Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these "prediction policy problems" requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of "machine learning" are particularly useful for addressing these prediction problems.

This paper investigates acute, compounded, and persistent deprivation in a representative sample of Chicago adolescents transitioning to young adulthood. Our investigation, based on four waves of longitudinal data from 1995 to 2013, is motivated by three goals. First, we document the prevalence of individual and neighborhood poverty over time, especially among whites, blacks, and Latinos.

Many modern progressives attribute the market's failings to conspiracies by powerful corporate actors to exploit workers and consumers. In this paper I defend the claim that many of the same failures are instead often rooted in competition among individuals for relative advantage. In the familiar stadium metaphor, all stand to get a better view, only to discover that no one sees any better than if all had remained comfortably seated.

This article discusses major methodological challenges in the comparative study of the drivers of international mobility (between different times and places) when using household surveys. Noting the difference between the study of coterminous and stage-specific drivers of migration, I highlight the problems of obtaining data with adequate representation across periods and geographies, which are pressing for all social science research but especially for cross-local comparative endeavors.

From the groundbreaking author team behind the bestselling Winner-Take-All Politics, a timely and topical work that examines what’s good for American business and what’s good for Americans—and why those interests are misaligned.