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A growing number of countries have established programs to attract immigrantswho can contribute to their economy. Research suggests that an immigrant'sinitial arrival location plays a key role in shaping their economic success.Yet immigrants currently lack access to personalized information that wouldhelp them identify optimal destinations. Instead, they often rely onavailability heuristics, which can lead to the selection of sub-optimal landinglocations, lower earnings, elevated outmigration rates, and concentration inthe most well-known locations. To address this issue and counteract the effectsof cognitive biases and limited information, we propose a data-driven decisionhelper that draws on behavioral insights, administrative data, and machinelearning methods to inform immigrants' location decisions. The decision helperprovides personalized location recommendations that reflect immigrants'preferences as well as data-driven predictions of the locations where theymaximize their expected earnings given their profile. We illustrate thepotential impact of our approach using backtests conducted with administrativedata that links landing data of recent economic immigrants from Canada'sExpress Entry system with their earnings retrieved from tax records.Simulations across various scenarios suggest that providing locationrecommendations to incoming economic immigrants can increase their initialearnings and lead to a mild shift away from the most populous landingdestinations. Our approach can be implemented within existing institutionalstructures at minimal cost, and offers governments an opportunity to harnesstheir administrative data to improve outcomes for economic immigrants.

Leveraging the Power of Place: A Data-Driven Decision Helper to Improve the Location Decisions of Economic Immigrants