There is a well-documented “gender profit gap” for small and medium enterprises in developing countries. Yet, observable differences between men and women entrepreneurs explain only a small portion of this earnings differential. Gender discrimination may be an important, yet understudied, factor inhibiting the success of female entrepreneurship. Policy solutions that reduce such discrimination, such as gender blinding, could increase gender equity and potentially improve efficiency in the allocation of capital. In addition, there is evidence that algorithms can be used to help avoid human prejudice in decisions prone to biases. On the other hand, by improving predictive accuracy, automated decision-making using available data could replicate or exacerbate underlying inequalities.
This research project explores how four different methods of evaluating businesses affect the equity and efficiency of capital allocation decisions. To test both human and algorithm-based loan decision-making, the research team is comparing decisions made by (i) loan officers with applicants’ gender information; (ii) loan officers that receive applicants in which the gender of the applicant has been randomized; (iii) an algorithm designed solely to predict business success and (iv) an algorithm designed to predict business success which incorporates discrimination-aware methods. This study is co-funded by CEGA’s Digital Credit Observatory.
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