Developed democracies are settling an increased number of refugees, many of whom face challenges integrating into host societies. We developed a flexible data-driven algorithm that assigns refugees across resettlement locations to improve integration outcomes. The algorithm uses a combination of supervised machine learning and optimal matching to discover and leverage synergies between refugee characteristics and resettlement sites. The algorithm was tested on historical registry data from two countries with different assignment regimes and refugee populations, the United States and Switzerland. Our approach led to gains of roughly 40 to 70%, on average, in refugees' employment outcomes relative to current assignment practices. This approach can provide governments with a practical and cost-efficient policy tool that can be immediately implemented within existing institutional structures.

With international migration at a record high, a burgeoning literature has explored the drivers of public attitudes toward migrants. However, most studies to date have focused on developed countries, which have relatively fewer migrants and more capacity to absorb them. We address this sample bias by conducting a survey of public attitudes toward Syrians in Jordan, a developing country with one of the largest shares of refugees. Our analysis indicates that neither personal- nor community-level exposure to the economic impact of the refugee crisis is associated with antimigrant sentiments among natives. Furthermore, an embedded conjoint experiment validated with qualitative evidence demonstrates the relative importance of humanitarian and cultural concerns over economic ones. Taken together, our findings weaken the case for egocentric and sociotropic economic concerns as critical drivers of antimigrant attitudes and demonstrate how humanitarian motives can sustain support for refugees when host and migrant cultures are similar.

A critical barrier to generating cumulative knowledge in political science and related disciplines is the inability of researchers to observe the results from the full set of research designs that scholars have conceptualized, implemented, and analyzed. For a variety of reasons, studies that produce null findings are especially likely to be unobserved, creating biases in publicly accessible research. While several approaches have been suggested to overcome this problem, none have yet proven adequate. We call for the establishment of a new discipline-wide norm in which scholars post short “null results reports” online that summarize their research designs, findings, and interpretations. To address the inevitable incentive problems that earlier proposals for reform were unable to overcome, we argue that decentralized research communities can spur the broader disciplinary norm change that would bring advantage to scientific advance. To facilitate our contribution, we offer a template for these reports that incorporates evaluation of the possible explanations for the null findings, including statistical power, measurement strategy, implementation issues, spillover/contamination, and flaws in theoretical priors. We illustrate the template’s utility with two experimental studies focused on the naturalization of immigrants in the United States and attitudes toward Syrian refugees in Jordan.