利用求职者的偏差进行精准帮扶：一项随机实验
Targeting Support Using Job Seekers' Biases: A Randomized Experiment
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中文总结 AI 辅助
该研究通过随机实验发现，针对悲观求职者的动机干预可提升求职努力与再就业结果，而职业推荐则惠及注意力分配有问题的求职者，展示了数字平台诊断求职者需求并优化干预的方法。
中文摘要 AI 辅助
大多数数字求职援助鼓励失业人员拓展至相关职业领域，以解决求职效率低下的重要来源之一：职业多元化不足。我们的分析表明，相关调整空间取决于潜在的求职问题。通过对求职者信念和求职行为的详细分析，我们识别出一大群悲观求职者，对他们而言，主要限制因素是求职努力程度低和职业期望低，而非职业多元化不足。这一诊断指向一种意想不到的干预措施：我们不鼓励这些求职者在新职业领域求职，而是鼓励他们在已考虑的职业领域内更密集地求职并申请薪资更高的岗位。我们与法国公共就业服务局合作开展了一项大规模随机实验，对这种基于诊断的干预措施以及标准职业推荐措施进行评估。结果显示，这种动机干预措施提高了求职努力程度、提升了保留工资，并在预测的维度上改善了再就业结果。相比之下，职业推荐措施主要惠及那些求职问题在于职业间注意力分配的求职者，其作用方式是激活现有认知而非纠正信念。更广泛而言，我们的研究结果表明，数字平台如何结合主观预期、行为数据、精准干预和随机实验，以诊断求职者的需求并迭代改进干预设计。
英文摘要
Most digital job-search assistance encourages unemployed workers to broaden their search toward related occupations, targeting one important source of search inefficiency: insufficient occupational diversification. Our analysis suggests that the relevant margin of adjustment depends on the underlying search problem. Building on a detailed analysis of job seekers' beliefs and search behavior, we identify a large group of pessimistic workers for whom the main constraints are low search effort and low aspirations, rather than insufficient occupational diversification. This diagnosis points to an unexpected intervention: rather than encouraging these workers to search in new occupations, we encourage them to search more intensively and apply for better-paying jobs within the occupations they already consider. We evaluate this diagnosis-based intervention, alongside a standard occupational recommendation, in a large-scale randomized experiment conducted with the French Public Employment Service. The motivational intervention increases search effort, raises reservation wages, and improves reemployment outcomes along the predicted margins. Occupational recommendations, by contrast, primarily benefit workers whose search problem lies in the allocation of attention across occupations and operate by activating existing perceptions rather than correcting beliefs. More broadly, our findings show how digital platforms can combine subjective expectations, behavioral data, targeted interventions, and randomized experimentation to diagnose job seekers' needs and iteratively improve intervention design.
