RAPID: Analysis of Job Posting in Recession and Recovery (JPRR)

摘要衰退和复苏中的职位发布项目将使用求职搜索引擎每天从公司网站下载的职位发布，按职业、地理区域和公司开发对新员工的近乎实时的劳动力需求指标。该项目将公司经济活动的公告与州和地方经济政策以及公告联系起来，帮助公司和政府就加快就业复苏的方法做出明智的决定。这是一个快速反应建议，因为该项目将在未来六个月内获得数据和分析，以便能够快速响应决策者的信息请求，并支持可以从这场危机中得出的关于经济政策的循证决策。2020年3月开始的史无前例的不连续就业崩溃，挑战了对正常经济时期劳动力需求的分析。该项目将探索危机期间企业就业反应的非线性模式，并从2020年夏秋两季的职位变化中提取复苏信号。利用差异性方法，该项目将审查州或地方政府政策以及坚定的人力资源政策对不同职业职位变化的影响。利用日常数据，该项目将应用高频计量经济学和统计技术来制定劳动力需求指数。该项目将通过使用高频近实时数据的劳动力需求结构以及对公司、职业、行业和地区的区分来补充供应方失业申领人报告。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

AbstractThe Job Posting in Recession and Recovery project will develop near real time indicators of labor demand for new workers by occupation, geographic area, and firms using job postings downloaded daily from company websites by a job search engine. The project will link postings of a firm's economic activity to state and local economic policies and postings that will help firms and governments make informed decisions about ways to speed the recovery of employment. This is a rapid response proposal because the project will have the data and analyses available in the next six months to be able to respond quickly to requests for information from decision-makers and to support evidence-based decisions about economic policy that can be drawn from this crisis.The unprecedented discontinuous collapse in employment that began in March 2020 challenges analyses of labor demand in normal economic times. The project will explore non-linear patterns in firm employment responses in the crisis and to extract signals of recovery from changes in postings in the summer and fall of 2020. Using difference-in-difference methodologies, the project will examine the effect of state or local government policies and of firm human resource policies on changes in postings for different occupations. Using daily data, the project will apply high frequency econometric and statistical techniques to develop indices of labor demand. The project will complement supply side unemployment claimant reports by constructs on labor demand using high frequency near real time data and by differentiating on firm, occupation, industry and area.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.