Women Are Less Likely to Access Consumer Credits

A study by CAF—development bank of Latin America—, the University of Chile and the Commission for the Financial Market of Chile found that loan applications submitted by women are 14.8% less likely to be approved than those by men with the same credit profile in the consumer credit market.

The study was conducted on a representative sample of credit officers who were randomly assigned loan applications of different amounts and terms from a sample of potential borrowers, which were matched with men and women profiles, in order to obtain a balanced sample of male and female borrowers with identical distributions in their ability to repay the loan.

The results suggest discrimination against women due to a gender bias in credit officers or account executives. Among pro-men credit officers, loan approval rates were 48% to 56% higher than approval rates for women, indicating that gender discrimination against borrowers is likely to be explained by taste-based sources.

In addition, banks with a higher number of male credit officers are associated with increased discrimination against women. The study also estimated that the benefits not received as a result of gender discrimination against female applicants amounted to 9.9% of the expected benefits associated with the approved loans.

“Latin America’s financial sector has the opportunity to bring about a shift in corporate and human resources management culture, leading to reduced loss of benefits from gender biases while adequately addressing and harnessing the financing potential of the women’s market segment,” said Jorge Arbache, Private Sector Vice President at CAF.

In terms of policy recommendations or actions to help eradicate gender biases, the study raises the need to design and implement recruitment and training mechanisms with a gender perspective. It also considers a cost-effective strategy for banks to assess the gender-based attitudes and preferences of men applying for jobs as a way to curb gender discrimination. In addition, automating the evaluation process by using algorithms that limit the influence of account executives on the final decision could also be beneficial in reducing discrimination.