Migration and Regional Unemployment in Italy

Basile, Roberto; Girardi, Alessandro; Mantuano, Marianna

doi:10.2174/1874942901205010001

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…Over the period from 2002 to 2014, thus, the distribution of real GVA per employee has become more unequal, despite a steady increas in within-as well as between movements. 2 On unemployment, Basile et al (2012) detect a negative relationship between net-migration and unemployment rates for Italian NUTS 3 regions, a result which is also confirmed in a recent investigation that additionally accounts for human capital flows that come with migration (Basile et al 2016). In contrast, Niebuhr et al (2012) conclude that migration in fact reduces disparities for German NUTS 3 regions.…”

Section: Introductionsupporting

confidence: 56%

Migration’s inability to alleviate regional disparities: the grass is still greener on the other side of the fence

Fischer

2018

Empirica

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This paper attempts to explain the seeming unresponsiveness of labor to react to economic disparities in terms of migration. In theory, the potential of workers to implicitly alleviate regional disparities in, for example, unemployment or wage levels by relocating appears potent, but finds little support empirically. To resolve this perplexity, a dynamic discrete choice model is used, which translates into a two stage estimation strategy for recovering structural parameters. Investigating Austrian bilateral movements on the NUTS 3 level from 2002 to 2014, the results suggest that this unresponsiveness builds on two pillars. First, estimated average migration costs are in the range of six times the average annual wage, which appears sizable enough to prevent taking advantage of economic opportunities for workers. These costs are shown to have decreased over time, though. Second, the relatively high variation in the random utility shifter can be interpreted as relative unimportance of regional disparities in forming migration decisions. Finally, a spatial approach on estimated regional valuations reveals an apparent 'beauty contest' of regions, where regions' own valuations suffer from proximity to highly attractive ones.

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Section: Introductionsupporting

confidence: 56%

Migration’s inability to alleviate regional disparities: the grass is still greener on the other side of the fence

Fischer

2018

Empirica

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“…models is a preliminary decomposition of spatial objects into several parts for which there may be a different spatial dependence. For example, Europe is often divided into east and west (see, for example, Basile, 2010), core and periphery (Basile, 2012). Regions of the same country are often also divided into parts, in particular, Germany into the eastern and western parts (Fuchs-Schundeln and Izem , 2012;Lottmann, 2012), Italy into the northern and southern parts (Basile, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spatial effects for the eastern and western regions of Russia: a comparative analysis

Demidova

2015

IJEPEE

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The purpose of this study is to identify the spatial effects of the main macroeconomic indicators of the eastern and western regions of Russia. These regions differ significantly in population density and the distances between cities. The main research question we are interested in is the following: how are events occurring in one of the western (or eastern) region, such as economic growth or a decrease in the unemployment rate, effecting similar indicators in other western and eastern regions. The spatial effects of the western and eastern regions, when considered separately, may differ both qualitatively and with of the 'flow on effect'. The determinants of the same macroeconomic indicators in the eastern and western regions may also differ. In order to test the hypothesis of a possible difference in the spatial effects and determinants for these regions, we have developed a special class of model with four spatial matrices (west-west, east-east, west-east, and east-west) and a double set of control variables (one for each type of region). As the macroeconomic indicators monitor the rate of unemployment in the region, the real regional wage and GRP growth for the year were chosen for our models. We controlled the variables describing the socio-demographic situation in the region, migration processes, economic development, and export-import activity in the region. The models were estimated by the Arellano-Bond method on panel data for Russian regions over 2000-2010. Our analysis revealed, 1) a positive spatial correlation of the main macroeconomic indicators for the western regions, 2) both positive and negative externalities for the eastern regions and 3) the asymmetric influence of eastern and western regions on each other. Usually "impulses" from the western regions have a positive effect on the eastern regions, but the "impulses" from the eastern regions usually do not affect the western regions.

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“…Evidence of notable disparities between regions in countries such as Norway, Israel, China, Japan (Portnov, 1999) and Italy (Fratesi and Percoco, 2014) has encouraged several scholars to study the relationship between regional levels of development and internal migration at an inter-regional level (inter-State for the USA) (Portnov, 1999;Chen and Rosenthal, 2008;Fielding, 2014;Fratesi and Percoco, 2014;Yang et al, 2015;Basile et al, 2012).…”

mentioning

confidence: 99%