Do “good neighbors” enhance regional performances in including disabled people in the labour market? A spatial Markov chain approach

MPRA Paper from University Library of Munich, Germany

Abstract: The purpose of this study is to examine whether the performance of regions in providing employment of disabled people according to Law 68/99 can be affected by the performance of neighbouring regions. Hence, we propose a two-step analysis focusing on the Italian regions for the years 2000-2009. In the first step, we verify by means of the Stochastic Frontier Approach that the regions of Central and Northern Italy are more efficient in the matching process between demand and supply of jobs for disabled people than the regions of Southern Italy. Then, the efficiency results are analyzed using a Markov Spatial Transition Matrix in order to provide insights into the transitions of regions between efficiency levels, taking their local context into account. The results of this analysis show that good neighbors are important in promoting the improvement of the performance of the regions. However, the effects produced by bad neighbors should not be underestimated, especially when they are concentrated in an area of the country and have a time-space persistence. The effect of a persistent dualism on the performance of the regions with respect to the application of Law 68/99 is a problem that must be seriously considered by policy makers; especially when the regions with a low efficiency score are surrounded by neighbors with poor efficiency score and show an unhealthy poorly performing labour market.

Keywords: Disabled People; Public Policy; Non-Labour Market Discrimination; Stochastic Frontier Approach; Spatial Markov Chain. (search for similar items in EconPapers)
JEL-codes: C23 C61 J14 J48 (search for similar items in EconPapers)
Date: 2013-05
New Economics Papers: this item is included in nep-geo and nep-ure
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Journal Article: Do “good neighbors” enhance regional performances in including disabled people in the labor market? A spatial Markov chain approach (2014)
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