THE SURVEY OF INCOME AND PROGRAM PARTICIPATION


                                                        RATIONALE FOR A SIPP-BASED MICROSIMULATION
                                                                  MODEL OF SSI AND OASDI


                                                                    WORKING PAPER #9117


                                                         BY:  BERNARD WIXON AND DENTON R. VAUGHAN
                                                              SOCIAL SECURITY ADMINISTRATION






































U.S. DEPARTMENT OF COMMERCE
BUREAU OF THE CENSUS


                                                       RATIONALE FOR A SIPP-BASED MICROSIMULATION MODEL OF SSI AND OASDI

                                             Bernard Wixon, Denton R. Vaughan, Social Security Administration
                                                      Bernard Wixon, 4301 Conn. Ave., NW, Suite 211,
                                                                   Washington, DC 20008

KEY WORDS: Microsimulation, SIPP, SSI, OASDI

I.  Introduction

                     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 long-standing
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 



 The views expressed are the authors' and do not necessarily
represent the position of the Social Security Administration or the
Department of Health and Human Services.


                               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.

II.  The Data Background

                     Several years ago, Nelson McClung, a prominent microsimulator,
wrote:

                               Continued improvement in microsimulation depends
                               mainly upon having better data.  I do not want to
                               discourage research on technique, but I suggest
                               that research be more willing to conclude with
                               specifications for a desirable data collection and
                               less willing to press on to imaginative
                               alternatives to better data.

Any contribution made by the model outlined here will stem from two
successful data development efforts.  Each was designed to address
a particular, well-recognized information gap:

                      There was no ongoing survey designed to support analysis of
income maintenance programs, including, for example, the estimation
of SSI participation rates.  The only regularly-available data used
to estimate SSI participation rates were from the Current
Population Survey (CPS), a survey designed to provide labor force
statistics, not to support program analysis; it measured neither
assets nor monthly income and had weak measures of disability.

                     To meet such needs, the Survey of Income and Program
Participation (SIPP) was developed to support evaluations of a
range of public programs, including means-tested programs.  The
survey, conducted by the Bureau of the Census, is nationally-
representative, ongoing, and its data are now widely used.

                      Analysts using administrative data to evaluate changes in
the OASDI program could not say to what extent the changes would
affect those with low or high family income.  For example,
administrative data do not tell us how proposed changes in OASDI
affecting elderly widows would affect those with low family
incomes, as opposed to those with higher incomes.  While family
income level is not relevant for all policy issues, for issues of
income adequacy, it is.  

                     In response, matching techniques have been used to combine
survey data with earnings history data and benefit data from SSA
administrative files.  With match files, earnings data allow us to
identify those affected by certain changes in OASDI, while survey
data allow us to describe them in terms of their family income from
all sources, assets, demographic traits, and household composition.
The Survey of Income and Program Participation

                     The SIPP is an ongoing, nationally-representative survey of
the civilian noninstitutional population with information on
income, assets, demographic traits, household characteristics, and
participation in many programs.  It was conceived in the seventies
by experts who concluded that a survey specifically designed to
support analysis of public programs was needed.

                     The Committee on National Statistics of the National Academy
of Sciences recently released an interim assessment of the SIPP. 
Because of its reputation for objectivity and expertise, some of
the Committee's findings are worth noting:

                     Overall, the committee finds that SIPP is making a vital
                     contribution to understanding the characteristics and
                     dynamics of the population at economic risk, and the ways
                     in which federal programs meet--or fail to meet--economic
                     needs...

                     One of the primary goals of SIPP is to meet the needs of
                     policy analysts for information on families eligible for
                     government income maintenance programs:  how much use is
                     being made of them; how well or poorly they are meeting
                     their objectives; and what factors appear to influence
                     entry and departure from them.

These findings suggest the SIPP's multi-purpose rationale.  First
of all, information is collected on various dimensions of economic
well-being.  By comparison to the CPS, the SIPP has improved income
measures and comprehensive measures of assets, both of which are
important for SSA policy research.  In addition, it collects the
information needed to determine eligibility and benefits for many
public programs, including monthly income (SSI and other programs
use a monthly accounting period) and more detailed measures of
disability than those in the CPS.  The SIPP's monthly reference
period, in contrast to the annual reference period of the CPS,
offers analysts an opportunity to consider the dynamics of program
participation, income flows, work behavior, and household
composition.    Furthermore, the data will support both cross-
sectional and longitudinal analysis.  On the one hand, the volume
of information collected and the SIPP's multi-purpose design make
it difficult to use and suggest the importance of developing
expertise.  On the other hand, we take the view that the
acknowledged difficulty of using the SIPP is not an accident of
poor design but rather an inevitable by-product of the SIPP's rich
content and multi-purpose rationale.  

                     As the SIPP is designed, a new sample panel is introduced each
year.  Because sample members are interviewed every four months for
two and a half years, the panels overlap.  Panels can be combined
to increase the precision of estimates.  The original panel size
was 20,000 households, but this was reduced to 12,000 due to cuts
in the Census Bureau's SIPP budget; however, the sample size has
been restored to 20,000, beginning in 1990.  

                     The SIPP also has limitations with respect to SSA policy
research.  For some applications, the sample size will limit the
amount of subgroup analysis which can be reliably undertaken. 
Furthermore, while the questions on health are extensive, using the
responses to those questions to decide whether someone would be
considered disabled in qualifying for public programs may be
problematic.

Matched Earnings and Benefit Data

                     As part of an ongoing data development effort of the Bureau of
the Census and SSA, SIPP data are matched to SSA data on earnings
and benefits for the same individual.  The earnings data are taken
from SSA's Summary Earnings Record which is based on employers'
reports of covered earnings from 1951 onward.  OASDI data are
extracted from the Master Beneficiary Record.  Data on SSI benefits
from the Supplemental Security Record will be included in future
matching efforts.  The use of these matched data is highly
restricted due to confidentiality considerations.

The Three-Part Focus of the Data

                     The analytical effort outlined here will exploit three focal
categories inherent in the matched SIPP data:

                     (1) evaluative variables, giving demographic characteristics
                     and the dimensions of economic well-being;
                     (2) the elements of eligibility and benefit determination for
                     major means-tested programs; and
                     (3) information on earnings histories and benefits--the
                     elements of eligibility and benefit determination for OASDI.

While variables may fall in more than one category, the three
categories, combined in a single analytical file, are central to
the effort described here.  They will permit analysis of some OASDI
issues from an income adequacy perspective, as well as comparisons
of SSI and OASDI alternatives with respect to issues of income
adequacy.  

III.  Aspects of Microsimulation

                     A procedure for determining current law eligibility or the
effects of an alternative benefit structure on eligibility,
benefits, or behavior would have to be added to any data collection
effort intended for rigorous program analysis.  As the term is used
here, a microsimulation model provides a way of relating data on
individuals and households to program provisions for such purposes.

                     From a historical perspective, microsimulation models have
provided a framework for incorporating data on individual persons
and households into the distributional analysis of benefits and
taxes.  The enabling developments have been advances in hardware
speed and software techniques (e.g., parameterization and
modularity).  As a result,  microsimulation offers the promise of
state of the art analysis in time frames once associated with
"quick and dirty" estimates.  What are some of the central features
of microsimulation?

                      Microsimulation prevents the loss of information frequently
associated with efforts to model program eligibility or the
behavior of individuals with aggregated data.  The relationships
that are modeled stem from program provisions (e.g., eligibility
for benefits) and behavioral responses to policy changes (e.g.,
decisions to apply for benefits).  The real world processes
simulated, such as the determination of SSI eligibility, depend on
combinations of traits of particular persons or families. 
Nonetheless, because disaggregated data are sometimes not available
or because of the tight deadlines associated with policy
discussions, estimates based on aggregated or group data are often
used.  But when only highly aggregated data are available,
information on combinations of traits or joint distributions is
often lost and assumptions must play a greater role.

                     The microsimulation approach is intended to avoid this loss of
information.  Under microsimulation the analytical process is
designed to mirror the real world process.  This begins with the
design of the underlying data; the SIPP, for example, collects
information at the level of persons and households and the
information collected is determined, in part, by reference to the
eligibility criteria of major public programs.  The model then uses
information on individuals and families on a case-by-case basis,
taking each through an eligibility determination process which
mimics the claims process itself.  

                      Survey-based microsimulation models give policymakers an
effective means of considering hypotheticals, such as:  (1) the
program eligibility of individuals who have never applied or (2)
the eligibility, benefits, and behavioral responses associated with
benefit structures which have been proposed, but not enacted.  One
criterion for evaluating a means-tested program is how well it
reaches its targeted population.  The main complication is that
nonparticipants within the targeted (or eligible) population are
not represented in the program data.  To address this issue,
eligibility models are used in conjunction with surveys
representing the general population, allowing analysts to estimate
the number of eligibles and their hypothetical benefits.

                     In addition, the model's step by step representation of the
eligibility and benefit determination procedures is analytically
powerful.  It allows us to consider "what if" alternatives to the
current structure, including the effects of particular structural
features (for example, the SSI asset test or the progressive design
of the OASDI primary insurance amount or PIA) or parameters (such
as SSI benefit standards).  

                      Disaggregated data and microsimulation techniques permit
flexibility in grouping persons within households and defining
population subgroups.  This flexibility is central to the way such
models evaluate programs and program options--in terms of the
current economic status of population subgroups.  Microsimulation
models define units within households according to program statutes
and economic relationships among household members.  Public
programs will often use different units in determining eligibility
(for example, a person's SSI benefits would be affected if he or
she were living rent-free in the household of an adult child, but
OASDI benefits would not be affected).  Moreover, since family
members share income to meet their common needs, an individual's
economic status depends on the composition and resources of the
family as a whole.  Hence, economic status is typically evaluated
in terms of the family unit.  Detailed survey information on
household composition permits such statutory and economic
relationships to be incorporated into estimates. 

                     Because of differences in resources and ability to work,
changes in benefit policies are often targeted for particular
groups.  The groups of interest to policymakers may be defined by
income and asset levels, demographic traits, family composition,
health, or work status, all of which are included in the SIPP.  In
a typical application, a program alternative would be considered in
the light of its effects on the current economic status of such
population subgroups.

IV.  Issues and Goals

                     Data from surveys such as the SIPP enhance what is known about
programs in two ways.  First, the SIPP represents the general
population, a broader universe than that of program data.  This
allows analysts to estimate the number and traits of
nonparticipating eligibles and to consider proposals which increase
program eligibility or participation.  This feature will be
advantageous for analysis of both OASDI and SSI.

                     Second, the SIPP will provide a richer characterization of
recipients than program data; program data are restricted in that
the only information collected is that necessary to the
determination of eligibility and benefits.  This advantage will
prove particularly valuable for OASDI analysis; analysts will be
able to characterize beneficiaries (or those affected by a specific
benefit provision) in terms of additional dimensions of current
financial status, family composition, and demographic traits.

The SSI Model

                     In the context of a means-tested program, a microsimulation
model which uses data representing the general population allows us
to consider the issue of program participation.  Measuring the
extent to which SSI reaches its target population implies
estimating both the eligible population (some of whom have never
applied and, hence, are not represented in the administrative data)
and the participant population.  Finding SSI participants is less
problematic; both surveys and administrative data have information
on participants.  Determining which non-participants are eligible
presents more obstacles.  In general, they are not represented in
program data.  Furthermore, they cannot simply be asked if they are
eligible for SSI as part of the survey process; many non-
participants will not know if they are eligible.  

                     One main purpose of the model, then, is to determine SSI
eligibility.  In simple terms, the computer procedures which
constitute the model take all survey respondents through an SSI
eligibility determination and benefit calculation process.  Their
survey responses to questions on income, assets, household
composition, and so on provide the basis for the estimates.

                     Some areas which may be considered using the SSI model
include:

                      Estimating SSI participation rates.
                      Characterizing nonparticipating eligibles.
                      Increasing the income guarantee levels.
                      Liberalizing or eliminating the SSI asset test.
                      Increasing SSI disregards.
                      Effects of the SSI accounting period and other
                       issues relating to program dynamics.

The OASDI Model

                     The report of the 1979 Advisory Council on Social Security
included a statement of the principles underlying the OASDI benefit
structure:

                     From its beginning those responsible for the design of
                     social security have sought to assure, on the one hand,
                     a reasonable relationship between the social security
                     taxes paid by individuals and the benefits they receive
                     and, on the other hand, at least a minimally adequate
                     income for long-term low-wage workers.  Maintaining a
                     reasonable relationship between taxes and benefits has
                     been described as the goal of individual equity. 
                     Assuring a basic level of income has been called the goal
                     of adequacy. 

A number of provisions, some in force and others proposed, are
motivated by the adequacy goal.  These include the "progressive"
structure of the PIA, the spouse benefit, other dependent benefits,
the minimum benefit (now repealed), and a special benefit for those
over the age of 85 (proposed).  Because of the scale of the OASDI
program, some of these adequacy-related provisions have major
effects.  For example, over three million persons receive spouse
benefits. 

                     Distributional analysis of such provisions has been impeded
because no single data source contained the necessary elements. 
Both current and historical information are required.  By design,
recurring household surveys, such as the SIPP or the CPS, have
information on current economic status, but their information on
social security benefits and earnings is not sufficiently detailed
for systematic program analysis.  Analysis of OASDI does not merely
require information on benefits received (which is reported on the
surveys), and the type of benefit, e.g., worker, spouse, survivor
(which can sometimes be inferred from survey information).  What
is needed are the details of earnings histories required to
calculate OASDI benefits and identify those affected by a specific
provision.  Yet due to respondents' limited recall, it is not
feasible to collect earnings history data that are sufficiently
detailed and reliable in surveys.

                     By contrast, administrative data have detailed information on
earnings and benefits.  However, since data on income other than
earnings and information on all members of the family are not used
to determine benefits, they are not available in the administrative
data for the program.  As a result, analysts often use the size of
the OASDI benefit to suggest the financial circumstances of
beneficiaries--a measure that does not take into account
considerable differences in pension income, property income, and
income received by other family members.  The OASDI model will
enhance analysts' capability to consider adequacy issues primarily
by providing better information on the current financial
circumstances, household composition, and demographic traits of
those affected.  Some issues for analysis include:

                      Income distributional effects of the spouse benefit and
                     proposals such as providing dropout years for caregivers.

                      The effects of specific OASDI provisions in reducing the
                     rate of poverty or the poverty gap.  For example, it has been
                     suggested that an increase in the worker's benefit, intended
                     to reduce inequities between one- and two-worker families,
                     would have important effects as well on the poverty rates of
                     widows.

                      Distributional effects of the "progressive" PIA structure,
                     in terms of the family incomes of those affected.

OASDI/SSI Comparisons

                     OASDI, combined with income from other sources, is intended to
provide adequate income for most of those with regular covered
employment. However, OASDI benefits and other income are less
likely to meet the needs of those who have neither worked regularly
in covered employment, nor are dependents of someone who has. 
Hence a means-tested program such as SSI can be viewed as a
necessary adjunct to a broadly-based contributory system.  

                     Not only SSI, but certain provisions of the OASDI program are
motivated by adequacy concerns.  Due to structural differences,
however, the programs "target" benefits in distinctly different
ways.  Although SSI can be thought of as a residual program for
those without sufficient resources from OASDI and other sources,
the evidence indicates there is a problem of SSI nonparticipation. 
By contrast, under OASDI even when a benefit provision has an
income adequacy objective, some benefits due to the provision are
likely to be received by middle and high income groups.  

                     For these reasons, when the policy objective is one of income
adequacy, comparing OASDI options with SSI alternatives would be a
promising analytical approach.  Using the microsimulation approach,
this suggests that the data source must have sufficient historical
data on covered earnings to support an OASDI model and sufficient
information on current resources and characteristics to determine
SSI eligiblity and to give a richer description of those affected,
in terms of the dimensions of current economic status.

                     The model development we have undertaken, in combination with
the SIPP match file we have described, should allow us to to make
such OASDI/SSI comparisons.  Some topics which may be considered
include:

                      An empirical analysis of basic structural alternatives.  A
                     broadly-based contributory system combined with a means-tested
                     supplementary program (such as we have in the United States)
                     could be compared with double decker systems used in other
                     countries.

                      Policy options with respect to the high rates of poverty
                     among nonmarried aged women.

                      Policy options with respect to the special needs of very
                     elderly recipients--those 85 or older.  

V.  Summary

                     The undertaking sketched in this paper builds on two long term
data development efforts.  First, a survey, the SIPP, was designed
specifically to support analysis of public programs.  Second, SSA
earnings and benefit data have been matched to the SIPP.  However,
rigorous program analysis requires not only data that meet certain
specifications, but an analytical tool--a capability to estimate
eligibility, benefits, and the effect of behavioral responses from
the underlying data.  SSA is now developing this capability;
because of the confidential nature of the matched data, an
OASDI/SSI model such as this would probably be developed only at
SSA.

                     This effort will support research on SSI participation rates
and other SSI policy issues.  In terms of OASDI research, analysts
will be able to consider those affected by particular provisions in
terms of the SIPP's rich data on current financial circumstances,
demographic traits, and family composition.  

                     Finally, this effort will allow policymakers to compare OASDI
proposals with SSI alternatives.  This should prove useful because
although provisions of both programs are motivated by concerns
about income adequacy, the programs have very different "targeting"
outcomes.

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                                                                                   ENDNOTES