The Elephant in the Corner: A Cautionary Tale about Measurement Error in Treatment Effects Models

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require the conditional independence assumption (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require the conditional independence assumption, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that conditional independence holds with the 'real' data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.treatment effects, propensity score, unconfoundedness, selection on observables, measurement error

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

Is the Quantity-Quality Trade-off a Trade-off for All, None, or Some?

Although the theoretical trade-off between the quantity and quality of children is well-established, empirical evidence supporting such a causal relationship − particularly on child health − is limited. We use two measures of child health to asses the quantity-quality trade-off across the entire distribution. Using data from the Indonesia Family Life Survey and controlling for the potential endogeneity of child quantity, we find evidence of a causal trade-off only for some and only in the short-run.intrahousehold allocation, health, human capital, fertility, quantile treatment effects, stochastic dominance

Does Private Tutoring Payoff?

We assess the causal effect of private tutoring on the probability of university placement in Turkey. We find that tutoring increases the probability of being placed in a university when non-random selection is ignored. Moreover, among those utilizing private tutoring, greater expenditure on tutoring is also positively associated with university placement. However, we find evidence of positive selection into tutoring, but negative selection into greater expenditures among those receiving tutoring. Accounting for this pattern of non-random selection, we conclude that private tutoring has a negative causal effect on university placement overall, but conditional on receiving any tutoring, spending more on tutoring has a positive causal effect on university placement.tutoring, Turkey, tertiary education, program evaluation

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

Three New Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous

The validity of existing empirical tests of the Pollution Haven Hypothesis (PHH) is constantly under scrutiny due to two shortcomings. First, the issues of unobserved heterogeneity and measurement error in environmental regulation are typically ignored due to the lack of a credible, traditional instrumental variable. Second, while the recent literature has emphasized the importance of geographic spillovers in determining the location choice of foreign investment, such spatial eects have yet to be adequately incorporated into empirical tests of the PHH. As a result, the impact of environmental regulations on trade patterns and the location decisions of multinational enterprises remains unclear. In this paper, we circumvent the lack of a traditional instrument within a model incorporating geographic spillovers utilizing three novel identication strategies. Using state-level panel data on inbound U.S. FDI, relative abatement costs, and other determinants of FDI, we consistently nd (i) evidence of environmental regulation being endogenous, (ii) a negative impact of own environmental regulation on inbound FDI in pollution-intensive sectors, particularly when measured by employment, and (iii) larger eects of environmental regulation once endogeneity is addressed. Neighboring environmental regulation is not found to be an important determinant of FDI. Key Words: Foreign Direct Investment, Environmental Regulation, Spillovers, Instrumental Variables, Control Function, Heteroskedasticity

Partial Identification of Economic Mobility: With an Application to the United States

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this paper, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identi…cation approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession. JEL: C18, D31, I3

School Nutrition Programs and the Incidence of Childhood Obesity

In light of the recent rise in childhood obesity, the School Breakfast Program (SBP) and National School Lunch Program (NSLP) have received renewed attention, despite the fact that they have existed for decades. The SBP, in particular, is viewed as a potentially important component of any policy reform designed to combat the increased prevalence of overweight children given the importance attributed to a nutritious breakfast. Using panel data on over 13,500 students from kindergarten through third grade, we assess the relationship between SBP and NSLP participation on (relatively) long-run measures of child weight. While we find more mixed evidence on the association between NSLP participation and child weight, we obtain a relatively robust positive association between SBP participation and child weight, particularly for white children, entering kindergarten in the `normal' weight range, with mothers of moderate education.

Federal Nutrition Programs and Childhood Obesity: Inside the Black Box

In response to the dramatic rise in childhood obesity, particularly among low income individuals, federal nutrition assistance programs have come under scrutiny. However, the vast majority of this research focuses on the direct relationship between these programs and child health, while little is known about the mechanisms by which such relationships arise. Using the 2007 American Time Use Survey and the Eating and Health Module, we explore differences in time use across families that participate in the Supplemental Nutriation Assistance Program (SNAP), the School Breakfast Program (SBP), and the National School Lunch Program (NSLP) to better understand behavioral differences across participants and non-participants. These differences have important implications for future research and policy.School Breakfast Program, National School Lunch Program, Supplemental Nutrition Assistance Program, time use, program evaluation