When two public programs have objectives which overlap to some degree, analysts must evaluate the most target efficient way to address a given policy issue. For example, the Social Security Administration (SSA) administers both a broadly-based social insurance program (Old Age, Survivors, and Disability Insurance or OASDI) and a means-tested program for the aged and disabled (Supplemental Security Income or SSI). Both SSI and certain provisions of the OASDI program have income adequacy objectives and, for example, either program could serve as a vehicle to address the needs of low-income elderly widows.

Comparative analysis would be useful because the principles underlying the two programs differ and, consequently, their 'targeting" outcomes vary. There is a participation or 'coverage' issue associated with SSI: Some of those with incomes low enough to receive benefits do not apply. The social insurance program, OASDI, poses another issue--,even when specific provisions are intended to help those with low (past) earnings or low (current) income, some benefits due to the provision are likely to be received by those with moderate or high incomes.

These issues are well understood by analysts, but there has been no regularly-available source of data rich enough to support systematic distributional analysis. Neither program data nor survey data, taken alone, would suffice. However, as a result of two long-term development efforts, the required data now exist and the Social Security Administration is expanding its longstanding activities in the area of microsimulation modeling to exploit them. The data sources are the Survey of Income and Program Participation (SIPP), and matched SSA earnings and benefit data.

This effort will enrich SSA's ability to address SSI and OASDI issues, taken alone, and permit comparative analysis. Within the federal government, no such model exists; indeed, because of the confidentiality issues surrounding the earnings and benefit data matched to the SIPP, in all likelihood an SSI/OASDI model of this type would only be developed at SSA.

This paper sets out the central ideas underlying the project in a nontechnical way. Section II describes the data sources. Section III discusses features of microsimulation modeling, the principal analytic technique to be used. Section IV considers possible contributions of the model with respect to SSI, OASDI, and issues which relate to both programs. The final section provides a summary.