SPATIAL CLUSTERS OF SELECTED POVERTY INDICATORS IN THE EUROPEAN UNION

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1 SPATIAL CLUSTERS OF SELECTED POVERTY INDICATORS IN THE EUROPEAN UNION Tomáš Želiský, Matúš Kubák Abstract Several poverty measures ca be used i order to aalyse poverty level i the society. All of them have their advatages as well as shortcomigs. The goal of this paper is to aalyse spatial distributio of poverty levels from the viewpoit of several poverty idicators. Differet approaches yield differet results ad hece, poverty levels based o differet idicators will be estimated ad compared (we will focus mostly o moetary poverty, subjective perceptio of poverty ad material deprivatio). Aalyses are based o spatial statistical methods (maily global ad local spatial autocorrelatio coefficiets). The results idicate that all ivestigated variables are positively spatially autocorrelated, i.e. similar values are clustered together. The available data further idicate that regios with high levels of poverty are clustered i the Easter (Romaia ad Bulgaria, ad the regio of Easter Slovakia) ad South-Wester parts of the EU, ad low values are cocetrated i the Norther ad Cetral part of the Europea Uio. Key words: Poverty, deprivatio, spatial statistics. JEL Code: I32, R11, R15. Itroductio Poverty assessmet ca be based o several cocepts. Absolute poverty cocept is based o the determiatio of a level of poverty which is fixed i terms of the welfare idicator used ad is also fixed i time ad for all the idividuals who are cosidered i these comparisos (Ravallio, 1992). The cocept of relative poverty is used frequetly i developed coutries, whereas the cocept of absolute poverty is more commo i less developed coutries. Oe of the ways of determiig the level of poverty is to compare the livig stadard of a idividual with a commo livig stadard i society (Hageaars ad va Praag, 1985), determiig a fixed ratio betwee the level of icome (or aother idicator of poverty ) of a idividual ad media or average icome i the whole society (Ravallio, 1998). Relative poverty refers to the positio of a idividual or household compared with the average icome i the coutry, 1705

2 while absolute poverty refers to the positio of a idividual or household i relatio to a poverty lie whose real value is fixed over time (World Bak, 1993). Regardless of the cocept, idividual poverty level depeds o a set of variables. The aim of this paper is to aalyse to what extet selected poverty measures are spatially clustered i the Europea Uio 1. The developmet of methods for spatial data aalyses allows the applicatio of such methods i various fields icludig social scieces. Durig the last decade a umber of poverty aalyses ivolvig methods for spatial data aalyses have bee published (see e. g. Chattopadhyay, Majumder ad Jama, 2014; Lawso ad Elwood, 2014; Thogdara et al., 2012; Zelisky ad Stakovicova, 2012 etc.). 1 Methodology 1.1 Observatio Uits ad Descriptio of Data The sample icludes data for all available regios at NUTS-2 (i some cases at NUTS-1) level. The followig poverty idicators are used i the aalyses: Disposable per capita icome: the total icome of a household, after tax ad other deductios, that is available for spedig or savig, divided by the umber of household members. Disposable per capita icome is measured i terms of purchasig power stadard based o fial cosumptio per ihabitat. Percetage of the populatio at risk of poverty or social exclusio defied as percetage of persos who are: at risk of poverty or severely materially deprived or livig i households with very low work itesity as a share of the total populatio, expressed i umbers or shares of the populatio, ad its sub-idicators: At-risk-of-poverty rate (after social trasfers) is defied as the share of persos with a equivalised disposable icome below the risk-of-poverty lie, which is set at 60% of the atioal media equivalised disposable icome after social trasfers. The disposable icome is defied as gross icome less icome tax, regular taxes o wealth, compulsory social isurace cotributios, while the gross icome is the total moetary ad o-moetary icome received by the household over a specified 1 Several studies o poverty levels, icomes/wages ad associated pheomea icludig coutries clusterig i the Europea Uio have bee published by the Czech ad Slovak scietists recetly (see e.g. Bartosova ad Forbelska, 2012; Bilkova, 2009; Laghamrova ad Fiala, 2013; Loster ad Pavelka, 2013; Marek, 2013; Megyesiova, Lieskovska ad Baco, 2013; Pivoka ad Loster, 2013; Sipkova, 2013; Stakovicova, Vlacuha ad Ivacikova, 2013). 1706

3 icome referece period. Icome is measured at household level, ad i order to gai equivalised disposable icome, the total disposable icome of a household has to be divided by equivalised household size accordig to the modified OECD scale (givig a weight of 1.0 to the first adult, 0.5 to other persos aged 14 or over ad 0.3 to each child aged less tha 14). Severe material deprivatio rate represets the share of the populatio lackig at least 4 items amog the 9 followig: the household could ot afford: i) to face uexpected expeses; ii) oe week aual holiday away from home; iii) to pay for arrears (mortgage or ret, utility bills or hire purchase istalmets); iv) a meal with meat, chicke or fish every secod day; v) to keep home adequately warm, or could ot afford (eve if wated to): vi) a washig machie; vii) a colour TV; viii) a telephoe; ix) a persoal car. Percetage of populatio livig i households with very low work itesity, i.e. households where workig-age adults (18-59) work less tha 20% of their total work potetial durig the past year. 1.2 Global ad Local Measures of Spatial Autocorrelatio Measures of spatial autocorrelatio quatify the existece of clusters i the spatial arragemet of a give variable, while global ad local versios of almost all measures ca be estimated. (Global) Mora s I is the most kow statistics widely used to test for the presece of spatial depedece i observatios take o a lattice. Uder the ull hypothesis that the data are idepedet ad idetically distributed ormal radom variates (Li, Calder ad Cressie, 2007). Mora (1950) proposed a test statistic to assess the degree of spatial autocorrelatio betwee adjacet locatios: I 0 i1 j1 ij X X X X X i X i1 i 2 j, where (1) X i ij is the variable of iterest, is a idicator such that ij =1 if the i th ad j th locatios are adjacet (defied a priori), ad ij =0 otherwise. 1707

4 Later, Cliff ad Ord (1981) proposed a statistic to test for a more geeral form of spatial depedece: ZWZ I, where (2) ZZ W is the spatial weight matrix. While the global spatial autocorrelatio measure (Mora s I) aalysis yields oe statistics to summarize the patter of poverty i the whole study area, i.e. it assumes homogeeity (Sowumi, 2012). Usig local spatial autocorrelatio measure we ca fid clusters also at a local level eve if there is o global spatial autocorrelatio or o spatial clusterig (Zhag, Mao ad Meg, 2010). Local Mora s I is the best kow local idicator of spatial autocorrelatio. Local Mora s I ca be used to evaluate the clusterig i idividual uits by calculatig Local Mora s I for each spatial uit ad evaluatig the statistical sigificace for each regio (Wag et al., 2012). Local Mora I (Aseli, 1995) is: I i zi m 2 j1 w ij z j, where (3) z i, z j are deviatios from the mea, w ij are the spatial weights betwee observatios i ad j, m 2 is the secod momet give: the variace). 2 z i i m 1 2 (a cosistet, but ot ubiased estimate of All estimatios ad calculatios are performed i R software (R Core Team, 2012) usig package spdep (Bivad et al., 2013). Admiistrative boudaries layer are dowloaded from Eurostat web-site (Eurostat, 2014). Cotiguity-based spatial weight matrix is used i estimatios. 2 Results ad Discussio Accordig to the Mora s I spatial autocorrelatio coefficiet all variables are sigificatly positively spatially autocorrelated, i.e. similar values are clustered together. From Fig. 1 (left) it is obvious that there is a strog degree of positive spatial autocorrelatio with very low levels of icome i the Easter part of the Europea Uio ad sigificatly high level of icome i the cetral part of the Europea Uio. 1708

5 Tab. 1: Mora s I spatial autocorrelatio coefficiet Variable Mora s I p-value Disposable icome < At-risk-of-poverty rate < At-risk-of-poverty rate or social exclusio < Very low work itesity < Severe material deprivatio < Source: Ow costructio The right part of Fig. 2 depicts sigificat local spatial autocorrelatio coefficiets ad it is obvious that i the Easter part of the EU low values of disposable icome are more ofte surrouded by high values ad oly i few cases low values are surrouded by low values. These results idicate that there is a umber of regios with sigificatly lower values of disposable icome. O the other had, i the cetral part of the EU high values are more ofte surrouded by high values tha high values surrouded by low values. Fig. 1: Disposable Icome Source: Ow costructio (left: spatial distributio of the variable; right: local spatial autocorrelatio) As for the aggregate idicator of poverty (at-risk-of-poverty or social exclusio rate), oe has to be very careful whe iterpretig spatial clusterig due to lack of data. The available data idicate that regios with high levels of the idicator are clustered i the Easter (Romaia ad Bulgaria, ad the regio of Easter Slovakia) ad South-Wester parts of the EU (see Fig. 2). Low values are cocetrated i the Norther ad Cetral part of the 1709

6 Europea Uio. Ufortuately Eurostat does ot publish data o Germay ad Frace, ad hece the data set does ot offer a complete iformatio. Local spatial autocorrelatio coefficiets idicate similar results i the Souther part of the EU regios with high values of the aggregate poverty measure are surrouded by regios with high values, exceptioally regios with higher values are surrouded by regios with low values. Fig. 2: At-risk-of-poverty or social exclusio rate Source: Ow costructio (left: spatial distributio of the variable; right: local spatial autocorrelatio) As already metioed, the aggregate poverty idicator cosists of three sub-idicators (see Fig. 3). At-risk-of-poverty rate (Fig. 3a) is oe of the most used measures of poverty, but it has both advatages ad disadvatages. As icome levels differ across coutries, iteratioal comparisos are doubtful or i some cases eve seseless. For istace, compare two coutries: coutry A with media persoal icome of EUR 6,000/year ad 13% at-riskof-poverty rate ad coutry B with media persoal icome of EUR 20,000/year ad 15% atrisk-of-poverty rate. Accordig to a simple compariso of poverty rates, coutry B would be perceived as a coutry with higher poverty level. O the other had media persoal icome i coutry B is more tha three-times higher tha i coutry A. The highest at-risk-of-poverty rates are cocetrated i the Souther ad Easter parts of the EU. Distributio of local spatial autocorrelatio coefficiets of at-risk-of-poverty rates is similar to the distributio of the aggregate poverty idicator. The results further idicate similar distributios of local spatial autocorrelatio coefficiets of all sub-idicators (see Figures 3a, 3b ad 3c). 1710

7 Fig. 3: Sub-idicators of aggregate poverty measure c) Severe material deprivatio rate b) Very low work itesity rate a) At-risk-of-poverty rate Source: Ow costructio (left: spatial distributio of the variable; right: local spatial autocorrelatio) 1711

8 Coclusio The article presets a simple aalysis of spatial distributio of poverty i the Europea Uio. The aalyses are based o Global ad Local Mora s coefficiets of spatial autocorrelatio. Accordig to the Mora s I spatial autocorrelatio coefficiet all variables are sigificatly positively spatially autocorrelated, i.e. similar values are clustered together. Further there is a strog degree of positive spatial autocorrelatio with very low levels of icome i the Easter part of the Europea Uio ad sigificatly high level of icome i the cetral part of the Europea Uio. The available data idicate that regios with high levels of at-risk-of-poverty rate or social exclusio are clustered i the Easter (Romaia ad Bulgaria, ad the regio of Easter Slovakia) ad South-Wester parts of the EU. Low values are cocetrated i the Norther ad Cetral part of the Europea Uio. Oe of the most sigificat limitatios of this study is the fact that regioal data o poverty are published i a very limited way ad hece a complete iformatio is ot provided. Ackowledgmet This work was supported by the Slovak Scietific Grat Agecy as part of the research project VEGA 1/0127/11 Spatial Distributio of Poverty i the Europea Uio. Refereces Aseli, L. (1995). Local Idicators of Spatial Associatio LISA. Geographical Aalysis, 27(2), Bartosova, J. & Forbelska, M. (2012). Modellig of the Risk of Moetary Poverty i the Czech Regios. I: Löster, T., Pavelka, T. (Eds.) The 6 th Iteratioal Days of Statistics ad Ecoomics, Coferece Proceedigs (pp ). Slaý: Meladrium. Bilkova, D. (2009). Pareto Distributio ad Wage Models.I Aplimat (pp ) Bivad, R. (2013). spdep: Spatial depedece: weightig schemes, statistics ad models. R package versio Chattopadhyay, S.; Majumder, A.; Jama, H. (2014). Decompositio of iter-regioal poverty gap i Idia: A spatial approach. Empirical Ecoomics, 46(1), Cliff, A. D.; Ord, J. K. (1981). Spatial Processes Models ad Applicatios. Lodo: Pio. Eurostat. (2014). EuroGeographics for the admiistrative boudaries. Luxembourg: Eurostat. Evelie, D. (2012). Urba Poverty, Spatial Represetatio ad Mobility: Tourig a Slum i Mexico. Iteratioal Joural of Urba ad Regioal Research, 36(4),

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10 Sipkova, L. (2012). Aalysis of Icome Iequality of Employees I the Slovak Republic. I: Löster, T., Pavelka, T. (Eds.) The 6 th Iteratioal Days of Statistics ad Ecoomics, Coferece Proceedigs (pp ). Slaý: Meladrium. Stakovicova, I., Vlacuha, R. & Ivacikova, L. (2013). Tred Aalysis of Moetary Poverty Measures i the Slovak ad Czech Republic. I Loster Tomas, Pavelka Tomas (Eds.), The 7th Iteratioal Days of Statistics ad Ecoomics (pp ). ISBN Thogdara, S. (2012). Usig GIS ad Spatial Statistics to Target Poverty ad Improve Poverty Alleviatio Programs: A Case Study i Northeast Thailad. Applied Spatial Aalysis ad Policy, 5(2), Wag, J. et al. (2012). A case research o ecoomic spatial distributio ad differetial of agriculture i Chia A applicatio to Hua provice based o the data of 1999, 2006 ad Agricultural Scieces, 3(8), World Bak. (1993). Poverty Reductio Hadbook. Washigto, DC, USA: The World Bak, ISBN Zelisky, T.; Stakovicova, I. (2012). Spatial Aspects of Poverty i Slovakia. I Löster; T., Pavelka, T. (Eds.), 6 th Iteratioal Days of Statistics ad Ecoomics (pp ). Slaý: Meladrium. Zhag, D.; Mao, X.; Meg, L. (2010). A Method Usig ESDA to Aalyze the Spatial Distributio Patters of Cultural Resource. The Iteratioal Archives of the Photogrammetry, Remote Sesig ad Spatial Iformatio Scieces. Vol. 38, Part II. Cotact Tomáš Želiský Faculty of Ecoomics, Techical Uiversity of Košice Němcovej 32, Košice, Slovakia tomas.zelisky@tuke.sk Matúš Kubák Uiversity of Prešov, Faculty of Maagemet Koštatíova 16, Prešov, Slovakia matus.kubak@uipo.sk 1714