灵活性(工程)
2019年冠状病毒病(COVID-19)
业务
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
2019-20冠状病毒爆发
失业
芯(光纤)
劳动经济学
创造就业机会
经济
计算机科学
管理
失业
病毒学
病理
传染病(医学专业)
疾病
爆发
生物
电信
医学
经济增长
作者
Murillo Campello,Gaurav Kankanhalli,Pradeep Muthukrishnan
标识
DOI:10.1017/s0022109023000522
摘要
Abstract Big data on job postings reveal multiple facets of the impact of COVID-19 on corporate hiring. Firms disproportionately cut new hiring for high-skill positions, with financially constrained firms reducing skilled hiring the most. Applying machine learning methods to job-ad texts, we find that firms have skewed their hiring toward operationally-core functions. New positions display greater flexibility regarding schedules and tasks. While job posting levels show signs of recovery starting in late-2020, changes to job descriptions and skill profiles persist through early-2022. Financial constraints amplify these changes, with constrained firms’ new hires witnessing greater adjustments to job roles and employment arrangements.
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