National Academies Press: OpenBook
Suggested Citation:"Labor Force Simulations." National Research Council. 1991. Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers. Washington, DC: The National Academies Press. doi: 10.17226/1853.
Page 126
Suggested Citation:"Labor Force Simulations." National Research Council. 1991. Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers. Washington, DC: The National Academies Press. doi: 10.17226/1853.
Page 127
Suggested Citation:"Labor Force Simulations." National Research Council. 1991. Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers. Washington, DC: The National Academies Press. doi: 10.17226/1853.
Page 128

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DYNASIM2 AND PRISM: EXAMPLES OF DYNAMIC MODELING 126 Given this sequence of events, demographic events can influence labor force events occurring in the same year (and demographic events occurring earlier in the sequence can affect those simulated later in the sequence), while events happening later in the sequence affect events in the following year. Events or characteristics that are simulated after the longitudinal histories are created are affected by those histories (e.g., supplemental security income [SSI] eligibility or tax liabilities), but cannot affect the sequence of demographic and labor force events. Therefore, the decision to include certain events in the simulation of longitudinal histories is an important one. Tables 2 and 3 list the events included in the simulation of longitudinal histories and the events that are simulated using the synthetic histories for DYNASIM2 (Table 2) and PRISM (Table 3). DYNASIM2 and PRISM both model birth, death, marriage, divorce, disability, and labor force events, but DYNASIM2 also models education, migration, and leaving home. The developers of DYNASIM2 wanted to create a model that could simulate a range of demographic and economic events for many different policy purposes. This objective led them to develop full capability to simulate population change. For example, the model simulates the education of children and young adults, including those born during the simulations and added to the file, because a labor force simulation might find these children as prime-age workers and a simulation with a very long time horizon might find these children as retirees. In contrast, the PRISM documentation states that the model is meant to simulate incomes and long-term care utilization of the elderly through the year 2025. Individuals who are 20 years old have completed enough schooling to provide a distribution of education levels in the population for the longitudinal employment simulations, and individuals who are 20 in 1979 will be 66 in 2025. Given this time horizon for the model, the model does not attempt to simulate life histories for individuals who were children in the base year or for those who were born during the simulations. PRISM LABOR FORCE AND PENSION SIMULATIONS Labor Force Simulations The labor force simulation in PRISM is based on a simple Markov probability model. An individual moves from one labor force category to another each year based on transition probabilities that vary by socioeconomic variables and previous employment patterns. The transition probabilities were estimated from two linked March CPS data files for a set of individuals (which provide employment information for 3 years, as employment status is recorded both at the time of the interview and for the previous year). The labor force categories among which the individual moves from year to year are five categories of annual hours of work: 0; 1–500; 501–1,000;

DYNASIM2 AND PRISM: EXAMPLES OF DYNAMIC MODELING 127 1,001–1,500; and more than 1,500. Excessive movement between annual hours of work categories is reduced by first classifying people into categories of annual hours over the past 3 years: full-time work, part-time work, and no work. (The categories are defined by the number of quarters of social security coverage earned in the previous 3 years.) Transition probabilities for annual hours of work were computed separately for individuals in the three categories of normal work patterns. Within these three groups, the sets of transition probabilities were computed separately for demographic groups defined by sex, marital status, age, and education. The actual number of hours of work assigned for the year is the average for the demographic group in that hours category, which is computed from the 3 years of CPS data. Wages are adjusted each year according to a straightforward rule. If the individual is simulated to be employed in 2 successive years, wages increase by a growth factor derived from the Lewin/ICF, Inc., Macroeconomic-Demographic Model (or another macroeconomic model). The growth factor varies by sex and age and is somewhat higher if the individual changed jobs in the current year. The model also updates wages for unemployed individuals annually (at 80% of the annual trend), although the individual will not receive a wage until he or she is employed. The PRISM simulation does not distinguish between unemployment and out of the labor force. Control totals for employment are projections of the proportion of persons who were ever employed during the year. Anyone working for fewer than 1,500 hours annually is considered to be part time for simulation purposes (rather than full time for part of the year and unemployed for the rest). The hours-of-work categories affect job- changing status, but the distinction between part-time work and unemployment is not necessary. A part-time worker is assigned a higher probability of changing jobs during the year than a full-time worker, in part because fewer annual hours of work may imply a period of unemployment and in part because a part-time worker may have less attachment to the job. There is no explicit attempt to model periods of unemployment as distinct either from periods out of the labor force or from part-time work. All individuals who did not work in the previous year and some of those who did are simulated to change jobs each year. Probabilities of a job change vary by part-time and full-time status, years on the current job, and the age of the worker and were computed from the matched CPS data. Individuals changing jobs are assigned a new industry based on transition probabilities that vary by previous industry and by sex of the worker. Individuals who enter or reenter the labor force are assigned an industry based on their age, education, and full- time or part-time work status. The industry assignment probabilities reflect expected shifts from manufacturing to service industries over time.

DYNASIM2 AND PRISM: EXAMPLES OF DYNAMIC MODELING 128 TABLE 2 Determinants of Major Events Simulated by DYNASIM2 Event or Characteristic Variables Used to Determine Event Simulation of Longitudinal Histories Demographic events Death Married women 45–64 Age, race, sex, marital status, education, number of children All others Age, race, sex, marital status, education Birth Age, marital status, number of children, race, education Multiple birth Race Sex of newborn Race Marriage Age 18–29 Age, race, sex, previous marital status, income, education, region, weeks worked, hourly wage, asset income, welfare, unemployment compensation Other ages or ever married Age, race, sex, previous marital status Mate matching Difference in age, difference in education Leaving homea Age, race, sex Divorce Distribution over time of expected divorces for this marriage cohort, age at marriage, education, previous marital status, presence of young children, weeks worked, wages Education Race, sex, age, years at current school level, parents' eduation Mobility Number of years married, size of family, age and sex of head, education of head, race, region, size of metropolitan statistical area (MSA) Disability Onset Age, race, sex, marital status Recovery Age, race, sex, marital status, education Labor force events Labor force participation Age, race, sex, education, South, disability, marital status, student, children, spouse earnings Hours in the labor force Age, transfer income, expected wage, disability, marital status, children Wage rate Race, sex, age, South, disability, marital status, education, student Unemployment Age, sex, race, education, marital status, South, disability, children

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Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers Get This Book
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This volume, second in the series, provides essential background material for policy analysts, researchers, statisticians, and others interested in the application of microsimulation techniques to develop estimates of the costs and population impacts of proposed changes in government policies ranging from welfare to retirement income to health care to taxes.

The material spans data inputs to models, design and computer implementation of models, validation of model outputs, and model documentation.

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