Nonparametric Partial Identification of Causal Net and Mechanism Average Treatment Effects

When analyzing the causal e§ect of a treatment on an outcome it is important to un- derstand the mechanisms or channels through which the treatment works. In this paper we study net and mechanism average treatment e§ects (NATE and MATE, respectively), which provide an intuitive decomposition of the total average treatment e§ect (ATE) that enables learning about how the treatment a§ects the outcome. We derive informative non- parametric bounds for these two e§ects allowing for heterogeneous e§ects and without re- quiring the use of an instrumental variable or having an outcome with bounded support. We employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations deÖned by the potential values of the mechanism variable under each treatment arm. We illustrate the identifying power of our bounds by analyzing what part of the ATE of a training program on weekly earnings and employment is due to the obtainment of a GED, high school, or vocational degree.causal inference, treatment effects, net effects, direct effects, nonparametric bounds, principal stratification

Partial Identification of Local Average Treatment Effects with an Invalid Instrument

We derive nonparametric bounds for local average treatment effects without requiring the exclusion restriction assumption to hold or an outcome with a bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations defined by the values of the potential treatment status under each value of the instrument. We illustrate the identifying power of the bounds by analyzing the effect of attaining a GED, high school, or vocational degree on subsequent employment and weekly earnings using randomization into a training program as an invalid instrument.causal inference, instrumental variables, treatment effects, nonparametric bounds, principal stratification

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

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.causal inference, causal mechanisms, post-treatment variables, principal stratification

Interpreting Degree Effects in the Returns to Education

Researchers often identify degree effects by including degree attainment (D) and years of schooling (S) in a wage model, yet the source of independent variation in these measures is not well understood. We argue that S is negatively correlated with ability among degree-holders because the most able graduate the fastest, while a positive correlation exists among dropouts because the most able benefit from increased schooling. Using data from the NLSY79, we find support for this explanation, and we reject the notion that the independent variation in S and D reflects reporting error.returns to education, degree effects

Bounds on Quantile Treatment Effects of Job Corps on Participants' Wages

This paper assesses the effect of the U.S. Job Corps (JC), the nation's largest and most comprehensive job training program targeting disadvantaged youths, on wages. We employ partial identification techniques and construct informative nonparametric bounds for the causal effect of interest under weaker assumptions than those conventionally used for point identification of treatment effects in the presence of sample selection. In addition, we propose and estimate bounds on quantile treatment effects of the program on participants' wages. In general, we find convincing evidence of positive impacts of JC on participants' wages. Importantly, we find that estimated impacts on lower quantiles of the distribution are higher, with the highest impact being in the 5th percentile where a positive effect on wages is bounded between 8.4 and 16.1 percent. These bounds suggest that JC results in wage compression within eligible participants.Job Corps, Nonparametric Bounds, Principal Stratification, Active Labor Market Programs., Labor and Human Capital, Public Economics, Research Methods/ Statistical Methods, J24, J68, C14, C21,

Unbundling the Degree Effect in a Job Training Program for Disadvantaged Youth

Government-sponsored education and training programs have the goal to enhance participants' skills so as to become more employable, productive and dependable citizens and thus alleviate poverty and decrease public dependence. While most of the literature evaluating training programs concentrates on estimating their total average treatment effect, these programs offer a variety of services to participants. Estimating the effect of these components is of importance for the design and the evaluation of labor market programs. In this paper, we employ a recent nonparametric approach to estimate bounds on the "mechanism average treatment effect" to evaluate the causal effect of attaining a high school diploma, General Education Development or vocational certificate within a training program for disadvantaged youth 16-24 (Job Corps) relative to other services pffered, on two labor outcomes: employment probability and weekly earnings. We provide these estimates for different demographic groups by race, ethnicity, gender, and two age-risk groups (youth and young adults). Our analysis depicts a positive impact of a degree attainment within the training program on employment probability and weekly earnings for the majority of its participants which in general accounts for 55 - 63 percent of the effect of the program. The heterogeneity of the key demographic subgroups is documented in the relative importance of a degree attainment and of the other services provided in Job Corps.Causal Inference, Treatment Effects, Mechanism Average Effects, Nonparametric Bounds, Potential Outcomes, Principal Stratification, Training Programs, Job Corps, Active Labor Market Policies, Labor and Human Capital, Public Economics, C14, I20, J01,

Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages

We assess the effectiveness of Job Corps (JC), the largest job training program targeting disadvantaged youth in the United States, by constructing nonparametric bounds for the average and quantile treatment effects of the program on wages. Our preferred estimates point toward convincing evidence of positive effects of JC on wages both at the mean and throughout the wage distribution. For the different demographic groups analyzed, the statistically significant estimated average effects are bounded between 4.6 and 12 percent, while the quantile treatment effects are bounded between 2.7 and 11.7 percent. Furthermore, we find that the program's effect on wages varies across quantiles and groups. Blacks likely experience larger impacts in the lower part of their wage distribution, while Whites likely experience larger impacts in the upper part of their distribution. Non-Hispanic Females show statistically significant impacts in the upper part of their distribution but not in the lower part.training programs, wages, bounds, quantile treatment effects

Land Use Change: A Spatial Multinomial Choice Analysis

Urban decentralization and dispersion trends have led to increased conversion of rural lands in many urban peripheries and exurban regions of the U.S. The growth of the exurban areas has outpaced growth in urban and suburban areas, resulting in growth pressures at the urban-rural fringe. A thorough analysis of land use change patterns and the ability to predict these changes are necessary for the effective design of regional environmental, growth, and development policies. We estimate a multinomial discrete choice model with spatial dependence using parcel-level data from Medina County, Ohio. Accounting for spatial dependence should result in improved statistical inference about land use changes. Our spatial model extends the binary choice “linearized logit” model of Klier and McMillen (2008) to a multinomial setting. A small Monte Carlo simulation indicates that this estimator performs reasonably well. Preliminary results suggest that the location of new urban development is guided by a preference over lower density areas, yet in proximity to current urban development. In addition, we find significant evidence of spatial dependence in land use decisions.Land Use Change, Multinomial Logit, Spatial Dependence, Community/Rural/Urban Development, Land Economics/Use, Research Methods/ Statistical Methods, R14, C21, C25,

Assessing the Sources of Changes in the Volatility of Real Growth

In much of the world, growth is more stable than it once was. Looking at a sample of twentyfive countries, we find that in sixteen, real GDP growth is less volatile today than it was twenty years ago. And these declines are large, averaging more than fifty per cent. What accounts for the fact that real growth has been more stable in recent years? We survey the evidence and competing explanations and find support for the view that improved inventory management policies, coupled with financial innovation, adopting an inflation targeting scheme and increased central bank independence have all been associated with more stable real growth. Furthermore, we find weak evidence suggesting that increased commercial openness has coincided with increased output volatility.

Do Dropouts Benefit from Training Programs? Korean Evidence Employing Methods for Continuous Treatments

Failure of participants to complete training programs is pervasive in existing active labor market programs both in developed and developing countries. The proportion of dropouts in prototypical programs ranges from 10 to 50 percent of all participants. From a policy perspective, it is of interest to know if dropouts benefit from the time they spend in training since these programs require considerable resources. We shed light on this issue by estimating the average employment effects of different lengths of exposure to a program by dropouts in a Korean job training program. To do this, we employ parametric and semiparametric methods to estimate effects from continuous treatments using the generalized propensity score, under the assumption that selection into different lengths of exposure is based on a rich set of observed covariates. We find that participants who drop out later – thereby having longer exposures – exhibit higher employment probabilities one year after receiving training, and that marginal effects of additional exposure to training are initially fairly small, but increase sharply past a certain threshold of exposure. One implication of these results is that this and similar programs could benefit from providing incentives for participants to stay longer in the program.training programs, dropouts, developing countries, continuous treatments, generalized propensity score, dose-response function