Understanding the Distributional Aspects of Microcredit Expansions

TL;DR

This paper analyzes the heterogeneous impacts of increased microcredit access on poor entrepreneurs using data from three RCTs, finding no average effects but significant variability depending on the analytical method used.

Contribution

It introduces a machine learning-based framework to evaluate heterogeneity in microcredit impacts across different subgroups and algorithms.

Findings

No average impact on consumption or profits.

Heterogeneous impacts exist but are sensitive to the machine learning method.

Methodology helps assess uncertainty in impact estimates.

Abstract

Various poverty reduction strategies are being implemented in the pursuit of eliminating extreme poverty. One such strategy is increased access to microcredit in poor areas around the world. Microcredit, typically defined as the supply of small loans to underserved entrepreneurs that originally aimed at displacing expensive local money-lenders, has been both praised and criticized as a development tool (Banerjee et al., 2015b). This paper presents an analysis of heterogeneous impacts from increased access to microcredit using data from three randomised trials. In the spirit of recognising that in general the impact of a policy intervention varies conditional on an unknown set of factors, particular, we investigate whether heterogeneity presents itself as groups of winners and losers, and whether such subgroups share characteristics across RCTs. We find no evidence of impacts, neither average nor distributional, from increased access to microcredit on consumption levels. In contrast, the lack of average effects on profits seems to mask heterogeneous impacts. The findings are, however, not robust to the specific machine learning algorithm applied. Switching from the better performing Elastic Net to the worse performing Random Forest leads to a sharp increase in the variance of the estimates. In this context, methods to evaluate the relative performing machine learning algorithm developed by Chernozhukov et al. (2019) provide a disciplined way for the analyst to counter the uncertainty as to which algorithm to deploy.