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DataWatch

Insuring Low-Income Adults: Does Public Coverage Crowd Out Private?

Abstract

 
During the mid-1990s Minnesota, Washington State, Oregon, and Tennessee implemented programs to provide subsidized health insurance for low-income persons who were not previously eligible for Medicaid. We estimate the effects of these programs on the health insurance status of low-income adults in these states. We find that among persons with family incomes below 100 percent of the federal poverty level, subsidized public coverage reduced the number of uninsured persons with very little effect on private coverage rates. Among persons with income between 100 percent and 200 percent of FPL, public coverage reduced the number of uninsured persons and crowded out some private insurance. The partial successes achieved by these programs should be kept in perspective: Even after program implementation, approximately 30 percent of low-income adults in the four states were uninsured.

The purpose of this paper is to assess the effects of subsidized insurance programs on the health insurance status of low-income adults. To what extent did these programs reduce the number of uninsured? To what extent did they crowd out private insurance or Medicaid coverage? Did the effects vary by state or by enrollees’ income level?

Factors that influence the extent of crowding out. Stimulated by David Cutler and Jonathan Gruber’s provocative 1996 work, a number of analysts have estimated whether the Medicaid expansions of the late 1980s and early 1990s crowded out private health insurance coverage.³ Most of the studies conclude that approximately 20 percent of the increase in Medicaid enrollment that occurred following the expansions resulted from crowding out of private insurance, while the remaining increase resulted from a reduction in the number of uninsured persons.⁴

Although the literature on crowding out has reached a consensus on the effects of Medicaid expansions on private insurance coverage for children, this work has limited applicability to understanding the effects of subsidized insurance programs for low-income workers. The state programs we studied expanded eligibility to many persons with incomes of 100–200 percent of the federal poverty level, while the Medicaid expansions primarily expanded eligibility to children below 100 percent of poverty (see Exhibit 1).

Second, Medicaid is an entitlement program, and Medicaid eligibility expansions were likely perceived to be "permanent." In sharp contrast, the state programs of subsidized insurance were less stable, which should have reduced the extent of crowding out, since employers and employees would be less willing to give up private insurance in favor of public coverage if that coverage might not be available in the future.

Finally, Minnesota and Tennessee adopted explicit protections against crowding out: Applicants were not eligible for MinnesotaCare if they were privately insured during the four months prior to application, or if employer-sponsored insurance was available during the previous eighteen months. TennCare restricted eligibility to persons who did not have an offer of employer coverage. The net effect of all of these factors is unclear.

Data And Methods

Top Data And Methods Study Results Discussion And Policy... NOTES

 
We present results from two types of analyses. The first is a simple pre-post analysis in which we compared insurance status in the four states before and after the subsidized coverage programs were implemented. We used a "difference-of-difference" approach in which we compared the change in insurance status in the states having subsidized insurance programs with that in other states in the region. The second approach estimates a multivariate logistic regression to predict insurance status as a function of individual demographic and employment characteristics, as well as fixed effects for each state and each year. We think that the second analysis produces more accurate estimates of program effects, but the simpler analysis enables one to assess the sensitivity of the results to alternative analytic methods.

Data sources. We used the March supplements to the Current Population Survey (CPS) from 1988 to 1999 to measure insurance status as well as individual employment and demographic characteristics. The CPS provides a large database, with detailed demographic and employment information and relatively consistent questions on health insurance status over the twelve-year period.⁷ We also obtained data on total state program enrollment among adults in December of each year from administrators of the four programs.⁸

Measuring insurance status—specifying the dependent variables. Starting in 1995 the CPS added a set of questions to identify recipients in state-sponsored insurance programs.⁹ After the standard set of questions asking about private insurance, Medicare, and Medicaid coverage, respondents were asked whether they were covered by any other form of insurance, such as MinnesotaCare (for Minnesota respondents), Basic Health Plan (for Washington respondents), and other state-specific programs. Respondents in our study states were much more likely to report coverage in these programs than were respondents from other states.¹⁰

Although these questions are useful in identifying enrollees in state-specific programs, they do have limitations. Most notably, in the March 1996–March 1999 surveys, "expansion" enrollment in TennCare and the Oregon Health Plan (OHP) cannot be separated from regular Medicaid enrollment. That is, respondents who were enrolled in TennCare or the OHP as a result of the eligibility expansions are treated in the CPS as regular Medicaid enrollees.

Because of this, we combined enrollment in Medicaid and in state-sponsored programs into a "public coverage" outcome. We combined employer-sponsored and nongroup coverage into a "private coverage" outcome. We analyzed the effects of state programs on three outcome variables: the probabilities of being privately covered, publicly covered, and uninsured.¹¹ We excluded from the analysis the few respondents with Medicare or Civilian Health and Medical Program of the Uniformed Services (CHAMPUS) coverage.

Our analysis was restricted to adults ages nineteen to sixty-four with incomes below 200 percent of poverty. We focused on adults for three reasons. First, much of the existing literature on crowding out deals with the effects on children, not adults. Second, many states moved to expand coverage to children in the mid-1990s, and specifying and measuring the size of these programs is beyond the scope of this work. Third, enactment of the State Children’s Health Insurance Program (SCHIP) largely resolved the policy question of whether to extend subsidized insurance to low-income children, while coverage for adults is still very much the subject of debate in state and federal legislatures. We restricted the main analyses to persons with family income below 200 percent of poverty because few persons with incomes above that level are enrolled in the programs. As discussed below, however, some CPS respondents with incomes above 200 percent of poverty did report being enrolled in the state programs, and we report the results of supplementary analyses including persons at 200–300 percent of poverty.

"Difference-of-difference" analysis. We provide a simplified view of the effects of these programs with a difference-of-difference analysis, in which we compared the insurance status in each state for the five years after program implementation with that for the seven years before implementation, using the change in insurance status in other states in the region as controls. Although the pace and timing of implementation varied somewhat among our four states, few people were enrolled prior to 1994 in any state. To simplify the analysis, we compared insurance status in the 1994–1998 time period with that in the 1987–1993 time period in each state.

This analysis produced an estimate of the effects of each program on the insurance status of low-income adults. As we see below, program size varied greatly across the four states. For example, in 1994 approximately 33 percent of adults with incomes below 200 percent of poverty in Tennessee were enrolled in TennCare, while the penetration rate of MinnesotaCare among low-income adults was under 4 percent. To estimate the effects of a 10 percent program penetration rate on insurance status, we divided the difference-of-difference estimates of program effects on insurance status by the estimated average program penetration rate from 1994 to 1998.

To estimate program penetration, we used responses to the CPS questions on "other government insurance" to allocate total program enrollment to adults under 100 percent, 100–200 percent, and above 200 percent of poverty.¹² On average, across the four states, we estimated that approximately 80 percent of adult program enrollees had family incomes under 200 percent of poverty. We divided the estimated number of adult enrollees at this poverty level by the total number of low-income adults to estimate the program penetration rates among low-income adults in each state and year.

Multivariate analysis. The difference-of-difference estimates are instructive, but they do not take full advantage of the information available to estimate program effects. The estimates assume that the changes in insurance status in the states we studied would have been similar to changes in the rest of the region. However, changes in economic conditions or demographic characteristics in our program states may have caused insurance coverage to evolve differently there. To control for such factors, we estimated a multivariate logistic regression in which we predicted a person’s insurance status as a function of demographic and employment characteristics, indicator variables for state and year, and a set of eight dummy variables indicating the presence of the programs. There are two program variables for each of the four states: one for adults below 100 percent of poverty, and a second for adults with family income of 100–200 percent of poverty.¹³

To concisely summarize the results of the multivariate logistic regressions, we used the parameter estimates from the model to predict the probabilities of private coverage, public coverage, and no coverage for each low-income respondent in our four states in the 1995–1999 CPS samples. We made two predictions for each respondent: first, assuming that the program did not exist (that is, setting the indicator variable equal to zero), and second, assuming that the program did exist. The average of the difference in probabilities across all respondents in the state provides our estimate of the effects of the state program on the probability of private coverage, public coverage, and no coverage. We calculated standard errors using the delta method.¹⁴

As in the difference-of-difference analysis, we divided the estimated program effects in each state by the estimated average program penetration in the state over the 1994–1998 time period, to estimate the effect of a 10 percent program penetration rate on the probability of public coverage, private coverage, and no coverage.

In addition to summarizing the results by state, we summarize the results separately for respondents under 100 percent and at 100–200 percent of poverty. In the summary by income level we combined data across the four states to increase the stability of the results. We divided the estimated effect of the state programs by the estimated program penetration rate at both income levels, to estimate the effects of enrolling 10 percent of the population at a given income level into a state program.

Supplementary analysis. In each of the four states, a small number of CPS respondents with incomes of 200–300 percent of poverty reported that they had non-Medicaid public coverage, despite the fact that few persons with incomes above 200 percent of poverty were eligible for subsidized coverage. In a supplementary analysiswe replicated the multivariate analysis described above, but we included respondents with incomes below 300 percent of poverty and an additional indicator variable in each state for persons at 200–300 percent of poverty.

Study Results

Top Data And Methods Study Results Discussion And Policy... NOTES

 
Penetration rates. The subsidized insurance programs enrolled substantial numbers of low-income adults (Exhibit 2). TennCare is the largest program, enrolling close to one-third of Tennessee adults with incomes below 200 percent of poverty in its first full year of operation; enrollment declined after the program was closed to most new adult enrollees and then rose in 1998 when enrollment was reopened for specified groups. The OHP enrolled approximately 15 percent of low-income adult Oregonians during 1995–1998. The penetration rate for Washington’s Basic Health Plan increased throughout the 1994–1998 period, rising to 17 percent of the low-income population by 1998. MinnesotaCare, primarily a program for children, enrolled relatively few adults—averaging only 7 percent of low-income adults over the 1994–1998 period.

View larger version (21K): [in this window] [in a new window]   EXHIBIT 2 Estimated Program Penetration Rates Among Adults Under 200 Percent Of Poverty, In Four States, 1992–1998

To estimate the effects of a 10 percent program penetration rate on coverage rates, we divided the difference-of-difference estimates of the program effect by the estimated average program penetration rate among adults below 200 percent of poverty over the 1994–1998 period (Exhibit 3). For each 10 percent of low-income adults who were enrolled in a state program, the proportion of low-income CPS respondents who reported being covered by a public program rose by approximately 5 percentage points. We expected closer to a one-to-one correspondence in these numbers; below we consider potential explanations for the discrepancy.

Logistic regression analysis. The results from the multivariate logistic regressions largely confirm the findings from the simple difference-of-difference analysis (Exhibits 3 and 4).¹⁶ Virtually all of the increase in public coverage in Oregon was associated with a decline in the number of uninsured persons. In contrast, in Tennessee only 55 percent of the increase in public coverage resulted from a decline in the number of uninsured; the remainder resulted from a decline in private coverage. In Minnesota the point estimates are that almost all of the increase in public coverage resulted from a decline in private coverage. In Washington the point estimates are that approximately 85 percent of the increased public coverage following implementation of the Basic Health Plan was accounted for by a decrease in the number of uninsured; the remaining 15 percent resulted from crowding out of private coverage. In both states the estimated effects are just barely significantly different from zero at the p = .05 level, and there is quite a bit of uncertainty about program effects on both private coverage and the uninsured.

View larger version (28K): [in this window] [in a new window]   EXHIBIT 4 Regression Estimates Of The Effects Of A 10 Percent Program Penetration Rate On Health Insurance Status, By State

View larger version (29K): [in this window] [in a new window]   EXHIBIT 5 Regression Estimates Of The Effects Of A 10 Percent Program Penetration Rate On Health Insurance Status, By Income Level

Discussion And Policy Implications

Top Data And Methods Study Results Discussion And Policy... NOTES

 
We have shown that in two states, Oregon and Washington, expansion of public coverage resulted in a decline in the number of uninsured and very little crowding out of private insurance. In Tennessee an expansion of public coverage was associated with a decrease in the number of both uninsured persons and privately insured persons. In Minnesota the implementation of MinnesotaCare was accompanied by a decline in the number of privately insured persons and virtually no change in that of uninsured persons.¹⁷

We do not have a convincing explanation for these differences in results across states. The Oregon program is targeted at persons below 100 percent of poverty, which might account, in part, for its relative success. But the Washington Basic Health Plan, which provides subsidies to persons up to 200 percent of poverty, resulted in little crowding out as well. Both TennCare and MinnesotaCare have greater explicit protections against crowding out than the Oregon and Washington programs; despite these protections, they appear to have experienced greater amounts of crowding out.

Estimated program effects vary widely by income group: Crowding out increases as adults with higher incomes enroll in the programs. Combining all four states together, we estimate that among persons below 100 percent of poverty, a 10 percent program penetration rate resulted in a 4.7 percent increase in public program enrollment, a 3.8 percent decrease in the percentage uninsured, and little change in the percentage with private insurance. Among adults at 100–200 percent of poverty, a 10 percent program penetration rate is estimated to have resulted in a 9.3 percent increase in public program enrollment, with approximately 45 percent of this increase coming from the crowding out of private coverage.

It is clear from our results that the programs in these four states led to little crowding out among adults with incomes below 100 percent of poverty, but crowding out did occur among persons with incomes 100–200 percent of poverty.¹⁸ However, there is much uncertainty about the amount of crowding out we might expect if similar programs were implemented in other states. There are two main sources of uncertainty. First, as we have shown, the effects of the state programs on the probability of having private coverage vary across our four states, and we were unable to convincingly connect variations in program characteristics with variations in program effects. This makes it difficult to know whether programs in other states might be expected to have effects closer to those in Oregon, where there was very little crowding out, or Tennessee, where there was more crowding out. Second, while our point estimate is that 45percentofthe increase in public coverage among persons with incomes of 100–200 percent of poverty results from a reduction in private coverage, the 95 percent confidence intervals for the estimated effects on private insurance and on uninsurance are consistent with a wide range of crowding-out estimates. Research on the effects of coverage expansions for low-income adults is in its infancy, and we expect that additional work using different data sources and studying other programs will be needed if we are to gain a more precise understanding of program effects.

One aspect of our results is somewhat puzzling: namely, the incomplete response in the CPS data to the programs we studied. For example, in Oregon we estimated that for every hundred adults who enroll in the program, sixty-six additional CPS respondents will report themselves as having public insurance and sixty-five fewer will report themselves as being uninsured, with virtually no change in the number reporting private insurance. What happened to the other thirty-four program enrollees? This is even more puzzling in Minnesota and Washington, where there is even less reflection in the CPS data of the enrollment in the states’ subsidized programs.

One possible explanation is that some people who enrolled in the public programs would have been enrolled in Medicaid if those programs hadn’t existed. Although federal participation in Medicaid gives states a strong financial incentives to maximize Medicaid enrollment, some persons may have preferred the state-specific programs to Medicaid. This explanation is supported by our finding that the CPS response to program enrollment is incomplete among persons below 100 percent of poverty, where "Medicaid crowding out" is most likely, but not among persons at 100–200 percent of poverty, where Medicaid crowding out is less likely.

A second possible explanation is that the CPS undercounts the number of persons enrolled in these public programs. It is well known that the CPS undercounts Medicaid coverage; apparently some who are covered by Medicaid do not report this coverage to the CPS interviewer.¹⁹ It is likely that the CPS undercounts coverage in states’ publicly subsidized programs as well. To the extent that the CPS undercounts program enrollment, then our estimates of program effects on the number of uninsured persons are biased downward; the true effect of the program on the number of uninsured persons is even larger than the effects we estimate in Exhibit 4.

Projections of the effects ofa national program. If subsidized insurance for low-income workers were implemented nationwide, and the program were similar to the programs we have studied here, we might expect from the results in Exhibit 2 that 15–20 percent of adults below 200 percent of poverty would enroll, or approximately 5.7–7.6 million adults. If the results in the nationwide program were similar to results in the average of the four states studied here, we would expect that 4–5 million enrollees would have been uninsured, with most of the remaining enrollees "crowded out" of private insurance.²⁰ However, if we used the current question wording in the CPS to measure coverage, we would expect to measure a decline of only 2–3 million uninsured adults.

There are approximately thirty-one million uninsured adults; a reduction of five million would be important but would still leave a sizable problem.²¹ As seen in Exhibit 1, even in the 1994–1998 time period after the subsidized insurance programs were implemented, approximately 30 percent of adults with incomes below 200 percent of poverty were uninsured in our four states. This was below the average for the region and below the preimplementation average (except in Minnesota), but it still represents a large number of persons without health insurance. Some may have been uninsured because they were not eligible for the programs; others, because enrollment in the program was closed, because they did not know about the program, because premiums were too high, or because they did not value health insurance highly.

If expanded programs of subsidized insurance for low-income adults are to greatly reduce the numbers of uninsured persons, they must be designed, implemented, financed, and marketed more successfully than were the programs we studied. Such programs have the potential to reduce private coverage, particularly as they are extended to persons with incomes above the federal poverty level. As has been discussed elsewhere, crowding out of private coverage will result in welfare-improving enhancements for low-income persons but does reduce the program’s target efficiency. Given the pressing problems created by the existence of close to forty million uninsured persons, we think that designing programs to maximize participation should be an overriding policy goal. However, in a voluntary market programs that are attractive enough to enroll large numbers of uninsured persons inevitably will be attractive enough to enroll large numbers of persons who would have had private insurance in the program’s absence.

Editor's Notes

 
Richard Kronick is an associate professor in the Division of Health Care Sciences, Department of Family and Preventive Medicine, University of California, San Diego in La Jolla. Todd Gilmer is an assistant adjunct professor there.

This research was supported by a grant from the California Program on Access to Care of the California Policy Research Center. The authors thank Sandra Hunt at PricewaterhouseCoopers for providing information on the Oregon Health Plan and TennCare, Carolyn Watts and Vicki Wilson for sharing insights on the Washington Basic Health Plan, and Gestur Davidson for information on MinnesotaCare.

NOTES

Top Data And Methods Study Results Discussion And Policy... NOTES

 

1. Hawaii also implemented subsidized coverage for low-income adults but is excluded from our analysis primarily because the mandate on employers to cover workers makes it likely that the effects on crowding out in Hawaii will be different from those elsewhere.
2. L. Ku and T.A. Coughlin, "Sliding-Scale Premium Health Insurance Programs: Four States’ Experiences," Inquiry (Winter 1999/2000): 471–480.
3. D.M. Cutler and J. Gruber, "Does Public Insurance Crowd-Out Private Insurance?" Quarterly Journal of Economics 111, no. 2 (1996): 391–430; L.C. Dubay and G.M. Kenney, "Revisiting the Issues: The Effects of Medicaid Expansions on Insurance Coverage of Children," Future of Children 6, no. 1 (1996): 152–161[Medline]; L.C. Dubay and G.M. Kenney, "Did Medicaid Expansions for Pregnant Women Crowd Out Private Insurance?" Health Affairs (Jan/Feb 1997): 185–193; K.E. Thorpe and C. Florence, "Health Insurance Coverage among Children: The Role of Expanded Medicaid Coverage," Inquiry 35, no. 4 (1998): 369–379; L. Shore-Sheppard, "Stemming the Tide: The Effect of Expanding Medicaid Eligibility on Health Insurance Coverage" (Working paper, University of Pittsburgh, November 1997); and L.J. Blumberg, L. Dubay, and S. Norton, "Did the Medicaid Expansions for Children Displace Private Insurance? An Analysis Using the SIPP," Journal of Health Economics (January 2000): 33–60.
4. The definition of crowding out is not entirely consistent across studies, but most researchers define it as the share of new program enrollees who would have had private coverage if the program did not exist. We use this definition in our research. For a detailed summary of work in this area, see E.Y. Yazici and R. Kaestner, "Medicaid Expansions and the Crowding Out of Private Health Insurance among Children," Inquiry (Spring 2000): 23–32.
5. Ku and Coughlin, "Sliding-Scale Premium Health Insurance Programs."
6. Conversely, required premiums will also deter the uninsured from enrolling, so the net effect of premiums on crowding out is not completely clear.
7. Our analytic sample is restricted to adults ages 19–64 with family incomes below 200 percent of poverty. When the CPS samples from 1988 to 1999 were combined, there were 2,561 adults in this group in Minnesota, 3,000 in Oregon, 3,737 in Tennessee, and 2,614 in Washington. The multivariate logistic regression, including adults from throughout the country, is estimated on 269,637 respondents. For a discussion of question wording changes in 1995, see K. Swartz, "Changes in the 1995 Current Population Survey and Estimates of Health Insurance Coverage," Inquiry 34, no. 1 (1997): 70–79.[Medline]
8. For the Oregon Health Plan we considered only "expansion" enrollment; for TennCare we considered only enrollment in the "uninsured"; and "uninsurable" categories; we considered regular subsidized enrollment Washington’s Basic Health Plan; we considered enrollment of all adults in MinnesotaCare.
9. Current Population Survey, March 1995 Technical Documentation, prepared by Administrative and Customer Services Division, Microdata Access Branch, Bureau of the Census, 1995.
10. In the 1995 CPS 1.7 percent of adults under 200 percent of poverty in the United States as a whole reported that they had other government coverage in response to these additional questions. By comparison, 24.1 percent of Tennessee adults under 200 percent of poverty responded positively, as did 14.6 percent of Oregon respondents. Over the 1995–1999 period our study states ranked 1–4 out of the fifty states in affirmative responses to these questions.
11. Respondents who reported both public and private coverage were assigned to public coverage.
12. The Washington Basic Health Plan provided data on the distribution of enrollees by income group, and these enrollment data correspond closely to our CPS estimates of enrollment by income group. This gives us confidence that the allocation of program enrollment to income group using estimates from the CPS is a reasonable approach.
13. The model is estimated by multinomial logistic regression, using the CPS sampling weight and a robust covariance matrix specification. As shown in Exhibit 1, eligibility for MinnesotaCare was originally limited to adults in families with children, and MinnesotaCare program data indicate that fewer than 20 percent of adult enrollees were childless. The dummy variables for Minnesota are weighted to reflect the higher probability of enrollment for adults in families. Detailed descriptions of the variables and models are available from Richard Kronick at <rkronick{at}ucsd.edu>.
14. W. Greene, Econometric Analysis (New York: Macmillan, 1993).
15. Standard errors for the difference-of-difference estimates are computed using the familiar formula for the standard error of a proportion. To compute the standard error for the estimated effect of a 10 percent increase in program penetration, we assumed the penetration rate to be fixed, and simply divided the standard error of the difference-of-difference estimate by the estimated average program penetration rate. To the extent that the program penetration rate is itself an estimate, with some nonzero, but unknown, standard error, the resulting estimated standard error of the estimated effect of a 10 percent program penetration is biased downward from its true value.
16. Results of the multivariate logistic regressions are available from the authors.
17. The nongroup insurance market in Minnesota shrank throughout the 1990s. Our model includes fixed effects for states and years but does not have enough power to estimate state-specific time trends during the preimplementation period. To the extent that private coverage was declining in Minnesota prior to MinnesotaCare, and that decline continued after MinnesotaCare was implemented, our model produces the result that MinnesotaCare was associated with a reduction in private coverage, when other features of the Minnesota environment may actually have caused the decline.
18. Refundable tax credits, one of the main alternatives to expansion of public programs, are likely to produce even higher levels of crowding out. See J. Gruber and L. Levitt, "Tax Subsidies for Health Insurance: Costs and Benefits," Health Affairs (Jan/Feb 2000): 72–85; and S. Glied, "Challenges and Options for Increasing the Number of Americans with Health Insurance," Inquiry 38, no. 2 (2001): 90–105.[Medline]
19. K. Lewis, M. Ellwood, and J.L. Czajka, Counting the Uninsured: A Review of the Literature (Washington: Mathematica Policy Research, June 1998).
20. This estimate assumes, following the experience in the four states, that 55 percent of enrollees would be below 100 percent of poverty, where 20 percent of program enrollment is estimated to result from crowding out, and 45 percent of enrollees would be at 100–200 percent of poverty, where 45 percent of enrollment is estimated to result from reduction in private coverage.
21. The estimated number of uninsured, like all other results reported in this paper, does not reflect adjustments to the CPS data to reflect the "verification" questions added in the March 2000 CPS. For details, see C.T. Mills and R.J. Nelson, "The March CPS Health Insurance Verification Question and Its Effect on Estimates of the Uninsured," August 2001, <www.census.gov/hhes/hlthins/verif.html>(17 October 2001).

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