This present work investigates the spatial distribution of the unemployment rate in Spain, one of the European countries with the highest recorded level of unemployment. The unemployment rate reached 16% during the pandemic. For this purpose, data from 2020 were used, such as the growth rate of the number of companies and entrepreneurs, the percentage of the workforce employed in the industrial sector, and the percentage of young people aged 16–25 years. All of these data were collected at the provincial level. The importance of spatiality in unemployment estimation is proven using regressions estimated in Geoda and GeodaSpace. The results support the introduction of the lag factor in regressions, improving the performance of the OLS model. However, the use of error models was found to be inefficient. Moreover, creating local estimates of coefficients can effectively adapt to the unique spatial characteristics of Spanish provinces. The research focuses on the sustainability challenges linked to regional inequalities in unemployment. It argues that these inequalities disrupt the balanced distribution of economic activities and hinder the achievement of long-term sustainable development across Spain’s regions. Resolving these inequalities is crucial for promoting regional competitiveness and overall economic growth.
Read full abstract