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Spinoffs in Germany: characteristics, survival, and the role of their parents

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Abstract

Using a 50 % sample of all private sector establishments in Germany, we report that spinoffs are larger, initially employ more skilled and more experienced workers, and pay higher wages than other startups. We investigate whether spinoffs are more likely to survive than other startups, and whether spinoff survival depends on the quality and size of their parent companies, as suggested in some of the theoretical and empirical literature. Our estimated survival models confirm that spinoffs are generally less likely to exit than other startups. We also distinguish between pulled spinoffs, where the parent company continues after they are founded, and pushed spinoffs, where the parent company stops operations. Our results indicate that in western and eastern Germany and in all sectors investigated, pulled spinoffs have a higher probability of survival than pushed spinoffs. Concerning the parent connection, we find that intra-industry spinoffs and spinoffs emerging from better-performing or smaller parent companies are generally less likely to exit.

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Notes

  1. The formation of spinoffs has been analyzed in a number of case studies about the circumstances in parent companies that lead to spinoffs; see, e.g., Klepper and Thompson (2010) and the review by Klepper (2009: 163−165).

  2. Using survey data on entrepreneurs and a matched employer-employee database from Denmark, Dahl and Sorenson (2014) show that the better performance of spinoffs is partly explained by the recruitment of more experienced employees, the better (un)employment history of founders, and the greater effort they exert.

  3. Although they do not investigate survival, Stephan (2014), and Czarnitzki et al. (2014) find performance premia of public research spinoffs in Germany in terms of innovation and employment growth respectively. Fryges et al. (2014) show for private firms that spinoffs that are based on an essential idea which the founder developed during his/her work as an employee outperform other spinoffs in terms of post-entry innovation activities but not in terms of employment growth.

  4. Bruneel et al. (2013) provide another categorization of spinoffs based on the event triggering the formation of the spinoff (opportunity or adverse development), and on the actor creating the company (employee or incumbent). A broader typology of spinoffs suggested by Fryges and Wright (2014) highlights the interaction between the environmental context a spinoff emanates from (university or commercial context) and the firm-level spinoff mode (distinguishing between new or existing activities).

  5. In our study we use an extended dataset with additional information on the parent companies. Using a 50 % sample poses the problem that not all parent companies are included. As part of a cooperation project with the Research Data Centre we were able to use the entire population to obtain information on all parent companies of the spinoffs in the sample. For replication studies the Research Data Centre will enable data access; in this case please contact: iab.fdz@iab.de.

  6. Since the BHP only contains annual snapshot information for June 30th, the number of entries and exits might be underestimated because establishments that enter after June 30th, and exit within 1 year before June 30th of the next year are not considered.

  7. An establishment is defined as a regional and economically delimited unity in which individuals are employed. Several branches of one employer in the same municipality and in the same industry may form an establishment, and one unique identification number is assigned to it.

  8. As 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 Centre.

  9. Since establishments first appear in the dataset when they first report having employees liable for social security, entry might have occurred earlier than recorded in the data. Similarly, exit could have occurred later.

  10. This procedure is applied because perforated establishment histories (e.g., if an establishment does not have any employees except the owner for some time) may become a problem at the beginning of the observation period and at the current edge.

  11. In addition, as 86 % of all establishments in Germany are separate firms comprising only one establishment (Koch and Krenz 2010), one can expect that new establishments are also new firms in most cases.

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

  13. Since 1999, marginal part-time workers have been included in the BeH, and therefore also in our BHP dataset. For time-consistency, those employment relationships were dropped in the analysis by Hethey-Maier and Schmieder (2013), 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 are not able to construct a fully time-consistent dataset, 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.

  14. Entries with only one initial employee are excluded, since it is not possible to distinguish between different types of entry in this group based on a worker-flow approach (Eriksson and Kuhn 2006: 1024f.). As not all entries will have recruited two employees in their first year, slowly developing entries are not considered. It is an open question whether this bias toward faster-growing startups affects spinoffs and other startups in different ways. In following the approach by Eriksson and Kuhn (2006) rather than applying the classification suggested by Hethey-Maier and Schmieder (2013), as was done in a working paper version of this study (Fackler and Schnabel 2013), we obtain a larger sample because Hethey-Maier and Schmieder (2013) only classified entrants with at least four initial employees. In addition, applying the more common 50 % threshold rather than the 80 % threshold by Hethey-Maier and Schmieder (2013) makes our analysis and its results more easily comparable to previous studies.

  15. The yearly number of entries in our sample for western Germany lies between 1546 and 2275 for pulled spinoffs, between 623 and 1910 for pushed spinoffs, and between 11,735 and 18,598 for other startups. In eastern Germany, the respective numbers are 485–1631 pulled spinoffs, 345–952 pushed spinoffs, and 4007–9909 other startups.

  16. As in previous studies by Eriksson and Kuhn (2006), and Andersson and Klepper (2013), spinoffs are identified only via the transfer of persons, since the data do not provide information on the transfer of technology or knowledge, which is often regarded as a key factor in the definition of spinoffs (e.g., Fryges and Wright 2014). The volume of the transfer of knowledge or technology can be considered larger in spinoffs than in other startups (Eriksson and Kuhn 2006: 1026). A certain shortcoming of our data is that we do not have information on the founder of a spinoff, and thus cannot be sure that he/she also stems from the parent company. However, Dahl and Sorenson (2014) report that spinoff entrepreneurs often rely on former colleagues in their hiring decisions, which gives us reason to believe that the spinoff founder stems from the same company.

  17. Note that the sample size is slightly smaller when the characteristics of parent companies are considered, since only spinoffs with parents in the private sector without agriculture and mining are included.

  18. The fact that in eastern Germany fewer parent companies pay wages above the (all-German) industry median indicates that most parent companies of eastern German spinoffs are also located in eastern Germany, where the level of wages and productivity is generally lower than in western Germany.

  19. Obviously wages only reflect productivity imperfectly, but we would argue that they may serve as a crude indicator of the quality of parent firms. Traditional neoclassical labor demand theory suggests that the wage should equal the value of the marginal product, and human capital theory also implies that wages rise with productivity. This relationship becomes looser in imperfect markets where workers may be able to share rents (via trade unions or works councils), or where employers pay efficiency wages. Nevertheless, paying higher wages usually reflects higher productivity, a competitive advantage (e.g. due to innovations), or some market power of the firm, and thus indicates that the parent company is better off.

  20. The Kaplan–Meier estimator is a non-parametric method that takes account of censoring and provides a graphical representation of the probability of survival at a certain point in time; for a detailed description, see Kalbfleisch and Prentice (2002: 14−19).

  21. Low qualified employees are those who do not have an upper secondary school-leaving certificate as their highest school qualification or do not have a vocational qualification. Skilled and highly skilled occupations are defined according to the occupational classification by Blossfeld (1987). Skilled occupations include skilled manual occupations, skilled services, skilled commercial and administrative occupations, and technicians; highly skilled occupations include semiprofessions, engineers, professions, and managers.

  22. Note that this insight does not change when we estimate a bivariate probit of being a spinoff and spinoff survival as a crude test of potential endogeneity, as suggested by one reviewer.

  23. Note that using a slightly different distinction, Buenstorf (2009) found that in the German laser industry, “opportunity spinoffs” performed best (although not statistically significantly different from “necessity spinoffs“).

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Acknowledgments

We would like to thank Joachim Wagner, participants in the research seminar at the University of Erlangen-Nürnberg, and two anonymous referees for very detailed and helpful comments on a previous version. The firm-level data used are confidential but not exclusive; see Spengler (2008) or Gruhl et al. (2012) for a description of how to access the data. To facilitate replication, the Stata do-files are available from the first author on request.

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Authors and Affiliations

  1. Halle Institute for Economic Research, Kleine Märkerstraße 8, 06108, Halle (Saale), Germany

    Daniel Fackler

  2. University of Erlangen-Nürnberg, Chair of Labour and Regional Economics, Lange Gasse 20, 90403, Nuremberg, Germany

    Claus Schnabel

  3. Institute for Employment Research, Regensburger Str. 100, 90478, Nuremberg, Germany

    Alexandra Schmucker

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  1. Daniel Fackler
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  2. Claus Schnabel
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Correspondence to Claus Schnabel.

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Fackler, D., Schnabel, C. & Schmucker, A. Spinoffs in Germany: characteristics, survival, and the role of their parents. Small Bus Econ 46, 93–114 (2016). https://doi.org/10.1007/s11187-015-9673-x

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