An Evaluation of the Performance of Regression Discontinuity Design on Progresa

While providing the most reliable method of evaluating social programs, randomized experiments in industrial and developing countries alike are accompanied by political risks and ethical issues that jeopardize the chances of adopting them. Buddelmeyer and Skoufias use a unique data set from rural Mexico collected for the purpose of evaluating the impact of the PROGRESA poverty alleviation program to examine the performance of a quasi-experimental estimator, the regression discontinuity design (RDD). Using as a benchmark the impact estimates based on the experimental nature of the sample, the authors examine how estimates differ when the RDD is used as the estimator for evaluating program impact on two key indicators - child school attendance and child work. Overall the performance of the RDD was remarkably good. The RDD estimates of program impact agreed with the experimental estimates in 10 out of the 12 possible cases. The two cases in which the RDD failed to reveal any significant program impact on school attendance of boys and girls were in the first year of the program (round 3). RDD estimates comparable to the experimental estimates were obtained when the authors used as a comparison group children from noneligible households in the control localities. This paper - a product of the Poverty and Gender Unit, Latin America and the Caribbean Region - is part of a larger effort in the region to develop and apply rigorous methods in the evaluation of poverty alleviation programs