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Job machine, think tank, or both: what makes corporate spin-offs different?


One way through which knowledge and technology transfer can take place is through the foundation of new firms by former employees of incumbent private firms. In this paper, we examine whether knowledge transferred from the incumbent causally affect employment growth and post-entry innovation activities of the new firm. We focus on start-ups for which a new idea (a new product, technology, production process, or management concept), which the founder developed during her work as an employee, was essential for setting up the new business. These firms are denoted corporate spin-offs. Using data from German start-ups founded in the period from 2005 to 2008, we apply nearest-neighbour propensity score matching. We find that corporate spin-offs outperform other start-ups founded by former employees of incumbent private firms that are not based on an essential idea in terms of post-entry innovation activities. However, we cannot show that corporate spin-offs benefit from the transferred idea in terms of employment growth. We conclude that a transferred idea is primarily an input factor and a stimulus for subsequent post-entry innovation activities of corporate spin-offs.

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  1. For a detailed description of the survey design, see Fryges et al. (2010).

  2. Interviews are preferably conducted with one of the founders of the firm. If the founders are no longer active, a member of management who is also owner of the firm is interviewed.

  3. The wording of the questions used to identify spin-offs is documented in Table 13 in the “Appendix”.

  4. In the following we only document unweighted descriptive statistics.

  5. Some interviewees indicated more than one idea, therefore the sum of shares exceeds 100 %.

  6. According to German law, mini jobbers are marginally employed persons who work either part-time or short-time with a maximum annual salary of 4.800 Euro.

  7. Employment growth rate as well as the Birch Index and R&D intensity (see below) are trimmed by excluding observations below the 1st and above the 99th percentiles. We also experimented with different definitions of outliers, but our core results remain unchanged.

  8. Please recall that the statistics displayed in Table 5 are unweighted sample shares. Since half the firms surveyed by the KfW/ZEW Start-Up Panel operate in a high-technology industry, the sample shares indicating innovation activities are upward biased. Using population weights reduces the share of spin-offs (non-spin-offs) that carried out in-house R&D in 2009 to 27 % (14 %).

  9. The number of observations of firms with (world) market novelties is too small to report complete sequences of these innovation indicators.

  10. The underlying assumption is that treatment and outcome are independent for entities with the same realisations x of the vector X (Conditional Independence Assumption, CIA, also referred to as the unconfoundedness or selection on observables assumption). For the CIA to be valid, the vector X must include all covariates that simultaneously influence both treatment and outcome (cf. Rubin 1977).

  11. The asymmetry of the bounded propensity score is relevant for matching techniques others than one-to-one nearest-neighbour matching. In order to test for the robustness of the chosen matching methodology, we experimented with other matching techniques like nearest-neighbour propensity score matching with calliper or kernel matching with varying bandwidths of the kernel function. The main conclusions, however, do not change.

  12. For details on the German education system, see European Commission (2010) or, for a short overview, European Commission (2011).

  13. There are a small number of founders that could not be coded into the four predetermined fields of specialisation. This includes, e.g., founders with a degree in sport science or founders without a completed vocational training.

  14. If a firm was set up by a team of founders that hold different highest degrees, this firm is represented by a set of dummy variables indicating these different degrees. As a consequence, the eight variables measuring educational degree are not orthogonal to each other. Thus, we do not have a base category for educational degree in the regression equation. The same argument applies for the set of variables representing the four different fields of specialisation.

  15. Unfortunately, this information is not available in our data to check this conjecture.

  16. Propensity score matching was conducted using the Stata programme psmatch2 (Leuven and Sianesi 2003).

  17. The common support condition that requires that conditional on the vector X firms have a positive probability of being both spin-offs and non-spin-offs is fulfilled for all but six spin-offs.

  18. The p value varies slightly depending on the outcome measure because not all outcome measures can be calculated for all firms due to missing values.

  19. The idea behind FILM is to make the OLS regression more flexible and to adjust it to propensity score matching. However, FILM still assumes linearity whereas propensity score matching does not. The common support condition guarantees that FILM, in accordance to the matching approach, only analyses firms that are comparable (cf. Goodman and Sianesi 2005).

  20. Of course, FILM regressions can only provide a first insight into the sources of heterogeneity. A more in-depth analysis is required but is beyond the scope of this paper.

  21. In the baseline model, the control group was constructed from the group of start-ups with at least one former employee of an established firm among the founders. It can be argued that all start-ups provide a better basis for the construction of the control group because they include, for example, academic spin-offs that might be particularly suitable matching partners for corporate spin-offs. In order to check whether our results are influenced by the choice of the comparison group from which the control group is selected, we replicated the analyses with the whole sample of firms. The results were essentially the same as the ones shown in Table 7.

  22. Note, however, that our results are confirmed by firms observed in their second business year, where the survival bias is presumably of minor importance.


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Valuable comments and suggestions of two anonymous referees are gratefully acknowledged. A previous version of this paper was presented at the ZEW Workshop on Spin-Off Entrepreneurship 2011 in Mannheim and the 42nd Australian Conference of Economists 2013 in Perth. We thank participants for their helpful remarks. Further, we thank Christian Rammer for useful suggestions and Sarah Gatenby-Clark for careful proof reading. All remaining errors are our sole responsibility.

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Correspondence to Helmut Fryges.

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See Tables 12, 13, 14, 15, 16.

Table 12 Composition of industry sectors
Table 13 Wording of questions that identify corporate spin-offs
Table 14 Means of explanatory variables (baseline model)
Table 15 Means of explanatory variables after matching (baseline model)
Table 16 Fully integrated linear model: interaction effects

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Fryges, H., Müller, B. & Niefert, M. Job machine, think tank, or both: what makes corporate spin-offs different?. Small Bus Econ 43, 369–391 (2014).

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  • Knowledge and technology transfer
  • Corporate spin-offs
  • Propensity score matching
  • KfW/ZEW Start-Up Panel

JEL Classifications

  • L26
  • L25
  • O31
  • C21