Leveraging the Power of Place: A Data-Driven Decision Helper to Improve the Location Decisions of Economic Immigrants
Jeremy Ferwerda, Nicholas Adams-Cohen, Kirk Bansak, Jennifer Fei,, Duncan Lawrence, Jeremy M. Weinstein, Jens Hainmueller

TL;DR
This paper introduces a data-driven decision support tool that uses machine learning and administrative data to help economic immigrants choose optimal landing locations, potentially increasing their earnings and diversifying settlement patterns.
Contribution
It presents a novel personalized recommendation system for immigrant location choices, integrating behavioral insights, administrative data, and machine learning to improve economic outcomes.
Findings
Simulations show increased earnings with recommended locations.
Potential for a mild shift away from popular destinations.
Implementation is feasible within existing institutional frameworks.
Abstract
A growing number of countries have established programs to attract immigrants who can contribute to their economy. Research suggests that an immigrant's initial arrival location plays a key role in shaping their economic success. Yet immigrants currently lack access to personalized information that would help them identify optimal destinations. Instead, they often rely on availability heuristics, which can lead to the selection of sub-optimal landing locations, lower earnings, elevated outmigration rates, and concentration in the most well-known locations. To address this issue and counteract the effects of cognitive biases and limited information, we propose a data-driven decision helper that draws on behavioral insights, administrative data, and machine learning methods to inform immigrants' location decisions. The decision helper provides personalized location recommendations that…
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Taxonomy
TopicsMigration and Labor Dynamics · Urban, Neighborhood, and Segregation Studies · Urban Transport and Accessibility
