Improving Modeling Behavioral Responses to Policy Change: Microsimulations in the Presence of Heterogeneity

Constantijn (Stan) Panis, RAND

This paper develops a method that improves researchers' ability to account for behavioral responses to policy change in microsimulation models. Current microsimulation models are relatively simple, in part because of the technical difficulty of accounting for unobserved heterogeneity. This is all the more problematic because data constraints typically force researchers to limit their models to a relatively small set of explanatory covariates, so that much of the variation across individuals is unobserved. Furthermore, failure to account for unobservables often leads to biased estimates of structural parameters, which are critically important for measuring behavioral responses. This paper develops a theoretical approach to incorporate (univariate or multivariate) unobserved heterogeneity into microsimulation models; illustrates computer algorithms to efficiently implement heterogeneity in continuous and limited dependent models; and evaluates the importance of unobserved heterogeneity by conducting Monte Carlo simulations.