Estimated Impacts of Final Public Charge Inadmissibility Rule on Immigrants and Medicaid Coverage
Appendix A: Methods
The findings presented in this brief are based on Kaiser Family Foundation analysis of Wave 3 of the Survey of Income and Program Participation (SIPP) 2014 Panel and 2017 American Community Survey (ACS) data. SIPP enables us to directly measure individuals’ immigration status at the time they entered the U.S. and health status. SIPP also provides measures of health coverage, but 2015 is the most recent year of data available. Because 2015 was a year of substantial transition for Medicaid due to the implementation of the Affordable Care Act, we base our Medicaid and CHIP disenrollment analysis on 2017 ACS data.
We classified people as not having LPR status when originally entering the U.S. based on a SIPP question that asks, “What was [respondent’s] immigration status when he/she first moved to the United States?” In addition to measuring people who might adjust to LPR status in the future, who would be subject to a public charge determination (unless they fall into an exempt category), this measure includes noncitizens who have adjusted to LPR status since arriving into the U.S. It also includes nonimmigrants and undocumented immigrants who do not have a current pathway to adjust to LPR status. Our testing of different citizenship measures led to overall similar patterns. The 2014 SIPP shows 17.8 million noncitizens, including 7.4 million of whom originally entered the country without LPR status. Due to underreporting of noncitizens and legal immigration status in the SIPP, these estimates may reflect an undercount of the total noncitizen population and especially the undocumented population. Given this potential undercount—and that the group of noncitizens without LPR status includes some individuals who have since adjusted to LPR status as well as nonimmigrants and undocumented immigrants who do not have a pathway to adjust to LPR status— our analysis of characteristics that DHS could consider negative in public charge determinations focuses on shares rather than absolute numbers of affected individuals.
For the estimates of the share of noncitizens without LPR status living within the U.S. who have characteristics that DHS could weigh negatively in a public charge determination under the proposed rule, we used SIPP to measure age, poverty and work status, insurance status, education, English proficiency, and health status and classified each factor as positive or negative based on the rule’s description of how DHS would consider the characteristic. DHS’ implementation and operationalization of its assessment of factors may differ from SIPP’s measurement of characteristics.
In our analysis of household income, we use 125% of the Census poverty threshold, or $23,848 for a family of three in 2015. Census poverty thresholds are measured slightly differently than HHS poverty guidelines but lead to similar poverty levels for incomes of similar household size. In the rule, DHS provides a specific definition of a household to be used in the calculation of household income. Thus, the final income cutoff for a particular family to meet the 125% of poverty rule as implemented may differ from our measurement or that used by other programs.
We base the Medicaid and CHIP potential disenrollment analysis on 2017 ACS data. These data show that over 13.5 million Medicaid/CHIP enrollees were noncitizens or living in a household with at least one noncitizen. These data on Medicaid enrollees reflect both an undercount of noncitizens in the survey data (as noted above) as well as an overestimate of the share of noncitizens participating in Medicaid as it includes some who may be reporting emergency Medicaid or other state or local health assistance programs as Medicaid coverage.
For estimates of potential changes in coverage due to public charge policies, we present several scenarios using different disenrollment rates for Medicaid and CHIP. These disenrollment rates drew on previous research that showed decreased enrollment in Medicaid and CHIP among immigrant families after welfare reform.1 For example, Kaushal and Kaestner found that after new eligibility restrictions were implemented for recent immigrants as part of welfare reform, there was 25% disenrollment among children of foreign-born parents from Medicaid even though the majority of these children were not affected by the eligibility changes and remained eligible.2 Using this 25% disenrollment rate as a mid-range target, we assume a range of disenrollment rates from a low of 15% to a high of 35%. However, it remains uncertain what share of individuals may disenroll from Medicaid and CHIP in response to the proposed rule. Although the welfare reform experience is instructive of chilling effects among immigrant families broadly, it was associated with changes to program eligibility for immigrants. In contrast, this rule would change the potential consequences of participating in programs on an individual’s immigration status.
Appendix B
Characteristics that DHS Could Consider in Public Charge Determinations by Citizenship Status, 2015 | |||||
Potential Positive or Negative Factor? | Heavily Weighted? | Non-LPR Noncitizen | Total Noncitizens | Citizens | |
Age | |||||
17 or younger | Negative | 7% | 8% | 22% | |
18 to 61 | Positive | 88% | 84% | 58% | |
62 or older | Negative | 6% | 8% | 21% | |
Health Status | |||||
No Physical or Mental Health Disability | Positive | 95% | 94% | 86% | |
Physical or Mental Health Disability | Negative | 5% | 6% | 14% | |
Excellent, Very Good, or Good Health | Positive | 90% | 90% | 86% | |
Fair or Poor health | Negative | 10% | 10% | 14% | |
Physical or Mental Health Disability and No Private Coverage | Negative | Y | 3% | 4% | 7% |
Family Income | |||||
Less than 125% Federal Poverty Level (FPL) | Negative | 32% | 28% | 16% | |
125% to less than 250% FPL | Positive | 32% | 29% | 22% | |
250% FPL or more | Positive | Y | 36% | 43% | 62% |
Health Coverage | |||||
Private Coverage | Positive | Y | 44% | 48% | 71% |
No Private Coverage | Negative | 56% | 52% | 29% | |
Employment | |||||
Employed (and age 18+) | Positive | 64% | 62% | 48% | |
Not employed and not a caregiver (and age 18+) | Negative | 29% | 30% | 30% | |
Not employed and not a student (and age 18+) | Negative | Y | 27% | 28% | 28% |
Education | |||||
Has high school diploma or higher (and age 18+) | Positive | 54% | 56% | 71% | |
No high school diploma (and age 18+) | Negative | 39% | 36% | 8% | |
English Proficiency | |||||
Does Not Have Limited English Proficiency | Positive | 77% | 77% | 99% | |
Limited English Proficiency | Negative | 23% | 23% | 1% | |
Any Positive Factor | 100% | 100% | 100% | ||
Any Heavily Weighted Positive Factor | 55% | 60% | 79% | ||
Any Negative Factor | 79% | 78% | 68% | ||
Any Heavily Weighted Negative Factor | 27% | 29% | 29% | ||
Notes: For each individual subject to a determination, DHS would take into account the totality of his or her circumstances and would retain discretion on how to weigh specific circumstances and factors; no single factor would govern a determination. How DHS would implement and operationalize its assessment of factors under the rule may differ from how SIPP measures characteristics. Source: Kaiser Family Foundation Analysis of Survey of Income and Program Participation 2014 Panel data. |