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Establishment exits in Germany: the role of size and age

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Abstract

Using comprehensive data for West Germany, this paper investigates the determinants of establishment exit. We find that between 1975 and 2006 the average exit rate has risen considerably. In order to test various “liabilities” of establishment survival identified in the literature, we analyzed the impact of establishment size and put a special focus on differences between young and mature establishments. Our empirical analysis shows that the mortality risk falls with establishment size, which confirms the liability of smallness. The probability of exit is substantially higher for young establishments which are not more than 5 years old, thus confirming the liability of newness. There also exists a liability of aging since exit rates first decline over time, reaching a minimum at ages 15–18, and then rise again somewhat. The determinants of exit differ substantially between young and mature establishments, suggesting that young establishments are more vulnerable in a number of ways.

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Notes

  1. In addition, related strands of the literature investigate individuals’ survival in self-employment (see, e.g., Millán et al. 2012), exits of founders leaving their firms (DeTienne and Cardon 2012), and the dynamics of entry and exit (Baptista and Karaöz 2011).

  2. The fact that establishments of solo self-employed individuals who do not employ any workers are not included in our data may affect the measurement of exits in two ways, where the net effect remains open. On the one hand, we cannot observe exits of establishments without employees which might induce an underestimation of the number of exits. On the other hand, establishments are counted as exits when they stop having employees liable to social security even if they continue to exist. This might lead to an overestimation of the number of exits. A similar problem applies to entries.

  3. Since there are breaks in the industry classification, a time-consistent industry classification variable based on the procedure by Eberle et al. (2011) was provided by the Research Data Center.

  4. Because establishments do not appear in the data until they employ their first employee liable to social security, entry could also have occurred earlier than recorded in the data.

  5. For a more detailed description of the problems concerning the identification of entries and exits see Brixy and Fritsch (2002).

  6. Since 1999 marginal part-time workers are included in the BLH and therefore also in our BHP data set. For time-consistency those employment relationships were dropped in the analysis of Brixy and Fritsch (2002) that makes use of personal level data. For the identification of establishments’ entries and exits we follow their approach. However, as we do not have access to the worker-level data, we were not able to construct a fully time-consistent data set, e.g. by calculating employment shares without marginal part-time workers in the numerator. Nevertheless, we decided not to exclude all establishments with marginal workers from our sample.

  7. To verify the robustness of our results, we also applied other classifications of entries and exits, but this did not change our main insights. Results are available upon request.

  8. A detailed discussion of the literature dealing with Gibrat’s Law is far beyond the scope of this paper; see Sutton (1997) for a survey of the theory and Santarelli et al. (2006) for a comprehensive tabular survey of empirical studies. Santarelli et al. (2006, p. 42) point out that the empirical studies on Gibrat’s Law are difficult to compare because the samples used and the methodologies applied differ widely.

  9. Related models were developed, for instance, by Hopenhayn (1992) or Ericson and Pakes (1995). For a survey of this literature, see e.g. Woywode (1998, p. 61 ff.).

  10. As higher exit rates for young establishments could simply occur because they are smaller than older ones, which may thus just reflect a liability of smallness, Fig. 4 was also split by size classes. The results (which are not shown here but are available on request) indicate that higher exit rates for young establishments can also be observed conditional on establishment size.

  11. For a more recent survey see Cafferata et al. (2009). A comparative analysis for ten OECD countries is provided by Bartelsman et al. (2005).

  12. Another potential determinant of firm survival and exit is innovation. As innovation is both risky and profitable (if successful), innovating firms may be both more likely to exit and more likely to perform better (for more detailed discussions of the innovation-survival nexus, see e.g. Audretsch 1991; Cefis and Marsili 2006). Unfortunately our data set does not contain information on firms’ innovation and R&D activities. Note that our estimations include the share of engineers in the workforce as a control variable, which may be regarded as a proxy for R&D activities. The results in Sect. 4 will show that a higher percentage of engineers is associated with a lower probability of exit and that this effect is more pronounced for young establishments. These results, however, do not necessarily indicate a R&D effect since a higher share of engineers may simply reflect a better qualified workforce.

  13. As a robustness check, we also estimated those models without interaction terms by probit or complementary log–log (a discrete time proportional hazard model), which did not make a substantial difference. Results are available upon request.

  14. Classifying young establishments as maximum 5 years old might appear quite arbitrary, but it is in accordance with empirical evidence for Germany by Brixy et al. (2006) showing that differences in labor fluctuation, wages and working conditions between newly founded and incumbent firms narrow and vanish within the first 5 years as the newly founded firms mature. Moreover, the picture does not change if we classify young establishments as maximum 4 or 6 years old. Results are available upon request.

  15. The observation period starts in 1981 rather than 1975 since we distinguish between young and mature establishments (which are older than 5 years).

  16. To test the hypothesis of an upward trend more directly, we also conducted our estimations with an additional linear time trend. The estimated coefficients of this time trend show that on average exit rates have increased by about 0.15 percentage points per year over our period of observation and that this upward trend has been more pronounced for young establishments. Results are available upon request.

  17. Note that in an earlier version of this paper we used regional unemployment rates (only available from 1985 onwards) rather than regional fixed effects to take account of regional economic conditions (see Fackler et al. 2012). The regional unemployment rate had a significant positive effect on the probability of exit, which was stronger for young establishments. This confirms that young establishments are more vulnerable to economic conditions and is in line with the results of previous empirical studies outlined in Sect. 3. The fact that young establishments are more strongly affected by regional unemployment may also reflect that new firms are more often founded by previously unemployed workers in regions where unemployment is high and are therefore less successful (Buehler et al. 2012).

  18. A potential solution to this problem may be to use lagged variables. However, this is problematic for at least two reasons: First, it is not clear when employment reactions anticipating an upcoming closure really begin, so that endogeneity may even be present when using employment information from the previous year(s) rather than contemporaneous employment. Second and even more important, we cannot use lagged variables in the case of establishments that exit in the first year of existence. Dropping these establishments would mean throwing away much valuable information, would reduce considerably the number of observations and would make it impossible to analyze the age-related liabilities.

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Authors and Affiliations

  1. University of Erlangen-Nürnberg, Lange Gasse 20, 90403, Nuremberg, Germany

    Daniel Fackler

  2. LASER, University of Erlangen-Nürnberg, Nuremberg, Germany

    Claus Schnabel

  3. IZA, Bonn, Germany

    Claus Schnabel & Joachim Wagner

  4. Institute of Economics, Leuphana University Lüneburg, P.O. Box 2440, 21314, Lüneburg, Germany

    Joachim Wagner

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  1. Daniel Fackler
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Correspondence to Joachim Wagner.

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We would like to thank two anonymous referees of this journal and participants of the 11th Comparative Analysis of Enterprise Data & COST Conference 2012 on 26–28 April 2012 in Nuremberg for helpful comments and suggestions. We also thank the German Research Foundation for financial support under the project SCHN 730/5-1 resp. WA 610/5‐1 “Firm exits” (Betriebsschließungen) and the team of the Research Data Centre of the Federal Employment Agency at the Institute for Employment Research for their exceptional support and cooperation.

Appendix

Appendix

See descriptive statistics in Appendix Table 3.

Table 3 Descriptive statistics (regression sample 1981–2006)
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Fackler, D., Schnabel, C. & Wagner, J. Establishment exits in Germany: the role of size and age. Small Bus Econ 41, 683–700 (2013). https://doi.org/10.1007/s11187-012-9450-z

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