Welfare reform and health insurance of immigrants

Health Services Research,  June, 2005  by Neeraj Kaushal,  Robert Kaestner

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Our research differs from that of Borjas (2003) in several ways. First, we focus on a group that is more likely to be affected by welfare reform--low-educated, unmarried women and their children--than the group examined by Borjas (2003). Second, we use other immigrants instead of natives as a comparison group. Third, we specifically look at changes in Medicaid and TANF policies, the two aspects of PRWORA that are most likely to affect health insurance of low-educated, unmarried women and their children. (3)

RESEARCH DESIGN

Our objective is to obtain estimates of the association between federal welfare reform (PRWORA) and health insurance coverage of low-educated, foreign--and U.S.-born single women and their children. Ideally, we would like to obtain estimates that can plausibly be given a causal interpretation. Therefore, we use multivariate regression methods and a pre- and post-test with comparison group research design. The starting point of this empirical approach is the following regression model:

(1) [Insurance.sub.icjt] = [alpha] + [[beta].sub.j] + [[delta].sub.jt] + [[lambda].sub.c] + [gamma] [Policy.sub.jt] + [Z.sub.jt][DELTA] + [X.sub.ijt][GAMMA] + [u.sub.icjt]

i = 1, ... , N(persons); c = 1, ...., C (country of birth);

j = 1, ... , 51(states); t = 1993, ..., 2000 (years)

In equation (1), health insurance status (e.g., Medicaid) of woman i from country c (if foreign born) in state j and year t is a function of welfare reform, which we measure as a dummy variable ([Policy.sub.jt]) that equals one if state j had implemented TANF in year t, and zero otherwise; state characteristics ([Z.sub.jt]) such as Medicaid income eligibility threshold, unemployment rate, and lagged unemployment rate; individual characteristics ([X.sub.ijt]) such as age, race, education, other family income, family composition, citizenship status (if foreign born), number of years lived in the U.S. (if foreign born), and whether arrived in the U.S. prior to 1996 (if foreign born); state-fixed effects ([[beta].sub.j]); and country-fixed effects ([[lambda].sub.c]). In addition, we include state-specific quadratic time trends ([[delta].sub.jt]) to capture unmeasured, time-varying state effects. Over a relatively short period, as in the current context, quadratic time trends may be expected to approximate reasonably well, unmeasured, time-varying state-specific influences. A similar model is used in the analysis of children's health insurance coverage.

We estimate equation (1) for two groups: those likely to be affected by PRWORA and those unlikely to be affected by it. We refer to the former as the target group and the latter as the comparison group. As the comparison group is mostly unaffected by PRWORA, estimates of the effect of policy on this group quantify the effect of unmeasured variables that affect health insurance and are correlated with welfare reform. To obtain the "causal" effect of PRWORA on the health insurance of the target group, we subtract the effect of PRWORA on the comparison group from the effect on the target group. This approach is commonly referred to as difference-in-differences (DD). The DD estimates can also be obtained directly using the following specification: