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Analyzing the determinants of entrepreneurship in European cities

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Abstract

This study investigated the determinants of business creation as a measure of entrepreneurship in European cities. It examined supply- and demand-side elements, actual and equilibrium rates of entrepreneurship, institutions and culture. These components were characterized using a dataset consisting of 21 indicators drawn from 184 cities in 20 European countries during the years 1999–2010. The study found that city size, self-employment, and tertiary education have a significant and positive impact on the number of new businesses registered. The implications of these findings are discussed in view of the European Commission’s Small Business Act, which provides guidelines for the conception and implementation of entrepreneurship policies in the European Union. This paper’s main contribution lies in the differentiation of factors that are context-specific (e.g., city-size) and others that can be influenced by policy (e.g., tertiary education).

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Notes

  1. Dynamic measures have their own drawbacks. For instance, the business creation rate does not consider whether these companies survive. However, the aim of this paper is to model entrepreneurial dynamism; whether new businesses are successful, being a subject worth analyzing, falls out of its scope.

  2. See Sect. 3 for the working definitions of self-employment and new business registration.

  3. Aggregations of U.S. counties based on commuting patterns.

  4. Further details of Eurostat’s Urban Audit data collection can be found in Eurostat (2004).

  5. However, paid employment indicators consider the jobs offered inside city limits, including employees who commute. For 'total employment/population in working age,’ some observations exceed 100 %.

  6. Eurostat (2004) acknowledged comparability limitations of LAU administrative boundaries. As a solution, Eurostat also aggregates data into Larger Urban Zone (LUZ) spatial units. In general, this unit is approximated using NUTS level 3 data, which corresponds to the administrative region surrounding the given city. During this paper’s writing, several of the included variables were not available in LUZ spatial units.

  7. Excluding mergers, break-ups, split-offs, restructuring of enterprises, changes of activity or in the name of the company.

  8. Eurostat (2007) argued it should be considered as a new company in the city.

  9. According to Eurostat (2010), changing location is not a sufficient reason to delete an existing company record in the previous location and create one in the new city.

  10. Regarding the access to formal finance, for example, Belitski and Korosteleva (2010) reported an overall insignificant effect.

  11. Audretsch et al. (2002) argues for the separation of institutions (formal) and culture (informal).

  12. However, in the absence of data for the period 1999–2002 on ‘domestic burglary’ and ‘male elected city representatives’ indicators, 2003–2006 values were used.

  13. Note that this example deals with positive correlations, although strong negative correlations would also violate the assumption of linear independence.

  14. Note, for example, the correlation of −0.5147 between young-age dependence and Factor 6 ‘tertiary education and high employment’ in Table 2.

  15. We do not use dummies for the periods 2003–2006 nor 2007–2010 as the lower proportion of observations yielded estimates that suffered from high variance inflation factors, which indicated high multicollinearity.

  16. This last model excludes Hungary, Latvia and Luxembourg as data was only available for one city in these countries.

  17. The coefficient for self-employment increased in value, suggesting that estimates were negatively biased in the first three models.

  18. Refer to the following section for more details.

  19. Logarithmic transformation. Summary statistics: obs = 264, mean = 3.82, std. dev. = 0.93, min = −0.51 and max = 6.50. Source Eurostat (2012).

  20. This approximation assumes that producers and consumers are evenly distributed within the area (A). However, the goal here is to capture the differences in magnitude of agglomeration economies across two different areas, i.e., within and outside city boundaries.

  21. For some cities, NUTS level 3 is roughly the same as LAU level two. Observations with city-land areas equivalent to 95 % or more of its region were excluded. Data extracted from the Eurostat Urban Audit (Eurostat 2012).

  22. The squares of the estimated values for new business registered \((\hat{n}_i^2)\) were found to have a significant explanatory power for the observed values (n i ). This suggests the presence of non-linear effects in the specification (Model 5).

  23. \(vif(\hat{B}_{i})={1}/({1-R^{2}_{i})}, \) where R 2 i corresponds to the R 2 of the regression in which the explanatory variable associated with \(\hat{B}_{i}\) becomes the explained variable, as a function of all the other explanatory variables of the original model. A large R 2 suggests a high goodness of fit and so, in this case, multicollinearity in the original model.

  24. Since only one observation is included per city, time effects are not included in these regressions.

  25. The SBA itself centers around policies that favor an “SME-friendly environment” (European Commission 2008). Support to business creation is included implicitly, as seen in some recommended practices (e.g., strengthening nascent entrepreneurship in educational programs and reducing administrative burdens for registering businesses) and more directly in the second policy principle which is about promoting recovery from bankruptcy.

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Acknowledgments

The author is grateful to three anonymous referees for their valuable comments. Special thanks to Ahmed Bounfour and Alejandro Hoyos for their useful suggestions and support. The usual disclaimer applies.

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  1. PESOR & European Chair on Intellectual Capital Management, University Paris Sud, 54 Bd Desgranges, 92331, Sceaux, France

    Andrés Barreneche García

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Correspondence to Andrés Barreneche García.

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Barreneche García, A. Analyzing the determinants of entrepreneurship in European cities. Small Bus Econ 42, 77–98 (2014). https://doi.org/10.1007/s11187-012-9462-8

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