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Estimating Federal Payments and Eligibility for Basic Health Programs: An Illustrative Example

The Characteristics of BHP Eligibles by State

The federal BHP payment formula depends on applicable benchmark premiums and on four characteristics of BHP enrollees: age (within ranges specified by the BHP federal payment methodology), income (within FPL ranges specified by the BHP federal payment methodology), number of persons in the tax unit (the household unit, as defined for purposes of determining eligibility both for BHP and QHP subsidies), and number of BHP-eligible persons in the tax unit who receive coverage through BHP. In order to compute payments, the joint distribution of these four characteristics—in other words, the number of enrollees at each benchmark premium level who possess every possible combination of the above four characteristics—must be known. For each state, we estimated the number of the joint distribution of these characteristics among people who would be eligible for BHP in 2016.1

We did not model how many of those eligible for BHP would actually enroll in the program. This depends to a large extent on the BHP premiums and beneficiary cost sharing, and states have a lot of flexibility in setting these elements of BHP policy.

Methods

To produce these estimates, we began with the Urban Institute’s Health Insurance Policy Simulation Model-American Community Survey (HIPSM-ACS). To obtain a large, representative sample population for each state, we pooled together the observations on the 2009, 2010, and 2011 American Community Surveys (ACS). Among national surveys conducted by the U.S. Census Bureau, the American Community Survey (ACS) has the largest state-specific samples and so is likely to provide the most reliable estimates. However, a limitation of both this data set and the other data set frequently used (the Current Population Survey-Annual Social and Economic Supplement) is that they do not include information about offers of employer-sponsored insurance (ESI), which almost always preclude subsidy eligibility.2 States that fail to take such offers into account will overestimate the prevalence of relatively high-income BHP-eligible consumers, since ESI offers grow increasingly common as income rises.3 As a result, such states will underestimate federal BHP funding per BHP enrollee, since QHP subsidies, hence BHP funding levels, decline as income rises. The estimates presented here do not share this problem, since HIPSM incorporates, via statistical matches with other data sources, information about unaccepted ESI offers.

Immigration Status. We impute documentation status for non-citizens in each year of survey data separately based on a year-specific model used in the CPS. Documentation status is imputed to immigrants in two stages, using individual and family characteristics, based on an imputation methodology that was originally developed by Passel, the most-used source of estimates of immigrants not lawfully present.4 Undocumented immigrants and lawfully present non-citizens, including immigrant adults who have been U.S. residents for less than five years, are generally ineligible for Medicaid.

Tax units and filing. To model tax units and filing behavior, we use 2011 tax rules (including thresholds for tax filing requirements), Earned Income Tax Credit (EITC) eligibility guidelines, and poverty guidelines as defined by the U.S. Department of Health and Human Services. Baseline coverage and post-ACA eligibility are based on estimates from HIPSM-ACS.

Tax units and filing status are determined based on the IRS guidelines set forth by the 2011 1040 Instructions and the 2011 EITC eligibility guidelines. The primary tax filing unit for each family is defined as the head of the family, the spouse, and any qualifying children or qualifying relatives (as defined by the IRS). In multi-generational households, nuclear subfamilies are tested for their filing status. If they are not found to file as a unit themselves, they are tested to qualify as dependents of the head of the household.

Tax filing status is determined based on characteristics of the head of the tax unit and pooled income within the tax unit. Married couples are assumed to be filing jointly to qualify for tax credits. As support within the household is not captured by the ACS, any unmarried tax unit head with dependents is considered filing as a head of household. Any other unmarried person without dependents is tested as single. To determine requirement to file, individual Adjusted Gross Income (AGI) is pooled for each person within the tax unit and compared to the 2011 minimum mandatory filing threshold.

Due to limitations of the income that is captured by the ACS, some taxable income categories could not be included in total income. Capital gains are not reported as investment income in the ACS, so it was not counted. Paid alimony was also excluded; however, internal analysis based on CPS alimony data suggests this exclusion would not affect our results. The ACS does not collect data on unemployment compensation, but because this was likely an important form of income for people at the margin of the Medicaid and subsidy eligibility thresholds, it was imputed based on reported unemployment compensation from the 2008 CPS.

None of the adjustments needed to calculate AGI are reported by the ACS, so we therefore take total income as a proxy for AGI. Total income is calculated as the sum of wages, business income, farm income, rents, most forms of positive investment income, retirement income, unemployment compensation, and the taxable portion of social security income.

EITC eligibility is calculated in a slightly different way. AGI is pooled only among the head of the tax unit, the spouse (if filing as a married couple), and qualifying children. Qualifying dependents are not tested to file for EITC individually because they are either childless dependents (ineligible for EITC) or are found not to file in subfamily analysis. However, because they are claimed on the tax unit head’s return, they take on the EITC eligibility status of their tax unit.

Once it was determined which tax units were required to file and which were eligible for EITC, units were assigned filing decisions. A 2005 Treasury Report estimated that about 7.4 million taxpayers who were required to file did not in Tax Year 2003.5 That year, approximately 131 million individual tax returns were filed,6 meaning the filing rate among those required to file was about 95%. A study by the IRS of Tax Year 2005 filings estimated the following EITC participation rates, by number of qualifying children: 55.6% among those without qualifying children, 73.6% among those with one qualifying child, and 85.9% among those with two or more qualifying children.7 Based on these rates, tax units were randomly assigned their decision to file or not file.

Eligibility for Medicaid/CHIP, QHP subsidies, and BHP. Medicaid and subsidy eligibility are determined using MAGI, which adds nontaxable social security income to AGI. Unit-level MAGI is pooled among the unit head, the spouse (if married), and any qualifying children with an individual AGI above the single tax filing threshold. The income of other qualifying children and qualifying relatives is not included. This is then used to calculate a ratio of MAGI to the applicable federal poverty level (FPL) of the unit. Special prorating of units that include undocumented parent(s) or childless spouses is used to scale the total AGI (including that of the undocumented family members) by a ratio of the FPLs including and excluding the undocumented family members.

Medicaid eligibility for some groups, particularly the blind and disabled, does not change under the ACA. We model their eligibility using pre-ACA rules. To determine Medicaid and CHIP eligibility for other groups, tax unit-level MAGI-as-a-percentage-of-FPL is assigned to the tax unit head, the spouse (if married), and qualifying children with individual AGI above the single tax filing threshold. Excluded qualifying children and qualifying relatives are automatically eligible for Medicaid under CMS regulations. Under the ACA, the children of non-filing qualifying dependents also automatically qualify for Medicaid. The remaining parents, childless adults, and children are then tested for Medicaid eligibility based on the corresponding eligibility threshold in their state of residence. Children who are found ineligible for Medicaid are tested for CHIP eligibility.

QHP subsidy eligibility is determined slightly differently. To be eligible for subsidies, one must have a MAGI-as-a-percentage-of-FPL between 100 and 400%. Eligibility for any public coverage precludes eligibility for subsidies, so subsidy-eligible consumers cannot be eligible for Medicaid or CHIP under the ACA, as determined above, nor can they currently be eligible for Medicare. Finally, no unit member can have an offer of single coverage that costs less than 9.5% of family MAGI. For this determination, we use the HIPSM-ACS imputation of employer offers and the affordability of those offers.

Those eligible for BHP are by definition those eligible for QHP subsidies who have incomes below 200% FPL.

Single Distributions of Each Characteristic. The resulting data allowed us to produce reliable estimates of the single distributions of BHP eligibles by state of age group, FPL income group, number of people in the tax unit, and number of BHP eligibles within the tax unit. These are Tables A1, A2, and A3.

Joint Distributions for Each State. As noted earlier, estimating federal BHP payments requires the joint distribution of all four characteristics by state. That is, one must know how many BHP-eligible residents of a state share a particular combination of age, FPL level, household size, and number of BHP-eligible household members. This would mean separating the BHP-eligible population for each state into 240 different groups.8 To get reliable estimates for so many small groups of people would require a sample size for each state far larger than what our data provide. We overcame this difficulty using a standard small area estimation technique that relies on our data having a large enough sample size to estimate this four-trait joint distribution among BHP-eligibles nationally. For each state, we reweighted the national joint distribution to match the individual state’s single distribution of age group, FPL income group, household size, and the number of BHP-eligible individuals per household.9 Thus, we used estimates in which we had confidence—state-level single distributions of characteristics and the national joint distribution—to estimate the state-level joint distribution, which could not itself be tabulated directly from the data. The single distributions for each state are shown in tables A1-A3 and the final joint distribution estimates are shown in Table A4. One additional single distribution, involving household size, is not included here, but is available upon request from the authors.

Results

The following tables present the data on the characteristics of the BHP-eligible population by state. Tables A1-A3 provide summary-level statistics on age, income range, and the number of BHP-eligible people in the household unit for all 50 states and the District of Columbia. Table A4 provides detailed estimates of the joint distribution of BHP-eligible consumers by the four characteristics listed above. These detailed estimated are not provided for several states (Alaska, Delaware, the District of Columbia, North Dakota, South Dakota, and Wyoming) due to small sample sizes in those states. Detailed estimates are also not provided for New York because more comprehensive Urban Institute estimates have already been incorporated into state budget projections. Because of sample size considerations, we did not distinguish between FPL income ranges below 138% FPL. The number of BHP-eligible persons in the household unit represents the maximum number of people in the household who can enroll in BHP. Because very few BHP-eligible people are in households with more than five members or in households with more than three BHP-eligible members, our largest listed categories included households with five or more members and with three or more BHP-eligible members. In Table A4, we present data for households with one to four members. You can access the complete data in a downloadable Excel file of Appendix Table A4 (.xls).

Table A1: BHP Eligibles by Age
State 19-20 21-34 35-44 45-54 55-64 Total
N % N % N % N % N % N
Alabama 4,042 5% 30,794 35% 16,405 19% 13,343 15% 22,587 26% 87,172
Alaska 730 4% 8,080 47% 2,040 12% 2,765 16% 3,744 22% 17,358
Arizona 4,614 4% 41,738 36% 20,834 18% 19,598 17% 29,125 25% 115,909
Arkansas 2,606 5% 19,441 35% 10,394 19% 9,470 17% 13,810 25% 55,720
California 46,615 6% 335,180 40% 154,246 19% 149,334 18% 147,330 18% 832,704
Colorado 4,900 5% 37,949 39% 16,602 17% 17,882 18% 20,136 21% 97,469
Connecticut 3,444 8% 17,814 41% 5,359 12% 6,128 14% 10,775 25% 43,520
Delaware 736 6% 4,909 39% 2,178 17% 1,800 14% 2,901 23% 12,523
DC 1,253 15% 3,065 38% 727 9% 843 10% 2,216 27% 8,103
Florida 23,137 5% 176,938 35% 98,005 20% 93,656 19% 107,119 21% 498,855
Georgia 10,465 5% 80,941 38% 41,128 20% 36,648 17% 41,607 20% 210,789
Hawaii 891 3% 8,720 34% 4,539 18% 5,365 21% 6,085 24% 25,600
Idaho 1,593 4% 15,628 41% 6,612 18% 5,537 15% 8,331 22% 37,701
Illinois 11,913 6% 81,309 38% 36,543 17% 38,332 18% 44,418 21% 212,515
Indiana 7,554 6% 50,822 38% 22,726 17% 21,858 16% 29,945 23% 132,905
Iowa 2,875 6% 18,301 41% 7,201 16% 7,370 17% 8,516 19% 44,263
Kansas 3,100 6% 19,360 39% 8,417 17% 9,056 18% 10,271 20% 50,203
Kentucky 2,982 4% 29,472 36% 13,878 17% 13,433 16% 22,069 27% 81,834
Louisiana 4,522 5% 36,219 39% 16,402 18% 14,606 16% 20,969 23% 92,717
Maine 945 4% 7,718 30% 3,491 14% 5,078 20% 8,189 32% 25,421
Maryland 4,455 5% 32,278 37% 16,674 19% 16,270 19% 17,541 20% 87,218
Massachusetts 5,941 8% 32,600 43% 11,939 16% 11,577 15% 13,413 18% 75,470
Michigan 8,396 4% 62,469 33% 29,357 16% 34,450 18% 52,527 28% 187,199
Minnesota 3,984 6% 25,776 37% 6,623 10% 10,723 15% 22,360 32% 69,466
Mississippi 2,189 4% 18,976 35% 10,368 19% 9,038 17% 13,971 26% 54,541
Missouri 5,343 4% 45,599 38% 22,000 18% 19,555 16% 26,792 22% 119,289
Montana 1,248 4% 11,455 39% 4,924 17% 5,102 18% 6,347 22% 29,075
Nebraska 1,232 4% 12,311 40% 5,552 18% 6,157 20% 5,243 17% 30,495
Nevada 2,224 4% 23,549 38% 11,811 19% 11,254 18% 13,012 21% 61,850
New Hampshire 1,193 5% 8,822 37% 3,779 16% 5,237 22% 4,715 20% 23,747
New Jersey 7,215 4% 61,796 38% 33,973 21% 28,459 18% 30,972 19% 162,416
New Mexico 2,239 5% 17,579 37% 8,649 18% 7,955 17% 10,740 23% 47,161
New York 23,288 6% 148,887 41% 67,099 18% 58,707 16% 66,749 18% 364,729
North Carolina 8,706 5% 65,002 35% 36,562 19% 32,422 17% 44,836 24% 187,528
North Dakota 575 4% 6,090 45% 1,910 14% 1,858 14% 2,967 22% 13,400
Ohio 8,202 4% 70,131 35% 35,944 18% 34,827 17% 51,463 26% 200,567
Oklahoma 3,498 5% 29,213 38% 14,672 19% 14,111 18% 16,101 21% 77,596
Oregon 3,959 5% 34,061 39% 15,765 18% 14,239 16% 19,600 22% 87,625
Pennsylvania 11,531 5% 77,880 34% 40,083 17% 42,014 18% 57,625 25% 229,132
Rhode Island 1,460 7% 8,172 40% 3,298 16% 3,407 17% 3,842 19% 20,179
South Carolina 5,488 6% 34,154 35% 16,509 17% 18,123 18% 23,826 24% 98,101
South Dakota 1,142 8% 5,731 39% 2,655 18% 1,980 14% 3,081 21% 14,588
Tennessee 5,369 4% 42,740 35% 21,458 18% 22,255 18% 29,572 24% 121,394
Texas 31,271 5% 231,706 41% 112,162 20% 94,753 17% 100,362 18% 570,254
Utah 3,547 6% 26,562 47% 9,865 18% 8,000 14% 8,142 15% 56,116
Vermont 788 6% 4,149 33% 2,245 18% 2,025 16% 3,402 27% 12,608
Virginia 7,742 6% 48,259 37% 24,876 19% 21,629 16% 28,898 22% 131,403
Washington 6,677 5% 53,526 41% 22,020 17% 23,129 18% 26,174 20% 131,526
West Virginia 899 3% 11,874 34% 5,037 14% 6,873 20% 10,174 29% 34,855
Wisconsin 5,119 6% 31,933 36% 15,401 17% 14,814 17% 22,402 25% 89,667
Wyoming 564 5% 3,593 35% 1,390 13% 1,672 16% 3,098 30% 10,318
* Data suppressed due to low sample size
** See the detailed estimates of BHP costs and savings in state budget projections, based on Urban Institute modeling
Source: Health Insurance Policy Simulation Model-American Community Survey, 2014
Table A2: BHP Eligibles by FPL
State Less than 138% 139-150% 151-175% 176-200% Total
N % N % N % N % N
Alabama 3,886 4% 17,145 20% 35,428 41% 30,712 35% 87,172
Alaska 951 5% 3,415 20% 6,239 36% 6,753 39% 17,358
Arizona 11,338 10% 18,931 16% 44,551 38% 41,089 35% 115,909
Arkansas 2,673 5% 11,373 20% 22,791 41% 18,882 34% 55,720
California 155,345 19% 124,611 15% 284,068 34% 268,680 32% 832,704
Colorado 8,803 9% 15,644 16% 37,503 38% 35,519 36% 97,469
Connecticut 8,211 19% 7,123 16% 14,854 34% 13,332 31% 43,520
Delaware 1,629 13% 1,839 15% 4,854 39% 4,202 34% 12,523
DC 1,253 15% 1,421 18% 2,063 25% 3,367 42% 8,103
Florida 82,116 16% 82,665 17% 175,162 35% 158,912 32% 498,855
Georgia 16,138 8% 35,579 17% 86,529 41% 72,543 34% 210,789
Hawaii 4,986 19% 4,192 16% 7,463 29% 8,960 35% 25,600
Idaho 1,685 4% 7,525 20% 13,914 37% 14,577 39% 37,701
Illinois 29,203 14% 36,676 17% 76,074 36% 70,562 33% 212,515
Indiana 9,717 7% 25,097 19% 50,598 38% 47,493 36% 132,905
Iowa 3,617 8% 7,287 16% 17,387 39% 15,972 36% 44,263
Kansas 4,218 8% 9,672 19% 20,045 40% 16,268 32% 50,203
Kentucky 6,125 7% 16,126 20% 32,247 39% 27,336 33% 81,834
Louisiana 4,675 5% 17,251 19% 37,264 40% 33,527 36% 92,717
Maine 370 1% 4,343 17% 10,734 42% 9,973 39% 25,421
Maryland 14,184 16% 12,562 14% 31,274 36% 29,198 33% 87,218
Massachusetts 18,102 24% 9,650 13% 24,250 32% 23,468 31% 75,470
Michigan 14,603 8% 33,357 18% 70,313 38% 68,926 37% 187,199
Minnesota 5,670 8% 12,507 18% 26,112 38% 25,178 36% 69,466
Mississippi 1,913 4% 10,908 20% 22,591 41% 19,129 35% 54,541
Missouri 8,456 7% 21,535 18% 45,324 38% 43,974 37% 119,289
Montana 720 2% 6,881 24% 11,339 39% 10,136 35% 29,075
Nebraska 2,702 9% 6,468 21% 10,360 34% 10,965 36% 30,495
Nevada 6,073 10% 9,055 15% 22,093 36% 24,628 40% 61,850
New Hampshire 1,629 7% 4,732 20% 7,943 33% 9,442 40% 23,747
New Jersey 32,395 20% 24,767 15% 55,651 34% 49,604 31% 162,416
New Mexico 3,620 8% 7,701 16% 17,630 37% 18,210 39% 47,161
New York 75,596 21% 58,100 16% 116,956 32% 114,077 31% 364,729
North Carolina 12,982 7% 34,247 18% 73,833 39% 66,465 35% 187,528
North Dakota 1,494 11% 1,869 14% 5,714 43% 4,324 32% 13,400
Ohio 12,274 6% 35,710 18% 79,895 40% 72,689 36% 200,567
Oklahoma 6,278 8% 12,899 17% 30,496 39% 27,923 36% 77,596
Oregon 6,508 7% 15,479 18% 32,799 37% 32,838 37% 87,625
Pennsylvania 17,804 8% 38,816 17% 88,365 39% 84,147 37% 229,132
Rhode Island 3,422 17% 3,034 15% 5,568 28% 8,155 40% 20,179
South Carolina 5,341 5% 18,444 19% 39,269 40% 35,046 36% 98,101
South Dakota 863 6% 2,376 16% 5,638 39% 5,712 39% 14,588
Tennessee 6,656 5% 25,992 21% 47,657 39% 41,089 34% 121,394
Texas 88,134 15% 99,013 17% 204,857 36% 178,251 31% 570,254
Utah 5,094 9% 9,483 17% 20,525 37% 21,014 37% 56,116
Vermont 502 4% 2,967 24% 5,045 40% 4,095 32% 12,608
Virginia 14,292 11% 20,550 16% 54,154 41% 42,407 32% 131,403
Washington 16,301 12% 20,672 16% 47,409 36% 47,144 36% 131,526
West Virginia 1,269 4% 6,799 20% 13,511 39% 13,275 38% 34,855
Wisconsin 4,959 6% 15,601 17% 37,217 42% 31,891 36% 89,667
Wyoming 481 5% 2,236 22% 4,598 45% 3,003 29% 10,318
* Data suppressed due to low sample size
** See the detailed estimates of BHP costs and savings in state budget projections, based on Urban Institute modeling
Source: Health Insurance Policy Simulation Model-American Community Survey, 2014
Table A3: BHP Eligibles in Tax Unit
State 1 2 3+ Total
N % N % N % N
Alabama 56,305 65% 27,988 32% 2,879 3% 87,172
Alaska 12,989 75% 4,202 24% 167 1% 17,358
Arizona 84,166 73% 28,859 25% 2,884 2% 115,909
Arkansas 35,385 64% 19,295 35% 1,040 2% 55,720
California 597,140 72% 198,287 24% 37,277 4% 832,704
Colorado 69,054 71% 26,906 28% 1,510 2% 97,469
Connecticut 36,893 85% 6,412 15% 214 0% 43,520
Delaware 9,451 75% 2,962 24% 110 1% 12,523
DC 7,360 91% 540 7% 203 3% 8,103
Florida 351,639 70% 124,291 25% 22,926 5% 498,855
Georgia 137,912 65% 62,847 30% 10,029 5% 210,789
Hawaii 20,086 78% 5,326 21% 188 1% 25,600
Idaho 22,092 59% 14,396 38% 1,213 3% 37,701
Illinois 155,046 73% 49,309 23% 8,160 4% 212,515
Indiana 86,382 65% 39,511 30% 7,012 5% 132,905
Iowa 31,612 71% 11,881 27% 771 2% 44,263
Kansas 33,461 67% 14,693 29% 2,049 4% 50,203
Kentucky 54,418 66% 26,073 32% 1,343 2% 81,834
Louisiana 62,935 68% 25,958 28% 3,824 4% 92,717
Maine 18,621 73% 6,408 25% 392 2% 25,421
Maryland 66,138 76% 19,184 22% 1,896 2% 87,218
Massachusetts 59,589 79% 13,715 18% 2,167 3% 75,470
Michigan 126,164 67% 55,244 30% 5,791 3% 187,199
Minnesota 54,391 78% 14,158 20% 916 1% 69,466
Mississippi 34,208 63% 18,456 34% 1,877 3% 54,541
Missouri 79,625 67% 35,647 30% 4,016 3% 119,289
Montana 17,601 61% 10,618 37% 857 3% 29,075
Nebraska 21,469 70% 8,531 28% 495 2% 30,495
Nevada 45,617 74% 14,956 24% 1,278 2% 61,850
New Hampshire 16,585 70% 6,208 26% 953 4% 23,747
New Jersey 116,794 72% 40,062 25% 5,560 3% 162,416
New Mexico 34,971 74% 10,710 23% 1,481 3% 47,161
New York 274,446 75% 79,740 22% 10,543 3% 364,729
North Carolina 129,275 69% 52,921 28% 5,332 3% 187,528
North Dakota 9,175 68% 4,022 30% 203 2% 13,400
Ohio 138,347 69% 57,442 29% 4,778 2% 200,567
Oklahoma 49,350 64% 24,731 32% 3,516 5% 77,596
Oregon 60,222 69% 24,456 28% 2,947 3% 87,625
Pennsylvania 151,848 66% 68,121 30% 9,163 4% 229,132
Rhode Island 14,947 74% 4,463 22% 769 4% 20,179
South Carolina 63,197 64% 29,718 30% 5,186 5% 98,101
South Dakota 9,103 62% 4,739 32% 747 5% 14,588
Tennessee 80,367 66% 36,806 30% 4,221 3% 121,394
Texas 381,480 67% 161,110 28% 27,664 5% 570,254
Utah 29,945 53% 22,363 40% 3,808 7% 56,116
Vermont 8,463 67% 4,067 32% 78 1% 12,608
Virginia 91,036 69% 34,880 27% 5,487 4% 131,403
Washington 90,448 69% 38,034 29% 3,045 2% 131,526
West Virginia 24,725 71% 9,950 29% 180 1% 34,855
Wisconsin 67,623 75% 20,248 23% 1,796 2% 89,667
Wyoming 6,004 58% 4,314 42% 0% 10,318
* Data suppressed due to low sample size
** See the detailed estimates of BHP costs and savings in state budget projections, based on Urban Institute modeling
Source: Health Insurance Policy Simulation Model-American Community Survey, 2014

Appendix: The Characteristics of BHP Eligibles by State by Matthew Buettgens and Jay Dev, Urban Institute Health Policy Center

Conclusion: placing federal payment estimates in context

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