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Firm size and efficiency in the German mechanical engineering industry

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Abstract

Research usually finds a positive size-efficiency relationship, but few studies focus on sectors dominated by small and medium-sized firms (SMEs). This paper fills this gap by analyzing this relationship in the German mechanical engineering industry sector, which is both successful and increasingly dominated by SMEs. The analysis, using a large and representative dataset, finds that small and large firms are, on average, the most efficient ones, while medium-sized firms have, on average, the greatest inefficiencies. Thus, the size-efficiency relationship is U-shaped rather than monotonically increasing. Additionally, the analysis finds that companies with active owner(s) are significantly more efficient and that capital firms are less efficient than firms with personally liable owners. Being located in either East or West Germany has no effect.

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Notes

  1. Here, these are firms with fewer than 500 employees.

  2. See Taymaz (2005) or Aggrey (2010) for an overview.

  3. During the financial and economic crisis of the years 2009 and 2010, the German government, in cooperation with state owned and private banks, set up funds in order to increase the equity base of SMEs.

  4. Among them, the small firms with fewer than 50 employees account for 10 % of all jobs and 6 percent of the industry sales in 2004. The larger ones, on the other hand, consist of firms with either 100–249 or 250–500 employees, each accounting for 19 % of sales and for 21 and 18 % of the industry employment (Federal Statistical Office Germany 2005).

  5. Data taken from the GENESIS database of the German Federal Statistical Office; https://www-genesis.destatis.de.

  6. Nevertheless, he also states that financial constraints still at least partly explain why small, but efficient, firms stay small, at least for some time (Jovanovic 1982).

  7. Within this section, studies on the efficiency-size relationship in industries of developing countries are not discussed. Taymaz (2005) and Aggrey (2010) provide an overview.

  8. We forego a repeated presentation here and refer to very good description of the method in Badunenko (2010).

  9. The approach is intuitive and easy to implement, based on the MATLAB code by Simar (2003).

  10. The data are confidential but not exclusive. Researchers can apply for their use. See Zühlke et al. (2004) and http://www.forschungsdatenzentrum.de/en/index.asp.

  11. For more information about the German Cost Structure Census, see Fritsch et al. (2004).

  12. Almost all characteristics entail information in monetary form. This study only uses and sums up the monetary information. The only exception is the number of employees, but this is not used as input an factor in the calculation of efficiency scores.

  13. Here we follow the approach of Simar and Wilson (2005), who use a capital proxy for the analysis of banking size. In the long run, though, the size of a firm cannot be regarded as exogenous. But since the efficiencies are estimated by means of input and output data on yearly frontiers, the assumption that the size of a firm is more or less stable within a year is imposed.

  14. To overcome this problem as much as possible one needs to have a representative dataset (as in this study), one needs to run a decent outlier detection method (as in this study) and one needs to apply the respective bootstrapping method (as in this study). Yet, pure efficiency numbers, derived in other studies by DEA or SFA and with different datasets, cannot be used to judge whether industry A in country A is more efficient than industry A (or B, C, etc.) in country B.

  15. In addition Martin-Marcos and Suarez-Galvez (2000) find an average efficiency for the Spanish engineering industry of 0.64. Moreover, the efficiency shown in Table 2 is biased corrected. Without that correction the mean efficiency would be 0.73 and more than 5 % of the firms have an efficiency score of one.

  16. Of course there are efficient firms in each size class in every year, as shown in Table 6 in the “Appendix”. The lower efficiency is therefore not just because of a frontier that is only defined by small and large companies.

  17. See Table 7 in the “Appendix”.

  18. The P values of the applied bilateral Wilcoxon rank-sum tests are not reported here, but are available upon request from the author.

  19. See Table 8 in the “Appendix”.

  20. This follows from the simple curve discussion in which the first differentiation is set equal to zero and solved for size.

  21. Given the results of Table 3, a robustness check with output defining size seems natural. However, no variable used in the first stage of the approach can be applied in the second stage, thus these estimations are not possible.

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Acknowledgments

I would like to thank the anonymous referees for useful comments and suggestions.

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Authors and Affiliations

  1. German Institute for Economic Research, Mohrenstraße 58, 10117, Berlin, Germany

    Alexander Schiersch

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  1. Alexander Schiersch
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Correspondence to Alexander Schiersch.

Appendix

Appendix

See Appendix Tables 5, 6, 7, 8.

Table 5 Descriptive statistics
Full size table
Table 6 Number of frontier observations in each size class in each year
Full size table
Table 7 Average efficiency in each size class for each year, with size defined by the number of employees
Full size table
Table 8 Average efficiency in each size class for each year, with size defined by output level
Full size table

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Schiersch, A. Firm size and efficiency in the German mechanical engineering industry. Small Bus Econ 40, 335–350 (2013). https://doi.org/10.1007/s11187-012-9438-8

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