Since the beginning of the all-volunteer era, millions of young Americans have chosen to enlist in the military. These volunteers disproportionately come from disadvantaged backgrounds, and while some aspects of military service are likely to be beneficial, exposure to violence and other elements of service could worsen outcomes. This article links the universe of army applicants between 1990 and 2011 to their federal tax records and other administrative data and uses two eligibility thresholds in the Armed Forces Qualification Test (AFQT) in a regression discontinuity design to estimate the effects of army enlistment on earnings and related outcomes. In the 19 years following application, army service increases average annual earnings by over $4,000 at both cutoffs. However, whether service increases long-run earnings varies significantly by race. Black servicemembers experience annual gains of $5,500 to $15,000 11–19 years after applying while white servicemembers do not experience significant changes. By providing Black servicemembers a stable and well-paying army job and by opening doors to higher-paid postservice employment, the army significantly closes the Black-white earnings gap in our sample.

How feasible is violence early-warning prediction? Colombia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These “best-case” scenarios with annual data fall short of workable early-warning systems.