Suchergebnisse

AbstractThe current refugee resettlement system is inefficient because there are too few resettlement places and because refugees are resettled to locations where they might not thrive. We outline how ideas from market design can lead to better resettlement practices. In particular, we discuss how market design can incentivize participation of countries in resettlement and improve the matching of refugees at international and local levels; some of these insights have already put into practice. Finally, we highlight several further applications of market design in refugee resettlement, including cardinal preference submission and matching with transfers.

Abstract
We show how to efficiently use costly testing resources in an epidemic, when testing outcomes can be used to make quarantine decisions. If the costs of false quarantine and false release exceed the cost of testing, the optimal myopic testing policy targets individuals with an intermediate likelihood of being infected. A high cost of false release means that testing is optimal for individuals with a low probability of infection, and a high cost of false quarantine means that testing is optimal for individuals with a high probability of infection. If individuals arrive over time, the policy-maker faces a dynamic trade-off: using tests for individuals for whom testing yields the maximum immediate benefit vs spreading out testing capacity across the population to learn prevalence rates thereby benefiting later individuals. We describe a simple policy that is nearly optimal from a dynamic perspective. We briefly discuss practical aspects of implementing our proposed policy, including imperfect testing technology, appropriate choice of prior, and non-stationarity of the prevalence rate.

Current refugee resettlement processes account for neither the preferences of refugees nor the priorities of hosting communities. We introduce a new framework for matching with multidimensional knapsack constraints that captures the (possibly multidimensional) sizes of refugee families and the capacities of communities. We propose four refugee resettlement mechanisms and two solution concepts that can be used in refugee resettlement matching under various institutional and informational constraints. Our theoretical results and simulations using refugee resettlement data suggest that preference-based matching mechanisms can improve match efficiency, respect priorities of communities, and incentivize refugees to report where they would prefer to settle. (JEL C78, D82, J15, J18)

Abstract
Over the past two decades, economists have made significant advances in understanding how networks affect individual behaviour and shape aggregate outcomes. We argue that insights from network economics can play an important role in the design of economic policy. Focusing on six policy domains, we show that network economics not only deepens our understanding of existing policy concerns but also suggests a number of new policy questions. In each of these policy areas, we evaluate the availability of data and assess the suitability of the network economics toolkit for policy work. We conclude with a discussion of challenges to the adoption of network-based methods in economic policy along with strategies to overcome them.