Cross-faculty proximity and academic entrepreneurship : The role of business schools

Over the past decades, entrepreneurial activity has started to be considered a third mission of higher education institutions. Our study examines the extent to which entrepreneurship at universities is driven by spatial proximity between university faculties. To this end, we use a new dataset that links information on business idea generation by faculties of German universities between 2007 and 2014 with comprehensive data on structural characteristics of these universities and faculties (e.g., number of academic staff, students, industry funding). Our analysis shows that the emergence of entrepreneurial ideas in natural sciences is positively affected by proximity to business schools. This pattern suggests the presence of knowledge flows between university faculties as an important source of science-based and technology-oriented business ideas.


Introduction
The increasing need to produce, transfer, and exploit commercially viable research findings has led today's universities to rethink and adjust their role. With the Massachusetts Institute of Technology (MIT) and Stanford University as early archetypes, higher education institutions have begun to expand their traditional mission of performing research and training of highly qualified people to include more applied research of greater commercial relevance, and to diffuse technical knowledge and provide technical support to industry (Etzkowitz, 2000;Thursby & Thursby, 2002). As a result, mainly in the U.S. and Western Europe, universities have become increasingly entrepreneurial (Shane, 2004;Siegel et al., 2007).
Ever since this transformation process started, fostering research commercialization has gained importance and became one of today's priority issues for policy-makers and public authorities. In recent decades, Western governments introduced many measures to actively promote the transformation of scientific knowledge into innovative and practical goods (Link & Scott, 2010;OECD, 2003). The Bayh-Dole Act of 1980 is probably the bestknown piece of legislation for that purpose.
Accompanying this development, spin-off entrepreneurship, patenting, licensing and other activities of knowledge and technology transfer from universities to the private sector, have attracted considerable scholarly attention (see Astebro & Bazzazian, 2011;Rothaermel, Agung, & Jiang, 2007;Perkmann et al., 2013 for comprehensive overviews). An important part of this literature has sought to explain institutional differences in technology transfer. For example, it has been found that the level of industry funding and the nature of research within the university (O'Shea et al., 2005;Powers & McDougall, 2005), the size and quality of research faculty (Di Gregorio & Shane, 2003;O'Shea et al., 2005), and a university's entrepreneurial tradition Shane, 2004) all are strong predictors of the probability and number of spinoff companies. These prior efforts notwithstanding, important questions are still unanswered.
From a process perspective of spin-off venture creation (Rasmussen, 2011), it is less clear whether and how characteristics of the university and its faculty exert influence on the initial stage, i.e., the emergence of the science-based business idea. Generally, the business idea represents the core of the entrepreneurial process, defined by Grandi and Grimaldi (2005, p. 826) as "the complex of products/services, knowledge, competencies, market, and technologies that are necessary to run a business". As Audretsch (2007) emphasized, not only is the success of a new venture rooted in the quality, newness, and potential of its business idea, but the success of whole entrepreneurial societies depends on the generation (and exploitation) of innovative business ideas. This paper seeks to elucidate the generation of innovative business ideas in the university context. We add to the academic entrepreneurship literature by proposing that knowledge flows between business schools (BS) and other university faculties are one important source of science-based and technology oriented business ideas. For our analysis, we use a comprehensive dataset that links information on business idea generation by faculties of German universities with data on structural characteristics of these universities and faculties. Moreover, our dataset allows shifting the analysis of institutional differences in entrepreneurial activity from the wellstudied university level to the relatively neglected faculty level. This is particularly important as previous organizational studies highlight the faculty level for understanding enterprising behavior among academic research scientists (Bercovitz & Feldman, 2008;Kenney & Goe, 2004;Rasmussen et al., 2014). Our empirical focus is on the role of business schools for the emergence of entrepreneurial ideas across natural science and engineering faculties of universities. Business schools can be conducive for the emergence of entrepreneurial ideas at universities (Wright et al., 2009). We will argue and show that proximity to Business Schools is positively related to the emergence of entrepreneurial ideas in other faculties.
Our key contribution to the literature is that, as far as we are aware, our paper is the first study to present systematic data that offers support for the idea that spatial proximity between faculties determines university entrepreneurship. While there is an emerging literature that stresses the importance of proximity between university actors and private sector partners (e.g., Adams, 2002;Abramovsky et al., 2007;Abramovsky & Simpson, 2011;Muscio, 2013), prior research does not consider the influence of intra-university proximity on the commercialization of scientific knowledge. We demonstrate that distance between university actors matters as well.
In particular, we find that spatial proximity to business schools has a positive impact on the generation of entrepreneurial ideas in natural science faculties while there is no relationship to entrepreneurship in engineering. These results are robust to several robustness checks including 2SLS instrumental variable regressions.
Overall, this study suggests that interdisciplinary social interactions and knowledge flows between faculties comprise important aspects of social context in university entrepreneurship. These results will resonate with scholars emphasizing contextual influences upon entrepreneurial behavior and innovation (Autio et al., 2014;Wright, 2014).

Proximity, cross-faculty knowledge flows, and academic entrepreneurship: Setting the scene
Our line of argumentation is embedded in the general literature on the role of proximity for peer effects, communication patterns, and knowledge flows (e.g., Bulte & Moenaert, 1998;Glaeser & Sacerdote, 2000;Grinblatt et al., 2008;Bandiera et al., 2010;Nanda & Sorenson, 2010). There is also a burgeoning strand of literature investigating social network formation in the university context (e.g., Sacerdote, 2001;Zimmerman, 2003;Mayer & Puller, 2008;Marmaros & Sacerdote, 2006). Peer effects and local network ties are important to understand scientists' engagement in technology transfer activities (e.g., Link et al., 2007;D'Este & Patel, 2007;Abreu & Grinevich, 2013;Heblich & Slavtchev, 2014;Tartari et al., 2014). In this respect, Murray (2004, p. 643) vividly describes the role of social capital. It comprises, for example, "the local laboratory networka network to current and former students and advisors established by the inventor through his laboratory life. The second form of social capital is a wider, cosmopolitan network of colleagues and co-authors established through the social patterns of collaboration, collegiality and competition that exemplify scientific careers." Bozeman and Mangematin (2004, p. 565), put it more literally: "scientists and engineers are as dependent upon social networks as they are upon such tangible scientific tools as electron microscopes, supercomputers and synchrotrons." We contribute to the literature by arguing that interdisciplinary network ties and peer effects exert an influence on the level of academic entrepreneurship. Entrepreneurship scholars already pointed out that people with an interdisciplinary background are better placed to recognize and act upon entrepreneurial opportunities (Shane, 2000;Bercovitz & Feldman, 2008). In this regard, we propose that business schools (BS) play a pivotal role as they may induce "crossfaculty knowledge spillovers" that spur the emergence of entrepreneurial ideas, for example, among faculties of natural science and technology. Wright et al. (2009) discuss two channels pertaining to the role of BSs for entrepreneurship at universities. The first channel refers to the provision of courses and seminars on financial planning, accounting, marketing as well as specific entrepreneurship classes where undergraduates and post-graduates learn about writing a business plan, marketing new products, or strategies to negotiate with potential investors. The second channel refers to direct involvement of BS faculty members in the start-up process through starting a firm themselves or serving as (co)-founders or board members of a start-up.
University researchers with significant technological know-how often do not possess the necessary skills to bring their inventions to the market or, for that matter, to spot new opportunities (Vohora et al., 2004). Attending general business and specific entrepreneurship classes offered at BSs could thus induce a formal transfer of generic managerial and entrepreneurial competences. 1 Moreover, members of BSs could join an entrepreneurial project and assume direct managerial responsibilities or take the role of an external "coach" providing businessrelated education to technically savvy would-be entrepreneurs.
In addition to formal entrepreneurship education and training, cross-faculty knowledge spillovers from BSs to other faculties like natural science and engineering may unfold through informal and occasional knowledge exchange. Social interaction with BS faculty on an informal basis (e.g., during lunch breaks, social events) might raise science and technology faculty's awareness of the commercial potential of their research and eventually increase their desire to exploit this potential by founding their own business. In fact, research showed that knowing where business-related advice is available already contributes to scientists' perceived efficacy with respect to starting an entrepreneurial career (Goethner et al., 2012). Altogether, crossfaculty knowledge spillovers emanating from BSs are about social contacts at the local university which make peers from other faculties aware of entrepreneurial ideas, promote their development and allow potential academic entrepreneurs to draw on local expertise (e.g., counseling, advice).
The effectiveness of cross-faculty knowledge flows from BSs should further be a function of spatial proximity. This assumption draws on insights from different strands of literature. First of all, papers on agglomeration externalities argue that density and proximity of actors increases the frequency of (face-to-face) interaction among heterogeneous actors, which provides the basis for serendipitous labor market matching, sharing of knowledge, and effective learning (e.g., Jacobs 1969;Helsley & Strange 1991;Glaeser and Sacerdote, 2000;Storper & Venables 2004).
Second, there is a literature stressing that spatial distance is also related to the costs of communication and the benefits of social interaction which was shown to matter in the university context (e.g., Festinger et al., 1963;Holahan et al., 1978;Sacerdote, 2001;Marmaros & Sacerdote, 2006;Agrawal & Goldfarb, 2008). The argument is illustrated by Marmaros and Sacerdote (2006) who argue that benefits of social interaction comprise the flow of information and ideas as well as the utility from sharing similar experiences. The benefit is likely to increase with the amount of previous contacts with the respective person. Costs are reflected by the time of interaction and search for useful social contacts. Distance is a huge cost factor if person and value of interaction are unknown. The authors find that proximity has a large effect on the likelihood of social interaction among individuals at university campus. Furthermore, increasing distance between faculties decreases the likelihood of chance encounters of students and faculty staff with different background in the urban neighborhood. The urban space provides interaction-promoting facilities similar to what Allen (1977) described in the organizational context. In a similar vein, much of the traffic in a neighborhood results from the movement of people to and from certain types of facilities they have to use during the course of the day and when these facilities are sprawled. Among them one can cite lecture theatres, student dorms, gyms, students clubs, bars, cafeterias. Allen (1977) shows that laboratory design that increases the number of interaction-promoting facilities can affect the extent of communications positively. Such mechanisms may also be at work for university architectures.
A positive effect of proximity on peer effects, communication patterns, and knowledge flows is also emphasized in sociological and psychological theories (for an overview, see Rivera et al., 2010, pp. 105-107;see also Allen, 1977, pp. 334-335 for earlier studies) and in economic geography. Regarding the latter Boschma (2005) discusses how geographic proximity relates to innovation. The argument is that spatial proximity is associated with knowledge externalities.
Short distance enhances opportunities for exchanging tacit (non-codified) knowledge which is crucial for innovation and entrepreneurship. The geographic bounding of knowledge transmission processes is confirmed in numerous empirical studies (e.g., Jaffe et al., 1993;Audretsch & Feldman, 1996;Howells, 2002;Singh & Marx, 2013;Belezon & Schankerman, 2013). Against this background, it comes as no surprise that recent research demonstrates the importance of local proximity for the formation and effectiveness of university-industry linkages (e.g., Adams, 2002;Abramovsky et al., 2007;Abramovsky & Simpson, 2011;Muscio, 2013). Abramovsky and Simpson (2011) argue that firms may benefit from proximity to universities by drawing on the expertise of university staff or research students through consultancy or direct recruitment, or to access codified and non-codified knowledge more informally and formally (e.g., in the form of research joint ventures). These mechanisms are similar to those we have in mind for the crossfaculty level. Therefore, we expect that research departments benefit from proximity to BSs, in terms of knowledge transfer (i.e., generation of university spin-offs) in a similar way.

Database
Our analysis is focused on the distribution of science-based business ideas across university faculties. We make use of publicly available information on business start-up grants assigned to university members between 2007 and 2014 under the framework of the German "EXIST-Gründerstipendium" initiative which is a main program line within the policy program "EXIST: Promotion of university-based start-ups" (http://www.exist.de).
Started in 1998, EXIST is a federally funded part of the German government's "Hightech Strategy for Germany" and is co-financed by the European Social Funds (ESF). The program aims at improving the conditions for academic entrepreneurship. Key objectives of EXIST thus include the establishment of an entrepreneurial culture at German HEIs, the continuous transfer of research results into marketable products and services, the promotion of highly innovative research-based business ideas, and a significant increase in entrepreneurial activity by academics.
The EXIST program follows a dual strategy. One building bloc is supporting universities and providing indirect assistance for individuals and start-up projects. In this respect, there have been several phases of EXIST program lines that aimed at creating entrepreneurship-facilitating structures at universities since the late 1990s. In support of these activities, universities receive an allowance from the German Federal Ministry of Economics and Technology over a three-year period (e.g., EXIST, 2013;Kulicke, 2014).
The so-called "EXIST-Gründerstipendium" (EXIST start-up grant), is one of the main policy instruments in terms of direct financial support to nascent academic entrepreneurs within the EXIST policy framework. The direct support is the second building bloc of the EXIST initiative. The EXIST start-up grant was introduced in 2007. The grant enables founders to develop a business model, test its economic viability, secure the required sources of funding and take the first steps in the process of developing the business (Kulicke, 2014

Sample
In order to be suited for the empirical analysis, a university has to have more than one faculty in   An observation refers to a faculty-year. If there was no grant awarded in a faculty-year combination, the observation is regarded as a 0-observation. Accordingly, non-0-observations refer to faculty-year combinations where there was at least one successful grant. In the empirical part of the paper, we run analyses on the level of faculties. For the faculties, we are investigating all faculty-year observations are considered.

Proximity to business schools (BS)
Our main variable of interest is proximity to business schools (BS) which we measure by distance in walking minutes. BSs at German universities are embedded in departments offering both economics and business administration classes. Therefore, "BS" henceforth refers to departments of Economics and Business Administration (EBA). Cross-faculty effects of BSs should increase with decreasing distance to other faculties due to a higher likelihood and extent of social interaction between faculty members. The distance is measured on the basis of the locations of faculties at the university campus. The measurement procedure required the building of "clusters" of important facilities for each faculty that can be regarded as the centers of research and teaching. Studying the campus maps of the universities in our final sample, it became apparent that significant research and teaching facilities of a faculty are clustered in no more than four sites. The mail addresses of the four largest sites of a faculty were used to calculate the distance in walking minutes in accordance to Google Maps. 4 The same source was used in previous research on proximity and university knowledge spillover (Belenzon & Schankerman, 2013).
We carried out the above exercise only for natural and engineering sciences. BSs are part of the social science faculty. Nearly all successful grant applications in Social Sciences can be assigned to BS. Therefore, analyzing how the prevalence of non-BS projects within the social sciences is affected by distance to the BS does not make much sense. For other sciences we abstained from an analysis since the number of facilities, for example, in humanities is so huge that determining distances of single facilities to the BS is not feasible. Given the lower number of entrepreneurial projects from these faculties, it is also unlikely to find systematic patterns in the data. Finally, data limitations prevented us from considering medical science as well (see Appendix A).

Model and method
We analyze the number of successful EXIST start-up grant applications across German universi- controls for serial correlation. This is important to account for the panel structure of the data (Cameron & Trivedi, 2009, p. 621;627). 5 We lag the time-varying continuous variables by three years in order to avoid simultaneity bias. 6 The basic relationship of the underlying model reads as follows: is the annual number of business start-up grants over the number of eligible faculty members (academic personnel, students, recent graduates) per faculty f at time t. This variable can assume the value of zero as well. In a robustness check, we use the number of business start-up grants as alternative outcome variable. Our main variable of interest is f BS PROX _ which measures proximity between natural science and engineering facul-4 Some faculties have small separate institutes at spots distant to sites hosting most of the teaching and research facilities of a faculty. We investigated whether regular teaching takes place at these spots. If there is just one separate institute without teaching activities, the respective sites are not considered as cluster of significant research and teaching. We thank Steve Langheinrich for outstanding research assistance for determining these clusters and distances between faculties. 5 For a similar application, see Fritsch and Falck (2007). 6 The lag is about three years since structural characteristics are likely to affect the emergence of business ideas in the longer run. There are also data restrictions for the most recent years of the observation period. Therefore, we would lose several observations when assuming a one or two year lag. ties to the local BS in walking minutes. It is time-invariant over the observation period. Furthermore, the analysis includes a vector of control variables that might affect university entrepreneurship. In the above equation, represents control variables for faculty specific characteristics and characteristics on the level of the university the faculty belongs to (see Table   A 2 for a definition of variables and Table A 3  The control variables for university and faculty characteristics focus on different organizational, institutional (faculty and university), and external determinants (regional) that may matter for the prevalence of academic entrepreneurship (for an overview, see O'Shea et al., 2005;Link & Scott, 2005;Rasmussen & Borch, 2010;Van Looy et al., 2011). We run separate analyses for specific faculties. So, there is always only one university-by-year observation. It should be noted that in this particular case, university fixed effects are similar to facultyuniversity fixed effects. Accordingly, dummy indicators for the different faculties and universities make no sense in this framework.
One crucial control variable at the university level is a dummy variable indicating a Technical university. Technical universities in Germany are one specific type of HEIs that has consistently been linked with a very entrepreneurship-supportive university policy. Since many areas of their research and teaching are traditionally linked very strongly to industry, Technical universities have been assumed to be more adept at fostering knowledge spillovers and technology commercialization than general universities (Audretsch & Lehmann, 2005a).
We also consider controls that describe current and time-varying characteristics of university-faculty structures. So, we control for the size of faculties as measured by the sum of faculty members eligible for a start-up grant (students, recent graduates, and academic staff). We also include the share of students among faculty members to account for potential differences in the entrepreneurial propensity within the group of eligible persons. We also control for the size of the EBA department which includes BS within the social science faculty at the respective universities. Unfortunately, information from university statistics does not allow separating the size of EBA and BS in terms of staff and students. However, it is possible to distinguish the size of EBA departments within social sciences in terms of financial budget resources. The relative size of the EBA department might be important for the level of business start-up grants at social science faculties since the entrepreneurial propensity of EBA staff and students is presumably relatively high within social sciences. The relative size of EBA might also play a role for the emergence of entrepreneurial ideas in other faculties due to cross-faculty knowledge flows.
We also exploit information on third-party funding per professor on the faculty level.
This measure is the total amount of third-party funding divided by the number of professors at a faculty. Third-party funding per professor is an indicator of the quality of universities (e.g., Fritsch & Aamoucke, 2013) and faculties accordingly. It is important to control for quality since previous studies indicate a positive relationship to academic entrepreneurship (Di Gregorio & Shane, 2003;Powers & McDougall, 2005).
Another potential determinant of university entrepreneurship that we control for is the private sector share among total third party funding to capture differences in the commercial orientation of research. Unfortunately, this information is only available at the level of universi- The variable is a reasonable proxy for quality of research which might be related to faculty spinoff activity as already mentioned above.
Entrepreneurial tradition and the historical record of universities in fostering entrepreneurship are important drivers of entrepreneurial activity of faculties (e.g., Kenney & Goe, 2004;O'Shea et al., 2005;Landry et al., 2006;Hsu et al., 2007). Universities experienced with start-up activity are likely to have entrepreneurship-facilitating infrastructures (network initiatives, incubators) in place. As entrepreneurial activity is more common in these institutions, entrepreneurial peers are likely to be present. Participation in the first wave of the EXIST program line (EXIST I) indicates such a tradition. The respective universities were the first ones to have a systematic entrepreneurship promotion program which was initiated in the late 1990s. There might be a long-term effect of these earlier activities on the emergence of entrepreneurial ideas in the observation period from 2007 to 2014. There have been further EXIST program waves that fall into our observation period which may affect the number of start-ups. EXIST participation implies that the technology transfer office (TTO) of the university pursues a clear entrepreneurial strategy.
Regional characteristics can play an important role for academic entrepreneurship and the entrepreneurship culture at universities as well (Fini et al., 2011;Fritsch & Aamoucke, 2013). The knowledge infrastructure of a region is often cited as a key factor determining spinoff activity (Audretsch & Feldman, 1996;O'Shea et al., 2005). If a university is geographically close to many firms interested in its research, its scientists may have more opportunities to share and exchange ideas. Moreover, in a region with higher levels of innovative entrepreneurship, an entire network infrastructure of customers, suppliers and employees is likely to be present, lowering the barriers to starting a university spin-off. The regional environment is captured by three dummy variables for the degree of agglomeration of the region hosting the university faculties. The degree of agglomeration is a "catch-all" variable for the regional economy since many of the regional variables are highly correlated with population density (stock of knowledge, house prices etc.). 7 The degree of agglomeration also captures selection of students in certain locations since some students and faculty members prefer large cities while others favor smaller places. Apart from that, we control for the start-up rate in knowledge-intensive industries (KIBS + R&D intensive manufacturing industries) to capture regional differences in the prevalence of high-quality entrepreneurship which is supposed to be strongly correlated with the entrepreneurial supporting infrastructure (e.g., access to venture capital, business consultancy support) available that might drive the decision to apply for a business start-up grant (e.g., Sorenson & Stuart, 2001;Cumming & Fischer, 2012).
We also assess dummy variables for the planning region in which universities and faculties are located in order to capture unobserved differences in the broader regional environment and with respect to policy making. Planning regions represent functionally integrated spatial units comparable to labor market areas in the United States. Every German Federal State comprises of several planning regions. Therefore, the dummies indirectly capture unobserved differences on the level of Federal States as well. This is important since university policy making is the task of the Federal States in Germany. We interact the regional control variables with year dummies to capture region-by-year-specific effects (e.g., business cycle) on the level of entrepreneurial activities across university-faculties.
A first assessment of the data reveals that the consideration of an array of region-specific effects implies that dummy controls for the participation in EXIST programs that are supposed to foster entrepreneurial culture at universities are perfectly collinear. That is, differences with 7 We use a classification that distinguishes between city regions, urbanized counties, and rural counties. This classification considers the density and centrality of the broader regional environment in which the university is located. This measure is superior to the population density measure on the county level in our context since some universities are located in city counties (Kreisfreie Staedte) which have naturally a higher density. University cities within other counties may have a high density as well which is however not captured by the county-wide density. respect to entrepreneurial tradition and culture across universities and regions are perfectly absorbed by the controls for regional conditions. One issue in the context of our empirical setting is that socialist policies in the former German Democratic Republic (GDR) affected university structures and urban planning (e.g., Connelly, 2000;Andrusz et al., 1996;Ott, 2001). We have no firm idea how these patterns confound with our prime mechanism but to err on the side of caution, we present models with and without post-socialist East Germany. Many universities faculties in East Germany underwent a significant renovation in the 1990s/early 2000s. This makes it more likely that business schools were deliberately placed close to highly entrepreneurial science departments since programs/initiatives fostering university entrepreneurship started in the late 1990s as well.
The use of an array of control variables for capturing specific difference across faculties, universities, and regions reduces a potential omitted variable bias. However, it does not address the issue of reverse causality, namely, that the prevalence of entrepreneurial activities explains differences in the location and proximity of faculties. To dispel such concerns, we also employ a 2 SLS approach. The first stage of this approach reads as follows:  (1). Determining the founding year was in most cases straightforward.
There have been some peculiarities which are documented in the notes of Table A 1 in the Ap-pendix. This table also lists the founding years of the universities in our final sample.
Finally, the variation in proximity that is due to age of universities ( f PROX  ) is used in the second stage to explain differences across faculties with respect to the emergence of entrepreneurial ideas. The second stage relationship reads as follows:

Cross-faculty knowledge flows and the emergence of entrepreneurial ideas
The main variable of interest in our models is the distance to the local business school (BS) in walking minutes. We employ log-transformed values of distance in order to rule out that major discontinuities in the distance distribution drive our results. 8 Results are illustrated in Table 2.
Column I only considers regional characteristics and whether the university is a Technical university which represents a university-fixed effect. The regional fixed effects are interacted with year dummies. Column II and III add control variables for university and faculty characteristics as discussed in the previous section. Column II includes East and West German universities. In column III the sample is restricted to universities in West Germany in order to account for any influences of socialist policies in Eastern Germany.
Distance to BS is negatively associated with the emergence of entrepreneurial ideas in natural sciences across German universities while there is no relationship for engineering (Table   2). This difference might have to do with the higher relevance of applied knowledge in engineering. Thus, it might be easier to develop entrepreneurial ideas without knowledge spillovers from BS as compared to natural sciences. The insignificant findings might also be driven by the lower variability of the distance measure (Table A 4). However, the change in the sign of the coefficient across specifications suggests that there is no stable negative relationship between distance and the prevalence of start-up grants in engineering.
The size of the coefficient estimates in the models for natural science is relatively small which comes as no surprise given the low overall number of entrepreneurial projects. An increase in distance by 10 percent (approximately: 20 walking minutes) 9 implies a drop in the EXIST rate by 0.9 units. This is approximately 1 start-up grant per 1,000 faculty members. For the sample restricted to West Germany the effect is larger. An increase in distance by 10 percent implies a drop in the EXIST rate by 2.4 units. Table A 6 and Table A 7 show the coefficients for control variables. Notably, distance to TTO is positively related to the EXIST rate for natural sciences. This pattern could be driven by multicollinearity with distance to BS but in a model excluding the distance to BS measure, dis-tance to TTO is still positively related to entrepreneurial activity (Table A 5). 10 One explanation for this somewhat puzzling finding is that researchers might be hesitant to contact the TTO if it is too close to their faculty. By approaching the TTO nearby it could be more easily noticed by their peers that they consider a start-up which they may have wanted to keep secret. On the other hand, contacts to people from BS are less obviously interpretable as attempts to start a company.

IV analysis
A concern by reviewers and other readers of this work could be that the results in Table 2 might suffer from endogeneity if faculty buildings were placed close to each other in order to foster entrepreneurial activities. Thus, BSs and other faculties might be "strategically" placed in proximity to each other in order to promote cross-faculty spillover and entrepreneurship. Therefore, OLS estimates on the effect of distance to BSs on entrepreneurship in other faculties might upward bias the coefficient estimates. This pattern should be relatively unlikely, however, since the "third mission" is a relatively new function that universities assume (Etzkowitz 2000;. Therefore, fostering cross-faculty knowledge spillovers should have played less of a role at the time when universities were founded. Nevertheless, in order to assuage remaining concerns, we run instrumental variables regressions as a robustness check to identify the causal effect of proximity between faculties on the emergence of entrepreneurial ideas. We focus on entrepreneurial projects within natural sciences since the OLS estimates indicate that distance to BS plays an important role in these faculties only. To overcome the reverse causality issue that entrepreneurial potential of faculties might determine the distance to other faculties, we use the age of universities as instrument for proximity in a 2 SLS setting. Our identification strategy relies on the idea that universities founded in recent times are more likely to have all university buildings near to each other since they were built up at the same time. By contrast, the location of faculty buildings of older universities, like those founded in medieval times, is more likely to be sprawled. One reason for this is the increasing breadth of university curricula over the course of centuries which is reflected, for example, by the emergence of natural science faculties and the expansion of tertiary education in the 20 th century. New faculties at old universities were not necessarily located in proximity to already existing facilities due to idiosyncratic historical city developments constraining opportunities for co-location, especially if the new faculties required some space for laboratories, equipment etc., like in the natural sciences (for a more detailed line of argumentation, see Appendix B). After presenting the IV results, we will show that age of universities is not related to their entrepreneurial and commercial orientation which assuages concerns regarding the validity of this instrument.

IV results
We employ the log-transformed value of age in our main specification. 11 Table 3 reports firstand second stage regression results. The structure of models I to III follows those in Table 2. The results clearly indicate that there is a positive relationship between university age and proximity of natural science faculties to BSs. The younger the university the shorter is the walking distance between respective university facilities. An increase in university age by 10 percent changes the distance of natural science faculties to BS by between 3.8 and 6.4 percent (walking distance: ca. 7.5 -13 minutes).
The first stages F-statistics have sufficiently high values (Stock et al., 2002) suggesting the relevance of age as an instrument. The results of the second stage estimations are shown in column IV to VI. Proximity to BS that is due to the age of universities has a positive and signifi-11 Kernel density estimates for university age before and after log-transformation is shown in Figure A 2. The results resemble those of Table 2 when using the non-transformed age (not reported). The distribution is smoothed with and without log-transformation. We also stick to the log-transformed value of the distance measure in order to interpret the change in distance that is due to age as elasticity (log-log model in the first stage estimation).
cant effect on the level of entrepreneurial activities in natural sciences. 12 In the analysis that is restricted to West Germany, an increase in walking distance by 10 percent (walking distance: ca. 20 minutes) implies a drop of approximately 1 start-up grant per 1,000 faculty members.
There is only a weakly significant 2 SLS relationship in the sample including East German natural science faculties when controlling for university and faculty characteristics. Note that the IV results indicate that university age explains differences in proximity between natural sciences and BSs in East Germany as well. However, proximity that is due age seems to be unrelated to entrepreneurship at East German universities. 13 The results for the full model for West Germany are robust when bootstrapping the standard errors (see Panel A of Table A 10). We also employed bootstrapped standard errors for robustness checks discussed in section 4.3. The results are also not sensitive when measuring region and year effects separately instead of interacting them (see Table A 11).
In a further assessment, we allow for functional flexibility of the age measure to rule out that our identification strategy is affected by arbitrary specification of the instrument. So, we use a binary indicator for age which assumes the value of 1 if a university was founded after 1945.
Our results remain robust and thus do not appear to be driven by the functional form of the instrument (Table A 12). The results show a significant second stage effect for East and West German universities. This suggests that universities founded in the GDR explain the insignificant second stage when employing the continuous age instrument. Table A 13 shows the reduced-form relationship between university age and entrepreneurial activities. Again, the structure of the models follows those in Table 2. A significant relationship between the instrument and the outcome variable in the reduced form in conjunction with a strong relationship between instrument and instrumented variable in the first-stage regression indicates the presence of a causal relationship. The results in Table A 13 reveal a robust relationship between university age and the emergence of entrepreneurial ideas for the sample of West German universities. If using the binary marker indicating whether a university was founded prior to 1945 yields a significant relationship for the full sample as well.
The results of the reduced-form estimates should not be interpreted as an indication that age has a direct effect on entrepreneurship levels (other than via proximity between natural science faculties and BSs). The next section explicitly deals with the exclusion restriction. 12 The explained variance (R 2 ) in the models is very high due to including planning region dummies as controls for regional characteristics. Using state dummies instead, reduces the R 2 to values around 0.6 to 0.7 (not reported). 13 We have no firm explanation for how this pattern could be affected by socialism or due to transitionspecific effects. There is a stable 2 SLS relationship when keeping East German universities but dropping the Technical University of Chemnitz. The coefficient estimates for the proximity measure in column VI of Table 3 resemble the one for the OLS estimates in Table 2. They are slightly larger but there are no severe biases in the coefficient estimates after instrumenting the proximity measure. This suggests that our baseline OLS results are not suffering from serious endogeneity problems. As previously argued, endogeneity might be not a particularly strong issue if we assume that location decisions are in most cases determined many decades before the period of analysis and before universities increased their activities in technology transfer. This might hold particularly for older universities.
Against this background, a good robustness check is to exclude, for example, universities aged less than 30 years in the baseline OLS regression. This exercise also circumvents the problem that cities, where a university had been recently created, are probably more dynamic and entrepreneurial than the average. This may be less true for cities where a university had been created in last centuries, especially since cities that were dynamic in last centuries are not necessarily dynamic today. The respective models are presented in the Appendix (Table A 14) and reveal a robust relationship between distance to BS and the EXIST rate in natural sciences.
We repeated the IV analysis also for engineering sciences to explore whether the insignificant results found in the OLS analysis are eventually due to potential endogeneity issues.
However, the results confirm the baseline OLS estimations (Table A 15). As expected, the first stage relationship shows that age is also positively related to distances between faculties of engineering and BS. However, the second stage regression shows that there is no relationship between distance to BS and the emergence of business ideas in engineering. Therefore, in the fol-lowing section on further robustness checks, we focus primarily on natural sciences. We will report results from OLS regression but also on IV estimates to address potential endogeneity concerns in the modified specifications even though the findings of this section suggest that endogeneity plays a minor role.

University age and "taste for entrepreneurship": Empirical analyses concerning IV validity
The credibility of the instrumental variables approach relies on the validity of the instrument.
Accordingly, the instrument should not have any influence on the outcome variable other than via the instrumented variable. In the present study, this would be violated if university age has a direct influence on the level of entrepreneurial activities at faculties. University age could be related to the "taste for entrepreneurship." In the following, we show analyses that rule out that there is such a relationship.
The first main concern in this regard is that more traditional universities might have a lower commercial orientation and therefore less entrepreneurship (e.g., Audretsch & Lehmann, 2005b). It can be noted, though, that the two stage relationship in our analysis remains robust when controlling for the share of private sector third-party funding (as indicator of a university's industry engagement). The second main concern is that age could also affect directly the number of start-ups by faculty members positively, for instance, because older universities are more established and better able to attract more talented researchers. A measure of such quality is the third-party funding by professor which we also control for in the analysis.  To assuage the two reasonable concerns, we regress the likelihood of having participated in the EXIST program on the age of universities. As previously mentioned, the EXIST program supports entrepreneurial activities at universities. University age should not be related to the likelihood of being an EXIST university. We run logit regressions including the same independent variables as in the previous analyses. As can be seen in Table 4, there is no relationship between university age and university engagement in entrepreneurship support policies. We run a similar analysis with the private sector share of third-party funding as dependent variable and include the other independent variables used in the previous analyses on the right hand side of the model. There is also no relationship of age with this indicator for commercial orientation (Table 5). Finally, we adjust third-party funding per professor, which indicates quality, by multiplying with the private sector share of third-party funding, which captures entrepreneurial orientation. There is also no meaningful relationship of age with this indicator. These analyses dispel the two main concerns namely that university age is related to quality and "taste for entrepreneurship."

Robustness checks
We conducted several robustness checks that confirm our main findings. So, we excluded universities that participated in EXIST programs over the entire observation period and those that did so before the observation period. As previously mentioned, am earlier participation indicated the presence of an entrepreneurial tradition and a historical record of universities in fostering entrepreneurship. Please note that binary indicators for participation are perfectly collinear with the region-by-year interactions in the main models which are therefore implicitly controlled for. When excluding the respective universities, the negative relationship between distance to BSs and academic entrepreneurship is confirmed (Table A 16). 14 In another specification, we exclude campus universities with very high proximity between natural science faculties and BSs. Similarly, we disregarded small and large faculties in terms of faculty members (Table   A 17). 15 We also checked whether observation years with extremely high EXIST rates in certain universities drive our findings. To this end, we excluded the upper 1 percent of the EXIST rate distribution. In further models, we also excluded observations without any EXIST grant to rule out that the skewed distribution of EXIST grants determined our baseline results. The case number is much reduced in the latter specification because there was no approved EXIST grant in 61 percent of all faculty-year observations for natural sciences (Table A 18). Throughout all alternative model specifications, our results remain robust and thus do not appear to be driven by specific types of universities, outliers, or by selection into entrepreneurship promotion programs.
As a further check, the number of EXIST start-up grants instead of the EXIST rate (i.e., number of start-up grants over eligible faculty member) is employed as an alternative outcome measure. The second stage results are in line with the main results. One difference is that there is also a significant 2 SLS relationship in the full model including East German natural science faculties. In terms of effect size, an increase in distance to BS by 1 percent reduces the number of grants by 1 (Table A 19).
We also add the number of start-up grants from BSs before calculating the EXIST rate. By this, we consider that start-up grants where natural science and BS staff have been involved (directly and indirectly) could have been assigned to BSs. The OLS regression and the IV 2 SLS relationship between university age, proximity, and the prevalence of start-up grants resemble the 14 In these models, the case number is too low to apply an IV approach with the same variables like in the OLS regression. 15 For definition of campus universities as well as small and large faculties, see notes of this table in the Appendix.
main results in this specification. 16 Interestingly distance to natural science is not significant when restricting the EXIST rate to start-up grants from BS only; a modification where potential knowledge flows to natural sciences are neglected (Table A 20). 17 We did a similar analysis for engineering sciences. That is, we add the number of start-up grants from BSs before calculating the EXIST rate in engineering, and we also restrict the EXIST rate to start-up grants from BS in universities hosting an engineering faculty. This additional exercise confirms again that distance between BS and engineering does not matter for the emergence of entrepreneurial ideas (Table   A 21).
One may object that the findings are an artifact of generally low distances of faculties at certain universities. In order to dispel such concerns, we calculated the distance (in walking minutes) between departments of computer sciences and other natural science departments.
Browsing the entrepreneurial projects that are supported by an EXIST start-up grant shows that the grand majority of the developed products or the business models (distribution channels etc.) are heavily relying on the increasing importance of information and communication technologies (ICT). Therefore, one could argue that distance to the department of computer sciences should play an important role for the emergence of entrepreneurial ideas. However, the analysis reveals no significant relationship between faculty proximity and the EXIST rate for the West German sample. Thus, the findings on proximity to BS are not coinciding with results on proximity to other potentially entrepreneurship-promoting university facilities. This pattern also suggests that it is distance to "managerial" and "commercial" knowledge is important for the actual emergence of entrepreneurial ideas. 18 In further placebo analyses we checked whether distance to faculty of sports sciences, and faculty of arts mattered. We expect that distance to these faculties should have no influence on the prevalence of entrepreneurial projects in natural sciences. This is confirmed empirically (Table A 22). 16 Faculty characteristics in this model are based on separate values for natural sciences and EBA. Since there is only information on faculty characteristics of Social Sciences in general, EBA values are obtained by multiplying the share of EBA within Social Sciences, in terms of financial spending, with the respective values of faculty characteristics of Social Sciences. 17 Please note that this analysis has some caveats. The first issue with this assessment is that we do not have information on the number of business school students and faculty members. There is only aggregate data for staff and students in the social sciences (incl. business schools). Moreover, there is no distinction between economics and business administration in the data. To obtain a crude number for members of business schools, we multiplied the number of university members in social science with the joint financial budget share of economics and business administration within social sciences. The faculty control variables are also only available at the social science faculty level. 18 It should be noted that there is a negative distance effect for the full sample including East German universities. We do not have a firm explanation why distance to computer science is apparently more important than distance to BS when considering East German universities as well. This might be explained by university-specific renovation activities in East Germany in the 1990s/early 2000s.

Concluding remarks
This study contributes to the literature on university entrepreneurship by investigating the emergence of entrepreneurial ideas at the level of university faculties. This is a novel approach which provides insights into the role of university structures for entrepreneurship and an as- Our results demonstrate that the level of entrepreneurial activities in natural science faculties is positively affected by proximity to BSs. The larger the distance between university facilities of natural sciences and local BSs, the lower is the magnitude of entrepreneurial ideas emerging out of natural sciences. To overcome the reverse causality issue that entrepreneurial potential of faculties may determine the distance to other faculties, we assess variation in proximity that is due to age of universities. We conducted robustness checks that confirmed the main findings. The cross-faculty effects that we find suggest the presence of knowledge spillovers within universities that lead to the commercialization of research and knowledge via entrepreneurship. Against this background, our paper adds a new perspective to the knowledge spillover theory of entrepreneurship (e.g., Acs et al., 2009;Audretsch et al., 2006). Furthermore, it contributes to the literature on social network formation in the university context (e.g., Sacerdote, 2001;Zimmerman, 2003;Mayer & Puller, 2008;Marmaros & Sacerdote, 2006). We do not find similar results for distance between engineering and BSs and also not for distance between natural science and engineering to the TTO. We offer reasonable explanations for these patterns. In a nutshell, the applied character of knowledge in engineering does not necessarily require knowledge input on commercialization opportunities from BSs. If a TTO is too close to a faculty potential entrepreneurs might dare to approach its staff because it is easily noticed by their peers that they consider a start-up which they do not want to disclose. Placebo analyses suggest that it is the distance to the BS-specific knowledge, not distance to other faculties in general, that matter. Altogether, the findings reveal a general underlying mechanism namely that technological knowledge of less applied character requires proximity to managerial and commercial knowledge input to emerge into entrepreneurial projects.
One data limitation is that we cannot identify the channel through which proximitybased cross-faculty spillovers are working since access to personal information of start-up grant holders is severely limited. It can be interdisciplinary team formation which is a direct channel or it can be indirect general social interaction on the university level. In this respect, potential entrepreneurs, for example, can draw on local expertise from university peers in other faculties.
Disentangling direct and indirect channels is clearly warranted in future research. Another limitation is that there is no publicly available information on the number of failed applications for start-up grants. Furthermore, we do not know whether the application succeeded in terms of creating and growing a venture. Exploring the latter and determining success are complex issues and come along with a lot of difficulties since track recording these ventures is hampered by restricted data access and privacy concerns. It is nevertheless an avenue for further research.
One particular interesting approach would be an assessment of the evolution of entrepreneurial competencies in spin-off ventures and venture success (e.g., Wennberg et al. 2011, Rasmussen et al., 2014  The findings of this study imply that policy initiatives aimed at sharpening the focus of universities at the cost of faculty diversity may be detrimental to the technology transfer potential of universities (at least in terms of commercializing knowledge via entrepreneurship). Due to these implications and the novel findings, we deliberately advocate for a much more finegrained assessment of university entrepreneurship, for instance, at the faculty level, as we do.
This research should also focus on the spillover channels to gain further interesting insights on knowledge spillovers within universities. Dropping the observations for this university comes along with neglecting 55 EXIST start-up grants. Furthermore, in 10 universities the application procedure for EXIST start-up grants was coordinated via a central service institution dealing with technology transfer (e.g., Career Center; TTO). For these universities we are not able to assign start-up grants to faculties (74 startup grants).
The sample attrition due to centralized grant applications should be rather a minor issue.
It is reasonable to assume that the decision to centralize grant applications at technology transfer institutions has to do with proximity of faculties. Such centralization of entrepreneurial projects makes sense where the average distances of all faculties to the central institution is low. At the same time the decision to centralize grant applications suggests that the university is interested in a track record of its entrepreneurial activities and centralized synergy effects which indicate emphasis on the entrepreneurial function of universities. Thus, it is likely that universities with a centralized institution coordinating the application process for EXIST start-up grants may have relatively many entrepreneurial activities and a relatively high proximity between faculties. 22 Thus, excluding these universities may rather induce a downward than an upward bias with respect to estimating the effect of proximity on faculty entrepreneurship. The loss of start-up grants due to dropping the Technical University of Berlin which is a campus university with low proximity between faculties and which accounts for one of the highest shares of startup grants should rather lead to a downward bias. 21 We also dropped faculties of veterinary science as well as for agriculture and nutrition because the number of such faculties is extremely low. An analysis makes no sense. 22 Mean comparison tests on the university level show indeed that universities with centralized grant applications are significantly more likely to be campus universities. There is at least no significant difference with respect to the level of entrepreneurship activity as compared to the remaining universities. Finally, comparing campus universities with centralized application procedures to those without such a system shows no differences with regard to the prevalence of entrepreneurial projects.

B University age and distance between faculties: Extended line of argumentation and confirmative observations
Investigating the role of spatial distance between faculties for the emergence of university entrepreneurship comes along with endogeneity concerns. It could be argued that spatial distance is endogenous to a strategy of university administrations and public authorities to encourage specific cross-faculty communication. This would appear in the data when specific faculties within a university are co-located for the purpose of encouraging close collaboration (e.g., natural science departments and BSs), while others are not. To circumvent this issue, in our analysis we make use of exogenous variation in proximity between departments and university scientists. A recent study by Catalini (2015) uses asbestos removal in university offices at medical and scientific complex Paris Jussieu as an exogenous shock to communication patterns. He finds that changing co-location patterns due to this measure affected scientific output. This identification strategy relies on exploiting a change in the legislation that affected where people were placed within universities. In a similar vein, we focus on historically-determined exogenous variation with respect to the placing of entire faculties within cities hosting universities. We rely on an instrumental variable approach which uses the age of universities as an instrument for distance between faculties to dispel endogeneity concerns. The idea behind this identification strategy is straightforward. Changes in the breadth of university curricula and the growing demand for tertiary education over the course of centuries came along with the formation of new faculties and faculty buildings that were not necessarily located in proximity to already existing facilities due to idiosyncratic historical city development constraining opportunities for co-location. These examples suggest that especially natural science faculties are likely to be located further away from city centers where historical sites of universities are based. Furthermore, universities founded after World War II have been often built as single campus on Greenfield sites. While historical universities had to develop further sites in order to cope with the unforeseen and unparalleled demand shock with regard to tertiary education after World War II, the location and size of new universities could be aligned with this demand right from the beginning.
The decision to start a university after 1945 was made by the Federal States (Laender) and the main motivation was to release existing universities and provide equally distributed possibilities for higher education across space while the foundation of historical universities was mainly due to idiosyncratic reasons (for examples, see Noseleit & Slavtchev, 2010).
Using age as instrument for distance overcomes the issue of "strategic" co-location of faculties in order to foster cross-faculty spillovers. It is quite unlikely that technology transfer has been a determinant of location of historical university buildings in pre-industrial times. Carlsson et al. (2009) describe medieval universities as places where utility or economic knowledge played no role since it was regarded rather as a skill than knowledge. Accordingly, the curriculum consisted of Greek and Latin classics and was focused on the study of the Bible.
The art of reading, writing, rhetoric, and logic have been important fields while ability and utility played a minor role. Similarly universities main tasks were to collect, codify, and teach general knowledge. Slight changes can be observed throughout industrialization since the late 18 th century which saw the foundation of natural science faculties and the first engineering schools and Technical universities which since the midst of the 19 th century started to apply science to invent (Drucker, 1998, p. 21). In Germany the first higher education institutions with a technical focus were founded in Karlsruhe and Dresden in the early 19 th century while the first natural science faculty opened at the University of Tuebingen in 1863. It could be argued that the location of buildings of the new Technical universities could have been driven by the idea to stimulate technology transfer and cross-faculty knowledge spillover. The Technical universities of Karlsruhe and Dresden are indeed among the oldest institutions that can be regarded as campus universities. 23 For older universities locating new faculties in close proximity may not have been an option due to idiosyncratic historical development of city structures. Altogether, we claim that university age is negatively related to proximity between its faculties.
Our argument is that university age matters for proximity between faculties due to historical developments since the midst of the 19 th century. If this is the dominant mechanism behind the first stage relationship then there should be no systematic relationship between university age and proximity for the sub-sample of universities that were founded prior to 1850.
This threshold was chosen since it reflects the approximate take-off of industrialization in Ger-many (e.g., Becker et al., 2011) and the time since science was increasingly applied for inventing (Drucker, 1998, p. 21). It should hence be less likely that prior to this threshold authorities planned to foster cross-faculty spillovers when making location decision for new faculties.
The data reveal that only 3 out of 26 universities of the sub-sample can be regarded as campus universities which have relatively low distances between faculties (see Table A 1). 24 The three exceptions are the Universities of Karlsruhe and Dresden, which are Technical Universities and were found in the early 19 th century, and the University of Mainz (founded in 1477) which was closed in the late 18 th century due to idiosyncratic historical events and reopened after World War II at a different site. 25 There is no other campus university founded prior to 1850 implying that pre-industrial universities created the newly emerging faculties at new sites after 1850 rather than next to the historical site which is likely to be due to space constraints. At the same time this pattern shows that there are no pre-industrial universities that did move their entire facilities to a new campus site in modern times apart from the University of Mainz.
As previously mentioned, there was a significant increase in the demand for tertiary education after World War II. Universities founded after 1945 might have been more likely to be built at Greenfield sites yielding a high proximity between faculties in order to cope with the demand while older universities may have had to develop new sites to do so. Table A 1 shows indeed that the bulk share of campus universities was founded after 1945. These preliminary observations underline the role of university age for determining proximity between faculties.
At the same time, we will also show that the relationship between age and proximity is not a pure "campus university" effect. 24 There is no clear-cut definition for campus universities. We define a campus university as a higher education institution where the bulk share of buildings used for the purpose of research and teaching is adjacent to each other. In the main empirical analysis the threshold was an average walking distance of less than 10 minutes between BS and main facilities of natural science faculties. 25 The French revolutionary army invaded the area of Mainz and established the Mainz Republic of 1792 until the Prussians recaptured the region. Due this turmoil the university became more or less dissolved. The faculty of medicine was still active until 1823 and the faculty of theology continued with teaching activities throughout the 19 th century. Since the reopening in 1946 the university is based in the remains of anti-aircraft warfare barracks that were built in Nazi times (http://www.unimainz.de/universitaet/96_DEU_HTML.php). One issue is the closure of universities over time. If a university was closed for more than 100 years, the new founding year is used. Universities are counted as campus universities if all major buildings devoted to teaching and research are situated at one site. If only specific facilities are at a distant location then the university is regarded as campus university as well. The Technical University Karlsruhe, for example, was renamed into Karlsruhe Institute of Technology (KIT) and merged with a research center for nuclear energy north of the city. The original university is based on one campus and therefore counted as a campus university in our sample. In two cases (Kassel, Cottbus) some natural science and engineering facilities are not based at the main campus. This explains the high distances to BS. + No natural science (engineering science) faculty at university University was successful in the program line "Institutional strategies for the project-based development of top-level university research" of the excellence initiative by the German government for the advancement of science and research at German universities Rate of high quality start-ups Regional number of start-ups in knowledge-intensive business services and R&D-intensive manufacturing industries divided by the regional number of employees (*1,000). City region (Yes=1) The county in which the university is based is counted as city region

D Tables Appendix
Urbanized county (Yes=1) The county in which the university is based is counted as urbanized county Rural county (Yes=1) The county in which the university is based is counted as rural county Sources: Federal German University Statistics, "funding catalogue" ("Förderkatalog") of the Federal Ministry of Economy and Technology, Google maps.    Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Region X Year-Effects comprise interactions of the year dummies with the rate of high-quality start-ups and dummies for the regional degree of agglomeration (groups: city regions; urbanized counties; rural counties). Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Region X Year-Effects comprise interactions of the year dummies with the rate of high-quality start-ups and dummies for the regional degree of agglomeration (groups: city regions; urbanized counties; rural counties).  Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Region X Year-Effects comprise interactions of the year dummies with the rate of high-quality start-ups and dummies for the regional degree of agglomeration (groups: city regions; urbanized counties; rural counties).        Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Coefficients for control variables and constant are not shown for brevity. Full models and first stage regressions of the IV approach can be obtained upon request. Universities with a walking distance of less than 10 minutes between BS and natural science faculty is regarded as campus university and excluded in model I to IV. The definition deviates from the one used in Table A 1. Here a campus university is defined as a university where all major buildings devoted to teaching and research are situated at one site which comprises large campuses where distance between BS and natural sciences is larger than 10 minutes. In two cases (Kassel, Cottbus) some natural science facilities are not based at the main campus. The models in column V to VIII disregard faculties with less than 1,000 and more than 10,000 EXIST-eligible faculty members. 9 percent of the observations were larger, 7 percent were smaller than these thresholds.   First stage F-Statistics 9.54*** 25.16*** 365.50*** 9.54*** 8.62*** 11.02*** Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Coefficients for control variables and constant are not shown for brevity. Full models and first stage regressions of the IV approach can be obtained upon request.  Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). The models of Panel B include the same variables like those in Panel A. Constant and coefficients for control variables are not shown for brevity. Full models can be obtained upon request. 27 IV results are not reported because university age turns out to be a poor predictor of average distance between faculties. Computer sciences are often based at the same site like other natural sciences. Faculties of Sports and Arts are much smaller on average than BS. Therefore, history is less of a constraint for colocation. The left-tail in Figure A 1 is due to "campus universities" where the distance is very low. 29 The figures refer to all universities of the final sample. Every university has a natural science faculty. The smaller peak around the age of 600 years (year 1400) is due to the exogenous increase in the founding of universities after the end of the papal schism (see Cantoni & Yuchtman, 2014