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 reflect immigrants' preferences as well as data-driven predictions of the locations where they maximize their expected earnings given their profile. We illustrate the potential impact of our approach using backtests conducted with administrative data that links landing data of recent economic immigrants from Canada's Express Entry system with their earnings retrieved from tax records. Simulations across various scenarios suggest that providing location recommendations to incoming economic immigrants can increase their initial earnings and lead to a mild shift away from the most populous landing destinations. Our approach can be implemented within existing institutional structures at minimal cost, and offers governments an opportunity to harness their administrative data to improve outcomes for economic immigrants.

为解决移民初到达时信息不足的问题以及认知偏差所带来的影响，我们提出了一种数据驱动的决策辅助工具，它利用行为洞察、行政数据和机器学习方法为移民的地点决策提供个性化的推荐，以反映他们的个人喜好以及数据预测在其个人档案下最大化其预期收入的地点。通过与加拿大经验类别移民系统的登陆数据以及他们从税收记录中提取的收入进行的回溯测试，说明了我们方法的潜在影响。不同的场景模拟表明，向入境的经济类移民提供地点建议可以增加他们的初始收入，并导致从人口最多的着陆地轻微转移。

发挥地域优势：数据驱动型决策助手，提高经济移民的定居决策