Regional knowledge , entrepreneurial culture and innovative start-ups over time and space : An empirical investigation

We investigate the role of entrepreneurship culture and the historical knowledge base of a region on current levels of new business formation in innovative industries. The analysis is for German regions and covers the time period 1907–2014. We find a pronounced positive relationship between high levels of historical self-employment in science-based industries and new business formation in innovative industries today. This long-term legacy effect of entrepreneurial tradition indicates the prevalence of a regional culture of entrepreneurship. Moreover, local presence and geographic proximity to a technical university founded before the year 1900 is positively related to the level of innovative start-ups more than a century later. The results show that a considerable part of the knowledge that constitutes an important source of entrepreneurial opportunities is deeply rooted in history. We draw conclusions for policy and for further research.


Regional knowledge and entrepreneurship
Knowledge is a key source for start-ups, particularly in innovative industries 2013;Fritsch 2011;Fritsch and Aamoucke 2013;. Accordingly, new businesses in general, and innovative startups in particular, can be regarded as manifestations of knowledge spillovers from extant knowledge sources 2013). There are at least two reasons to expect an important role of geographic proximity in the process of entrepreneurial knowledge spillovers. First, new knowledge does not flow freely across space but tends to be regionally bounded (Anselin et al. 1997;Asheim and Gertler 2006;Boschma 2005).
Second, founders have a pronounced tendency to locate their firms in close spatial proximity to their former workplace, or near where they reside (Figueiredo et al. 2002;Dahl and Sorenson 2009). Hence, the regional knowledge stock, the regional workforce, and the regional conditions for entrepreneurship are important factors in the emergence of innovative new businesses.
While a number of studies have shown the importance of regional knowledge for innovative start-ups (Audretsch et al. 2005;Fritsch and Aamoucke 2013;, the historical roots of the current knowledge base and their role for innovative entrepreneurship have remained largely unexplored. 1 Clearly, knowledge does not suddenly fall on regions 'from heaven', but emerges and develops over longer periods of time shaping types of regional activity and industry structures. Empirical research has also clearly shown that the entrepreneurial spirit that is necessary to recognize and realize entrepreneurial opportunities is not evenly spread across space (Sternberg 2009). In particular, it is well documented that such spatial differences in entrepreneurship tend to be highly persistent over longer periods of time 1 For an overview of studies that find long-term persistence of entrepreneurship, see Fritsch and Wyrwich (2017b). Most studies that investigate the sources of regional knowledge and entrepreneurship (e.g., Grabher 1993;Saxenian 1994 and the contributions in Braunerhjelm and Feldman 2006) are on a case-study basis so that the results can hardly be generalized. Recent quantitative approaches based on larger sets of regions analyze the evolution of industries and industrial path-dependencies in regions in the medium run (e.g., Klepper 2009;Boschma 2017). (Andersson and Koster 2011;Fritsch and Wyrwich 2014;2017a;Fotopoulos and Storey 2017). Fritsch and Wyrwich (2014;2017a) argue that persistence of entrepreneurship over time indicates the role of a region-specific 'culture' understood as an informal institution that changes only gradually and over rather long periods of time (North 1994;Williamson 2000). It is, however, largely unclear what the main constituents of such an entrepreneurial culture are, how it emerges, and what other factors might contribute to the explanation of persistence of entrepreneurship.
We investigate the extent to which a historical tradition of entrepreneurship and the historical knowledge base of a region contribute to new business formation in innovative industries today. We focus on innovative entrepreneurship for two reasons. First, there is good reason to assume that innovative entry that exerts fierce competitive pressure on incumbents is particularly important for stimulating regional growth (Fritsch 2011). Second, the knowledge intensity inherent in innovative new businesses makes them a well suited source for analyzing the role of regional knowledge for entrepreneurship. The aim of this study is to gain a better understanding of the historical roots of contemporaneous regional differences in innovative entrepreneurship. We want to contribute to answering the following question "Why do some regions have better prospects of gaining from knowledge-based developments than others?" Our data cover the development path of regions in Germany from 1907 to 2014, a period of more than one hundred years including two lost World Wars and a number of additional disruptions, such as drastic changes of the political regime and massive inflows of refugees after World War II. Given these developments, persistence of entrepreneurship may be regarded as an indication of a regional culture of entrepreneurship. Based on the knowledge spillover theory of entrepreneurship 2013), we hypothesize that there is stronger persistence of innovative entrepreneurship in regions that had a relatively large knowledge base and high levels of self-employment in science-based industries at the outset of the 20 th century.
In Section 2, we briefly survey on the literature on the role of regional knowledge and an entrepreneurial tradition of entrepreneurship.
Section 3 presents our data and the empirical approach. The results are presented in Section 4. Section 5 concludes and draws implications for policy and for further research.

The role of history: knowledge trajectories and entrepreneurial tradition
The basic idea of the knowledge spillover theory of entrepreneurship 2013) is that knowledge, particularly new knowledge, is an important source of entrepreneurial opportunities. For this reason, a large and dynamically growing knowledge base should have the potential to provide rich opportunities for many start-ups. This should be especially true for innovative new businesses as they are particularly dependent on knowledge inputs. Consistent with these considerations, research has documented a pronounced relationship between indicators of regional knowledge and new business formation (particularly with start-ups in innovative and knowledge-intensive industries), such as the presence of academic institutions and the level of R&D activities (Audretsch et al. 2005;Fritsch and Aamoucke 2013;. Since a larger part of the available knowledge is tacit, it is attached to people and, therefore, regionally bounded. Due to this stickiness of tacit knowledge, it tends to remain in the local population and is transferred across generations. This characteristic, as well as the continuity of wellestablished institutions of higher education and research (such as universities), influences the persistence and scope of regional knowledge levels and knowledge profiles over longer periods of time. Hence, there are significant differences in the amount and character of the available knowledge across regions.
The knowledge spillover theory of entrepreneurship 2013) argues that a rich regional knowledge base does not automatically give rise to new businesses, but that entrepreneurial people

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who recognize and seize the available opportunities are also required. 2 Hence, the propensity of the regional population to start a venture is important for entrepreneurial spillovers to occur. Empirical studies have identified a number of factors that appear to be conducive to entrepreneurial behavior, such as qualification of the workforce, employment in small businesses (e.g., Chinitz 1961;Parker 2009) and personality traits of the regional population (Stuetzer et al. 2017;). Research has particularly highlighted the role of social acceptance of entrepreneurial behavior (Etzioni 1987;Kibler et al. 2014), or a regional entrepreneurship culture (Beugelsdijk 2007;Fritsch and Wyrwich 2014;2017b). Chinitz (1961) argues that an entrepreneurial culture is more likely to emerge in areas with high employment shares in small businesses. This argument is further developed in Stuetzer et al. (2016). In a nutshell, workers in small firms are in closer contact with an entrepreneurial role model and can acquire entrepreneurial skills more easily than workers in large firms. Such role model effects may trigger a positive perception of entrepreneurship and hence stimulate a personal decision to start a firm. 3 A regional culture of entrepreneurship can be characterized as an "aggregate psychological trait" (Freytag & Thurik 2007, 123) in the regional population that favors entrepreneurial values, such as individualism, independence, and motivation for achievement. As already mentioned, it can be regarded as a sticky and slowly changing informal institution (North 1994;Williamson 2000). Several studies have indeed shown that regional levels of entrepreneurship tend to be rather persistent over longer periods of time, even surviving massive shocks such as devastating wars or drastic changes of the political regime (Fritsch and Wyrwich 2017b). In Germany, these shocks did not hit all regions in the same way and 2 Saxenian's (1994) comparison of the computer industry in Silicon Valley and the East Coast provides an impressive example of the role of entrepreneurship for the successful commercialization of knowledge.
3 Based on an empirical analysis of the development of the German Ruhr area that is dominated by large-scale industries, Grabher (1993) argues that the old established incumbents may show a tendency to suppress the emergence of novel ideas and entrepreneurship.
reshaped the economic and social landscape quite differently (see Fritsch and Wyrwich 2017a). Since these developments rule out that the persistence of entrepreneurship is caused by enduring structural conditions, we argue that a positive relationship between an entrepreneurial tradition (as measured by historical levels of selfemployment) and current start-up activity indicates that the presence of a local entrepreneurship culture is the key mechanism behind this persistence.
Analyzing the role of history for new business formation in innovative industries today, we combine measures of historical entrepreneurship with indicators of regional industry structures and with information on the presence of universities. In particular, we investigate whether both factors are complementary in their effect on current new business formation. Our data suggests that, not only regional differences in entrepreneurship, but also regional differences of the knowledge stock and the level of knowledge generation tend to be rather persistent over time . Our main hypothesis is that it is not the historical knowledge base, per se, but it is the interaction of this knowledge base with an entrepreneurial tradition that has an enduring effect on the formation of innovative new businesses today. Given the severe structural shocks that German regions experienced over the course of the observation period, the key mechanism behind any persistence effect of an entrepreneurial tradition and its interaction with historical knowledge indicates the local presence of an entrepreneurship culture.

Data and measurement
Our empirical analysis focuses on German regions and is based on data drawn from current start-up activity and information about historical selfemployment rates, industry structure and knowledge sources. as well as chemical industries as science-based, and divide the number of establishments in these industries by the total number of employees. The share of establishments in these industries is 3.27%. 4 The information in this data base is originally collected by Creditreform, Germany's largest credit rating agency, and is prepared by the Center for European Economic Research (ZEW) (Bersch et al. 2014). An alternatively data source to measure entrepreneurship at the regional level is the Establishment History Panel ( focus on natural sciences and engineering and were much more oriented towards the commercial application of knowledge (Drucker 1998, 21).
While it was rather unusual for German CUs at that time to have cooperative links with private firms, the pronounced collaboration of TUs with the private sector could have made the figure of the entrepreneur more legitimate in regions hosting a TU and may in this way have been conducive to higher levels of self-employment.
All TUs in Germany that existed in the year 1900 emerged from technical colleges (Polytechnische Hochschulen) that were founded earlier in the 19 th century as a reaction to the rapidly growing general demand for scientific research and education (Drucker 1998;Carlsson et al. 2009).
The main political force behind the upgrading of technical colleges to TUs was the German Association of Engineers (Verband Deutscher Ingenieure, VDI). 6 All technical colleges that became TUs before 1900 were located in the capital cities of the Federal States (for details see König 2006, andManegold 1989). There is no indication that they were strategically placed primarily in regions with high levels of selfemployment. Today, TUs in Germany represent just one specific type of higher education institution that has relatively strong links to private sector firms.
There are at least three reasons why the presence of higher education institutions in the early 20 th century is a meaningful indicator of the historical knowledge base. First, universities play an important role for the absorption, storage and diffusion of knowledge, and they are also engaged in the generation of new knowledge. Second, they provide innovation-related inputs and contribute to the regional stock of human capital (Schubert and Kroll 2016)  presence of a university fairly captures differences in the regional knowledge base and the quality of human capital as compared to regions that do not have higher education institutions. 7 The idea behind these distance measures is that knowledge spillovers are found to be highly localized and sticky (Anselin et al. 1997;Fritsch and Aamoucke 2013).
Thus, the spillover effects of TUs and CUs should decay with increasing geographic distance. A further advantage of the distance measure is that it rules out that the spillover effect is driven by the low number of regions with TUs and CUs, as indicated by the binary variables.
While the spatial definition of administrative districts in the early 20 th century differs from the organization of current planning regions, we are able to assign the historical districts to current planning regions. Planning regions represent functionally integrated spatial units based on travel-towork patterns and are comparable to labor market areas in the United States. 8 Although all planning regions host at least one university today, the presence of higher education institutions does not play any role in the definition of these regions. If a historical district is located in two or more current planning regions, we assigned the employment based on each region's share of the geographical area. Our regression framework includes the 92 planning regions of Germany.
7 At the same time, we agree that there could have been differences in the quality of universities in the early 20th century which we cannot measure. Please note that there is no regional variation in literacy levels in Germany between 1907 and 1925, since schooling was compulsory.   Distance to technical university founded before 1900 Distance in km.
Self-employment rate in science-based industries 1907 Total number of establishments in science-based industries ("machine, apparatus, and instruments" and "chemical industry") over all employees.
Self-employment rate in non-agricultural nonscience based private sector industries 1907 Total number of establishments in non-agricultural private sector industries (excluding science-based industries) over all employees.
Self-employment rate in science-based industries 1925 Total number of self-employed persons in knowledgeintensive industries ("machine, apparatus, and vehicle construction", "electrical engineering, precision mechanics, optics", "chemicals", and "rubber-and asbestos") over all employees.
Self-employment rate in non-agricultural nonscience based private sector industries 1925 southwest of Berlin. The relatively low self-employment rates in the Ruhr area north of Cologne, a region that was dominated by large-scale industries for a long time, is also noteworthy. Most of the relatively few TUs were located in regions with high levels of self-employment in science-based industries. This pattern is more pronounced in 1925. 9 Freelance professions are not considered in the historical self-employment rates because they are included in the "state" sector and cannot be disentangled.   Table 1 lists the definition of the variables used in the analysis. Tables 2 and Table A2 in the Appendix present summary statistics and a correlation matrix for these variables.

Persistence of regional knowledge
In a first step of our analysis we investigate the persistence of regional knowledge. A first indication of the persistence of regional knowledge intensity is that all of the universities that were present in 1900 still exist today. To further explore the persistence of regional knowledge we regress the information on the presence of a university in the year 1900 on two indicators for innovation activity today: the number of patents per person employed, 10 and the employment share of R&D employees. 11 Population density in the year 1907 is included as a "catch-all" variable that controls agglomeration effects and general economic conditions such as wage level, house prices, etc. Dummy variables for the Federal States are intended to capture differences in state-level policies that may affect entrepreneurship. We also include the employment share in manufacturing in the year 1907 to control for the effects of the regional industry structure.
The distance to the nearest coalfield is intended to control for effects of natural resource endowments. 12 Since all continuous variables are logged, the respective coefficients can be interpreted as elasticities that indicate the relative importance of the respective measure.
We find that both indicators for the historical knowledge base (the presence of a CU and/or of a TU) are highly significant (Table 4). The coefficients for the presence of a TU are much larger than those for the presence of a CU, suggesting a relatively strong effect of a regional tradition in natural sciences and engineering. The estimated coefficients indicate that regions with a TU have 81% more patents per working population today than regions without any university (Model I in Table 3).
For CUs this effect is about 36%. The presence of a TU increases the employment share of R&D employees by 57%, while the presence of a CU increases this share by 25% compared to regions without a CU or TU (Model III in Table 3). The estimates also clearly suggest (Models II and IV in Table 3) that geographic proximity to CUs and TUs matters. A 1% increase in distance to CUs reduces the patenting rate by 0.1%, while a 1% increase in distance to TUs is associated with a drop of 0.2%. The effects are slightly smaller for the employment share of R&D employees (0.06% for CUs; 0.13% for TUs).
The results are robust when considering regional control variables for the year 1925 instead of 1907 (Table 3, model V to VIII). These results clearly demonstrate a pronounced persistence of regional knowledge intensity over rather long periods of time. In an additional analysis, we distinguished between large and small CUs and TUs in terms of the number of students registered in 1911. 13 We split the data at the median value which implies that CUs with less than 2,000 students are marked as small while the respective threshold for TUs is 1,000 students. The results indicate that the effects of historical knowledge on today's innovation activities are stronger for larger CUs and TUs (Table A3 in the Appendix). 14 Table 4 shows the main results of our analysis of the effects of historical knowledge and historical self-employment rates on regional levels on new business formation in innovative industries. We do not consider indicators of modern day regional entrepreneurship and knowledge because these measures are probably caused by historical levels and may cause multicollinearity problems with the measures of historical entrepreneurship  Table 4). There is no significant effect of CUs or of the control variables. 17 Table 5: The interaction between historical entrepreneurial tradition and regional knowledge and its role for start-ups in technology-intensive industries today I II III IV V VI Self-employment rates 1907

Persistence of entrepreneurship
Self-employment rates 1925 Self-employment rate in science-based industries 0.296** 0.302** 0.327*** 0.510*** 0.515*** 0.517*** (0 In order to analyse the interplay of entrepreneurial tradition and the regional knowledge base we interact our indicators for historical entrepreneurship with the measures for the historical regional knowledge base (Table 5). For ease of interpretation, we focus on the binary indicators for the presence of a CU or a TU. In the models of Table 5 the constitutive term of the self-employment rate represents the effect of historical self-employment in regions that had no CU or TU in 1900. In terms of effect size, there is a positive and significant effect of historical sciencebased entrepreneurship for these regions that resembles the findings of Interacting non-science based entrepreneurship with the dummies for the presence of a CU or a TU yields an interesting pattern. The insignificance of the constitutive term of historical non-science based entrepreneurship indicates that this type of self-employment had no long-term effect on technology-intensive entrepreneurship today in those regions that did not host a university in the year 1900. However, the results of the estimates using data for the year 1907 reveal a significantly positive effect for the interaction of historical non-science based selfemployment with the presence of a CU, as well as a TU (Models II and III in Table 5).
In the models with data for 1925 we find significantly positive effects of interaction between the presence of a TU and the self-employment rate in sciencebased industries, as well as with non-science based industries. There is, however, no significant relationship for the interaction between both types of self-employment and the presence of a CU. A 1% increase in non-science based self-employment in 1907 implies a 1% to 1.5% higher start-up rate in high-tech entrepreneurship today (Models II and III in Table 5). For 1925, we find an even higher effect of nearly 2.2% (Models V and VI in Table 5). 18 A technical note concerns the TU and CU dummy variables. In interaction models these binary variables measure the specific effect of the local presence of 18 We ran models with only one interaction term to rule out that the results are driven by using more than one interaction term. This method does not change the results (see Table A7 in the Appendix).
CUs or TUs for the hypothetical case that the self-employment rate(s) are zero.
Therefore, the coefficients of the dummy variables for CUs and TUs in Table 5 cannot be interpreted as an effect at the mean value (for details see Brambor et al. 2006). Plotting marginal effects of hosting a university at different levels of the selfemployment rates reveals that there is a positive stand-alone effect in regions with high levels of historical entrepreneurship. 19 Splitting the sample of CUs and TUs into smaller and larger institutions reveals that the persistent effect of regional knowledge is driven by larger universities (Table A8 in the Appendix).
Altogether, the results suggest that entrepreneurial tradition interacts with knowledge of a more applied character (presence of a TU), but also with knowledge of a more general character as represented by the presence of a CU. The insignificance of the interactions between science-based entrepreneurship and the presence of a CU in 1907 confirms the well-known fact that German CUs in the early 20 th century had a rather low propensity to cooperate with private firms (Manegold 1989;König 2006). Although the links between TUs and private sector firms at that time were much more pronounced, these relationships were more with well- The considerable correlation between population density and the employment share in manufacturing (see Table A2 in the Appendix), may give rise to multicollinearity concerns. However, the mean VIF presented for all models suggests that multicollinearity is not a critical concern here. To err on the side of caution, we run all models without the employment share in manufacturing as a robustness check. The results of this exercise reveal no meaningful differences to the set of models presented in Tables 5 and 6 (Table A9 and A10 in the Appendix).
For the year 1925, information about the employment share of science-based industries is also available. This variables is highly correlated with the employment share in manufacturing (r=0.68). Considering this variable instead of the employment share in manufacturing does not change the main results (Table A11, and A12 in the Appendix). The coefficient for the share itself is not significantly different from zero.
This clearly indicates that it is not the historical presence of science-based industries as such that is important for persistence of entrepreneurship, but the prevalence of self-employment in these industries. 20 As a further step of analysis we investigate the effect of the universities that were founded before the year 1900 with those that were established at a later point in time. Particularly in the 1960s and 1970s, the German university system was significantly extended by adding several new locations. We introduce dummy variables indicating regions hosting a CU or TU founded after 1900 (see Table A14).
We additionally interact our historical entrepreneurship measures also with the binary markers for universities (see Table A15). The results demonstrate that new universities are not related to high technology entrepreneurship. This result suggests that the historical knowledge base is more important for the effect of entrepreneurial tradition on today's technology-intensive entrepreneurship than the newly created universities.
Altogether, the results demonstrate that there is a positive relationship between the historical level of science-based entrepreneurship and current start-up activity in innovative industries. There is also an interesting interaction between the level of non-science based entrepreneurship and the presence of a university. This interaction is particularly pronounced for applied knowledge, as indicated by the presence of a TU, while the effect of more general knowledge (presence of a CU) seems to decrease over time.

Discussion
Analyzing the effect of historical levels of knowledge and entrepreneurship on the formation of innovative new businesses today, we found a number of highly significant relationships that indicate a strong persistence of both regional knowledge and entrepreneurship. One important result is that a history of academic knowledge in natural sciences and engineering, as indicated by the presence of a technical university in the year 1900, has a pronounced effect on the rate of innovative startups today, showing remarkable long-term effects of a relatively strong regional knowledge base. We also found a positive effect of recently founded universities on innovative entrepreneurship. This effect is, however, smaller than the effect of institutions that were already in place in the year 1900. This result suggests that it may take longer periods of time for the effects of universities on the local economy to unfold.
A second important result is that our analyses clearly indicate that it is the historical self-employment rate in science-based industries and not the level of selfemployment in non-science based non-agricultural industries that has a long-lasting effect on innovative entrepreneurship. However, non-science based self-employment seems to be conducive to technology-intensive start-ups today in regions that hosted a classical or a technical university. Our results suggest that a historically-grown regional knowledge base and a tradition of science-based entrepreneurship, as well as the interaction between the knowledge base and the level of general selfemployment are important for explaining entrepreneurial activities in innovative industries today. These findings are consistent with the knowledge spillover theory of entrepreneurship 2013).
Given that Germany experienced a number of disruptive shocks in the last century that reshaped the country's economic structures, the positive effects of high levels of self-employment in the past indicate the presence of a long-lasting entrepreneurship culture. This conclusion is supported by a study of Fritsch et al.
(2017) that attempts to identify a regional culture of entrepreneurship understood as an "aggregate psychological trait" (Freytag and Thurik 2007, 123) of the local population. Using data drawn from the personality profiles of the local population, this study finds that regions with high historical self-employment rates tend to have high shares of people with an entrepreneurship-prone personality profile today.
Our study has, of course, a number of limitations. First, we have no information about the quality of the universities that existed in the early 20 th century that might provide important insights about their effect on the economy in their region. Moreover, we have no data that would allow us to judge if parts of the effects that we observe are caused by particularly high government transfers at that time.
Another limitation is that we do not have any direct measures of a historical entrepreneurship culture, such as the treatment of self-employment in the local media or the entrepreneurship-friendliness of the local government.
A major challenge for further research is to identify the sources of a regional culture of entrepreneurship and how it is transferred over time despite disruptive changes of the framework conditions. It would be interesting to know how regional entrepreneurship cultures have emerged. Hypotheses in this regard stress the role of geographic location, the conditions of the soil and the inheritance law that prevailed in a region (e.g., Freytag and Thurik 2007;Stuetzer et al., 2016). For example, a popular explanation for the pronounced entrepreneurial spirit that is still found in many areas of Baden-Wuerttemberg in southwest Germany argues that the inheritance law in this region created incentives to shift economic activity from agriculture toward some type of craft businesses and this has led to a relatively large number of small businesses (for details, see Fritsch and Wyrwich 2014;2017a). In contrast, the Ruhr area with its rich coal deposits, was dominated by coal mining for a long time and is characterized by related large-scale industries that prevented the emergence of an entrepreneurship culture (Grabher 1993). 21 We believe that the basic results of our analysis apply to many countries and that they convey two important messages for policy makers. Finally, policy measures that promote networking among actors, particularly between public research institutes and private sector firms, could be helpful for the creation, recognition and realization of entrepreneurial opportunities. In any case, policy makers should be aware that creating an entrepreneurship culture is a long-term task but that its effect-once established-is long-lasting.      Notes: Dependent variable is the average start-up rate in innovative industries in the period 2000-2014. Robust standard errors in parentheses. The number of observations is 91 regions in all models. ***: statistically significant at the 1% level; **: statistically significant at the 5% level; *: statistically significant at the 10%. level. All continuous variables are log-transformed. To avoid any confusion, the dummy variable for East Germany is not reported. It cannot be interpreted due to perfect multicollinearity with State Dummies. Notes: Dependent variable is the average start-up rate in innovative industries in the period 2000-2014. Robust standard errors in parentheses. The number of observations is 92 regions in all models. ***: statistically significant at the 1% level; **: statistically significant at the 5% level; *: statistically significant at the 10% level. All continuous variables are log-transformed. Notes: Dependent variable is the average start-up rate in innovative industries in the period 2000-2014. Robust standard errors in parentheses. The number of observations is 92 regions in all models. ***: statistically significant at the 1% level; **: statistically significant at the 5% level; *: statistically significant at the 10% level. All continuous variables are log-transformed. Please note that constitutive variables of interactions must not be interpreted as mean effects. The coefficients measure the effect for the case that the other constitutive variable is zero.   Robust standard errors in parentheses. The number of observations is 92 regions in all models. ***: statistically significant at the 1% level; **: statistically significant at the 5% level; *: statistically significant at the 10% level. All continuous variables are log-transformed. Please note that constitutive variables of interactions must not be interpreted as mean effects. The coefficients measure the effect for the case that the other constitutive variable is zero.     Notes: Dependent variable is the average start-up rate in innovative industries in the period 2000-2014. Robust standard errors in parentheses. The number of observations is 92 regions in all models. ***: statistically significant at the 1% level; **: statistically significant at the 5% level; *: statistically significant at the 10% level. All continuous variables are log-transformed.

Figures: Marginal Effects Plots
Supportive  Figure A11: Marginal effect (with 95% CI) of hosting a classical university on current technology entrepreneurship at different levels of non-science-based selfemployment (SERNOSCI) rate (log) in 1925 (Table 5, column V) Figure A12: Marginal effect (with 95% CI) of hosting a technical university on current technology entrepreneurship at different levels of non-science-based selfemployment (SERNOSCI) rate (log) in 1925 (  (SERNOSCI 1925, log)