Re-evaluating the role of refugee integration factors for building more equitable allocation algorithms

Numerous studies in the social sciences have examined how individual and location-level characteristics influence refugees’ integration outcomes. A more recent, smaller body of computational research has developed algorithmic tools that aim to improve refugee integration by optimizing matching to resettlement locations based on predicted outcomes. These tools, which are piloted in a number of countries, raise a number of concerns. This includes, first, their reliance on a narrow set of individual-level predictors – most of which are protected attributes under global anti-discrimination laws – overlooking valuable insights from migration studies that may improve predictive accuracy. Second, they guide refugee placement decisions without assessing group fairness, potentially reinforcing existing inequalities. Against this background, we draw on comprehensive refugee panel data from Germany and study the economic integration of refugees through the lens of predictive modeling. Specifically, we develop prediction models that integrate and test a wide range of integration factors from migration research. We then compare our extended model configurations with existing refugee-location matching algorithms, and evaluate group model performance to assess generalizability and fairness. Overall, we highlight the importance of integrating insights from migration studies into the development of algorithmic decision-making tools to improve their reliability and promote fair outcomes across diverse groups.