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.

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,

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

Has Monetary Policy Become More Efficient? A Cross Country Analysis

Over the past twenty years, macroeconomic performance has improved in industrialized and developing countries alike. In a broad cross-section of countries inflation volatility has fallen markedly while output variability has either fallen or risen only slightly. This increased stability can be attributed to either: 1) more efficient policy-making by the monetary authority, 2) a reduction in the variability of the aggregate supply shocks, or 3) changes in the structure of the economy. In this paper we develop a method for measuring changes in performance, and allocate the source of performance changes to these two factors. Our technique involves estimating movements toward an inflation and output variability efficiency frontier, and shifts in the frontier itself. We study the change from the 1980s to the 1990s in the macroeconomic performance of 24 countries and find that, for most of the analyzed countries, more efficient policy has been the driving force behind improved macroeconomic performance.

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

Agricultural Productivity and Anticipated Climate Change in Sub-Saharan Africa: A Spatial Sample Selection Model

A cereal yield response function is estimated conditional upon environmental and topographical features to detect the effects of spatial heterogeneity and spatial dependence in explaining agricultural productivity across Sub-Saharan Africa. Controlling for direct and localized spillover effects, we then estimate the effect that projected changes in temperature and precipitation as a result of global climate change will have on agricultural production. We find that the estimated declines found in the climatological literature may overestimate actual declines, and factors such as spatial heterogeneity (i.e., country fixed effects) are profoundly more important to agricultural production.Agricultural Production, Climate Change, Applied Spatial Econometrics, Sample Selection, Generalized Method of Moments Estimation, Environmental Economics and Policy, Productivity Analysis, I3, Q18, C50,

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

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

Estimating the Effects of Lenght of Exposure to Traning Program: The Case of Job Corps

Length of exposure to a training program is important in determining the labor market outcomes of participants. Employing methods to estimate the causal effects from continuous treatments, we provide insights regarding the effects of different lengths of enrollment to Job Corps (JC)— America’s largest and most comprehensive job training program for disadvantaged youth. We semiparametrically estimate average causal effects (on the treated) of different lengths of exposure to JC, using the “generalized propensity score” under the assumption that selection into different lengths is based on a rich set of observed covariates. “Placebo tests” are performed to gauge the plausibility of this assumption. We find that the estimated effects are increasing in the length of training, and that the marginal effects of additional training are decreasing with length of enrollment. We also document differences in the estimated effects of length of exposure across different demographic groups, which are particularly large between males and females. Finally, our results suggest an important “lock-in” effect in JC training.Training Programs, Continuous Treatments, Generalized Propensity Score, Dose-Response Function