International Entrepreneurship Review (Mar 2024)
Unveiling the dynamic job-matching landscape in the post-COVID-19 labour market through the lens of the Beveridge curve
Abstract
Objective: The objective of the article is the identification and assessment of labour market operation efficiency and stability of matching mechanisms from the microstructure perspective of labour market dynamics via the relationship between distortions in the level and slope of the Beveridge curve. We studied the US labour market dynamics and identified potential structural changes in the job matching mechanisms. The long-term relationship between the level and slope of the Beveridge curve suggested that the underlying level of unemployment matched the labour market’s elasticity in response to changes in job vacancies. Research Design & Methods: The main design of our article was a quantitative analysis of the Beveridge curve shifts. We used a data set consisting of monthly information on job vacancies and the unemployment rate of the US labour market from 2000 to 2022. Using a dynamic rolling scheme with a fixed window size, we extracted time series parameters of the Beveridge curve. We then proceed to apply the VECM(1) model to establish the relationship between these parameters. Findings: We have found the stability of the matching mechanism in the long term and the relatively weaker or instantaneous adjustment mechanisms in the short term. We also addressed the absence of evidence regarding Granger causality and its implications. Implications & Recommendations: The identification of elasticity of the Beveridge curve should guide policymakers towards integrated and adaptive strategies that address both structural and dynamic aspects of the labour market. Regular monitoring and evaluation will be crucial to ensuring that policies remain effective in the evolving economic landscape. Contribution & Value Added: Unlike previous literature on the subject, which primarily relied on static analyses and limited time frames, we took a pioneering step by incorporating monthly data on job vacancies and unemployment rate spanning over two decades, from 2000 to 2022. This inclusion allowed us to encompass the period of the COVID-19 outbreak, providing a comprehensive understanding of the matching mechanism’s behaviour as a cornerstone for entrepreneurship during times of extraordinary economic stress.
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