Languages di¤er widely in the ways they encode time. I test the hypothesis that languages that grammatically associate the future and the present, foster future-oriented behavior. This prediction arises naturally when well-documented e¤ects of language structure are merged with models of intertemporal choice. Empirically, I …nd that speakers of such languages: save more, retire with more wealth, smoke less, practice safer sex, and are less obese. This holds both across countries and within countries when comparing demographically similar native households. The evidence does not support the most obvious forms of common causation. I discuss implications for theories of intertemporal choice.Comments are welcome at keith.chen@gmail.com. I am indebted to

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homes—and the role these connections serve in spreading a highly contagious respiratory infection—is currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study period—even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a home’s staff network connections and its centrality within the greater network strongly predict COVID-19 cases.

provided invaluable technical and research assistance throughout this project. Through Emily Oehlsen, Uber Technologies provided the data used in this paper. Under the agreement granted the authors, Uber has the right to review the paper "solely to confirm that confidential information is being represented in a nonmisleading fashion" but not to dispute or influence the findings or conclusions of the paper. Chevalier and Rossi have no material financial relationships with entities related to this research. Oehlsen is an employee of Uber Technologies. Chen is a former employee of Uber, and as a result, continues to hold stock options that may constitute a material financial position. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau ofhttps://www.nber.org/people/william_mann Economic Research. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w23296.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.