Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called “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. In the second step weighted sample versions of inequality measures are computed using weights based on the estimated propensity-score. Root-N consistency, asymptotic normality, semiparametric efficiency and validity of inference based on the bootstrap are shown for the semiparametric estimators proposed. In addition of being easily implementable and computationally simple, results from a Monte Carlo exercise reveal that its good relative performance in small samples is robust to changes in the distribution of latent selection variables. Finally, as an illustration of the method, we apply the estimator to a real data set collected for the evaluation of a job training program, using several popular inequality measures to capture distributional impacts of the program.inequality measures, treatment effects, semiparametric efficiency, reweighting estimator

Efficient Semiparametric Estimation of Quantile Treatment Effects

This paper presents calculations of semiparametric efficiency bounds for quantile treatment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric estimation and a computation of the difference between the solutions of two distinct minimization problems. Root-$N$ consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound is shown for one of the three estimators. In the final part of the paper, an empirical application to a job training program reveals the importance of heterogeneous treatment effects, showing that for this program the effects are concentrated in the upper quantiles of the earnings distribution.Quantile Treatment Effects, Propensity Score, Semiparametric Efficiency Bounds, Efficient Estimation, Semiparametric Estimation

Identification and estimation of interventions using changes in inequality measures

This paper presents semiparametric estimators of changes in inequality measures of adependent variable distribution taking into account the possible changes on the distribu-tions of covariates. When we do not impose parametric assumptions on the conditionaldistribution of the dependent variable given covariates, this problem becomes equivalent toestimation of distributional impacts of interventions (treatment) when selection to the pro-gram is based on observable characteristics. The distributional impacts of a treatment willbe calculated as di¤erences in inequality measures of the potential outcomes of receivingand not receiving the treatment. These differences are called here Inequality TreatmentEffects (ITE). The estimation procedure involves a first non-parametric step in whichthe probability of receiving treatment given covariates, the propensity-score, is estimated.Using the inverse probability weighting method to estimate parameters of the marginal dis-tribution of potential outcomes, in the second step weighted sample versions of inequalitymeasures are computed. Root-N consistency, asymptotic normality and semiparametrice¢ ciency are shown for the semiparametric estimators proposed. A Monte Carlo exerciseis performed to investigate the behavior in finite samples of the estimator derived in thepaper. We also apply our method to the evaluation of a job training program.

Identification and estimation of distributional impacts of interventions using changes in inequality measures

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called '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. In the second step weighted sample versions of inequality measures are computed using weights based on the estimated propensity-score. Root-N consistency, asymptotic normality, semiparametric efficiency and validity of inference based on the bootstrap are shown for the semiparametric estimators proposed. In addition of being easily implementable and computationally simple, results from a Monte Carlo exercise reveal that its good relative performance in small samples is robust to changes in the distribution of latent selection variables. Finally, as an illustration of the method, we apply the estimator to a real data set collected for the evaluation of a job training program, using several popular inequality measures to capture distributional impacts of the program

The relationship between school violence and student proficiency

School violence has recently become a central concern among teachers, students, students' parents andpolicymakers. Violence can induce behaviors on educational agents that go against the goals ofimproving the quality of education and increasing school attendance. In fact, there is evidence thatschool environmental characteristics and student performance and behavior at school are related.Although school violence may have a direct impact on students’ performance, such impact has not yetbeen quantified. In this paper, we investigate this issue using Brazilian data and show that, on average,students who attended more violent schools had worse proficiency on a centralized test carried out bythe Brazilian Ministry of Education, even when we controlled for school, class, teachers and studentcharacteristics. We also show that school violence affects more the students from the bottom of theproficiency distribution. Furthermore, we find out that besides the direct effect on student proficiency,it seems that school violence has an indirect effect on it operating through teacher turnover. Indeed, weshow that the occurrence of violent episodes in a school decreases the probability of a class in thatschool having only one teacher during the academic year, and increases the probability of that classhaving more than one teacher (teacher turnover).

Bounds on functionals of the distribution treatment effects

Bounds on the distribution function of the sum of two random variableswith known marginal distributions obtained by Makarov (1981) canbe used to bound the cumulative distribution function (c.d.f.) of individualtreatment effects. Identification of the distribution of individualtreatment effects is important for policy purposes if we are interested infunctionals of that distribution, such as the proportion of individuals whogain from the treatment and the expected gain from the treatment forthese individuals. Makarov bounds on the c.d.f. of the individual treatmenteffect distribution are pointwise sharp, i.e. they cannot be improvedin any single point of the distribution. We show that the Makarov boundsare not uniformly sharp. Specifically, we show that the Makarov boundson the region that contains the c.d.f. of the treatment effect distributionin two (or more) points can be improved, and we derive the smallest setfor the c.d.f. of the treatment effect distribution in two (or more) points.An implication is that the Makarov bounds on a functional of the c.d.f.of the individual treatment effect distribution are not best possible.

Identifying and measuring economic discrimination

Differences in wages between men and women, white and black workers, or any two distinct groups are a controversial feature of the labor market, raising concern about discrimination by employers. Decomposition methods shed light on those differences by separating them into: (i) composition effects, which are explained by differences in the distribution of observable variables, e.g. education level; and (ii) structural effects, which are explained by differences in the returns to observable and unobservable variables. Often, a significant structural effect, such as different returns to education, can be indicative of discrimination

Occupational Tasks and Changes in the Wage Structure

This paper argues that changes in the returns to occupational tasks have contributed to changes in the wage distribution over the last three decades. Using Current Population Survey (CPS) data, we first show that the 1990s polarization of wages is explained by changes in wage setting between and within occupations, which are well captured by tasks measures linked to technological change and offshorability. Using a decomposition based on Firpo, Fortin, and Lemieux (2009), we find that technological change and deunionization played a central role in the 1980s and 1990s, while offshorability became an important factor from the 1990s onwards.wage inequality, polarization, occupational tasks, offshoring, RIF-regressions

Unconditional Quantile Regressions

We propose a new regression method to estimate the impact of explanatory variables on quantiles of the unconditional distribution of an outcome variable. The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables. The influence function is a widely used tool in robust estimation that can easily be computed for each quantile of interest. We show how standard partial effects, as well as policy effects, can be estimated using our regression approach. We propose three different regression estimators based on a standard OLS regression (RIFOLS), a Logit regression (RIF-Logit), and a nonparametric Logit regression (RIFNP). We also discuss how our approach can be generalized to other distributional statistics besides quantiles.Influence Functions, Unconditional Quantile, Quantile Regressions.

Electoral rules, political competition and fiscal spending : regression discontinuity evidence from Brazilian municipalities

We exploit a discontinuity in Brazilian municipal election rules to investigate whether political competition has a causal impact on policy choices. In municipalities with less than 200,000 voters mayors are elected with a plurality of the vote. In municipalities with more than 200,000 voters a run-off election takes place among the top two candidates if neither achieves a majority of the votes. At a first stage, we show that the possibility of runoff increases political competition. At a second stage, we use the discontinuity as a source of exogenous variation to infer causality from political competition to fiscal policy. Our second stage results suggest that political competition induces more investment and less current spending, particularly personnel expenses. Furthermore, the impact of political competition is larger when incumbents can run for reelection, suggesting incentives matter insofar as incumbents can themselves remain in office.Electoral Systems; Strategic Voting; Political Competition; Regression Discontinuity; Fiscal Spending. JEL Codes: H72; D72; C14; P1