Measuring Human Capital and its Effects on Wage Growth

Ever since Mincer (1974), years of labor market experience were used to approximate individual's general human capital, while years of seniority were used to approximate job specific human capital. This specification is restrictive because it assumes that starting wages at a new job depend only on job market experience. In this article I investigate the effects of human capital on wage growth by using a more flexible specification of the wage equation, which allows for rich set of information on past employment spells to affect the starting wages. In addition, I endogenize the labor mobility decision. In order to illuminate the effects of human capital accumulation patterns on wage growth, I compare counterfactual career paths for representative individuals

Search Costs and Medicare Plan Choice

There is increasing evidence suggesting that Medicare beneficiaries do not make fully informed decisions when choosing among alternative Medicare health plans. To the extent that deciphering the intricacies of alternative plans consumes time and money, the Medicare health plan market is one in which search costs may play an important role. To account for this, we split beneficiaries into two groups--those who are informed and those who are uninformed. If uninformed, beneficiaries only use a subset of covariates to compute their maximum utilities, and if informed, they use the full set of variables considered. In a Bayesian framework with Markov Chain Monte Carlo (MCMC) methods, we estimate search cost coefficients based on the minimum and maximum statistics of the search cost distribution, incorporating both horizontal differentiation and information heterogeneities across eligibles. Our results suggest that, conditional on being uninformed, older, higher income beneficiaries with lower self-reported health status are more likely to utilize easier access to information.Search, Medicare Health Plan Choice, Discrete Choice Models, Bayesian Methods

The Role of Social Norms in Child Labor and Schooling in India

This paper aims to summarize the unexplained propensity of children to engage in work, school, or neither. After controlling for a wide range of determinants of child labor, schooling, and idleness, we estimate a hierarchical model that allows for heteroskedastic, spatially correlated random effects. We use the posterior distribution of ranks of random effects to capture social norms toward children’s activities in each district and thus identify those Indian districts where social attitudes favor education and oppose child labor and idleness. We propose that government intervention be targeted at districts with pro-schooling, anti-child-labor, and anti-idleness social attitudes if limited government resources necessitate implementing minimal cost policies that have the greatest potential to succeed.Child Labor, Education, Spatial Dependence, Social Norms, India

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.Treatment Effects, Propensity Score, Bias, Unconfoundedness, Selection on Unobservables

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.treatment effects, propensity score, bias, unconfoundedness, selection on unobservables

The Connection Between Maternal Employment and Childhood Obesity: Inspecting the Mechanisms

This paper investigates the channels through which maternal employment affects childhood obesity. We use time diaries and interview responses from the Child Development Supplement of the Panel Study of Income Dynamics which combine information on children’s time allocation and mother’s labor force participation. Our empirical strategy involves estimating the effect of children’s activities and meal routines on BMI, estimating the effect of maternal employment on these activities and routines and then combining these two estimates. We find that maternal employment affects child weight through two main mechanisms – supervision and nutrition, however, the particular channels vary by mother’s education.Childhood Obesity, Labor Supply, Time Allocations

On the Specification of Propensity Scores: with an Application to the WTO-Environment Debate

The use of propensity score methods for program evaluation with non-experimental data typically requires the propensity score be estimated, often with a model whose specification is unknown. While theoretical results suggest that estimators utilizing more flexible propensity score specifications perform better, this has not filtered into applied research. Here, we provide Monte Carlo evidence indicating the benefits of over-specifying the propensity score when using weighting estimators, as well as using normalized weights. We illustrate these results with an application assessing the environmental effects of GATT/WTO membership. We find that membership has a mixed impact, and that under-fitting the propensity score yields misleading inference in several cases