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FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 146 Static models can be deterministic or stochastic. Stochastic or probabilistic models have operating characteristics that determine specific outcomes on the basis of probability distributions and use pseudorandom number generators to select those outcomes. Dynamic models require some stochastic processes in order to provide a meaningful representation of some of the processes being modeled. Deterministic models contain operating characteristics that have fixed rules, such as are embodied in an income tax algorithm, with no variability due to chance selection. In practice, most models contain a mix of deterministic and stochastic operating characteristics.7 Static models do not require solutions for simulated future time periods, although they are almost always used in this way. Dynamic models have little meaning unless their solution provides a number of results over the course of simulated time. There is general acceptance that dynamic models provide a more realistic representation of micropopulation unit behavior. However, static models are regarded as more effective at times for specific short-run projection purposes because of their greater simplicity and the often lower costs associated with building such models and obtaining computer-generated model solutions. Historical Background The path-breaking work that created the field of socioeconomic microsimulationâMicroanalysis of Socioeconomic Systems: A Simulation Study (1961)âwas performed by Guy Orcutt and his colleagues in the late 1950s. The underlying behavioral model was dynamic and stochastic, and the simulation system was implemented in assembly language on an IBM 704 computer system. Initial public policy analysis based on this methodology was first applied to the federal individual income tax system in the United States (Pechman, 1965) and Canada (Bossons, 1967). Initial versions of these models were strictly static accounting models that embodied neither behavioral assumptions nor forward projections in time. Both models were implemented in FORTRAN using IBM 7090/94 and System 360 computers, respectively, and both have been widely used. Additional Canadian models were subsequently built, using similar methodology, for tax reform analysis for the province of Ontario and to study tax and transfer program integration for the province of Quebec. Another initial use of microanalytic simulation methodology was to project the economic status of retired older persons (Schulz, 1968). The underlying 7For example, while an algorithm ascertaining eligibility for a specific public assistance transfer program may be completely deterministic, a user may choose to assume that only a percentage of those eligible (less than 100%) actually choose to participate in the program. Such an operating characteristic has a stochastic condition for participation, with a deterministic rule applied for those who do participate.
FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 147 microanalytic model was dynamic and stochastic, and it emphasized labor market participation and accrual of private and public pension rights. Interest in the late 1960s in welfare reform and negative income tax proposals led to the creation of the RIM model (Wilensky, 1970) for use by the President's Commission on Income Maintenance in the United States. RIM embodied a static model used to project the effects of alternative tax and transfer policies on families in the United States. Its success in supporting the work of the commission led to the development of TRIM in 1971 and TRIM2 in 1979 to support continued exploration of tax and transfer policy alternatives focused on the lower end of the income distribution. RIM was implemented on a Control Data computer system, while TRIM and TRIM2 have largely been implemented in FORTRAN on IBM System 360/370/3090 computing environments. At the same time, another approach was undertaken under the leadership of Guy Orcutt in the 1970s to develop DYNASIM, a dynamic microanalytic simulation model embodying expanded household-sector submodels (Orcutt et al., 1976). The initial underlying computer system, MASH, was written in FORTRAN for a DECsystem-10 (Sadowsky, 1977). A later implementation, MASS, was created at Yale University by Amihai Glazer and his colleagues in PL/I for an IBM System 370. In 1981 the DYNASIM model was reimplemented as DYNASIM2 for reasons of efficiency and portability. The development of TRIM2 spawned several other microeconomic modeling developments. MATH (Doyle and Neyland, 1979), a model based on TRIM, was developed by Mathematica Policy Research, Inc., to provide more precise estimates of U.S. Department of Agriculture transfers in kind, such as the food stamp program.8 The KGB model (Betson, Greenberg, and Kasten, 1980) was developed by the U.S. Department of Health and Human Services to provide official cost and distributional analyses for President Carter's major welfare reform initiative in 1977. Independently, ICF, Inc., developed HITSM, a proprietary model used by, inter alia, the Office of the Assistant Secretary for Planning and Evaluation in 1987 to estimate the impact of proposed legislation regarding the Aid to Families with Dependent Children program. More recently, Statistics Canada initiated a substantial modeling effort and has developed SPSD/M (Statistics Canada, 1989a), a microanalytic model of the Canadian household sector, on an MS-DOS-based microcomputer platform. SPSD/M is a static model and is oriented toward assessment of the revenue and distributional effects of Canadian household tax and transfer policies. Further developments of dynamic socioeconomic simulation models has continued at Cornell University under Steve Caldwell. The Cornell CORSIM model is written in C language and is currently being used on an IBM System 3090â600 mainframe and on MS-DOS microcomputers. CORSIM exemplifies 8See Lewis and Michel (1990) for a discussion of the development and use of the MATH model.