We implement a lab-in-the-field experiment with 334 Turkish loan officers to document gender discrimination in small business lending and to unpack the mechanisms at play. Each officer reviews multiple real-life loan applications in which we randomize the applicant's gender. While unconditional approval rates are the same for male and female applicants, loan officers are 26 percent more likely to require a guarantor when we present the same application as coming from a female instead of a male entrepreneur. A causal forest algorithm to estimate heterogeneous treatment effects reveals that this discrimination is strongly concentrated among young, inexperienced, and gender-biased loan officers. Discrimination mainly affects female loan applicants in male-dominated industries, indicating how financial frictions can perpetuate entrepreneurial gender segregation across sectors.
|Name||CentER Discussion Paper|
- Gender bias
- bank credit
- implicit association test
- causal forest