Microsimulation and Population Distribution of Pathways With An Application *

This article develops an approach that extends the methods that simulate individual pathways and describe and explain the population distribution of individual pathways. Conventional microsimulation often bases on static models, ignores its randomness, and fails to provide the sampling distribution of simulated individual pathways. Our approach uses dynamic modeling, corrects for the randomness of microsimulation, and provides sampling distribution of simulated individual pathways. Conventional methods to describe and explain the population distribution of pathways, such as multi-state life tables (MSLTs), are often non-model-based, relying on subgroup comparisons. The quantities of pathways in MSLTs are limited. Macrosimulation of MSLTs based on a static model with very few covariates does not provide proper tests of hypotheses about the partial effect of explanatory variables. Our approach constructs individual pathways based on dynamic modeling that includes a sufficient set of theoretically important explanatory variables, produces an unlimited set of pathway quantities, and is able to test hypotheses about the partial effect of explanatory variables on pathway quantities with inferential statistics. Throughout the article, an example examining the effect of welfare reform on pathways to high school graduation is used to illustrate the approach. The approach developed here will be useful in various sociological areas applying life course perspective to describing the patterns of pathways and testing hypotheses about pathways. Microsimulation and Population Distribution of Pathways With An Application Life course perspective has been widely applied to many sociological areas because of its dynamic, interdependent views and its linkage between micro-level actions and macro-level societal and historical factors. Many life course events, such as pathways to high school graduation, sequences of school-to-work transitions, trajectories of unemployment and poverty, and life course patterns of delinquency and crime are discrete and interdependent in nature. In an example used in this article, before high school graduation, an adolescent can move among four states—enrolled only, both enrolled and employed, neither enrolled nor employed, or employed only. The sequence of these states constitutes pathways to graduation or non-graduation. A largescale social change, a specific institutional development, an organizational transformation, and individual trajectories of another life course domain may produce different population distribution of these pathways. Our objective is to examine how variations in these driving forces change the population distribution of pathways. For this objective we develop an approach that extends methods that simulate individual pathways and describe and explain population distribution of individual pathways. Conventional microsimulation often bases on static models, ignores its inherent randomness from Monte Carlo experiments and from the specification of the behavioral model it bases, and fails to provide the sampling distribution of simulated individual pathways. Our approach addresses these limitations by dynamic modeling that examines the relationships between time-varying explanatory variables and individual transitions, correcting for the Monte Carlo randomness of microsimulation, attending to the specification randomness, and providing the sampling distribution of simulated individual pathways using the variance-covariance matrix of the parameter estimates from the dynamic model. Conventional methods to describe and explain the population distribution of