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Infrastructure and entrepreneurship


This paper is one of the first studies to examine the link between infrastructure and entrepreneurship. Because infrastructure can enhance connectivity and linkages that facilitate the recognition of entrepreneurial opportunities and the ability of entrepreneurs to actualize those opportunities, a hypothesis is developed suggesting that startup activity is enhanced by infrastructure. However, not all types of infrastructure have a homogeneous impact on the entrepreneurial decision, so that a second hypothesis is developed suggesting that certain types of infrastructure which facilitate connectivity and linkages among people are more conducive to startup activity. The empirical results suggest that startup activity is positively linked to infrastructure in general, but that certain specific types of infrastructure, such as broadband are more conducive to infrastructure than are highways and railroads. Finally, we hypothesize that the types of infrastructure have varying influences in different sectors. Our empirical analyses support this view and we conclude that particular infrastructure policies can be used to facilitate regional startup activities and, furthermore, to foster startup activities in desired industries.

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  2. The MUP is implemented by the Centre for European Economic Research (ZEW) in cooperation with Creditreform, the largest credit rating agency in Germany.

  3. We use the logarithm because the distribution of interchanges is highly skewed.

  4. Because some railway stations changes their status during the observation period, we checked the respective city’s webpage. For more details regarding the Deutsche Bahn route map refer to the following link:

  5. We are grateful to Deutsche Telekom AG which provided us with information on the broadband rollout in Germany between 2000 and 2004.

  6. The calculation of the broadband penetration index is considerably more complicated. A more detailed description is provided in the “Appendix”.

  7. As a number of counties exhibit no new firm formation in high technology manufacturing, we check whether OLS might result in biased estimation results due to left-truncation of the dependent variable. Therefore, we estimate the model for high technology manufacturing using the Tobit regression model. As Table 5 in the “Appendix” shows results discussed in this section remain basically unchanged (see Table 5 in the “Appendix”).

  8. When all control variables are included in the estimated regression model, the effect of highway infrastructure investments increases and turns significant at the 10 % level.

  9. Note that the coefficient of East Germany almost doubles when we also control for unemployment. This pattern may be explained by the fact that structural differences in the unemployment existed between East and West Germany which may result in some kind of multicollinearity issue. The value of the coefficient drops again when we further include the Ich-AG policy scheme which was primarily launched to help unemployed to get self-employed if labor market opportunities were scarce. The correlation table in the “Appendix” reveals that the correlation between Ich-AG policy and East Germany is substantial and positive.

  10. The distinction between those groups of manufacturing is made by looking at the sector’s R&D intensity, i.e., total sector R&D expenditures divided by the aggregate sector’s sales. If this exceeds 3.5 % the sector is attributed to be a high technology industry.

  11. Please note that we do not consider broadband with bid rates of 16 kbits or even higher but rely on the very first provision of broadband which was mainly at a transmission quality of 1 or 2 kbits.


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Correspondence to Tobias Veith.



See Tables 3, 4 and 5.

Table 3 Descriptive statistics
Table 4 Correlations
Table 5 Tobit regression for high technology manufacturing

1.1 Calculation of broadband penetration index

In contrast to publicly available information on the other types of infrastructure, details on broadband rollout and its availability are not publicly available in Europe, at least not on a county level. The impact of broadband on startups is twofold: First, broadband enables entrepreneurs to access knowledge without being close to a knowledge incubator and accessing a broad range of customers beyond regional proximity. Second, the transmission rate, i.e., the quality of broadband, is the key pre-condition to provide completely new types of services. In this second regard, broadband itself is a platform for completely new and innovative types of entrepreneurs whose business model completely depends on the high transmission rate of broadband. For our cross-infrastructure comparison, we focus on the impact of broadband availability on startups. Broadband was introduced in Germany mainly starting in 2000. Since then, no substitute infrastructure, such as cable or mobile infrastructure has been available which could have influenced the distribution of broadband in Germany.

We measure broadband availability using the date of the upgrade of MDFs. An MDF connects the local loop infrastructure and the backbone infrastructure. Thus, each MDF belongs to only one area with one common area code. As soon as an MDF is upgraded to provide access to higher-speed backbone infrastructure, this MDF can be used for local broadband access.Footnote 11 Households and companies with standard subscriber lines connected to an upgraded MDF by standard copper lines can directly switch to broadband without major physical arrangements.

Main distribution frames are not directly related to a particular county but serve households and companies also across county borders. Similarly, the capacity of MDFs is limited. Therefore, multiple MDFs are required to provide telecommunication services to a larger county. Taking into account these obstacles, we approximate broadband availability with the so-called broadband penetration index per county. Based on the area code, we know the subscriber lines per area and we also know the subscriber lines per MDF. Thus, we calculate the share of upgraded MDFs in an area as

$$shbb_{mt} = \frac{{\sum\nolimits_{a} {\# sl_{amt} } }}{{\# sl_{mt} }}$$

which is the share of subscriber lines at upgraded MDFs per total subscriber lines in the area m at time t.

We use households as the linking pin between area codes and counties as households is the majority of broadband users and as households usually own one subscriber line. In so doing, we calculate the average broadband penetration based on the areas covering a county as follows:

$$PI_{it} = \sum\nolimits_{s} {\frac{{\# hh_{ist} }}{{\sum\nolimits_{n} {\# hh_{\text{int}} } }}} shbb_{st}$$

with \(\frac{{\# hh_{imt} }}{{\sum\nolimits_{n} {\# hh_{\text{int}} } }}\)as the share of households in county i with subscriber lines to MDFs in area m.

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Audretsch, D.B., Heger, D. & Veith, T. Infrastructure and entrepreneurship. Small Bus Econ 44, 219–230 (2015).

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