During the first round of open enrollment under the ACA, millions of Americans learned whether they qualify for subsidized health insurance coverage through the law’s exchanges. At that time, one significant feature of the law became especially apparent: the dramatic cutoff of premium assistance for individuals who do not qualify for Medicaid in their state, but also earn too little to qualify for subsidies through the exchanges.

For example, if, during enrollment, a sixty year-old Miami man projected that he would earn $11,500 in 2014, he could purchase a bronze plan using a full subsidy so there would be no cost to him in premiums.  But if the same man projected earning just $500 less, $11,000, he would no longer qualify for any subsidy at all and the same coverage would require him to pay the full $6,573 premium.

Here is how the coverage gap works: Under the ACA, people earning below the key threshold of 100 percent of the federal poverty level (FPL) do not qualify for any premium subsidies through the exchanges; the law was written to provide them with Medicaid coverage instead. In reality, however, Florida and 23 other states chose not to expand Medicaid under the ACA, creating a gap into which roughly five million people with annual incomes less than 100 percent FPL ($11,490 for an individual or $23,550 for a family of four) will likely fall.

Discussions of the coverage gap typically overlook the strong incentives that these uninsured individuals may have to modify their incomes in order to qualify for subsidies. Such behavior is a particularly important possibility because the US treasury will not force exchange enrollees to pay back subsidies if their projected income at enrollment exceeds the Federal poverty level but their year-end taxable income falls below it.

The Subsidy Cliff

With premium data available, we can now measure the magnitude of this sharp cutoff in subsidies (the “subsidy cliff”) and characterize the population in its proximity.

Economic principles tell us that the promotion of equity through the tax and transfer system or in the form of means-tested public programs often comes at the cost of efficiency, typically by distorting individuals’ incentives to work. To minimize these distortions, economists tend to favor policies that gradually adjust eligibility for benefits as in the case of the earned income tax credit (EITC) rather than steep benefit “cliffs.”

Supporting this position is research demonstrating that low-income individuals respond to kinks or sharp cliffs in the tax schedule. Congress designed the ACA subsidies with these lessons in mind, but they did not anticipate the Supreme Court ruling striking down the mandatory Medicaid expansion. No longer required to expand Medicaid, 25 states chose not to, thereby introducing a sharp difference in insurance access for individuals just below 100 percent FPL and those just above.

Figure 1 demonstrates the magnitude of the subsidy cliff based on recently released premium data from states not expanding Medicaid. The EITC, a major tax credit for lower-income working families, provides a useful side-by-side comparison. While the magnitudes of the benefits are similar, their structures are very different; the ACA provides an “all-or-nothing” benefit at the 100 percent FPL threshold that is slowly phased-out, while the EITC benefits are phased in and phased out.

Notably, while the ACA cliff is substantial for all age groups, it is largest for the near-elderly, who receive higher subsidies because they face higher premiums. Although some health insurance consumers have expressed frustration with the drop-off in ACA subsidies at 400 percent FPL, little attention has been paid to the discontinuity in benefits at 100 percent FPL, which is demonstrably larger.

For those in the coverage gap who would receive no subsidies at their current income, we estimated that the average premium subsidy at 100 percent of FPL would be $2,831 per year, and the combined value of premium and cost sharing subsidies would be $3,727 per year. Thus, the incentive to exceed the eligibility threshold is large, equivalent to 24.6 percent of household income and 32.4 percent of household income, respectively.

Figure 1

2014 ACA Premium Tax Credit Schedule vs. Earned Income Tax Credits


ACA Source: Author calculations using 2013 Current Population Survey (CPS) Integrated Public Use Microdata Series, KFF Subsidy Calculator, and premium data from healthcare.gov. EITC Source: 2014 EITC parameters taken from the Tax Policy Center.

Given the size of the subsidy cliff, individuals near the threshold have a strong incentive to increase their reported income. For instance, workers with flexible hours, such as part-time or hourly employees, may respond by increasing their number of hours worked. Some workers may choose to report income they otherwise would not have, while others may over-report or manipulate their projected income to be above 100 percent FPL so they qualify for subsidies.

Age And Employment Characteristics

Figure 2 demonstrates the age and employment characteristics of the highest wage-earner in the households containing the roughly two million individuals near the subsidy threshold. This population includes a substantial number of part-time workers, whose labor supply may be flexible, and older individuals, who qualify for higher subsidies. Roughly 300,000 people are members of households that are especially close to the subsidy cliff, with annual household incomes less than $1,000 below the threshold.

Figure 2­

Characteristics of Individuals Near Subsidy Threshold (75-125% FPL) 


Source: Author calculations using 2013 Current Population Survey (CPS) Integrated Public Use Microdata Series and KFF “The Coverage Gap: Uninsured Poor Adults in States that Do Not Expand Medicaid.” See Appendix for sample construction method.

Despite the large financial incentive introduced by the subsidy cliff, it is unclear whether it will affect individuals’ behavior. There are many cases of eligible individuals failing to respond to public benefits, including those that are free or highly subsidized like Medicaid. In the case of the ACA, responding to the subsidy cliff would require both labor supply flexibility and some sophistication regarding the subsidy rules.

On the other hand, online subsidy calculators, the large network of the ACA’s health care exchange navigators, and the tax preparation industry may heighten awareness of the subsidy and its eligibility requirements. Moreover, many workers with earnings close to 100 percent of FPL hold part-time or hourly positions, giving them some flexibility to take on additional shifts and push their earnings above the threshold.


States that have not expanded Medicaid under the ACA have created substantial, and likely unintended, incentives for individuals to ensure their incomes exceed 100 percent of FPL. This quirk in the new health care law merits policymakers’ attention for several reasons.

First, the magnitude of the cliff and the fact that individuals can earn too little to qualify is, as far as we can tell, unprecedented in social welfare programs. Second, if individuals respond to this incentive by seeking a second job or working longer hours, then the policy may increase labor force participation for individuals near the coverage threshold, which may be a desirable outcome.

Third, any response will increase the number of people covered by exchange plans and the cost of subsidized coverage above initial projections. Finally, low-income individuals who are genuinely uncertain regarding their future income could benefit from clarification regarding the legal consequences of their projections.

For all these reasons, policymakers should carefully monitor how individuals respond to the subsidy cliff. For academic researchers, variation in the subsidy cliff across state, age and time can be exploited to study how consumers value health insurance and to what extent they respond to tax incentives.

Appendix: Methods

To analyze the population likely to respond to the sharp cliff at 100 percent FPL, we used the 2013 Current Population Survey March Supplement from the Integrated Public Use Microdata Series (IPUMS). We restricted the sample to include only nonelderly adults (ages 19-64) living in the 25 states that have not opted into the ACA Medicaid expansions.

We restricted the sample further to uninsured citizens or legal residents (residing in the U.S. for at least five years) with incomes greater than state Medicaid eligibility limits. Family income was calculated at the level of each person’s health insurance unit and the corresponding federal poverty guidelines in their state, both available in the CPS IPUMS. We reweighted our sample so that the total number of individuals in each state whose incomes were below the 100 percent FPL cutoff was consistent with the Kaiser Family Foundation’s prior analysis of the coverage gap.

Figure 1 was constructed using the average second-cheapest silver plan available to individuals in the sample. This value was calculated for each state using premium data from healthcare.gov. Premiums for Idaho, which has a state-based exchange, were imputed based on premiums in three surrounding states: Wyoming, Montana and Utah. The average potential subsidy for individuals in the coverage gap was calculated as the average second-cheapest silver plan premium minus 2 percent of what the individuals’ household income would be at 100 percent FPL.

Because individuals at 100 percent FPL are also eligible for cost sharing subsidies that increase the actuarial value of insurance to levels somewhat greater than that of platinum level plans, we approximated the added value of cost sharing subsidies as the difference between the most affordable platinum plan in each state and the silver plan premium used to calculate premium subsidies for each individual. (This may overestimate the value of the cost sharing subsidies if the price differential between silver and platinum plans is not purely a function of more generous cost sharing but also reflects larger networks, improved customer service or the possibility that individuals choosing more comprehensive plans have greater health needs [adverse selection].)

Figure 2 was constructed based on the characteristics of the highest income individual in each health insurance unit. The part-time and full-time categories only include employees (i.e. not self-employed people). The self-employed category includes both part-time and full-time workers.