High-growth firm shares in Austrian regions: the role of economic structures

ABSTRACT

This paper explores the structural determinants of high-growth firm shares in Austrian regions. The regional level of analysis allows one to uncover regularities that are not detectable in firm-level studies. It is found that lower mobility barriers, firm exits and technological opportunities, measured by digitalization intensities, and, to a lesser extent, agglomeration effects are associated with a larger share of high-growth firms. The results suggest that comparisons of shares of high-growth firm across countries and regions should consider differences in the industrial structures together with the often-emphasized differences in policies and regulations.

KEYWORDS:

• high-growth firms
• industrial structure
• information and communication technology
• Austria
• variety
• NUTS-3

JEL:

• D22
• L25
• R11

ACKNOWLEDGEMENTS

The authors are grateful to Alexandros Charos and Kathrin Hofmann for assistance with data handling. Previous versions of this paper benefited from comments received at: a seminar at the Max Planck Institute for Economics, Jena, May 2014; the 54th European Regional Science Association (ERSA) Conference in St Petersburg, Russia, August 2014; and an Institut d’Economia de Barcelona (IEB) seminar in Barcelona, Spain, November 2015.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. HGFs are firms with an annualized growth rate of > 20% over a three-year period and more than 10 employees at the beginning of the period (OECD & Eurostat, 2007).

2. The data cover the NACE Rev. 2 sectors A–N.

3. Entry is defined as the hire of the first employee and not the establishment of the firm. This may be a drawback, but the HGF definition used only considers firms with more than 10 employees, which is why the deviation of the establishment year from the hiring date should not affect HGF shares. Also, firms that first exit and then re-enter are not recorded as exit and entry, because the social security numbers are kept on record, which allows one to distinguish ‘true’ entries from re-entries.

4. In 2014, Eurostat changed the growth criterion used to define HGFs from 20% per year over three years to 10% per year over three years. There does not seem to be any published rationale for this change (Anyadike-Danes et al., 2018). Eurostat still reports data for the 20% criterion.

5. An average of 3.9% of HGFs in our sample are classified as HGFs in two consecutive three-year periods. Coad et al. (2014) stress that high growth is not persistent over time, which is one of the seven stylized facts identified by the empirical literature.

6. The indicators used comprise the share of ICT tangible and intangible (i.e., software) investment, the share of purchases of intermediate ICT goods and services, the stock of robots per hundreds of employees, the share of ICT specialists in total employment, and the share of turnover from online sales.

7. We also used employment growth based on regional statistics. This covers the whole economy. Differences are negligible.

8. However, Bos and Stam (2014) provide evidence that the presence of HGFs is associated with subsequent industry employment growth.

9. When time dummies were excluded, the entry rate turned significant, indicating that economy-wide cyclicality can induce an association between the entry rate and the HGF share, which is otherwise captured by the time effects (see the supplemental data online).

10. See the supplemental data online.

 

Additional information

Funding

Klaus Friesenbichler used funding from the European Union Horizon 2020 Research and Innovation action under grant agreement number 822781 for the final revisions.