Public guarantee schemes, corruption and gender: a European SME-level analysis

ABSTRACT

Does a corrupt politico-institutional environment affect the demand of public subsidies for credit access – so-called public guarantee schemes – by small and medium-sized enterprises (SMEs) helmed by female entrepreneurs? The paper tackles this question by using a large sample of European SMEs over 2010–2014 while also carefully addressing possible endogeneity issues. It provides robust evidence that, compared to their male counterparts, female entrepreneurs: (a) tend to demand more public subsidies, and (b) are more sensitive to the quality of the politico-institutional environment. The upshot is that a corrupt environment is not gender neutral: in light of ‘essential gender features,’ corruption negatively influences SMEs helmed by female entrepreneurs more than male ones.

KEYWORDS:

• corruption
• European SMEs
• female entrepreneurship
• gender
• public subsidies

JEL CLASSIFICATION:

• H20
• J16
• J18
• K40

Acknowledgments

We are grateful to the Editor, David Peel, and an anonymous referee for very useful comments. We also thank Emma Galli and Tania Masi for suggestions, and the European Public Choice Society (EPCS) scientific board that selected the paper for the 2020 EPCS annual conference that should have been held in Lucerne (April 15-18). Nadia Fiorino and Giampaolo Garzarelli are grateful for the financial support from the Research project MIUR PRIN 2017 – 2017CRLZ3F: PolitiCanti. Finally, Stefania P. S. Rossi gratefully acknowledges the research grants from University of Trieste (FRA-2018). The usual disclaimers apply.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

¹ Take note that this is not to say that a grant is a panacea and cost-free policy tool. When, for example, a subsidy-supported SME would have been privately financed anyways, there would be no impact on private sector access to credit. Or think of when a subsidy may exacerbate adverse selection and moral hazard because it limits SME liability – in this case, there likely would be an increase of so-called bad loans. More generally, a grant is a policy tool that can have both positive (value creation) and negative (rent seeking) aspects (Garzarelli and Keeton 2018). Here, we focus on the positive aspect of grants through the facilitation of credit access for female entrepreneurs. For an exploration of the negative aspect of grants in a germane context, see Gustafsson et al. 2020 on ‘subsidy entrepreneurship.’

² An alternative view is proposed by Sung (2003) and Hazarica (2018). The argument here is that measures of female participation in the labour force and in politics are potentially endogenous in liberal democracies. Other studies suggest that the relationship is specific to democracies (Esarey and Chirillo 2013) and is present when electoral accountability is high (Esarey and Schwindt-Bayer 2017).

³ SAFE data do not allow to disentangle the three roles of CEO, director and owner. We believe that especially for the micro and small firms often there is no separation between ownership and control. Therefore, to produce robust estimates in Section 4.3, we report the results obtained on a sub-sample of micro and small firms only.

⁴ The WGI reports aggregate and individual governance indicators for over 200 countries and territories over the period 1996–2018. The set of indicators includes six dimensions of governance, viz. the traditions and institutions by which authority in a country is exercised. The construction of these composite indices is complex (it is based on an unobserved components model), drawing upon data from over 30 individual data sources produced by a variety of survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms in industrial and developing countries.

⁵ The stratification by firm size class included micro (1 to 9 employees), small (10 to 49 employees) and medium-sized firms (50 to 249 employees). A group of large firms (250 or more employees) is also considered. The selected sectors relate to the four largest activities: industry, construction, trade and services. Agriculture, forestry, fishing, financial intermediation, public administration, activities of households, extra-territorial organizations, as well as bodies and holding companies are excluded. The age of the firms includes very recent, which equals one if the firm is less than 2 years old, recent, which has the value of one if the firm is between 2 and 5 years old, and old, which equals one if the firm is between 5 and 10 years old.

⁶ As the SMEs included in the survey are randomly selected in each wave, the original SAFE is built as an unbalanced panel dataset. The unbalanced panel prevents the tracking of demand for public grants through time for all the firms of the sample. One could obtain a balanced panel by tracking the limited number of firms that are present in the consecutive waves, but the cost would be a significant reduction in number of observations.

⁷ We compute data on this instrumental variable as averages of quarterly data that have been linked to each wave of our sample.

⁸ $Controlofcorruption$ was introduced several years after the CPI. Yet it is our primary measure of institutional quality because it has expanded and improved Transparency International’s measure in several ways, particularly with regard to the treatment of statistical uncertainty contained in the CPI.

⁹ Our results are robust to the inclusion of the interaction between bank loan application and female. The results are available upon request.

¹⁰ Table 2 presents the results of the second stage IV probit estimates. The results of the first stage and the Wald test on the exogeneity of the instrumental variables used are reported in Table A4 in the online Appendix.

¹¹ First stage results and the Wald test are reported in Table A5 in the online Appendix.

Additional information

Funding

This work was supported by the MIUR PRIN 2017 – 2017CRLZ3F: PolitiCanti. Università degli Studi di Trieste [Rossi FRA-2018].