Explaining the pattern of regional unemployment: The case of the Midi Pyrénées region

Aragon, Yves; Haughton, Dominique; Haughton, Jonathan; Leconte, Eve; Malin, Eric; Ruiz‐Gazen, Anne; Thomas‐Agnan, Christine

doi:10.1111/j.1435-5597.2003.tb00009.x

Cited by 14 publications

(27 citation statements)

References 29 publications

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“…He finds that unemployment is more of disequilibrium nature. Cracolici et al (2007) explores spatial dependence of unemployment in Italy, claiming that the best model contains spatially lagged dependent variable, which is in line with the Aragon's conclusion for French regions (Aragon et al 2003). Cracolici finds that unemployment in Italy is more driven by the disequilibrium factors rather than by equilibrium ones; hence, unemployment disparities are mainly caused by labor demand factors.…”

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confidence: 77%

“…. 41 opportunity costs (Aragon et al 2003). Therefore, the adjustment mechanism works better when there are more young people in the labor force.…”

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confidence: 97%

“…They are also better informed about the economic conditions in the regions and thus are more willing to migrate. Hence, better education levels result in faster adjustment of the labor market (Aragon et al 2003). Thus, regions with a big share of higher-educated people experience lower rates of unemployment.…”

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confidence: 99%

“…The speed of adjustment can be affected both by labor supply and labor demand functions. Particularly, equalizing patterns are determined by the flexibility of wages, households' decisions on migration and labor force participation, and businesses locations (Aragon et al 2003). The equilibrium view assumes that external shocks or economic disturbances in the labor market affect the unemployment rate for a short period of time, allowing it to converge back to its mean value.…”

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confidence: 99%

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

Semerikova

2015

Geographical Labor Market Imbalances

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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...

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mentioning

confidence: 77%

“…. 41 opportunity costs (Aragon et al 2003). Therefore, the adjustment mechanism works better when there are more young people in the labor force.…”

mentioning

confidence: 97%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Semerikova

2015

Geographical Labor Market Imbalances

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“…There are only a very few analyses using spatial data and spatial parametric tools (see Molho 1995;Aragon et al 2003;Niebuhr 2003;López-Bazo et al 2002).…”

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confidence: 99%

A spatial analysis on Italian unemployment differences

2008

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Using spatial econometric models, this paper focuses attention on the spatial structure of provincial unemployment disparities of Italian provinces for the year 2003. On the basis of findings from the economic literature and of the available socio-economic data, various model specifications including supply-and demandside variables are tested. Further we use ESDA analysis as equivalent to integration analysis on time series; therefore it is applied on each variable, dependent and independent, involved in the statistical model. The suggestions of ESDA lead us to the most adequate statistical model, which estimates indicate that there is a significant degree of neighbouring effect (i.e. positive spatial correlation) among labour markets at the provincial level in Italy; this effect is present notwithstanding we controlled for local characteristics. The unemployment shows a polarized spatial pattern that is strongly connected to labour demand and to a much lesser extent to the share of young population and economic structural composition.

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