Pavlína Netrdová (p), Vojtěch Nosek

The regional and spatial variability of socio-economic indicators, their determinants, and evolution in time, are one of the biggest research topics in human geography. In this study, we focus on unemployment as a complex indicator of socio-economic performance of regions and we examine the relative influence of individual and spatial/regional factors in determining the risk of unemployment. The main research questions of this study are: to what extent does unemployment in Czechia vary based not only on personal characteristics of individuals, but also according to specific areas (e. g. municipalities, Regional Labour Markets) in which they live? What is the role of “space” in determining the risk of unemployment?
To answer these questions, we use multilevel regression approach, which provides an appropriate and comprehensive framework for analysing individual and contextual influences on the risk of unemployment. It enables us to analyse data with nested or clustered organization. This is typical for geographical data, which are hierarchical or multilevel in nature – people are nested in localities such as municipalities, municipalities are nested in regions etc. The multilevel analysis overcomes disparities between models constructed on micro-level and macro-level. At the same time, multilevel modelling deals with the basic problem every geographical research is facing, i.e. the choice of appropriate hierarchical scale of analysis.
Using data from 2011 Czech census, the individual and regional determinants of unemployment among population at working age are studied. To reveal the real and long term contextual effects we work only with population living in the same region where they were born. All undergoing analyses are two-level with one individual (micro) level and one regional (macro) level. The regional levels differ in respective analysis (municipal level, NUTS3 level, NUTS4 level), thus by comparing the results, some conclusions can be made regarding the significance of different regional levels in determining the risk of unemployment. The individual data include the information about gender, age, education, marital status etc.; the regional data are partly aggregate from individual data (unemployment rate, demographical and educational structure) and partly gained from official statistics (e.g. GDP per capita).

Angel Martin-Roman, Jaime Cuellar (p), Alfonso Moral (p)

The differences in the regional unemployment rates, as well as their formation mechanism and persistence, have given rise to a number of papers both at a national and international level. This work contributes to that strand of literature from two different perspectives. In the first part of our work, we follow the methodological proposal established by Hofler and Murphy (1989) and Aysun et al. (2014). We make use of an estimation of a stochastic cost frontier to breakdown the effective unemployment in two different components: first one associated with aggregate supply side factors, and the other one more related to the aggregate demand side factors. The second part of our research analyzes the existence of spatial dependence patterns among the Spanish provinces in the effective unemployment and in both above mentioned components. The decomposition performed in the first part of our research will let us know the margin that the policymakers have when they deal with unemployment reductions by means of aggregate supply and aggregate demand policies. Finally, the spatial analysis of the unemployment rates amongst the Spanish provinces can potentially have also significant implications from an economic policy viewpoint since we find that there are common formation patterns or clusters of unemployment at the provincial (NUTS-3) level in Spain.

Tatiana Blinova (p), Victor Rusanovskiy, Vladimir Markov

Significant interregional differences in unemployment rates are the main feature of the Russian labour market. Therefore the analysis of the youth labour market based only on the Russian averages conceals the acuteness of the problem hiding an almost tenfold gap in unemployment rates between regions. When the interregional differentiation of the youth labour market is excessive the common economic space disintegrates and the efficiency of the universal instruments and methods of the labour market regulation decreases. It should be noted that in Russia the interregional differences in youth unemployment rates are stable over time. The paper presents the results of modeling the degree and the dynamics of the interregional differences in the youth unemployment rates in Russia. We decompose the interregional differentiation of the youth unemployment rate into “within-groups” differences and “between-groups” differences. We analyze the dynamics of the within- and between- groups differences in the rates of youth unemployment and estimate their contribution to changes in the interregional differentiation of youth unemployment. Additionally we estimate the degree and the dynamics of the interregional differences of the youth labour market in Russia in the time of crisis. The results of the analysis show the reduction in the interregional differences in unemployment rates between 2005 and 2008, while in 2009-2013 the interregional differentiation of the youth labour market increased. The statistical database for this study was the Rosstat data posted on the official website of the Federal State Statistics Service. We found that the socio-economic effects of youth unemployment, as well as the behavioral response to economic shocks in the age groups of 15-19 and 20-29 years were significantly different. Additionally the study contributes to the evaluation of the impact of changing economic conditions on the interregional differentiation of the youth labour market. The report is based on the paper “Empirical Study of Spatial Differentiation of Youth Unemployment in Russia”. Acta Oeconomica, 2016, Vol.66 (3). The study was conducted at the Institute of Agrarian Problems of RAS with the financial support from the Russian Scientific Foundation (RSF), project # 17-18-01319.