Does Entrepreneurship Capital Matter?

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Economics has identified three types of capital as the drivers of economic growth—physical capital, human capital, and knowledge capital. This article introduces the conceptof entrepreneurship capital and suggests that it is also an important factor shaping the eco-nomic performance of an economy. We define entrepreneurship capital as those factorsinfluencing and shaping an economy’s milieu of agents in such a way as to be conduciveto the creation of new firms. The hypothesis that entrepreneurship capital is positively linkedto economic growth is then tested by examining the relationship between several differentmeasures of entrepreneurship capital and regional economic performance, measured asper-capita income for Germany. The empirical evidence suggests that there is indeed a pos-itive link between entrepreneurship capital and regional economic performance. Theseresults suggest a new direction for public policy that focuses on instruments to enhanceentrepreneurship capital.

Introduction

One of the basic questions in economics has to do with what drives the economicperformance of an economy. While the neoclassical tradition identified investment inphysical capital as the driving factor (Solow, 1956), the endogenous growth theory(Romer, 1986, 1990) put the emphasis on the process of the accumulation of knowledge,and hence the creation of knowledge capital. The concept of social capital (Putnam, 1993;Coleman, 1988) can be considered as a further extension because it identifies a socialcomponent to those factors shaping economic growth and prosperity. According toPutnam (2000, p. 19), “Whereas physical capital refers to physical objects and humancapital refers to the properties of individuals, social capital refers to connections amongindividuals—social networks and the norms of reciprocity and trustworthiness that arise from them. In that sense social capital is closely related to what some have called ‘civic virtue.’ The difference is that ‘social capital’ calls attention to the fact thatcivic virtue is most powerful when embedded in a sense network of reciprocal social relations. A society of many virtues but isolated individuals is not necessarily rich insocial capital.”

Putnam also challenged the standard neoclassical growth model by arguing that socialcapital was also important in generating economic growth: “By analogy with notions ofphysical capital and human capital—tools and training that enhance individual produc-

PTE &Does EntrepreneurshipCapital Matter?David B. AudretschMax Keilbach

1042-2587Copyright 2004 byBaylor University

Please send all correspondence to: David B. Audretsch at [email protected] and to Max Keilbach [email protected].

tivity—social capital refers to features of social organization, such as networks, norms,and trust, that facilitate coordination and cooperation for mutual benefits.”

A large and compelling literature has emerged linking social capital to entrepreneur-ship (Aldrich & Martinez, 2003; Thorton & Flynne, 2003). According to this literature,entrepreneurial activity should be enhanced where investments in social capital aregreater (Amin, 2000; Simmie, 2003; Smith, 2003). However, while it was clear thatPutnam was providing a link between social capital and economic welfare, this link didnot directly involve entrepreneurship. The components of social capital that Putnamplaced the greatest emphasis on were associational membership and public trust. Whilethese may be essential for social and economic well being, it was not obvious that theyinvolved entrepreneurship, per se.

The purpose of this article is to suggest that what has been called social capital inthe entrepreneurship literature may actually be a more specific subcomponent, which weintroduce as entrepreneurship capital. Entrepreneurship has typically been defined as anaction, process, or activity. By entrepreneurship capital of an economy or a society wemean a regional milieu of agents that is conducive to the creation of new firms. Thisinvolves a number of aspects such as social acceptance of entrepreneurial behavior butof course also individuals who are willing to deal with the risk of creating new firms1

and the activity of bankers and venture capital agents that are willing to share risks andbenefits involved. Hence entrepreneurship capital reflects a number of different legal,institutional, and social factors and forces. Taken together, these factors and forces con-stitute the entrepreneurship capital of an economy, which creates a capacity for entre-preneurial activity (Hofstede et al., 2002). Thus, entrepreneurship capital manifests itselfby the creation of new firms.

It should be emphasized at the outset that entrepreneurship capital should not be con-fused with social capital. The major distinction is that, in our view, not all social capitalmay be conducive to economic performance, let alone entrepreneurial activity. Sometypes of social capital may be more focused on preserving the status quo and not neces-sarily directed at creating challenges to the status quo. By contrast, entrepreneurshipcapital could be considered to constitute one particular subset of social capital. Whilesocial capital may have various impacts on entrepreneurship, depending on the specificorientation, entrepreneurship capital, by its very definition, will have a positive impacton entrepreneurial activity.

In the second section of this article the link between entrepreneurship capital andeconomic growth is explained. In the third section an empirical test based on a simplemodel is introduced, along with the data and methodology. Finally, in the last section asummary and conclusions are provided. In particular, the empirical results provide com-pelling evidence that economic performance is positively related to the presence of entre-preneurship capital. While these findings do not enable us to shed any light on whatexactly constitutes entrepreneurship capital, or which type of public policies would bestenhance entrepreneurship capital, they do shed light on the current race among publicpolicy makers to “create the next Silicon Valley” in an effort to promote entrepreneurialactivity.

420 ENTREPRENEURSHIP THEORY and PRACTICE

1. As Gartner and Carter (2003) state, “Entrepreneurial behavior involves the activities of individuals whoare associated with creating new organizations rather than the activities of individuals who are involved withmaintaining or changing the operations of on-going established organizations.”

Regional Economic Performance and Entrepreneurship Capital

The main contribution of the social capital literature is that endowments with “tra-ditional factors” such as capital, labor, and (since not too long) knowledge are not ade-quate to sufficiently explain economic performance. Rather, as Putnam argues, socialinteraction facilitates the creation of communities, personal commitments, and socialfabric. A sense of belonging and the concrete experience of social networks, whichinvolves relationships of trust and tolerance, will ultimately be transmitted into economicperformance.

As explained in the previous section, in this article we suggest that the notion ofentrepreneurship capital may be more useful in the entrepreneurship literature. Entre-preneurship capital refers to a specific type of social capital that explicitly generates thestartup of new enterprises.

Even if the concept of entrepreneurship capital might be acknowledged, the exactlink between entrepreneurship capital and economic performance is less than certain. Inan article in the Harvard Business Review, Ferguson (1988, p. 61), actually argued thatentrepreneurship would actually reduce rather than increase economic performance. Heconsidered entrepreneurship in Silicon Valley to be a drag on economic performance,because the “fragmentation, instability, and entrepreneurialism are not signs of well-being. In fact, they are symptoms of the larger structural problems that afflict U.S. indus-try. In semiconductors, a combination of personnel mobility, ineffective intellectualproperty protection, risk aversion in large companies, and tax subsidies for the formationof new companies contribute to a fragmented ‘chronically entrepreneurial’ industry. U.S.semiconductor companies are unable to sustain the large, long-term investments requiredfor continued U.S. competitiveness. Companies avoid long-term R&D, personnel train-ing, and long-term cooperative relationships because these are presumed, often correctly,to yield no benefit to the original investors. Economies of scale are not sufficiently devel-oped. An elaborate infrastructure of small subcontractors has sprung up in Silicon Valley.Personnel turnover in the American merchant semiconductor industry has risen to 20percent compared with less than 5 percent in IBM and Japanese corporations. . . . Frag-mentation discouraged badly needed coordinated action—to develop process technologyand also to demand better government support.”

The opposite view, suggesting that entrepreneurship capital will have a positiveimpact on economic performance, is provided by Saxenian (1994), who examines thesame region, Silicon Valley, “It is not simply the concentration of skilled labor, suppli-ers and information that distinguish the region. A variety of regional institutions—including Stanford University, several trade associations and local business organizations,and a myriad of specialized consulting, market research, public relations and venturecapital firms—provide technical, financial, and networking services which the region’senterprises often cannot afford individually. These networks defy sectoral barriers: indi-viduals move easily from semiconductor to disk drive firms or from computer to networkmakers. They move from established firms to startups (or vice versa) and even to marketresearch or consulting firms, and from consulting firms back into startups. And they con-tinue to meet at trade shows, industry conferences, and the scores of seminars, talks, andsocial activities organized by local business organizations and trade associations. In theseforums, relationships are easily formed and maintained, technical and market informa-tion is exchanged, business contacts are established, and new enterprises are conceived.. . . This decentralized and fluid environment also promotes the diffusion of intangibletechnological capabilities and understandings” (Saxenian, 1990, pp. 96–97). Saxenianclaims further (pp. 97–98) that even the language and vocabulary used by technical

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specialists can be specific to the entrepreneurship capital associated with that region, “. . . a distinct language has evolved in the region and certain technical terms used bysemiconductor production engineers in Silicon Valley would not even be understood bytheir counterparts in Boston’s Route 128.” Amin and Thrift (2003) address this phenom-enon as well, denoting it “institutional thickness.” A similar observation for the Cam-bridge, United Kingdom, cluster has been provided by Keeble (1997), Keeble et al. (1999)and Keeble and Wilkinson (1999).

These arguments put in economic terms, suggest that entrepreneurship capital can beseen as a mechanism for knowledge spillovers because it facilitates the exchange andflow of ideas. Just as Romer (1986) addressed what he perceived to be a shortcoming inthe Solow (1956) model—the lack of an explicit contribution by knowledge—we wouldargue that a shortcoming of the Romer model is that the spillover of knowledge isassumed to be automatic. But this assumption by Romer is inconsistent with Arrow’s(1962) claim that knowledge is not automatically transmitted into commercial knowl-edge. The factor of knowledge differs from the traditional economic factors of labor andcapital in that it is characterized by a significantly greater degree of uncertainty, moreasymmetries, and higher costs of transactions across agents and organizations. Theseknowledge conditions create greater divergences in the perceived valuation of new ideas,resulting in a high variance of expected values associated with those new ideas. If a newidea is not highly valued by an incumbent firm, knowledge workers, such as scientistsand engineers, may resort to starting a new enterprise in order to appropriate the poten-tial value of their own knowledge. Thus, the decision to start a new firm also involvesthe (potential) spillover of knowledge from the incumbent enterprise to the startup.

The literature identifying mechanisms that actually transmit knowledge spillovers issparse and remains underdeveloped. However, one important area where such transmis-sion mechanisms have been identified involves entrepreneurship, i.e., the startup andgrowth of new enterprises. In the metaphor provided by Albert O. Hirschman (1970), ifvoice proves to be ineffective within incumbent organizations, and loyalty is sufficientlyweak, a knowledge worker may resort to exiting the firm or university where the knowl-edge was created in order to form a new company.2 In this spillover channel, the knowl-edge production function is actually reversed. The knowledge is exogenous and embodiedin a worker. The firm is created endogenously in the worker’s effort to appropriate thevalue of his knowledge through innovative activity. Thus, entrepreneurship serves as themechanism by which knowledge spills over from the source creating a new firm whereit is commercialized.

Regions with a high degree of entrepreneurship capital facilitate the startup of newfirms based on uncertain and asymmetric ideas. On the other hand, regions with a lowdegree of entrepreneurship capital impede the ability of individuals to start new firms.Thus, entrepreneurship capital promotes the spillover of knowledge by facilitating thestartup of new firms. Acs et al. (2003) refer to the gap between knowledge and com-mercialized knowledge as the knowledge filter. By commercializing ideas that otherwisewould not be pursued and commercialized, entrepreneurship serves as one mechanismfacilitating the spillover of knowledge. Thus, entrepreneurship capital promotes economicperformance by serving as a conduit of knowledge spillovers. As Meister and Werker(2004), Bode (2004), and Pinch et al. (2003) have emphasized, the localized nature ofknowledge spillovers would suggest that the impact of knowledge capital should also belocalized.


2. See Audretsch (1995) for a more detailed analysis of this issue.

Production Function Model and Measurement Issues

As stated in the introduction, the goal of this article is to identify the impact of entre-preneurship capital on regional economic performance. We use a standard measure ofeconomic performance, labor productivity, i.e., a region’s economic output relative to itslabor force. Dividing output by the input of labor corrects for the size of a region, henceincreases the pertinence of this measure. We link this measure of regional economic per-formance to the traditional factors of capital, labor, and knowledge, along with our (new)factor of entrepreneurship capital by using a Cobb-Douglas production function of theform

Based on this function, dividing output by labor we obtain

(1)

where Y represents economic output, L represents labor (hence Y/L is labor produc-tivity), K represents the factor of physical capital (K/L being the capital intensity of theregion), R represents knowledge capital, and E represents entrepreneurship capital. Thesubscript i refers to German regions. Equation 1 represents the classical Cobb-Douglasproduction function in its intensive form under the assumption that the production elas-ticities of capital and labor sum to unity. bj represents output elasticities of the respec-tive variables, i.e., an increase of the corresponding variable by 1% increases the left-handside (labor productivity) by bj%.

Our data will consist of a cross section of 327 West German regions, or Kreise, forthe year 1992 (if not indicated otherwise). Sources and construction of the data are asfollows. We measure Output as Gross Value Added corrected for purchases of goods andservices, VAT, and shipping costs. Statistics are published every two years for Kreise bythe Working Group of the Statistical Offices of the German Länder, under “Volkswirt-schaftliche Gesamtrechnungen der Länder.” Data on labor are published by the FederalLabor Office, Nürnberg, which reports number of employees liable to social insuranceby Kreise.

The stock of physical capital used in the manufacturing sector of the Kreise has beenestimated using a perpetual inventory method, which computes the stock of capital as aweighted sum of past investments. Data on investments at the level of German Kreise are published annually by the Federal Statistical Office in the series “E I 6.” Thesefigures however are limited to firms of the producing sector, excluding the mining in-dustry, with more than 20 employees. The vector of the producing sector as a whole has been estimated by multiplying these values such that the value of the capital stockof Western Germany—as published in the Statistical Yearbook—was attained. Note thatthis procedure implies that estimates for Kreise with a high proportion of mining mightbe biased.

Knowledge Capital is measured as the number of employees engaged in R&D in the public (1992) and in the private sector (1991). R&D inputs have been used in anumber of previous studies as a proxy indicator of knowledge capital (Griliches, 1979;Jaffe, 1989; Audretsch & Feldman, 1996). The data were obtained by the Stifterverbandfür die Wissenschaft under obligation of secrecy. With these data, it was impossible tomake a distinction between R&D employees in the producing and nonproducing sectors.Regression results therefore will implicitly include spillovers from R&D of the nonpro-ducing sector to the producing sectors. We presume, however, that this effect is ratherlow.

Y L K L R Ei i i i i i( ) = ( )a b b b1 2 3 ,

Y K L R E .i i i i i= -a b b b b1 1 2 31

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Measurement of entrepreneurship capital is no less complicated than is measuringthe traditional factors of production. Just as measuring capital, labor, and knowledgeinvokes numerous assumptions and simplifications, creating a metric for entrepreneur-ship capital presents a challenge. Many of the elements determining entrepreneurshipcapital defy quantification and as our definition has emphasized, entrepreneurship capitalis a multifaceted and heterogeneous phenomenon.3 Direct measurement of entrepreneur-ship capital would include a broad spectrum of institutions, policies, historical, social,and cultural traditions, as well as personal characteristics associated with the particularregion. Such measurement, if accurately and correctly undertaken, would clearly be non-trivial, and of great significance. While we anticipate future research will rise to the chal-lenge of directly measuring entrepreneurship capital, in this article we instead rely on anindicator that is a reflection or a manifestation of entrepreneurship capital—the numberof new firms started in a region relative to the size of that region.

There are several important qualifications which must be emphasized using this indi-cator of entrepreneurship capital. First, it does not at all directly measure entrepreneur-ship capital, but indirectly reflects an underlying economic phenomenon that, as of now,cannot be measured. However, ceteris paribus, new-firm startups would be expected tobe relatively low in those regions with a low stock of entrepreneurship capital, but higherin those regions endowed with a high stock of (the unmeasurable) entrepreneurshipcapital. Second, startup rates are a flow measure reflecting the stock variable entrepre-neurship capital. This should not be interpreted as suggesting that entrepreneurship capitalis being measured as a flow. Rather, it suggests that the underlying latent stock of entre-preneurship capital generates a flow of new-firm startups that can be observed and mea-sured. That a flow measure is used to reflect an underlying latent stock factor is notwithout precedent. In fact, the use of R&D expenditures is often used to reflect the under-lying stock of knowledge that is also latent and cannot be directly measured.

With these two important qualifications in mind, we compute an indicator of entre-preneurship capital as the number of startups in the respective region relative to its pop-ulation, which reflects the propensity of inhabitants of a region to start a new firm. Inaddition, to avoid stochastic disturbance implicit in this approach, we compute thismeasure based on startup rates over a longer time period. In particular, we use the numberof startups between 1989–1992, thus covering four periods. The choice of these periodsand the length of the lag involved are driven by data restrictions, 1992 being the baseyear of our analysis and our data on startups starting in 1989.4 The data on startups aretaken from the ZEW foundation panels that are based on data provided biannually byCreditreform, the largest German credit-rating agency. This data contains virtually allentries—hence startups—in the German Trade Register, especially for firms with largecredit requirements, such as high-technology firms.5 As of 2003 there were 1.6 millionentries for West Germany.

One might argue that our measure of entrepreneurship capital is endogenous in thesense that regions with higher labor productivity will generate a higher number of star-


3. Actually, this applies for physical capital too. The measure of capital condenses such diverse input factorsas buildings, machinery, as well as raw materials and supplies into one singular measure.4. A correlation analysis has shown that measures of entrepreneurship capital involving more periods arehighly correlated with the one we used. This implies that adding more periods would not improve the qualityof the measure of entrepreneurship capital.5. Firms with low credit requirements, with a low number of employees, or with unlimited legal forms areregistered only with a time lag. These are typically retail stores or catering firms. See Harhoff and Steil (1997)for more detail on the ZEW foundation panels.

tups rather than the other way around. However, this argument would certainly apply tophysical capital as well. The argument could even be stronger, since monetary invest-ments (which build up capital) will flow across regions more easily than persons (henceentrepreneurs). Thus, the application of the production function is consistent with previ-ous studies in the growth literature. Equally important, the use of lagged values of startuprates enables us to avoid problems of simultaneity and endogeneity between output andentrepreneurship capital. This lagged relationship reflects causality between entrepre-neurship capital in one period and economic output in subsequent periods.

In our definition of entrepreneurship capital given above, we stated that a region’sentrepreneurship capital is also related to its capacity to deal with the risk involved withthe creation of new businesses. To address this aspect, we compute two more measuresof entrepreneurship capital that involve risk in a stronger way since they concern high-tech- or R&D-oriented activities. The first one restricts entrepreneurship capital to includeonly startup activity in high-technology manufacturing industries (whose R&D-intensityis above 2.5%). The second measure restricts entrepreneurship capital to include onlystartup activity in the ICT industries, i.e. firms in the hardware and software business.Some of these industries are also classified under high-technology manufacturing.

Table 1 shows a matrix of correlations between all variables involved in this study.A few interesting insights follow from this table. First (from column 1), as the high cor-relation indicates, entrepreneurship capital tends to be higher in densely populated areas,suggesting that it is an urban phenomenon. Second, the correlation rates between the mea-sures of entrepreneurship capital and labor productivity are rather low (though signifi-cant). Third, it is interesting to observe, that while all of the different indicators ofentrepreneurship capital are highly and positively correlated, in fact the correlation coef-ficients among those variables and the measure of knowledge capital is positive but ratherlow (though significant). This provides at least an indication that startups are slightlystronger in regions with a greater proportion of R&D employees, and would suggest that

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Table 1

Correlation between Variables and Population Density for 327 German Kreise

Population Startups StartupsDensity Y/L K/L R All High Tech

Y/L 0.2040(0.000)

K/L 0.3842 0.2478(0.000) (0.000)

R 0.5068 0.2072 0.2811(0.000) (0.000) (0.000)

Entrepr. Cap. (E) 0.3367 0.1117 0.0501 0.3036(0.000) (0.044) (0.366) (0.000)

High-Tech E 0.2668 0.1716 0.0437 0.3404 0.8153(0.000) (0.002) (0.431) (0.000) (0.000)

ICT E 0.2870 0.1881 0.0304 0.3396 0.8164 0.9138(0.000) (0.001) (0.584) (0.000) (0.000) (0.000)

Notes: p-values in brackets denote probability of correlation to be nondifferent to zero. As long as these are below 0.05(0.1), the correlation is significant at the 5% (10%) level.

an R&D employee might have a greater propensity to start a new business. The weak but positive correlation, on the other hand, might indicate that other factors will also havean influence on the regional startup activities.

Empirical Results

Estimation of Equation 1 produced the results displayed in Table 2. To test for robust-ness, different specifications were estimated. In the first column, results are shown forthe estimation of regional productivity in Germany using the traditional Cobb-Douglasmodel, (relating output to capital and labor) in its intensive form. The implicitly esti-mated output elasticities for capital (b1) and labor (1 - b1) are in the usual range.6 As hasbeen consistently verified in previous studies, those regions with a higher capital inten-sity exhibit greater levels of productivity. In the second column knowledge capital isadded. The positive and statistically significant coefficient of this variable lends supportto the Romer view that knowledge matters as a factor of production and generates higherlevels of productivity.

For columns 3 to 5, we added the three different indicators of entrepreneurshipcapital. All three estimations provided positive and significant results. This supports ourhypothesis that entrepreneurship capital is positively linked to economic performance. It


Table 2

Results of Estimation of Model (1) for German Regions

(1) (2) (3) (4) (5) (6) (7) (8)

Constant -1.888 -2.2340 -1.1249 -0.9627 -0.8606 -1.6671 -1.4603 -1.3589(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

K/L 0.332 0.2662 0.3188 0.3094 0.3126 0.2659 0.2709 0.2736(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

R 0.039 0.0344 0.0270 0.0266(0.000) (0.000) (0.000) (0.000)

Entrepreneurship (E) 0.1676 0.1145(0.000) (0.032)

High-Tech E 0.1326 0.0951(0.000) (0.000)

ICT E 0.1401 0.1038(0.000) (0.000)

F-Test 46.44 34.50 29.01 35.35 36.34 24.80 26.63 27.26(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adjusted R2 0.122 0.170 0.146 0.174 0.178 0.178 0.190 0.195

Notes: p-values in parentheses denote probability of estimate to be nondifferent to zero. As long as these are below 0.05,the estimate differs significantly from zero at the 5% level.

6. Cobb and Douglas (1928) estimated a production elasticity of 0.75 for labor and 0.25 for capital, imply-ing that an increase of labor (capital) input by 1% increases output by 0.75% (0.25%). Virtually all of sub-sequent estimates that have been done for different regions or industries have found results in ranges between0.25 and 0.33 for capital and 0.66 to 0.75 for labor.

is interesting to observe that the estimated output elasticities of the different measures ofentrepreneurship capital (b3) are larger than the output elasticities of knowledge capital(b2). More precisely, the effect of a 1% increase in the indicator of entrepreneurshipcapital on regional labor productivity is three to four times larger than a 1% increase ofR&D.

Columns 6 to 8 combine the measure of knowledge along with the three differentindicators reflecting entrepreneurship capital. All of the estimated coefficients are statis-tically significant at the 5% level. Hence both variables—the indicators of knowledgecapital and of entrepreneurship capital—have a joint positive impact on a region’s laborproductivity. Still, the impact of entrepreneurship capital seems relatively larger in thesense that its estimated production elasticity is three to four times larger than that ofknowledge capital. A policy implication of this finding is that it would be more efficientto increase a region’s entrepreneurship capital rather than its R&D input. However, thisis only preliminary evidence, to be interpreted carefully. Subsequent research needs tobe undertaken to identify specifically (a) what actually constitutes entrepreneurshipcapital, and (b) which, if any, public policies could actually be undertaken to promoteentrepreneurship capital.

Summary and Conclusions

In this article, we have attempted to link entrepreneurship to the economic perfor-mance of a society. To do so, we introduce the concept of entrepreneurship capital as asubcomponent, or specific aspect, of social capital. Entrepreneurship capital differs fromsocial capital in that it focuses solely on those aspects of social capital that promote entre-preneurial activity. There are other aspects of social capital that actually may inhibit entre-preneurship. However, this article clearly follows the social capital tradition, fuelled bythe writings of Putnam and Coleman, among others, by arguing that a high presence ofentrepreneurship capital will enhance economic performance.

Since the degree of entrepreneurship capital in an economy ultimately manifests itselfin the form of newly created businesses, we measure it indirectly, as being reflected bythe amount of business startups in that economy relative to the respective population.Using data from German regions, we find convincing evidence consistent with thehypothesis that entrepreneurship is positively linked to economic performance.

There are two important qualifications in concluding this article. First, entrepreneur-ship capital is not directly measured, but rather is inferred by the observable degree ofstartup activity. While we are not able to directly measure entrepreneurship capital, weare able to infer something about relative magnitudes across regions based on a mani-festation of that entrepreneurship capital—startup activity within that region. Second, theamount of entrepreneurship capital in a region is taken as being exogenous. The articlenever considers why entrepreneurship capital varies across regions and which factorsactually shape entrepreneurship capital. We leave this for further research.

Taken together, these two qualifications suggest the sole public policy implicationfrom the article. Public policies promoting entrepreneurship capital would be expectedto have a positive impact on economic performance. However, which types of publicpolicy instruments are best suited to promote entrepreneurship capital are beyond thescope of this article. On the basis of our definition, all policy measures that increase aneconomy’s entrepreneurship capital will be useful. In a recent study, Storey (2003) iden-tified a broad range of public policy measures spanning a broad spectrum of countriesthat have been used to promote entrepreneurship.

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However, the results of this article do provide an important interpretation of thosepublic policies that have been undertaken to promote entrepreneurship in most, if not virtually all cities and regions in the developed countries. They clearly represent anattempt to increase the amount of entrepreneurship capital and therefore improve eco-nomic performance. Such policy attempts might be considered to constitute the newentrepreneurship policies and will no doubt be the focus of a growing area of researchin the future.

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David B. Audretsch is at Indiana University, CEPR and the Max Planck Institute for Research into EconomicSystems in Germany.

Max Keilbach is at the Max Planck Institute for Research into Economic Systems in Germany.

We are grateful to the editors of this special issue and two anonymous referees for helpful comments.

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Does Entrepreneurship Capital Matter?