Estimation of treatment effects for social program evaluation

Because impact evaluation is an important tool that guides public policy decisions and because applying impact evaluation is a rigorous process, we must generate examples of how impact evaluation methodologies apply to the Mexican context. To this end, we have used nonexperimental methodologies to estimate treatment effects for Mexican social programs. In order to quantify the effects that a social program has on its beneficiaries’ welfare and productive activities, we have used Stata to estimate treatment effects and to generate an adequate database with information from the Mexican Family Life Survey for the years 2002 and 2005. Two central Stata commands were used: pscore and psmatch2. The pscore command estimates the propensity score and stratifies individuals according to the propensity-score distribution, using for this a series of covariables that are assumed to be related to both treatment status and the result variable. This command also checks that the balancing property is satisfied. psmatch2 performs a variety of matching estimation methods to obtain estimates of the average treatment effect on the treated. Additionally, we used database handling commands such as foreach, merge, collapse, gen, egen, recode, and replace. Our panel will discuss the advantages and disadvantages of these commands when applied to the evaluation of social programs.