A spatial analysis on Italian unemployment differences

Abstract

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.

On Italian Households’ Economic Inadequacy Using Quali-Quantitative Measures

“…Specifically, we observe some overlap in the distribution of poverty risks, with higher values in southern Italy than in northern Italy. The southern areas are characterised by the highest unemployment risk, the lowest gross domestic product (GDP) per capita, the lowest ratio of enterprises per inhabitant, the lowest municipal expenditures on social assistance, and the lowest allotment of public services for children (e.g., the nursery schools that represent an important service in the implementation of policies aimed at the reconciliation of home and work commitments), to name a few (e.g., D'Antonio and Scarlato 2008; Cracolici et al 2009;Fabrizi et al 2014;. These factors also explain why foreign citizens who enter in the country choose to locate their residence in northern Italy rather than in southern Italy (see Marra et al 2012).…”

Geographic distribution of livestock products in China- an application of spatial autocorrelation analysis

“…Although spatial autocorrelation was defined decades ago, its application has been limited by spatial datasets availability and computation capacity (Saizen et al, 2010;Zhang et al, 2011). Current studies on spatial autocorrelation for spatial dependence and heterogeneity in either global or local scales have focused on spatial econometrics (Anselin and Rey 1997;Ping et al, 2004;Cracolici et al, 2009). As for all types of statistical analysis, the choice of a specific analytical technique must be based on a consideration of the characteristics of the data and on prior knowledge of the observations.…”

Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis

“…The determinants of unemployment have been studied extensively and are not the focus of this study [20][21][22][23]. Most of these studies treat regions as homogeneous and cross-sectionally independent [11]. As a result, spatial correlation or spatial heterogeneity is not taken into account.…”

“…The study of regional unemployment attempts to explain the differences among geographical areas in terms of unemployment rates, by focusing mainly (a) on the persistence of unemployment differentials and (b) on the creation of models that investigate its determinants [11,12]. There are three main characteristics that describe regional unemployment.…”