Abstract
Researchers have developed logical, demographic, and statistical strategies for imputing immigrants’ legal status, but these methods have never been empirically assessed. We used Monte Carlo simulations to test whether, and under what conditions, legal status imputation approaches yield unbiased estimates of the association of unauthorized status with health insurance coverage. We tested five methods under a range of missing data scenarios. Logical and demographic imputation methods yielded biased estimates across all missing data scenarios. Statistical imputation approaches yielded unbiased estimates only when unauthorized status was jointly observed with insurance coverage; when this condition was not met, these methods overestimated insurance coverage for unauthorized relative to legal immigrants. We next showed how bias can be reduced by incorporating prior information about unauthorized immigrants. Finally, we demonstrated the utility of the best-performing statistical method for increasing power. We used it to produce state/regional estimates of insurance coverage among unauthorized immigrants in the Current Population Survey, a data source that contains no direct measures of immigrants’ legal status. We conclude that commonly employed legal status imputation approaches are likely to produce biased estimates, but data and statistical methods exist that could substantially reduce these biases.
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Notes
Indicators of legality include U.S. citizenship, migration from countries and periods that correspond with known patterns of refugee flows, being newly arrived with characteristics that would qualify for certain visa categories, working in occupations or industries that require legal status; receipt of public assistance or social services, and having moved to the United States before 1982 (thus qualifying for IRCA legalization).
Regressors include insurance coverage, all the controls described earlier, and several additional variables: marital status, spouse’s citizenship, occupational status, English proficiency, parental status, household size, homeownership, employment status, occupation, state of residence, and selected squared and interaction terms.
We tested variations where we altered which half of the non–probably legal were coded as unauthorized: those most likely to be unauthorized, the most disadvantaged, and a random half. None performed better than the demographic accounting method described here.
We also tried (1) coding unauthorized status to 0 for those who are “probably legal” before multiply imputing, (2) including “probably legal” and the predicted probability separately in the imputation model, and (3) coding the “probably legal” as legal and those with very high probabilities of being unauthorized (>.8) as unauthorized prior to multiple imputation. None of these variations outperformed the logical-CSMI method.
References
Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage.
Bachmeier, J. D., Van Hook, J., & Bean, F. D. (2014). Can we measure immigrants’ legal status? Lessons from two U.S. surveys. International Migration Review, 48, 538–566.
Batalova, J., Hooker, S., & Capps, R. (2014). DACA at the two-year mark: A national and state profile of youth eligible and applying for deferred action. Washington, DC: Migration Policy Institute.
Bohn, S., Lofstrom, M., & Raphael, S. (2014). Did the 2007 Legal Arizona Workers Act reduce the state’s unauthorized immigrant population? Review of Economics and Statistics, 96, 258–269.
Bozick, R., & Miller, T. (2014). In-state college tuition policies for undocumented immigrants: Implications for high school enrollment among non-citizen Mexican youth. Population Research and Policy Review, 33, 13–30.
Bustamante, A. V., Fang, H., Garza, J., Carter-Pokras, O., Wallace, S. P., Rizzo, J. A., & Ortega, A. N. (2012). Variations in healthcare access and utilization among Mexican immigrants: The role of documentation status. Journal of Immigrant and Minority Health, 14, 146–155.
California Health Interview Survey. (2013). CHIS 2013–2014 sample design. Los Angeles, CA: UCLA Center for Health Policy Research.
Caponi, V., & Plesca, M. (2014). Empirical characteristics of legal and illegal immigrants in the USA. Journal of Population Economics, 27, 923–960.
Capps, R., Bachmeier, J. D., Fix, M., & Van Hook, J. (2013). A demographic, socioeconomic, and health coverage profile of unauthorized immigrants in the United States. Washington, DC: Migration Policy Institute.
Clark, R. L., Glick, J. E., & Bures, R. M. (2009). Immigrant families over the life course: Research directions and needs. Journal of Family Issues, 30, 852–872.
Clark, R. L., & King, R. B. (2008). Social and economic aspects of immigration. Annals of the New York Academy of Sciences, 1136, 289–297.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330–351.
Durand, J., Massey, D. S., & Capoferro, C. (2005). The new geography of Mexican immigration. In V. Zuniga & R. Hernandez-Leon (Eds.), New destinations: Mexican immigration in the United States (pp. 1–22). New York, NY: Russell Sage Foundation.
Flores, S. M. (2010). State dream acts: The effect of in-state resident tuition policies and undocumented Latino students. Review of Higher Education, 33, 239–283.
Greenman, E., & Hall, M. (2013). Legal status and educational transitions for Mexican and Central American immigrant youth. Social Forces, 91, 1475–1498.
Hall, M., Greenman, E., & Farkas, G. (2010). Legal status and wage disparities for Mexican immigrants. Social Forces, 89, 491–513.
Heer, D. M., & Passel, J. S. (1987). Comparison of two methods for estimating the number of undocumented Mexican adults in Los Angeles County. International Migration Review, 21, 1446–1473.
Jasso, G., Massey, D. S., Rosenzweig, M. R., & Smith, J. P. (2000). The New Immigrant Survey Pilot (NIS-P): Overview and new findings about U.S. legal immigrants at admission. Demography, 37, 127–138.
Javier, J. R., Huffman, L. C., Mendoza, F. S., & Wise, P. H. (2010). Children with special health care needs: How immigrant status is related to health care access, health care utilization, and health status. Maternal and Child Health Journal, 14, 567–579.
Kaushal, N. (2006). Amnesty programs and the labor market outcomes of undocumented workers. Journal of Human Resources, 41, 631–647.
Ku, L. (2009). Health insurance coverage and medical expenditures of immigrants and native-born citizens in the United States. American Journal of Public Health, 99, 1322–1328.
Little, R. J. A., & Rubin, D. B. (2002). Statistical analyses with missing data (2nd ed.). New York, NY: John Wiley and Sons.
Marcelli, E. A. (2004). Unauthorized Mexican immigration, day labour and other lower-wage informal employment in California. Regional Studies, 38, 1–13.
Marcelli, E. A., & Heer, D. (1997). Unauthorized Mexican workers in the 1990 Los Angeles County labour force. International Migration, 35, 59–83.
Marcelli, E. A., & Heer, D. (1998). Unauthorized Mexican immigration and welfare: A comparative statistical analysis. Sociological Perspectives, 41, 279–302.
Markides, K. S., & Eschbach, K. (2005). Aging, migration, and mortality: Current status of research on the Hispanic paradox. Journals of Gerontology: Series B, 60(Special Issue 2), S68–S75.
Massey, D. S., & Bartley, K. (2005). The changing legal status distribution of immigrants: A caution. International Migration Review, 39, 469–482.
Passel, J. S. (2006). The size and characteristics of the unauthorized migrant population in the U.S. Washington, DC: Pew Hispanic Center.
Passel, J. S., & Clark, R. L. (1998). Immigrants in New York: Their legal status, incomes, and taxes. Washington, DC: Urban Institute.
Passel, J. S., & Cohn, D. V. (2009). A portrait of unauthorized immigrants in the United States. Washington, DC: Pew Hispanic Center.
Potochnick, S. (2014). How states can reduce the dropout rate for undocumented youth: The effects of in-state resident tuition policies. Social Science Research, 45, 18–32.
Rässler, S. (2004). Data fusion: Identification problems, validity, and multiple imputation. Austrian Journal of Statistics, 33(1–2), 153–171.
Rendall, M. S., Ghosh-Dastidar, B., Weden, M. M., Baker, E. H., & Nazarov, Z. (2013). Multiple imputation for combined-survey estimation with incomplete regressors in one but not both surveys. Sociological Methods & Research, 42, 483–530.
Resche-Rigon, M., White, I. R., Bartlett, J. W., Peters, S. A. E., & Thompson, S. G. (2013). Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Statistics in Medicine, 32, 4890–4905.
Rodgers, W. L. (1984). An evaluation of statistical matching. Journal of Business and Economic Statistics, 2, 91–102.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: John Wiley and Sons.
Schenker, N., Raghunathan, T. E., & Bondarenko, I. (2010). Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey. Statistics in Medicine, 29, 533–545.
Sommers, B. D. (2013). Stuck between health and immigration reform—Care for undocumented immigrants. New England Journal of Medicine, 369, 593–597.
StataCorp. (2013). Stata statistical software: Release 13. College Station, TX: StataCorp LP.
State Health Access Data Assistance Center. (2013). State estimates of the low-income uninsured not eligible for the ACA Medicaid expansion (Issue Brief No. 35). Minneapolis, MN: University of Minnesota.
Stevens, G. D., West-Wright, C. N., & Tsai, K.-Y. (2010). Health insurance and access to care for families with young children in California, 2001–2005: Differences by immigration status. Journal of Immigrant and Minority Health, 12, 273–281.
U.S. Census Bureau. (2013). Survey of income and program participation. Washington, DC: U.S. Census Bureau.
Acknowledgments
This research was supported by grants from the National Institutes of Health (RC2 HD064497, P01 HD062498, K01MH087219, and 2R24HD041025). We thank Michelle Frisco, Molly Martin, Nancy Landale, Claire Altman, Susana Sanchez, and the anonymous reviewers for helpful comments. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Van Hook, J., Bachmeier, J.D., Coffman, D.L. et al. Can We Spin Straw Into Gold? An Evaluation of Immigrant Legal Status Imputation Approaches. Demography 52, 329–354 (2015). https://doi.org/10.1007/s13524-014-0358-x
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DOI: https://doi.org/10.1007/s13524-014-0358-x