Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling Volume I, Review and Recommendations (1991) / Chapter Skim
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Appendix: Microsimulation Models, Databases, and Modeling Terms
Appendix: Microsimulation Models, Databases, and Modeling Terms
Pages 290-310
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From page 290... ...
Department of Health and Human Services, ASPE, SSA, Administration on Aging, National Institute of Child Health and Human Development. Programs Simulated SSI, social security, employer pensions, individual retirement accounts, social security payroll ~c, federal income tax.
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Programs Simulated AFIRE, SSI, food stamps, energy assistance, unemployment insurance, in-kind benefits from Medicare, Medicaid, housing assistance, and school lunch programs, federal income tax, social security payroll tax, state income tax, sales tax. Main Database CPS March income supplement.
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Projection Strategy Static aging of demographic characteristics, employment, and income amounts is integrated into the model and always carried out prior to program simulations. Behavioral Responses Simulated Basic program participation decision (see Citro and Ross, in Volume II)
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Projection Strategy Usual practice is to age a recent March CPS file forward about 4 years, using static procedures to adjust demographic, economic, and employment characteristics; process is repeated every 2 or 3 years. Behavioral Responses Simulated Basic participation decision in AFDC, SSI, general assistance, and food stamp program and F~r~cipation response to change in food stamp benefits (see Citro and Ross, in Volume II)
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Department of Health and Human Services; Social Security Advisory Council; Congressional Budget Office. Programs Simulated SSI, social securing, employer pensions, individual retirement accounts, federal income tax, social security payroll tax, state income tax.
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. Behavioral Responses Simulated Basic participation decision for SSI and decision to retire—partial as well as full retirement—and accept public or private retirement benefit, but no feedback effects of simulated future program changes (e.g., on hours of work or savings behavior)
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Major Database Enhancements Imputations of deductible expenses for nonitemizing taxpayers and earnings attributable to husbands and wives; statistical match with the CES to obtain information on consumption; statistical match with the CPS March income supplement to obtain sources of income not currently subject to taxation, links between taxpayers and family or household units, and information on low-income people not required to file a return under current law; imputations to simulate the 1986 Tax Reform Act and He 1989 Omnibus Budget Reconciliation Act, simulate catastrophic health insurance
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Programs Simulated ~FDC, SSI, food stamps, school nutrition programs, Medicare, Medicaid (for the noninstitutionalized population) , employer-sponsored heals insurance, federal income tax, social security payroll tax, state income tax.
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and also the decision to itemize taxes; a module to simulate labor supply response to income benefits is under development. Calibration of Baseline Simulations AFDC participants are controlled to administrative data on state caseloads and a few national characteristics; SSI and food stamp participants are controlled to administrative data on national characteristics and some state-level targets, including number of units; federal income tax deductions and capital gains are calibrated to IRS totals by adjusted gross income class; Medicaid caseloads are calibrated to state-level enrollment and cost data Computer Implementation Hardware is mainframe IBM; software is FORTRAN, Assembly (creates output files for standard packages such as SAS)
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From page 299... ...
. Data collected in the March supplement include labor force participation and job history in the prior calendar year, annual income for the prior year by detailed source, including earnings, self-employment, public and private transfers, and assets; participation in noncash benefit programs, including energy assistance, food stamps, public housing, school lunch; and health insurance coverage.
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Data collected included expenditures and sources of payment for all major forms of medical care, demographic and socioeconomic characteristics of respondents, insurance coverage of respondents, information from medical providers about respondents, and access to medical care. The NMCUlES, sponsored by the National Center for Health Statistics with the Health Care Financing Administration, consisted of five rounds of data collection over a 15-month period for a national sample of 6,000 households and samples of 1,000 Medicaid cases each in New York, California, Texas, and Michigan.
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These panels were completed or are planned for eight waves (1985~; seven waves (1986, 1987~; six waves (1988~; three waves (1989~; eight waves (1990, 1991~. The data collected for each interview include demographic characteristics; monthly information on labor force participation, job characteristics, and earnings; monthly information on detailed sources and amounts of income from public and private transfer payments, noncash benefits (such as food stamps, Medicaid, Medicare, and health insurance coverage)
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For example, DYNASIM2 dynamically ages the following characteristics of the records in the database: birth, death, first marriage, remarriage, divorce, work disability, education, migration, wage rate, labor force participation, hours of work, unemployment, job change, industry movement, and pension coverage. Dynamic models typically calibrate their simulated longitudinal histories using aggregate population and economic growth assumptions from outside sources such as the Social Security Actuary's trust fund model.
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The MATH and TRIM2 unemployment adjustment algorithm resembles dynamic aging techniques in that employed people are selected to experience unemployment (or vice versa) , with other variables adjusted accordingly, on the basis of transition probabilities estimated using panel data Static aging is typically carried out for a short period, 2-5 years; however, the method can be used to generate a cross-sectional database for any year, no matter how far into the future, provided the needed population and economic projections are available from outside sources.
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Or, a decrease in marginal tax rates in the personal income tax may increase work effort, thereby leading to increased labor supply, lower wage rates, and higher employment in the labor market. CGE models are calibrated
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See, for example, Beebout (1980:Table 2.8~. Dynamic Model This term refers to microsimulation models that generate a database of longitudinal histories for a population sample through means of applying transition probabilities to individual records and Den use these histories to simulate alternative policies.
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The Census Bureau uses very complex item nonresponse imputation methods for household surveys such as the CPS and SIPP, including the hot-deck method and what it refers to as statistical matching. Hot-deck methods assign a nearest neighbor value: that is, the data records are sorted by geographic area and processed sequentially, and reported values are used to update ("hot deck")
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Statistical matching is carried out on two or more data sets when they share variables in common" such as age, sex, and incom~but lack a common unique identifier or come from nonoverlapping samples. In some cases, statistical matches have been performed when it was theoretically possible but not feasible for confidentiality or other reasons—to carry out exact matches.
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Static Model This term refers to microsimulation models that operate on a database representing a cross-section of the population at a given time. Such models typically simulate the direct effects of policy changes, assuming full implementation of the program changes without any feedback effects due to behavioral responses; however, they can also simulate behavioral responses to program changes.
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; adjustment factors for household nonresponse; adjustment factors to reduce the variance among primary sampling units; and adjustment factors so that the weighted counts approximate estimates of the total civilian, noninstitutionalized population by age, race, Hispanic origin, and sex. The last set of adjustment factors is developed from the previous decennial census updated by vital records.
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Key Terms
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