Rising Premiums, Charity Care, and the Decline in Private Health Insurance. Michael Chernew University of Michigan and NBER

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1 Rising Premiums, Charity Care, and the Decline in Private Health Insurance Michael Chernew University of Michigan and NBER David Cutler Harvard University and NBER Patricia Seliger Keenan NBER December 2004 This work was funded by the Economic Research Initiative on the Uninsured, the National Institute on Aging, and the Sloan Foundation. Patricia Keenan gratefully acknowledges support from the National Institute on Aging, Grant Number T32-AG00186.

2 Over the past several decades health care costs have increased substantially and the share of the population with insurance coverage has decreased. Relative to GDP, medical care today accounts for 75 percent more of the economy than it did in While health care costs have been rising, the share of the nonelderly population that is uninsured has increased by roughly 4 percentage points since To what extent is the increase in the cost of health insurance responsible for the decline in coverage? Individuals will purchase coverage if the utility of coverage exceeds that of being uninsured. Textbook economic theory suggests that rising medical expenditures ought to increase the utility of coverage because insurance mitigates risk. As medical costs increase, the variability of spending will usually rise as well, and so the insurance product becomes more valuable. 1 This implies that rising costs would be associated with insurance coverage, but in the opposite direction: rising medical costs should lead to more, not less insurance coverage (Phelps, 1997). The rising demand for pharmaceutical coverage following the rise in spending on pharmaceuticals is consistent with this model. In the textbook model, rising costs would be associated with falling coverage only if the cost increases were driven by increased administrative loads. Empirically, though, most medical spending is a result of increased quantities of care received, owing to technological changes in medicine, not greater administrative burden (Cutler and McClellan, 2001). There are two extensions to the textbook model that can help explain a negative relation between health insurance costs and insurance coverage. The first extension focuses on the value of medical care relative to its cost (which determines the utility of coverage). While increased spending buys additional medical services, these services may not be of sufficient value to justify

3 their costs, at least for some consumers, and these people rationally decline coverage as a result. In this theory, rising quantities of care function, in essence, as an increase in administrative loads. Of course, if consumers had the option, they would exclude unvalued care from the insurance policy, but determining which care is valuable and which is not may be difficult, and contracting imperfections may make it difficult for individuals to purchase a plan that limits access to particular services. One type of unvalued care is traditional moral hazard: services are provided because of insurance but are worth less than they cost. Cutler (2004) argues that increased medical spending overall has bought care that is more than worth its value. But that does not imply that the differential growth of service use in some areas of the country relative to other areas is purchasing services of high value. It may be that high cost growth areas are disproportionately buying care with little marginal value. A more subtle story about the relative value of new services concerns the implicit pooling of costs across individuals. Insurance is generally not priced at the individual level. Most insurance is bought in employment groups. Although there may be a group specific wage offset associated with coverage (Pauly and Herring, 1999; Sheiner, 1997), it is likely that relatively healthy groups of individuals would experience a greater increase in premiums, compared to the value they receive, relative to less healthy individuals. Why should a relatively healthy 30 yearold pay 10 percent more each year for insurance, if most of the costs are for services that are very unlikely to be used? Technological advances and rising costs may alter the nature of equilibrium by imposing an implicit tax on the low risk individuals. This could cause markets to unravel and the healthy consumers to turn down coverage. 1 This needn t be true in all contexts; the mean of spending could increase while the spread narrows. However, if technology increases spending proportionately at each point in the distribution, or disproportionately affects 2

4 The second extension to the textbook model focuses on the utility of being uninsured, stressing the availability of care for the uninsured as a mechanism for avoiding the increased costs of coverage (Herring, 2001). People who are uninsured do not pay for all medical care outof-pocket. Rather, much of the care is uncompensated, written off by the provider as bad debt or charity care. Evidence suggests that a lack of coverage is associated with worse health outcomes. Yet the incremental value of the access to care associated with coverage may change over time because the new technology that drives cost increases may also be integrated into charity care, changing the value of access at the margin. Moreover, the impact of new technology on the value of coverage, relative to relying on charity care, may depend on health status. Lack of coverage may have its most pronounced effect on health outcomes for those with chronic illness where access to physician services and maintenance therapies for stable patients may be substantially inhibited. These extensions suggest that trends in coverage associated with new technology (and the associated rising costs) will reflect the value of those services for different subgroups and the availability of charity care to cushion the fiscal and clinical consequences associated with being uninsured. In this work we examine the relationship between coverage declines, premiums and charity care for different subgroups of the population. I. Medical Costs and Insurance Coverage To examine the factors predicting changes in insurance coverage, we use data on coverage in different MSAs at the beginning and end of the 1990s. 2 We create two cohorts of non-elderly individuals from the Current Population Survey: one cohort from and the spending for the very sick, greater spending would be associated with greater variance. 2 See Chernew, Cutler, and Keenan (2004) for discussion of the data and its construction. 3

5 second from Respondents are divided into health insurance units (HIUs), the typical unit in which insurance is purchased. The CPS provides a rich array of HIU and market-level variables that we control for, including basic demographics, income, employment characteristics, and MSA characteristics such as the overall demographic composition. We measure health insurance premiums using premium data from the Health Insurance Association of America and the Kaiser Family Foundation/Health Research and Educational Trust Survey of Employer-Sponsored Health Benefits (KPMG Survey, 1988, 1989, 1998) and (Kaiser Family Foundation, 1999). Surveys are pooled from 1988 and 1989 for the early years and 1998 and 1999 for the later years. To purge the premium data of differences in benefit design, we regress premiums on plan type, whether an employer offers multiple plans, and interactions of these variables with each other and year dummies; cost-sharing parameters such as deductible, coinsurance, and copay amounts; dummy variables for service coverage, including coverage for prescription drugs, outpatient mental health benefits, inpatient mental health benefits, and maternity benefits; firm size and industry dummies; and MSA dummies and their interaction with years. The coefficients on MSA dummies in this regression give adjusted premium estimates at the MSA level. 3 Premiums have increased substantially over time. From the late 1980s to the late 1990s, the cost of a standard insurance policy for an individual increased by $818. Clearly, premium changes may be endogenous to changes in insurance coverage and measurement error in the premium data can bias the estimates. Yet Chernew, Cutler, and Keenan (2004) report that in models for any insurance coverage, IV models using per capita 3 The coefficients from this regression indicate that more generous benefits led to greater premiums; the R 2 from this regression was The resulting premium estimates are quite reasonable: the correlation between adjusted premiums and Medicare part B spending at the MSA level is

6 MSA level Medicare Part B spending and state level personal spending for the nonelderly as instruments for premiums were even a bit larger than OLS estimates (presumably because of measurement error). We control for several other variables suggested in the literature that might lead to increased levels of uninsurance. Traditional theories of insurance have focused on the tax price of insurance: marginal tax rate reductions over the past two decades have lowered the tax subsidy to health insurance purchases, and thus possibly led to reduced coverage. We measure the tax subsidy for health insurance along the lines of Gruber (2001). Other theories are about the demand for health insurance. A lengthy literature has argued that increased generosity of public insurance, primarily Medicaid, has crowded out private insurance, and thus affected the mix, and perhaps the overall level of coverage. We generate measures of the generosity of Medicaid coverage of children following the approach of Cutler and Gruber (1996). Crowdout may also be generated by the availability of charity care providers. We measure the availability of charity care in each metropolitan area as the number of public and teaching hospital beds per capita. 4 We measure these variables only in 1990 to minimize reverse causality issues that may arise because these measures could be affected by insurance, rather than the reverse. The average area had 1,279 public and teaching hospital beds per 1 million people in Finally, some have hypothesized that a greater share of working women should lead to lower insurance coverage, as employers provide less generous benefits in the hope of 4 We include this variable as an interaction with time period, to examine whether the effect of charity care availability changes over time. 5

7 encouraging employees to take up coverage elsewhere (Farber and Levy, 2000; Dranove, Baker, and Spier, 2000). The first column of Table 1 shows the results of these regressions. 5 The estimates suggest that a $1,000 increase in premiums reduces the propensity to purchase private coverage by 2.67 percentage points. The coefficient is statistically significantly different from zero. Further, it is large. Health costs have increased by $818 per person for a single policy over the past decade, so this estimate suggests that rising health costs can explain about two-thirds of the total 3.3 percentage point reduction in private insurance coverage in our sample over time. The estimates on the other covariates are generally consistent with the existing literature. A higher tax price discourages private coverage. Individuals in markets with a larger share of working women and with greater Medicaid generosity are less likely to have private coverage, consistent with the public health insurance crowdout literature. II. Charity Care and Administrative Loads We examine the reasons why increased premiums might affect insurance coverage in several ways. The second column of Table 1 reports estimates for the models which include the measure of charity care and its interactions with premiums. The results are consistent with the charity care explanation. In particular, insurance coverage is much more responsive to insurance premiums in areas with greater charity care availability. Using marginal effects, we estimate that the responsiveness to private coverage from premiums is nearly twice as great at the mean value of charity care, as compared to if charity care were not available. 5 We use linear probability models in Table 1 and throughout the paper for ease of interpretation, and to avoid issues of different magnitudes of the effect based on the baseline characteristics (Ai and Norton, 2003). Non-linear estimation methods such as probit for column 1 give similar results with a somewhat higher effect of premiums on coverage declines. The standard errors in all cases are adjusted for intra-msa correlation. 6

8 To shed more light on the different theories of the impact of premiums, the remaining columns differentiate the population into younger and older adults (ages 18 through 25 and 50 through 64) as a proxy for healthy and sick, by education level of the family head as a proxy for permanent income (high school or less or greater than high school), and by family income ($30,000 or less or greater than $30,000). The impact of premiums themselves, and of the charity care interaction, is strongest in the young, lower educated and lower income population. The magnitudes of the responsiveness to premiums are depicted in Figure 1 for subgroups by age, education, and income. Younger adults are on average healthier than older adults so that a greater premium response for younger adults relative to older adults is consistent with the pooling hypothesis. It is also consistent with a model in which young adults have greater access to charity care (or are more willing to use it ), a model where the young view new services as less valuable than the old, or a model where the young are less risk averse than the old. It is interesting to note that premiums have no impact on coverage for the older population, regardless of the level of charity care availability. Put another way, for the high risk population, rising costs do not result in a reduction in insurance coverage. Possibly, this suggests that the pure moral hazard explanation for the impact of premiums on costs that additional care is not clinically beneficial is not empirically important. However, note that the impact of the tax price, a measure of pure load, is also not very salient in the older population, suggesting this group is very risk averse and perceives the value of coverage relative to relying on charity care to be substantial. The greater sensitivity of lower educated and income individuals to premiums can be explained by a variety of models. Of particular interest is the result that the availability of charity care increases the premium sensitivity for our lower educated and income groups much 7

9 more than in our higher educated and income samples. This is consistent with the view that education (and income) are inversely related to access to or willingness to rely on charity care. These results also highlight that the crowdout effect of Medicaid is concentrated among lower educated and lower income groups. III. Summary Overall, our results point to several conclusions. First, the evidence is very clear that rising health insurance costs lead to significant reductions in insurance coverage. The link between rising costs and reduced coverage explains the bulk of why health insurance coverage fell in the 1990s. We estimate that up to half of this response to higher costs is related to the availability of charity care. This estimate is rough because our estimates of the availability of charity care are based solely on the availability of beds in public and teaching hospitals. Yet the models consistently demonstrate that the availability of beds in facilities that are relied upon for charity care increases the sensitivity of coverage to rising premiums. By providing access for the uninsured, charity care providers inadvertently create the conditions for crowding out of private health insurance. We suspect that the remaining impact of premiums on coverage is due to diminished utility of coverage associated with rising premiums, particularly for the young and the low income. Of particular importance may be the pooling of high and low risk enrollees, which leads to identifiable transfers from the healthy to the sick. As medical costs increase, the size of these transfers rises, and the willingness of the healthy to make them declines. This finding suggests that the new era of rising medical spending we have recently entered could have a major impact on private insurance coverage. Moreover, the decline in 8

10 coverage caused by rising premiums will place a greater burden on charity care providers. Though important in a time of declining coverage, bolstering the strained charity care system may further exacerbate the decline in coverage, posing a policy dilemma in responding to increases in the uninsured population. 9

11 Table 1: Linear Probability Models Explaining Private Health Insurance Coverage Sample Total Total Ages Ages High sch. or less > High school Income <=$30,000 Income >$30,000 Premiums *** ** * ** *** (000s) (0.0072) (0.0082) (0.0166) (0.0144) (0.0106) (0.0094) (0.0135) (0.0083) Charity ** ** * Care (3.5165) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Charity care* ** ** ** ** premium (5.8094) ( ) ( ) (8.4325) (6.7061) ( ) (4.8264) Tax price *** *** * *** * (0.1846) (0.1844) (0.2349) (0.2512) (0.2681) (0.1896) (0.2428) (0.0737) State Insur * Reforms (0.0060) (0.0060) (0.0097) (0.0099) (0.0092) (0.0051) (0.0114) (0.0038) % Working ** ** ** ** *** Women (0.0615) (0.0627) (0.1634) (0.1058) (0.0956) (0.0702) (0.1068) (0.0739) Medicaid *** *** *** ** *** *** ** Share (0.0181) (0.0182) (0.0439) (0.0694) (0.0311) (0.0216) (0.0370) (0.0196) Obs R-squared Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 10

12 Figure 1: Charity Care and the Impact of Premiums on Coverage Impact on Prremiums on Coverage Base Age Age <= High Sch > High Sch Inc <30,000 Inc >30, Public and Teaching Hospital Beds per Million Population 11

13 References Ai C. and E.C. Norton Interaction Terms in Logit and Probit Models. Economics Letters 80: Chernew M., D. Cutler, and P. Keenan Increasing Health Insurance Costs and the Decline in Insurance Coverage. Mimeo. Cutler, D.M Your Money or Your Life: Strong Medicine for America s Health Care System. Oxford: Oxford University Press. Cutler, D.M. and M. McClellan Is Technological Change in Medicine Worth It? Health Affairs 20(5): Cutler, D.M. and J. Gruber Does Public Insurance Crowd Out Private Insurance? Quarterly Journal of Economics 111(2): Dranove D., K. Spier, and L. Baker Competition Among Employers Offering Health Insurance. Journal of Health Economics 19(1): Farber, H.S. and H. Levy Recent Trends in Employer-Sponsored Health Insurance Coverage: Are Bad Jobs Getting Worse? Journal of Health Economics 19(1): Gruber, J. July The Impact of the Tax System on Health Insurance Decisions. Mimeo, MIT. Herring B Does Access to Charity Care For the Uninsured Crowd Out Private Health Insurance Coverage. Mimeo. KPMG Survey of Employer-Sponsored Health Benefits, 1988, 1989, Kaiser Family Foundation/Health Research and Educational Trust Survey of Employer- Sponsored Health Benefits, Pauly M.V. and B. Herring Pooling Health Insurance Risks. The AEI Press, Washington, D.C. Phelps C Health Economics. New York: Addison Wesley. Sheiner L Health Care Costs, Wages, and Aging. Mimeo (Federal Reserve Board of Governors, Washington, DC). 12