A Quantile Regression Analysis of Micro-lendings Poverty Impact

This paper aims to evaluate the impact of a microlending program on ameliorating measured poverty within its client population, with the aim of improving that impact.  We analyze over 18,000 women micro-finance clients of the Negros Women for Tomorrow Foundation (NWTF), a database using the Progress out of Poverty (PPI) Scorecard as a measure of poverty.  Analysis using both OLS and quantile multivariate regression models shows how observable borrower attributes affect the ability of clients to reduce their measured poverty.  Loan size, duration, and the economic activity supported all have strongly identifiable effects.  Moreover, estimates suggest which among the poor are receiving the greatest effective help by the program.  Results offer specific advice to the NWTF and other micro-lenders:  impact is greatest with fewer, larger loans in particular economic sectors (sari-sari, service and trade) but require patience as each additional year increases the client’s average change in poverty score.