Clusters of poverty in Poland

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Publishing House of Wrocław University of Economics Wrocław 2016

Quality of Life.

Human and Ecosystem Well-being

PRACE NAUKOWE

Uniwersytetu Ekonomicznego we Wrocławiu

RESEARCH PAPERS

of Wrocław University of Economics

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Copy-editing:RafałGalos Layout:BarbaraŁopusiewicz Proof-reading:BarbaraŁopusiewicz Typesetting:AdamDębski Coverdesign:BeataDębska Informationonsubmittingandreviewingpapersisavailableonwebsites: www.pracenaukowe.ue.wroc.pl www.wydawnictwo.ue.wroc.pl ThepublicationisdistributedundertheCreativeCommonsAttribution3.0 Attribution-NonCommercial-NoderivsCCBY-NC-ND © CopyrightbyWrocławUniversityofEconomics Wrocław2016 ISSN 1899-3192 e-ISSN 2392-0041 ISBN 978-83-7695-590-2 Theoriginalversion:printed PublicationmaybeorderedinPublishingHouse WydawnictwoUniwersytetuEkonomicznegoweWrocławiu ul.Komandorska118/120,53-345Wrocław tel./fax713680602;e-mail:econbook@ue.wroc.pl www.ksiegarnia.ue.wroc.pl Drukioprawa:TOTEM

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Introduction

OnSeptember21-22,2015,6thInternationalScientificConference“QualityofLife 2015.HumanandEcosystemsWell-being”washeldinWrocław. Theconferencewasapartofthecycleoftheconferencesonthetopicofquality oflifethathavebeenorganizedbytheDepartmentofStatistics(WrocławUniversity ofEconomics)since1999.Theaimofthecycleistoparticipateinthestillrising alloverthewordwaveofscientificstudiesonqualityoflife:ethicalbackground anddefinitionsofqualityoflife,investigating(howtomeasureit),presentingthe resultsofdifferencesofqualityoflifeovertimeandspace,itsinterdependences with natural environment, mathematical methods useful for the methodology ofmeasuringqualityoflifeandfinally–possiblemethodsofimprovingit.The conferencesaremeanttointegratethePolishscientificcommunitydoingresearch onthesetopicsaswellastomakecontactswithforeignscientists.

ThisyearourhonoraryguestwasProfessorFilomenaMaggino,pastPresident of International Society for Quality-of-Life Studies (ISQOLS), who presented aplenarylecture. Wehostedabout30participants,amongthemscientistsfromSpain,Romania, ItalyandJapan.Wehad24lecturesonsuchavarietyoftopicsascarbonfootprint andmathematicalpropertiesofsomeestimators.Thecommonbackgroundofall ofthemwastobettercomprehend,measureandpossiblytoimprovethequalityof humans’life. Thepresentvolumecontainstheextendedversionsofsomeselectedlectures presented during the conference. We wish to thank all of the participants of the conference for co-creating very inspiring character of this meeting, stimulating productivediscussionsandresultinginsomepotentiallyfruitfulcooperationover new research problems. We wish also to thank the authors for their prolonged cooperationinpreparingthisvolume,thereviewersfortheirhardworkandformany valuable,althoughanonymous,suggestionsthathelpedsomeofustoimprovetheir works.

Finally, we wish to thank the members of the Editorial Office of Wrocław University of Economics for their hard work while preparing the edition of this volume,continuouskindnessandhelpfulnessexceedingtheirdutiesofthejob.

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PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU RESEARCH PAPERS OF WROCŁAW UNIVERSITY OF ECONOMICS nr 435 ● 2016

Quality of Life. Human and Ecosystem Well-being ISSN 1899-3192

e-ISSN 2392-0041

Anna Sączewska-Piotrowska

UniversityofEconomicsinKatowice

e-mail:anna.saczewska-piotrowska@ue.katowice.pl

CLUSTERS OF POVERTY IN POLAND

KLASTRY UBÓSTWA POLSCE

DOI:10.15611/pn.2016.435.04

Summary: Themainobjectiveofthispaperistostudyspatialautocorrelationofpovertyin

Polandusingthesyntheticmeasure.Theanalysiswillbeconductedonthelevelofsubregions. Spatial analysis of the data has allowed to evaluate the overall similarity of subregions in Poland in the field of poverty.There were separated groups of similar subregions and subregionsdifferingfromneighboringsubregions.TherewereusedglobalandlocalMoran statistics and traditional method without using information regarding localization of the syntheticmeasure.

Keywords:globalstatistic,localstatistic,poverty,subregions,Poland.

Streszczenie: Głównymcelemartykułujestanalizaprzestrzennejautokorelacjiubóstwaw

Polscezużyciemmiarysyntetycznej.Analizajestprzeprowadzananapoziomiepodregionów. Analiza przestrzenna pozwoliła ocenić ogólne podobieństwo podregionów w Polsce ze względunapoziomubóstwawPolsce.Wyróżnionogrupypodobnychpodregionówiregionów różniącychsięodsąsiadów.WanaliziezastosowanoglobalnąilokalnąstatystykęMorana oraztradycyjnąmetodę,niewykorzystującąinformacjiolokalizacjizmiennejsyntetycznej. Słowa kluczowe:statystykaglobalna,statystykalokalna,ubóstwo,podregiony,Polska.

1. Introduction

PovertyisaphenomenonthreateninghouseholdsinwholePoland.However,this phenomenon is spatially differentiated by regions, voivodeships, subregions and even smaller territorial units. Poverty may be described by several variables concerning,interalia,rangeandintensityofthisphenomenon.Inthissituation,to describethephenomenonasyntheticmeasuremaybeused.Themainobjectiveof thispaperistostudyspatialautocorrelationofpovertyinPolandusingthesynthetic measure.Theanalysiswillbeconductedbysubregions.Spatialanalysisofthedata willallowtoevaluatetheoverallsimilarityofsubregionsinPolandinthefieldof poverty. Spatial autocorrelation can be considered as an indicator of clustering.

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70 AnnaSączewska-Piotrowska

Therewillbeseparatedclustersofsimilarsubregionsandsubregionsdifferingfrom neighboring subregions. There will be used global and local Moran statistics to achievetheaimofthispaper.Theresultswillbecomparedwithresultsobtained using the traditional method, which means without using information regarding localizationofthesyntheticmeasure.

2. Data and methods

TheanalysisofpovertybysubregionsinPolandwasbasedondatafromSocial Diagnosis[SocialMonitoringCouncil2013].Inthedatabasetherewereincluded 12355householdsdividedinto66subregions1_. Povertyisaphenomenonstudiedinmanyways.Theproblemappearsatthe beginningandisconnectedwithdefiningpoverty.Alldefinitionscanbefitintoone ofthefollowingcategories[Hagenaars,deVos1988]: a)povertyishavinglessthananobjectivelydefined,absoluteminimum, b)povertyishavinglessthanothersinsociety, c)povertyisfeelingyoudonothaveenoughtogetalong. Accordingtothefirstcategoryofdefinitions,povertyisabsolute,accordingto thesecondcategory–isrelativeandaccordingtothethirdcategorymaybeabsolute orrelative,orsomewhereinbetween.Anotherdifferencebetweenthecategoriesis thatthethirdcategorydefinespovertysubjectively,whilethefirstandseconddefine povertytobeanobjectivesituation.

The choice of certain poverty definition implies a certain way of poverty measurement, i.e. we have several choice possibilities of the poverty threshold, equivalence scales etc. The description of poverty methodology is available in foreign [Hagenaars, van Praag 1985; Atkinson et al. 2002] and Polish literature [Panek2011]. Povertymaybedescribedbyseveralvariables.Themostpopularmeasuresused inpovertystudiesareheadcountratio(alsoknownasat-risk-of-povertyrate)andthe medianpovertygapratioamongthepoor(alsoknownasrelativemedianat-risk-of-povertygap).At-risk-of-povertyratemeasurestheshareofindividuals(persons, householdsorfamilies)withincomebelowpovertythresholdandrelativemedianat-risk-of-povertygapmeasuresthedifferencebetweenthemedianincomeofthepoor individualsandthepovertythreshold,expressedasapercentageofthisthreshold [Laeken indicators…2003].

Poverty is connected with income inequality: the greater the inequality, the morepoorindividuals.Forthisreason,inequalitymeasuresaretakenintoaccount inthepovertystudy.OftenusedmeasuresareGinicoefficientandincomequintile shareratio.TheGinicoefficientisdefinedastherelationshipofcumulativeshares ofthepopulationarrangedaccordingtothelevelofequivaliseddisposableincome,

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ClustersofpovertyinPoland 71 tothecumulativeshareoftheequivalisedtotaldisposableincomereceivedbythem. Theincomequintileshareratioisdefinedastheratiooftotalincomereceivedbythe 20%ofthepopulationwiththehighestincome(topquintile)tothatreceivedbythe 20%ofthepopulationwiththelowestincome(lowestquintile)[Laeken indicators… 2003]. BasedoncalculatedvaluesofmeanincomeandGinicoefficientSenindexcan becomputed[Rusnak2007]: IS=µ 1

(

−G

)

, (1) where:μ–meanincome;G–Ginicoefficient. ThehighervaluesofSenindex,thegreaterwelfare.

Hellwig’s method allows to create ranking objects (in our case – subregions in Poland) described by more than one variable. On the basis of the matrix of standardizedinputvariablesthereferenceobjectisdetermined.Coordinatesofthe referenceobjectaredeterminedbythefollowingformula[Hellwig1968]: z z j S z j D j i ij i ij 0 = ∈ ∈     max | min | , (2) where:S–setofstimulants;D–setofdestimulants;z_ij–standardizedvaluefori-th objectandj-thvariable. Thenwecalculateforeachobjectitsdistancefromareferenceobject,using Euclideandistanceasgivenbyformula: (3) Finally,syntheticmeasureisdefinedas: q d d i= −1 i0 0 , ₍₄₎ where: d₀=d₀+2s₀, ₍₅₎ whereby: d n i d n i 0 1 0 1 = =

∑

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72 AnnaSączewska-Piotrowska and s n i d d n i 0 1 0 0 2 1 =

(

−

)

=

∑

. (7) Valuesofsyntheticmeasure(formula4)belongtotheinterval[0,1]andonly inexceptionalcasesgobeyondthisrange–thisparticularplacewhentheobject dramaticallylagsbehinddevelopmentallyfromremaininginthetestarea.Ahigher valueofthismeasureindicatesabetterpositionoftheobject.Thedivisionofobjects intoclassescanbemadeonthebasisofstatisticalcriteriausingthearithmeticmean q̅ andstandarddeviations_qofthesyntheticmeasure[Nowak1990]: (8)

This is a classic way of division of territorial units into groups. If we have in database information about the localization of studied variable (in our case – syntheticmeasure),wewilllosethisinformation.Thisinformationmayrelatetoarea boundariesorneighbors.Explorativespatialdataanalysis(ESDA)usesinformation aboutvaluesofstudiedvariableandadditionallyaboutlocalization.ESDAisoften usedtovisualizationandquantitativeanalysisofspatialdata.ESDAtechniquesare anefficientwaytotesttheexistenceofspatialautocorrelationprocesses.Measures ofspatialautocorrelationallowtoevaluatecorrelationofvariablesregardingspatial location.Spatialautocorrelationmeansthatgeographicallycloseobservationsare more similar than distant observations. ESDA techniques were used in Poland, interalia,intheanalysesofblooddonation[Ojrzyńska,Twaróg2011],landprices [Pietrzykowski2011]orbudgetincomes[Wolny-Dominiak,Zeug-Żebro2012].

Thekeyelementofspatialanalysesisspatialweightsmatrix.Thismatrixis usuallydefinedasn ×nrow-standardizedfirstordercontiguitymatrix.“Firstorder contiguity” means regions bordering with studied territorial unit are neighbors. Weightsmatrixiscreatedbystandardizationtooneofbinaryneighborhoodmatrix. Inbinarymatrixvalueonemeansthatunitshavecommonborder,zero–unitsdo nothavecommonborder.Row-standardizationmeansthatforeachrowiwehave .Intheempiricalresearchthereareoftenusedstandardizationinvolvingthe assumptionthatw_ijareequalto ,whenaregionhasnneighbors.

FortestingglobalspatialautocorrelationglobalMoran’sIstatisticisused,which isgivenbyformula[Kopczewska2011]:

AllcalculationsandgraphsweremadeinR[RDevelopmentCoreTeam2015]using spdep [Bivand 2015b] and maptools [Bivand 2015a] packages. The map of Poland with division into subregions is available on the Eurostat website http:// ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.

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ClustersofpovertyinPoland 75

The study was conducted based on database of Social Diagnosis project for 2013.Databasecontainsinformationaboutmorethan12000households,interalia, informationabouttheirincome.WeusedmodifiedOECDequivalencescaleinorder tocompareincomeofhouseholdswithdifferentsizeandcomposition.Thisscaleis [Hagenaarsetal.1994]equaltooneforfirstadult,0.5foreachadditionaladultin householdand0.3foreachpersonof14yearsoryounger.Povertythresholdwasset at60%ofthemedianequivalisedincome. TostudypovertybysubregionsinPoland,sixdiagnosticvariableswereselected: X₁–at-risk-of-povertyrate, X₂–relativemedianat-risk-of-povertygap, X₃–incomequintileshareratio, X₄–Ginicoefficient, X₅–meanincome, X₆–Senindex.

Variables from initial list were tested due to the level of variability and correlationwithothervariablesfromthelist.Allofthevariableshadrequiredthe levelofvariability,becausenoneofthevariableshadcoefficientofvariationlower thanorequalto10%.UsingHellwig’sparametricmethod,threevariables:X₁,X₄,

X₆ were the so-called satellite variables (they were too strongly correlated with othervariablesfromthelist)andthereforethesevariablesweredeletedfromthe list.Thevaluesofthevariablesfromthefinallistwerestandardizedbeforefurther calculations.VariablesX₂ and X₃weredestimulantsandvariableX₅wasstimulant. Valuesofsyntheticmeasurewerecalculatedinthenextstepand,basedonthem subregionsweredividedintogroupsusingclassicway(formula8).Theresultsofthe subregionsgroupingareshowninFigure2.

Itcanbenoticedthattherearesimilaritiesregardingpovertylevel.Thelowest poverty level (the highest values of synthetic measure) is in four neighboring subregionsfromsouthernPoland(Bielski,Gliwicki,RybnickiandTyski),intwo subregions bordering with Warszawa (subregions: Warszawsko-wschodni and Warszawsko-zachodni) and in one subregion bordering with Poznań (Poznański subregion), and also in big cities (Szczecin, Wrocław, Trójmiejski subregion, i.e. Gdańsk,GdyniaandSopot).Theworstmaterialsituationisinthreesubregionsfrom eastern Poland (Bialski, Lubelski, Przemyski) and in a few subregions scattered acrossPoland. Inthenextstepananalysiswasconductedtoevaluatethecorrelationofsynthetic variableinregardtospatiallocation.Firstofallthespatialweightmatrixwassetfor 66subregionsinPoland.Thesubregionswereconsideredasneighborsiftheyhada commonboundary.SpatiallinksinweightmatrixareshowninFigure3. Thenumberofnonzerolinksisequalto312andaveragenumberoflinksis4.73. Therearefivetheleastconnectedsubregionswithonelink(Kraków,Łódź,Poznań, WrocławandTrójmiejskisubregions)andonethemostconnectedsubregionwith ninelinks(Sandomiersko-jędrzejowski).

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76 AnnaSączewska-Piotrowska less than 0.11 0.11–0.22 0.22–0.33 0.33 and over Figure 2. SpatialdifferentiationofpovertybysubregionsinPoland

Source:own calculations based on [Social Monitoring Council 2013], © EuroGeographics for the administrativeboundaries.

Figure 3. Spatiallinksinweightmatrix

Source: own calculations based on [Social Monitoring Council 2013], © EuroGeographics for the administrativeboundaries.

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ClustersofpovertyinPoland 77

InthenextstepMoran’sIglobalstatisticwascalculatedusingthetestunder randomization.Moran’sIisstatisticallysignificant(p-valueat0.011)andindicates poorspatialautocorrelation(I=0.177).(Thismeansthatthereisasmallsimilarity between neighboring subregions in terms of poverty. Moran’s global statistic is showninMoran’sscatterplot(Figure4).

Figure 4. Moran’sscatterplotforsyntheticmeasure

Source: own calculations based on [Social Monitoring Council 2013], © EuroGeographics for the administrativeboundaries.

Whiletheoverallpatternofspatialassociationisclearlypositive,asindicatedby theslopeoftheregressionline(Moran’sI),thirtyobservationsshowanassociation betweendissimilarvalues:14intheupperleftquadrantand16inthelowerright quadrant. Seven selected subregions may be considered as “outliers” (Bielski, Lubelski,Poznań,Rybnicki,Trójmiejski,TyskiandWrocławsubregions).Fiveof them(Bielski,Lubelski,Rybnicki,TrójmiejskiandTyskisubregions)havevalues

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ofsyntheticmeasure(horizontalaxis)approx.twostandarddeviationsawayfrom themeanandsimultaneouslythesesubregionshavequitefarvaluesforthespatial lag(verticalaxis).Bielski,RybnickiandTyskisubregionsarepotentialhotspots (subregions with high values with similar neighbors) and Lubelski subregion is apotentialcoldspot(subregionwithlowvalueswithsimilarneighbors).Finally, Trójmiejskisubregionisapotentialspatialoutlier.PoznańandWrocławsubregions havenotquitefarvaluesforsyntheticmeasure(Poznań)orforspatiallag(Wrocław). Forthisreason,theyhavelesschancetobehotspots.

Table1containsthelocalMoranstatisticsI_i and p-valueoflocalMoranstatistics. Table 1. ValuesoflocalMoran’sI_iinsubregions

Subregion I_i Pr(z>0) 1 2 3 Bialski 0.959 0.011 Białostocki −0.046 0.518 Bielski 2.497 0.000 Bydgosko-toruński 0.742 0.136 Bytomski −0.203 0.670 Chełmsko-zamojski 0.774 0.032 Ciechanowsko-płocki −0.072 0.568 Częstochowski 0.300 0.207 Elbląski −0.303 0.750 Ełcki 0.088 0.415 Gdański −0.599 0.888 Gliwicki 1.244 0.002 Gorzowski −0.182 0.652 Grudziądzki 0.136 0.334 Jeleniogórski 0.135 0.377 Kaliski −0.016 0.501 Katowicki −0.299 0.723 Kielecki −0.072 0.540 Koniński −0.006 0.491 Koszaliński 0.001 0.486 Krakowski −0.411 0.848 Kraków −0.518 0.695 Krośnieński 0.589 0.078 Legnicko-głogowski −0.039 0.520 Leszczyński −0.051 0.540 Lubelski 1.664 0.001 Łomżyński 0.121 0.374 Łódzki −0.482 0.834 Łódź −0.405 0.654 Nowosądecki 0.177 0.345 Nyski 0.045 0.444

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ClustersofpovertyinPoland 79 1 2 3 Olsztyński −0.094 0.565 Opolski 0.020 0.463 Ostrołęcko-siedlecki 0.184 0.287 Oświęcimski 0.417 0.155 Pilski 0.018 0.459 Piotrkowski 0.068 0.400 Poznań 1.241 0.101 Poznański 0.488 0.119 Przemyski 0.580 0.108 Puławski 0.245 0.214 Radomski −0.029 0.515 Rybnicki 2.825 0.000 Rzeszowski −0.036 0.514 Sandomiersko-jędrzejowski 0.351 0.117 Sieradzki −0.285 0.794 Skierniewicki −0.117 0.613 Słupski 0.054 0.436 Sosnowiecki −0.020 0.505 Stargardzki −1.008 0.981 Starogardzki 0.144 0.370 Suwalski −0.026 0.508 Szczecin −0.553 0.782 Szczeciński 0.311 0.318 Tarnobrzeski 0.640 0.032 Tarnowski 0.797 0.028 Trójmiejski −2.574 0.995 Tyski 1.821 0.000 Wałbrzyski 0.159 0.378 Warszawa −0.078 0.536 Warszawskiwschodni −0.202 0.686 Warszawskizachodni −0.009 0.494 Włocławski 0.201 0.288 Wrocław 0.365 0.350 Wrocławski 0.017 0.464 Zielonogórski −0.008 0.494 Source:owncalculationsbasedon[SocialMonitoringCouncil2013],©EuroGeographicsforthead-ministrativeboundaries. ThelocalMoran’sI_iaresignificantfor11subregions:nineofthem(boldvalues in Table 2) are surrounded by subregions with similar values (Bialski, Bielski, Chełmsko-zamojski, Gliwicki, Lubelski, Rybnicki, Tarnobrzeski, Tarnowski and Tyskisubregions)andtwoofthem(boldanditalicvaluesinTable2)aresurrounded bysubregionswithdifferentvalues(StargardzkiandTrójmiejskisubregions).These

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twosubregionsarespatialoutliers.Statisticallysignificantlocalstatisticsareshown inFigure5.

surrounded by similar values, locM > 0 not significant

surrounded by different values, locM < 0

Figure 5. SubregionswithsignicicantlocalMoran’sIi

OnthebasisoflocalMoran’sI_iandsubregionsbelongingtoquartersinMoran’sI scatterplotspatialregimesareidentified(Fig.6),i.e.subregionswithsubstantially similarordissimilardistributionoftheanalysedvariable[Szubert2014].

Spatialclusterofhighvalues(hotspot)isformedbysubregionsfromsouthern Poland (Bielski, Gliwicki, Rybnicki and Tyski subregions) and spatial cluster oflowvalues(coldspot)–bysubregionsfromeasternandsouth-easternPoland (Bialski,Chełmsko-zamojski,Lubelski,TarnobrzeskiandTarnowskisubregions). ThespatialoutliersareStargardzkiandTrójmiejskisubregions.Theothervaluesof localMoran’sI_iarenotstatisticallysignificant.LocalMoranstatisticsconfirmthe resultsobtainedbasedonscatterplot.Theindicatedpotentialcoldspots,hotspots andspatialoutliersareinfactstatisticallysignificant“outliers”.Itshouldbenoted thatonthebasisofscatterplotnoteverysignificantlocalstatisticswereidentified.

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ClustersofpovertyinPoland 81 I quarter – HH II quarter – LH III quarter – LL IV quarter – HL not significant Figure 6. Spatialregimes

4. Conclusions

Theanalysisofpovertywasperformedintwoversions:usingtraditionalmethod andspatialautocorrelationstatistics.Thepovertyhasbeendescribedbysynthetic measureinbothcases.Basedonaclassicway(divisionintothegroupsusingmean andstandarddeviationofsyntheticmeasure)itcanbeconcludedthatfoursubregions from southern Poland form a cluster the least at-risk-of-poverty subregions. The extended analysis on information about neighboring subregions confirms these results,butadditionallyshowsthatthereisaclusteroffivethepoorestsubregions fromeasternandsouth-easternPoland.

Usingspatialmethodsallowstoconductamorecompleteanalysis.Incontrast to traditional methods spatial autocorrelation does not ignore information about

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localizationofvariable.Spatialmethodsallowafullerdefinitionoftheconnections anddependenciesbetweenterritorialunitsandtheyallowtodefinespatialstructures.

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