Carlos A. Flores

Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps

We semiparametrically estimate average causal effects of different lengths of exposure to academic and vocational instruction in the Job Corps (JC) under the assumption that selection into different lengths is based on a rich set of observed covariates and time-invariant factors. We find that the estimated effects on future earnings increase in the length of exposure and that the marginal effects of additional instruction decrease with length of exposure. We also document differences in the estimated effects across demographic groups, which are particularly large between males and females. Finally, our results suggest an important lock-in effect in JC training.

Partial Identification of Local Average Treatment Effects With an Invalid Instrument

We derive nonparametric bounds for local average treatment effects (LATE) without imposing the exclusion restriction assumption or requiring an outcome with bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential and counterfactual outcomes within or across subpopulations defined by the values of the potential treatment status under each value of the instrument. The key element in our derivation is a result relating LATE to a causal mediation effect, which allows us to exploit partial identification results from the causal mediation analysis literature. The bounds are employed to analyze the effect of attaining a GED, high school, or vocational degree on future labor market outcomes using randomization into a training program as an invalid instrument. The resulting bounds are informative, indicating that the local effect when assigned to training for those whose degree attainment is affected by the instrument is at most 12.7 percentage points on employment and

Lessons From Quantile Panel Estimation of the Environmental Kuznets Curve

64.4 on weekly earnings.

Bounds on Treatment Effects in the Presence of Sample Selection and Noncompliance: The Wage Effects of Job Corps

We employ quantile regression fixed effects models to estimate the income-pollution relationship on NO x (nitrogen oxide) and SO 2 (sulfur dioxide) using U.S. data. Conditional median results suggest that conditional mean methods provide too optimistic estimates about emissions reduction for NO x , while the opposite is found for SO 2. Deleting outlier states reverses the absence of a turning point for SO 2 in the conditional mean model, while the conditional median model is robust to them. We also document the relationships sensitivity to including additional covariates for NO x , and undertake simulations to shed light on some estimation issues of the methods employed.

Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies

Randomized and natural experiments are commonly used in economics and other social science fields to estimate the effect of programs and interventions. Even when employing experimental data, assessing the impact of a treatment is often complicated by the presence of sample selection (outcomes are only observed for a selected group) and noncompliance (some treatment group individuals do not receive the treatment while some control individuals do). We address both of these identification problems simultaneously and derive nonparametric bounds for average treatment effects within a principal stratification framework. We employ these bounds to empirically assess the wage effects of Job Corps (JC), the most comprehensive and largest federally funded job training program for disadvantaged youth in the United States. Our results strongly suggest positive average effects of JC on wages for individuals who comply with their treatment assignment and would be employed whether or not they enrolled in JC (the “always-employed compliers”). Under relatively weak monotonicity and mean dominance assumptions, we find that this average effect is between 5.7% and 13.9% 4 years after randomization, and between 7.7% and 17.5% for non-Hispanics. Our results are consistent with larger effects of JC on wages than those found without adjusting for noncompliance.

Going Beyond Late: Bounding Average Treatment Effects of Job Corps Training

We study the effectiveness of nonexperimental strategies in adjusting for comparison group differences when using data from several programs, each implemented at a different location, to compare their effect if implemented at alternative locations. First, we adjust for individual characteristics differences simultaneously across all groups using unconfoundedness-based and conditional difference-in-difference methods for multiple treatments. Second, we adjust for differences in local economic conditions and stress their role after program participation. Our results show that it is critical to have sufficient overlap across locations in both dimensions and illustrate the difficulty of adjusting for local economic conditions that differ greatly across locations.

The role of degree attainment in the differential impact of job corps on adolescents and young adults

We derive bounds on the population average treatment effect (ATE) and the average treatment effect on the treated (ATT) with an instrumental variable and employ them to evaluate the effectiveness of the Job Corps (JC) training program using data from a randomized evaluation with noncompliance. We find positive effects of JC on earnings and employment, and negative effects on public benefits dependence for eligible applicants (ATE) and participants (ATT). Some of our results also point to positive average effects on the labor market outcomes of “never-takers” (individuals who never enroll in JC regardless of their treatment assignment).

Cooperative tag communications

Abstract Job Corps is the United State’s largest and most comprehensive training program for disadvantaged youth aged 16–24 years old. A randomized social experiment concluded that, on average, individuals benefited from the program in the form of higher weekly earnings and employment prospects. At the same time, “young adults” (ages 20–24) realized much higher impacts relative to “adolescents” (ages 16–19). Employing recent nonparametric bounds for causal mediation, we investigate whether these two groups’ disparate effects correspond to them benefiting differentially from distinct aspects of Job Corps, with a particular focus on the attainment of a degree (GED, high school, or vocational). We find that, for young adults, the part of the total effect of Job Corps on earnings (employment) that is due to attaining a degree within the program is at most 41% (32%) of the total effect, whereas for adolescents that part can account for up to 87% (100%) of the total effect. We also find evidence that the magnitude of the part of the effect of Job Corps on the outcomes that works through components of Job Corps other than degree attainment (e.g., social skills, job placement, residential services) is likely higher for young adults than for adolescents. That those other components likely play a more important role for young adults has policy implications for more effectively servicing participants. More generally, our results illustrate how researchers can learn about particular mechanisms of an intervention.

Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment Under Unconfoundedness

In this paper we propose the use of multiple tags cooperatively backscattering the same message to improve reverse-link range of passive communications. Since no control of relative backscatter phase is available, backscattered amplitude is approximately Rayleigh-distributed in a line-of-sight environment. Selection of optimized subsets is projected to provide diversity gain, particularly in a fading environment. Preliminary experimental data demonstrates the expected increase in backscattered amplitude from multiple tags. We propose an approach for extending the 18000-6C protocol to support cooperative tags query operations and subset selection.