Immigrants and Employer- Provided Health Insurance Anthony T. Lo Sasso, Ph.D., Northwestern University Thomas C. Buchmueller, Ph.D., UC-Irvine and NBER Ithai Z. Lurie, M.A., Northwestern University

Immigrants are Much More Likely to Lack Health Insurance Versus Natives  31.6% of immigrants were uninsured in 2000 versus 11.8% of natives The difference in group health insurance coverage is striking: 66% for natives vs. 48.7% for immigrants the difference in the Medicaid coverage is less than one percentage point: 10.4% for natives, 9.9% for immigrants  It is important to understand the factors that contribute to the low rates of employer-provided insurance coverage of immigrants

Objectives  To understand the factors affecting the differential rates of employer-sponsored coverage between natives and non-citizens  To examine the role of various characteristics in explaining the differential rate of coverage Education and other human capital factors Employer and industry characteristics State unobservables

Data: 1996 Survey of Income and Program Participation  Longitudinal survey in which respondents are interviewed every four months over four-year period  Contains detailed (i.e., better than CPS) measures of the reasons why respondents are uninsured Can identify offer, eligibility, reason R did not take-up  Contains detailed (i.e., better than CPS) measures of immigrant status natives, naturalized citizens, and non-naturalized residents, who can be broken down into permanent and non- permanent residents

Decomposition of the coverage difference between natives and non-citizens

Decomposition of the coverage difference between natives and non-citizens: Men

Decomposition of the coverage difference between natives and non-citizens: Women

Employment and Insurance Coverage by Immigration Status, Men 25-64

Employment and Insurance Coverage by Immigration Status, Women 25-64

Insurance Coverage by Immigration Status, Employed Men 25-64

Insurance Coverage by Immigration Status, Employed Women 25-64

Methods  Estimate regressions among employed persons that sequentially add blocks of variables to explain the difference in offer rate Pr(Offer i )= β 1 Immigrant i + β 2 (Human Capital) i + β 3 (Employer Chars) i + β 4 State i + ε i  We will examine the change in β 1 that results from adding additional variables

Baseline Regression Marginal Probability Naturalized Citizens-3.5%** Permanent Residents-21.2%** Non-Permanent Residents-34.8%**

Human Capital Variables Included Marginal Probability Naturalized Citizens-1.8% Permanent Residents-11.2%** Non-Permanent Residents-23.6%** Age, gender, education, marital status, #children **

Employer/Industry Characteristics Included Marginal Probability Naturalized Citizens-2.3%* Permanent Residents-5.7%** Non-Permanent Residents-14.7%** Age, gender, education, marital status, #children ** Firm size, industry dummies, years on job **

State Fixed Effects Included Marginal Probability Naturalized Citizens-1.8% Permanent Residents-5.1%** Non-Permanent Residents-13.6%** Age, gender, education, marital status, #children ** Firm size, industry dummies, years on job ** State fixed effects**

Discussion  The vast difference in insurance coverage between natives and non-natives is largely explained by differences in offer rates  Differences in the rate of eligibility and take-up between natives and non-natives are tiny and do not account for a significant portion of the employer- provided health insurance coverage gap  We can explain three-quarters & three-fifths of difference in employer offers for permanent residents and non-permanent residents, respectively, with observable characteristics

Conclusion  Our results point to the difficulties that policymakers face in crafting solutions to the employer-provided health insurance gap between natives and non- naturalized residents  The employers in question are small, low-wage firms most likely with high turnover rates that are unlikely to be able to afford even a basic health insurance policy, thus policy options must take care to avoid causing job loss in an effort to increase insurance coverage