A Profile of Health Insurance Exchange Enrollees

Based on the 2007 Medical Expenditure Panel Survey, examines the demographic, health status, and healthcare utilization characteristics of the population expected to obtain coverage through state-run exchanges in 2019. Considers policy implications

Examining Moral Hazard in the Healthcare Insurance Market

This study aims to examine the effect of insurance coverage on medical expenditure in the United States. The data was gathered from the Household Component Medical Expenditure Panel Survey and is a cross-sectional data set with a sample size of approximately 1500 observations. The study also distinguishes between public and private insurance coverage to compare the potential moral hazard in the two separate markets. The results of this study suggest that insurance status, specifically public, has a strong positive effect on healthcare expenditure. This result, combined with a negative relationship between household income and healthcare expenditure, suggests that the source of financial funds rather than the ability to pay determines the demand for healthcare services. The study indicates that individuals are very sensitive to the financial incentives provided by public insurance and inefficiencies within the public insurance market should be examined by future research

Modeling Usage of Medical Care Services: The Medical Expenditure Panel Survey Data, 1996-2000

We explore the determinants of usage of six different types of health careservices, using the Medical Expenditure Panel Survey data, years 1996-2000.We apply a number of models for univariate count data, including semiparametric, semi-nonparametric and finite mixture models.We find that the complexity of the model that is required to fit the datawell depends upon the way in which the data is pooled across sexes andover time, and upon the characteristics of the usage measure.Pooling across time and sexes is almost always favored, but when more heterogeneous data is pooled it is often the case that a more complex statisticalmodel is required.medical care; count data; maximum likelihood

The Benefits and Costs of Newer Drugs: Evidence from the 1996 Medical Expenditure Panel Survey

The nation's spending for prescription drugs has grown dramatically in recent years. Previous studies have shown that the replacement of older drugs by newer, more expensive, drugs is the single most important reason for this increase, but they did not measure how much of the difference between new and old drug prices reflects changes in quality as better, newer drugs replace older, less effective medications. In this paper we analyze data from the 1996 Medical Expenditure Panel Survey (MEPS) to provide evidence about the effect of drug age on mortality, morbidity, and total medical expenditure, controlling for sex, age, education, race, income, insurance status, who paid for the drug, the condition for which the drug was prescribed, how long the person has had the condition, and the number of medical conditions reported by the person. The results provide strong support for the hypothesis that the replacement of older by newer drugs results in reductions in mortality, morbidity, and total medical expenditure. People consuming new drugs were significantly less likely to experience work-loss days and to die by the end of the survey than people consuming older drugs. The estimates indicate that reductions in drug age tend to reduce all types of non-drug medical expenditure, although the reduction in inpatient expenditure is by far the largest. Reducing the age of the drug results in a substantial net reduction in the total cost of treating the condition. Allowing people to use only generic drugs would increase total treatment costs, not reduce them, and would lead to worse outcomes.

Sample selection correction in panel data models when selectivity is due to two sources

This paper proposes a specification of Wooldridge's (1995) two step estimation method in which selectivity bias is due to two sources rather than one. The main objective of the paper is to show how the method can be applied in practice. The application concerns an important problem in health economics: the presence of adverse selection in the private health insurance markets on which there exists a large literature. The data for the empirical application is drawn from the 2003/2004 Medical Expenditure Panel Survey in conjunction with the 2002 National Health Interview Survey.

Sample Selection Correction in Panel Data Models When Selectivity Is Due to Two Sources

This paper proposes a specification of Wooldridge's (1995) two step estimation method in which selectivity bias is due to two sources rather than one. The main objective of the paper is to show how the method can be applied in practise. The application concerns an important problem in health economics: the presence of asymmetric information in the private health insurance markets on which there exists a large literature. The data for the empirical application is drawn from the 2003/2004 Medical Expenditure Panel Survey in conjunction with the 2002 National Health Interview Survey.multiple sample selection bias; panel data; asymmetric information.

Adverse selection in the U.S. health insurance markets: Evidence from the MEPS

We use the 2003/2004 Medical Expenditure Panel Survey in conjunctions with the 2002 National Health Interview Survey to test for adverse selection in the U.S. private health insurance market. The key idea is to test whether the individuals who are more exposed to health risks also buy insurance contracts with more coverage or higher expected payments. The critical statistical problem is that the extension of insurance is only measured for those who are insured and face positive health care expenditure. So there is a possible sample selection bias effect. The procedure used is based on a method suggested by Wooldridge (1995). The method also accounts for heterogeneity across individuals. The simultaneous account taken of both possible sources of bias is new for this kind of application.adverse selection, health insurance, risk profile

Partially Identifying Treatment Effects with an Application to Covering the Uninsured

We extend the nonparametric literature on partially identified probability distributions and use our analytical results to provide sharp bounds on the impact of universal health insurance on provider visits and medical expenditures. Our approach accounts for uncertainty about the reliability of self-reported insurance status as well as uncertainty created by unknown counterfactuals. We construct health insurance validation data using detailed information from the Medical Expenditure Panel Survey. Imposing relatively weak nonparametric assumptions, we estimate that under universal coverage monthly per capita provider visits and expenditures would rise by less than 8% and 16%, respectively, across the nonelderly population.

Compensating Differentials and Fringe Benefits: Evidence from the Medical Expenditure Panel Survey 1997-2004

In this paper, we revisited the question of the existence of a tradeoff between wages and health insurance by extending previous work in the following way: 1) we exploit richer information on health insurance in terms of whether the worker holds health insurance or whether it is offered at the firm but he/she does not hold it, 2) we analyze possible combinations of health insurance with other fringe benefits (retirement, sick leave and paid vacation), 3) we include information on workers health (self-reported) as a determinants of workers wage and mobility decision, and 4) we use an econometric framework and GMM estimations which allow us to treat the issues of endogenous choice of benefits and mobility into benefits sectors encountered in the literature and estimate the extent of worker selection into jobs with/without benefits based on unobserved individual-specific traits, skills and health status.