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Improving refugee integration through data-driven algorithmic assignment by Kirk Bansak, Jeremy Ferwerda, Jens Hainmueller, Andrea Dillon, Dominik Hangartner,

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Presentation on theme: "Improving refugee integration through data-driven algorithmic assignment by Kirk Bansak, Jeremy Ferwerda, Jens Hainmueller, Andrea Dillon, Dominik Hangartner,"— Presentation transcript:

1 Improving refugee integration through data-driven algorithmic assignment
by Kirk Bansak, Jeremy Ferwerda, Jens Hainmueller, Andrea Dillon, Dominik Hangartner, Duncan Lawrence, and Jeremy Weinstein Science Volume 359(6373): January 19, 2018 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

2 Fig. 1 Variation in refugee employment in the United States.
Variation in refugee employment in the United States. (A to C) Refugee employment at 90 days after arrival varies as a function of refugees’ assigned resettlement location (A), personal characteristics (pooled across refugees assigned to all locations) (B), and synergies between characteristics and locations (two example locations) (C). In (B) and (C), dots with horizontal lines indicate point estimates with robust 95% confidence intervals from ordinary least-squares regression. The open circles on the zero line denote reference categories. The data for all three panels include working-age refugees resettled by one of the largest U.S. resettlement agencies during the 2011–2016 period (n = 33,782). These results are replicated for only working-age refugees without U.S. ties (i.e., “free cases”) in fig. S1. Kirk Bansak et al. Science 2018;359: Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

3 Fig. 2 Employment gains from data-driven refugee assignment in the United States.
Employment gains from data-driven refugee assignment in the United States. (A) Empirical cumulative distribution functions (ECDFs) of the refugees’ predicted 90-day employment probabilities under their actual and algorithmic assignments. (B) Actual and algorithmic employment rates by resettlement location. Kirk Bansak et al. Science 2018;359: Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

4 Fig. 3 Employment gains from data-driven refugee assignment in Switzerland.
Employment gains from data-driven refugee assignment in Switzerland. (A) ECDFs of the refugees’ predicted third-year employment probabilities under their actual and algorithmic assignments. (B) Actual and algorithmic employment rates by canton. See table S3 for canton names. Kirk Bansak et al. Science 2018;359: Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

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