Location Attributes and Start‐ups in Knowledge‐Intensive Business Services

Abstract

This paper examines start‐ups in knowledge‐intensive business services (KIBS) across Swedish regions by individuals with a formally recognized capacity to produce and develop advanced business services. The empirical analysis focuses on whether their involvement in entrepreneurship may be explained by location attributes. As much as 75 percent of the KIBS founders have prior work experience from business services, suggesting that KIBS start‐ups are more frequent in regions where the KIBS sector is already large. Controlling for the stock of potential entrepreneurs and the stock of KIBS firms, it is shown that variables reflecting both supply‐side conditions and market size influence KIBS start‐up activity. Results are consistent with the hypothesis that KIBS entrepreneurship in a region is stimulated by the simultaneous presence of (i) knowledge resources conducive for the generation and diffusion of knowledge and ideas upon which new firms can be established and (ii) a large market.

Keywords:

• Knowledge‐intensive business services
• KIBS
• spillovers
• start‐ups
• entrepreneurship
• market size

Acknowledgements

The authors are grateful for constructive comments from Olof Ejermo, Annika Rickne, Per Thulin, Johan Wiklund and three anonymous referees which improved earlier versions of the paper. Martin Andersson acknowledges financial support from the Swedish Governmental Agency for Innovation Systems (VINNOVA).

Notes

1. Productivity in non‐service sectors is measured by value‐added per employee.

2. Variations in start‐up activity across regions are often explained by differences in the potential for knowledge spillovers emanating from differences in accessibility to knowledge resources, such as R&D investments by universities and firms (Acs and Audretsch, 2003). At the individual level absorptive capacity, idiosyncratic prior knowledge and experience have received much attention (Shane, 2000).

3. These ideas have a clear bearing on place‐specific increasing returns to scale and the comprehensive concept of agglomeration economies (cf. Fujita and Thisse, 2002).

4. Audretsch and Lehmann (2005) maintain that most of the theoretical reasons why proximity to knowledge sources might enhance entrepreneurial performance emanate from the resource theory of entrepreneurship.

5. This definition builds on Venkataraman (1997) and Shane and Venkataraman (2000).

6. A related literature focuses on how characteristics of individuals, for example, demographic and psychological, influence the decision to exploit an entrepreneurial opportunity. See inter alia Parker (2004) and Davidsson (2005) for overviews.

7. Non‐rivalry means that the use by one person does not limit the use by another. A piece of knowledge can be used over and over again in as many contexts as desired. Incomplete excludability means that new knowledge cannot be kept as a completely private asset, but tends to diffuse and spill over.

8. Regarding the role of customer contact, it should be emphasized that research by, for example, Henderson (1988) and Henderson and Clark (1990) illustrates that too close contacts with existing customers may make firms vulnerable to technological shifts or discontinuities.

9. This also leads to an inherent feature of services: their quality depends not only of the competences and skills of the seller but also of the buyer.

10. Coffey and Bailly (1991: 109) remark for instance that “… it is the cost of maintaining face‐to‐face contacts between the producer on the one hand, and their inputs and markets, on the other hand, that is potentially the most expensive element of intermediate‐demand service production”.

11. We have checked various definitions of prior work experience such as 1, 2 or 3 years before entering self‐employment. All definitions give the same result. Individuals working in a sector 1 year before self‐employment typically worked in the same sector 2 years before self‐employment.

12. We use NUTEK’s definition (1998) of such regions in Sweden of which there are 81.

13. The definition of a large share of public‐sector employment follows the one used by Andersson and Karlsson (2007).

14. Results using a narrow classification of KIBS, that is, NACE 71–74, are available from the authors upon request.

15. Following the arguments developed in Audretsch and Fritsch (1994b), most analyses of start‐ups are based on start‐up rates where start‐ups are normalized by the labor market population. One advantage of this approach over, for example, normalizing by the total number of firms, is that if some regions are dominated by a few large firms results may be distorted by creating an “artificially” high start‐up rate simply because of few firms in the region rather than a large set of start‐ups. The total number of STM individuals is really small in several municipalities in Sweden and ranges from 6 to about 36,000. If we normalize the number of KIBS start‐ups by STM individuals in each municipality, we end up precisely with a very high start‐up rate in a set of very small municipalities accounting for a small fraction of the total number of start‐ups. Therefore, we estimate a NEGBIN model and include the number of STM individuals, that is, the potential number of entrepreneurs, as a control variable. A similar estimation strategy can be found in Fritsch and Falck (2007).

16. However, when excluding the number of STM individuals, the estimated parameter for the R&D dummy becomes significant and positive.