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Size matters: entrepreneurial entry and government

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Abstract

We explore the country-specific institutional characteristics likely to influence an individual’s decision to become an entrepreneur. We focus on the size of the government, on freedom from corruption and on “market freedom” defined as a cluster of variables related to protection of property rights and regulation. We test these relationships by combining country-level institutional indicators for 47 countries with working-age population survey data taken from the Global Entrepreneurship Monitor. Our results indicate that entrepreneurial entry is inversely related to the size of the government, and more weakly to the extent of corruption. A cluster of institutional indicators representing “market freedom” is only significant in some specifications. Freedom from corruption is significantly related to entrepreneurial entry, especially when the richest countries are removed from the sample, but unlike the size of government, the results on corruption are not confirmed by country-level fixed-effects models.

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Notes

  1. Entrepreneurship is a multidimensional concept (see Acs and Szerb 2009), and our results may not apply to measures other than entry. We motivate our focus by the work of Lumpkin and Dess (1996) who state “the essential act of entrepreneurship is new entry” (1996, p. 136). See also Acs (2006).

  2. For example, the highly corrupt environment in Russia may explain the low levels of entrepreneurship there (see also Aidis and Adachi 2007; Aidis, et al. 2008).

  3. An example of predatory entrepreneurship in this case would be “private” security companies which provide business protection at a price and that profit from maintaining weak local property rights (e.g. Sicily). This is a form of destructive entrepreneurship and has a negative overall effect on economic growth.

  4. This variable measures the existence and extensiveness of private firms or non-governmental organizations that maintain databases on the creditworthiness of borrowers (Van Stel et al. 2007, p. 178).

  5. Koellinger (2008) applies similar methodology to GEM data focussing on innovativeness.

  6. World Bank “Governance” indicators would be an attractive alternative. Unfortunately, it is only in the most recent period that these have been reported annually. For the period we cover, some years are missing.

  7. This indicator is available since 2005.

  8. As accessed in February 2008. Since labour freedom is available from 2005 only, it was not included. However, we verified that it did not affect the results significantly. When we run factor analysis for a shorter period but with freedom of labour included, the first two factors still explain most of the variance, and loadings of labour freedom are not high on either of them. Empirically, labour freedom is negatively correlated with the size of government spending, and therefore its impact is difficult to separate when the size of the government is taken into account (see Aidis et al. 2007).

  9. However, retaining two factors comes at the cost of a high uniqueness value for the trade freedom indicator (at 0.71); i.e. this variable is not well explained by the extracted factors. Generally, however, sampling adequacy is high: the overall Kaiser–Meyer–Olkin measure is 0.85.

  10. As argued by Costello and Osborne (2005), orthogonal rotation does not utilise all available information. Moreover, if factors are truly uncorrelated, the results of oblique rotation are very similar to the results of orthogonal rotation. In our case the correlation between the two factors after oblique rotation is −0.14. We use oblimin method following recommendation by Fabrigar et al. (1999) and Russell (2002). We also applied promax and verified that the results based on the two approaches are almost indistinguishable for our data.

  11. However, while the size of government measure as reported by the Heritage Foundation is simply based on the volume of government expenses, the tax measure includes the impact of marginal tax rates in addition to tax revenues.

  12. These results can be obtained from the authors upon request. Estimation with GDP per capita squared does not produce a credible Wald statistics for the probit model. At the same time, the main results are not affected.

  13. We also used a 20% threshold. The results were not affected.

  14. Additional results can be obtained from the authors upon request.

  15. The Polity IV project captures global trends in governance and currently includes 163 countries. The Polity conceptual scale examines concomitant dimensions of democratic and autocratic authority in governing institutions. It is based on six components that measure three key qualities: executive recruitment, constraints to executive authority and political competition (see also http://www.systemicpeace.org/polity/polity4.htm).

  16. It is likely that there is a U-shaped relationship between the level of development and entrepreneurial entry, as postulated by Acs et al. (1994), Carree et al. (2002) and Wennekers et al. (2005). Unfortunately, with regards to our data, when we attempt to enter a linear and quadratic GDP per capita term in our specifications, the overall Wald statistics for the probit regression cannot be produced. For this reason, we stick to the logarithmic transformation.

  17. Given the high significance of the size of the government we also explored what happens if a quadratic term is added; it is insignificant.

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    Ruta Aidis, Saul Estrin & Tomasz Marek Mickiewicz

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Aidis, R., Estrin, S. & Mickiewicz, T.M. Size matters: entrepreneurial entry and government. Small Bus Econ 39, 119–139 (2012). https://doi.org/10.1007/s11187-010-9299-y

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