Spatial Patterns of German Labor Market: Panel Data Analysis of Regional Unemployment

Abstract

This chapter is devoted to the investigation of spatial spillover effects of the regional unemployment in Germany. Due to historical reasons, the differences between eastern and western regions of Germany persist over time. We explore the differences in the determinants of the regional unemployment as well as the differences in spatial effects by estimating spatial models. We use panel data for 407 out of 413 German regions (using the NUTS III regional structure) for 2001 through 2009. In order to account for possible spatial interactions between regions, we use a spatial weighting matrix of inverse distances. We estimate static and dynamic models by the maximum likelihood estimation approach, developed by Anselin (Spatial econometrics: Methods and models, Berlin: Springer, 1988) specifically for spatial models and elaborated by Lee and Yu (Journal of Econometrics, 154, 165-185, 2010a; Regional Science and Urban Economics, 40, 255-271, 2010b). We reveal that the unemployment in western regions is more of disequilibrium nature, while the unemployment in eastern regions is more of equilibrium nature. Using System GMM approach, we estimate the extended specification of the dynamic model and find that the unemployment in eastern regions affects both the unemployment in western and eastern regions of Germany, whereas the unemployment in western regions has an impact only on other western regions. Keywords Germany • Regional unemployment • Spatial panel data analysis JEL classification C21, C23, R1 IntroductionThere exist numerous macroeconomic approaches which explain the severity of unemployment at the national level. However, disparities in unemployment are not only observed among the countries but also among the regions within the same country. Treating unemployment on a more detailed level might lead to more reliable results. Moreover, the reduction of unemployment diversities between regions leads to desirable outcomes such as higher national product and lower inflation (Taylor 1996). The country benefits from more equal regional unemployment rates also because the reduction of disparities "lessens the adverse effect related to geographical concentrations of high unemployment and counteracts the downward spiral effect of economically depressed regions" (Elhorst 2003). Therefore, the issue of persistent regional inequalities between the local labor markets is widely investigated (e.g., Boeri 2000;Overman and Puga 2002;Bornhorst and Commander 2006;Huber 2007;Ferragina and Pastore 2008) as well as the issue of regional unemployment rates (see among others Bornhorst and Commander 2006;Newell and Pastore 2006;Jurajda and Terrell 2009;Marelli et al. 2012).Decisions of labor market participants are not restricted by regional borders. Those in search of jobs also consider possibilities to move to other regions. On the other hand, firms' decisions on the location are dependent on situations in local labor markets. Bronars and Jansen (1987) established that a one-period shock in the local labor market has an imp...

Changes in the labour market during the COVID-19 pandemic and their spatial interactions – evidence from monthly data for Polish LAU

“…They showed a significant spatial dependence that has been growing over time and characterized by a low distance decay. Similarly, Semerikova (2015) presented a study of spatial spillover effects of the regional unemployment in Germany at the NUTS 3 level, using both spatial crosssectional and spatial-tempral econometric models. While, Kivi (2019) presented modelling with the use of spatial econometrics tools for the relationship between all regional labour markets in the European Union at the NUTS 2 level.…”

Spatio‑temporal Modelling of the Influence of the Number of Business Entities in Selected Urban Centres on Unemployment in the Kujawsko‑Pomorskie Voivodeship

“…The labour market is a heterogeneous category in different aspects of the analysis (Tokarski, 2005;Perugini, Signorelli, 2007;Gawrycka, Szymczak, 2010;Szulc, 2011;Bal-Domańska, 2014;Nosek, Netrdova, 2014;Pillet et al, 2014;Semerikova, 2015;Vega, Elhorst, 2014;Blinova, Markow, Rusanovskiy, 2016). In particular, in addition to variability in time, the labour market situation is characterised by spatial differentiation occurring at different levels of data aggregation.…”