Quantile Regression with Censoring and Endogeneity

In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator combines Powell (1986) censored quantile regression (CQR) to deal semiparametrically with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are nonadditive in the unobservables. The first stage estimates a nonadditive model with infinite dimensional parameters for the control variable, such as a quantile or distribution regression model. The second stage estimates a nonadditive censored quantile regression model for the response variable of interest, including the estimated control variable to deal with endogeneity. For computation, we extend the algorithm for CQR developed by Chernozhukov and Hong (2002) to incorporate the estimation of the control variable. We give generic regularity conditions for asymptotic normality of the CQIV estimator and for the validity of resampling methods to approximate its asymptotic distribution. We verify these conditions for quantile and distribution regression estimation of the control variable. We illustrate the computation and applicability of the CQIV estimator with numerical examples and an empirical application on estimation of Engel curves for alcohol.Censored, Quantile, Instrumental variable, Censoring, Endogeneity, Engel curve, Alcohol

Doing More When You\u27re Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments

I examine treatment effect heterogeneity within an experiment to inform external validity. The local average treatment effect (LATE) gives an average treatment effect for compliers. I bound and estimate average treatment effects for always takers and never takers by extending marginal treatment effect methods. I use these methods to separate selection from treatment effect heterogeneity, generalizing the comparison of OLS to LATE. Applying these methods to the Oregon Health Insurance Experiment, I find that the treatment effect of insurance on emergency room utilization decreases from always takers to compliers to never takers. Previous utilization explains a large share of the treatment effect heterogeneity. Extrapolations show that other expansions could increase or decrease utilization

What Do Longitudinal Data on Millions of Hospital Visits Tell Us about the Value of Public Health Insurance as a Safety Net for the Young and Privately Insured?

Young people with private health insurance sometimes transition to the public health insurance safety net after they get sick, but popular sources of cross-sectional data obscure how frequently these transitions occur. We use longitudinal data on almost all hospital visits in New York from 1995 to 2011. We show that young privately insured individuals with diagnoses that require more hospital visits in subsequent years are more likely to transition to public insurance. If we ignore the longitudinal transitions in our data, we obscure over 80% of the value of public health insurance to the young and privately insured

Quantile regression with censoring and endogeneity

In this paper, we develop a new censored quantile instrumental variable (CQIV)estimator and describe its properties and computation. The CQIV estimator combines Powell(1986) censored quantile regression (CQR) to deal semiparametrically with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are nonadditive in the unobservables. The first stage estimates a nonadditive model with infinite dimensional parameters for the control variable, such as a quantile or distribution regression model. The second stage estimates a nonadditive censored quantile regression model for the response variable of interest, including the estimated control variable to deal with endogeneity. For computation, we extend the algorithm for CQR developed by Chernozhukov and Hong (2002) to incorporate the estimation of the control variable. We give generic regularity conditions for asymptotic normality of the CQIV estimator and for the validity of resampling methods to approximate its asymptotic distribution. We verify these conditions for quantile and distribution regression estimation of the control variable. We illustrate the computation and applicability of the CQIV estimator with numerical examples and an empirical application on estimation of Engel curves for alcohol.

Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment

I aim to shed light on why emergency room (ER) utilization increased following the Oregon Health Insurance Experiment but decreased following a Massachusetts policy. To do so, I unite the literatures on insurance and treatment effects. Under an MTE model that assumes no more than the LATE assumptions, comparisons across always takers, compliers, and never takers can inform the impact of polices that expand and contract coverage. Starting from the Oregon experiment as the “gold standard,” I make comparisons within Oregon and extrapolate my findings to Massachusetts. Within Oregon, I find adverse selection and heterogeneous moral hazard. Although previous enrollees increased their ER utilization, evidence suggests that subsequent enrollees will be healthier, and they will decrease their ER utilization. Accordingly, I can reconcile the Oregon and Massachusetts results because the Massachusetts policy expanded coverage from a higher baseline, and new enrollees reported better health

The Early Impact of the Affordable Care Act State-by-State

I examine the impact of state policy decisions on the early impact of the ACA using data through the first half of 2014. I focus on the individual health insurance market, which includes plans purchased through exchanges as well as plans purchased directly from insurers. In this market, at least 13.2 million people were covered in the second quarter of 2014, representing an increase of at least 4.2 million beyond pre-ACA state-level trends. I use data on coverage, premiums, and costs and a model developed by Hackmann, Kolstad, and Kowalski (2013) to calculate changes in selection and markups, which allow me to estimate the welfare impact of the ACA on participants in the individual health insurance market in each state. I then focus on comparisons across groups of states. The estimates from my model imply that market participants in the five “direct enforcement” states that ceded all enforcement of the ACA to the federal government are experiencing welfare losses of approximately $245 per participant on an annualized basis, relative to participants in all other states. They also imply that the impact of setting up a state exchange depends meaningfully on how well it functions. Market participants in the six states that had severe exchange glitches are experiencing welfare losses of approximately$750 per participant on an annualized basis, relative to participants in other states with their own exchanges. Although the national impact of the ACA is likely to change over the course of 2014 as coverage, costs, and premiums evolve, I expect that the differential impacts that we observe across states will persist through the rest of 2014

Censored quantile instrumental variable estimation with Stata

Many applications involve a censored dependent variable and an endogenous independent variable. Chernozhukov, Fernandez-Val, and Kowalski (2015) introduced a censored quantile instrumental variable estimator (CQIV) for use in those applications, which has been applied by Kowalski (2016), among others. In this article, we introduce a Stata command, cqiv, that simplifes application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, we describe the use of the cqiv command, and we provide empirical examples.https://arxiv.org/abs/1801.05305First author draf