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

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

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 Estimates of the Price Elasticity of Expenditure on Medical Care

The extent to which consumers respond to marginal prices for medical care is important for policy. Using recent data and a new censored quantile instrumental variable (CQIV) estimator, I estimate the price elasticity of expenditure on medical care. The CQIV estimator allows the estimates to vary across the skewed expenditure distribution, it allows for censoring at zero expenditure nonparametrically, and it allows for the insurance-induced endogenous relationship between price and expenditure. For identification, I rely on cost sharing provisions that generate marginal price differences between individuals who have injured family members and individuals who do not. I estimate the price elasticity of expenditure on medical care to be stable at -2.3 across the .65 to .95 conditional quantiles of the expenditure distribution. These quantile estimates are an order of magnitude larger than previous mean estimates. I consider several explanations for why price responsiveness is larger than previous estimates would suggest.

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

Health Reform, Health Insurance, and Selection: Estimating Selection into Health Insurance Using the Massachusetts Health Reform

We implement an empirical test for selection into health insurance using changes in coverage induced by the introduction of mandated health insurance in Massachusetts. Our test examines changes in the cost of the newly insured relative to those who were insured prior to the reform. We find that counties with larger increases in insurance coverage over the reform period face the smallest increase in average hospital costs for the insured population, consistent with adverse selection into insurance before the reform. Additional results, incorporating cross-state variation and data on health measures, provide further evidence for adverse selection.

Health Reform, Health Insurance, and Selection: Estimating Selection into Health Insurance Using the Massachusetts Health Reform

We implement an empirical test for selection into health insurance using changes in coverage induced by the introduction of mandated health insurance in Massachusetts. Our test examines changes in the cost of the newly insured relative to those who were insured prior to the reform. We find that counties with larger increases in insurance coverage over the reform period face the smallest increase in average hospital costs for the insured population, consistent with adverse selection into insurance before the reform. Additional results, incorporating cross-state variation and data on health measures, provide further evidence for adverse selection.Adverse selection, Massachusetts, Health reform

Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform

We model the labor market impact of the key provisions of the national and Massachusetts mandate-based health reforms: individual mandates, employer mandates, and subsidies. We characterize the compensating differential for employer-sponsored health insurance (ESHI) and the welfare impact of reform in terms of sufficient statistics. We compare welfare under mandate-based reform to welfare in a counterfactual world where individuals do not value ESHI. Relying on the Massachusetts reform, we find that jobs with ESHI pay $2,812 less annually, somewhat less than the cost of ESHI to employers. Accordingly, the deadweight loss of mandate-based health reform was approximately 8 percent of its potential size

Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform

We model the labor market impact of the three key provisions of the recent Massachusetts and national “mandate-based” health reforms: individual and employer mandates and expansions in publicly-subsidized coverage. Using our model, we characterize the compensating differential for employer-sponsored health insurance (ESHI) — the causal change in wages associated with gaining ESHI. We also characterize the welfare impact of the labor market distortion induced by health reform. We show that the welfare impact depends on a small number of sufficient statistics” that can be recovered from labor market outcomes. Relying on the reform implemented in Massachusetts in 2006, we estimate the empirical analog of our model. We find that jobs with ESHI pay wages that are lower by an average of $6,058 annually, indicating that the compensating differential for ESHI is only slightly smaller in magnitude than the average cost of ESHI to employers. Because the newly-insured in Massachusetts valued ESHI, they were willing to accept lower wages, and the deadweight loss of mandate-based health reform was less than 5% of what it would have been if the government had instead provided health insurance by levying a tax on wages