Microfinance Impact: Bias from Dropouts
This paper critically examines the cross-sectional impact assessment tool developed by the United States Agency for International Development (USAID), and seeks to highlight the consequences of ignoring biases inherent in it.
The tool recommends comparing veteran members of a microcredit program to new members, and attributes any difference to the impact of the program. It introduces a potential source of bias into estimates of impact by not instructing organizations to include program dropouts in their calculations. The paper uses data from a longitudinal study in Peru of Mibanco borrowers and non-borrowers to quantify some of the biases in the cross-sectional approach. To measure dropout bias, the study recalculates impact on a data set that includes dropouts. Study results indicate that:
- Not including dropouts overestimates impact of the credit program;
- Sample composition shifted over the two years, introducing further bias into cross-sectional impact assessment.
The study concludes that the additional cost incurred in interviewing individuals who have left the program is justified because it provides a more accurate impact assessment as well as valuable information on the causes of dropout.