JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. University of Wisconsin Press andThe Board of Regents of the University of Wisconsin System are collaborating with JSTOR to digitize, preserve and extend access to The Journal of Human Resources. ABSTRACTData from the Survey of Income and Program Participation are used to investigate ways in which health influences a single mother's decision whether to work: the direct effect of a woman's health on work effort and potential wage; the impact of her children's health on hours available to work; and the impact of health on the values of health insurance and Medicaid associated with work and AFDC participation, respectively. Simulations suggest that wage subsidies and decreases in AFDC benefits are unlikely to increase the labor force participation of single mothers in poor health or with disabled children, as they face limitations on work hours and the kinds of work they can perform that prohibit them from earning enough to stay out of poverty. Extending health insurance coverage to all children of single mothers regardless of AFDC status would induce a large percentage of these mothers to seek and accept employment, as would a pay-or-play insurance plan covering all workers (and their dependents) who work 15 or more hours a week. Barbara L. Wolfe is professor of economics and preventive medicine at the University of Wisconsin-Madison. Steven C. Hill is a graduate student in economics at the University of Wisconsin-Madison.

This paper assesses the quality of the Medical Expenditure Panel Survey (MEPS) drug data and the impact that misreporting prescription drug data has on descriptive and behavioral analyses. It does this by matching MEPS participants with Medicare Part D coverage during the period 2006-2007 to their Part D claims data. In the validation sample, the number of drug fills and total expenditures are reasonably accurate compared with claims. Household respondents tended to underreport the number of different drugs taken, but tended to overreport the number of fills of each drug. Behavioral analyses of the determinants of medication use and expenditures were largely unaffected because underreporting cut across most sociodemographic groups.

Under the ACA, many of the uninsured and a larger proportion of survivors facing financial hardship will be eligible for Medicaid or premium tax credits in the Marketplaces. ACA implementation will dramatically enhance insurance availability and is likely to reduce financial hardship for vulnerable cancer survivors.

Health-care expenditure regressions are used in a wide variety of economic analyses including risk adjustment and program and treatment evaluations. Recent articles demonstrated that generalized gamma models (GGMs) and extended estimating equations (EEE) models provide flexible approaches to deal with a variety of data problems encountered in expenditure estimation. To date there have been few empirical applications of these models to expenditures. We use data from the US Medical Expenditure Panel Survey to compare the bias, predictive accuracy, and marginal effects of GGM and EEE models with other commonly used regression models in a cross-validation study design. Health-care expenditure distributions vary in the degree of heteroskedasticity, skewness, and kurtosis by type of service and population. To examine the ability of estimators to address a range of data problems, we estimate models of total health expenditures and prescription drug expenditures for two populations, the elderly and privately insured adults. Our findings illustrate the need for researchers to examine their assumptions about link functions: the appropriate link function varies across our four distributions. The EEE model, which has a flexible link function, is a robust estimator that performs as well, or better, than the other models in each distribution.

Health insurance disparities associated with rural residence are related to the structure of employment. Major factors include smaller employers, lower wages, greater prevalence of self-employment, and sociodemographic characteristics.

We study the effect of public insurance on smoking cessation medication prescriptions and financing. We leverage variation in insurance coverage generated by recent Affordable Care Act expansions to Medicaid. We estimate differences‐in‐differences models using administrative data on the universe of Medicaid‐financed prescriptions sold in retail and online pharmacies 2011–2017. Our findings suggest that these expansions increased Medicaid‐financed smoking cessation prescriptions by 34%. This increase reflects new medication use and a shift in payment from private insurers and self‐paying patients to Medicaid. Adjusting our estimate for changes in financing implies that Medicaid expansion led to a 24% increase in new medication use. (JEL I1, I13, I18)

Background: The homeless population

The Affordable Care Act (ACA) has dramatically increased the number of low-income nonelderly adults eligible for Medicaid. Starting in 2014, states can elect to cover individuals and families with modified adjusted gross incomes below a threshold of 133 percent of federal poverty guidelines, with a 5 percent income disregard. We used simulation methods and data from the Medical Expenditure Panel Survey to compare nondisabled adults enrolled in Medicaid prior to the ACA with two other groups: adults who were eligible for Medicaid but not enrolled in it, and adults who were in the income range for the ACA's Medicaid expansion and thus newly eligible for coverage. Although differences in health across the groups were not large, both the newly eligible and those eligible before the ACA but not enrolled were healthier on several measures than pre-ACA enrollees. Twenty-five states have opted not to use the ACA to expand Medicaid eligibility. If these states reverse their decisions, their Medicaid programs might not enroll a population that is sicker than their pre-ACA enrollees. By expanding Medicaid eligibility, states could provide coverage to millions of healthier adults as well as to millions who have chronic conditions and who need care.