Overcoming Selection Bias in Microcredit Impact Assessments: A Case Study in Peru

There are several potential sources of bias in microcredit impact assessments. This paper uses a panel data set from a Peruvian MFI to test for impact of credit on microenterprise profits, while controlling for these biases. We find that those who will eventually become borrowers have significantly higher incomes than those who will not become borrowers, implying that selection into the lending programme is a substantial problem. After controlling for this selection, we find that an average microentrepreneur who borrows earns significantly higher enterprise profits than one who does not borrow, and that na�ve models, which do not control for selection, overestimate this impact. Fixed effects estimates give roughly the same results as the quasi-experimental cross-section analysis.