Identification and estimation of interventions using changes in inequality measures

This paper presents semiparametric estimators of changes in inequality measures of a dependent variable distribution taking into account the possible changes on the distributions of covariates. When we do not impose parametric assumptions on the conditional distribution of the dependent variable given covariates, this problem becomes equivalent to estimation of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. The distributional impacts of a treatment will be calculated as differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called here Inequality Treatment Effects (ITE). The estimation procedure involves a first non-parametric step in which the probability of receiving treatment given covariates, the propensity-score, is estimated. Using the inverse probability weighting method to estimate parameters of the marginal distribution of potential outcomes, in the second step weighted sample versions of inequality measures are computed. Root-N consistency, asymptotic normality and semiparametric efficiency are shown for the semiparametric estimators proposed. A Monte Carlo exercise is performed to investigate the behavior in finite samples of the estimator derived in the paper. We also apply our method to the evaluation of a job training program.

Lighting and violent crimes: evaluating the effect of an electrification policy in rural Brazil on violent crime reduction

This paper estimates the effect of lighting on violent crime reduction. We explore an electrification program (LUZ PARA TODOS or Light for All - LPT) adopted by the federal government to expand electrification to rural areas in all Brazilian municipalities in the 2000s as an exogenous source of variation in electrification expansion. Our instrumental variable results show a reduction in homicide rates (approximately five homicides per 100,000 inhabitants) on rural roads/urban streets when a municipality moved from no access to full coverage of electricity between 2000 and 2010. These findings are even more significant in the northern and northeastern regions of Brazil, where rates of electrification are lower than those of the rest of the country and, thus, where the program is concentrated. In the north (northeast), the number of violent deaths on the streets per 100,000 inhabitants decreased by 48.12 (13.43). This moved a municipality at the 99th percentile (75th) to the median (zero) of the crime distribution of municipalities. Finally, we do not find effects on violent deaths in households and at other locations. Because we use an IV strategy by exploring the LPT program eligibility criteria, we can interpret the results as the estimated impact of the program on those experiencing an increase in electricity coverage due to their program eligibility. Thus, the results represent local average treatment effects of lighting on homicides.