investment is typically high (e.g. Beiseand Stahl, 1999; Manseld, 1991, 1995, 1997; Narin et al., 1997;Salter and Martin, 2001; Tijssen, 2002; Toole, 2012 Such nd-ings have, in turn, supported continuing public R&D spending overtime.The conceptual underpinnings of such work are also strong.Investments in R&D tend to be risky, mainly due to limited knowl-edge appropriability and uncertainty of outcomes ( 1971 Such characteristics can discourage private parties from investingin R&D because the expected private rate of return is low. In fact,the social rate of return from R&D investments often outweighsthe private rate ( 1992; Hall, 1996 Consequently, gov-ernments be able to correct this market failure by funding 0048-7333/$ see front matter 2013 Elsevier B.V. All rights reserved. 122C. Kolympiris et al. / Research Policy 43 (2014) 121 137 R&D and increase the odds of socially desirable outcomes ( 1962; Nelson, 1959 sort of argument for government intervention relies heavilyon a complete understanding and accounting of the benets frompublic R&D funding. Yet, one potential benet from public R&Dfunding, rm creation, has received relatively limited attentionin the academic literature despite the strong link between rmcreation and economic growth ( Praag and Versloot, 2007;Wennekers and Thurik, 1999 Indeed, there are good theoreticalreasons to expect that public R&D funding encourage rmcreation. For instance, increased R&D expenditures can expand theknowledge base developed in universities and other research insti-tutions and a part of it can be commercially exploited throughrm spinoffs ( and Logothetidis, 2008; Lockett andWright, 2005 rms also be formed to capitalize onnon-appropriated knowledge (e.g. Acs et al., 2009; Audretsch andKeilbach, 2007 In this study we focus on the question of whether publiclyfunded R&D expenditures lead to rm births in knowledge-intensive industries and in particular in biotechnology. A number ofstudies have examined the relationship between R&D expendituresand rm births but most have not delineated the sources of fundsthat support R&D ( and Nerlinger, 2000; Goetz and Morgan,1995; Karlsson and Nystrm, 2011; Kim et al., 2011; Kirchhoff et al.,2007; Woodward et al., 2006 Accordingly, our knowledge on theimpact of public R&D funding on rm creation is limited.In our review of the literature we have identied only that have focused on the impact of public R&D fundingon the creation of biotechnology startups: Chen and Marchioni(2008) and Zucker et al. (1998) Both studies nd a positive rela-tionship between indicators of publicly funded R&D activity andlocal biotechnology rm births. Our study adds to the ndingsof these two studies and introduces a number of methodologi-cal and measurement improvements. For instance, these previousstudies do not distinguish between the type of organization thatreceives the public funding and performs the R&D. Here, we rec-ognize the potential for differential efciencies between industrialand academic R&D organizations on the rate of rm creation ( and Nerlinger, 2000; Karlsson and Nystrm, 2011 and examinethe impacts of public R&D funds directed to universities, privaterms, research institutes and research hospitals separately. Thetwo previous studies have also measured the impact of federalR&D outlays on rm creation in the biotechnology industry forrather short periods of time (up to two years). Here, extend theperiod of analysis to 18 years (19922010) recognizing the inherentlong cycles involved in R&D funding, knowledge development andpotential rm creation from such new knowledge. As well, insteadof proxies of R&D intensity employed in the two previous studies(a life sciences index and a count of faculty members with grants)we use a more direct and sharper measure of R&D activity, namely,the dollar amount of R&D funding awarded to universities, privaterms, research institutes and research hospitals. focus on rm births in the biotechnology industry for severalreasons. First and foremost, because the biotechnology industry isa core part of the knowledge economy and understanding how itgrows is important. Second, because the industry is a heavy recip-ient of federal research funds ( and Tulum, 2011 it is a 2 and Martin (2001) and Chaminade and Edquist (2006) elaborate that addi-tional considerations, besides the market failure arguments, are often in place beforethe government intervenes in the market place.3Data from the Association of University Technology Managers (AUTM) suggestthat over the last twenty years more than 9000 university spinoff rms were cre-ated based on knowledge and intellectual property developed at major researchuniversities in the U.S. fertile ground for our investigation. because of the closelinkage between basic biotechnology research and commercialapplications, there is potential for a strong relationship betweenthe level of R&D activity and rm births ( and Liebeskind,1998; McMillan and Narin, 2000 Fourth, because the biotech-nology industry exhibits a strong tendency to cluster in narrowgeographies ( and Stephan, 1996; Powell et al., 2002;Zucker et al., 1998 biotechnology rm births tend to concentratein regions with large venture capital pools, specialized labor pools,and anchor institutions, like large biotechnology rms and uni-versities ( and Stephan, 1996; Powell et al., 1996, 2012;Zucker et al., 1998 These are exactly the types of institutions andgeographies that a large share of public biotechnology R&D invest-ment is typically directed to. For these and other reasons, we expectthat if a relationship between public funding of R&D and rm cre-ation exists, it should be possible to detect in the biotechnologyindustry.For our empirical analysis construct a rich dataset thatincludes all R&D funds from the largest funding source, the NationalInstitutes of Health (NIH), directed towards biotechnology researchfrom 1992 up to 2010. complement this dataset with informa-tion about biotechnology rm births, venture capital investmentsand other relevant variables from Thomsons Financial SDC Plat-inum Database and other sources. organize the rest of the paper as follows: In the next sec-tion briey discuss the biotechnology industry and some of itscharacteristics that make it attractive for our analysis. In Sections 3and 4 review the relevant literature and develop our theoreticalexpectations on the effects of federal R&D monies on biotechnol-ogy rm births. In Section 5 describe our econometric modeland estimation procedures, and in Section 6 we review the data In Section 7 present the estimation results and in Section we discuss how test the robustness of those results. Finally,in Section 9 we offer concluding comments, implications for policyand suggestions for further research. 2. The biotechnology industry The scientic origins of biotechnology can be traced back to theadvancements of molecular biology and related elds in the 1950s( 1986 However, biotechnology as an industry began todevelop after the discovery of the basic technique for recombinantDNA in 1973 from Stanley Cohen of Stanford University and HerbertBoyer of University of California San Francisco.The fundamental discoveries in genetic engineering led to anever-increasing rate of innovation. By the mid-1980s, a large num-ber of novel products and processes were being pursued in avariety of industries ( and Nelson, 1999 For instance,in the pharmaceutical industry regulatory proteins (e.g. humaninsulin and growth hormone), vaccines, antibiotics and monoclonalantibodies for diagnostic and therapeutic uses, were early targets.In agriculture, animal health products and growth promotants,and genetically engineered plants (e.g. plants resistant to herbi-cides, insects, diseases, and drought), were also broadly pursued.Improved amino acids, enzymes, vitamins, lipids, were the maintargets in the specialty chemicals industry. And, R&D activitiesextended in various other industries, from food processing to envi-ronmental remediation.The development of waves of biotechnology innovations andassociated competencies, such as genetic engineering, bioprocess-ing, genomics, proteomics, metabolomics and others, has sincecontinued and has led to an ever-expanding range of potential 4Biotechnology is not a heavy recipient of public R&D investments only in the US,but across the world (for instance see Dohse (2000) C. Kolympiris et al. / Research Policy 43 (2014) 121 137123 commercial applications and in the scope of the industry ( et al., 2001 In parallel, scores of scholarly works have documentedthe industrys growth, its reliance on scientic talent and newknowledge development and transmission as well as its emergenceat the core of the knowledge economy (e.g. Audretsch and Stephan,1996; Liebeskind et al., 1996; Whittington et al., 2009; Zucker et al.,1998 Since its initial emergence, an important feature of biotech-nology has been that new knowledge development in the lab hasfound immediate commercial applications in the market place. Assuch, biotechnology has continued to blur the distinction betweenbasic and applied research. This feature, has led to its lasting suc-cess in attracting risk capital and in rm creation ( & Young,2012 It has also proved a good t and a catalyst for the emergenceof the entrepreneurial university ( 2006 With regard toscience, biotechnology has promoted interdisciplinary research,largely because it draws upon a number of diverse knowledgebases and housing all relevant knowledge under one roof is increas-ingly difcult ( 2006 Furthermore, it has promoted intenseuniversity-industry collaboration and commercialization of uni-versity research as well as rm creation. sum, the biotechnology industry is at the center of the knowl-edge economy, it is heavily supported through government R&Dfunds, and many of its features make it an important industry toanalyze the relationship between public funding of R&D activities,knowledge development and rm creation. 3. How can federal R&D expenditures lead to rm births? Except, perhaps, in exceptional cases, there are inherent mea-surement difculties with establishing a direct causal relationshipbetween specic government R&D funds, ensuing new knowledgeoutcomes and resulting rm births by tracing some sort of lineage.In this study, we instead ask the question whether an increase in theamount of public R&D investment in some location leads to a par-allel and measurable increase in rm births within some distancefrom the location where the R&D activity takes place. In princi-ple, there are, at least, two mechanisms that public R&D spending lead to rm creation. First, government R&D expenditures canlead to rm births because they can expand the level of knowl-edge that can be pursued commercially by research organizationswith voluntary rm spinoffs and other forms of ventures that haveformal ties to these organizations (e.g. joint ventures, subsidiaries,etc.). they can strengthen localization economies, whichcan attract and support the creation of new rms in a given region.The rst mechanism is straightforward. Federal funds canexpand R&D outlays which, in turn, would tend to yield moreknowledge ( and Levinthal, 1989 As Klevorick et al. (1995) public R&D expenditures can expand the scope ofopportunities that can be pursued commercially. As such, volun-tary spinoffs, which rely on the research and knowledge ows ofresearch organizations, could materialize ( 1983; Ndonzuauet al., 2002 Indeed, in knowledge industries, such as biotech-nology, voluntarily spinoffs from incumbent rms as well asfrom public research institutions are common ( and It is not clear how many of the university spinoffs in the U.S. are biotechnologyrms as AUTM does not provide details about the industrial focus of these newrms. However, from the few universities that do provide such details as well asfrom survey results, biotechnology startups appear to constitute a large majority( et al., 2012; Zhang, 2009 6Often, in the literature on the determinants of rm births there is a distinctionbetween startups which are dened as new rms without a specic scientic ori-gin, and between spinoffs/spinouts which refer to rms that spawn from particularinstitutions such as universities and private rms. In order to develop our theoreticalexpectations consult with research that uses both terms. 2008; Lockett and Wright, 2005 For example, therst ever biotechnology rm, Genentech, a spinoff fromthe University of California, San Francisco. Similarly, IntermunePharmaceuticals was a spin out of Connetics Corporation andGuidant of Eli Lilly ( and Zipkin, 2002 The second mechanism that can encourage rm births throughthe strengthening of localization economies is more indirect.Localization economies are typically dened as gains from the co-location of similar rms. They can be partitioned to gains in theknowledge base of rms (knowledge spillovers, network exter-nalities and the like) and gains from efciencies in the costs ofdoing business (access to a skilled labor pool and specialized suppli-ers, availability of rms in complementary industries) ( andSchnellenbach, 2006 An increase in the level of R&D expendituresin a particular location can lead to improvements in localizationeconomies and, hence, in the odds of local rm creation. specically, confronted with substantial research expend-itures, long research cycles, scientic complexities and a strictregulatory environment ( and Grabowski, 2007; Hausslerand Zademach, 2007 rms in high technology industries oftenleverage the imperfect appropriability of knowledge ( 1962; Nelson, 1959 and its inherent difculty of transfer overphysical space ( 1998 by sourcing knowledge andknow-how from nearby research-intensive institutions. This hap-pens via interpersonal interactions of economic actors working insimilar problems, collaboration between nearby rms, participa-tion in local professional networks and labor mobility of highlytrained employees ( et al., 2004; Dahl and Pedersen, 2004;Kolympiris and Kalaitzandonakes, 2013; Liebeskind et al., 1996;Saxenian, 1991 As the knowledge base of research organizations increases withtheir R&D investment ( and Levinthal, 1989 the opportunityfor knowledge acquisition by closely located rms should increaseas well. Indeed, especially young rms located in proximity toresearch intensive institutions often achieve higher innovative out-comes potentially due to such knowledge acquisition effects ( et al., 1994; Anselin et al., 1997, 2000; Fischer and Varga, 2003;Jaffe, 1989 Accordingly, the potential for knowledge acquisitioncan provide strong incentives for newly founded rms to locate inproximity to sources of knowledge that can be exploited ( and Helpman, 1992 A somewhat different form of localization economies that isalmost unique to the biotechnology industry, has been described bya stream of research developed by Powell and his colleagues (e.g. 1996; Powell et al., 1996, 2002, 2012; Whittington et al.,2009 they explain, the diversity of local organizations (e.g.rms, universities, etc.) in a region can boost the local biotechnol-ogy birth rate. When there is diversity in the local environment,communities can more easily overcome declines and increasedcompetition can create different standards, practices, rules andstrategies that are most relevant for success. This is particularly 7As we discuss in this section, a signicant body of research demonstrates theexistence and strength of knowledge spillovers between closely located actors. Nev-ertheless, other contributions, including Breschi and Lissoni (2001) have questionedthe relevance of knowledge spillovers. Similarly, physical proximity among actors also increase the chances that ones ideas can be appropriated by others ( and Flyer, 2000 perhaps due to increased local competition. For an opposing viewsee Feldman and Kelley (2006) and Audretsch and Stephan (1999) 8These studies have also noted that networks across relevant actors are an impor-tant source of innovation in biotechnology. Such networks can potentially inuencethe startup rate of a region as long as new startups are attracted to them. To capturesuch ties we would need access to the formal and, potentially, informal linkagesof the biotechnology rms, universities and research institutes in our dataset. Suchinformation is not available to us. However, insofar as organizations that receivemore funds tend to engage more heavily in networks, concerns of the impact of thisdata limitation on our work should be alleviated. 124C. Kolympiris et al. / Research Policy 43 (2014) 121 137 relevant for biotechnology perhaps because its broad knowl-edge base favors numerous competing experimentations beforereaching the most desired outcome. Among the diverse organiza-tions, Powell and his colleagues highlighted the importance of alocal anchor entity (e.g. large rm, research university, etc.) that. . .becomes a scaffolding that, either intentionally or unexpect-edly, assists subsequent connections. . . ( et al., 2012 In thiscontext of the biotechnology industry, federal R&D funds are allo-cated to different types of research institutions and might increasethe diversity of local research organizations. At the same time, thebulk of such funding goes to large research universities that as anchor organizations in various regions. As such, public R&Dfunding might increase rm births in the biotechnology industrythrough the location effects described by Powell and his colleagues.Indeed, there is signicant evidence that most early biotechnologystartups were founded around universities, where major break-throughs took place ( and Stephan, 1996; Owen-Smithand Powell, 2004 Investments in R&D can augment knowledge spillovers amongproximate rms but can also induce efciencies in the costs ofdoing business of knowledge industries by enhancing specializedand localized labor pools and encouraging the presence of rmsin complementary industries. Specically in biotechnology, serviceproviders and other suppliers such as rms with expertise in biolog-ical materials and advanced laboratory equipment tend to locate inregions with high research intensity ( and Sorenson, 2003a Insofar as newly founded biotechnology rms are attracted bythe resource endowment of a given region ( and Sorenson,2003b an increase in the availability of research dollars can boostthe attractiveness of the region as a potential startup location.Along the same lines, an increase in R&D spending, especiallythrough government grants to universities that train new scien-tists through research, can enhance the talent in the local labor pooland accordingly boost the local availability of highly skilled labor( and St. John, 1996 Employee turnover from incumbentrms can also enhance the local labor pool ( and Marschke,2005 A region with ample highly skilled labor could thereforeattract and encourage the creation of new local rms.Regions rich in knowledge base also lead to rm birthsthrough a mechanism described under the Knowledge SpilloverTheory of Entrepreneurship (KSTE). Knowledge originally devel-oped at incumbent organizations be commercially pursuedby alert individuals, often previous employees, who recognize thepotential of these projects and mobilize local networks and othermeans towards the development of their rm ( et al., 2009;Audretsch and Keilbach, 2007 Empirical evidence on federal R&D expenditures andrm births Empirical evidence in the literature is generally consistent withthe arguments in the previous section and suggests a positive rela-tionship between R&D expenditures and rm creation. A stream ofresearch has documented a consistent positive association betweenproxies of R&D intensity, such as the number of R&D employees ina region, and the rate of local rm births ( and Nerlinger, 2000; Previous employers can guard against the use of knowledge acquired internally( and Marschke, 2005 but signicant evidence of rm spinoffs without formalties with the parent company ( et al., 2004; Klepper and Sleeper, 2005 suggests that such protection schemes are often bypassed.10 (1994) provides indirect evidence towards such effects by reporting that71 percent of rm founders in his sample stated that their business originating ideacame either through replication or modication of an idea encountered throughprevious employment. and Morgan, 1995; Karlsson and Nystrm, 2011; Kim et al.,2011; Kirchhoff et al., 2007; Woodward et al., 2006 several studies in this stream of research havesought to distinguish the relative impact of academic and industrialR&D on rm creation. These studies have found that conductsthe R&D matters to the rate of rm creation and there appearsto be a stronger linkage between the creation of new rms withR&D activity occurring in rms rather than with R&D taking placein academic institutions ( and Nerlinger, 2000; Karlsson andNystrm, 2011 What is difcult to infer from all these previousstudies, however, is the impact of federally funded R&D on rmcreation because they measure R&D activity without any referenceto its funding source.A handful of studies have examined directly the relationshipbetween government R&D funding and rm creation and here theevidence is more limited and nuanced. Kim et al. (2011) examinedthe factors that inuence the annual rate of rm births and deathsin the U.S. (without any specic industry focus) and found thatindustrial R&D expenditures had a positive effect on rm births butgovernment R&D investments had no distinguishable effect. Samilaand Sorenson (2010) also studied the relationship between feder-ally funded R&D grants to academic institutions and the annual rateof rm births in the U.S. (again without a specic industry focus). and Sorenson (2010) concluded that while in isolation fed-eral R&D funding to academic institutions did not affect the rmbirth rate in the regions where the R&D activity occurs, in regionsrich in venture capital it did exhibit a positive effect. The authorsthen concluded that the local availability of venture capital actedas a catalyst to rm creation.Two more studies, Chen and Marchioni (2008) and Zucker et al.(1998) have examined the linkage between federally funded R&Dand biotechnology rm births and both have documented a positiverelationship. More specically, Chen and Marchioni (2008) exam-ined the impact of federal research funds given to an MSA overthe 20032005 period on the number of venture capital-backedbiotechnology rms in an in 2006. The level of federal fundsgiven to an was used in a principal component analysis alongwith other indicators, such as the number of life scientists, uni-versities, institutes and hospitals in an MSA, in order to constructa composite index of biotechnology research intensity in an MSA.In turn, this index was found to have a strong explanatory powerin the number of biotechnology rm births. Hence, Chen andMarchioni (2008) provide indirect empirical evidence for a positiveimpact of public R&D funding on biotechnology rm creation butthe marginal effect of such funding could not be computed. Zuckeret al. (1998) studied the births of biotechnology rms in a givenU.S. Functional Economic Area in 1990. They evaluated the impactof federal funding on rm creation by examining the relationshipbetween the number of faculty members in local universities thathave received federal grants between 1979 and 1980 and biotech-nology rms ten years later. They found that federal R&D fundinghad a positive impact on biotechnology rm births but, as in thecase of Chen and Marchioni (2008) the marginal impact of suchfederal investments on local rm creation was not part of the anal-ysis.In broad strokes then, existing empirical evidence indicates that:(a) more R&D spending tends to increase rm creation; (b) whoperforms the R&D matters and rm R&D activities appear to have 11Indirect evidence is also provided by studies that examine the relationshipbetween the density of institutions that conduct R&D in a region and the rate ofnew rm births at that region as well as from studies that analyze the rate thatacademic institutions and private organizations spawn startups (e.g. Baptista andMendonc a, 2010; Klepper and Sleeper, 2005; Steffensen and Rogers, 2000; Stuartand Sorenson, 2003a C. Kolympiris et al. / Research Policy 43 (2014) 121 137125 a higher marginal impact than university R&D activities on rmbirths; and (c) increased government R&D spending have apositive effect on rm creation under some conditions, especiallyin the biotechnology industry. The existing literature and empiri-cal evidence, however, leave a number of questions unanswered,including the most basic one just how many more rm birthsmight one expect from, say, an additional $1 million in public R&Dfunding in a given region? This is the principle question we addressin this study within the context of the U.S. biotechnology indus-try. To answer this question, we make a number of methodologicalimprovements in what we consider to be potential limitations inprevious studies.Specically, the cycles of R&D grant acquisition, performance ofR&D, creation of new knowledge, and application of the new knowl-edge through the creation of new rms can be long and variable inlength from one year to another. As such, we propose that the rela-tionship between public R&D funding and rm creation must beexamined over a long period of time in order to allow for suf-cient lags in the process and year-to-year variations in the ows offunds and rms. the capacity of different types of researchinstitutions to translate knowledge into rm births might be differ-ent, we also propose that the effects of public R&D expenditures onrm creation should be considered separately by the type of recip-ient. Finally, we propose that instead of various indirect indicatorsof publicly funded R&D intensity, a direct measure of public R&Dfunding (dollars spent) should be used in this analysis so that themarginal effects on such spending can be calculated. implement the proposed improvements in the analysis thatfollows. More specically, we use a direct measure of public R&Dspending; we extend the period of analysis to 18 years (19922010)to ensure adequate consideration of lags and year to year variationsin R&D investments and rm births; and we partition public R&Dfunds to those directed to universities, private rms and researchinstitutes and hospitals.Consistent with previous work that nds the impact of R&Dactivities on rm creation to materialize within a narrow geo-graphic scope, we measure the number of rm births at the same where federal funds are allocated ( and Nystrm,2011; Samila and Sorenson, 2010 The is used as the unit ofanalysis because it is small enough to capture the spatially boundednature of localization economies and the tendency of spinoffs tolocate close to parent organizations but it is large enough to exhibitindependent economic activity ( and Deitz, 2012; Samila andSorenson, 2011 Further, MSAs are generally more homogeneousacross U.S. states cities or other geographic units whichallows us to provide more meaningful comparisons across MSAsof rural and urban regions. 12For instance, in Chen and Marchioni (2008) and Zucker et al. (1998) the timespan of public R&D outlays measured and the lags between outlays and the newrm counts are rather limited. Chen and Marchioni (2008) measure federal R&Dspending for a two year period and Zucker et al. (1998) for a one year period. Itis therefore possible, that the results could weaken or strengthen if their periodof analyses were lengthened allowing for multiple funding cycles, potential longerlags between funding and rm creation as well as natural variations in externalconditions that also affect rm births over time (e.g. overall business climateand conditions in nancial markets).13Previous measures of public R&D activity have been somewhat crude. In additionto making difcult the derivation of a direct measure of the marginal effect of publicR&D spending on rm creation, they also distort the potential relationship.For example, the count of faculty members supported by federal grants used in et al. (1998) mask important differences across the size of grants andaccordingly the level of knowledge generated and rm created from public R&Dfunding.14The MSAs are U.S. population centers and exhibit less economic and geographicheterogeneity than the component spatial units (e.g., cities, towns, suburbs, villages,neighborhoods, and boroughs). 5. Methods and procedures specify a two-way xed effects model in which the depend-ent variable yitis the number of biotechnology rm births inMetropolitan Statistical Area i and year t. Given that the depend-ent variable is an observed count, the general form of the expectedcount is formulated as follows: E(yit|Xit, Ai, t) = mXit +iaiAi+ttt(1) where Xitis the 1 13 row vector that contains thirteen non-constant explanatory variables, which we describe later in thissection. Function m is a link function that maps the linear com-bination of the explanatory variables into an expected count thatis non-negative. two-way unobserved components in our model are repre-sented with the second and third terms in Eq. (1) In particular, Aiequals 1 for i and is 0 otherwise, and tequals 1 for year tand is zero otherwise. The year dummies can capture time-varyingeffects, such as favorable or unfavorable environments in nan-cial markets on rm births (e.g. a hot IPO market ( andSchwert, 2002 The variables are used to capturetime-constant factors that affect rm location choice for a widerange of industries, including biotechnology. For example, the can approximate the local effect of taxes ( 1985,1989; Rathelot and Sillard, 2008 and economic initiatives ( and Rottner, 2008 which despite potential deviations from year toyear are expected to be largely time constant. set of explanatory variables in Eq. (1) includes the laggeddependent variable (i.e. the rm birth rate in each at time t1)in order to capture potentially dynamic relationships in rm births.Regions conducive to new rm creation are expected to show ahistorical pattern of rm births ( and Koster, 2011 sowe expect a positive sign for the coefcient of the lagged dependentvariable. also use the estimate of this coefcient to distinguishbetween the short and long run effects of federal funds on the localrm birth rate ( 2009 Since the lagged count variable in Xitis a function of thecross-sectional unobserved component (represented by the time-invariant variable Aiin (1) the presence of laggeddependent variables in panel models generally causes bias and 15To estimate the model in (1) we use the Poisson maximum likelihood estima-tor. Following earlier contributions to this literature, the standard Poisson varianceassumption of equal conditional means and variances be too restrictive formuch of the economic count data encountered in practice, so we relax this con-dition in our estimated model. When relaxed, the computed standard errors arenot sensitive to any conditional variance assumption and allow for arbitrary serialcorrelation ( 1999 16 should note that because some MSAs cross state borders, local taxes andeconomic initiatives may vary within an MSA. Accordingly, the interpretation of theregion-specic dummy variable as a true xed effect may be limited. As a robustnesscheck, whenever the analysis is limited to the MSAs that do not cross state bordersthe results remain qualitatively similar to those reported in Table 3 17 have also run xed effects models using procedures that are included inpopular statistical software, such as SAS and STATA, (e.g. TCOUNTREG). Unfortu-nately, some of these procedures are still experimental and as such they only reportlimited t statistics. Furthermore, the available routines are suitable mainly for one-way count panel models but in our application need to estimate a restricted orpartial-two-way model (some cross-sectional effects are restricted to zero) becausesome cross-sectional observations have limited variability. In addition, in all the pro-cedures used, the number of observations drops drastically because the standardFE Poisson model as described in Wooldridge (2002) drops all observed zeros fromthe log-likelihood. As a result, the sample of such models is quite different fromthe base model and as such the results are not directly comparable. Given all theseproblems as well as the ndings of Greene (2002) that the xed effects estimatorin different families of nonlinear models is biased, opted to not present xedeffects estimates here. 126C. Kolympiris et al. / Research Policy 43 (2014) 121 137 inconsistency problems in the standard estimators for panel mod-els ( 2002 For one-way panel models of count data,the potential inconsistency problem be resolved by usingquasi-differenced data or an instrumental variables estimator.Given that our data have two-way unobserved components,we use the variable approach, which is roughly compara-ble to the within estimator. Although the within estimator is alsobiased for data sets with a xed temporal dimension, the bias con-verges to zero as the number of time series observations becomeslarge. Our data set has 18 years of time-series observations, so thepotential bias in the two-way estimator of Eq. (1) be smallfor this relatively large data set, and we present alternative ver-sions of the estimated model in order to evaluate the evidence ofpotential bias in the estimator.To test whether federal funds relate to local rm births andwhether the type of the recipient mediates that effect, we includethe average total amount of federal grants awarded at is uni-versities, private rms and institutes/hospitals from t1 to t5 linear and quadratic form. The amount of funds that each from NIH within a ve year window is fairly stable dueto the common multiyear nature of grants awards. Due to thesimilarity across adjacent observations of the lagged variables,strong correlations exist among yearly lagged observations, and an average value versus separate year lags in the empiricalmodel. use a ve year lag average because the period up to veyears before rm birth is perhaps the most relevant in capturingthe true effects of federal funds because a large number of grantsexpire after the ve year window. quadratic form of the vari-ables in question is included in the analysis in order to account forpotential nonlinearities in the relationship between federal R&Dmonies and rm births at the level.In line with our theoretical expectation, we anticipate an overallpositive contribution of NIH funds to rm births, and the rate increasing at an increasing rate (i.e. the quadratic coefcientis positive) or increasing at a decreasing rate (i.e. the quadraticcoefcient is negative). Also in line with our theoretical expec-tations, we expect the magnitude of each variable that measuresthe amount of funds provided to a given type of institution tovary among types of institutions which would signal that differ-ent types of recipients have distinct effect on the generation ofnew rms from federally funded research. As well, we include threeinteraction terms (university funds * private rm funds, universityfunds * research institute funds, research institute funds * privaterms funds) to account for potential synergies (positive sign of theinteraction terms) or congestion effects (negative sign of the inter-action terms) that arise when funds are allocated to differentrecipient types/organizational forms in the same MSA.To account for time-varying factors that can inuence the yearlybirth rate of a given region we add three relevant control factors inthe analysis. The rst control variable is the average GDP of the in the ve years prior to a rm birth. The variable is usedto capture the effect of overall economic conditions on the yearlyrm birth rate. These conditions are expected to arise from factorssuch as regional cost advantages, amenities and a regions prestige( and Gray, 2002; Frenkel, 2001; Stuart and Sorenson, 2003b 18In order to include early years in the empirical analysis, use a 5 year averagefor available observations. For example, the observation for 1995 is the averagevalue of years 19921994, which is a 3 year average. Thus, the empirical analysisonly omits data on the dependent variable for 1992. As show in section 8, thisempirical choice did not have a noticeable impact on our results.19Previous research has found 5-year windows for lag structures to be appropriate(e.g. Aharonson et al., 2008; Baum et al., 2000 As shown in Table 5 to test therobustness of our ndings to the specication of the year lag structure speciedthe relevant variables with different lag structures and found nearly identical resultsto those reported in Table 3 Given the highly localized nature of venture capital investments inbiotechnology ( et al., 2011; Powell et al., 2002 and thecontribution of venture capital to regional rm birth rate ( and Sorenson, 2010, 2011 the second control variable is the aver-age total venture capital funds invested in biotechnology rms inthe in the ve years prior to a rm birth. expect a positivesign for the variable in question. Note that the aforementionedcontrol variables are specied as 5-year averages so that we couldcompare them with the corresponding variables that measure the5-year average inow of NIH dollars at a given MSA. To ensure thatour empirical analysis is robust to the size of the MSA, alsoinclude a control variable for the population of the MSA at year t. 6. Data sources and presentation The data used to construct the variables that measure the yearlyfederal funds allocated to universities, private rms, and researchinstitutes/hospitals (and the associated interaction and quadraticterms) were obtained from NIHs Research Portfolio Online Repor-ting Tools (RePORT). collected data from 1992 2010 onthe amount awarded by NIH to every principal investigator (PI)as well as each funded projects title and each PIs afliation atthe time the project was funded. order to identify biotech-nology grants, a keyword search performed for all projecttitles. we sorted out the biotechnology grants, adjustedthe nominal award money to 2007 values using the CPI and clas-sied each projects PI afliation to universities, private rmsand research institutes/hospitals after consulting with the catego-rization of each institution as private rm, university and so onprovided by RePORT. Whenever in doubt, visited each institu-tions website. Then, we constructed the MSA-specic explanatoryvariables by adding the ination adjusted award monies for eachtype of institution. The effect of NIH money on local rm births could have been larger in the earlyyears because the industry have not attained complete maturity during theseyears. Accordingly, rm births prior to 1992 would also be of interest to the presentstudy. Unfortunately, 1992 is the rst year for which NIH data are available. Further,the boom in the biotechnology industry occurred some years later than 1992, so1992 is still among the early years.21NIHs RePORT reports the PI and the institution that each project is awarded. Itis possible that the PI may have more than one afliation. Nevertheless, this doesnot present a problem as we allocate the funds to the institution reported in theNIH grant. NIH grants are made, principally, to an institution rather than a PI andas such the research is expected to occur in the recipient institution listed in theaward. While no ofcial statistics exist, information provided by the Ofce of Statis-tical Analysis and Reporting (OSAR) of NIH, indicates that the majority of NIH grantsare individually allocated to a single institution and a single PI. As such, we allocateall awarded monies in each grant to the location of the primary recipient insti-tution and PI. For the small number of NIH grants involving multiple institutions,this attribution scheme might lead to misallocation of funding among institutionsand locations but do not expect this type of error to be signicant. indi-rectly tested for the signicance of such a potential error by taking advantage of arecent change in NIH grant awards. Specically, since the beginning of 2007, NIHhas allowed grants to have multiple investigators ( 2006 When re-estimated our base model on a limited data set for the 19922007 period, we foundthe results to be qualitatively similar to the results of Table 3 where the analysisextended to 2010.22The list of biotechnology keywords was constructed after consulting withbiotechnology researchers employed at the authors institutions. Almost 400 key-words were used but 155 of them characterized 99% of all grants in our dataset. Theabbreviated list is presented in Appendix Table A1 It should be noted that med-ical and biotechnology research often overlap. There are, however, many cases ofmedical research that is different from biotechnology. Examples include researchon clinical diagnostics, clinical test kits, infectious diseases and magnetic resonanceimaging (MRI).23Starting in 2007, NIH implemented a new system to measure the amount offunds for biotechnology. Our measure of biotechnology funds, which is comparableto the updated NIH system, is conservative when compared to the original NIHestimate; the 2007 real total biotechnology amount estimate of NIH is about 5 billiondollars while our estimate for the same year is about 3 billion dollars. C. Kolympiris et al. / Research Policy 43 (2014) 121 137127 Fig. 1. Historical NIH nancial outlays towards biotechnology. Table 1Correlation coefcients between award amounts per year. IN PR UN 1.00 0.55 0.57IN 0.55 1.00 0.52PR 0.57 0.52 1.00 UN: average NIH funds from t1 to t5 towards universities per MSA year; IN,average NIH funds from t1 to t5 towards institutes and hospitals per MSA year;PR, average NIH funds from t1 to t5 towards private rms per year. 1 presents the historical real NIH funding levels for biotech-nology by institution type. Biotechnology funds increased throughthe 1990s, attened-out between 2003 and 2004, and stabilizedstarting in 2005. The proportion of funds directed to the differ-ent types of institutions remained stable over time mainly becausemost grants are multiyear awards. Universities attract the largestshare of the funds while private rms receive the least amount ofgrants from NIH. as seen in Table 1 the correlationcoefcients between monies for the different types of institutionson a per-MSA-year base are moderate, ranging from 0.52 to 0.57.The magnitude of those correlations suggests that there is variationin funding levels across the different types of institutions within theMSAs, and this should help us to estimate the separate effects of thefunds on rm births. used the Thomsons Financial SDC Platinum Database, theZoominfo web-based database, and the web-based Moneytreereport to identify biotechnology rm births and to construct the 24NIH is currently required to set aside 2.5 percent of its extramural R&D bud-get exclusively for grants of the Small Business Innovation Research (SBIR) program( (Ed.), 2009 which mainly go to private rms. The required percentage hasslightly uctuated over time but some of the NIH funds issued to private rms areSBIR grants. Also note that the majority of funds for private rms do not come fromthe federal government but from other sources like venture capital funds. Hence,the overall rm-generation capacity of private rms will be underestimated herebecause do not include the total research amount received by private rms(besides NIH funds). dependent variable and the time lag variable. Each rms loca-tion and founding date were generally available in all three datasources, but missing observations were gathered from the web-sites of the individual rms. All three data-sources report rmsthat during their lifetime received funds from venture capital rms.Perhaps due to the highly selective nature of venture capital invest-ments, venture capital-backed rms are often overperforming andgenerate substantial revenues and associated increases in valueadded and jobs; they are then precisely the type of rms that (2009) argues public policy should focus on. exten-sion, venture capital-backed rms appear a suitable sample for ourstudy. Fig. 2 presents the number of rm births by year, whichexhibit an increasing trend over time even though year to year 25 (2009) even suggests that the criteria used by venture capitalists in choos-ing the rms they invest in should be adopted by federal agencies, such as NIH, thatprovide funds.26Approximately 80 percent of the rms were common in all three databases butSDC was more comprehensive in reporting the foundation date of each rm as wellas its address, which were the two main pieces of information we sought from thesedata sources in order to assign the birth of each rm to an at a certain year.Importantly, for about 90 percent of the rms we used to construct our dependentvariable SDC provided the status of each rm as of the end of 2010, which is theyear our analysis ends. The large majority of the rms were in business for at least7 years after their births and some were merged or acquired.27Although venture capitalbacked rms often locate where venture capitalinvestments occur, because of our focus on rms of this kind have over-looked some rms in regions with less venture capital activity, which could leadto a potential bias in our estimates. In general, we do not expect this issue to beimportant as 60 percent of the rms in our sample were founded outside the threetraditional venture capital hubs of San Francisco, Boston and San Diego. Further-more, existing empirical evidence from a broad set of industries indicates that theeffect of regional venture capital activity per se on rm foundings is not particularlystrong ( and Sorenson, 2010, 2011 Empirical evidence from the biotechnol-ogy industry suggests that the impact of venture capital activity on rm births iseither weakly positive and lessens even more when other factors (e.g. universitypresence) are explicitly considered ( and Sorenson, 2003a or it is negative,potentially due to the fact that venture capital activity favors the creation of a smallnumber of large rms ( et al., 1998 128C. Kolympiris et al. / Research Policy 43 (2014) 121 137 Fig. 2. Biotechnology rm births from 1992 to 2010 (1298 rm births). variations are substantial. Firm births peaked in 2000 with 102 newbiotechnology rms and declined after 2005.To illustrate the spatial character of rm births, Fig. 3 presentsthe cumulative amount of NIH grants collected from 1992 to 2010for all MSAs in the U.S. along with their cumulative rm birthsfor the same period. Each is represented by its principal cityas dened by the U.S. Census Bureau; for those MSAs with cities, the more geographically central city in the isdepicted in the map. MSAs are classied according to their NIH fundaccumulations, and larger symbols in the gure indicate MSAs withmore biotechnology rm births. The general pattern observed from 3 is that the MSAs that host institutions that have attractedlarge amounts from NIH have also experienced more rm births.Only 12 percent of the MSAs (9 of 75) with the highest NIH fundaccumulations did not have any rm births while the correspond-ing percentage for MSAs with lower or no NIH fund accumulationswas 76 percent (107 of the 140) and 97 percent (151 of the 156),respectively. For example, Bostons had 181 rm births andthe largest funds accumulation of all MSAs with more than $4.1billion from 1992 to 2010. Also, San Francisco had 201 rm birthswhile having the 6th highest total NIH fund accumulation. In con-trast, Los Angeles had only 27 rm births while having received the3rd total largest amount from NIH with more than $2 billion from1992 to 2010. While Fig. 3 implies a positive association betweenrm births and total NIH funds, it does not provide a comprehen-sive picture of the relationship under consideration because it doesnot account for temporal, spatial, and other structural effects thatmight shape rm rms in various locations. The estimated countdata model is therefore expected to provide more specic evidenceon the conditional impact of NIH funds on rm births across the U.S.For the remaining variables, we used data from the U.S. Bureauof Economic Analysis to construct the GDP for each and the nominal GDP values to 2007 dollars using the CPI.Data available at the U.S. Census Bureau was employed to formthe population variable. Finally, the variable that measuredthe venture capital investments at a given was built with data from the SDC Platinum database. SDC provided the nominal venturecapital amounts awarded to biotechnology rms per year. In orderto construct the variable in question, converted these amountsto 2007 dollars using the CPI and summed up the values for all rmslocated in each MSA. 2 presents descriptive statistics for the dependent vari-able and selected independent variables. The average number ofrm births per year is 0.18 with a standard deviation of 1.The dependent variable is right-skewed because most of the MSAsdid not have any rm births in a given year. On average, universi-ties receive about $3.69 million per year, research institutesreceive about $1 million per year, and private rms attractabout $0.12 million per year. Note that regardless of the typeof the institution, the standard deviation of NIH funds is greaterthan the variable mean, which indicates the wide range of values inthe observed funding levels. As with the dependent variable, theseexplanatory variables exhibit strong right skewness because mostobservations in a given year equal zero. Most MSAs did not receivebiotechnology-related funds from venture capitalists but the rel-ative size of the standard deviation of the average venture capitalinvested as compared with its mean (36.8 versus 4.9) indicates thatwhen (and where) venture capital investments occurred, they wereof signicant magnitude. Finally, the average GDP for a given almost $19 billion with most MSAs having a GDP of more than$2.4 billion. 7. Estimation results The t statistics reported at the bottom of Table 3 comefrom a maximum likelihood (ML) estimation of the previouslydescribed Poisson count model which based on an exponen-tial specication for the conditional mean. however, that 28For the cross-sectional xed effects, the estimators of these parameters areonly identied when the associated dependent variable exhibits changes during the C. Kolympiris et al. / Research Policy 43 (2014) 121 137129 Fig. 3. MSA NIH funds (2007$) and rm births from 1992 to 2010. Table 2Descriptive statistics of variables used in the empirical analysis. Mean Median Mode Standard Number of biotechnology rm births for each year 0.184 0.000 0.000 1.019Average federal funds amount towards universities in the 5 years proceeding rm birth (2007 3.695 0.000 0.000 13.795Average federal funds amount towards private rms in the 5 years proceeding rm birth (2007 M$)0.119 0.000 0.000 0.692Average federal funds amount towards institutes in the 5 years proceeding rm birth (2007 0.996 0.000 0.000 7.460Average total venture capital funds invested in the in the 5 years proceeding rm birth (2007 M$) 4.881 0.000 0.000 36.817 population at time t (Million) 0.654 0.225 0.110 1.512Average GDP in the 5 years proceeding rm birth (2007 18,955.818 4653.830 2473.650 55,877.052 there might be unobserved or difcult to measure time-varyingregional characteristics (for instance, the quality of support towardsentrepreneurship provided in the MSA) that can boost or hinderthe rm birth rate in a given across years. If such unobservedfactors do exist, they lead to a violation in the assumptionof independence across observations ( and Schaffer, 2007;Stimson, 1985 For this reason we compute standard errors clus-tered at the level using generalized estimating equations(GEE). practice accounts for potential clustering effects andyields identical estimates to These last estimates of the ttedPoisson count model are reported in the rst column of Table 3 Aswe discuss in detail in Section 8 a number of robustness checkssuggest that our estimates are generally consistent across differentestimators, sets of regressors, lag structures, and model specica-tions.To evaluate the potential bias due to the presence of the xedeffects and the lagged dependent variables in (1) estimate analternative model and report the results in the second column of 3 The second model includes all the independent variablespreviously discussed except the temporal lag. The small differencesbetween the estimates of the two models suggest that the temporal sample period ( 2008 Accordingly, the set of MSA-specic dummies wasdened as the set of 104 MSAs that over the sample period had at least one rmbirth, and use a partial or restricted form of the xed effects specication.29GEE is a method to estimate the standard errors which rst estimates the vari-ability within the dened cluster and then sums across all clusters ( 2006 lag does not induce substantial bias in the parameter estima-tors. Accordingly, in the following discussion we refer only to theestimates of the full model in the rst column.The joint signicance tests reported at the bottom of Table 3 strong signicance for the MSA-specic and the year-specic variables. well, the condition number for the setof explanatory variables (39.67) reduces inference concerns thatrelate to multicollinearity because it is within the range of the gen-erally regarded as safe level of 30 and well below the worrisomecondition number of 100 ( et al., 1980 The estimated coefcients for the NIH variables are largelypositive and provide empirical support to the proposition that fed-eral R&D spending contributes to local biotechnology rm births.Because the NIH funding variables for the recipient institutionsappear in multiple terms (i.e. linear, quadratic, and cross-productterms), have to combine the individual marginal effects to eval-uate the overall marginal effect from NIH funding on rm births.The semi-elasticities for the three recipient types are reported forthe two estimated Poisson models in Table 4 For research universi- 30 also estimated models with one-way (only MSA-specic) xed effects, andthese have largely similar results with those reported in Table 3 31Separate year-specic and MSA-specic xed effects were mostly statisticallysignicant as well and are not reported in Table 3 for ease of exposition. Importantly,the signicance of the MSA-specic dummy variables suggests that time-invariantcharacteristics such as scal policies at the regional level have an impact on thelocation patterns of biotechnology rm births. 130C. Kolympiris et al. / Research Policy 43 (2014) 121 137Table 3Count estimates of Poisson model with mean equal variance assumption relaxed. The dependent variable is the number of biotechnology rm births at time t in i. Model 1: Poisson (without variancerestriction) full modelModel 2: Poisson (without variancerestriction) excluding temporal lag Estimate S.E. Estimate S.E. Intercept 4.2939 4.2626 Biotech rm births in in 10.0186 0.0235Average amount awarded ($2007 to MSA universitiesfrom t1 to t5 0.0612 Average amount awarded ($2007 to MSAinst./hospitals from t1 to t5 0.0538 Average amount awarded ($2007 to MSA private rmsfrom t1 to t5 0.6245 (Average amount awarded ($2007 M.) to universitiesfrom t1 to 0.00010.0002 0.00010.0002 amount awarded ($2007 M.) to from t1 to t5)20.0002 0.0002 0.0002 0.0002(Average amount awarded ($2007 to MSA private rmsfrom t1 to t5)20.0014 0.0117 0.0017 0.0119University funds * inst./hospitals funds 0.0003 0.0004 0.0004 0.0004University funds * private rms funds 0.0065 0.0065 Private rms funds * inst./hospitals funds 0.0073 0.0076 Average amount of venture capital funds invested tobiotechnology rms from t1 to 0.00090.0007 0.0008 0.0007Population of the at t 0.0267 0.1093 0.0288 0.1110Average GDP observed at the MSA from t1 to t5 0.0000 0.0000 0.0000 0.0000Year xed effects Yes Yes xed effects included Yes YesScale 0.5613 0.5615Fit statistics (AIC and loglikelihood from maximumlikelihood estimation)Test of joint signicance of year xed effects 3.660 of joint signicance of xed effects 41.160 41.890 likelihood 1890.148 1391.756AIC 3504.438 3504.477Multicollinearity condition number 39.673 38.395Number of observations 6480 6480 Notes: 1. The log link function was used for the Poisson model. 2. Standard errors reported are clustered at the level.a In order to include years 19921996 in the analysis, the averages are calculated as the average of available observations. For year 1996 for example, the average used inthe model is the average NIH$ from 1992 to 1995, which is a 4 and not 5 year average. b The omitted year is 2007.*0.10 signicance. ** 0.05 signicance. *** 0.001 signicance. Table 4Semi-elasticities for the variables that measure the rate at which federal funds towards different types of institutions associate with biotechnology rm births at the Model 1: Poisson (withoutvariance restriction) full modelModel 2: Poisson (without variancerestriction) excluding temporal lag Percentage change in the expected number of rm births in the focal at year t, given a1 million increase in the average NIH funding level of universities located in the t1 to t55.9388 5.9288Percentage change in the expected number of rm births in the focal at year t, given a1 million increase in the average NIH funding level of research institutes/hospitalslocated in the from t1 to t54.9923 5.2518Percentage change in the expected number of rm births in the focal at year t, given a1 million increase in the average NIH funding level of private rms located in the t1 to t558.1078 59.2508 ties, the results of Table 4 indicate that, on average, an additional $1million of public R&D funding awarded to universities over a 5 yearperiod is expected to generate in the following year an increaseof 5.93 percent in local rm births per MSA. Regarding the shortrun and long run effects of federal money on local rm births, estimated long-run effect is close to 2 percent higher than theshort-run effect (0.0612 and 0.0623 respectively).With regard to the marginal effect of federal R&D funds awardedto private rms on the regional rm birth rate, the estimated 32The long run effect is estimated with /1  where  is the short run effect and is the estimated coefcient of the lagged births variable ( 2009 semi-elasticity reported in Table 4 implies that, on average, an addi-tional $1 million of federal R&D funds awarded to private rms overa 5 year period is expected to increase the number of local rmbirths in the following year by 58.11 percent per MSA. This nd-ing provides empirical support to the proposition that the impactof federal R&D funding on rm creation is sensitive to the recipi-ent type. What makes this nding striking is the sheer differencein magnitude. Federal R&D funding directed to private rms isfound to have an impact on rm creation that is almost ten timeslarger than that directed to research universities. This differenceis consistent with the ndings of previous studies that documentdifferential effects between industrial and academic R&D on rmbirths ( and Nerlinger, 2000; Karlsson and Nystrm, 2011 C. Kolympiris et al. / Research Policy 43 (2014) 121 137131 Similar to the results for universities, the estimated short-run andlong-run multipliers for private rms are nearly identical, and themarginal effects do not exhibit a higher order relationship that iseconomically relevant.With regards to federal R&D funds awarded to research insti-tutes and hospitals we nd that the impact of such funds on localbiotechnology rm creation is slightly lower than that of researchuniversities but substantially lower than that of private rms. Inparticular, our results show that, on average, an additional $1 mil-lion of federal R&D funds awarded to research institutes/hospitalsin a ve year period is expected to increase the number of biotech-nology rm births in the following year by about 5 percent for the of interest. Given that unlike private rms, universities andresearch institutes are not driven mainly by prot maximization, apossible impetus of the observed difference be the potentialtendency of private rms to direct their efforts mainly towards theend of research with higher commercial value. It also be theresult of diminishing returns of public R&D funding to rm creation(note the difference in the relative size of public R&D spending inuniversities and private rms), or some other factor.The statistical insignicance of the lagged dependent variablesuggests that prior local rm births do not have strong explanatorypower in the rate of local rm formation and the same holds forventure capital investments, the size of the and its economicgrowth. 8. Sensitivity analysis In order to evaluate the robustness of our ndings performeda number of tests and we present the results in Table 5 First, we tested for the case under which our results are inu-enced by a virtuous cycle where funds go to regions that alreadyhost many rms and in turn (mainly due to the enhanced capacityof rms to generate new rms from federal funds) these regionsend up with more rms. To address the issue, we computed theannual NIH funds towards private rms in the same MSA measur-ing only those funds that went to rms that were at least sevenyears old. shown in Model 1 in Table 5 the results once againindicate that federal funds towards incumbent rms promote localrm births and imply that the estimates reported in Table 3 a lower bound on the impact of such funds on the localstartup rate. The estimates of the university and institutes impactsare in the same range with the estimates of Table 3 but the instituteimpact is no longer statistically signicant.The estimated scale parameter for the Poisson model reported in 3 indicates under-dispersion in the variance of the dependentvariable (i.e. the observed variance is less than a standard Poissonrandom variable), which be due to the large number of obser-vations with a zero count. to this potential under-dispersion,we also constructed a Generalized Poisson model ( 1993;Famoye and Singh, 2003 which has properties that are appropri-ate for under-dispersed data ( 2011 The results of this modelare presented as Model 2 in Table 5 and are qualitatively similar the results presented in Table 3 In Model 3 we present estimates of a linear probability modelthat tests the sensitivity of our results to the exclusion of MSAswithout rm births across years from the analysis that relates to 33 are grateful to two anonymous reviewers for pointing this potential issueand for suggesting ways to address it.34The Poisson model with the variance equal to the mean is not reported in Table 3 the Poisson variance assumption was not supported by the data.35The main difference is the statistical signicance of the quadratic terms. But,their magnitude is so small that it suggests a nearly nonexistent economic impactfrom these higher-order terms. the Maximum Likelihood estimator used for the count mod-els. In Model 3, those MSAs are included in the analysis. AlthoughOLS ignores the count feature of the dependent variable, the esti-mates largely corroborate our previous ndings that funds directedtowards private rms have a much stronger marginal effect on localrm births than funds directed towards universities and researchinstitutes/hospitals.Another estimator that ignores the count feature of our databut addresses potential endogeneity between the dependent andthe independent variables is the Arellano-Bond estimator ( and Bond, 1991 our application, in Model 4, we use it totest the possibility that our estimates are plagued by the poten-tial endogeneity of the lagged dependent variable. The estimatedcoefcients do not provide evidence that such an issue is presentin our models.Models 58 test the sensitivity of our estimates to the modelspecication. In particular, we build models that include each fund-ing category separately and then a model that does not includethe interaction terms. The estimates of these models are largelycomparable with the estimates presented in Table 3 Models 9 and 10 are built to test the robustness of our estimatesto the construction of the time lag we used to estimate the effect offederal funds (5 year average). In model 9, the 5 year average lag isreplaced by a one year lag and in model 10 the 5 year average lagis replaced by a 3 year average lag. Both models suggest that thechoice of the lag structure does not signicantly impact the results.Finally, as note at the bottom of Table 3 in order to includeearly years in the empirical analysis, we use a 5 year average foravailable observations where for example, the observation for 1995is the average value of years 19921994, which is a 3 year average.In model 11 we test the potential inuence of that choice in ourestimates and build models that use observations only after 1997.The results are nearly identical to those presented in Table 3 In conclusion, the tests we conducted support the overall con-clusions have drawn from our main results reported in Table 3 indicate that our ndings are robust to alternative model spec-ications and data constructions. 9. Summary, discussion and concluding comments Partly due to a strategic decision of the U.S. government to sub-stantially increase funding towards R&D in order to boost the U.S.economy during the severe downturn of the late 2000s, interestin the relationship between public R&D spending and economicgrowth has been revitalized. A longstanding academic literaturehas established that public funding matters for economic growthas it increases innovation, productivity and the like. Nevertheless,the impact of public R&D funding on the creation of new rms hasreceived little attention despite strong theoretical constructs thatsupport the association. Indeed, empirical evidence on the relation-ship between public R&D funding and rm creation has been scantand indirect.In this study, we have analyzed the relationship between federalR&D funds and local rm births in the U.S. biotechnology indus-try and our ndings suggest that government R&D spending has apositive impact on rm creation. Our results, therefore, corrobo-rate the empirical evidence that has been provided by a handful of 36To construct the instruments we use a one year lag of the independent variables,which model as weakly exogenous implying that we allow for the case thatthey are correlated both with past and . . .possibly current realization of the error(pg. 86 Roodman, 2009 Different congurations of the set of variables we use toconstruct the instruments yield qualitatively similar results to those reported in 5 The estimates presented in Model 4 of Table 5 are derived from the Arellano-Bond system estimator, which, in our application, yields similar estimates to thedifference estimator. 132C. Kolympiris et al. / Research Policy 43 (2014) 121 137Table 5Estimates of models testing the robustness of the results. The dependent variable is the number of biotechnology rm births at time t in i. number 1 2 3 4Model description Model where the NIH funds towardsprivate rms measures only fundstowards rms that were at least 7 yearsold at the year in question. Standarderrors are clustered at the MSA levelGeneralized Poisson Linear probability model withheteroskedasticity robuststandard errorsArellano bond estimator Estimate S.E. Estimate S.E. Estimate S.E. Estimate S.E. Intercept 4.3297 4.3212 0.0381 0.0934 0.2554 Biotech rm births in MSA in 10.0525 0.0084 0.0161 0.1876 0.0620 Average amount awarded ($2007 to universities from t1 to t5 0.0676 0.0138 0.0129 0.0043Average amount awarded ($2007 to inst./hospitals from t1 to t5 0.0423 0.0546 0.0881 0.0942 Average amount awarded ($2007 to private rms from t1 to t5 0.5855 0.2635 0.1638 (Average amount awarded ($2007 to universities from t1 to t5)20.0000 0.0001 0.0001 0.0001 0.0001 0.0000 0.0000(Average amount awarded ($2007 to inst./hospitals from t1 to t5)20.0000 0.0002 0.0002 0.0001 0.0002 0.0011 (Average amount awarded ($2007 to private rms from t1 to t5)20.0292 0.0301 0.0050 0.0062 0.0152 0.0187 0.0083 0.0054University funds * inst./hospitals funds 0.0005 0.0006 0.0002 0.0002 0.0002 0.0004 0.0009 University funds * private rms funds 0.0095 0.0073 0.0074 0.0054 0.0017 0.0011Private rms funds * inst./hospitals funds0.0081 0.0099 0.0080 0.0014 0.0080 0.0039 0.0034Average amount of venture capital fundsinvested to biotechnology rms from t1to t50.0016 0.0018 0.0008 0.0008 0.0023 0.0044 0.0002 0.0007Population of the at t 0.2593 0.0366 0.0494 0.1564 0.9392 Average GDP observed at the fromt1 to t50.0000 0.0000 0.0000 0.0000 0.0000 Year xed effects Yes Yes Yes Yes xed effects Yes Yes Yes YesScale 0.5849Fit statistics (for countmodels the log likelihoodand the AIC are frommodels estimated withmaximum likelihood)Test of joint signicance of year xedeffects6.690 of joint signicance of xedeffects31.360 likelihood 1992.011AIC 3678.757 3379.300Adjusted R20.739Wald 2297.350 condition number 41.079 39.673 41.579 33.978Number of observations6480 6480 6480 5831 C. Kolympiris et al. / Research Policy 43 (2014) 121 137133 4.4941 3.3875 3.5403 4.3855 Biotech rm births in in t1 0.1172 0.0824 0.1137 0.0398 0.0250Average amount awarded ($2007 to universities from t1 to t5 0.0692 Average amount awarded ($2007 to inst./hospitals from t1 to t5 0.0244 Average amount awarded ($2007 to private rms from t1 to t5 0.5981 (Average amount awarded ($2007 to universities from t1 to t5)20.0004 0.0003 (Average amount awarded ($2007 to inst./hospitals from t1 to t5)20.0003 0.0000 0.0001 0.0001(Average amount awarded ($2007 to private rms from t1 to t5)20.0668 0.0291 0.0184University funds * inst./hospitals fundsUniversity funds * private rms fundsPrivate rms funds * inst./hospitals fundsAverage amount of venture capital fundsinvested to biotechnology rms from t1to t50.0002 0.0009 0.0006 0.0004 0.0018 0.0008 0.0044 Population of the MSA at t 0.1028 0.1475 0.2478 0.3224 0.0295 0.1134Average GDP observed at the MSA fromt1 to t50.0000 0.0000 0.0000 0.0000 0.0000 0.0000Year xed effects Yes Yes Yes Yes xed effects included Yes Yes Yes YesScale 0.6101 0.6418 0.6396 0.5802Fit statistics (for countmodels the log likelihoodand the AIC are frommodels estimated withmaximum likelihood)Test of joint signicance of year xedeffects4.110 0.310 8.030 of joint signicance of xedeffects10.100 likelihood 2091.908 2197.114 2190.437 1974.693AIC 3356.823 4109.498 4091.873 3637.283Adjusted R2Wald 2Multicollinearity condition number 14.822 12.412 12.763 18.177Number of observations 6480 6480 6480 6480 134C. Kolympiris et al. / Research Policy 43 (2014) 121 137Table 5 (continued ) number 9 10 11Model description Model where the 5 year lagaverage of the NIH funds isreplaced with a one year lag.Standard errors are clusteredat the levelModel where the 5 year lagaverage of the NIH funds isreplaced with a 3 year lagaverage. Standard errors areclustered at the levelModel where all observationshave a 5 year lag average.Standard errors are clusteredat the level Estimate S.E. Estimate S.E. Estimate S.E. Intercept 4.3137 4.3607 4.2763 Biotech rm births in in 10.0499 0.0323 0.0161 0.0277 0.00020.0232 amount awarded ($2007 M.) toMSA universities from t1 to t530.0598 0.0571 0.0596 Average amount awarded ($2007 M.) toMSA inst./hospitals from t1 to t530.0337 0.0488 0.0478 Average amount awarded ($2007 M.) toMSA Private Firms from t1 to t530.4764 0.6066 0.5731 (Average amount awarded ($2007 toMSA universities from t1 to 0.00010.0001 0.00010.0001 0.0000 0.0002(Average amount awarded ($2007 toMSA inst./hospitals from t1 to t5)0.0000 0.0001 0.0002 0.0001 0.0002 0.0002(Average amount awarded ($2007 toMSA private rms from t1 to 0.00260.0017 0.00620.0067 0.00280.0119 funds * inst./hospitals funds 0.0001 0.0002 0.0003 0.0003 0.0004 0.0003University funds * private rms funds 0.0046 0.0051 0.0074 Private rms funds * inst./hospitals funds 0.0037 0.0071 0.0069 Average amount of venture capital fundsinvested to biotechnology rms from t1to t50.0006 0.0008 0.0007 0.0006 0.0009Population of the MSA at 0.00670.1164 0.0226 0.1140 0.02820.1319 GDP observed at the MSA fromt1 to t50.0000 0.0000 0.0000 0.0000 0.0000 0.0000Year xed effects included Yes Yes Yes xed effects included Yes Yes YesScale 0.5637 0.5613 0.5559Fit statistics (for countmodels the log likelihoodand the AIC are frommodels estimated withmaximum likelihood)Test of joint signicance of year xedeffects4.370 of joint signicance of MSA xedeffects38.930 likelihood 1901.148 1890.202 1160.255AIC 3521.668 1633.262 2760.192Adjusted R2Wald 2Multicollinearity condition number 34.609 37.300 39.569Number of observations6480 6480 5040 The log link function used for the Poisson model.a In order to include years 19921996 in the analysis, the averages are calculated as the average of available observations. For year 1996 for example, the average used in the model is the average NIH$ from 1992 to 1995, whichis a 4 and not 5 year average. In models 9 and 10 the estimates represent a one year lag and a three years average respectively. The square terms as well as the interaction effects are calculated accordingly. b The omitted year is 2007. * 0.10 signicance. ** 0.05 signicance. *** 0.001 signicance. C. Kolympiris et al. / Research Policy 43 (2014) 121 137135 previous studies that have also found a positive relationshipbetween public R&D funds and regional rm births, especially inthe biotechnology industry. Our analysis focused on biotechnologybecause it is central to the knowledge economy; it is a heavy recip-ient of federal research funds; it displays a close linkage betweenbasic research and commercial application; and it tends to clusteraround geographies and institutions that receive signicant gov-ernment R&D funding. As such, we presumed that if a positiverelationship between public R&D spending and rm creation exists,it should be evident in the biotechnology industry.By developing separate measures of the effect of federal R&Dfunds awarded to different types of recipient research institutions,we nd that public funds dispensed for R&D to existing rmshave, proportionally, a much stronger positive impact on localrm births than funds directed to universities and research insti-tutes/hospitals. also nd that temporal and spatial factors inour analysis (two-way xed effects) explain part of the variation inthe birth rate of biotechnology rms across different MetropolitanStatistical Areas in the U.S. Hence, year-to-year variation in marketconditions (e.g. hot IPO markets) as well as regional differencesin infrastructure, business climate, taxes, initiatives and other fac-tors tend to condition the overall impact of public R&D funding onrm creation. These ndings reinforce our view that analysis of therelationship between public R&D spending and rm creation mustbe done over a long period of time and across large geographies to allow for lags as well as spatial and temporal variation that areinherent in such a relationship.The specic quantitative results in our study are of particu-lar interest. nd that, depending on the recipient of funds, a$1 million increase in the average amount of federal R&D fund-ing associates with an increase of 558 percent in the number oflocal biotechnology rm births a few years later. Therefore, themagnitude of the effect has a considerable range. The specic sizeof this marginal effect is important because it relates to the job-creation process and other direct economic benets associated withentrepreneurship and rm creation ( Praag and Versloot, 2007 Given that such marginal effects have not been produced in otherstudies, it is important that future studies conrm and rene them.Having a deeper understanding of such marginal effects across sec-tors and geographies as well as of the conditions that determinethem could boost the overall impact of public R&D funding oneconomic growth and employment. It could also provide a moreaccurate accounting of the extent public R&D funding corrects mar-ket failures in certain knowledge sectors.Measurement of the marginal effects of public R&D on rmcreation, like those we provide in this study, also offer use-ful insights in the debate about the gradual emergence of theentrepreneurial university, which in addition to its teaching andresearch mission, promotes local rm births and economic devel-opment ( 1998 Our empirical estimates suggest that Table A1Keywords used to identify biotechnology related grants. chromatography Chinese Hamster Ovary gene silencing oligonucleotideagarose cho cellsgene targeting oligo-nucleotideAgarose gel electrophoresis chromatid Gene therapy oligonucleotide ligation assayallele chromatin gene transfer oligonucleotide microarrayamplied fragment lengthpolymorphismChromatography geneow oligonucleotide probesanticodon chromosomal fragmentation genetic PCRAntigen chromosomal mapping genetic discoveries pcr testAntisense chromosomal mutation genetic disorders peptideArabidopsis Chromosome genetic engineering plasmidascites chromosome walking genetic map Polyacrylamide gel electrophoresisassay chrondocyte differentiation genetic marker polyclonal antibodiesbacillus Cistron genetic modication polymerase chain reactionBacillus thuringiensisclone genetic parametersPolymerase chain reaction (PCR)bacteriophage coli genomics recombinant adenovirus technologyBeta-glucans collagen glycopeptides recombinant allergensbeta-glucuronidase combinatorial biocatalysis glycoprotein recombinant antigensbioassay Combinatorial chemistry glycosaminoglycan recombinant collagensbiocomputing Cyclic (cyclic adenosinemonophosphate)glycosidase recombinant enzymesBioltration Cytogenetic glycosylation recombinant genesbiolistics cytokines glycosyltransferases recombinant proteinsbiomass cytokinesis hormone Restriction-fragment-length polymorphism (RFLP)biomedicine cytosine immunoafnityReverse transcriptasebiopharma Directional cloning immunoassay Southern blotBiopharming dna Interferon Southern hybridization (Southern blotting)bioplastics dna biosensor Introgression stem cellsbiopolymers dna chips kinase tissue engineeringbioprocess dna detection knockout mice transcription factorsBioprocessing dna inihibitors ligase chain reaction Transgenic plantsBioreactor dna modication Microarray Western blotBioremediation dna polymerases microbial biotechnologybiosensor dna-chip mitosisbiosynthesis elisa monoclonal antibodiesBiotelemetry embryoandgenetic Mouse modelBioticstress enzyme Mutagenesisblastocyst Enzyme-linked assay (ELISA):mutagenic substancebovine somatotropin fab mutationCdna Field trial nanobiotechnologycell culture functional genomics neuroncellular assays gene neuropithelial stem cellscellular signaling Gene (DNA) sequencing Northern blotcentromere gene amplication nucleotidechimeraplasty Gene mapping d-glycolsylation 136C. Kolympiris et al. / Research Policy 43 (2014) 121 137 publicly funded R&D in universities and research institutes has apositive impact on local rm creation. Characterizing the differ-ences in such marginal effects between universities and privaterms as well as across locations and sectors help identify theirsources and improve the efciency of the entrepreneurial univer-sities and their impact on local economies through the creation ofnew ventures.Before closing, we note that our empirical models contain someunexplained variance. This variance can be reduced in a numberof ways. have discussed a number of mechanisms that havebeen identied in the literature and could explain the differencesin the marginal effects of public R&D spending we found in thisstudy. However, we have not tested the relevance of such mech-anisms in our analysis. Explicitly accounting for the mechanismsthat shape the differential impact of government R&D spending onrm creation across recipient institutions, is an important area forfurther investigation. Also, we treat all institutions in our three fundrecipient categories (university, private rm, and research instituteor hospital) as homogeneous (aside from MSA-specic difference),but there are distinct types of academic institutions ( Gregorioand Shane, 2003 or private rms that affect the local rate ofassociated rm births differently. Future research can determineif these within-group differences are signicant and how the dif-ferent types of subgroups perform. Finally, data limitations do notallow us to directly account for the network ties in the biotech-nology industry that have been noted in previous work. Testingwhether federal funds inuence the creation of such ties, whichcan promote local rm creation, could yield important insights.AcknowledgementsResearch funding provided by the Ewing Marion KauffmanFoundation Strategic Grant #20050176 is gratefully acknowledged.An earlier version of the paper was presented at the 35th DRUID Cel-ebration Conference at ESADE Business School in Barcelona, Spain. thank Peter Klein and James Kaufman for valuable inputs dur-ing the development of the paper and Sebastian Hoenen and LifengTu for assistance in data collection. Appendix A. See Table A1 ReferencesAbel, J., Deitz, R., 2012. capital? Z.J., Audretsch, D.B., Feldman, M.P., 1994. Managerial Z.J., Braunerhjelm, P., Audretsch, D.B., Carlsson, B., 2009. spillover R., Echambadi, R., Franco, A.M., Sarkar, M.B., 2004. throughof B.S., Baum, J.A.C., Plunket, A., 2008. the P.D., 2008. Koster, S., 2011. ratesevidence L., Varga, A., Acs, Z.J., 1997. sity422448. 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