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

该研究项目将使用匿名的真实的时间手机位置数据，结合其他数据来源，调查COVID 19大流行对就业的影响。 该研究将在颗粒层面上开发一个创新的就业破坏和创造就业的理论模型，以应对大流行病，并使用收集的数据来估计模型。 该模型不仅考虑到不同地点的就业机会的破坏和创造，而且还考虑到所创造的就业机会类型的变化以及不同地点创造就业机会的行业的变化。 新模型可能会影响研究人员如何调查流行病对各地就业的影响。 研究结果将为如何制定政策以应对就业影响（特别是当前和未来的流行病）以及一般的经济中断提供重要投入。 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导者地位。本研究项目基于MTI现有的高频匿名手机数据，调查COVID 19对就业的破坏和创造影响。 PI将联合收割机将MTI数据与其他数据源（如HERE、QCEW等）结合起来。并使用数据和丁格尔尼曼方法来构建职业构成指数的基础上，所有968职业就业调查（OES），允许远程工作在不同的地点。 PI将制定一个工作破坏和特定地点各种工作类别的工作破坏模型。然后，PI将使用基于构建的数据的索引来估计粒度级别的工作破坏/创造模型。 数据的面板结构允许PI研究经济冲击的短期和长期就业影响。 除了本研究的方法创新外，研究结果还将为应对当前和可能的未来流行病影响的政策提供指导。 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导地位。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.