Unstable Ground? Comparing Income, Poverty & Health Insurance Estimates from Major National Surveys Michael J. O’Grady, Ph.D. Senior Fellow, National Opinion Research Center (NORC) at the University of Chicago Uninsured Estimates from National Surveys Method of Estimate Survey Most Recent Year Current Population Survey (CPS), Census Medical Expenditure Panel Survey (MEPS), HHS* National Health Interview Survey (NHIS), HHS Survey of Income and Program Participation (SIPP), Census Uninsured At Any Time During The Year Uninsured For Full Year At The Time Of The Survey 2003 45.0 million 15.6% N/A N/A 2002 32.4 million 11.5% 47.3 million 16.6%** 62.6 million 22.3% 2003 28.8 million 10.0% 42.0 million 14.6% 51.4 million 17.9% 2001 18.9 million 6.8% 38.7 million*** 14.0% 66.5 million 24.0% Notes: - “N/A“ = Survey does not capture this dimension; and - Point in time refers to the period from Jan. 1 through the interview date, which is 5 months on average. *non-elderly **Estimate for 2003. ***Estimate for March 2001. The Current Work • Both papers are just what is needed to move the policy debate forward. The timing could be better. • Both papers do what they were asked to do, but are too academically polite for policymakers. CPS needs to invest time and money in improving the HI questions. MEPS need to invest time and money in improving income questions. The Need for a New Methodology? • The borders between surveys, linked data sets and models? For income - are we better off using a hybrid approach, e.g., SIPP for the lowest quartile, SoI for the other four? For health insurance – Survey/Medicare & Medicaid admin data? Where is the line between imputing missing answers and using probabilistic modeling? The Need For Real Data Policy • Data policy is a means to an end – Part D, the uninsured, Medicare payment policy. • Hard for policymakers to see the relevance and that it's worth the return on investment. • Do we approach these new challenges in a piece meal fashion or a coordinated approach? Will we still be presenting slide two to President a decade from now? How Do We Get To a Viable Data Policy? • The needs: Better coordination across survey teams. Realistic funding of major surveys and other data collection, links, models, etc. Aggressive investment in methodological work. How Do We Get To a Viable Data Policy? (continued) • Where does the leadership come from? Has to come from within the government. Senior Civil Servants (OMB, HHS Data Council)? − Pros - Great expertise, in it for the long haul. − Cons - Most to lose in a turf battle, not enough control over budget. Political leadership (OMB,ASPE, Under Secretary of Commerce for Economic Affairs) − Pros – Make it a policy and budget priority. − Cons – The wrong politico could politicize the system. Conclusions • The new Administration is well positioned to advance this policy. • Not a partisan issue – A priority issue.