ABSTRACT

Microfinance is known as a direct and indirect contributor to the welfare of the poor in Pakistan. Data from the Pakistan Bureau of Statistics (PBS) Household Integrated Economic Survey (HIES) for the year 2018-19 has been used in this study.

An Econometric approach called “Propensity score matching (PSM)” technique have grown incredibly over the past decades. They are used primarily to match treatment and control units to estimate the causal treatment effect from observational studies or to link two or more data sets that share a common subset of covariates. This study analyzes the impact of microfinance institutions on the well-being of poor people in the literature on microfinance in Pakistan using PSM and matching methods like Nearest Neighbor Matching, Radius Matching, Kernal Method, and Stratification Method.

This study found out that microfinance institutions have a positive impact on poverty reduction and hence it is an effective tool for poverty reduction in many countries including Pakistan. This study found that most of the loan has been used for income-generating activities and many other functions. The Effect of microfinance on household well-being in Punjab, Sindh, and Balochistan has less effect by (-0.094), (-0.084), and (-0.119) coefficient values than KP (0.121) respectively. Propensity score matching (PSM) generated slightly different results. The study also shows that the treatment group who received microfinance had lower income than the control group. The best matching criteria are the Radius matching criteria, which is similar to unmatched difference and has a higher effect on well-being than rest three criteria for example nearest neighbor, kernel matching, and stratification matching criteria. The PSM has been preferred over OLS, DID, and RDD because it does provide us with some households which have similar characteristics.