The United States operates the world’s largest refugee resettlement program. However, there is almost no systematic evidence on whether refugees successfully integrate into American society over the long run. We address this gap by drawing on linked administrative data to directly measure a long-term integration outcome: naturalization rates. Assessing the full population of refugees resettled between 2000 and 2010, we find that refugees naturalize at high rates: 66% achieved citizenship by 2015. This rate is substantially higher than among other immigrants who became eligible for citizenship during the same period. We also find significant heterogeneity in naturalization rates. Consistent with the literature on immigration more generally, sociodemographic characteristics condition the likelihood of naturalization. Women, refugees with longer residency, and those with higher education levels are more likely to obtain citizenship. National origins also matter. While refugees from Iran, Iraq, and Somalia naturalize at higher rates, those from Burma, Ukraine, Vietnam, and Liberia naturalize at lower rates. We also find naturalization success is significantly shaped by the initial resettlement location. Placing refugees in areas that are urban, have lower rates of unemployment, and have a larger share of conationals increases the likelihood of acquiring citizenship. These findings suggest pathways to promote refugee integration by targeting interventions and by optimizing the geographic placement of refugees.

Targeted killing by drones is a systemic driven instrumental practice that overrides societal non-instrumental practices that are essential for international society. Doing so, targeted killing by drones is not simply another form of inflicting violence by technical means to political opponents. It also inflicts the agents applying this practice, tempting them to frame it as a permissible measure to preserve international society. The reliance on drones for targeted killing is a pursuit of non-societal practices that seek individual and retributive justice and anticipatory and preventive self-defence by means of force relying on technological advantage. Eventually, this practice permits military tactics to steer political strategy, mitigating standards and practices agreed on in international society’s norms, rules of conduct, and institutions.

Soon after Raphael Lemkin coined the term “genocide” in Axis Rule in Occupied Europe in 1944, he began working on a world history of genocide to popularize his neologism. Correspondence with funding organizations and publishers shows that he was soliciting interest in a book on the subject as early as 1947 and that he had produced substantial draft chapters by the following year. Before his death in 1959, he had almost completed the book on genocide in world history but, in marked contrast to the present, publishers were uninterested in the project, which was neither completed nor published. Both Lemkin and his approach were forgotten until the 1980s, when a small group of social scientists founded a marginal field called comparative genocide studies.

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are modeling artifacts or are at best highly model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.

I spent my adolescence around maps of places that didn’t exist. An older cousin read The Lord of the Rings over the course of a hot summer when I was nine, and I watched in fascination as he traced the Fellowship’s progress across the foldout map that came with the book in those days. This, I decided, had to be what grown-up reading looked like.

The United States is embroiled in a debate about whether to protect or deport its estimated 11 million unauthorized immigrants, but the fact that these immigrants are also parents to more than 4 million U.S.-born children is often overlooked. We provide causal evidence of the impact of parents’ unauthorized immigration status on the health of their U.S. citizen children. The Deferred Action for Childhood Arrivals (DACA) program granted temporary protection from deportation to more than 780,000 unauthorized immigrants. We used Medicaid claims data from Oregon and exploited the quasi-random assignment of DACA eligibility among mothers with birthdates close to the DACA age qualification cutoff. Mothers’ DACA eligibility significantly decreased adjustment and anxiety disorder diagnoses among their children. Parents’ unauthorized status is thus a substantial barrier to normal child development and perpetuates health inequalities through the intergenerational transmission of disadvantage.