[论文解读] Leveraging the Power of Place: A Data-Driven Decision Helper to Improve the Location Decisions of Economic Immigrants

Jeremy Ferwerda, Nicholas Adams-Cohen+5 more|arXiv (Cornell University)|Jul 27, 2020

Migration and Labor Dynamics参考文献 23被引用 6

一句话总结

本文提出了一种数据驱动的决策辅助工具，利用机器学习和行政数据，为经济移民提供个性化的定居地点推荐，通过引导他们避开过度拥挤的次优目的地，提高其初始收入。利用加拿大联邦快速通道（Express Entry）数据进行的回溯测试表明，该工具可显著提升移民的预期收入，且实施成本极低。

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 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.

研究动机与目标

• 解决经济移民在选择初始定居地点时缺乏个性化信息的问题。
• 减少因认知偏见（如可得性启发法）导致的次优地点选择。
• 通过利用行政数据和机器学习预测各地区收入，改善移民的经济成果。
• 设计一种低成本、可扩展的决策辅助工具，补充现有移民流程，而不取代个人自主权。
• 使政府能够利用现有数据基础设施，提升经济移民的融合成果。

提出的方法

• 该决策辅助工具使用基于行政数据训练的机器学习模型，关联近期加拿大经济移民的抵达地点与纳税记录。
• 应用基于结果的匹配算法，预测具有相似背景的移民在不同地点的预期收入。
• 推荐结果根据个人偏好以及基于数据的经济表现预测进行个性化定制。
• 该系统作为信息性提示（nudge）发挥作用，而非具有约束力的指令，以支持知情决策。
• 模型持续通过新数据更新，以反映不断变化的劳动力市场状况和个体结果。
• 通过加拿大快速通道系统的历史数据进行回溯测试，以模拟推荐的影响。

实验结果

研究问题

• RQ1 在缺乏足够信息的情况下，经济移民在多大程度上因认知偏见而选择次优的定居地点？
• RQ2 如果移民获得个性化、数据驱动的地点推荐，其预期收入能提高多少？
• RQ3 通过有针对性的推荐，减少热门目的地地理集中度的潜力有多大？
• RQ4 在现有政府行政系统中，此类决策辅助工具的可扩展性和成本效益如何？
• RQ5 该决策辅助工具能否调整以优先考虑短期收入以外的结果，如长期融合或区域发展？

主要发现

• 加拿大相当一部分经济移民目前定居在次优地点，收入预测显示，若选择更优地点，存在显著的收入提升潜力。
• 回溯测试表明，提供数据驱动的推荐可使经济移民的初始年收入实现可测量的提升。
• 该推荐将适度降低对最热门抵达城市的集中度，促进更广泛的地理分布定居模式。
• 由于所需行政数据已由政府常规收集，该系统可实现极低的边际实施成本。
• 该决策辅助工具通过提供推荐而非强制执行，支持个人自主权，使移民能够做出知情且自愿的选择。
• 该方法具有可扩展性，可进一步拓展以优先考虑其他融合成果，如长期收入或区域经济发展。