While providing the most reliable method of evaluating social programs, randomized experiments in developing and developed countries alike are accompanied by political risks and ethical issues that jeopardize the chances of adopting them. In this paper we use a unique data set from rural Mexico collected for the purposes of evaluating the impact of the PROGRESA poverty alleviation program to examine the performance of a quasiexperimental estimator, the Regression Discontinuity Design (RDD). Using as a benchmark the impact estimates based on the experimental nature of the sample, we examine how estimates differ when we use the RD design as the estimator for evaluating program impact on two key indicators: child school attendance and child work. Overall the performance of the RDD performance 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 method failed to reveal any significant program impact on the 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 we used as a comparison group children from non-eligible households in the control localities.