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Gender Biases in Bank Lending: Lessons from Microcredit in France

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The evidence on gender discrimination in lending remains controversial. To capture gender biases in banks’ loan allocations, we observe the impact on the applicants of a microfinance institution (MFI) and exploit the natural experiment of a regulatory change imposing a strict EUR 10,000 loan ceiling on microcredit. Descriptive statistics indicate that the presence of the ceiling is associated both with bank-MFI co-financing and with harsher treatment of female borrowers. To investigate causal links, we develop an econometric approach that addresses the concerns of selection biases, multicollinearity, and endogeneity. Our empirical findings suggest that the change in the MFI’s gender-related attitude was triggered by banks through co-financing. Hence, we speculate that co-financing pushes ceiling-constrained MFIs to import whatever biases in loan granting that the banks are prone to. Overall, this paper stresses that apparently benign regulations such as loan ceilings can significantly harm the women’s empowerment efforts made by MFIs.

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  1. Riding and Swift (1990), Coleman (2000) and Bellucci et al. (2010) find that collateral requirements are gender-related in Canada, the UK, and Italy, respectively. Alesina et al. (2013) and Agier and Szafarz (2013a) show that female micro-entrepreneurs receive smaller loans than male ones in Italy and Brazil, respectively. More generally, Bagus et al. (2015) discuss the ethicality of banks’ actions.

  2. According to Field et al. (2014, p. 10) “much of today’s microcredit arrangements bear little resemblance to loans offered by organizations such as the United States Small Business Administration (SBA), which are also designed, ostensibly, to support the kind of entrepreneurial risk-taking necessary for success.”

  3. This ceiling is significantly lower than the EUR 25,000 threshold recommended by the European Commission.

  4. Most of them are unemployed people aiming at self-employment.

  5. MFIs and banks have different statuses. MFIs are subsidized institutions maximizing social performance within a budget constraint, while banks are driven by profit maximization (Aubert et al. 2009). However, Armendariz and Szafarz (2011) provide evidence that the social mission varies across MFIs.

  6. In the United States, the loan ceiling for microcredit is USD 50,000. The European Union (EU) recommends the use of a EUR 25,000 ceiling, but member states remain free to set their own rules. Some countries (Romania, Italy) have adopted the EU recommendation, while others, like Hungary, Portugal, Slovakia, and the UK, allow MFIs to grant loans exceeding EUR 25,000. France is the only EU member to impose a ceiling below the EU recommendation.

  7. In our dataset, 71 % of the applicants with a secured bank loan ended up with a co-financing arrangement. Moreover, in 3 years out of four (2009, 2010, and 2011, but not 2012) the interest rates charged by the banks are significantly higher than the rate charged by the MFI.

  8. As pointed out by Vanroose and D’Espallier (2013), microfinance reaches more clients in countries with low financial inclusion, which is not the case of developed countries in general, and France in particular.


  10. In 2010, the MFI opened two new branches and its staff passed from six to ten employees.

  11. The granted loan size was smaller than the requested one in just 7.6 % of our sample. In contrast, most MFIs determine loan sizes in house (Agier and Szafarz, 2013b).

  12. This threshold was hardly binding.

  13. The sample size is smaller for the first period, which may result in larger standard deviations and less rejections of H0.

  14. However, as pointed out by Johnson (2014), characteristics that are highly correlated with gender can hide an underlying reality involving gender discrimination.

  15. Due to the specific nature of our sample, introducing the interaction term in Eq. (4) would make little sense. During the ceiling-free period, only six applicants came to the MFI with a bank loan and none of them ended up with a loan from the institution.

  16. This argument involving credit risk is speculative since we do not observe the outcomes of the loans. This being said, the literature amply documents that women are more creditworthy than men, all else equal (D’Espallier et al. 2011).

  17. This approach is consistent with the 2PLS estimation sequence that starts with project size. Another option would be to select only the applications involving no bank loan. In addition to making a break with our estimation strategy, this option would likely introduce a massive selection bias.

  18. However, the authors detect a gender gap for loans exceeding EUR 25,000.

  19. Conversely, reforms aiming at gender equality can also backfire (Bøhren and Staubo 2014).

  20. In contrast, experimental data addresses the problem of unobservable characteristics (Beaman et al. 2009).

  21. This approach is used by Bertrand and Mullainathan (2004) on the labor market.


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The authors thank Renaud Bourlès, Olivier Chanel, Habiba Djebbari, Supriya Garikipati, Isabelle Guérin, Susan Johnson, Robert Lensink, Thierry Magnac, Marc Sangnier, and the participants in the “Microfinance and Women’s Empowerment: The Road Ahead” workshop (Liverpool, July 2013) for valuable comments. The two authors benefited from the financial support of the “Interuniversity Attraction Pole” on social enterprise, funded by the Belgian Science Policy Office. Anastasia Cozarenco is member of the Labex Chair “Entrepreneurship & Innovation”.

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Correspondence to Anastasia Cozarenco.

Appendix 1

Appendix 1

See Tables 9 and 10.

Table 9 Correlations of project size and bank loan with other covariates
Table 10 Descriptive statistics on other financial characteristics

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Cozarenco, A., Szafarz, A. Gender Biases in Bank Lending: Lessons from Microcredit in France. J Bus Ethics 147, 631–650 (2018).

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