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Investigating the impact of the technological environment on survival chances of employer entrepreneurs

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Abstract

Given the big employment losses in the current economic and financial crisis, not only the creation of new jobs is important for economic and social welfare, but also the conservation of existing jobs. In this respect, it is crucial that firms that employ personnel survive. In this article, we investigate the role of the technological environment in determining the survival chances of employer entrepreneurs, defined as owner-managers of firms that employ personnel. We estimate survival models to analyze durations as an employer entrepreneur, using micro-panel data from EU-15 countries drawn from the European Community Household Panel. As indicators for the technological environment, we use a country’s R&D expenditures, a country’s employment share of high-tech and knowledge-intensive sectors, and a country’s number of patent applications to the European Patent Office. We find strong support for a positive relationship between these indicators of the technological environment in country j and year t and survival chances of employer entrepreneurs in that same country and year. Our analysis also suggests that a selection effect may be part of the explanation in the sense that in a more advanced technological environment, relatively more ‘high-quality’ individuals select into entrepreneurship.

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Notes

  1. The uncertain nature of knowledge also explains that in an entrepreneurial regime, innovations tend to be more radical and less incremental.

  2. We are aware that many new firms in the entrepreneurial economy do not survive for long, in particular the smallest of new firms (i.e., one-person businesses). We would like to stress here though that our empirical analysis actually considers small employer firms but not necessarily new firms or one-person businesses.

  3. The ECHP data are used with the permission of Eurostat (contract ECHP/2006/09, held with the Universidad de Huelva).

  4. France, Luxembourg and Sweden were excluded from our analysis for different reasons. Regarding France, own-account workers cannot be distinguished from employers because of the high number of missing values observed in the variable which enables such a distinction to be made. Regarding Sweden, longitudinal analyses are not possible since the data set in this country has not been collected by following a panel structure. Finally, Luxembourg presents missing values in some of our variables capturing the technological environment.

  5. Those entrepreneurs with no employees are considered own-account workers and are excluded from the survival analysis.

  6. Those individuals entering employership before 1981 are excluded from our sample because we do not have information about the exact employership spell duration. However, the number of employers within this group is smaller than 3 % of the initial sample, and their exclusion does not affect our results in a significant way.

  7. The way we handle left-truncation and right-censoring problems is described in Sect. 3.3.

  8. Exits to inactivity involve education or training, early retirement (before 59 years of age), doing housework, looking after children or other persons, and some other activities.

  9. We excluded 752 observations from our sample because they present missing values in some of the following control variables: cohabiting, number of children under 14; educational attainment, and business sector dummies. As a robustness check, we imputed these missing values using chained equations and re-estimated our models, obtaining similar results to those presented later in Sect. 4. These results are available on request.

  10. This section draws especially on the Stephen P. Jenkins’ Lecture Notes corresponding to the course Survival Analysis by Stephen P. Jenkins, provided by the University of Essex Summer School.

  11. For persons with censored spells, all observations are censored; for persons with a completed spell, all observations are censored except the final one.

  12. The multinomial logit model imposes the assumption of independence from irrelevant alternatives (IIA), which implies that the probability of choosing between two outcomes is not affected by the characteristics of the other alternatives. In this regard, McFadden (1974) argued that multinomial logit models should be used only in cases where the alternatives can plausibly be assumed to be distinct and weighted independently. In our view, the assumption of IIA in the context of our analysis is reasonable. In addition, we performed a set of Wald and likelihood ratio tests to examine the null hypothesis that the coefficients of the alternatives do not differ significantly from each other for all possible combinations. In both tests, none of the categories should be combined because the null hypothesis is rejected. Therefore, the multinomial logit specification seems to be appropriate. For brevity, the results of these tests are not shown but are available upon request.

  13. This standard error correction reflects associations across the spells and therefore, addresses the issue of repeated spells of employership. Let us stress that this issue is relatively small in our sample (less than 15 % of cases).

  14. According to criteria such as change in log-likelihood value, AIC or BIC values, the inclusion of technological environment measures improves the goodness of fit (with respect to our baseline model including only control variables), without affecting the sign and significance of other coefficient estimates.

  15. Presenting marginal effects on the basis of 1 % increases of these variables seems unrealistic, given their sample means: 1.2 % for R&D expenditure and 3.5 % for high-technology employment.

  16. As a robustness check, we disaggregate our variables capturing R&D expenditure and patent applications by sector of performance: (1) business enterprise sector, (2) government sector and (3) higher education sector. As regards R&D expenditure, all exit probabilities decrease when these efforts are made by the business enterprise sector or the higher education sector. As regards expenditures by the government sector, we only obtain negative and significant effects for exits to own-account work. Hence, for the R&D measure, the positive influence of the technological environment on survival chances is particularly strong for business R&D and R&D by the higher education sector. With respect to patent applications, all exit probabilities decrease, no matter the sector that is behind the patent application. These results are available on request.

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Acknowledgments

Mirjam van Praag provided helpful comments on an earlier draft. The research has been supported by the framework of the research program SCALES, carried out by Panteia/EIM and financed by the Dutch Ministry of Economic Affairs.

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Authors and Affiliations

  1. Panteia/EIM, Zoetermeer, The Netherlands

    André van Stel

  2. Department of Economics, University of Huelva, Huelva, Spain

    José María Millán & Concepción Román

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  1. André van Stel
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  2. José María Millán
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  3. Concepción Román
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Correspondence to André van Stel or José María Millán.

Appendix

Appendix

See Tables 2, 3 and 4.

Table 2 Variables definition
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Table 3 Mean values of innovation variables by country (1994–2001)
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Table 4 Correlations between innovation variables
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van Stel, A., Millán, J.M. & Román, C. Investigating the impact of the technological environment on survival chances of employer entrepreneurs. Small Bus Econ 43, 839–855 (2014). https://doi.org/10.1007/s11187-014-9565-5

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