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Economic failure and the role of plant age and size


This paper introduces a large-scale administrative panel data set on corporate bankruptcy in Germany that allows for an econometric analysis of involuntary exits where previous studies mixed voluntary and involuntary exits. Approximately 83 % of all bankruptcies occur in plants with not more than 10 employees, and 61 % of all bankrupt plants are not older than 5 years. The descriptive statistics and regression analysis indicate substantial negative age dependence with respect to bankruptcy risk but confirm negative size dependence for mature plants only. Our results corroborate hypotheses stressing increasing capabilities and positional advantage, both predicting negative age dependence with respect to bankruptcy risk due to productivity improvements. The results are not consistent with the theories explaining age dependence via imprinting or structural inertia.

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  1. Fackler et al. (2013a) provide evidence for the reasons for plant closure in Germany. They do not use bankruptcy information and do not distinguish between voluntary and involuntary closure.

  2. A similar argumentation can, e.g., be found in Honjo (2000:560).

  3. Coad (2013) proposes to use the term ‘death’ instead of ‘failure.’ As ‘death’ summarizes voluntary and involuntary closures, however, this term is not very useful for our discussion.

  4. German bankruptcy law distinguishes between consumer bankruptcy and regular bankruptcy, the latter accounting for corporate bankruptcy. According to these regulations, local district courts are responsible for the implementation of bankruptcy proceedings. Bankruptcy proceedings are initiated at the request of creditors or debtors and require illiquidity or over-indebtedness on the part of the firm. The court will not initiate the process if these conditions are not met. The two most important decisions in the course of the process are whether to formally begin the bankruptcy proceeding and whether to reject claims because the remaining assets are insufficient to cover the expenses of the bankruptcy proceedings. Both decisions also underlie the official figures and are part of our data.

  5. For a case in point, see Jirjahn et al. (2011). They provide a theoretical discussion of how the relationship between management and a formal employee representation evolves over time and how it may increase firm productivity.

  6. This includes, for instance, the IAB Establishment Panel, the Linked-Employer-Employee Data (LIAB), and individual-level data (SIAB and IEB) provided by the Institute for Employment Research of the BA (IAB).

  7. The BHP is described in Hethey-Maier and Seth (2010).

  8. Because we intend to measure involuntary closures, our definition of bankruptcy risk deliberately assigns plants that survive the bankruptcy process to the denominator.

  9. Throughout the paper, we will use the same age and size categories as presented in the figures. The categories are chosen such that there are a sufficient number of observations per category interaction, e.g., plants being small and old. Note that we do not opt for continuous age and size variables as it turns out that restrictions such as linearity or log-linearity in the interaction effects are not supported by our multivariate results.

  10. Computing plant age from the first appearance of a plant ID in the data can lead to underestimation of true plant age, as a new plant ID may, e.g., reflect a change in ownership instead of the true date of plant foundation. Note that all our results concerning age dependence hold if we use worker flows to indicate and exclude ambiguous entries (see Hethey and Schmieder 2010). However, using worker flows to exclude ambiguous entries yields somewhat more pronounced negative size dependence. See the corresponding results in Figs. 5 and 6. We do not employ the restricted sample in subsequent analysis, as a considerable number of plants are dropped due to missing values in the entry classification scheme.

  11. As discussed previously, all results of the cross-tabulation remain qualitatively unchanged when excluding ambiguous entries.

  12. As described in Sect. 2, however, we include bankruptcies where the court refuses to open a formal bankruptcy trial because assets are too low to pay the liquidator. We therefore capture the vast majority of cases.

  13. We do not disaggregate exits according to the classifications in Hethey and Schmieder (2010) as it is necessarily arbitrary how to define the exit classes and to decide with which class our data should be compared. What is more, we think that using all vanishing IDs is more instructive for researchers who do not have linked employer–employee data necessary to apply the worker flow measures in Hethey and Schmieder (2010).

  14. The number of observations in Table 5 is higher than in Table 1 because, as described in the text, in Table 1 all observations with vanishing ID but without bankruptcy filing are excluded from the denominator, while in Appendix Table 5 we exclude the much smaller number of exits with bankruptcy.

  15. Fackler et al. (2013a) use a subset of the data on vanishing plant IDs used in our study. Looking at the years 2001–2006, they also find evidence for U-shaped age dependency. Also using the BHP data but not reporting how exit is measured, Schindele and Weyh (2011) show increasing hazard rates for older plants in their survival analysis of entry cohorts.

  16. The advantages of OLS over probit or logit estimators are the straightforward interpretation of coefficients as marginal effects and, given 7,822,643 observations, its computational efficiency. See Fackler et al. (2013a) for a detailed explanation.

  17. Note that our results for size dependence in failure risk could be influenced by what Griliches and Regev (1995) have termed the ‘shadow of death.’ A “shadow of death” in employment means that plants shrink for a considerable amount of time before they ultimately exit the market (see also Fackler et al. 2013b). In our data, plant size categories are defined by the number of employees on the last 30th of June on which the plant exists and may therefore not reflect the plant’s long-run employment level. However, a ‘shadow of death’ cannot explain why we do not find negative size dependence because any reduction in employment just prior to exit moves bankrupt plants into lower size categories and would therefore artificially produce negative size dependence.

  18. The share of part-time workers working fewer than 18 h per week may seem implausibly high at first glance, in particular within smaller plants. The figures represent means over plant-level averages, such that each plant has the same weight. Within each size category, the smallest plants constitute the highest share among all plants, and hence, employment shares at the plant level may be very different from the corresponding shares at the worker level, as is typically reported in official statistics.

  19. For comparison, Fig. 7 in Appendix presents results from a regression analysis of vanishing plant IDs instead of bankruptcies. The definition of the dependent variable is described in Sect. 5. Not surprisingly, the insights derived from the regression are also quite similar to those from the descriptive evidence and therefore differ much from the analysis of bankruptcy risk.

  20. As discussed previously, all results remain qualitatively unchanged when we exclude ambiguous entries.

  21. Comparing columns (1) and (3) of Table 4 reveals that controlling for wages reverses the sign of the East Germany dummy. Thus, conditional on the same wage structure, East German plants are, ceteris paribus, less likely to fail than their West German competitors. Because East German wages and productivity levels are on average much lower than those in West Germany, conditioning on wages implies comparing a high-wage (high-productivity) East German plant with a low wage (low productivity) West German plant. As long as the relative position in the local productivity distribution affects bankruptcy risk, the change in the sign of the East Germany regressor demonstrates that wages capture unobserved productivity differences that would otherwise be reflected in the coefficients of other regressors, whether in the region dummy or in the age or size dummies.

  22. Audretsch and Mahmood (1995:101) also report that entrants reduce failure risk by increasing start-up size.


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The authors thank Daniel Fackler, Claus Schnabel, Till von Wachter, and two anonymous referees for helpful comments. This study is based on customized data. We therefore thank Cerstin Rauscher, other IAB staff members, and staff members of the German Federal Employment Agency for their indispensable support concerning data provision.

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Correspondence to Steffen Mueller.



See Figs. 5, 6, 7 and Table 5.

Fig. 5
figure 5

Bankruptcy risk by plant age and year (years 2007–2010), w/o ambiguous entries

Fig. 6
figure 6

Bankruptcy risk by plant size and year (years 2007–2010), w/o ambiguous entries

Fig. 7
figure 7

Effects on exit probability (w/o bankruptcies), OLS coefficients for interaction terms of plant age and size (years 2007–2010). Notes: The figures illustrates the results from a regression as presented in Panel B of Table 3 but where the dependent variable indicates exit for all plants that closed without bankruptcy filing

Table 5 Exit probability (w/o bankruptcies) by age and size (in  %), years 2007–2010

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Mueller, S., Stegmaier, J. Economic failure and the role of plant age and size. Small Bus Econ 44, 621–638 (2015).

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  • Bankruptcy
  • Plant exit
  • Start-ups
  • Organizational mortality

JEL Classifications

  • D21
  • L10
  • L25
  • L26
  • L29
  • M13