Abstract
This paper investigates the relationship between industrial dynamics in terms of firm entry, market turbulence and employment growth. Do entry of firms, the composition of industry dynamics (net entry) and market turbulence (entry and exit) influence industrial employment growth? This paper provides an empirical investigation, using unique data for 42 disaggregated Swedish industrial sectors during the period 1997–2001. It is hypothesised that the importance of entering firms, net entry and market turbulence may differ significantly across industries. A quantile regression method is used in order to detect industrial differences in the response to industrial employment growth. The empirical evidence shows that, on the one hand, firm entry and market turbulence have a positive effect on employment for fast growing industries and that the effect is larger for high growth industries. On the other hand, the composition of industry dynamics in terms of net entry rates has a more dispersed effect across all industries, even though the effect of net entry is larger for high growth industries.
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Notes
Defined as market turbulence (sum of entry and exit) minus net entry.
Measured as per capita income growth.
The SIC code at the 4-digit level corresponds to NACE Rev. 1.
Financial intermediation (SIC codes 65–67), real estate activities (SIC code 70) and activities of membership organizations (SIC code 91) are not included in the dataset since they are not covered by the collection method applied by Statistics Sweden.
These industries were: extraction of crude petroleum and natural gas (SIC code 11), mining of metal ores (SIC code 13), manufacture of tobacco products (SIC code 16) and collection, purification and distribution of water (SIC code 41).
Note that entry and exit rates are defined according to the ecological approach (see Armington and Acs 2002).
One alternative approach would of course be to estimate different subsets of the data (e.g. high growth, medium growth, and low growth industries) separately. In this way a lot of information will be lost since only a limited number of observations can be used in each regression. The advantage with using the quantile regression is that all observations are used in the estimations.
It might be worth mentioning that in a quantile regression framework there is less need to assign, e.g. industry dummy variables since each quantile is estimated separately.
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Nyström, K. Entry, market turbulence and industry employment growth. Empirica 36, 293–308 (2009). https://doi.org/10.1007/s10663-008-9086-z
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DOI: https://doi.org/10.1007/s10663-008-9086-z