The basic strategy behind our approach to estimating the cost of a paid leave program in Massachusetts was to, as much as possible, base estimates of program costs on actual known leave-taking behavior, and where this was not possible, to estimate a range of program costs reflecting a range of reasonable assumptions about unknown aspects of behavior in the presence of a paid leave program. We wanted to be able to estimate the sensitivity of program costs estimates to these assumptions. We also wanted to be able to analyze the distribution of program benefits by demographic characteristics. Furthermore, we wanted users to be able to estimate the costs of similarly structured paid leave benefit programs in other states, to be able to have some control over the assumptions about behavior that affect program cost estimates, and to be able to undertake their own distributional analyses.
We chose a simulation strategy as the best way to accomplish these goals. To obtain the best estimates possible about known leave-taking behavior, we use the Public Use Family and Medical Leave survey data collected by Abt Associates in 2012 for the Department Labor (referred to here as the DOL Survey) (McGarry, et al, 2013) to estimate behavioral models of leave-taking behavior conditional on the demographic characteristics of individuals, and use the Census Bureau’s American Community Survey Public Use Microdata Sample (hereinafter referred to as the ACS or ACS PUMS) to predict leave-taking behavior conditional on the demographic characteristics of individuals.