Geospatial Characteristics of Unemployment for the Period 2010–2020 in the South-West Oltenia Development Region, Romania

This chapter aims to analyse the dynamics of unemployment in the South-West Oltenia Region, through comparative approaches at the level of the counties and localities, for the period 2010–2020, and the factors that influenced the level of this indicator. In the current economic context generated by the COVID-19 pandemic, which has affected the activity of many companies and has even led to a restructuring of the labour force, the unemployment rate recorded in Romania is 3.4% in 2020, and the region with the highest rate is South-West Oltenia, with 5.5% (NIS/National Institute of Statistics. Tempo online database. Accessed at http://statistici.insse.ro:8077/tempo-online/ in December 2021, 2021), being one of the weakest regions in the country in economic terms. In the analytical approach, the statistical data used are sourced from the National Institute of Statistics and have been used to create databases in GIS and Excel and will be represented through cartograms and charts. The Pearson coefficient was also used to determine whether there is a correlation between the level of gross domestic product and the unemployment rate within the region. Analysing the dynamics of the unemployment rate, in the period 2010–2020, at the level of South-West Oltenia Region, a significant decrease (about 4 percentage points) was observed, both due to the stabilization of the economy after the 2009 crisis and to the development of new branches in the tertiary sector. Within the study area, discrepancies between the region’s counties were highlighted, with Dolj and Mehedinti counties having the highest unemployment rate due to the accentuation of the demographic aging phenomenon, the decrease in the young population and the low level of education of the population. The present study can make a significant contribution to the literature because three statistical indicators of unemployment (unemployment rate, number of unemployed and their proportion in labour resources) are analysed in correlation with other variables such as GDP, education level of the population, the structure of the total and employed population by age group and economic activity.