Using Matched Survey and Administrative Data to Estimate Eligibility for the Medicare Part D Low Income Subsidy Program

The 2003 Medicare Prescription Drug Improvement and Modernization Act added a new prescription drug benefit to the Medicare program known as Part D (prescription drug coverage), as well as the Low-Income Subsidy (LIS) program to provide extra help with premiums, deductibles, and copayments for Medicare Part D beneficiaries with low income and limited assets. In this paper, the authors report on the use of matched survey and administrative data to estimate the size of the LIS-eligible population as of 2006. In particular, they employ individual-level data from the Survey of Income and Program Participation (SIPP) and the Health and Retirement Study (HRS) to cover the potentially LIS-eligible noninstitutionalized and institutionalized populations of all ages. The survey data are matched to Social Security Administration (SSA) administrative data to improve on potentially error-ridden survey measures of income components (e.g., earnings and beneficiary payments from Supplemental Security Income and Old Age, Survivors, and Disability Insurance) and program participation (e.g., participation in Medicare or a Medicaid/Medicare Savings program). The administrative data include the Master Beneficiary Record/Payment History Update System, the Master Earnings File, and the Supplemental Security Record. The survey data are the source of information on asset components, as well as the income components (e.g., private pensions) and individual characteristics (e.g., health status) not covered in the administrative data. Their baseline estimate, based on the matched data, is that about 12 million individuals were potentially eligible for the LIS as of 2006. A sensitivity analysis indicates that the use of administrative data has a relatively small effect on the estimates but does suggest that measurement error is important to account for. The estimate of the size of the LIS-eligible population is more sensitive to the relative weight they place on the two survey data sources, rather than the choice of methods we apply to either data source.

Prescription drug coverage/Medicare/Measurement Error/ADMINISTRATIVE DATA

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