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Start-ups, long- and short-term survivors, and their contribution to employment growth

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Abstract

This study documents that the survival of start-ups is central in explaining the relationship between entry and regional employment growth. Distinguishing between start-ups according to the period of their survival shows that the positive effect of new business formation on employment growth is mainly driven by those new businesses that are strong enough to remain in the market for a certain period of time. This result is especially pronounced for the relationship between the surviving start-ups and employment growth in incumbent businesses indicating that there are significantly positive indirect effects of new business formation on regional development. We draw conclusions for policy and make suggestions for further research.

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Notes

  1. E.g., Birch (1981, 1987), Davis et al. (1996), Spletzer (2000), Neumark et al. (2006), Haltiwanger et al. (2010), Horrell and Litan (2010), Stangler and Kedrosky (2010), Schindele and Weyh (2011).

  2. Aghion et al. (2004, 2009), Caves (1998), Disney et al. (2003), Fritsch and Noseleit (2012a).

  3. Indeed, entirely different results are found if, for example, the relationship between the level of start-ups and subsequent employment change is analyzed at the level of regions instead of at the level of industries (see Fritsch 1996). Therefore, geographical units of observation are much better suited for such an analysis than are industries.

  4. In our data, new businesses are recorded at the time when they hire their first employee. Hence, a number of those businesses that are started without any employee but hire someone later on are recorded in that later year. Unfortunately, those start-ups that never have an employee, the solo-entrepreneurs, are not recorded in our data. Although it is known that this type of entrepreneurship has become more important in Germany during the 1990s (Boegenhold and Fachinger 2010), there is no information about solo-entrepreneurship available for smaller regional units such as planning regions. Although we cannot say anything in detail about the regional distribution of solo-entrepreneurs, we expect that the variables for regional industry structure, time dummies, and the regional fixed effects in our empirical model should prevent any significant distortion of the results caused by their omission. Given that these firms have no employee, we assume that their direct effects may be rather small if not negligible.

  5. Analyzing regional differences of the direct employment effect of new business formation, Fritsch and Schindele (2011) take the number of employees in a start-up cohort after a certain period of time (2 or 10 years, respectively) over regional employment in the year before the new businesses have been set up as indicator for the direct effects. This measure is, however, not appropriate for a comparison with the indirect effects of new business formation because the effect of a certain start-up cohort on incumbent employment can hardly be empirically isolated for two reasons. First, the indirect effects take a period of about 10 years before they become fully evident so that the effects of different start-up cohorts overlap. Second, regional start-up rates tend to be rather constant over time so that the start-up rate for a certain year may not differ much from the average rate calculated for a longer period of time. Comparing the number of jobs that have been created in the start-up cohorts of the previous 10 years and that still exist in year \(t=\) 0 with change of incumbent employment is inappropriate for the same reason. The average employment share of start-up cohorts of the previous 10 years in year \(t=\) 0 is 23.1 percent (median: 22.5 percent).

  6. This is also the main reason why firms that are older than three and a half years are classified as representing established business ownership in the Global Entrepreneurship Monitor (GEM); see Reynolds et al. (2005).

  7. All cross-model tests are based on results of seemingly unrelated estimations in order to account for cross-equation correlation.

  8. Some studies separately analyze start-ups in manufacturing and those in the service sector (e.g., van Stel and Suddle 2008; Andersson and Noseleit 2011). Baptista and Preto (2011) perform analyses for knowledge-intensive service industries and compare the results with other sectors such as non-knowledge-intensive services.

  9. The obvious reason why the vast majority of work on the effect of new business formation uses employment as an indicator of performance is data availability. Exemptions are Carree and Thurik (2008), who use GDP and labor productivity as dependent variables, and Thurik et al. (2008), who investigate the effect of self-employment and unemployment.

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Authors and Affiliations

  1. School of Economics and Business Administration, Friedrich Schiller University Jena, Carl-Zeiss-Str. 3, 07743, Jena, Germany

    Michael Fritsch

  2. Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747, AE, Groningen, The Netherlands

    Florian Noseleit

Authors
  1. Michael Fritsch
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  2. Florian Noseleit
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Correspondence to Michael Fritsch.

Appendix

Appendix

Table 6 Descriptive statistics for variables used in the empirical models
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Table 7 Correlations
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Table 8 The impact of new business formation on employment change in incumbent businesses, and employment change in new and young businesses (un-weighted employment change measures)
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Fritsch, M., Noseleit, F. Start-ups, long- and short-term survivors, and their contribution to employment growth. J Evol Econ 23, 719–733 (2013). https://doi.org/10.1007/s00191-012-0301-5

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