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.