Deciding the Future of Refugees: Rolling the Dice or Algorithmic Location Assignment?

MCML Authors

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Abstract

Upon arrival in Germany, refugees are distributed among the 16 federal states. This distribution decision is based on a fixed formula consisting of two components: tax revenue and the population size of the federal state. Research suggests that optimal refugee-location matching enhances refugee integration into the labor market. However, the current mechanism fails to align refugees’ characteristics with their assigned locations, resulting in a missed opportunity to leverage synergies. To this end, we use comprehensive refugee data in Germany and exploit an existing machine learning matching tool to assign refugees to states algorithmically. Our findings reveal potential improvements in refugee employment, depending on the modeling setup. Our study provides two key contributions. First, we evaluate the effectiveness of an algorithmic matching tool within Germany. Second, we investigate the fairness implications of such an algorithmic decision-making tool by evaluating the impact of different train data setups on group-specific model performance.

inproceedings

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EWAF 2024

3rd European Workshop on Algorithmic Fairness. Mainz, Germany, Jul 01-03, 2024.

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Authors

C. Strasser Ceballos • C. Kern

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Links

PDF

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Research Area

 C4 | Computational Social Sciences