so again.2 Thus, the development of a prospective measure of MCER will have to be accomplished without accounting fully for the impact of health insurance coverage on potential out-of-pocket costs.
Other survey-specific data gaps exist, which limit how fully each survey could support the modeling of MCER. MEPS does not collect Medicare premium payments (although these might be imputed based on income) or most of what distinguishes disposable income from money income. MEPS collects data on assets only once, in the final interview for each panel. Such data would therefore not be usable as a baseline characteristic. Liquid assets could be projected backward, however, and that might be acceptable even though some assets may have been used in paying for exceptional medical costs. With only a portion of liquid assets being included in resources, following the panel’s recommendation in Chapter 2, the asset component of resources is relatively insensitive to this type of error.
SIPP collects no data on chronic medical conditions, which is likely to be one of the most important predictors of subsequent medical expenditures, given that none of the surveys collects information on the details of health insurance coverage. SIPP does collect information on in-kind benefits and commuting and child care costs, but its data on taxes paid do not appear to be useful.
The HRS lacks information on insurance premiums paid since the prior interview, although it obtains current premiums, which may provide a reasonably good proxy. The HRS has the same limitations as MEPS with respect to disposable versus money income, but, unlike MEPS, liquid assets are available as a baseline characteristic. The HRS captures some additional information that could be useful in modeling the economic burden of medical expenditures. The survey asks respondents with large out-of-pocket medical expenses how they financed these expenditures (although the response categories combine earnings and savings, which would be useful to separate), and it collects information on assistance that children may have provided with payments. In addition, Medicare claims data have been linked to the HRS, expanding the survey’s available data on expenditures— primarily for the population ages 65 and older.3
2 MEPS has added a few more questions on types of health insurance coverage to support analysis of the ACA and is considering what additional questions and content might be tested and added to the Insurance Component. Interest centers on employer plans and offerings, firm size, actuarial value, stop-loss policies, wellness programs, and additional detail on the characteristics of self-insured plans and small employer anticipated exchange participation. (See http://aspe.hhs.gov/hsp/12/surveyenhancements/ib.shtml.)
3 The HRS also serves as the central data source for the Future Elderly Model (FEM), a microsimulation model developed by the University of Southern California and RAND Roybal Center for Health Policy Simulation. The FEM combines data from the HRS, MEPS, the Medi care Current Beneficiary Survey, and the National Health Interview Survey and can be used to predict health status and economic outcomes for individuals 51 and older. For an example of an application of this model see Lakdawalla, Goldman, and Shang (2005).