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The geographic determinants of bankruptcy: evidence from Switzerland

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Abstract

This paper examines the geographic determinants of firm bankruptcy. We employ hazard rate models to study the bankruptcy risk of a firm, allowing for time-varying covariates. Based on a large sample from all geographic areas and the major sectors of the Swiss economy, we find the following main results: (1) Bankruptcy rates tend to be lower in the central municipalities of agglomerations; (2) bankruptcy rates are lower in regions with favorable business conditions (where corporate taxes and unemployment are low and public investment is high); (3) private taxes and public spending at the local level have little impact on bankruptcy rates.

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Notes

  1. See Feld and Kirchgässner (2001) for an analysis of income tax competition at the state and local level.

  2. Fractionalization is the most common measure of aggregate ethnic diversity. It is defined as the probability that two individuals selected at random from a country will be from different ethnic groups. Formally, if the shares of ethnic groups are given by p 1, …, p n , then fractionalization is defined as \(F\equiv1-\sum_{i=1}^{n}p_{i}^{2}\) (Fearon 2003, p. 208).

  3. To measure cultural diversity, Fearon (2003) defines a resemblance factor r ij  ∈ [0, 1] for two ethnic groups i and j, which is zero if the groups’ languages come from completely different families and one if the groups speak the same language. Cultural diversity is then defined as \(D\equiv1-\sum_{i=1}^{n}\sum_{j=1}^{n}p_{i}p_{j}r_{ij}.\)

  4. The Roman Catholic Church and the Evangelic Reformed Church are the dominating religious denominations in Switzerland (see Sect. 3.2 for further details).

  5. It is well known that exit rates vary considerably across industries. Dunne et al. (1988), for instance, report substantial and persistent differences in exit rates across U.S. manufacturing industries. There are a number of potential explanations for such differences, including the intensity of competition (Symeonidis 2002), the industry life cycle (Mata et al. 1995; Agarwal and Gort 1996), and the speed of innovation (Geroski 1995; Audretsch 1995; Segarra and Callejón 2002).

  6. See Puga (2010), Melo et al. (2009), and Rosenthal and Strange (2004) for recent surveys.

  7. These economies are also known as Marshall–Arrow–Romer (MAR) externalities.

  8. The firm selection effect tends to work in the opposite direction: Due to the left-trunction of the firms’ productivity distribution, even firms with relatively high productivity become vulnerable to negative productivity shocks.

  9. Alcácer and Chung (2007) further highlight that firms not only gain from inward knowledge spillovers, but also suffer from outward spillovers. Analyzing U.S. data, they find that technologically advanced firms tend to avoid locations with industrial activity to distance themselves from competitors.

  10. Note that a reversed chain of causation, where firm exits lead to increases in unemployment, would also lead to a positive relation between exit rates and unemployment.

  11. Using data from India, Audretsch et al. (2007) document the impact of religion on the decision of people in India to become entrepreneurs.

  12. For instance, the population ratio of the largest and the smallest canton is 85/1. The expansion ratio amounts to 192/1.

  13. Note that agglomeration 3787 is not a metropolitan agglomeration, such that St. Moritz does not qualify as a central municipality.

  14. A more detailed discussion of the categorization into municipality types and the process of aggregation into main types is beyond the scope of this paper. See Swiss Federal Statistical Office (2005, p. 115) for further details.

  15. In our estimates below, all other religious denominations are pooled in the reference group OtherRel (in which “No Affiliation” dominates, see Table 3).

  16. Numerous other (non-official) languages are being spoken by subgroups of the population in each canton, including English, Serbian, Croatian, Albanian, and Turkish.

  17. The majority of firms in our sample are single-plant firms. Multi-plant firms are associated with the municipality of their headquarters. We are well aware, though, that the bankruptcy risk of a large—perhaps multi-national—firm is unlikely to be determined by the characteristics of their headquarters’ host community.

  18. Note, however, that many other covariates in our study are time variant (see Table A1 in the Appendix—ESMl).

  19. See Buehler et al. (2005) for further details.

  20. The average (median) age at the time of entering the survey was 35 quarters (28 quarters, respectively).

  21. Size variables are commonly log-transformed, as it is natural to assume that the marginal effect of size on bankruptcy decreases.

  22. Since we use a 1-year lag specification, we use values from 1994 to 1999 for our estimations.

  23. The use of dummy variables to proxy for the aggregate movement of the economy caused collinearity problems.

  24. That is, we aggregate migration into and out of a given canton.

  25. Since the number of firms in the Rhaeto-Romanic region is very small, we use the five main regions to associate each firm with the relevant official language.

  26. See Kiefer (1988) for further details.

  27. We are grateful to the editor and one of the referees for suggesting this approach.

  28. To check for robustness, we have also employed a generalized estimating equation (GEE, see Hardin and Hilbe 2007). The results (available on request from the authors) are very similar to those reported in Tables 5 and 6.

  29. See Schary (1991) for a theoretical analysis.

  30. We had to aggregate the cantons of Berne and Jura as well as Appenzell IR and Appenzell AR to assure a sufficient number of observations in each geographic area.

  31. Inspection of the estimated hazard ratios indicates that bankruptcy rates in the cantons of Neuchâtel, Valais, Vaud, Ticino, Thurgau, Solothurn, and Fribourg are more than 30% higher than those in Zurich.

  32. Recall that, according to the Weberian perspective, bankruptcy rates should be lower in religious regions because firms employ more productive workers with strong work ethic and drift.

  33. These estimates are available on request from the authors.

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Acknowledgments

We are grateful to the associate editor, Philipp Koellinger, and three anonymous referees for many detailed comments. We also thank Dirk Burghardt, Manfred Fischer, Bruno Frey, Dennis Gärtner, Luigi Guiso, Daniel Halbheer, Ulrich Kaiser, Mariko Klasing, Rico Maggi, Gianmarco Ottaviano, Dieter Pennerstorfer, Armin Schmutzler, Enrique Schroth, Christoph Weiss, and seminar participants at Arlington (IIOC 2008), Berne (Swiss IO Day 2006), Brussels (EcoMod), Lugano, Milan (EEA 2008), Munich (VfS 2007), Valencia (EARIE 2007), and Zurich for helpful discussions and suggestions. Support from the Swiss National Science Foundation (through grant no. PP0012-114754), the Swiss Federal Statistical Office, and the Cantonal Bank of St. Gallen is gratefully acknowledged. The usual disclaimer applies. In particular, the opinions expressed in this paper are those of the authors and are not attributable to HSBC. The authors are solely responsible for the contents.

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Authors and Affiliations

  1. University of St. Gallen, IFF-HSG, Varnbüelstr. 19, 9000, St. Gallen, Switzerland

    Stefan Buehler

  2. HSBC, 8 Canada Square, London, E14 5HQ, UK

    Christian Kaiser

  3. Executive School of Management, Technology and Law, University of St. Gallen, Holzstr. 15, 9010, St. Gallen, Switzerland

    Franz Jaeger

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  1. Stefan Buehler
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Correspondence to Stefan Buehler.

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Buehler, S., Kaiser, C. & Jaeger, F. The geographic determinants of bankruptcy: evidence from Switzerland. Small Bus Econ 39, 231–251 (2012). https://doi.org/10.1007/s11187-010-9301-8

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