摘要翻译：
越来越多的国家已经制定了吸引移民的计划，这些移民可以为他们的经济做出贡献。研究表明，移民最初到达的地点在塑造他们的经济成功方面起着关键作用。然而，移民目前缺乏帮助他们确定最佳目的地的个性化信息。相反，他们往往依赖可用性启发式，这可能导致选择次优的着陆地点、较低的收入、较高的外迁率以及集中在最著名的地点。为了解决这个问题并抵消认知偏差和有限信息的影响，我们提出了一个数据驱动的决策帮助器，它利用行为洞察、管理数据和机器学习方法来通知移民的位置决策。decision helper提供个性化的地点建议，反映移民的偏好，以及根据他们的个人资料，对他们最大限度地提高预期收入的地点进行数据驱动预测。我们利用行政数据进行的回溯测试来说明我们的方法的潜在影响，这些数据将加拿大快速入境系统中最近经济移民的登陆数据与他们从税收记录中检索的收入联系起来。对各种情况的模拟表明，向入境的经济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登陆目的地轻微转移。我们的方法可以在现有体制结构内以最小的成本实施，并为政府提供了利用其行政数据改善经济移民结果的机会。
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英文标题：
《Leveraging the Power of Place: A Data-Driven Decision Helper to Improve
  the Location Decisions of Economic Immigrants》
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作者：
Jeremy Ferwerda, Nicholas Adams-Cohen, Kirk Bansak, Jennifer Fei,
  Duncan Lawrence, Jeremy M. Weinstein, Jens Hainmueller
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最新提交年份：
2020
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computers and Society        计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2007.13902