Facilitating the integration of refugees has become a major policy challenge in many host countries in the context of the global displacement crisis. One of the first policy decisions host countries make in the resettlement process is the assignment of refugees to locations within the country. We develop a mechanism to match refugees to locations in a way that takes into account their expected integration outcomes and their preferences over where to be settled. Our proposal is based on a serial dictatorship mechanism that allows the government first to specify a threshold for the minimum level of expected integration success that should be achieved. Refugees are then matched to locations based on their preferences subject to meeting the government's specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the government's threshold. We demonstrate our approach using simulations and a real-world application to refugee data from the United States.
is an assistant professor of political science at Stanford. He is a formal political theorist and political economist whose work ranges across a diverse set of topics including voting theory, bargaining theory, principal-agent theory, behavioral political economy, distributive politics, and long run development. Avi’s papers have been published (or are forthcoming) in the leading journals of political science, including the American Political Science Review, American Journal of Political Science, and Journal of Politics, as well as the top journals in economic theory, including Econometrica, Journal of Economic Theory, and Games and Economic Behavior. Avi earned his PhD in political economy from Princeton University, and taught in the political science and economics departments of the University of Rochester before coming to Stanford.
is a Ph.D. candidate in political science and a data scientist at the Immigration Policy Lab at Stanford University. His research interests are in causal inference, experimental design and analysis, immigration policy, survey methodology, international and comparative political economy, and computational social science. He holds an M.S. in statistics from Stanford, an M.A.L.D. from the Fletcher School (Tufts), and a B.A. in anthropology from Harvard.
During the 2018-2019 academic year, he will be supported by a Facebook Ph.D. Fellowship (Computational Social Science). Beginning in July 2019, he will be an Assistant Professor in the Department of Political Science at the University of California, San Diego.
is a Professor in the Department of Political Science at Stanford University and holds a courtesy appointment in the Stanford Graduate School of Business. He is also the Faculty Co-Director of the Stanford Immigration Policy Lab that is focused on the design and evaluation of immigration and integration policies and programs.
His research interests include immigration, statistical methods, political economy, and political behavior. He has published over 40 articles, many of them in top general science journals and top field journals in political science, statistics, economics, and business. He has also published three open source software packages and his research has received awards and funding from the Carnegie Corporation, the Russell Sage Foundation, the Robin Hood Foundation, the National Science Foundation, the Swiss SNF, the American Political Science Association, Schmidt Futures, the Society of Political Methodology, the National Bureau of Economic Research, and the Midwest Political Science Association.
Hainmueller received his PhD from Harvard University and also studied at the London School of Economics, Brown University, and the University of Tübingen. Before joining Stanford, he served on the faculty of the Massachusetts Institute of Technology.