Languages differ widely in the ways they encode time. I test the hypothesis that the languages that grammatically associate the future and the present, foster future-oriented behavior. This prediction arises naturally when well-documented effects of language structure are merged with models of intertemporal choice. Empirically, I find 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.

Behavioral economics has demonstrated systematic decision‐making biases in both lab and field data. Do these biases extend across contexts, cultures, or even species? We investigate this question by introducing fiat currency and trade to a colony of capuchin monkeys and recovering their preferences over a range of goods and gambles. We show that capuchins react rationally to both price and wealth shocks but display several hallmark biases when faced with gambles, including reference dependence and loss aversion. Given our capuchins’ inexperience with money and trade, these results suggest that loss aversion extends beyond humans and may be innate rather than learned.

Equal access to voting is a core feature of democratic government. Using data from hundreds of thousands of smartphone users, we quantify a racial disparity in voting wait times across a nationwide sample of polling places during the 2016 U.S. presidential election. Relative to entirely white neighborhoods, residents of entirely black neighborhoods waited 29% longer to vote and were 74% more likely to spend more than thirty minutes at their polling place. This disparity holds when comparing predominantly white and black polling places within the same states and counties and survives numerous robustness and placebo tests. We shed light on the mechanism for these results and discuss how geospatial data can be an effective tool to measure and monitor these disparities going forward.

Research on policing has focused on documented actions such as stops and arrests—less is known about patrols and presence. We map the neighborhood movement of nearly 10,000 officers across 21 of America’s largest cities using anonymized smartphone data. Police spend 0.36% more time in neighborhoods for each percentage point increase in Black residents. This neighborhood-level disparity persists after controlling for density, socioeconomic status, and crime-driven demand for policing, and may be lower in cities with more Black police supervisors (but not officers). Patterns of police presence statistically explain 57% of the higher arrest rate in more Black neighborhoods.

Technology has facilitated new, nontraditional work arrangements, including the ride-sharing company Uber. Uber drivers provide rides anytime they choose. Using data on hourly earnings and driving, we document driver utilization of this real-time flexibility. We propose that the value of flexibility can be measured as deriving from time variation in the drivers' reservation wage. Measuring time variation in drivers' reservation wages allows us to estimate the surplus and labor supply implications of Uber relative to alternative, less-flexible work arrangements. Despite other drawbacks to the Uber arrangement, we estimate that Uber drivers earn more than twice the surplus they would in less-flexible arrangements.