Digitalization and Unemployment During the Covid-19 Pandemic in Selected Countries of Europe and Commonwealth of Independent States

Abstract

This study focuses on unemployment in selected European and CIS countries in 2019-2020 and analyses the determinants of unemployment rates, focusing on the impact of digitalization. The paper measures the impact of macroeconomic determinants, such as GDP growth rate, inflation rate, population growth rate, labour productivity, foreign direct investment, exports of goods and services, external debt, and the level of countries’ digitalization defined as the Global Connectivity Index, on unemployment. The estimation is done by relying on the panel data of 33 European and CIS countries and employing Pooled OLS, Fixed Effects, and Random Effects regression. Two main and two additional econometric models are tested. Obtained results for two main models demonstrate that Exports of goods and services, Labour Productivity, Global Connectivity Index and Interaction term of COVID-dummy and the GCI, and Exports of goods and services, Average of The Four Technology Enablers and Interaction term of COVID-dummy and The Four Technology Enablers are the significant determinants of unemployment rates in 33 European and CIS countries in 2019-2020. Two more detailed models are also tested for robustness check. Obtained results for the additional model with detalization of The Four Pillars of the GCI demonstrate that Labour productivity and Demand-indicator are statistically significant. The results for the additional model with detalization of The Four Technology Enablers demonstrate that Inflation rate, Foreign direct investment, COVID-dummy, Cloud-indicator, and Interaction Term COVID-dummy and Cloud- indicator are the significant determinants of unemployment rates in 33 European and CIS countries in 2019-2020. Since the number of observations is relatively small the results of the research are supposed to be considered with consciousness; furthermore, it should be also admitted that there could be some omitted variable bias. To handle at least some of the issues with endogeneity, three estimation methods are used (Pooled OLS, Fixed Effects, and Random Effects) and results are compared. Based on the results of the analysis some policy implications are presented.