Abstract

In addition to direct negative effects in terms of morbidity and mortality, the pandemic caused by SARS-CoV-2 also has indirect negative effects that concerned, among others, the labour market. This study analysed changes in the unemployment rate that were observed at the level of Polish Local Administrative Units (LAU) during the ten months of the pandemic. Both annual and monthly data were applied. Using cross-sectional and panel econometric modelling with spatial interactions it was shown that the observed increase in unemployment was strongly influenced by the share of employment in services, especially in less knowledge-intensive services such as: trade, accommodation and gastronomy. Moreover, it turns out that a higher share of women working in services was associated with a higher increase in unemployment than in the case of men working in services. Significant positive spatial relationships between local labour markets in LAUs were also identified. It was also shown that both the timing and severity of containment measures were significant. The strongest effect of the lockdown was observed three months after its introduction, while after six months the effect was significantly smaller. The study's findings may be important for post-pandemic recovery plans.

Highlights

  • The COVID-19 pandemic is not over yet, and it is not known when it will end, it is already noticeable that it caused one of the greatest crises in modern history

  • Just like most countries affected by the COVID-19 pandemic, Poland has experienced an increase in the unemployment rate

  • It turns out that there were units where there was a slight decrease in the unemployment rate, but it was only units, while for units the increase was higher than 2 percentage points

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Summary

Introduction

The COVID-19 pandemic is not over yet, and it is not known when it will end, it is already noticeable that it caused one of the greatest crises in modern history. Spatial error autocorrelation (SEM model) – random factors from neighbouring locations influenced the change in the unemployment rate in a given unit,

Results
Conclusion
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