Abstract
This paper uses a unique dataset which gives a complete picture of the pattern of entry and exit in industrial sectors in Sweden during the period 1997–2001. The importance of profitability, industrial market growth, tangible capital intensity, intangible capital intensity and economies of scale for entry and exit are investigated. A fixed effects panel data model is used, and it is shown that the inclusion of unobserved industry-specific effects explains many of the inter-industrial differences in entry and exit rates. For policymakers, this implies that it is difficult to formulate an entrepreneurship policy that can be expected to be equally successful across all industries. It is also shown that investments in intangible assets is one way to compete, while economies of scale tend to deter entry rates.
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Notes
These intangible capital assets should not be confused with what Siegfried and Evans (1994) denote managerial intangible assets. Managerial intangible assets refers to, for example, the firm-specific human capital that managers have or the their emotional attachment to the firm, which may impede exit. However, as noted by Siegfried and Evans (1994), these managerial intangible assets are very difficult or even impossible to measure.
Note that the empirical analysis on the industry-level makes it difficult to include variables reflecting, for example, non-pecuniary benefits associated with entrepreneurship.
The SIC code at the four 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.
The consistency of the original data has been carefully checked by Statistics Sweden, and therefore just a few observations that were regarded as inconsistent remained.
Further details about this dataset can be found in Statistics Sweden (1998).
The dataset used does unfortunately not allow us to identify mergers and acquisitions. When two firms merge or when a firm acquires another firm, one of them is reported as an exit. Also note that in the case of reconstruction of a firm, an exit and later an entry will be reported. However, it should be noted that other ownership changes, which do not cause changes in the unique identification code, are not reported as an entry or an exit. Furthermore, the identification code does not change if the firm changes its name, and hence no entry or exit will be reported. Further information regarding the dataset is available in Nyström (2006).
To some extent, this aggregation must be regarded as an aggregation across product markets to rather broad industrial sectors. However, as previously mentioned this aggregation was necessary in order to get sufficiently many observations in each industry.
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).
The random effects model is estimated by using generalised least squares (GLS).
In a two-way fixed effect model, unobservable time-specific effects are also included. These time-specific effects would, for example, take care of fluctuations in the business cycle, but since there is already a variable reflecting the business cycle in terms of the industrial market growth rate, the inclusion of a time-specific variable might cause problems with multicollinearity.
Air transport (SIC code 62) and Post and telecommunication (SIC code 64) sectors. These industrial sectors are sectors which consist of few firms and underwent deregulation during the 1990s, which caused the entry and exit rates to be very high.
For details about this heteroscedasticity correction method see, for example, Greene (2003).
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The author would like to thank David Storey, Charlie Karlsson, Niclas Berggren and two anonymous referees for valuable comments on a previous version of this paper.
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Nyström, K. Patterns and determinants of entry and exit in industrial sectors in Sweden. J Int Entrepr 5, 85–110 (2007). https://doi.org/10.1007/s10843-007-0017-z
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DOI: https://doi.org/10.1007/s10843-007-0017-z