RAPID: The Impact of COVID-19 on Job Loss and Job Creation

这项研究项目将使用匿名的实时移动电话位置数据，并结合其他数据来源来调查COVID19大流行对就业的影响。这项研究将在颗粒级开发一个创新的就业破坏和创造就业的理论模型，以应对大流行，并使用收集的数据来估计该模型。该模型不仅考虑到不同地点的就业破坏和创造，而且还考虑到创造的就业类型的变化以及在不同地点创造就业的不断变化的行业。新的模型可能会影响研究人员调查流行病对不同地区就业的影响的方式。研究结果将为如何制定政策以应对当前和未来大流行病以及一般经济中断的就业影响提供重要投入。研究结果还将确立美国在了解流行病对就业的影响以及如何制定政策以减少其影响方面的全球领先地位。这项研究项目建立在MTI现有的高频匿名移动电话数据的基础上，以调查COVID19对就业的破坏和创造就业的影响。PI将MTI数据与其他数据源(例如，此处为QCEW等)相结合。并使用数据和Dingell&Amp；Nieman方法构建基于所有968个允许在不同地点远程工作的职业就业调查(OES)的职业构成指数。绩效指标将制定工作破坏模式和特定地点不同工作类别的工作破坏模式。然后，绩效指标将使用基于构建的数据的指数来估计细粒度的就业破坏/创造模型。数据的面板结构使采购经理人能够研究经济冲击对就业的短期和长期影响。除了这项研究的方法创新之外，研究结果还将为应对当前和可能未来大流行的影响的政策提供指导。这一结果还将使美国在了解流行病对就业的影响以及如何制定政策以减少其影响方面成为全球领先者。这一奖项反映了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。

This research project will use anonymized real time cellular phone location data combined with other sources of data to investigate the employment effects of the COVID19 pandemic. The research will develop an innovative theoretical model of job destruction and job creation, at the granular level, in response to the pandemic and use the data assembled to estimate the model. The model does not only account for job destruction and creation at various locations but also changes in the types of jobs created as well as the changing industries in which the jobs are created at the various locations. The new model is likely to influence how researchers investigate the effects of pandemics on employment at various locations. The research results will provide important inputs into how to craft policies to counter the employment effects the current as well as future pandemics particularly, and economic disruptions generally. The results will also establish the US as the global leader in understanding the employment effects of pandemics and how to develop policies to reduce their effects.This research project builds on existing high frequency anonymized cellular telephone data at the MTI to investigate the job destruction and job creation effects of COVID19. The PIs will combine the MTI data with other data sources (e.g. HERE, QCEW, etc.) and use the data and Dingell & Nieman method to construct occupational composition indices based on all 968 Occupational Employment Survey (OES) that allows for teleworking at various locations. The PIs will develop a model of job destruction and job destruction of the various job categories at particular locations. The PIs will then use the indices based on the data constructed to estimate the job destruction/creation model at the granular level. The panel structure of the data allows the PIs to study the short term as well as the long term employment effects of economic shocks. Besides the methodological innovation in this study, the results will also provide guidance on policies to counter the effects of the current and possibly future pandemics. The results will also establish the US as the global leader in understanding the employment effects of pandemics and how to develop policies to reduce its effects.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.