Entrepreneurship Capital, Knowledge Spillovers and Regional ...

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Entrepreneurship Capital, Knowledge Spillovers and Regional Productivity: Some Empirical Evidence from European Regions Werner Boente, Schumpeter School of Business and Economics Stephan Heblich, University of Jena Monika Jarosch, Schumpeter School of Business and Economics

November 2008 Working Paper IAREG WP3/05

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 216813

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IAREG – Intangible Assets and Regional Economic Growth

IAREG Working Paper WP3/5

Entrepreneurship Capital, Knowledge Spillovers and Regional Productivity: Some Empirical Evidence from European Regions *

Werner Boente , Stephan Heblich‡, Monika Jarosch+, *

Schumpeter School of Business and Economics, Gaußstraße. 20, D-042119 Wuppertal (Germany), Phone: +49 202 439 2446, Fax: +49 202 439 3852, Email: [email protected] ‡ Max Planck Institute of Economics, Entrepreneurship, Growth, and Public Policy Group, Kahlaischestr. 10, D-07745 Jena (Germany), Phone: +49 3641 686 733, Email: [email protected]. +

Schumpeter School of Business and Economics, Gaußstraße. 20, D-042119 Wuppertal (Germany), Phone: +49 202 439 2446, Fax: +49 202 439 2583, Email: [email protected]

Abstract

This paper analyses the relationship between regional economic performance and a region’s endowment with knowledge and entrepreneurship capital. The analysis is based on a dataset comprising 145 European regions over the period from 2000 to 2003. The results of panel regressions suggest that there is a positive relationship between a region’s total factor productivity and its endowment with knowledge and entrepreneurship capital. Moreover, we find a positive relationship between regional entrepreneurship capital – proxied by self-employment intensity – and the nationwide entrepreneurship capital – proxied by entrepreneurial attitude. Keywords

Entrepreneurship Capital, Region, Productivity, Knowledge, Spillovers. JEL Classification L26; O47; R11.

1. Introduction In recent years entrepreneurship policies that aim at encouraging entrepreneurial activities have been implemented in many industrial economies. Indeed, the European Commission (2003) postulates in its ‘Green Paper – Entrepreneurship in Europe’ that “Europe needs to foster entrepreneurial drive more effectively. It needs more new and thriving firms willing to reap the benefits of market opening and to embark on creative or innovative ventures for commercial exploitation on a larger scale.” The strong interest in knowledge based, innovative new ventures might be explained by the fact that in a global economy the comparative advantage of developed, high labor cost countries has continuously shifted towards knowledge-based activities and innovation being the key to economic growth and employment. The relevance of knowledge-based activities for economic growth is also emphasized by the endogenous growth theory. Romer (1986, 1990), Grossman and Helpman (1991), and Aghion and Howitt (1992) found that the knowledge creating activities, like R&D efforts, are the main drivers of economic growth because knowledge exhibits, at least partly, characteristics and properties of a public good, i.e. it is non-excludable and non-rival in use. Consequently, knowledge spillovers occur if innovating firms cannot fully protect their proprietary knowledge. These knowledge spillovers may improve the economic performance of existing firms or may induce entrepreneurial activities and in turn knowledge-based new ventures. Entrepreneurial activities as well as knowledge spillovers tend to have a geographical dimension. The results of empirical studies suggest that there are considerable regional differences with regard to the regional ability to stimulate entrepreneurial activities and Sternberg and Rocha (2007) conclude that entrepreneurship is a regional event. Moreover, results of empirical studies point to localized knowledge spillovers (Audretsch and Feldman 1996, Audretsch and Stephan 1999, Jaffe et al. 1993). There are only a few empirical studies that analyze the relationship between the economic performance of regions and the regions’ entrepreneurial activities as well as their knowledge endowments. Audretsch and Keilbach (2004) introduced the notion ‘entrepreneurship capital’ and defined it as those factors influencing and shaping a region’s milieu of agents in such a way as to be conducive to the creation of new firms. Using regional start-up intensity as an indicator for entrepreneurship capital they found for 327 West German counties (Kreise) that both, knowledge as well as entrepreneurship capital, have a joint positive impact on a region’s labor productivity. Audretsch et al. (2008) using a similar dataset and employing a structural equation model found that regional innovation activities have a positive impact on the regional level of entrepreneurship and that entrepreneurship in turn positively affects regional economic performance. Hence, they identified an indirect effect of innovation efforts on economic performance via entrepreneurship capital. Currently there is a gap in our knowledge, as no empirical study analyzes the relationship between regional economic performance and a region’s endowment with knowledge using data from different countries. Hence, we do not know whether results found for West German region can be generalized to other European regions. We aim to close this gap with the present study. We examine the relationship between productivity and the intangible assets entrepreneurship capital and knowledge capital using a dataset consisting of 145 European NUTS 2 regions over the years 2000 to 2003. This panel structure allows us to control for region-specific effects. Moreover, we investigate whether regional self-employment intensity – our regional proxy for entrepreneurship capital – is related to a national measure of individuals’ attitude toward entrepreneurship – our national proxy for entrepreneurship capital. The paper is arranged as follows: In Section2, we present a brief survey of the relevant literature and develop our hypotheses. Section3 explains the econometric specification used in 1



this study and describes the data. Section4 presents and discusses the empirical results and Section5 concludes. 2. Literature and Hypotheses Development The importance of entrepreneurial activities and the creation of new ventures for economic development was first recognized by Schumpeter (1934, p. 66) who stated that “it is not essential to the matter – though it happen – that the new combinations should be carried out by the same people who control the productive or commercial process which is to be displaced by the new. On the contrary, new combinations are, as a rule, embodied, as it were, in new firms which generally do not arise out of the old ones but start producing beside them; …in general it is not the owner of stage-coaches who build railways.” However, given the fact that new ventures are often very innovative, it is unclear where their ability to innovate does come from. Audretsch (1995, p. 179) provided an explanation for this phenomenon: “How are these small and frequently new firms able to generate innovative output when undertaking a generally negligible amount of investment into knowledgegenerating inputs, such as R&D? One answer is apparently through exploiting knowledge created by expenditures on research in universities and on R&D in large corporations.” This is the basic idea of the ‘knowledge spillover theory of entrepreneurship’. As mentioned in the introductory section, entrepreneurial activities and knowledge spillovers tend to be localized. According to the spillover theory of entrepreneurship one would expect that both phenomena are interlinked. In other words, if there is more innovative activity in a region – usually performed in large firms and universities – this may induce spillovers and thus lead to more entrepreneurial activity. Apparently, this link is most likely to apply to knowledge based start-ups. However, many start-ups are not knowledge-based and innovative at all but even non-innovative entrepreneurship tends to be a regional event (Michelacci and Silva 2007). Hence, regional factors may exist that facilitate entrepreneurship in general. Audretsch and Keilbach (2004) introduced the concept of entrepreneurship capital and defined it as those aspects of a region that are conducive to the creation of new businesses. Bönte et al. (2008) proposed a more narrow definition of regional entrepreneurship capital where regional entrepreneurship capital (REC) is defined as the entrepreneurial orientation of all individuals in a region, i.e., their basic willingness to start a new business. This narrow definition of REC implies that individuals’ entrepreneurial orientation has a regional dimension. In other words, there may be a regional entrepreneurship culture that influences an individual’s entrepreneurial orientation. Since entrepreneurship capital cannot be directly observed various indicators, like start-up rates or self-employment intensity, may be used to proxy it. 2



In order to analyze the growth effects of entrepreneurship at the country level, Wong et al. (2005) use cross-sectional data on the 37 countries participating in GEM 2002. They estimate an augmented Cobb–Douglas production to explore firm formation and technological innovation as separate determinants of growth. In particular they distinguish between different types of entrepreneurial activities as measured using the Global Entrepreneurship Monitor’s (GEM) Total Entrepreneurial Activity (TEA) rates, i.e. high growth potential TEA, necessity TEA, opportunity TEA, and overall TEA. They report that only high growth potential entrepreneurship has a significant impact on economic growth. In contrast, countries with higher levels of overall TEA do not exhibit higher growth rates. Acs and Varga (2005) investigate whether variations across countries in entrepreneurial activity and spatial structure can explain differences in effects of knowledge spillovers and hence differences in economic growth. They make use of GEM cross-national data and proxy the level of entrepreneurship by total entrepreneurial activity (TEA). They report positive and statistically significant effects of entrepreneurial activity and agglomeration on economic growth for countries of the European Union. A positive relationship is also reported at a more disaggregated regional level. In several studies, Audretsch, et al. (2008), Audretsch and Keilbach (2004, 2007), and Audretsch, et al. (2006) analyze the relationship between a region’s economic performance and the region’s entrepreneurship capital using data from West German regions (counties). Although various econometric specifications and differing time periods are used, the estimation results remain robust and provide empirical evidence for a positive relationship between economic performance and entrepreneurial activity. From the existing literature we derive the following two hypotheses: Hypothesis 1: There is a positive relationship between regional economic performance and entrepreneurship capital (self-employment intensity) Hypothesis 2: There is a positive relationship between regional economic performance and knowledge capital (patent intensity) Although we argue that entrepreneurship is a regional event, we expect that the level of entrepreneurship capital at the regional level is at least to some extent related to entrepreneurship capital at the national level. A positive relationship may exist, for instance, because there is something like a nationwide entrepreneurship culture. Eventually, this leads to our third hypothesis: Hypothesis 3: There is a positive relationship between entrepreneurship capital at the national level (entrepreneurial attitude) and regional entrepreneurship capital (selfemployment intensity) 3. Econometric Specification and Data 3



Econometric Specification In order to test our Hypotheses 1 and 2, we make use of a simple Cobb-Douglas production function:

Yit = Lit αi Cit (1−αi ) Ait , (1) where Yit is the output of region i in Period t , Lit is labor, Cit is physical capital and Ait is the efficiency level of the respective region. The parameter α denotes the output elasticity of labor and we assume constant returns to scale, i.e. the output elasticity of physical capital is (1-α). Moreover, we assume that a region’s efficiency level Ait is a function of a region’s endowment with knowledge capital ( K ) and a region’s endowment with entrepreneurship capital ( EC ). In particular, we assume a log linear relationship between a region’s efficiency level and its endowment with knowledge and entrepreneurship capital: ln( Ait ) = β + γ ln( ECit ) + δ ln( K it ) + eit , Substitution of Equation (2) in the logarithmic form of Equation (1) and rewriting Equation (1) yields: ⎛ ⎞ Y ln( Ait ) = ln ⎜ αi it (1−αi ) ⎟ = β + γ ln( ECit ) + δ ln( K it ) + eit , ⎝ Lit Cit ⎠ or expressed in growth rates ln( Ait − Ai (t −1) ) = β + γ ln( ECit − ECi ( t −1) ) + δ ln( K it − K i ( t −1) ) + eit − eit −1 ,

(2)

(2a)

(2b)

where ei = μ i + ν it and μ i denotes the unobservable region-specific effect and ν it is the remainder disturbance. The former controls for region-specific effects that are not included in the regression, such as omitted variables and misspecifications. We take the fixed-effects into account when estimating Equation (2a). Note, that first differencing, Equation (2b), eliminates the fixed-effects.

Data For our empirical analysis we use the NEW CRONOS database, which is part of the EUROSTAT Dissemination database provided by the European Commission. We analyze European regions at the NUTS 2 level, which is the lowest level of aggregation where data are still available across Europe. Using a panel structure to examine effects across countries and over time, data are sparse. Limited by self-employment data available from 1999 and by data about patents available to 2003, our panel data analysis covers four years. Due to missing data, the number of observations is reduced such that our final sample consists of 145 regions across 18 European countries. Table 1 summarizes the countries taken into account. The regional efficiency level is measured by the region’s total factor productivity (TFP ). Following Coe and Helpman (1995), TFP is defined for each region as

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TFPit = Ait =

Lit

sei


Yit . Cit (1− sei )

(3)

where Yit is a region’s GDP at time t , Lit is labor, Cit is the physical capital stock, and sei is the income share of labor. We used real GDP adjusted for purchasing power parities, i.e. in Euros based on the EU 25. The input labor is measured by total employment, the number of a region’s economically active population where total employment covers both, self-employed and dependently employed individuals. The income share of labor sei , which is an estimate of the output elasticity of labor, is measured by the share of annual average of total nominal compensation of employees in nominal gross value added. Annual average of sei is computed for a 4-year period, covering the investigation period 2000 to 2003. The physical capital stock Cit is the real gross fixed Table 1: sample of unbalanced panel fixed -effects regressions method simple OLS regressions

between-effects regression

country

2000

2001

2002

2003

2000

2001

2002

2003

Belgium (11)

11

11

11

11

11

11

11

11

Czech Republic (8)

7

7

7

7

-

-

-

-

Germany (39)

7

7

7

7

30

33

33

36

Estonia (1)

1

1

1

1

-

-

-

-

Greece (13)

4

4

4

5

4

4

4

5

Spain (19)

17

17

17

17

17

17

17

17

France (22)

22

22

22

22

22

22

23

23

Italy (21)

19

19

19

18

21

21

21

20

Cyprus (1)

1

1

1

1

-

-

-

-

Latvia (1)

1

1

1

1

-

-

-

-

Hungary (8)

7

7

7

7

-

-

-

-

Netherlands (12)

-

12

12

12

-

12

12

12

Poland (16)

10

12

13

11

-

-

-

-

Portugal (7)

5

4

4

3

5

4

4

3

Slovakia (4)

4

4

4

4

-

-

-

-

5



Finnland (5)

3

4

3

3

3

4

3

3

Sweden (8)

8

8

8

8

8

8

8

8

Lithuania (1)

1

1

1

1

-

-

-

-

136

136

138

Sum of regions 128 142 142 139 121 sample for fixed-effects regression and OLS regressions (145 regions belonging to 18 countries) between-effects regression (142 regions belonging to 10 countries)

capital formation data in purchasing power parities. Using the perpetual inventory method like e.g. Coe and Helpman (1995), the depreciation rate was assumed to be 10 percent. In order to estimate the initial physical capital stock we make use of a method suggested by Griliches (1980). 1 We computed the average logarithmic growth of investments over a 9-year period, investments from 1995 until 2003. We define capital stocks as beginning of period stocks, obtaining first physical capital stock data in 1995. Along empirical practice, patents are used to proxy a region’s endowment with knowledge, computed by the number of patents over total employment. In order to account for a potential lag between patenting and the corresponding impact on productivity, the current average of the total number of patents over a 5-year period is used. Moreover, this also balances temporal fluctuations. Hence, we are able to compute patent intensity for the period from 1999 to 2003. Entrepreneurship Capital is proxied by the self-employment intensity at the regional NUTS 2 level, i.e. the number of self-employed population over the number of total employment. We assume that self-employment affects productivity in the subsequent period. Unfortunately, data that would allow for direct measurement of entrepreneurship capital in narrow definition as defined in Bönte et al. (2008), i.e. the basic willingness to start a new business, are not available. The same is true for other measures like, for instance, start-up rates are not available at the regional level across Europe. There is also no detailed information on knowledge based entrepreneurship as the self-employment data lack an industry dimension that allows distinguishing e.g. high-technology sectors or ICT. An alternative indicator for a region’s entrepreneurship capital is the entrepreneurial attitude of individuals living in the regions. Such data are available at the national level and can be obtained from the FLASH EUROBAROMETER “Entrepreneurship” survey, where each

1

In our calculation, the procedure suggested by Griliches (1980) is C0 = I0 / (g + δ), were C0 is initial physical capital stock, I0 is the investment in t =0, g is the average logarithmic growth and δ is depreciation.

6

Table 2: Deskriptive Statistics Variable self-employment intensity patent intensity self-employment itensity, growth rate * total factor productivity, growth rate *

Observations 550 550 405 405

Mean 0.162 0.00017 0.003 -0.011

from 2000 to 2003 Std. Dev. 0.068 0.00023 0.087 0.056

Min 0.06 1.68E-06 -0.363 -0.153

Max 0.44 0.002 0.384 0.21

share of self-employment++ 530 0.148 0.06 0.06 0.429 entrepreneurial attitude++ 530 0.464 0.106 0.258 0.702 Mean, standard derivation, minimum and maximum across 145 European regions and 142 European regions respectively. * time period covers 2001-2003 ++Variables taken into account for analysis of relationship between share of self-employment and entrepreneurial attitude, restriction of remaining observation for TFP is lifted

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sample of the survey is representative on the national level for the population aged fifteen years and above. The target size of national samples is 500 or 1000 individuals, who were interviewed by telephone for this survey. In order to test whether national entrepreneurial attitude influences regional self-employment, we regress self-employment intensity on entrepreneurial attitude. This empirical analysis covers 10 European countries out of the sample used for analyzing entrepreneurship capital proxied by self-employment intensity, including142 regions 2 Measuring entrepreneurship capital at the national level, we compute the preferred occupational choice in view of being self-employed and therefore use the following item of the FLASH EUROBAROMETER “Entrepreneurship” survey: Suppose you could choose between different kinds of jobs. Which one would you prefer: … •

being an employee

•

or being self-employed?

•

(none of these)

Entrepreneurial attitude occurs, if the interviewee prefers being self-employed, otherwise not. The mean of a nation’s respondents answering being self-employed should indicate the entrepreneurial attitude on a national level. 4. Empirical Results Our empirical results suggest a positive relationship between productivity and entrepreneurship capital. A fixed-effects regression 3 shows a positive coefficient of selfemployment intensity which is significantly different from zero at the 1 percent level. This implies that entrepreneurship capital has a positive impact on regional productivity. These results hold if year dummies are included (Table 3a and 3b). To check the robustness of our results, we used the lagged self-employment intensity. As can be seen from Table 3b, the estimated coefficient increases (regressions I & III and IV &VI of tables 3a and 3b). This may imply a causal affect of entrepreneurship capital on productivity (Audretsch and Keilbach, 2004). This is an interesting finding since our proxy of entrepreneurships capital is likely to comprise non-innovative self-employment.,. According to Baumol (2005), there is little correlation between an economy’s number of “replicative” entrepreneurs and its growth rate, where the replicative entrepreneur is understood as reorganizer of a new business firm. Since we do not have any information about the kind of self-employment, we do not know whether the positive effect of self-employment intensity is due to innovative or non-innovative selfemployment. Moreover, our empirical results suggest a positive relationship between knowledge capital and productivity. The estimated coefficient of patent intensity from a fixed-effects regression 2

Because of lack of data, we decided to give up the restriction concerning availability of data for TFP. A comparison of regressors by the Hausmann specification test advises estimation via fixed-effects model relative to random-effects model.

3

8



with robust standard errors is positive and significantly different from zero at the 1 percent level and stays significant at the 10 percent level excluding year dummies (regressions II & V of Tables 3a and 3b). Taking the explaining variables self-employment intensity and patent intensity simultaneously into account and including year dummies, both turn out statistically significant at the 1 percent level. Anyhow, when excluding year dummies, patent data turn out statistically insignificant in case of lagged self-employment intensity. Next, we analyze the growth of TFP . Since fixed effects are eliminated by first differences and by the limited time period we conduct simple OLS regressions. In our analyses, we use lagged growth rates of the explanatory variables as wells as current growth rates (Tables 4a and 4b). The estimated coefficient of the growth rate of self-employment intensity is positive

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Table 3a: Results

self-employment intensity (ln)

patent intensity (ln)

R² within year dummies F Hausmann specification test (Chi²)

Total factor productivity (ln) III IV

I

II

V

VI

0.121*** (0.043)

---

0.11*** (0.041)

0.127*** (0.041)

---

0.095*** (0.036)

---

0.058* (0.031)

0.051* (0.03)

---

0.157*** (0.037)

0.149*** (0.037)

0.026

0.0203

0.0415

0.1052

0.18

0.1958

---

---

---

Yes*** 11.83

Yes*** 26.03

Yes*** 25.58

111.80***

45.76***

65.35***

128.35***

6.45

20.53***

Method: fixed-effects regression with robust standard errors Time: 2000-2003, Regions: Europe, NUTS 2 (145 regions belonging to 18 countries) Robust standard errors in parenthesis ***significant at the 1 percent level; **significant at the 5 percent level; *significant at the 10 percent level under robust

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Table 3b: Results

self-employment intensity (ln) lagged one year patent intensity (ln)

R² within year dummies F Hausmann specification test (Chi²)

Total factor productivity (ln) III IV

I

II

V

VI

0.185*** (0.052)

---

0.175*** (0.049)

0.176*** (0.054)

---

0.13*** (0.046)

---

0.058* (0.031)

0.047 (0.023)

---

0.157*** (0.037)

0.142*** (0.036)

0.0515

0.0203

0.0645

0.1224

0.18

0.2041

---

---

---

Yes*** 10.66

Yes*** 23.11

Yes*** 22.72

123.59***

47.76***

75.6***

124.42***

6.45

22.96***

Method: fixed-effects regression with robust standard errors Time: 2000-2003, Regions: Europe, NUTS 2 (145 regions belonging to 18 countries) Robust standard errors in parenthesis ***significant at the 1 percent level; **significant at the 5 percent level; *significant at the 10 percent level under robust

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Table 4a: Results

growth rate of self-employment intensity (ln) growth rate of patent intensity (ln)

R² year dummies F

Growth Rate of TFP (ln) III IV

I

II

V

VI

0.076* (0.043)

---

0.067* (0.037)

0.075* (0.043)

---

0.059 (0.037)

---

0.096*** ( 0.033)

0.093*** (0.031)

---

0.145*** (0.041)

0.14*** (0.039)

0.0138

0.0423

0.0531

0.0148

0.0691

0.0773

---

---

---

Yes 0.25

Yes** 4.01

Yes** 3.67

Method: OLS regression with robust standard errors Time: 2001-2003, Regions: Europe, NUTS 2 (145 regions belonging to 18 countries) Robust standard errors in parenthesis ***significant at the 1 percent level; **significant at the 5 percent level; *significant at the 10 percent level

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Table 4b: Results Growth Rate of TFP (ln) III IV

I

II

growth rate of self-employment intensity, lagged one year (ln)

0.15*** (0.054)

---

0.101** (0.039)

growth rate of patent intensity, lagged for one year (ln)

---

0.231*** (0.045)

0.0346 ---

R² year dummies F

V

VI

0.15*** (0.053)

---

0.099** (0.039)

0.221*** (0.044)

---

0.233*** (0.045)

0.222*** (0.044)

0.1789

0.1942

0.0348

0.18

0.1948

---

---

Yes 0.05

Yes 0.34

Yes 0.19

Method: OLS regression with robust standard errors Time: 2001-2003, Regions: Europe, NUTS 2 (145 regions belonging to 18 countries) Robust standard errors in parenthesis ***significant at the 1 percent level; **significant at the 5 percent level; *significant at the 10 percent level

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and statistically significant at the 1 percent level throughout all regressions if lagged values are included. It is significant at the 10 percent level in three of four regressions if current growth rates are used. The estimated coefficient of patent intensity is always positive and statistically significant at the one percent level. Furthermore, we analyze the relation of the two indicators for entrepreneurship capital, selfemployment intensity and entrepreneurial attitude. Following Bönte et al. (2008), we argue that the higher the level of regional entrepreneurial culture, the higher the entrepreneurial activity. According to our Hypothesis 3 we explain regional entrepreneurship capital (selfemployment intensity) by a country’s entrepreneurial attitude EAc , as a proxy for the nationwide entrepreneurship capital. We assume the following log linear relationship: ln( ECi ) = β + γ ln( EAc ) + eci

(3)

Table 5: Results

regional self-employment intensity (ln)

entrepreneurial

0.935***

attitude (ln) (0.132)

R² between

0.264

14



Method: between-effects regression

Time: 2000-2003, 142 European regions belonging to 10 countries)

standard errors in parenthesis

***significant at the 1 percent level; **significant at the 5 percent level;

*significant at the 10 percent level

Appling a between-effect regression, we found a positive coefficient of entrepreneurial attitude to be statistically significant at the 1 percent level (Table5). We identify countries with the highest self-employment intensity as those countries with the highest average entrepreneurial attitude in our sample. d Regarding differences across countries concerning the level of self-employment intensity, it turns out that the farm sector – which traditionally has a high self-employment rate – got a noticeable influence, e.g. for Greek and Portuguese regions. Considering Torrini (2005), industrial composition plays a minor role in explaining large disparities in self-employment rates across countries. Anyhow, a detailed analysis of regional entrepreneurship capital eventually requires data at the industry level. Furthermore, we argue that entrepreneurial attitude as phenomenon of culture stays constant over time or changes only in the long run. Therefore, we would not expect any variation in the entrepreneurial attitude over a 4-year period. Consistently, fixed-effects regression does not show any relation between the two measures of entrepreneurship capital. The results are not reported here but are available upon request.

d

These countries are Greece, Portugal, Italy and Spain.

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What we cannot test in this analysis is the positive relationship between knowledge based entrepreneurship and knowledge in a region that has been found by Audretsch et al. (2008) for German regions. In our analysis, neither the relationship between self-employment intensity and patent intensity nor between their growth rates turns out statistically significant. This result may be explained by data limitations. It is likely that aggregate self-employment intensity is an inappropriate indicator for knowledge based entrepreneurship. So, observed insignificance is consistent with findings by Audretsch and Keilbach (2007).They report that there is no statistically significant relationship between knowledge and low-technology entrepreneurship. 5. Conclusion

Our empirical analysis provides the following findings: firstly, there is a positive relationship between regional economic performance and entrepreneurship capital (self-employment intensity). Secondly, there is a positive relationship between regional economic performance and knowledge capital (patent intensity). Thirdly, there is a positive relationship between entrepreneurship capital at the national level (entrepreneurial attitude) and regional entrepreneurship capital (self-employment intensity). Our results suggest that the entrepreneurship policy measures which induce entrepreneurial activities can improve economic performance of regions. Policy programs that have been introduced to support knowledge based start-ups and the program design of many of the new policy approaches, like the organization of contests to select supported start-ups, are promising. However, as yet it is unclear whether such programs were successful or not. Future research could analyze the impact of such programs at the regional level. Two types of data limitation restrict our empirical analysis: First, appropriate indicators for entrepreneurship capital are not available at the regional level. By employment a rather crude self-employment measure comprising both, innovative as well as non-innovative 16



entrepreneurship, we cannot analyze in detail the impact of innovative entrepreneurship which may have a stronger positive effect on growth than non-innovative entrepreneurship. A better indicator would be, for instance, the regional start-up rates in various industries. The second type of limitation is imposed by the sample. Most indicators are not available for all regions or years, which implies a lot of missing values. Hence, panel estimations are limited. References

Acs, Z., and A. Varga (2005) Entrepreneurship, Agglomeration and Technological Change.

Small Business Economics 24, 323–334. Aghion, P., and P. Howitt (1992) A Model of Growth Through Creative Destruction,

Econometrica, 60, 323–351. Audretsch, D.B. (1995) Innovation and Industry Evolution, MIT Press. Audretsch, D., B., W. Bönte, and M. Keilbach (2008) Entrepreneurship capital and its impact on knowledeg diffusion and economic performance. Journal of business venturing, 23, 687698. Audretsch, D. B. and M. Feldman (1996) R&D Spillovers and the Geography of Innovation and Production, American Economic Review 86(3): 630-640. Audretsch, D.B., and M. Keilbach (2007) The Theory of Knowledge Spillover Entrepreneurship, Journal of Management Studies 44-7. Audretsch, D., and M. Keilbach (2004) Entrepreneurship Capital and Economic performance,

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