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- Jena Economic Research Papers 2017 31 ties because it is difficult to distinguish structural characteristics of medical departments from university hospitals with the dataset at hand.21 In total, 778 EXIST start-up grants have been assigned to the universities remaining in the sample (N=69). This is the full sample of universities of interest based on conceptual reasons mentioned above. Unfortunately, there are missing values for some faculty-year observations.
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- Jena Economic Research Papers 2017 32 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.
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- Jena Economic Research Papers 2017 38 Table A 5: Cross-faculty spillovers BS to natural and engineering sciences: sensitivity check for potential multicollinearity between distance measures I II III IV Dep var: EXIST rate All West All West Controls variables Yes Yes Yes Yes Panel A: Natural Sciences Panel B: Engineering Sciences Distance to BS - - - -(log) - - - -Control: distance to TTO 0.0431*** 0.0507** 0.0514-0.134 (log) (0.0153) (0.0189) (0.120) (0.474) Observations 408 312 168 104 R2 0.383 0.506 0.307 0.352 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.
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- Jena Economic Research Papers 2017 43 Table A 11: Baseline model natural science: IV Regressions (column IV to VI, Table 3) without region-by-year interactions I II III IV V VI All All West All All West Controls variables No Yes Yes No Yes Yes First stage: Dep Var: Distance to BS Second stage: Dep Var: EXIST rate Age of university (log) 0.380*** 0.677*** 0.511*** - - -(0.118) (0.0831) (0.0188) - - -Distance to BS (log) - - - -0.164** -0.0964** -0.259** (IV: Age of university (log)) - - - (0.0652) (0.0478) (0.103) Observations 408 408 312 408 408 312 R2 0.981 0.993 0.999 0.309 0.332 0.391 First stage F-Statistics 10.15*** 65.25*** 722.92*** - - -Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Constant and coefficients for control variables are not shown for brevity.
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- Jena Economic Research Papers 2017 44 Table A 12: Baseline model natural science: IV Regression with alternative instrument I II III IV V VI All All West All All West Controls variables No Yes Yes No Yes Yes First stage: Dep Var: Distance to BS Second stage: Dep Var: EXIST rate Foundation of University < Year 1945 1.043*** 1.373*** 1.293*** - - -(0.174) (0.0250) (0.0613) - - -Distance to BS (log) - - - -0.143*** -0.0828** -0.309*** (IV: Foundation of University < Year 1945) - - - (0.0527) (0.0343) (0.0906) Observations 408 408 312 408 408 312 R2 0.989 0.997 0.999 0.365 0.388 0.521 First stage F-Statistics 35.49*** 2971.84*** 435.07*** - - -Notes: Significance levels: *** p<0.01, ** p<0.05, * p<0.1. Clustered robust standard errors in parentheses (cluster: university). Constant and coefficients for control variables are not shown for brevity.
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- Jena Economic Research Papers 2017 45 Table A 13. Baseline model natural science: Reduced form estimates I II III IV V VI Dep Var: EXIST rate All All West All All West Controls variables No Yes Yes No Yes Yes Age of university (log) -0.0630*** -0.0630* -0.152*** (0.0165) (0.0318) (0.0524) Foundation of University-0.149*** -0.114** -0.399*** < Year 1945 (0.0371) (0.0521) (0.130) Observations 408 408 312 408 408 312 R2 0.368 0.387 0.525 0.370 0.386 0.526 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.
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- Jena Economic Research Papers 2017 49 Table A 19: Cross-faculty spillover from BS to natural sciences: checking for sensitivity to employing an alternative dependent variable Dep Var: Number of EXIST grants I III IV All All West Controls variables No Yes Yes Panel A: OLS regression Distance to BS-1.047*** -0.985*** -0.794*** (log) (0.324) (0.123) (0.268) Observations 408 408 312 R2 0.707 0.724 0.796 Panel B: IV second stage regression (IV: Age of university (log)) Distance to BS-1.122*** -0.930*** -0.914*** (log) (0.325) (0.126) (0.252) Observations 408 408 312 R2 0.707 0.724 0.796 First stage F-Statistics 21.46*** 65.24*** 746.02*** 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.
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- Jena Economic Research Papers 2017 51 Table A 21: Sensitivity of main results to considering EXIST grants from BS: Analysis for engineering faculties Dep Var: EXIST rate I II III IV V VI All All West All All West Controls variables No Yes Yes No Yes Yes EXIST Grants: Business Schools + Natural Sciences EXIST Grants: Business Schools Panel A: OLS regression Distance BS-Natural Science-0.0343-0.575* -0.779* 0.0334 0.0271-0.0885 (log) (0.0314) (0.283) (0.362) (0.0503) (0.0686) (0.132) Observations 168 168 104 168 168 104 R2 0.408 0.518 0.528 0.243 0.353 0.562 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.
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- Jena Economic Research Papers 2017 53 E Figures Appendix Figure A 1: Density estimates for the distance to BS28 Figure A 2: Density estimates for age of universities29 28 The figures refer to all facilities of natural science and engineering science faculties of the final sample. 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.
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- Our argument is that university age matters for proximity between faculties due to historical developments since the midst of the 19th 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 Ger23 The Technical University Karlsruhe 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.
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- Table A 14: OLS regressions excluding young universities (age less than 30 years in 2007) I II III IV V VI Dep var: EXIST rate All All West All All West Controls variables No Yes Yes No Yes Yes Panel A: Natural Sciences Panel B: Engineering Sciences Distance to BS-0.0936** -0.0936** -0.256** -0.0108 0.382-0.153 (log) (0.0362) (0.0350) (0.0957) (0.0909) (0.242) (0.978) Observations 376 376 296 152 152 104 R2 0.454 0.473 0.524 0.273 0.334 0.352 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.
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- 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 19th century, and the University of Mainz (founded in 1477) which was closed in the late 18th 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.
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- 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.
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- The faculty of medicine was still active until 1823 and the faculty of theology continued with teaching activities throughout the 19th 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).
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- 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). 0 .01 .02 .03 Density 0 50 100 150 200 Distance to BS kernel = epanechnikov, bandwidth = 4.3767 Kernel density estimate 0 .1 .2 .3 .4 Density 0 2 4 6 Distance to BS (log) kernel = epanechnikov, bandwidth = 0.2385 Kernel density estimate 0 .001 .002 .003 Density 0 200 400 600 800 Age of university kernel = epanechnikov, bandwidth = 53.8363 Kernel density estimate 0 .1 .2 .3 .4 Density 2 3 4 5 6 7 Age of university (log) kernel = epanechnikov, bandwidth = 0.3049 Kernel density estimate Jena Economic Research Papers 2017
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- 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, Jena Economic Research Papers 2017 33 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.
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- We dropped all faculties of a university when there was at least one missing value for the respective faculty between 2007 and 2014. This came along with the loss of 7 start-up grants. We further had to drop two universities (University of Duisburg-Essen and University of ErlangenNuremberg) which have two different campus sites at two different cities. The University Statistics database does not allow to consistently assigning information on structure to the different locations. There is no start-up grant assigned to the faculty-year observations for these universities.
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