Are the CPS Uninsurance Estimates Too High? An Examination of Imputation
Davern, M., H. Rodin, K. T. Call, and L. A. Blewett. 2007. “Are the CPS Uninsurance Estimates Too High? An Examination of Imputation.” Health Services Research 42(5): 2038-2055.
RESEARCH OBJECTIVE: To determine whether the imputation procedure used to replace missing data by the U.S. Census Bureau produces bias in the estimates of health insurance coverage in the Current Population Survey's (CPS) Annual Social and Economic Supplement (ASEC). DATA SOURCE: 2004 CPS-ASEC. STUDY DESIGN: Eleven percent of the respondents to the monthly CPS do not take the ASEC supplement and the entire supplement for these respondents is imputed by the Census Bureau. We compare the health insurance coverage of these "full-supplement imputations" with those respondents answering the ASEC supplement. We then compare demographic characteristics of the two groups and model the likelihood of having insurance coverage given the data are imputed controlling for demographic characteristics. Finally, in order to gauge the impact of imputation on the uninsurance rate we remove the full-supplement imputations and reweight the data, and we also use the multivariate regression model to simulate what the uninsurance rate would be under the counter-factual simulation that no cases had the full-supplement imputation. POPULATION STUDIED: The noninstitutionalized U.S. population under 65 years of age in 2004. DATA EXTRACTION METHODS: The CPS-ASEC survey was extracted from the U.S. Census Bureau's FTP web page in September of 2004 (http://www.bls.census.gov/ferretftp.htm). PRINCIPAL FINDINGS: In the 2004 CPS-ASEC, 59.3 percent of the full-supplement imputations under age 65 years had private health insurance coverage as compared with 69.1 percent of the nonfull-supplement imputations. Furthermore, full-supplement imputations have a 26.4 percent uninsurance rate while all others have an uninsurance rate of 16.6 percent. Having imputed data remains a significant predictor of health insurance coverage in multivariate models with demographic controls. Both our reweighting strategy and our counterfactual modeling show that the uninsured rate is approximately one percentage point higher than it should be for people under 65 (i.e., approximately 2.5 million more people are counted as uninsured due to this imputation bias). CONCLUSIONS: The imputed ASEC data are coding too many people to be uninsured. The situation is complicated by the current survey items in the ASEC instrument allowing all members of a household to be assigned coverage with the single press of a button. The Census Bureau should consider altering its imputation specifications and, more importantly, altering how it collects survey data from those who respond to the supplement. IMPLICATIONS FOR POLICY DELIVERY OR PRACTICE: The bias affects many different policy simulations, policy evaluations and federal funding allocations that rely on the CPS-ASEC data. PRIMARY FUNDING SOURCE: The Robert Wood Johnson Foundation.