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Introduction

Constance F.Citro

The papers in this volume, prepared by members, staff, and consultants of the Panel to Evaluate Microsimulation Models for Social Welfare Programs, provide technical background on topics related to microsimulation modeling. The panel drew heavily on these papers in forming the recommendations that are presented in Volume I, Part II, of this report. However, the views expressed in the papers are the authors’ and should not be attributed to the panel.

The papers relate to all of the elements that are part of using microsimulation models for policy analysis purposes: that is, to estimating the budgetary and population effects of alternative programs and policies. As approached through microsimulation techniques, this task entails simulating the effects of program changes at the level of the individual decision units involved—such as families for changes to income support programs or doctors and hospitals for changes to health care cost reimbursement programs. The elements that contribute to microsimulation modeling of policy alternatives include the input databases, the design of the models themselves, the models’ computing environments, methods for evaluating the models’ outputs, and the models’ documentation.

This introduction provides an overview of the volume and brief descriptions and references for the major microsimulation models that are referred to by the authors. Chapter 4 and the Appendix to Part II in Volume I present a more detailed explanation of the microsimulation modeling approach.



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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers Introduction Constance F.Citro The papers in this volume, prepared by members, staff, and consultants of the Panel to Evaluate Microsimulation Models for Social Welfare Programs, provide technical background on topics related to microsimulation modeling. The panel drew heavily on these papers in forming the recommendations that are presented in Volume I, Part II, of this report. However, the views expressed in the papers are the authors’ and should not be attributed to the panel. The papers relate to all of the elements that are part of using microsimulation models for policy analysis purposes: that is, to estimating the budgetary and population effects of alternative programs and policies. As approached through microsimulation techniques, this task entails simulating the effects of program changes at the level of the individual decision units involved—such as families for changes to income support programs or doctors and hospitals for changes to health care cost reimbursement programs. The elements that contribute to microsimulation modeling of policy alternatives include the input databases, the design of the models themselves, the models’ computing environments, methods for evaluating the models’ outputs, and the models’ documentation. This introduction provides an overview of the volume and brief descriptions and references for the major microsimulation models that are referred to by the authors. Chapter 4 and the Appendix to Part II in Volume I present a more detailed explanation of the microsimulation modeling approach.

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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers OVERVIEW Databases and Methods of Data Enhancement Typically, microsimulation models operate on large databases containing detailed information for individual decision units to obtain the necessary input to simulate current and alternative programs. The quality and scope of such databases are critical to the quality and detail of the estimates the models can provide. In Chapter 1 Citro compares and contrasts the March Current Population Survey (CPS) with the new Survey of Income and Program Participation (SIPP) from the perspective of their utility for simulating changes to income support programs, such as food stamps and Aid to Families with Dependent Children (AFDC). The March CPS is currently widely used by microsimulation models, but it exhibits a range of data quality problems. SIPP was designed to provide improved data for microsimulation, and policy research generally in the area of economic well-being, but it has been plagued by start-up problems. SIPP clearly will play a role in generating higher quality data for microsimulation, but the precise nature of its role is uncertain at this time. The data sets provided by specific surveys, such as the March CPS or SIPP, or by specific administrative records systems, such as federal income tax returns, are rarely adequate for microsimulation modeling without enhancement, either to add needed variables or to improve quality. Imputation techniques of various types—such as evaluating a regression equation estimated from another data set—are often used to supply values for one or more missing variables in the primary database. Exact matches of two or more data sets, based on common identifiers such as social security number, have been used to generate more comprehensive databases for models. However, such matches have not been generally available for policy analysis or research use because of concerns about protecting the confidentiality of individual records. In Chapter 2 Cohen discusses another technique, statistical matching, that has been used to link two or more data sets, in cases in which it is not possible or feasible to perform an exact match. Statistical matching involves strong assumptions about the relationships between the variables that are common to the input files used in the matching and the variables that are unique to each file. However, the technique is worth serious consideration because of the difficulties involved in alternatives, such as exact matches or seeking to expand the content of specific surveys. Model Design Currently, two major types of microsimulation models—static and dynamic—are widely used for social welfare policy modeling. Static models operate on cross-sectional databases that provide a snapshot of the population at one time.

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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers They typically include static “aging” routines to bring their databases up to date or project them into the future. Such routines reweight the individual records to match outside control totals for key demographic characteristics and make other adjustments for changes in income and employment. Dynamic models operate on longitudinal databases that contain individual histories. They “grow” their databases forward in time by applying transition probabilities to each record for such events as birth, death, marriage, labor force status change, and so on. Within these two distinct model types, there are variations in handling common functions that result from such factors as differences in client needs and in styles of the model developers. In Chapter 3 Citro and Ross describe the different approaches taken by three static models—TRIM2, MATH, and HITSM (see below)—to two important functions of models that simulate income support programs such as AFDC and food stamps: the routines to simulate the participation decision and the routines to convert annual to monthly values. In Chapter 4 Ross compares and contrasts two major dynamic models—DYNASIM2 and PRISM (see below)—and reflects generally on the dynamic modeling approach. Computing Technology Given their complexity and size, microsimulation models are very dependent on computer hardware and software capabilities to operate in a cost-effective manner. Most models that are in widespread use today are designed for mainframe, batch-oriented processing that minimizes the cost of single computer runs but imposes barriers to access and inhibits flexible, timely adaptation to meet new policy needs. In Chapter 5 Cotton and Sadowsky compare and contrast the mainframe computing environment for the TRIM2 model with the personal computer-based environment for the model developed by Statistics Canada, SPSD/M (see below). Cotton and Sadowsky assess likely future directions for computer hardware and software that offer potential benefits for improved microsimulation model capabilities. Model Evaluation Assessment of the quality of outputs from models is a vitally important component of the process of using model estimates in the policy debate and of determining fruitful directions for investment in improved model capabilities. However, for a variety of reasons, validation of microsimulation models has been a largely neglected activity. In Chapter 6 Cohen discusses the potential for using relatively new, computer-intensive sample reuse techniques for developing variance estimates for the outputs of microsimulation models. In Chapter 7 Cohen reviews the scanty literature of previous microsimulation model validation studies.

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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers Chapter 8, by Cohen, Billard, Betson, and Ericksen, documents and discusses the results of a validation experiment that the panel conducted with the TRIM2 model. The experiment combined a sensitivity analysis, which varied three TRIM2 components, with an external validation study. The three components were the input database, namely, whether or not the records were adjusted for population undercount; the routines used to age the database; and the routines used to convert annual income and employment variables in the March CPS to monthly values. External validity was assessed by having the model use a 1983 database to simulate AFDC and supplemental security income (SSI) program law in 1987 and then comparing the results with administrative data on AFDC recipients for 1987 from the Integrated Quality Control System (IQCS). In Chapter 9 Grummer-Strawn and Espenshade review the literature on evaluations of the quality of projections of the U.S. population produced by the Census Bureau and the Social Security Actuary through the use of cell-based models. These projections are important to assess because they are used by many microsimulation models as controls in the database aging process. In addition, the authors’ discussion of the experience in validating these types of projections offers a useful contrast to the experience in microsimulation model validation discussed in Chapters 7 and 8. Model Documentation Good documentation is essential for people who want to use or develop a model and for people who want to understand the model outputs. In Chapter 10 Hollenbeck provides a critical review of the extant documentation of TRIM2, MATH, and HITSM. Hollenbeck compares the documentation for these models with a software documentation standard developed by the Institute of Electrical and Electronics Engineers, Inc. MODELS Seven major microsimulation models are discussed in one or more of the chapters. See the Appendix to Part II of Volume I for more detailed descriptions; see also Lewis and Michel (1990) for a recent review of the use of microsimulation modeling in tax and transfer policy analysis, including histories of the development of TRIM2, DYNASIM2, and PRISM. Dynamic Simulation of Income Model 2 (DYNASIM2) DYNASIM2 is a dynamic model of demographic and labor force processes and public and private retirement income programs, including social security, employer pensions, SSI, and individual retirement accounts. The model also

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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers simulates federal income taxes and social security payroll taxes. The original DYNASIM model was developed by the Urban Institute in the early 1970s; see Johnson and Zedlewski (1982) and Johnson, Wertheimer, and Zedlewski (1983). Household Income and Tax Simulation Model (HITSM) HITSM is a static model of government tax and transfer programs, including AFDC, SSI, food stamps, energy assistance, unemployment insurance, Medicare, Medicaid, housing assistance, school lunch programs, federal income taxes, state income taxes, sales taxes, and social security payroll taxes. The model is proprietary and was developed by Lewin/ICF, Inc., in the mid-1980s; see Lewin/ICF, Inc. (1988). Micro Analysis of Transfers to Households (MATH) Model MATH is a static model of government tax and transfer programs, including AFDC, general assistance, SSI, food stamps, federal income taxes, and social security payroll taxes. The model was developed by Mathematica Policy Research, Inc., in the mid-1970s; see Doyle et al. (1990). Multi-Regional Policy Impact Simulation (MRPIS) Model MRPIS is a hybrid model, with microsimulation, cell-based, and input-output components. It is designed to estimate the second-round effects of tax and transfer program changes on regional product mix and therefore on regional employment. The model was developed by the Social Welfare Research Institute at Boston College in the mid-1980s; see Social Welfare Research Institute (n.d.) and Havens and Clayton-Matthews (1989). Pension and Retirement Income Simulation Model (PRISM) PRISM is a dynamic model of labor force and selected demographic processes and retirement income programs, including social security, employer pensions, SSI, and individual retirement accounts. The model also simulates federal income taxes, social security payroll taxes, and state income taxes. The model was developed by Lewin/ICF, Inc., in the early 1980s. In the mid-1980s the model was expanded to include a long-term care financing submodel; see Kennell and Sheils (1986) and Kennell et al. (1988). Social Policy Simulation Database/Model (SPSD/M) SPSD/M is a static microsimulation model of Canadian tax and transfer programs

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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers that is implemented with personal computing technology. It was developed by Statistics Canada in the late 1980s; see Statistics Canada (1989). Transfer Income Model 2 (TRIM2) TRIM2 is a static model of government tax and transfer programs, including AFDC, SSI, food stamps, school nutrition programs, Medicare, employer-sponsored health insurance, Medicaid, federal income taxes, state income taxes, and social security payroll taxes. The original TRIM was developed by the Urban Institute in the early 1970s; see Webb et al. (1982) and Webb et al. (1986). REFERENCES Doyle, Pat, Trippe, Carole, Huff, Ann, and Coffin, Kirsten, eds. 1990 MATH Technical Description: Current Services Files. Washington, D.C.: Mathematica Policy Research, Inc. Havens, John, and Clayton-Matthews, Alan 1989 The MRPIS Model: Notes for Presentation to Committee on National Statistics. Paper prepared for the Panel to Evaluate Microsimulation Models for Social Welfare Programs. Social Welfare Research Institute, Boston College, Chestnut Hill, Mass. Johnson, Jon, and Zedlewski, Sheila R. 1982 The Dynamic Simulation of Income Model (DYNASIM). Vol. II, The Jobs and Benefits History Model. Washington, D.C.: The Urban Institute Press. Johnson, Jon, Wertheimer, Richard, and Zedlewski, Sheila R. 1983 The Dynamic Simulation of Income Model (DYNASIM). Vol. I, The Family and Earnings History Model. Washington, D.C.: The Urban Institute Press. Kennell, David L., and Sheils, John F. 1986 The ICF Pension and Retirement Income Simulation Model (PRISM) with the ICF/Brookings Long-Term Care Financing Model. Draft technical documentation. ICF, Inc., Washington, D.C. Kennell, David, et al. 1988 Financing of Long-Term Care. Final report submitted to the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. Washington, D.C.: Lewin/ICF, Inc. Lewin/ICF, Inc. 1988 The Household Income and Tax Simulation Model (HITSM): Methodology and Documentation. Washington, D.C.: Lewin/ICF, Inc. Lewis, Gordon H., and Michel, Richard C., eds. 1990 Microsimulation Techniques for Tax and Transfer Analysis. Washington, D.C.: The Urban Institute Press. Social Welfare Research Institute No date Multi-Regional Policy Impact Simulation (MRPIS) Model, Level 3.0: Analyst’s Guide. Boston College, Chestnut Hill, Mass. Statistics Canada 1989 SPSD/M Introductory Manual. Ottawa: Statistics Canada.

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Improving Information for Social Policy Decisions—The Uses of Microsimulation Modeling: Volume II, Technical Papers Webb, Randall L., Hager, Clara, Murray, Douglas, and Simon, Eric 1982 TRIM2 Simulation Modules. 2 vols. (A–M and N–Z). March 1982 plus updates. Washington, D.C.: The Urban Institute Press. Webb, Randall L., Bergsman, Anne, Hager, Clara, Murray, Douglas, and Simon, Eric 1986 TRIM2 Reference Manual: The Framework for Microsimulation. Working Paper 3069–091. Washington, D.C.: The Urban Institute Press.

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