Determinants of regional unemployment: some evidence from Austria

Abstract

Badinger H. and Url T. (2002) Determinants of regional unemployment: some evidence from Austria, Reg. Studies 36, 977-988. Using a spatial filtering technique we estimate an empirical model of regional unemployment for a sample of Austrian regions. The spatial filtering technique removes the spatial correlation from the data and allows us to assess the importance of purely spatial effects. We find significant relations between the regional unemployment rate and relative regional wages transaction costs as reflected by the share of public housing, job vacancies, the share of employees in the manufacturing sector and a social assistance variable. Spatial effects account for about one-fifth of the variation in the unemployment rate.Local labour market, Spatial filtering, Austria,

Youth labour market performance in European regions

“…Much empirical evidence also suggests explicit consideration of the spatial autocorrelation of regional labour market performances (e.g., Elhorst 2003;Cracolici et al 2007;Overman and Puga 2002;Molho 1995;Badinger and Url 2002;Niebuhr 2003;Aragon et al 2003;Blanchard and Katz 1992;Decressin and Fatàs 1995). Consistent with this evidence, Table 3 shows positive and significant spatial autocorrelations for youth unemployment rates in both samples.…”

The evolution of the commuting network in Germany: Spatial and connectivity patterns

“…Moreover, distance has already been shown to lead to greatly diminished labor market interactions, when over a certain threshold (e.g. Badinger and Url 2002), and accessibility is also seen as a possible source of spatial dependence (Anselin and Florax 1995). In this framework, the value added of network analysis is that its set of analytical tools supports an intuitive inspection of commuting-related topology and accessibility.…”

Analysing Local Employment and Unemployment in Greece Under Conventional Zoning Regimes and Partitions Extracted from the Data

“…This is usually achieved via an iterative process (algorithm) that takes into account residence-and workplace-based self-containment criteria (e.g., Coombes et al, 1986;Casado-Díaz, 2000;van der Laan and Schalke, 2001;Poper, 2005); -spatial dependence by incorporating in the regression measures of spatial contiguity or proximity in order to estimate non-spatial parameters that are corrected for spatial-autocorrelation. This is usually achieved with the use of a weight matrix the elements of which capture contiguity, straight-line distances or travel times among the population centres of the localities involved (e.g., Molho, 1995;Badinger and Url, 2002;Elhortst and Zeilstra, 2005;Pattacchini and Zenou, 2007).…”

“…For instance, with regard to (a), the obvious next step would be to regress employment, unemployment or workforce participation data collected at fairly disaggregated levels using functional spatial regressors. With regard to (b), the path may have already been laid out by Badinger and Url's (2002) discovery of spatial patterns in the residuals of a specification lacking spatial regressors, 2 and the suggestion of constructing spatial regressors fashioned after these patterns (preferably orthogonal towards other regressors) and introducing them into the expression. The coefficients associated with them are obviously interpreted in the same manner as those corresponding to conventional territorial regressors in typical econometric analyses, i.e., as estimates of the level of dissimilarity of the areas in question from the intercept.…”