Regional Labour Markets as a Result of the Impact of the COVID-19 Pandemic—A Polish-German Borderland Case Study

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Abstract

The COVID-19 pandemic, as an external factor, quite strongly disrupted the existing trends in the development of unemployment in the Polish-German borderland. The objective of the article is to analyse the resilience of Polish and German Local Administrative Units (LAU) to the COVID-19 pandemic. The authors hypothesised that large differences in unemployment rates would turn out to exist while comparing the Polish and German sides. The study uses the counterfactual before-after comparison method. The novelty in this approach to the study of this hypothetical rate of unemployment involves filling the gap in the literature regarding research of contact regions, while also developing the existing approaches in the research method used. To assess the differences between the observed and the hypothetical unemployment rate, the ATE index was used, which evaluates the total average impact of a pandemic expressed as the difference between the mean values of the outcome variables in the event of a pandemic and a counterfactual situation (Svabova et al. in J Econ Econ Policy 16(2):261–284, 2021). The hypothetical counterfactual value of the outcome variable can be estimated with various methods. In this study, due to the nature of the available data, we decided to use the SARIMA model (Hyndman and Athanasopoulos in Forecasting: principles and practice, 2018), which enables us to model both the trend and the seasonal effects. The results of the survey indicate that the unemployment rate in the Polish-German borderland area has risen. There are some major differences in how the pandemic has impacted the labour market. Considering the counterfactual approach used, this difference can be described as the impact of the COVID-19 pandemic.

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APA

Łaźniewska, E., Górecki, T., & Płac, K. (2022). Regional Labour Markets as a Result of the Impact of the COVID-19 Pandemic—A Polish-German Borderland Case Study. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 335–349). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-10190-8_23

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