Increasing health insurance costs and the decline in insurance coverage
The 1990s were a decade of relative prosperity, yet the percentage of Americans without health insurance coverage rose over 17 percent between 1990 and 1998. This decline generally reflects a drop in the rates of employer-sponsored coverage, a trend that began in the late 1970s (Farber and Levy 2000). The drop in coverage has raised concern among policy makers in light of a variety of studies that highlight the difficulty that the uninsured have in accessing care, and their resulting poorer outcomes (Institute of Medicine 2002; Serafini and Stone 2002). Designing policies that will effectively address this problem requires understanding why coverage rates have fallen and anticipating how coverage will change in the future. Despite a relatively large literature investigating the determinants of insurance coverage, relatively few studies use multivariate techniques to examine factors contributing to the decline in coverage over time. These studies show that increased reliance on part-time workers (Fronstin and Snider 1996), industry shifts (Long and Rogers 1995), a combination of labor market factors (Kronick and Gilmer 1999; Glied and Stabile 2000; Glied and Jack 2003), or crowdout (Curler and Gruber 1996a, b; Currie and Yelowitz 1999; Blumberg, Dubay, and Norton 2000) only partially explain the decline in employer-provided insurance.
An alternative explanation is that coverage has dropped because the cost of insurance has risen. In contrast to substantial media coverage linking rising premiums to declining coverage rates, empirical evidence quantifying the relationship between premiums and coverage is limited. The studies that use multivariate techniques to examine the relationship between health care costs and coverage rates find support for the view that increasing costs decrease coverage (Fronstin and Snider 1996; Kronick and Gilmer 1999; Curler 2002; Glied and Jack 2003). Kronick and Gilmer (1999) rely on national measures of health care costs, relative to income, and generate most of the variance in the cost to income ratio from variation in income, not health care costs. Fronstin and Snider (1996) analyze state-level data from 1988 to 1992 and include only one cost proxy, the price of a hospital day. Cutler (2002) uses national-level data on employee contributions. Glied and Jack (2003) use state-level Medicare per capita spending excluding home health, adjusted by the ratio of private spending per enrollee to Medicare spending per enrollee. Thus these studies do not directly measure the effects of rising premiums on coverage, nor do they attempt to adjust for potential reverse causality that arises because declining coverage may lead to higher premiums. Further, existing studies typically focus on employer-sponsored coverage, which, although important, does not give a full picture of the effects of rising premiums on coverage because some individuals may substitute public for private coverage. Finally, these studies typically do not devote substantial attention to controlling for potential confounding explanations for the decline in coverage such as the expansion in Medicaid or changing tax policy.
This paper explores the relationship between health care premiums and coverage rates. It takes advantage of wide geographic variation in changes in premiums and coverage rates. Thus the variation in premiums that we use is broader than that used in existing literature and less likely to be confounded with other secular trends. In contrast to existing work, we also use instrumental variable (IV) techniques to address the potential for reverse causality between rising costs and coverage rates. The IV techniques also adjust for potential measurement error in our premium data.
We focus on coverage from any source, which gives a more complete picture of coverage because some individuals may switch from private to public coverage. We also focus only on the ultimate coverage decision, without attempting to explain the detailed set of decisions such as employer offer or employee take-up, which lead to coverage. Finally, we control for a wide range of factors associated with alternative explanations of coverage declines. We thus quantify the link between rising health insurance premiums and rates of insurance coverage, addressing limitations of the existing literature.
We include the following demographic variables for each individual and the head of their HIU: age, gender, race/ethnicity, education, marital status, industry, occupation, full/part-time work status, government versus private employer, and firm size. We also include indicators of whether there are no workers or more than one worker in the HIU; interactions of being a spouse or a child in a family with multiple workers; binary variables for the income decile the HIU falls into, calculated separately for singles and married people, and interactions of income decile and marital status of the HIU head. We include interaction terms between these variables and a binary variable capturing observations in the later period to allow for the possibility that their effect changes over time.
Several metropolitan area-level demographic factors are included based on CPS data. These capture market-level effects and competing explanations for the decline in coverage. The MSA-level covariates include the share of the population that is foreign born, the share of the population in the metropolitan area that is nonwhite, the share that is elderly, average HIU income, and the share of women that are working. We also include the local unemployment rate, which is from BLS data available on the Area Resource File (ARF). Unless otherwise indicated, we do not interact the MSA-level or policy covariates with a time period dummy.
Two of the important potential explanations for declining coverage that have been explored in the literature are rising Medicaid eligibility and falling tax subsidies. We control for these explanations using the approaches followed by studies focusing on these explanations. Specifically, we generate measures of the generosity of Medicaid coverage of children following the approach of Cutler and Gruber (1996a), using information from the Intergovernmental Health Policy Project (1988, 1990, 1991) and the National Governors’ Association (1990, 1999). They measure Medicaid eligibility by the fraction of HIU health spending eligible for Medicaid, based on family composition, which captures the role of Medicaid eligibility in the context of family health insurance decisions. This is calculated by applying state regulations to CPS data to assess generosity at the state level and adding controls for the fraction of family health spending attributable to each child age.