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Improving refugee integration through data-driven algorithmic assignment

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Science  19 Jan 2018:
Vol. 359, Issue 6373, pp. 325-329
DOI: 10.1126/science.aao4408
  • Fig. 1 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.

  • Fig. 2 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.

  • Fig. 3 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.

Supplementary Materials

  • Improving refugee integration through data-driven algorithmic assignment

    Kirk Bansak, Jeremy Ferwerda, Jens Hainmueller, Andrea Dillon, Dominik Hangartner, Duncan Lawrence, Jeremy Weinstein

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Supplementary Text
    • Figs. S1 to S11
    • Tables S1 to S3
    • References and Notes

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