Abstract

It is now widely accepted that knowledge generation is a key determinant of regional economic performance. This

However, it is not enough to have knowledge spillovers: positive economic growth depends on regionalentrepreneurship capital. Regional entrepreneurship capital is the capacity of a region, city or state to not just

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Journal of Business Venturing 23 (2008) 687698encourage entrepreneurs, but actually support entrepreneurs as they navigate the bureaucracy to start new businesses,paper states that this is only partially true. Economic performance is not just determined by new knowledge creation butalso by the ability and the willingness of innovative entrepreneurs to develop new products and processes based on newknowledge. In fact, the economic value of new knowledge is typically uncertain, so the transformation of newknowledge into new products and processes requires risky investment with an uncertain outcome. If this investmentoccurs, it often comes in the form of a new venture started by an entrepreneur.

This paper highlights the central role played by these entrepreneurs who bring the benefit of new knowledge toregional economic performance. New knowledge, spilling over from both public and private R&D, encouragesregional knowledge based entrepreneurship; often manifesting itself in new high-tech or information andcommunication technologies (ICT) companies. These companies improve the regional economic performance.In this paper, we develop two hypotheses: First, regional innovation efforts have a positive impact on regional knowledge basedentrepreneurial activity. Second, knowledge based entrepreneurship positively affects regional economic performance. We testthese hypotheses using county level data from West Germany, employing a structural equation model to analyze the relationshipsbetween latent variables. Our empirical analysis provides evidence supporting both hypotheses. In particular, our results suggestthat innovation efforts have an indirect effect on economic performance via entrepreneurship. This indirect effect is neglected inexisting empirical studies focusing on the direct effect of innovation on economic performance. 2008 Published by Elsevier Inc.

Keywords: Innovation; Entrepreneurship; Economic performance; Structural equation model

1. Executive summaryEntrepreneurship capital and its impact on knowledgediffusion and economic performance

David B. Audretsch a, Werner Bnte b,, Max Keilbach a

a Max-Planck Institute of Economics, Entrepreneurship, Growth and Public Policy Group, Kahlaische Street 10, 07745 Jena, Germanyb Bergische Universitt Wuppertal, Fachbereich Wirtschaftswissenschaft, 42097 Wuppertal, Germany Corresponding author. Tel.: +492024392446; fax: +492024393852.E-mail addresses: audretsch@econ.mpg.de (D.B. Audretsch), boente@wiwi.uni-wuppertal.de (W. Bnte), keilbach@econ.mpg.de (M.Keilbach).

0883-9026/$ - see front matter 2008 Published by Elsevier Inc.doi:10.1016/j.jbusvent.2008.01.006

seek money to start their firms, and provide moral support when the entrepreneur has problems. Hence, regionalinnovation efforts may not only affect economic performance directly but also indirectly because of their influence on

688 D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698knowledge based entrepreneurship.This paper contributes to the existing literature by presenting an analysis of the direct and indirect effects of

innovation on economic performance. In particular, the relationship between innovation efforts, technical knowledge,entrepreneurship capital, and economic performance is analyzed. Previous studies focused on the direct effects ofinnovation and entrepreneurship on regional economic performance while neglecting the interrelationship betweenthem. This paper uses structural equation modeling to discover the underlying interrelationships, an approach notpreviously used in this context.

The analysis uses a sample of 310 West-German counties. The results suggest that innovation efforts have both adirect and indirect effect on regional economic performance. On the one hand, innovation efforts of incumbent firmslead to an increase in regional technical knowledge. This, in turn, directly improves economic performance of theregional manufacturing sector. On the other hand, regional innovation efforts also increase entrepreneurship capitalwhich in turn improves regional economic performance. Indeed, indirect effects account for 40% of innovation'simpact on economic performance.

Our results suggest that to focus policy solely on knowledge generation may not be sufficient to generate strongereconomic performance. By putting more emphasis on entrepreneurship policy, policy-makers can facilitate thetransformation of new knowledge into new products and technology that ultimately fosters regional economicperformance.

2. Introduction

Knowledge generation is an essential driver of regional economic performance. This is the main message of theendogenous growth theory (Lucas, 1988; Romer, 1990; Grossman and Helpman, 1991). In this literature the publicgood aspect of knowledge is stressed: knowledge cannot always be fully protected and it can be used by many firmssimultaneously. Hence, in this theory, innovative firms' technical knowledge spills over automatically to other firmswhere it is used productively.

While this theory was extraordinarily influential, the underlying assumption is strongly simplifying. Arrow (1962)argues that new knowledge is intrinsically uncertain in its potential economic value. Therefore, transforming generallyavailable new economic knowledge into viable new products or technologies requires investments with uncertainoutcomes. This links to Knight (1921) who distinguishes this incalculable uncertainty from calculable risks. Knightargues that the entrepreneur is the economic functionary who undertakes the responsibility of dealing with uncertainty.

Uncertainty is also a cornerstone of Kirzner's theory of entrepreneurial opportunities. In fact, according to Kirzner(1997), discovery of entrepreneurial opportunities requires vision and alertness. For Schumpeter (1911, 1942) thefunction of the entrepreneur consists of the recognition and realization of new economic opportunities. Opportunitiesare not just potential products but also potential production processes and opportunities in marketing. Schumpeter'semphasis on innovation implies that risk and uncertainty are implicitly part of entrepreneurial opportunity.

In this paper, we focus on knowledge based opportunities and the entrepreneur's role in developing them into newproducts and technologies. Often investment into developing new technological knowledge is made by entrepreneurs.By starting a new venture, entrepreneurs literally bet on the product, thus taking on the risk that it involves. In theprocess, entrepreneurs commercialize ideas that otherwise would not have been pursued, thus increasing the amount ofutilized knowledge.1 Hence, entrepreneurship and its function of risk taking is an important component of theinnovation process.

Baumol (2002a, 2002b) explicitly distinguishes the entrepreneurial function of risk taking in the innovation processfrom the role of larger incumbent corporations that are engaged into routinized processes of large scale innovation.While these processes are quantitatively more important in that R&D expenditure and patents generated are greater, anumber of systematic studies found that breakthroughs and new products are very often introduced by small and youngfirms, i.e. entrepreneurs.2

1 Acs et al. (2003) refer to the gap between knowledge and commercialized knowledge as the knowledge filter, stating that the larger the gap

between available knowledge and actually commercialized knowledge, the less permeable the knowledge filter.2 See, for instance, Scherer (1980), CHI Research Inc. (2002) or U.S. Small Business Administration (1995, p.114).

The aim of this paper is to investigate empirically whether the creation of new technological opportunities throughpast innovative activities of incumbent firms leads to an increase in regional economic performance, and to investigatethe role of entrepreneurship in that process. Regional economic performance is principally associated with regionalproductivity which is a key determinant of a region's wealth. As Krugman (1994, p. 13) puts it: Productivity isn'teverything but in the long run it is almost everything. We analyze the impact of innovation efforts, technicalknowledge and entrepreneurship on productivity for a sample of West-German regions.

The paper is arranged as follows: In the following section we describe the theoretical underpinning of our analysisand derive two hypotheses on the interaction between new technical knowledge, entrepreneurship and economicperformance. Section 4 explains the empirical model used in this study and describes the data. Section 5 discusses theempirical findings. Section 6 concludes.

689D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 6876983. Entrepreneurship and knowledge diffusion

In the previous section, we suggested that entrepreneurs take knowledge based opportunities and develop them intonew products. This increases the amount of knowledge spillovers and, we argue, has a positive impact on economicperformance. In this section, we propose to consider the behavior of potential entrepreneurs with respect to differentlevels of opportunities.

New knowledge is created through public and private R&D with the bulk of private R&D conducted by largeincumbent firms. For a variety of reasons, these firms may not be able or willing to fully exploit the new knowledge.Innovative entrepreneurs who consider investment in the development of new knowledge even the riskier parts of itas a business opportunity can then take advantage of this gap. Hence, the amount of knowledge based entrepreneurshipshould be greater in locations with greater levels of knowledge. Moreover, geographic proximity may reduce the cost ofaccessing and absorbing knowledge spillovers. While information and communication technology has eased the globaltransfer of codified knowledge, face-to-face contact is crucial for the transfer of tacit knowledge (Lawson and Lorenz,1999). Since face-to-face contact is facilitated by geographical proximity, it is especially likely that the transfer of tacitknowledge is geographically localized. Indeed, empirical studies support this assertion (Jaffe et al., 1993; Audretsch andFeldman, 1996). That is, regions with higher levels of knowledge creation (i.e. innovation or innovation efforts) areexpected to show greater levels of knowledge based (innovative) entrepreneurial activity with more start-ups in high-techand ICT industries. This is the essence of the Knowledge Spillover Theory of Entrepreneurship, put forward byAudretsch, Keilbach, and Lehmann (2006). By discovering and investing in opportunities resulting from newtechnological knowledge, entrepreneurship is an important, though in our view neglected, mechanism in thetransformation of new knowledge into economic performance. Two hypotheses result from this discussion:

Hypothesis 1. Regional innovation efforts have a positive impact on regional knowledge based entrepreneurialactivity.

Hypothesis 2. Knowledge based entrepreneurial activity facilitates knowledge spillovers and is therefore conducive toregional economic performance.

These theoretical considerations suggest a path model as the one illustrated in Fig. 1 where the arrows indicate acauseeffect structure.Fig. 1. Direct and indirect effects of innovation efforts, technical knowledge and entrepreneurship capital on economic performance.

The model consists of four latent variables, shown in circles, with corresponding hypothesized direction of impacts.Innovation efforts is a variable that expresses the effort taken in a region to create new knowledge; technical

690 D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698knowledge is the level of knowledge embedded in a region. Economic performance is the ability of firms in a regionto transform efficiently production inputs, like capital and labor, into outputs. Entrepreneurship capital is the milieuof agents, routines, traditions and institutions of an economy, a region or a society that is conducive to entrepreneurialbehavior and a culture of risk taking. Hence entrepreneurship capital reflects a number of different legal, institutionaland social factors.3 As such, entrepreneurship capital is unobservable and thus is a latent variable (e.g. Bartholomewand Knott, 1999). The empirical method presented in the following section explicitly deals with these kinds ofvariables.

The first arrow in Fig. 1 depicts the impact of innovation efforts on the creation of technical knowledge; togetherwith the third arrow, it indicates the assumed direct effect from innovation efforts via the creation of technicalknowledge on economic performance as developed in the literature on endogenous growth and examined in anumber of empirical studies.4 Here, our interest is in explicitly analyzing the role of entrepreneurship capital in theprocess of knowledge diffusion. Fig. 1 depicts Hypothesis 1, the effect of innovation efforts and technicalknowledge on entrepreneurship capital as the second and fourth arrow. Hypothesis 2, the effect of entrepreneurshipcapital on economic performance, instead, is the fifth arrow. The second arrow expresses the effect of innovationefforts on entrepreneurship capital. This effect is expected to be positive if innovative spirit translates intoentrepreneurial spirit, or if innovation efforts directly translate into the creation of new ventures. Taken together, thesecond, fourth and fifth arrows map the indirect effects of innovation efforts and technical knowledge oneconomic performance mediated by entrepreneurship capital.

On the basis of the distinction between direct and indirect effects, we make the following contributions to theexisting literature: We recommend an integrated analysis of the direct and indirect effects of innovation efforts andtechnical knowledge on economic performance. That is, we take the indirect effect of innovation efforts and technicalknowledge via entrepreneurship explicitly into account. In other empirical studies production functions are specified inorder to analyze the impact of entrepreneurship on economic performance (Acs and Storey (2004), Audretsch andKeilbach (2004b), Audretsch and Keilbach (2004a), Wong et al. (2005)). However, in these studies technicalknowledge and entrepreneurship capital are treated as separate production inputs and hence only their direct effect oneconomic performance is analyzed. Although Audretsch and Keilbach (2005) extend this approach by taking intoaccount that regional R&D intensity is a determinant of entrepreneurship, indirect effects of innovations efforts andtechnical knowledge are not analyzed in their study. Moreover, we make use of structural equation modeling (SEM) inorder to estimate the relationship between hypothetical constructs (latent variables) like entrepreneurship capital andeconomic performance. Previous empirical studies of the relationship between entrepreneurship and economicperformance employed classical regression analysis which is not an appropriate method for analyzing relationshipsbetween latent variables. The OLS estimator, for instance, is based on the assumption that the explanatory variables areclearly defined, observable and measured without error. If this assumption is violated as in the case of latentvariables OLS estimates are biased. Moreover, for each latent variable, several highly correlated indicators mayexist and multicollinearity may be a problem if such indicators are included in the regression analysis. In contrast, SEMallows for linking latent variables to multiple indicators.

4. Method and data

4.1. The model

The variables under investigation; innovation efforts, technical knowledge, entrepreneurship capital andeconomic performance are hypothetical constructs, or latent variables, rather than directly measured variables.Therefore, we make use of the LISREL model, which consists of a measurement model that takes measurement errorinto account and a structural equation model that allows us to estimate causal relationships among the latent variablesinvolved (see Jreskog and Srbom, 2001).

3 In that respect the notion of entrepreneurship capital is close to the one of social capital (e.g. Putnam, 1993), though not identical. See Audretsch

and Keilbach (2004a) for an in-depth discussion of this issue.4 See Griliches (1995) for a survey of the empirical literature.

1.1. Measurement modelThe LISREL approach differentiates between latent variables and observed indicators. The values of the observed

indicators are presumed to be determined by the underlying latent variables and the measurement model links the

disadvantages. Specifically, a large number of observations is needed (typically more than 200) and the maximum-likelihood estimation of the model requires normally distributed variables.

691D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 6876984.2. Data

4.2.1. SampleTo estimate the model we make use of data from 310 West-German counties or Kreise, which are the smallest

geographical units for which data can be obtained.6 One might have doubts whether counties are the appropriategeographical units for this study. It could be argued, for instance, that they do not constitute the relevant economicspatial unit for knowledge spillovers. However, tacit knowledge goes short distances since face-to-face contact iscrucial for its transfer. Moreover, one could argue that the institutional background, like propriety rights oradministrative barriers, which is important for the ability of individuals to start new ventures, is country- or state-specific rather than county-specific. Then, one would expect a high variation of entrepreneurship capital betweencountries or states but not between counties. Our data, however, show a high level of variation at the county levelsuggesting that the endowment of entrepreneurship capital is at least to some extent county-specific.

4.2.2. Entrepreneurship capitalA number of aspects that we mentioned in our definition of this variable defy quantification. However,

entrepreneurial and risk taking behavior certainly manifests itself in the creation of new ventures. As observedindicators for this latent variable we therefore choose the cumulated numbers of start-ups within a county from 1998 to2000, per capita. In other words, we assume that the higher this entrepreneurship intensity is, the higher the level of thelatent variable entrepreneurship capital becomes. The data on start-ups are taken from the start-up panel developed bythe Center for European Economic Research (ZEW). Moreover, we focus on start-ups in high-tech and ICT industries

5 A variable is exogenous if it only affects other variables of the model but is itself never affected by other variables. A variable is endogenous if itis directly affected or influenced by at least one of the other variables (Hayduk, 1987).6endogenous (exogenous) latent variables () to observable endogenous (exogenous) indicators y (x):5

y Kyg e; 1

x Kxn d; 2

where y and x are the matrices of unknown parameters (iiy ) and (ii

x ) and and are the vectors of errors. Thus, themeasurement model takes measurement errors explicitly into account and, by controlling for such errors, allows us toobtain unbiased estimates of the structural coefficients.

4.1.2. Structural equation modelThe following equation comprises all direct effects between the endogenous and exogenous latent variables:

g Bg Cn n; 3where is the vector of endogenous latent variables (in our case technical knowledge, economic performance andentrepreneurship capital), is the vector of exogenous latent variables (innovation efforts), B is the matrix of unknownparameters (ii) that reflect the effect of endogenous variables on each other, is the matrix of unknown parameters(ii) which reflect the influence of the exogenous variables on the endogenous variables, and is an error variable.

LISREL allows us to analyze relationships between latent variables (constructs) and to estimate variables' direct andindirect effects on each other. Of course, in addition to the advantages described above, LISREL presents also someThe total number of West-German counties is 328 but we have excluded 18 counties from the analysis because data were not available orbecause they were detected as outliers.

Table 1Covariance matrix of the observed indicators

692 D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698since we are interested in knowledge based entrepreneurship. The ZEW defines a high-tech industry as one whoseR&D intensity is above 2.5% and ICT industries comprise products and services that are related to information andcommunication technologies. For a further discussion of this measure see Audretsch and Keilbach (2004b).

4.2.3. Economic performanceWe define economic performance as the regions' production efficiency. Compared with other regions, more efficient

regions are able to produce a higher (given) level of output with a given (lower) level of inputs. Our observed indicators forthe latent variable economic performance are the level of average labor productivity (output/labor input) and the level ofaverage capital productivity (output/capital input) in a region's manufacturing sector.7 We restricted our attention to theproductivity of themanufacturing sector because the bulk of private innovation efforts takes place within this sector andwetherefore expect the direct as well as indirect effects of innovation efforts and technical knowledge on productivity to bestronger in the manufacturing sector than in other sectors.

Output ismeasured asGrossValueAdded in year 2000 of themanufacturing industries corrected for purchases of goodsand services, VAT and shipping costs. The stock of physical capital used in the manufacturing sector of each Kreis wasestimated using a perpetual inventorymethod, which computes the stock of capital as a weighted sum of investments in theproducing sector from 1980 to 2000. Audretsch and Keilbach (2004b) provide a detailed description of this procedure.Statistics including output and investment are published every two years at the Kreise level by the Working Group of theStatistical Ofces of the German Lnder, under Volkswirtschaftliche Gesamtrechnungen der Lnder. Labor is expressedas the number of employees in the manufacturing industries in 2000. This data is published by the Federal Labor Ofce,Nrnberg, that reports number of employees liable to social insurance on the Level of German counties.

4.2.4. Technical knowledgeIn empirical practice it is common to use patents to proxy for a region's technical knowledge. The observed

indicator for the latent variable technical knowledge is a region's patent intensity. That is, the number of patentsrelative to our measure of labor. We use German patent data for the years 1995 and 1997. This data is taken from Greifand Schmiedl (2002).

R&D91 R&D95 Pat97 HITECH ICT Y/C Y/L Pat95

R&D91 1.266R&D95 0.792 1.195Pat97 0.230 0.246 0.512HITECH 0.234 0.268 0.155 0.220ICT 0.188 0.205 0.104 0.162 0.170Y/C 0.019 0.009 0.057 0.026 0.026 0.107Y/L 0.051 0.055 0.056 0.030 0.023 0.017 0.035Pat95 0.215 0.241 0.460 0.152 0.101 0.060 0.053 0.487

Notes: Number of observations: 310.4.2.5. Innovation effortsThe observed indicator for the latent variable innovation efforts is a region's R&D intensity, which is measured as

the number of non-public R&D-employees in all industries relative to our measure of labor for the years 1991 and1995. This data has been provided by the Stifterverband fr die Deutsche Wissenschaft.

5. Analysis and results

Three different path models of the relationship between innovation efforts, technical knowledge, entrepreneur-ship capital and economic performance are conceivable: first, a partially mediated model, as illustrated in Fig. 1,

7 Note, that our latent variable economic performance is related to well-known measures of total factor productivity (TFP) because theproductivity of both, labor as well as capital input, is taken into account (see Solow, 1957). A higher level of efficiency due to, for instance, (Hicksneutral) technological innovations implies an increase in both, labor as well as capital productivity.

693D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698where both the direct and indirect effects of innovation efforts and technical knowledge are relevant. Second, a fullymediated model where the third arrow connecting technical knowledge and economic performance is eliminated.This implies that only the indirect effect of technical knowledge on economic performance exists. Third, a non-mediational relationship model where the fifth arrow is eliminated, implying that entrepreneurship capital is notimportant for economic performance.

We estimated these models employing the maximum-likelihood (ML) estimator. The ML estimation providesparameter values that generate a model-implied covariance matrix, which reproduces the observed sample covariancematrix as closely as possible. The covariance matrix of the observed indicators used in this study is reported in Table 1.The results of 2-difference tests suggest that the partially mediated model clearly provides a better fit than the fullymediated model and a slightly better fit than the non-mediated model.8

Fig. 2 displays the non-standardized estimates for the path coefficients of the partially mediated model. Theestimation results suggest that the latent variable innovation efforts has a direct effect on entrepreneurship capitaland technical knowledge. The latter has a direct impact on economic performance.Moreover, it is also positivelylinked to entrepreneurship capital which in turn influences economic performance. All these relationships arepositive and statistically significant. Table 2 reports the parameter estimates of the structural equation model as well asrespective t-values. In addition, Table 2 contains parameter estimates of the measurement model and the estimates ofthe error variances.

We turn next to the discussion of the direct and indirect effects as well as the total effects of exogenous andendogenous latent variables. The total effect of the exogenous latent variable innovation efforts on economicperformancemediated by the latent variables technical knowledge and entrepreneurship capital is estimatedat 0.05 (with a t-statistic of 5.44, hence significant at =0.01). Thus an increase in innovation efforts by 1%increases economic performance by 0.05%.9 Roughly 40% of this effect can be attributed to the mediating effect ofentrepreneurship capital. The total effect of innovation efforts on entrepreneurship capital is estimated at 0.30(with a t-statistic of 9.08) while the direct effect is estimated at 0.24 and the indirect effect, via technical knowledge,is 0.06 (with a t-statistic of 4.47).10 Since the direct effect of entrepreneurship capital on economic performance is

Fig. 2. Innovation efforts, technical knowledge, entrepreneurship capital and economic performance: partially mediated model ( indicates that theestimate is significant at =0.01.).estimated at 0.07, the impact of innovation efforts on economic performance via entrepreneurship capital is0.021(=0.300.07).

Moreover, results suggest that the direct effect of the latent variable technical knowledge on the latent variableeconomic performance is 0.10 and the indirect effect on economic performance via entrepreneurship capital is0.01 (with a t-value of 2.59). Consequently, the total effect of an increase in technical knowledge on productivity is0.11 with a t-value of 7.44.

8 The difference between the 2-statistic of fully mediated model and the partially mediated model is 36.6. The difference between the 2-statisticof the non-mediated and the partially mediated model is 8.5. Since these values exceeds the 0.99 percentage point of the 2 distribution with onedegree of freedom (6.63), we can reject the fully mediated and the non-mediated model in favor of the partially mediated model.9 All values of the observed indicators are transformed to logarithms and therefore each coefficient can be interpreted as an elasticity.10 The positive direct effect of innovation efforts on entrepreneurship capital may also reflect the relevance of non-patented knowledge forentrepreneurship capital. Innovative firms are often not able or not willing to patent the knowledge generated by their innovation efforts(Griliches, 1990). We also tested a model where we allowed for a direct link between innovation efforts and economic performance but this link didnot improve the fit of the model.

Table 2Estimation results: partially mediated model

Latent variables Observed variables Parameter Estimate t-value

() Innovationefforts

R&D 91 kx11 0.88 (10.04)

R&D 95 kx21 1

(1) Technicalknowledge

Patents 95 ky510.97 (24.50)

Patents 97 ky611

(2) Economic Labor productivity ky13 1

Performance Capital productivity ky231.01 (4.57)

(2) Entrepreneurshipcapital

High-tech start-ups ky321

ICT start-ups y 0.75 (17.28)

694 D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698k42b21 0.20 (5.58)

b31 0.10 (6.07)

b32 0.07 (2.90)

g11 0.28 (5.80)

g21 0.24 (7.51)

Var(f1) 0.41 (10.33)In summary, the estimation results of the partially mediated model provide evidence in favor of the two hypothesesdeveloped in Section 3: First, regional innovation efforts have a positive impact on the region's level of knowledgebased entrepreneurial activity. Second, knowledge based entrepreneurial activities are conducive to regional economicperformance since these activities facilitate knowledge spillovers.

Although structural equation modeling is a powerful technique for analyzing relationships between latent variables,there are some critical issues in applying it, namely data characteristics, measures' reliability and validity or model

Var(f2) 0.12 (8.23)Var(f3) 0.01 (2.55)

he11 0.04 (2.31)

he22 0.02 (4.76)

he33 0.09 (11.08)

he44 0.00 (0.49)

he55 0.05 (7.29)

he66 0.04 (2.66)

hd110.57 (7.64)

hd220.30 (3.75)

Notes: Number of observations: 310. Number of distinct sample moments: 36, number of distinct sample parameters to be estimated: 21, degrees offreedom 15. For identification of themodel and in order to assign each latent variable to ametric the value of the respective structural coefficient (ii

y, iix )

is set to one. Software package LISREL 8.54 was used to computeML estimates of coefficients, variances of measurement errors (, ) and variancesof equation errors (var()).

Table 3Tests for normality

Variable Min Max Skewness Critical ratio Kurtosis Critical ratio

Y/L 3.897 5.058 0.179 1.288 0.165 0.593Y/C 2.468 0.618 0.236 1.698 0.424 1.524High-tech 8.87 5.817 0.028 0.202 0.252 0.904ICT 8.341 5.764 0.362 2.6 0.297 1.067Pat97 7.34 3.049 0.181 1.303 0.222 0.797Pat95 7.344 3.164 0.326 2.342 0.299 1.075R&D95 7.352 1.46 0.227 1.63 0.052 0.187R&D91 8.101 1.345 0.25 1.798 0.179 0.644

Notes: Number of observations: 310. Values of the observed indicators are transformed to logarithms.

Table 4Goodness of fit statistics

Statistic Structural equation model Measurement model

2 /df 1.182 1.251RMSEA 0.024 0.025SRMR 0.03 0.03

695D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698specification (see Shook et al., 2004). As a result, we performed additional tests and estimations in order to check therobustness of our results. Our estimates are based on ML estimations. The latter, however, are only correct if theobserved indicators are normally distributed. Tests of univariate normality reported in Table 3 suggest that the observedindicators tend to be normally distributed.11

The reliability of the measurement model is another critical issue for the application of structural equation modeling.The fit indicators, reported separately for the structural equation model and the measurement model in Table 4, suggestthat the models fit the data well. For instance, the value of the goodness of fit index, adjusted for degrees of freedom, is0.97 for the measurement model and the structural equation model which indicates a nearly perfect fit.12

Following Anderson and Gerbing (1988, p. 416), we assessed convergent validity and discriminant validity fromthe measurement model. A separate estimation of the measurement model shows that all factor loadings are statisticallysignificant, which indicates convergent validity. In order to test discriminant validity for each pair of estimatedconstructs we constrained the correlation parameter between the respective constructs to 1.0 and compared the 2 ofthis constrained model with the 2 of the unconstrained model. For all pairs of constructs the 2 value was significantlylower for the unconstrained model. This indicates that constructs are not perfectly correlated, which supportsdiscriminant validity.

In addition, we computed the average variance extracted (AVE) for each latent variable following Fornell andLarcker (1981). The results confirm convergent and discriminant validity for the latent variables innovation efforts,technical knowledge and entrepreneurship capital.13 For the latent variable economic performance AVE isrelatively low which means that the variance due to measurement error is large relative to the variance captured by the

Goodness of Fit Index (GFI) 0.99 0.99Adjusted Goodness of Fit Index (AGFI) 0.97 0.97Normed Fit Index (NFI) 0.99 0.99Non-Normed Fit Index (NNFI) 1.00 1.00Comparative Fit Index (CFI) 1.00 1.00Critical N(CN) 518.7 515.3

Notes: Number of observations: 310; The quality of the measurement model was analyzed separately by using confirmatory factor analysis and inorder compute estimates for all factor loadings the variance of each factor was fixed at 1.0 (diagonal of the phi matrix). Moreover, factors wereallowed to correlate. RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Square Residual; Critical N: Hoelter's'Critical N' for a significance level of 0.01; Software package LISREL 8.54 was used to compute fit indices.latent variable. Closer inspection shows that the reliability of the indicator capital productivity is especially low. Thismight be explained by the conceptual problems associated with the computation of capital stocks. The latter may causesevere measurement errors. In order to check the robustness of results, we estimated a model where economicperformancewas related to a single indicator, namely labor productivity. The results of this estimation suggest that themain findings, in terms of sign and significance of the estimated coefficients, hardly change.

Furthermore, the assumed direction of causality is critical. One might argue, for instance, that there is not a uni- butrather a bidirectional causality between technical knowledge and entrepreneurship capital since entrepreneurialactivity may also impact the creation of technical knowledge. Shook et al. (2004, p. 398) argue that the strongestinference of causality may be made only when the temporal ordering of variables is demonstrated. Since the observed

11 Standard errors of the ML estimation are robust against moderate departures from normality. However, in order to check the robustness of theresults, we also estimated the partially mediated model using the software AMOS 6 and the asymptotically distribution free (ADF) estimator thatdoes not require multivariate normality. The estimation results show that the estimated coefficients and standard errors of the ADF estimator and theML estimator are similar.12 Fit is good if 2 divided by degrees of freedom is between 3 and 1, if RMSEA is below 0.05, if SRMR is below 0.09, if GFI, AGFI, NFI, NNFI,and CFI exceed 0.90 and if critical N is more than 200. For a discussion of global fit indicators refer to Jreskog and Srbom (2001).13 According to Fornell and Larcker (1981), discriminant validity requires that the squared correlation between two latent variables is not greaterthan their individual AVEs. Otherwise shared variance between the latent variables would exceed their internal (extracted) variances.

indicators for the latent variables technical knowledge and innovation efforts are taken for periods earlier than 1998while the indicators for entrepreneurship capital are taken for the years 1998 to 2000, one might favor assumingunidirectional causality.14 However, Anderson and Gerbing (1988, p. 421) point out that temporal order is not aninfallible guide to causal relations. Therefore, we estimated a variant of the partially mediated model where

696 D.B. Audretsch et al. / Journal of Business Venturing 23 (2008) 687698bidirectional causality was allowed between the latent variables technical knowledge and entrepreneurship capital.However, allowing reverse causality does not improve the model fit and the reverse link is insignificant.15 Thissupports a unidirectional causality running from technical knowledge toward entrepreneurship capital.

6. Discussion and conclusion

In this paper, we highlight the central role of knowledge based entrepreneurship for regional economic performance.We argue that innovative entrepreneurs, discovering and investing in opportunities given by new technical knowledge,are essential for knowledge diffusion. Two hypotheses are developed stating that innovation activities within a regionpositively influence regional knowledge based entrepreneurial activities and that the latter are conducive to regionaleconomic performance as knowledge spillovers are encouraged.

In order to test these hypotheses, we estimate a structural equation model of the relationship between the latentvariables of innovation efforts, technical knowledge, entrepreneurship capital and economic performance for a sampleof West-German counties. We find that innovation efforts have both a direct and an indirect effect on regional economicperformance. On the one hand, innovation efforts of incumbent firms lead to an increase in regional technicalknowledge which in turn directly improves economic performance of the regional manufacturing sector. On the otherhand, we find that regional innovation efforts increase entrepreneurship capital which in turn increases regionaleconomic performance. Indeed, we find that this indirect channel accounts for 40% of innovation efforts' impact oneconomic performance.

6.1. Policy implications

Our results suggest that in a knowledge based economy, it is not sufficient to focus policy solely on knowledgegeneration in order to generate stronger economic performance. We provide evidence that entrepreneurship capital iscritical in translating innovation from the laboratory into economic performance. Regions with high R&D expendituresdo not necessarily show a stronger economic performance. For example, this phenomenon has been given the nameEuropean Paradox, because while Europe has large investments in knowledge generation, it is comparatively weakin exploring that new knowledge and transforming it into economic growth.16 In other words, the investment is nottranslating into stronger economic performance.

Given the central role of knowledge based entrepreneurship in the process of economic growth, policy-makers canencourage the transformation of generally available new knowledge into viable new products and technology byputting greater emphasis on entrepreneurship policy. However, the implementation of any sound entrepreneurshippolicy requires knowledge of how regional knowledge based entrepreneurship can be fostered.

Traditional policy measures aiming at the improvement of public infrastructure and the provision of risk capitalmay not be sufficient. Regional entrepreneurship capital is the capacity of a region to not just encourage entrepreneurs,but actually support entrepreneurs as they navigate the bureaucracy to start new businesses, seek money to start theirfirms, and provide support when the entrepreneur has problems. Venkataraman (2004, p. 153), for instance, postulatesthat intangibles, including, access to novel ideas, role models, informal forums, region-specific opportunities, safetynets, access to large markets, and executive leadership are especially important for regional technologicalentrepreneurship. Moreover, Venkataraman (2004, p. 155), states that the creation of these very important intangibles,however, does not happen overnight and requires the collaboration among leaders from prominent firms, market-enhancing governments, universities, and other public institutions.

14 Of course, this does not mean that we try to impose a certain lag-structure.15 For the identification of the parameters of this non-recursive model we had to eliminate the link between innovation efforts and

entrepreneurship capital.16 This has been stated in the EU Commissions Green Paper on Innovation (COM (95) 688).

6.2. Future research

Future research should further investigate intangibles that affect entrepreneurship capital. Such research may behelpful to guide entrepreneurship policy since understanding the interrelationships between these intangibles isimportant for fostering a culture of innovative entrepreneurship.

Our study demonstrated that structural equation modeling can be used for estimating the relationship between thelatent variables entrepreneurship capital and economic performance. However, we think that structural equationmodeling is also a promising tool for future empirical work in this field of research since it is allows for analyzing therelationships between intangibles (latent variables) that are relevant for regional knowledge based entrepreneurship.

Moreover, similar studies of other countries as well as studies based on additional indicators for entrepreneurshipcapital should be conducted. We expect that this will allow generalization of our results.

Acknowledgements

We thank two anonymous referees and the editors for the very helpful comments. We are also grateful to AdamLederer, Erik Monsen, Holger Patzelt as well as participants of the International Schumpeter Society conference inNice/Sophia-Antipolis and seminar participants at the University of Hamburg.

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Entrepreneurship capital and its impact on knowledge diffusion and economic performanceExecutive summaryIntroductionEntrepreneurship and knowledge diffusionMethod and dataThe modelMeasurement modelStructural equation model

DataSampleEntrepreneurship capitalEconomic performanceTechnical knowledgeInnovation efforts

Analysis and resultsDiscussion and conclusionPolicy implicationsFuture research

AcknowledgementsReferences