The Affordable Care Act Reduced Income Inequality In The US

Income inequality is growing in the United States and is a cause for concern. Wealth concentration was high in the beginning of the twentieth century before falling from 1929 to 1978, but it has continuously increased since then.¹ For example, the share of national income among the poorest half of the US population steadily declined from more than 20 percent in 1980 to 13 percent in 2016, and the income share among the top 1 percent doubled from around 10 percent in 1980 to 20 percent in 2016.² In addition to the political, economic, and social concerns related to rising economic inequality, there is also a growing literature linking income inequality to health disparities.^3,4

The Affordable Care Act (ACA) ushered in the biggest health insurance coverage expansion in the US health care system since the creation of Medicare and Medicaid in 1965 and may have redistributed income between different populations. After implementation of the ACA, the number of people without health insurance in the US declined by 13.3 million from late 2013 through 2017.⁵ However, the uninsured population rose by 1.9 million between 2017 and 2018, to 27.5 million people.⁶ Enrollment in Medicaid and the Children’s Health Insurance Program (CHIP) increased by about 16.6 million (29.2 percent) between 2013 and December 2017,^7,8 and as of the 2018 plan year, 11.8 million people were enrolled in health plans through federal or state-based Marketplaces.⁹ Coverage gains mirrored states’ decisions to expand Medicaid eligibility, as the decline in uninsurance was significantly larger in states that expanded than in states that did not.^10–14 Given the large and growing cost of health care—the overall level of health care spending in the United States was $3.6 trillion in 2018, or 17.7 percent of the economy¹⁵—it is important to understand how the changes in health insurance programs under the ACA affected income inequality.

We investigated the impact the ACA has had on income inequality, considering the new health coverage benefits and government revenue needed to finance them. Although most analyses on inequality focus on earnings or other forms of income,¹⁶ this study incorporated a broader measure of income that shows how government taxes and transfers affect real resources. This measure is based on the health-inclusive poverty measure, developed by Sanders Korenman and Dahlia Remler,¹⁷ and includes the value of Medicaid and CHIP benefits, financial assistance for health insurance premiums provided by the government or employers, and the value of health insurance in reducing families’ risk for high out-of-pocket health care spending. We also accounted for the tax revenue needed to pay for the ACA’s health benefits.

To demonstrate the impact of ACA on income inequality at a point in time, we used the Urban Institute’s Health Insurance Policy Simulation Model to replicate health coverage and costs under the ACA based on the most recent available enrollment data and compared these estimates to a simulated baseline scenario without the ACA. Simulating health coverage in 2019 without the ACA is not the same as simply going back to health coverage in 2013; the simulation model allowed us to incorporate changes in demographic, economic, and other contextual factors during the period and to measure these elements consistently in the presence and absence of the ACA.

Other analyses have assessed how the ACA would reduce income inequality by providing benefits to and increasing household incomes for the lower half of the income distribution.^18,19 However, the estimates in this study provide a more comprehensive picture of the impact of the ACA on inequality. First, prior studies assessed only the potential effects of the ACA on resource inequality, whereas this study allows us to compare two scenarios at the same point in time. Second, our resource measure accounts for the value of key health coverage components—consistent with the health-inclusive poverty measure—that other studies do not account for. Third, we show how inequality changes both within and between various subpopulations, such as racial and ethnic groups.

Study Data And Methods

Simulation Of Scenarios

The Health Insurance Policy Simulation Model is a detailed microsimulation model of the health care system designed to estimate the cost and coverage effects of proposed policy options. The model has been used extensively to estimate the cost and coverage implications of health reforms at the national and state levels and has been widely cited, including in the Supreme Court’s majority opinion in King v. Burwell.²⁰ Unlike survey data, which are published after a time lag of at least a year, the simulation model allows us to incorporate 2019 data from Medicaid and Marketplace enrollment in each state. Survey data also generally underreport Medicaid enrollment and lack details such as whether a family received premium tax credits. Additional information on the Health Insurance Policy Simulation Model is in the online appendix.²¹

Although individual records in the Health Insurance Policy Simulation Model are based on two years of data from the American Community Survey, we regularly update the model to reflect published Medicaid and Marketplace enrollment and costs in each state. The enrollment experience in each state under current law affects how the model simulates policy alternatives. The current version of the model is calibrated to state-specific targets for Marketplace enrollment after the 2019 open enrollment period, 2019 Marketplace premiums, and late 2018 Medicaid enrollment from the Centers for Medicare and Medicaid Services (CMS) monthly enrollment snapshots.^22,23 Because no data are currently available on off-Marketplace or non-ACA-compliant nongroup coverage, these were simulated by the model.

We used the Health Insurance Policy Simulation Model to simulate health coverage and costs among nonelderly adults both under the ACA as implemented and under a scenario in which the ACA had not been implemented. We simulated the impact of the coverage provisions of the ACA, comparing it with insurance coverage and health care spending without the ACA at the national and state levels. The current-law estimates account for the federal individual mandate penalties being set to $0 beginning in plan year 2019, as well as Massachusetts, New Jersey, and Washington, D.C., having their own individual mandate penalties. The current-law estimates also incorporate other recent policy changes, including the expanded availability of short-term, limited-duration policies; a shortened annual open enrollment period; and reduced funds for outreach and enrollment assistance.

We treated states (Idaho, Nebraska, and Utah) in which the ACA Medicaid expansion had been approved by ballot initiative in November 2018 but not yet implemented by the beginning of 2019 as nonexpansion states.

To develop estimates of the impact of the ACA on income inequality, we compared estimates from the current-law scenario with estimates from a simulated baseline scenario without the ACA. This baseline scenario was drawn from the approach used in a recent Health Insurance Policy Simulation Model analysis that estimated the impact of ACA repeal on health insurance coverage and costs.²⁴ We assumed that the seven states with substantial Medicaid coverage expansions for adults before the ACA (Arizona, Delaware, Hawaii, Massachusetts, New York, Vermont, and Wisconsin) could return to pre-ACA eligibility levels. For this to happen, CMS would have to approve new Medicaid Section 1115 waivers. If such waivers are not approved, ACA repeal would result in substantially greater losses of coverage in these states.

Income Measures

No commonly used income measure considers the full monetary value of health coverage under the ACA. Both the Census Bureau’s official poverty measure and family modified adjusted gross income as a percentage of the Department of Health and Human Services Poverty Guidelines (the income measure on which program eligibility is based) omit noncash benefits, such as health insurance coverage, which can reduce out-of-pocket health spending or lower the risk of having to pay very high medical expenses.

For this analysis we created two alternative income measures. The first measure is consistent with the Census Bureau’s Supplemental Poverty Measure, which reduces income by deducting out-of-pocket health care spending.²⁵ As a more comprehensive alternative, we created a measure consistent with the health-inclusive poverty measure,¹⁷ which includes the value of Medicaid and CHIP benefits, financial assistance for health insurance premiums provided by the government or employers, and the value of health insurance in reducing families’ risk for high out-of-pocket health care expenses. We also accounted for the tax revenue needed to pay for the ACA’s health benefits.

We first explored the ACA’s impact on income inequality using the Supplemental Poverty Measure concept. The Supplemental Poverty Measure extends the Census Bureau’s official poverty measure by taking into account many government programs (but not Medicare, Medicaid, or subsidized health care programs) designed to assist low-income individuals and families that are not included in the official poverty measure. The Supplemental Poverty Measure includes the sum of cash income, plus noncash benefits that families can use to meet their needs, minus taxes (or plus tax credits), work expenses, medical expenses (out-of-pocket medical expenses and premiums), and child support paid.²⁵ To construct a measure similar to the Supplemental Poverty Measure that deducts these same medical expenses, we constructed the family’s modified adjusted gross income from pretax income components reported on the American Community Survey and deducted the out-of-pocket expenses for health insurance premiums and health care costs (net cost-sharing subsidies received in the Marketplace).

However, the major limitation of the Supplemental Poverty Measure is that it does not incorporate the value of Medicaid benefits and the receipt of financial assistance to pay for health insurance premiums. We used the concept of the health-inclusive poverty measure to fill this gap by adding family health insurance benefits to family resources. To create our health-inclusive poverty measure, we started with the Supplemental Poverty Measure and made the following modifications. First, we added the receipt of financial assistance to pay for health insurance premiums. This includes the part of the family’s health insurance premiums paid for by the government through premium tax credits or by employer contributions to employer-sponsored insurance premiums.

Second, we added the fungible Medicaid benefit, where the amount of the Medicaid benefit cannot exceed income. The fungible value approach adds the dollar value of the Medicaid benefit to income to the extent that having the insurance would free up resources that would have been spent on medical care.²⁶ Unlike the Census Bureau, we did not include food and housing cost requirements in our definition of fungible value because they are not available in the American Community Survey data.

Third, we added a valuation of the financial risk associated with high medical expenses. Finally, we subtracted the family’s share of new federal and state spending under the ACA, allocated by federal and state income tax incidence. New government spending under the ACA includes the federal and state shares of the cost of new Medicaid enrollment because of the ACA and federal premium tax credits for Marketplace coverage. This modification to income is not incorporated in the original health-inclusive poverty measure.

Additional details on these income components and on how we created a measure consistent with the health-inclusive poverty measure are in the appendix.²¹ Details on how the original health-inclusive poverty measure was constructed and its context can be found elsewhere.¹⁷

Measures Of Inequality

We began by assessing differences in inequality between scenarios with and without the ACA by computing our modified measure of modified adjusted gross income within income percentiles, a common statistic used in analyzing inequality. However, inequality can be presented more concisely as an index. The most widely used index, the Gini index, ranges from 0 (perfect equality) to 1 (maximum inequality) and is derived from the Lorenz curve, which measures the difference between the cumulative income distribution and a perfectly equal income distribution.²⁷ For this analysis we used the Theil index because it has an important decomposition property that the Gini index lacks.²⁸ Similar to the Gini index, the Theil index measures the difference between the cumulative income distribution and a perfectly equal distribution. The Theil index also ranges from 0 to 1, with higher values indicating greater inequality, but it allows researchers to decompose inequality that occurs within demographic groups and across groups (for example, the White-Black income gap).²⁹ This allowed us to examine changes in inequality by race/ethnicity, age, and family educational attainment. More information on the Theil index is in the appendix.²¹

Study Results

We first compared the overall results using the Supplemental Poverty Measure and health-inclusive poverty measure definitions. Next, using the health-inclusive poverty measure, we analyzed the overall impact of the ACA separately for states that expanded Medicaid under the ACA and states that did not. Finally, using the health-inclusive poverty measure, we decomposed inequality in both expansion and nonexpansion states by race/ethnicity, age, and family educational attainment.

Alternative Measures

Appendix table 1 compares the impact of the ACA on inequality across the Supplemental Poverty Measure and health-inclusive poverty measure definitions.²¹ Under the Supplemental Poverty Measure–like income measure, those with the lowest incomes as a percentage of the federal poverty level see their incomes increase under the ACA, whereas those at middle and higher incomes see little change. Those in the lowest-income group—many of whom gained Medicaid coverage under the ACA—are better off because their out-of-pocket health spending is reduced and they do not pay private health insurance premiums. Middle-income groups gain no income benefit under the ACA. For the Marketplace enrollees with premium tax credits in this group, the Supplemental Poverty Measure deducts insurance premiums from income and incorporates reductions in out-of-pocket spending, but the premium tax credits are not counted as income. Those with high incomes do not qualify for ACA programs, so the Supplemental Poverty Measure registers virtually no change for them.

The effects of the ACA on income inequality are clearer using the modified health-inclusive poverty measure. Income gains for those in the lowest-income groups are even larger because this measure adds the fungible value of Medicaid to income. In addition, those in the middle-income groups see gains in income under the ACA because premium tax credits for Marketplace coverage are counted. Those with the highest incomes are less well off under the ACA because their taxes help pay for the ACA’s benefits, but their incomes are too high to qualify for those benefits.

For the rest of this analysis, we use the more comprehensive health-inclusive poverty measure–based definition to analyze the impact of the ACA on inequality.

Income Inequality And Medicaid Expansion

In exhibit 1 we show the impact of the ACA on income inequality, nationwide and by Medicaid expansion status. For those in these lowest-income percentiles, gaining Medicaid coverage virtually eliminated out-of-pocket health care spending; thus, the ACA increased average income as a percentage of the federal poverty level by 18.8 percent, 13.0 percent, 8.4 percent, and 8.4 percent among those in the tenth, twentieth, thirtieth, and fortieth income percentiles, respectively. Our model results are consistent with recent studies of the ACA’s impact on affordability and access to health care.^30,31

Average income increased by smaller margins under the ACA among those at the fiftieth (2.2 percent) and sixtieth (0.5 percent) income percentiles and slightly decreased among those in the top three percentiles. Overall, the ACA, relative to a scenario without the ACA, reduced income inequality by 10.6 percent as measured by the Theil index.

The bottom two panels of exhibit 1 show the impact of the ACA separately for states that have expanded Medicaid eligibility and those that have not. The ACA had a far larger impact on health coverage in expansion states than nonexpansion states. Thus, the gains in health benefits, declines in out-of-pocket spending, and changes in risk premiums—key components of our modified health-inclusive poverty measure—were also much larger in expansion states. In contrast, the funding of those benefits is allocated by income tax, which was far more even across expansion and nonexpansion states. Funding of benefits varied between the two groups only as much as they differed in income distribution and state tax rates.

These factors explain the major differences in changes in income inequality under the ACA between expansion and nonexpansion states. First, using the Theil index, the decline in income inequality under the ACA was much higher in expansion states than in nonexpansion states: The Theil index decreased by 11.9 percent for expansion states compared with 8.3 percent for nonexpansion states. Second, looking at income percentiles, the increases in income under the ACA were much larger at the bottom two percentiles in expansion states compared with nonexpansion states. This is due to greater gains in benefits and declines in out-of-pocket spending for Medicaid enrollees in expansion states. Third, in the fortieth and fiftieth income percentiles, the ACA had comparable impacts on income in expansion and nonexpansion states. This is largely because many households in these percentiles receive tax credits for Marketplace health coverage in both groups of states. Finally, in the highest-income percentiles, the ACA was associated with reductions in income in both expansion and nonexpansion states because federal Medicaid expansion costs are funded by households in these income groups across all states.

Income Inequality And Race/Ethnicity

Using the health-inclusive poverty measure–based income definition, in exhibits 2, 3, and 4 we decompose the total change in inequality under the ACA by race/ethnicity, age, and family educational attainment, respectively. More detailed estimates of the levels of inequality with and without the ACA and the distribution of between-group inequality versus within-group inequality are in the appendix.²¹

SOURCE Authors’ analysis of the Health Insurance Policy Simulation Model, 2019. NOTES Income is defined to be consistent with the health-inclusive poverty measure. We use the Theil index to measure income inequality, as explained in the text.

SOURCE Authors’ analysis of the Health Insurance Policy Simulation Model, 2019. NOTES Income is defined to be consistent with the health-inclusive poverty measure. We use the Theil index to measure income inequality, as explained in the text.

SOURCE Authors’ analysis of the Health Insurance Policy Simulation Model, 2019. NOTES Income is defined to be consistent with the health-inclusive poverty measure. We use the Theil index to measure income inequality, as explained in the text.

Overall, estimates in exhibit 2 show that the ACA reduced between-group inequality by 8.5 percent, with larger reductions seen in expansion states (10.2 percent) than in nonexpansion states (6.1 percent). Inequality under the ACA also significantly declined within each racial/ethnic group, with larger declines seen in Medicaid expansion states. American Indians/Alaska Natives, Hispanics, and Black non-Hispanics generally saw the largest decreases in within-group inequality. Between-group income inequality (for example, differences in income between the five racial/ethnic groups) made up only about 6 percent of total inequality, whereas within-group inequality made up roughly 94 percent of total inequality, as shown in appendix table 2.²¹

Indian Health Service funding was not included in our analysis. However, this program has a fixed annual budget that generally does not change with the availability of other funding for health coverage. Thus, the greater availability of Medicaid coverage, and to a lesser extent Marketplace coverage, to American Indians/Alaska Natives under the ACA represents a true increase in health benefits.

Income Inequality And Age

Similar to the estimates by race and ethnicity, most of the total age-related income inequality fell within age groups (94 percent), rather than between them (6 percent). However, that is largely because of our choice to use only five age groups to simplify the presentation (appendix table 3).²¹ Overall, between-group inequality declined by 5.3 percent under the ACA, with large reductions in expansion states (8.1 percent) and no change in nonexpansion states (exhibit 3).

Within-group inequality nationwide also declined by at least 10 percent for each age category, with larger reductions seen in expansion states across most categories. In expansion states, the youngest age group experienced the largest decline (23.5 percent) in within-group inequality under the ACA. In contrast, in nonexpansion states, adults ages 55–64 experienced the largest decline (15.8 percent) in within-group inequality, whereas young adults were less affected. This largely reflects the age distribution of Marketplace enrollees, who are older, and Medicaid enrollees, who are younger (exhibit 3).

Income Inequality And Educational Attainment

Looking at family educational attainment, we found that between-group inequality made up more than a fifth of total inequality, which is a much higher share than for race/ethnicity and age (appendix table 4).²¹Exhibit 4 shows that under the ACA, between-group educational attainment inequality declined by 9.3 percent. Within-group inequality declined for each educational attainment group, with larger reductions seen in expansion states. For both expansion and nonexpansion states, the largest decrease in within-group inequality was among those with a high school education, with large declines among those with some college education and those with less than a high school education as well. The smallest declines in inequality were among those with a college degree.

We also computed Gini indices for each characteristic value (appendix tables 2–4).²¹. In every case, changes in the Gini index showed a decline in inequality under the ACA.

Discussion

This analysis focused on how changes in health insurance coverage and spending under the ACA affected the distribution of income and resources in the US, considering various monetary benefits associated with health insurance. Overall, we found that that the ACA significantly reduced income inequality. Inequality decreased both in states that have expanded Medicaid and in those that have not, although the impact was larger among expansion states. We also found that the ACA reduced income inequality within and between groups defined by race/ethnicity, age, and family educational attainment, with larger declines in inequality occurring in Medicaid expansion states.

These findings provide additional insight into the effect of potential repeal of the ACA. Despite the reluctance of Congress to take up repeal-and-replace legislation since the failure of several proposals in 2017, it remains important to consider the consequences of repealing the law. Efforts to repeal and replace the ACA have shifted to the courts, specifically with California v. Texas.³² The plaintiffs in this case argued that because the Tax Cuts and Jobs Act of 2017 set the ACA’s individual mandate penalty to zero dollars, the entire ACA cannot operate or be sustained. Therefore, they argued that the ACA should be invalidated or effectively repealed in its entirety. The US Supreme Court held oral arguments on this case November 10, 2020, with a decision expected by summer 2021.

Eliminating the ACA would significantly change the distribution of health insurance coverage and allocation of health care spending in the US. Based on a newly developed projection that accounts for the coronavirus disease 2019 (COVID-19) pandemic, more than twenty million people would lose health insurance, primarily through decreases in Medicaid and nongroup coverage, if the ACA were to be repealed.³³ Although eliminating the ACA would decrease federal and state spending, it would also significantly increase uncompensated care costs and the medical financial burden for families, particularly those with low and middle incomes, who were the chief beneficiaries of the ACA. This would primarily occur through the repeal of Medicaid expansion, which has been shown to increase the financial security of the newly insured^34,35 and improve hospital finances through lowered uncompensated care costs.^36,37

Eliminating the ACA could also worsen other outcomes, such as access to primary care and prescription drugs and self-reported health. Prior studies found that through 2015, the ACA substantially increased the share of nonelderly people who reported having a personal physician (3.5 percentage points) and easy access to medicine (2.4 percentage points) and decreased the share who reported being in fair or poor health (3.4 percentage points) and who reported that they could not afford care (5.5 percentage points) relative to the pre-ACA trend.^31,38,39

In addition, between the third quarter of 2013 and the first quarter of 2017, there were significant increases in the shares of adults with a usual source of care and with a routine checkup in the past year, whereas the shares of adults reporting an unmet need for medical care because of cost and problems paying family medical bills both declined.⁴⁰

Without the valuable components of health insurance coverage provided by the ACA, income inequality as measured inclusive of various health coverage benefits would revert to pre-ACA levels. The ACA reduced income inequality between racial/ethnic groups, age groups, and people of higher and lower educational attainment. Overturning the law would put these gains in serious jeopardy.

ACKNOWLEDGMENTS

This research was supported by the Robert Wood Johnson Foundation. The views expressed are those of the authors and should not be attributed to the Robert Wood Johnson Foundation or the Urban Institute, its trustees, or its funders. The authors thank Steve Zuckerman, Linda Blumberg, John Holahan, and the anonymous reviewers for their feedback and reviews.

NOTES

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