Rescuing a `Sick' Labour Market: Using Online Vacancy Data to Track COVID-19's Economic Impact.

Lead Research Organisation: University of Warwick
Department Name: Economics


The outbreak of COVID-19 pandemic is likely to cause the worst recession the world economy has experienced since the Great Depression. Millions of people have already lost their jobs, and the functioning of the labour market has been profoundly disrupted by social distancing measures. In this context, it is fundamental to quantify the impact of the pandemic on job creation. This project will use a unique data set of daily online job postings to provide answers to key questions: which firms and sectors are expanding or contracting during the pandemic? Which jobs are being demanded? What skills and tasks are required in these jobs and how are work activities being delivered? How fast will the dynamics of job creation change as lockdown measures are eased? To answer these questions, our project will carry out an articulated analysis, employing multiple econometric techniques.

Firstly, we will provide a detailed descriptive analysis on the evolution of job creation across occupations, sectors, and regions in order to deliver essential insights on the economic consequences of the pandemic, including the crucial distributive impacts across regions and types of jobs.

Second, we will make use of advanced techniques in text analysis to study the wording of job postings in order to shed light on whether and how the structure of jobs changes as a result of the COVID-19 shock. In light of the intensity of the COVID-19 induced economic disruption, we may expect to see persistent structural changes to the design of work activities and the remuneration patterns associated with different jobs. The granular and high frequency data that we will employ will allow us to comprehensively assess the occurrence and importance of such changes.

Finally, we plan to identify the firm-level characteristics that play a crucial role in ensuring firms' production continuity, and labour demand resilience. Among other factors, the degree of automated work may be crucial to ensure firms' production continuity under lockdown restrictions. For example, robots assembling product components or production processes that are compatible with remote work may allow firms to remain more active while social distancing measures are in place.

Coupled with a detailed analysis on the skills demanded, the study will be provide essential inputs for the design and roll-out of targeted interventions that support the most severely affected areas, jobs and industries. These inputs will also be useful for the informing longer run investment decisions on skill training programs and government assistance.


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