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The Annals of Regional Science

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01-08-2014 | Original Paper

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

Author: M. Agovino

Published in: The Annals of Regional Science | Issue 1/2014

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Abstract

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

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In the Appendix 1, we report some information on the Law 68/99.

These studies were conducted using the nonparametric approach to efficiency measurement, represented by DEA (Data Envelopment Analysis).

In this paper, we consider the production of a service by the region. In particular, we refer to the service related to the implementation of Law 68/99.

Our period of analysis consists of nine years, so we have \(T=8\) annual transitions.

For reasons of space, we do not report the results of the transition matrix not conditioned; interested readers can request them to the author.

We describe more in detail the five states in Sect. 5.

The temporal dimension of our database is 9 years instead of 10 years, because ISFOL does not provide the percentage of disabled workers for the year 2002.

More specifically, the areas of the macro-branch “Government, Ministry of Education, Department of Health” in which people with disabilities are employed are the following ones: public administration services, education, health and social services, other public, social and personal services, recreational, cultural and sporting activities, other services.

We report only the information for the years 2005 and 2008 because ISFOL does not provide further information for the other years considered in our analysis.

We do not consider construction, financial brokerage and business services, and other service sectors, because they do not hire many disabled people.

We construct this indicator by using regional employment rates (see Cracolici et al. 2007).

We construct this indicator by using the regional employment rates (see Cracolici et al. 2007).

We consider the ratio between female labor force over total females at working age and male labor force over total males at working age (see Cracolici et al. 2007).

We consider the ratio between the population over 65 years and the population of 15–29 years (see Cracolici et al. 2007).

The difference between the maximum and the minimum value.

Table 1

Statistical summary

Variable		Mean	SD	Min	Max	Observations
EDP	Overall	8.110	4.097	0.487	34.619	N \(=\) 180
	Between		2.914	2.962	13.732	\(n =\) 20
	Within		2.945	1.528	30.475	\(T =\) 9
ERIS	Overall	12.594	5.898	3.974	23.738	\(N =\) 180
	Between		6.014	4.375	22.423	\(n =\) 20
	Within		0.481	10.387	14.127	\(T=\) 9
ERAS	Overall	3.264	1.415	1.092	6.986	\(N =\) 180
	Between		1.421	1.193	6.433	\(n=\) 20
	Within		0.273	2.358	4.221	\(T=\) 9
ERSS	Overall	41.941	7.398	29.726	56.113	\(N=\) 180
	Between		7.417	30.815	54.212	\(n = 20\)
	Within		1.479	37.045	45.228	\(T =\) 9
Government, ministry of education	Overall	12.574	1.899	9.595	18.360	\(N=\) 140
	Between		1.922	9.805	17.363	\(n=\) 20
	Within		0.266	11.423	13.571	\(T=\) 7
Trade	Overall	9.068	1.597	6.428	11.497	\(N=\) 140
	Between		1.609	6.721	11.258	\(n =\) 20
	Within		0.273	8.386	9.751	\(T=\) 7
Transport and communication	Overall	3.039	0.797	1.760	5.408	\(N =\) 140
	Between		0.809	1.863	5.108	\(n=\) 20
	Within		0.093	2.763	3.402	\(T=\) 7
FM	Overall	0.667	0.087	0.490	0.818	\(N =\) 180
	Between		0.084	0.522	0.777	\(n=\) 20
	Within		0.030	0.550	0.740	\(T=\) 9
OY	Overall	0.621	0.161	0.316	1.124	\(N =\) 180
	Between		0.146	0.360	0.949	\(n=\) 20
	Within		0.074	0.447	0.837	\(T=\) 9
EC1	Overall	0.210	0.008	0.181	0.232	\(N=\) 180
	Between		0.004	0.202	0.220	\(n=\) 20
	Within		0.006	0.189	0.226	\(T=\) 9
EC2	Overall	0.509	0.014	0.467	0.550	\(N=\) 180
	Between		0.004	0.497	0.519	\(n=\) 20
	Within		0.013	0.478	0.543	\(T=\) 9

EDP, percentage of employed disabled people; ERIS, total employment rate of industry sector; ERAS, total employment rate of agriculture sector; ERSS, total employment rate of service sector; Government, Ministry of Education, employment rate of public services sector; Trade, employment rates of trade sector; Transport and Communication, employment rates of transport and communication sector; FM, ratio of female-to-male labor force; OY, ratio between old and young population; EC1, structural disequilibrium variable; EC2, short-time disequilibrium variable

In Appendix 2 we report the motivation of the sign for variable YO.

For an analysis of the negative sign of the FM see Appendix 2 used for variable YO.

The Moran scatter plot provides a tool for visual exploration of spatial autocorrelation (Anselin 1996, 2002). The four different quadrants of the scatterplot identify four types of local spatial association between a region and its neighbors:

(HH) a region with a high efficiency score surrounded by neighbors with high efficiency scores (quadrant I);
(LH) a region with a low efficiency score surrounded by neighbors with high efficiency scores (quadrant II);
(LL) a region with a low efficiency score surrounded by neighbors with low efficiency scores (quadrant III);
(HL) a region with a high efficiency score surrounded by neighbors with low efficiency scores (quadrant IV).

Quadrants I and III represent positive spatial dependence, while quadrants II and IV represent negative spatial dependence (Rey and Montouri (1999)).

We obtain the same results by using a rook and a bishop contiguity matrix.

The null hypothesis of the Moran’s I test is spatial independence. According to the results, we reject the null hypothesis at the 1 % level and we conclude that the annual average of regions’ efficiency scores presents spatial autocorrelation.

The Moran’s I test, implemented on efficiency scores for each year analyzed, always rejects the null hypothesis of spatial independence. For reasons of space we do not show these results, but interested readers can request them to the author.

We obtain the same results for the efficiency scores of the second model. For reasons of space, we do not show them, but interested readers can request them to the author.

We do not have nine possible transitions because data for the year 2002 are not available.

With \(n\) regions, \(K\) states and \(t\) years, there are \((t-1)*K*n\) possible cases of transitions.

The spatial lag is the average efficiency score of neighboring regions. The spatial lag is a weighted average, where the weights are represented by the elements of the contiguity matrix.

For more details on the ergodic distribution concept, see Rey (2001) and Le Gallo (2004).

In this case, we will have a probability and not an index because the ratio between numerator and denominator is equal to the ratio of the number of actual occurrences to the total number of possible occurrences.

The same decomposition is valid for variable FM (see Cracolici et al. 2007).

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Title: Do “good neighbors” enhance regional performances in including disabled people in the labor market? A spatial Markov chain approach
Author: M. Agovino
Publication date: 01-08-2014
Publisher: Springer Berlin Heidelberg
Published in: The Annals of Regional Science / Issue 1/2014
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-014-0619-z

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