Using AI to Expand the Job Search of Displaced Workers in the Aftermath of the Covid-19 Crisis

该奖项将支持使用人工智能（AI）和机器学习的研究，以改善低技能工人与现有工作的匹配。 尽管美国就业市场从COVID 19疫情的影响中恢复良好，但尽管未填补的职位空缺增加并保持在高位，但代表性不足的群体的失业率仍然相对较高。 这表明求职者和雇主所获得的信息不匹配。 本研究将采用现场实验的方法来研究人工智能辅助的技能和心理测量技能的算法匹配到在线空缺是否可以帮助求职者获得更好的工作匹配。 该研究项目有可能改善技能分布低端工人的劳动力市场的运作，从而增加这一群体工人的就业。 除了为人工智能和计算算法用于提高劳动力市场效率的机制提供证据外，这项研究的结果还可以为增加就业、劳动生产率和经济增长的政策提供指导。 由于这项研究的重点是低工资工人，因此研究结果可以减少贫困，减少收入不平等，并帮助美国成为减贫政策的全球领导者。 该项目利用关于求职者特征和在线求职搜索引擎发布的职位要求的数据，并使用随机对照试验（RCT）设计来调查将AI辅助算法匹配分配给职位发布是否会改善职位匹配。 随机对照试验设计有三个处理组：（i）求职者领域的工作机会，没有特定的顺序;（ii）空缺职位，没有特定的顺序，但具有预测的匹配质量;（iii）空缺职位，以最佳匹配求职者的背景和技能，具有预测的匹配质量。 对照组是不按特定顺序列出的作业，也没有附加匹配质量。随机对照试验将有2600名求职者的样本，这些求职者在性别和空间上具有异质性，他们是通过在Monster.com的社交媒体账户上发布的广告招募的。 第一组与对照组的比较将回答治疗是否降低搜索成本从而改善匹配的问题，而第二组和第三组与对照组和第一组的结果比较将回答治疗是否有助于降低不完全信息的成本并改善工作匹配的问题。 该研究项目的成果，不仅可以证明人工智能和计算算法在改善劳动力市场功能方面的作用机制，还可以为增加就业、提高生产率、促进经济增长等政策提供指导。该奖项体现了NSF的法定使命，根据基金会的学术价值和更广泛的影响评价标准，被认为值得支持。

This award will support research that uses artificial intelligence (AI) and machine learning to improve matching of low skilled workers to available jobs. While the US job market recovered well from the COVID 19 pandemic disruption, the unemployment rate remains relatively high for under-represented groups even as unfilled vacancies have risen and stayed high. This suggests a mismatch between the information available to jobseekers and employers. This research will use field experimental methods to investigate whether AI-assisted algorithmic matching of skills and psychometric skills to on-line vacancies can help job seekers get better job matches. This research project has the potential to improve the functioning of the labor market for workers at the lower end of the skill distribution, hence increase employment for this group of workers. Besides providing evidence on the mechanisms through which AI and computational algorithms can be used to improve labor market efficiency, the results of this research can provide guidance on policies to increase employment, labor productivity, and economic growth. Because the research focuses on low wage workers, the results can decrease poverty as well as decrease income inequality and help establish the US as a global leader in poverty reduction policies. This project leverages data on job seeker characteristics and the requirements of jobs posted by online job search engines and use a randomized control trial (RCT) design to investigate whether assigning an AI-assisted algorithmic matching to job postings improves job matching. The RCT design has three treatment arms: (i) job offers in the job-seekers field in no particular order; (ii) vacancies in no particular order but with predicted match quality; and (iii) vacancies sorted to best match backgrounds and skills of jobseekers with predicted match quality. The control group are jobs listed in no particular order and without a match quality attached. The RCT will have a sample of 2600 job seekers, with heterogeneity across gender and space, recruited via advertisements posted on Monster.com’s social media accounts. Comparison of the first group with the control group will answer the questions of whether treatment reduces search cost hence improves matching, while comparing outcomes for the second and third arms to those of the control group and the first arm will answer the questions as to whether treatment help to lower the cost of incomplete information and improves job match. Besides providing evidence on the mechanisms through which AI and computational algorithms can be used to improve the functioning of labor markets, the results of this research project can provide guidance on policies to increase employment, increase productivity, and economic growth.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.