Jurisdictions across the United States have adopted “ban the box” (BTB) policies preventing employers from conducting criminal background checks until late in the job application process. Their primary goal is to increase employment for those with criminal records. If individuals with criminal records view these policies as improving their labor market opportunities, they might move to BTB-adopting places in search of employment. In this paper, we consider BTB's effects on the demographic composition of labor markets and the likelihood that residents report recently moving from other labor markets. We find no evidence that BTB affects migration.

Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.