Os Beneficiários do programa Bolsa Família trabalham menos? Uma análise utilizando pareamento por escore de propensão

Abstract

It is typical to hear criticism stating that the Bolsa Família program discourages people from working. To verify if this is true, it is not enough to compare the activity rate among beneficiaries and non-beneficiaries, since they are different in other aspects that may also decrease the activity rate. It’s necessary a method that consider this information to be used. Therefore, the aim of this final paper is to evaluate the impact of the Bolsa Família Program on the participation of beneficiaries in the labor market, using regression analysis, blocks and propensity score matching. This impact is measured by the ATE (Average Treatment Effect). Data analyzed came from the 2005 Impact Evaluation of the Bolsa Família Program (AIPBF), carried out by the Center for Regional Development and Planning (Cedeplar) from the Department of Economics of the Federal University of Minas Gerais, through which 15,426 households were interviewed (from 10/24/2005 to 12/5/2005), in 269 municipalities from 24 states, regarding expenditures on food, anthropometry, clothing, school attendance, healthcare, labor and social benefits received (not only the PBF). It was used potential results approach, which involves the estimation of the propensity score, defined as the probability of an individual to receive treatment, conditional on X, the vector of confounding variables. In order to calculate the propensity score matching [^e(x)], logistic regression analysis was used. After selecting covariates, reducing the sample and dividing it into strata, three methods of ATE estimation were applied: ordinary least squares, blocks and propensity score matching. From the analysis, the people who receive the benefit do not tend to work less, since the effect obtained by the methods considered superiors (blocks and matching) was not significant. In order words, the analysis indicated that the Bolsa Família Program does not make people work less.