According to the U.S. Census Bureau, 45 million Americans were without health.cm insurance in 2003 and their numbers continues to grow. Health benefits in the U.S. remain heavily employer-based with about 60 percent of all insured individuals being enrolled through employer-sponsored plans. While the indigent can often qualify for public assistance programs such as Medicaid, many participants in the labor force may not have adequate access to coverage through their employer. Although it is possible to purchase individual plans privately, these may only be available at prohibitively high rates compared with the group rates available through large employers. As a consequence, 16 percent of full-time workers are uninsured (Fronstin 2003). Not surprisingly, much of the policy discussion focuses on ways of expanding coverage to all workers. In 2005, Congress enacted Health Savings Accounts to allow small employers and their employees to purchase high deductible insurance.cm plans at low premiums. During the last presidential election campaign the Bush administration proposed a tax credit of up to $1,000 to help low income workers purchase insurance and health savings accounts, while the Kerry-Edwards campaign favored expanding employer-based coverage by allowing private buy-ins through the health cm insurance program for Federal government employees or through Medicare.

The potential impact of health insurance of the health of the population is one important issue that underlies the policy debate. Evidence to this effect may provide further support in favor of policies designed to expand health insurance coverage, irrespective of the policy mechanism ultimately chosen. In a recent review of the literature, Hadley (2003) found broad evidence of positive impacts in studies based on observational data. The magnitude of these effects, however, varied with the type of population sampled, type of illness and the particular measure of health outcomes chosen and thus merits further investigation. Most of the previous research focused on mortality as the outcome measure, rather than actual measures of health status. Moreover Hadley notes the paucity of research that adjusts for endogeneity of insurance participation in health equations, suggesting that previous studies may have underestimated the insurance effect (p. 43). In this paper, we attempt to fill both of these gaps. First, we employ a composite health score as our outcome measure, rather than the more commonly used mortality probabilty (e.g., Franks, Clancy, and Gold 1993; Sorlie et al. 1994). Second, we attempt to adjust for potential endogeneity. Our identification approach is similar to that of Goldman et al. (2001), who focused on the effect of Medicaid on declines in mortality probabilities for HIV-positive individuals. However, given our interest in employment related insurance we focus on working-age adults. While our findings are in agreement with Goldman et al. in that both studies yield greater insurance effects after adjusting for endogeneity, we are also careful to point out difficulties in statistical identification inherent in such models and we note the possible range of estimates for the insurance co effect. With this caveat noted, our results provide deeper evidence that expanding insurance will have direct benefits in terms of improved health.cm outcomes. The rest of the paper proceeds as follows: the second section summarizes the relevant literature on health scores, and on the effect of insurance on outcomes. The third section presents the methodological approach and estimation framework. The fourth section presents data and variable definitions. The fifth section presents results from the insurance participation equation and the health status equation for the full sample and a summary of number of tests for endogeneity bias. The sixth section replicates this analysis for subsets of survey respondents based on groupings of chronic conditions. This was done in order to test whether insurance effects are repeated across various settings in which symptoms of the underlying medical condition may not be equally observable to the individual. Finally, implications and limitations of the results are discussed in the last section.

PREVIOUS LITERATURE

Previous literature on the impact of insurance on health has tended to focus on mortality as the outcome measure of choice. In this study we focus instead on a now commonly accepted health scoring methodology, similar in content to the physical component summary scale of the SF-36 and SF-12 (Ware and Kosinski 2001). Below we briefly review the literature on these measures. In addition, we provide a brief review of the literature on insurance and mortality, including the issue of treating potential endogeneity bias.

Health Scores

There is a substantial body of literature on using survey-based measures of health status. These measures appear with similar wording in major household surveys such as the Medical Expenditures Panel Survey (MEPS), the Health and Retirement Survey, and the National Health Interview Survey. Indicators are generally classified into three types: Subjective measure, i.e., self rated overall health (poor, fair, good, very, excellent); objective measures based on a general criterion, especially physical limitation, defined as inability to perform certain tasks defined in the survey; and objective measures that pertain to self-reporting of specific diagnoses or medical conditions. In general, these measures have been shown to perform well. Perry and Rosen (2001, p. 19) find that "objective measures give exactly the same answer as subjective measures" when testing for differences in health status between wage earners and the self-employed. Specifically in the Health and Retirement Study, Hurd and McGarry (1995) find that subjective probabilities of survival vary with health predictors in the same way as actual outcomes.

 

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