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.

Some projects take time to build or are slow to yield cash flows. This may impact the dynamics of investment and liquidity management, although few studies test their financial implications. We exploit the peculiar advantages of copper mines as a laboratory to identify cash-flow sensitivities. In this context, investment decisions depend on the expectations of the long run price of the commodity, while the spread between the spot price and this long run expectations shifts current cash-flows. For this study we compiled a sample of copper firms between 2002 and 2012. We do not find significant effects of cash flow on current capital expenditures, but we do observe a systematic cash flow sensitivity of cash holdings, meaning that some of these transitory earnings are retained as liquidity. This cash stockpiling is stronger among financially constrained firms. In a context of time-to-build, our findings support financial theories emphasizing the salience of cash as buffer stock for liquidity in preparation for future investment opportunities.

The accepted manuscript version (last revised 5 Jan 2018 (v8)) has 118 pages, 3 tables, 11 figures, and includes supplementary appendix. This version corrects some typos in Example 2 of the published version. This supplement contains 11 appendices with additional results and some omitted proofs. Appendices F-J include additional results for Sections 2-7, respectively. Appendix K gathers auxiliary results on algebra of covering entropies. Appendices L and M contain the proofs of Sections 4 and 5 omitted from the main text. Appendix N contains the proofs of Sections 6 omitted from the main text, together with the proofs of the additional results for Section 6 in Appendix I. Appendix O reports the results of a simulation experiment.

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.