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Start-ups and employment dynamics within and across sectors

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Abstract

We use a decade of longitudinal data on start-ups and employment in Swedish regions to analyze the effect of start-ups on subsequent employment growth. We extend previous analyses by decomposing the effect of start-ups on total employment change into within- and cross-sector effects. We find that start-ups in a sector influence employment change in the same as well as in other sectors. The results illustrate that the known S-shaped pattern can be attributed to the different effects of start-ups in a sector on employment change in the same sector and in others. Start-ups in a sector have a positive impact on employment change in the same sector. The effects on employment change in other sectors may be negative or positive, and depend on the sector under consideration. In particular, start-ups in high-end services deviate from manufacturing and low-end services in that they have significant negative impacts on employment change in other sectors. The findings are consistent with the idea that start-ups are a vehicle for change in the composition of regional industry.

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Notes

  1. The role of small and new firms is maintained to be amplified by the increased pace of technological change and innovation, which shorten product cycles. Small and new firms are often maintained to have innovation and growth advantages in such contexts (cf. Acs and Audretsch 1987; Christensen and Rosenbloom 1995).

  2. Moreover, a significant fraction of European Union (EU) funding, for instance, from the European Regional Development Fund (ERDF), is devoted to projects aimed at supporting start-ups in EU regions.

  3. A special issue of Small Business Economics (2008, vol. 30) collected a number of contributions applying a similar methodology, which produced comparable results from regions in different countries. Analyses were conducted for Portugal (Baptista et al. 2008), The Netherlands (van Stel and Suddle 2008) Germany (Fritsch and Mueller 2008), the USA (Acs and Mueller 2008), Spain (Arauzo Carod et al. 2008), and Great Britain (Mueller et al. 2008). An analysis was also conducted for a set of Organisation for Economic Co-operation and Development (OECD) countries (Carree and Thurik 2008).

  4. Moreover, high-end services are characterized by small-scale firms among incumbents as well as entrants, which suggests that demand effects may be relatively limited.

  5. These are the same data as Andersson and Koster (2009) make use of in their analysis of persistence in start-up rates across Swedish municipalities.

  6. Data have been corrected for change in municipality classifications between 1994 and 2004.

  7. In relation to some previous Swedish analyses of start-ups and employment we employ a more precise measure of start-ups. Data on start-ups in, e.g., Nyström (2008b) is based on tracing new firm identity numbers in the business register on a year-to-year basis. In this case, firm identity may change due to changes in, for instance, legal form or simply an error. Borgman and Braunerhjelm (2007) measure entrepreneurship by the change in the number of establishments with zero or one employee.

  8. As a robustness check we have also estimated our empirical model with start-up rates that also include start-ups with personal liability. This does not change the results we present in the sequel. Additional tables are available from the authors.

  9. Low-end services are defined by NACE code 50–64 and include logistics and transport services, retail, wholesale, hotels, restaurants, and repair shops. High-end services are defined by NACE code 65–99 and include advanced producer services, R&D institutions, education services, etc.

  10. A potential explanation for the observed decline in manufacturing and low-end service start-ups since 1994 is that 1994 marked the end of a recession in the early 1990s. Improved economic conditions and a consequent recovery of the labor market in 1994 and onwards may have impeded start-up activity.

  11. Weighted with the employment share of sector s in region r.

  12. Weighted with the employment share of other sectors in region r.

  13. In addition to the variables listed in the table, we have included median income in the estimations. This can be theoretically motivated but is correlated to education. When it is included in the model it has a negative parameter estimate and does not change the results presented in the sequel. Results from the estimations which include median income are available from the authors upon request.

  14. Most studies use population per square kilometer or similar as a density measure. Our accessibility measure is also a measure of density. It combines information about the magnitude of economic activity in a municipality with information about the time distances in terms of travel by car between different zones within the municipality. In this sense it is a more precise density measure than density measures based on the geographical scope of a region (e.g., square kilometers). Actual time distances is a better description of actual interaction opportunities and proximity in a region than crude measures such as square kilometers.

  15. As stated in the previous sections, we also tested models including start-ups with personal liability and also estimated the models with median income included among the regressors. Population density as an alternative density measures has been tested as well. The results presented here are robust to these alterations. The results with different model specifications, including polynomial lag estimates, are available from the authors upon request.

  16. The total number of start-ups is the sum of the start-ups in low- and high-end services as well as manufacturing.

  17. As stated previously, it is evident that the effect of total employment growth as given by the sum of the estimated parameters in column 1 (within sector) and 2 (cross sectors) is consistent with the estimated effect of high-end service start-ups on total employment change in Appendix Table 3 for all lags but t − 3. Despite the difference being statistically significant at the 10% level, it is small in magnitude.

  18. Moreover, the measurement of start-ups applied here excludes start-ups due to splits of existing establishments.

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Acknowledgments

We are grateful for comments from Michael Fritsch, Marcus Dejardin, Kristina Nyström, and two anonymous referees that substantially improved earlier versions of this paper. We also thank Jan Andersson at Statistics Sweden for support with data issues. Martin Andersson acknowledges financial support from the Swedish Governmental Agency for Innovation Systems (VINNOVA) and from the EU sixth framework program (MICRODYN project).

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Authors and Affiliations

  1. Centre of Excellence for Science and Innovation Studies (CESIS), Royal Institute of Technology and Jönköping International Business School (JIBS), Stockholm, Sweden

    Martin Andersson

  2. School of Economics and Business Administration, Friedrich-Schiller-University Jena, Jena, Germany

    Florian Noseleit

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  1. Martin Andersson
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Correspondence to Martin Andersson.

Appendix

Appendix

See Tables 3, 4, 5, and 6.

Table 3 The impact of start-up rates on regional employment change
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Table 4 The impact of start-up rates in manufacturing on regional employment change
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Table 5 The impact of start-up rates in low-end services on regional employment change
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Table 6 The impact of start-up rates in high-end services on regional employment change
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Andersson, M., Noseleit, F. Start-ups and employment dynamics within and across sectors. Small Bus Econ 36, 461–483 (2011). https://doi.org/10.1007/s11187-009-9252-0

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