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CostoflivinginSpain(2006‐2011):regionaldifferentialsandtheimpactofeconomiccrisis

ElenaLasarteNavamuel

EstebanFernándezVázquez

FernandoRubieraMorollón

REGIOlab‐UniversityofOviedo,Oviedo(Spain)

Abstract

Inthisarticle,wehaveestimatedademandsystemofSpanishhouseholdstomeasurethecostoflivingofthesehouseholdsacrosstheSpanishregionsandalongtheperiod2006‐2011.ThedatacomesfromSpanishHouseholdBudgetSurveyandDeatonandMuellbauer (1980) approach was applied with a two‐step methodology ofShonkwillerandYen(1999)toaddresstheproblemsderivedofhavingcensoreddatain thesurvey.Theresultsobtainedshowsstrikingdifferences inprices levelsacrossregionsinalltheperiodofstudycontrarytotheevidenceobtainedwiththeCPIserieswhichdonotreflectanyregionalpricedifferences.WealsocanobservethatourCOLIevolves ina similarway than the economic cyclewhile theCPIpresentsa smoothincrease of price every year. Finallywe find different impacts of prices across theincome range implying important policy issues relating with welfare. This papershow how differences in price levels across regions exist andmatter for economicoutcomesandshouldbetakenintoaccountineverywelfarepolicy.

Keywords: Almost Ideal Demand Systems (AIDS), Spatial Cost of Living (SCOL),SpatialEffectsoftheEconomicCrisis,RegionalEconomicsandSpain.

JELClassification:D12,R11andR22.

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1. Introduction.

Regionalpricelevelsdifferentialsindicatedifferencesintheregionalcostoflivingand thus have important individual and social welfare implications. For thisreason,differences in livingconditionsand inthequalityof lifealwayscapturealot of attention by citizens and local governments. In Spain as well as in othercountries this issue is currently in a phase in which their quantification isincreasinglyimportant(Juradoetal.,2012).

The disparities in well‐being are evidenced by the Spanish AutonomousCommunities by some authors, such as Ayala et al. (2011), García‐Luque et al.(2009), Pérez‐Mayo (2008), or Poggi (2007) among others. However, there is alittleresearchonthisissueinSpaincomparedtotheUSorotherEUcountries.Dueto the structure of Spanish political system,where almost all policies that affectcitizens’ well‐being are responsibly of the regional or local administrations, theneglectof this issuehas importantpolitical implications (Juradoet al., 2012).ASKosfeld et al. (2008) pointed out the lack of area‐wide price level data, regionalpolicyhastorelyonnominaldata, inthesecasesdisparities instandardof livingcanbedistorted and spatial planningpolicies are expected to be differentwhenbasedonrealdata.

ThemainreasonwhichexplainsthelackofstudiesinthisareafortheSpanishcaseisthelimitationsofdataavailability.InSpain,likeinthemajorityofthecountries,theofficialConsumerPrice Index(CPI) isoftenused for thesepurposes.But thisCPIevaluateschangesintheaveragepricesfortheacquisitionofabasketofgoodsand services which is considered to be representative of the expenditure of allconsumers independently of the region where the consumer is situated. ThischaracteristicmakesthattheCPIisalimitedindicatorformeasuringhowthecostof living(COL) is in thedifferentSpanishregionsandhowthisCOLevolvesovertime because even in the case that consumers face the same nominal prices,variations in the cost of living can arise because of differences in expenditurepatterns. There is a great importance of having a proper price index forcomparisonsofcostsof livingbetween,forexample,urbanandruralareas,alongtheincomedistributionoralongtheagecohortbecauseit issupposedthattheseindexshowincomeandpovertydifferencesmoreaccuratelythanotherwise.Also,it isofgreat importancetake intoaccountspatialdifferences inthecostof livingovertimeforthemeasurementofaveragestandardofliving.Despitethis,welfareprograms in Spain do not make any adjustment for cost of living in differentregions.

Theobjectiveofthisstudyisprovideevidenceonintra‐countryregionalpriceleveldifferences in Spain across regions and along the time through a Cost of LivingIndex(COLI)calculatedwith therepresentativebasketofeachconsumer ineachregion, and highlights the limits of the CPI as an indicator of the regional price

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disparities over time as Alberola et al. (2001) and Garrido‐Yserte et al. (2012)evidence for Spain in their respectiveworks. Inorder to calculate costsof livinganditsdistribution,weuseexpendituredatafromtheSpanishHouseholdBudgetSurvey provided by the National Statistical Institute. This survey record theexpenditure of many items but it has information neither about quantitiespurchased nor about prices except for the food and energy groups of goods.BecausetheCOLIestimatesrequireinformationaboutpricesfacedbyconsumersweareonlyabletocalculatetheCOLIforfoodproductsandexcludingtheenergygroups because the aforementioned group only represents a 4% of the totalexpenditure of a Spanish household. Despite this limitation we can obtain theadvantage of knowing regional price differences in dairy products which aresupposedtohavelesspricedispersionthanothers.Differencesinpricelevelsareobvious in goods such as housing, but the critical question is whether thedispersion inother representative consumergoods ispervasiveandof sufficientmagnitudetoinfluencehouseholds’costsoflivingsignificantly(Slesnick,2002).

There aremany studieswhichprovide evidence on spatial pricedifferences andhow these spatial differences affect poverty and inequality. Thomas (1987)measuresdisparities in living standardswithinBrazil, remain these,particularly,between the Northeast and the Southeast. Asra (1999) demonstrates theimportance of urban‐rural differences in Indonesia and their impact on povertymeasuresfindingthattheurban‐ruralfoodpricedifferentialsduring1987‐96was13%‐16%andnot28%‐52%asimpliedbytheofficialdata.WefindthesamekindofstudiesforIrelandinSomerville(2004)andfromChinatheworkofBrandtetal.(2006) where they emphasize the spatial price differences and thedisproportionally impact across the income range that clearly raises importantwelfare policy issues. In Germany, as in the US, we find many studies aboutregional price differences, some examples are Roos (2006) and Kosfeld et al.(2008)forGermancase,andSlesnick(2002)andJolliffe(2006),amongothers,forthe US case. Both conclude that adjust for differences in cost of living havecomplete reversal implications in terms of prevalence, depth and severity ofpoverty.

After this introductionof theproblemof the spatial costof livingdifferenceswefollowwithsection2wherethegeneralmethodologyofprice indicesandcostoflivingindicesisexplained.Then,insection3,wemakeananalysisandadiscussionofthemethodologicalapproachappliedtotheSpanishcase.Section4setsouttheempirical findings of spatial price differences along the period 2006‐2011,includinganexplorationoftheimpactofpricedifferencesacrosstheincomerange.Finally,conclusionsaredrawninsection5.

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2.Methodology: aprocedure to estimate a true spatio‐temporalcostofliving.

ThetheoryoftheCostofLiving(COL)wasinitiallydevelopedbyKonüs(1939).InthisseminarworkKonüsfocusedhisapproachoncomparingtwoperiodsoftime:a householdwhich faces two different price levels tries to adjust its commoditybasket in order to maintain a constant level of utility with the minimumexpenditurecost.AlaterlineintheliteratureappliestheKonüs’ideabutstudyingpricedifferentialsacrossspace:seeDesai(1969),Nelson(1991),Timmins(2006)orAtuestaandParedes(2012)amongothers.

Thisanalysisdepartfromtheestimationofdemandmodels.Thisestimationcanbeperformed by means of various methods. A summary and evaluation of thedifferent options can be found in Kakhki et al. (2010). One of the most widelyappliedproposalshasbeenthatofDeatonandMuellbauer(1980).Theirapproachhas important advantages over other procedures; the most important of thesebeing the fact that it considers non‐homothetic preferences for each householdincome group. This makes it especially valuable for comparisons across space,given that it allows the basket of preferences between consumers of differentregionsorareastobevaried.Withthisapproachwecanknowthenotobservableutility function, and, consequently, to derive the cost function and to calculate aCOLindex(COLI)byfixingtheutilitylevelinsteadthebasketofconsumergoodsasinthecaseofthepriceindicescalculatedbystatisticalagencies.

Keeping fixed the quantities consumed instead of the utility level generates asubstitution bias derived from ignoring the substitutionsmade by consumers inresponse topricevariations.Recent literatureshowsthat thesubstitutionbias ismore significant in spatial comparisons than in a time series context (see, forexample,ParedesandIturra,2013).Therearetwomainreasonsthataggravatethebiasprobleminthisspatialcontext:(i)becausetransportationcostsaffectpricesindifferentwaysacrossspace,eveninthesameperiodof time;and,(ii)becausetheconsumptionbasketismoreheterogeneousamongregionsbecauseofproductatoutletheterogeneityandbecauseofgeographicalandweatherfactors.Forthesereasons, the assumption that the consumption basket is fixed across the spacecouldbeahighlyunrealisticassumptionwhenspatialpricevariationsarestudied.

The estimation of the AIDS is used to recover the expenditure function and tocalculatetheCOLforarepresentativehouseholdineachregion.ThedefinitionofaCOLI(CostofLivingIndex)betweenregionshandris:

COLI ̅ ,̅ ,

[1]

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where is thecost function; and arevectorscontainingthepricespaidbythereferenceconsumerintheregionshandr,respectively;and isanutilitylevelsetascommonforbothregions.

The point of departure for estimating anAIDS starts by defining a PIGLOG classcost or expenditure function consistentwith themicroeconomic theory that setstheminimum expenditure necessary in order to attain a specific utility level at

givenpricesforasetofnproducts1:

log c p, u = 1 u log a p u log b p [2]

where istheexpenditurefunction, isthepricevectorand istheutilitylevel.With someexceptions, liesbetween0 (subsistence level) and1 (bliss level) solog and log canbe consideredas the logof the costsof subsistenceandbliss,respectively.Theirrespectivefunctionalformsare:

log a p α α log p12

γ log p log p [3]

log b p log a p β p [4]

where the sub‐index ( ) denotes the products included in the demand system.Thedemandfunctionscanbederivedsubstituting(3)and(4)inthecostfunction[2],fromwhichweobtain:

log c p, u =α ∑ α log p ∑ ∑ γ log p log p uβ ∏ p [5]

By applying the Shepard’s lemma to [5], i.e., price derivates are equal to thequantitiesdemanded,andmultiplyingbothsidesoftheequation[5]by ,⁄ ,weobtain:

,

,wi [6]

wherew isthebudgetshareofgoodi:

w α γ log p β uβ p [7]

1 This formulation of the AIDS is based on Deaton and Muellbauer (1980).

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Toobtainanestimablesystemweneedtosolvefor asafunctionofobservedandknownparametersfromequation[5]:2

u=, ∑ ∑ ∑

∏ [8]

Substitutinguinequation[7]weobtain:

w α γ log p β log c p, u α α log p

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γ log p log p [9]

Thesharesin[9]aredeterminedfrompricesandtheexpenditurefunction,plusaset of parameters to be estimated. These shares are theAIDS demand functionsandtheycanbeexpressedas:

w α γ log p β log x P⁄ [10]

where , and aretheparameterstobeestimated, isthetotalexpenditureonthefoodgroupandPisapriceindexdefinedas:

+∑ ∑ ∑ [11]

SomeempiricalstudiesusetheStonePriceIndextoavoidproblemsofnon‐linearestimations. However, we estimated the original model as suggested by DeatonandMuellbauer(1980)usingthetranslogpriceindexdescribedin[11]3.

The parameters included in the AIDS model should satisfy a set of constrains.Firstly, they must hold the adding‐up restriction (∑ 1), which requiresequality of the sum of individual commodity expenditures and the totalexpenditures:

∑ α 1,∑ γ 0,∑ β 0 [12]

2 This develop is based on Nelson (2010).

3 Asanalternativeto[11],CooperandMcLaren(1992)suggestamodificationofAIDScalledMAIDS,whichpreservesregularityinawiderregionoftheexpenditure‐pricespace.Nevertheless,themostusualforminthe

literatureisAIDSoritslinearapproximation,LAIDS.

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Furthermore,theparametersarehomogeneousofdegreezeroinpricesandtotalexpenditure taken together. This means that if prices and total expenditureincreasebythesameamountthedemandremainsunchanged:

γ 0 [13]

Moreover,thetotalexpendituremustverifytheSlutskysymmetry,whichrequiresthat the compensated cross‐price derivative of commodity i with respect tocommodity j equals the compensated cross‐pricederivativeof commodity jwithrespecttocommodityi:

γ γ [14]

The and parameterscanbe interpreted ineconomic terms.The elements

quantify theeffectofchanges inrelativeprices, representing the%ofchangeontheithbudgetshareproducedbya1%increaseinthepriceofthejthproduct,being( ⁄ held constant. The effects of changes in the real expenditure operatethrough the coefficients, which are positive for luxuries and negative fornecessities(DeatonandMuellbauer,1980).

ThefollowingsectionwilldiscusshowtoapplythisproceduretotheSpanishcaseanddatabases.

3. Application to the Spanish case: data limitations and firstresults.

As inmanyother countries, the applicationof this approach to the Spanish caseentailsthedifficultiesarisingfromthelackofavailabilityofdata.Theonlysurveythatcontainsinformationonhouseholdexpenditureandconsumptionpatternsisthe Household Budget Survey (HBS), an extensive survey of Spanish householdpurchases, income and other socioeconomic characteristics with 21,790observations.TheSpanishStatisticalInstitute(INE)conductsthissurveyannuallywith different households each year. The estimation of the AIDS requiresinformationonprices,quantitiesandhouseholdexpenditure.Asallthepricesmustbe observable to estimate the model, the unitary prices at which householdspurchase the commodities are recoveredbydividing expenditures by quantities.Thisproceduretoobtaintheunitpricesisacceptedintheliteratureanditiswellknownasunitvalues(Deaton,1988).Alltheseinformationrequirementslimittheapplied estimation to be feasible only for the foodstuffs group, divided into tenfood sub‐groups in the Household Budget Survey classification: (i) Bread andcereals, (ii) Meat, (iii) Fish, (iv) Milk, cheese and eggs, (v) Oil, (vi) Fruits, (vii)

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Vegetables, (viii)Sugar, (ix)Coffee, teaandcacao; and (x)Mineralwaterand softdrinks.

Anadditional issue in theestimationprocess,derivedfromtheSpanishdatabasecharacteristics, is the existence of censored data or households that report zeroconsumption.Consequently,pricesarenotavailableforallitemsinallhouseholds.This situation can happenwhen the consumed quantities are not reported by ahousehold, or because the household do not really consume that specific group,beingtheconsequencethatthepriceoftheitemcannotbeobtainedbymeansofunitvalues.Inbothcasesthepriceoftheitemisreplacedbyageometricmeanofthepricesofthisiteminthesameregion4,distinguishingifthisitemispurchasedby a household situated in a capital city or not. In the first case, the price isreplaced by the average price of the same item in the same capital city. In thesecond case, the price is replacedby the averageprice of the item in the regionwherethehouseholdislocated.Controllingbytheeffectofcapitalcityisexpectedtoproducemore accurate estimatesbecauseprices in capital cities are assumeddifferentthanpricesintherestoftheregion.Thisisassumedbecausethecapitalcity is normally themain city of the region (and the largest one inmost of thecases),whichresultsinanagglomerationprocessofpublicandprivateservicesin

thistypeoflocations.5

Additionally, another particular characteristic in our formulation is that weincorporateaspatialfactorintothemodel.ThemodeltobeestimatedinourcaseisaspecificversionoftheAIDSmodelwherecensoreddataandspatialfactorareconsidered.Themodelingofdemandsystemswithhousehold‐levelmicrodatahasthe advantage of providing a large and statistically rich sample avoiding theproblemofaggregationoverconsumers.Intheotherhanddetailedmicrodatamaycause a problem of censored commodity purchases, especially when a verydetailed classification for the commodities is used. Not accounting for the zeroconsumption biases the estimation of the parameters of the model and it mayproduce a selection bias if we do not incorporate these observations into theestimationprocess.Dealingwithcensoreddataismorecomplicatedinthecaseofdemand systems than in a case of the econometric estimation of one singleequation. The complication arises from the necessity of ensuring nonnegativeestimatesofthequantitiesconsumed;therequirementofincludingtheconstraintsimposed by economic theory; and the numerical problem of having to evaluate

4This isausualproceduretoreplacepricesthataremissing,Dongetal. (2004)andAtuestaandParedes(2012)usethesameprocedureforMexicoandColombia,respectively.5 This procedure to determinemissing prices implies assuming that the households that do notreport the quantities consumed or households with censored data are facing the averagecommodityprices.

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high‐dimension cumulative density functions during the estimation (Dong et al.,2004).

To address these problems we will follow the two‐step method proposed byShonkwiler and Yen (1999), which improves the previous “favorite” two‐stepestimationprocedureofHeienandWessells(1990).InthefirststepweestimateaPROBIT regression with a dependent binary variable that represents thehousehold decision of consuming or not, which takes the value of 1 if thehouseholdpurchasesthecommodityandthevalueof0ifnot,whichdependsonaset of socioeconomic variables that are used as regressors. The PROBIT modeldeterminestheprobabilitythatagivenhouseholdconsumesagivengoodanditisusedtoestimatethecumulativedistributionfunction(Φ)andthenormaldensityfunction(ϕ).ThesecondstepincludesthecumulativefunctionΦ asascalarinthe equations for shares,while the density functionϕ is included as an extraexplanatoryvariable:

w Φ x α γ log p β log x P⁄ c R δϕ x [15]

where are dummy variables for the 17 Spanish regions, called AutonomousCommunities (NUTS II regions in European nomenclature), that representunobservableheterogeneityacrossspatialunitsandidiosyncraticcomponents; isaparameterassociatedtotheregionaldummy ;and isanextraparameterassociatedwiththedensityfunction.

The set of n– 1 equations like [15] conform the demand system,where n is thenumber of shares, being the last share recovered as a residual of the remaining

n– 1 ones. Once this demand system is estimated, the parameters are used torecover the expenditure function of a representative household for each spatialunitandtheSCOLindexdefinedin[1]iscalculated.

Theestimatesof thePROBITmodel for the first stepof theShonkwillerandYen(1999)methodologyarenotshownhere,wehavePROBITestimatesforeachyearofthestudysince2006to2011.Abinaryvariablewhichrepresentsthedecisionofconsumption of each one of the ten food groups at the sample is regressed as afunction of the socioeconomic variables, demographic variables represented asregiondummies, that isonedummy foreachof theregion;andonedummythattakesthevalue1ifthehouseholdislivinginacapitalcityand0otherwise.

Theestimationresultsshowthatallthesocio‐economicvariablesaresignificantatthe 1% level. There is evidence that there are significant different purchasepatterns across NUTS‐II regions, given that all the regional dummies, with theexceptionofonlyafew,aresignificantatthe1%levelforallthecommodities.Theestimatesalsoshowthat theeffectofbeing located ina capital city isanegativefactor in the decision of consumption of all commodities, since in all the

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commodity regressions the coefficient of the capital city dummy is negative andsignificant at the 1% level. The results of the PROBIT models will be used tocalculatethecumulative(Φ)andthedensity(ϕ)functions,whichareincludedasregressorsinthesecondstepintheestimationoftheAIDS.

As in the first step, in the estimation of the second step we have one demandsystemestimationforeachyear,2006to2011.TheparametersoftheAIDSmodelare recovered by applying Nonlinear Seemingly Unrelated Regression (NLSUR),which estimates a system of nonlinear equations by Feasible GeneralizedNonlinear Least Squares (FGNLS). The parameters estimates are neither shownhere6,butmostoftheestimatesaresignificantatthe1%level.Theseestimatesarerequired in order to recover the utility level and the expenditure equationdescribed in [2] as a function on prices and income. More specifically, thehouseholdinthemedianoftheexpendituredistributionineachregionistakenasrepresentative household, being the prices in [2] set to median prices on eachregion. Once the value for this expenditure function is calculated, it is used tocalculate the SCOL index defined in equation [1]. Note that this formulationrequireshavingacommonutilitylevel inordertomakeregionalcomparisonsatconstantutility.Inourcasetheutilitylevelofthemedianhouseholdintheregionof Madrid in year 2011 is taken as reference for the computation of the index,whichmeans thatMadrid2011 is thebenchmark forouranalysis.TheresultsofthisSCOLindexareshowninTable1.

>>>INSERTHERETABLE1<<<

ThisTable1shows theresultsobtained forourconstructedCostofLiving Indexforthe17regionsandfortheperiod2006‐2011.Thesedatareflectthespatialandthetemporaldimension,sotheypermitcomparisonsacrossthespaceindifferenttimemomentssimultaneously.Moreexactly,showstheCOLinEurosofattainingthe utility level ofMadrid in year 2011. These quantities show the total annualexpenditurethatthemedianhouseholdofeachregionneedstoacquiretheutilitylevel of reference, Madrid in 2011. Note, that we emphasize in the issue of theutility level because the approach is very different to the fixed basket approachthat reflect the cost of attaining a fix basket of goods in different places or indifferentmomentsoftime.So,Table1doesnotrepresentthecostofattainingthebasketofgoodsofMadrid2011,buttheutilitylevel.

6 All the estimates are available on request to authors.

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4.Resultsdiscussion.

4.1.Anoverview.

ThisdatarepresentedinTable1,previoussection,isthefirstevidencethatthereexist relevant differences in price levels across the regions in Spain. The sameresultsarepresentedinTable2butintheformofanindex.WehavedividedalltheexpenditurefunctionsofalltheSpanishregionsbytheexpenditurefunctionoftheregionofMadrid(thatcontains thecapitalandthe largestcityof thecountry) inyear2011,forthisreasonMadrid2011takesthevalue1.

>>>INSERTHERETABLE2<<<

Wecanseetheevolutionofthecostoflivingduringtheentireperiodthatshowsthatallregionshadadownwardevolutionofpricesinyear2006thatturnedinasharpincreasesince in2007to2009whenpricesreturntodecrease.TheregionmostexpensiveisBasqueCountryin2009a34%higherthanthecheapestregion(CastileLaMancha2007).ThismeansthataconsumerintheBasqueCountrypaida37%more thana consumer inCastileLaMancha forattaining the sameutilitylevel.

Most of the regions have lower prices than Madrid 2011. The exceptions areCatalonia,BasqueCountryandNavarra.Thesethreeregionshavemaintainedthistendencyinpricessince2006,exceptCataloniain2006and2007andNavarrain2007,when the costof livingwas less than the costof livingofMadrid in2011.Additionally,intheperiod2008‐2009wecanobservethatafewmorecaseswerehigh price regions, showing a cost of living higher than Madrid in 2011. TheseregionsareCanaryIslands,ValenciaandMurcia,allofthemareimportanttouristregions.

In the works of Alberola & Marqués (2001) and Garrido et al. (2012) we findsimilarconclusionsfortheSpanishcase.Theformerarrivedtotheseconclusionswith their constructed series of regional CPIs finding that regional pricedivergences at a regional level are evident and persistent. Their study usesquarterlydataoftheINEfrom1961to1998andlikeustheyfindZamorainCastileLa Mancha the lowest inflation region and Vizcaya in the Basque Country thehighestone.Garridoetal.(2012)proposedaCostofLivingIndexthatincorporatesthe cost of acquiring or living in owned housing. Their data proceed from theMinistryofHousingfrom1995to2007andtheynotethattheregionaldifferenceshavegrownovertime.

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4.2.Differentbehaviorsbetweentherichestandpoorestregions.

In Figure1we represent theCPI samples from2006 to 2011 in someof the 17Spanishregions.Thisisarepresentationoftimeseriesofpriceofthepoorestandthe richest regions but it is not a representation of price levels. Our alternativeconstructeddataisnecessarytoassesswhetheratagivenpointoftimethepricesarehigherorlowerinoneregionrespecttoanotherone.

>>>INSERTHEREFIGURE1<<<

Wecanobservethattheoveralltrendsarethesameacrossregions.Thereisnogapbetween the poorest (Andalusia, Castile La Mancha and Extremadura) and therichest (Catalonia, Navarra and Basque Country) throughout the sample period.This result is not in the line neitherwith the literature about regional prices inSpain,norwiththeregionaleconomics.Alberola&Marqués(2001)andGarridoetal.(2012)evidencesubstantialandpermanentdifferencesinpricesamongSpanishregions.ThesameisobservedinFigure2:

>>>INSERTHEREFIGURE2<<<

InFigure2weshowtheevolutionofourconstructedcostof living for thesamethreerichestregionsandthesamethreepoorestregionsfortheperiod2006‐2011derivedforthedataoftheTable1.BycontrastwiththeFigure1andaccordingtothe regional economics, with the data represented in Figure 2, we appreciateconsiderable differences in the cost of living among Spanish regions, moreprecisely, between the poorest (with the discontinuous line) and the richestregions(solidline).Wecanappreciatethisheterogeneityinthewayofanexistinghigh‐priceareaformedbyNavarra,CataloniaandBasqueCountryandalow‐priceareaformedbyAndalusia,CastileLaManchaandExtremadura.

One could think that an important reasonbehind the strike differences betweentwoindexescouldbethattheCPIdatarepresentstheentireconsumptionbasketwithallkindofgoodsandservices,incontrastwithourCostofLivingIndexwhichrepresentsonlythefoodgroup. InFigure3wecancontrastthis issue.Again,thefoodCPIdatashowthesameresultsasthewholeCPIrepresentedinFigure1,thatis, no price differences among regions. After that, the Cost of Living datarepresentedinFigure2couldbeconsideredabetterandmorerealisticindicatoroftheeconomicsituationbothforthewholecountryandataregionallevel.

>>>INSERTHEREFIGURE3<<<

TheFigure3showsupanimportantproposaloftheregionaleconomics,whichisthat the high income regions support higher prices than lower income regions(Kosfeld et al., 2007). Suedekum (2006) in his paper also indicates a strongcorrelationbetweenCostofLivingIndexandnominalearningsindicatorssuchasincomeandwage.AswecouldseeinFigure1theseproposalscannotbeinferredfromtheCPIofficialdata.

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Anotherimportantissuetobetreatedinthepaperistheevolutionofbothindexesoverthetime.WeseethisrepresentedinFigure4theCostofLivingIndexgrowthratederivedfromthedatareportedinTable2.

>>>INSERTHEREFIGURE4<<<

WhiletheCPIpresentssimilarincreasesinpricesinallSpanishregionsaround12‐15%in2011respecttotheyear2006,ourCOLIndexshowsbothincrementsanddecrementsinpricesveryfarfromthemagnitudesrecoverbytheCPIrangingfroman 8.04% increase in Catalonia to a decrease of 9.82% in Balearic Islands. ThemaximumgapintheCPIin2011amongregionsis5.19%whilewithourCOLthegapisalmost18%.Inaverage,thedivergencebetweenthemost inflationaryandthelessinflationaryregionsoverthewholeperiodisaround15‐19%withourCOL.

Also,derivedfromthecomparisonbetweenFigures1and4isthecontrastintheevolutionofpricesintheperiodstudied.RespectthisissuetheBalassa‐Samuelsonmodel postulates a positive correlation between economic performance andincreases in prices. This implies that higher growth should be associated withhigherinflation(Alberola&Marqués,2001).Ifweobserve,again,theFigure4wecan see that, effectively, theworst years of the actual economic crisis, 2010 and2011, present a downward trend in the cost of living, contrary to the evidencefoundintheCPIdatarepresentedinFigure1whichevidenceasharpincreaseinpricesinthisperiod.

4.3.TheCOLindexfordifferentincomegroups.

Itmakessense touse theCostofLiving Indexasageneral inflationmeasure fordeciding on basic political issues, as the Spatial Cost of Living Index is moreappropriate for regional policieswewould need to construct a separate Cost ofLivingIndexfordifferentgroupsofpeopleifgovernmentswouldliketoreachthemore effectiveness of social policies targeted to groups of vulnerable anddisadvantagedpeople(Wardetal.,2011).

As Deaton (1998) argue for constructing a separatemeasure by income groups,thereisnoguaranteethatchangesinpricesfacedbyhouseholdsacrosstheincomedistributionwere similar and the index shouldhave the sufficiently flexibility toallow inflation to vary across the income groups. Also, Kuznets (1996), amongothers,hasdocumentedthetendencyforthepricefoodtoriserelativetothepriceof other goods as income grows. This clearly disfavors the poor households.Loughrey (2012) andSlesnick (2002) argue thatmovements in consumerpricescombined with the heterogeneous consumption baskets of different householdsmeansthatthecostoflivingduetopricechangescandifferconsiderablybetweenhouseholds.

ForalltheseargumentswehaveestimateaCostofLivingforthe1stdecileand9thdecile of the distribution. Table 3 and Table 4 show these estimations. Table 3

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showtheestimatedCostofLivingIndexofattainingtheutility levelofreference,that is, as always, Madrid in 2011, for the households of the 1st decile of thedistributionby regions and for theperiod2006 to2011; andTable4 shows thesameestimatesbut forthe9thdecileof thedistribution.Thedifferencesbetweenthe highest and the lowest cost of living each year is significantly bigger forhouseholdsofthe1stdecilethanforthe9thdecileones.Thisdifferencerangesfrom36% tomore than 50% for the formers, and between 23% and 30% for the 9thdecile,dependingoftheobservedyear.Forexample,forthe1stdecilehouseholdsin 2011, Castile La Mancha had the lowest cost of living with a value of 873.4Euros,whilethepooresthouseholdsinNavarrahada50.7%highercostoflivinginthe same yearwith a value of 1,315.5 Euros. Therewere not, however, so largedifferences in the costs of living of the richest households; the gap between thehighestcostoflivinginEurosin2011,reachedinBasqueCountry,andthelowestcostoflivinginEurosin2011,reachedinCastileLaMancha,was30.21%.

In the case of the 1st decile of the distribution Castile La Mancha and Navarramaintainedtheirpositions(thelowestandthehighestcostofliving,respectively)since2008,butthegapbetweenthemgrowthcontinuouslysincethen,morethana10%andbeingtheincrementbetween2010and2011,whentheeconomiccrisiswasstronger,of3%.Isveryinterestingmakethesameanalysisforthe9thdecileofthe distribution. In this case, the cheapest (Castile La Mancha) and the mostexpensive (Basque Country) regions havemaintained their positions since 2007withacontinuouslygrowthofthegapbetweenthemuntil2010.Butbetween2010and2011,thisgrowthturnedtoadecreaseinthecostoflivingofalmosta4%inthehardperiodoftheeconomiccrisis.Importantevidencederivedfromthisdataisthatrichesthouseholdshavebeenbeneficiatedfromtheconsequencesandfromthepricepoliciesderivedfromtheeconomiccrisis.Whilepooresthouseholdsnotonlytheyarenotinthesamestandardoflivingaftertheeconomiccrisis,buttheyare considerably worst. This is the evidence that price influence inequality in averyimportantway,andinflationisoftenreferredtoasthe“cruelesttax”becauseofitsdisproportionatereductioninthewelfareofthepoor(Slesnick,2002). Thesame result has been found in Somerville (2004) for Ireland where there is anegativecorrelationbetweenincomerankandtheindexgrowth.Incontrast,IrvineandMcCarthy (1978) found in Ireland thegrowthof the costof livingpositivelyrelatedwiththeincomebutonly0.14%perannumandforaveryremoteperiod:1968‐1974.

WeillustratetheseresultsinFigure5andFigure6.Inbothfigureswecanseetheevolution of the Cost of Living Index respect year 2006 for the 1st decileshouseholds,with thediscontinuous line, and for the9th decileshouseholds,withthesolidline.Figure5representstherichestregionsforbothdecile1standdecile9th.TheevolutionoftheCostofLivingIndexfrom2006to2010isalmostidenticalin all regions and for both deciles, but in 2010 the differences between the twodeciles are illustrated. We clearly see the rise in the cost of living in the three

15

regionsforhouseholdsofthe1ºdecile,and,contrary,weseethedownwardcostofliving for the richesthouseholds.Themost striking fall in the cost of living tookplaceinNavarrafortherichesthouseholdswhichin2011theyhadacostofliving3.78%belowthecostoflivingof2006.

>>>INSERTHEREFIGURE5AND6<<<

InFigure6theevolutionoftheCOLforthepoorestregionsisrepresented.Therearemoreheterogeneousevolutionbetweentheregionsand,also,betweenthetwodecilesofthedistributioninsidethem.ThemostremarkableinthisFigure6istheenormousgapbetweenthe1standthe9thdecilesinCastileLaManchawheresinceyear2006theCostofLivingIndexhasbeenfallinginthepooresthouseholdswhiletheoppositehasbeenoccurinthesamemagnitudeforrichesthouseholds.Also,inthis Figure 6 we can observe the decline in the cost of living for the 9th decilehouseholdsinthethreeregions,althoughwecannotseeasclearasinfigure5theriseinthecostoflivingforthepooresthouseholds.Fromthen,itclearlyseenthatpoorhouseholdsintherichestregionsaremoredamagedthanpoorhouseholdsinthepoorestregions,and,inaddition,thesehouseholdssupportedin2011aCostofLivingwhichwasabovetheCostofLivingoftherichesthouseholds.

5.Summaryandconclusions.

In this article, we have estimated a demand system of Spanish households tomeasure the Cost of Living of these households across the Spanish regions andalongtheperiod2006‐2011.FollowintheKonus’(1939)ideaitispostulatedthatcomparing two periods of time or two regions in the space, a household whichfaces two different price levels tries to adjust its commodity basket in order tomaintain a constant utility level with the minimum expenditure cost. Since theSpanish CPI provides a poor approximation of the Cost of Living, our proposedindexisabetterestimatebecauseisconsistentwiththemicroeconomictheoryanditmaintainstheutilitylevelconstantinsteadofthebasketofgoodsastheCPIdoes.Moreover, the CPI only permits comparisons along time taken a base year asreference,butwithourCOLIwecancompareacrossthespaceandalongthetimesimultaneously. For calculating the COLI with the Spanish Household BudgetSurvey we apply the AIDS approach of Deaton and Muellbauer (1980) and weapply the two‐step methodology of Shonkwiller and Yen (1999) to address theproblemsderivedofhavingcensoreddatainthesurvey.

TheresultsobtainedshowsstrikingdifferencesinpriceslevelsacrossregionsinalltheperiodofstudycontrarytotheevidenceobtainedwiththeCPIserieswhichdonot reflect any regional price differences. The COLI shows different behaviorsamongregionsaccordingtotheregionaleconomics,wecanseeagapbetweentherichest regions and the poorest ones, clearly distinguishing a high price areaformedby the regionswith thehighestCOLanda lowprice area formedby the

16

regionswiththelowestCOL.Thisresultisinlinewithanimportantproposaloftheregionaleconomics:thehighincomeregionssupporthigherpricesthanthelowerincome regions and the strong correlation between nominal indicators such aswagesand incomeand theCOLI.Respect to the timedimension,wecanobservethat our COLI evolves in a similar way than the economic cycle. While the CPIpresentsasmooth increaseofpriceeveryyear,ourCOLIevolvesaccordingwiththe economic performance, presenting a strong decrease in prices in the worstyearsoftheeconomiccrisisthatis2010‐2011.

Anotherinterestingpointof theCOLI is thepossibilityofestimate it fordifferentincome groups.We find a disproportionately impact of prices across the incomerange implying important policy issues relating with welfare. We find that thepoorest households are considerableworst in terms of standard of living in theeconomiccrashin2010thantherichestones.Wecanobservethatin2010thefirstdecilehouseholdhaveexperimentedanincreaseintheircostofliving,contrarytothehighestincomedecilewhichhasadecreaseintheircostofliving.

Thispapershowhow differences inprice levelsacrossregionsexistandmatterforeconomicoutcomesandshouldbetakenintoaccountineverywelfarepolicy.AlthoughourCOLIisnotwithoutshortcomings,weconsiderthatitcontributestoabetterunderstandingofthecostoflivingandwehopethatprovideshelpinfurtherresearchresults.

17

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GARCÍA‐LUQUE,O.,LAFUENTE,M.andFAURA,U.(2009)Disparidadterritorialdela pobreza dinámica en España. Estudios de Economía Aplicada, 27(2), pp.417‐436.

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Table1.CostofLivinginEurosoftheutilitylevelofMadrid‐2011forSpanishRegions(2006‐2011).

2006 2007 2008 2009 2010 2011

ANDALUSIA 3,005.79 2,872.02 3,088.22 3,117.23 3,092.39 2,962.75

ARAGON 3,099.97 3,016.56 3,306.99 3,443.64 3,335.55 3,206.52

ASTURIAS 2,970.93 2,872.15 3,214.60 3,181.32 3,202.55 3,090.60

BALEARICISLANDS 3,299.84 2,889.32 3,235.34 3,315.72 3,167.62 2,975.65

CANARYISLANDS 3,301.14 3,204.38 3,485.03 3,530.19 3,409.86 3,299.80

CANTABRIA 3,194.50 2,920.81 3,274.56 3,405.51 3,251.09 3,214.63

CASTILELEON 2,887.03 2,796.28 3,002.50 3,150.07 3,069.40 2,921.94

CASTILELAMANCHA 2,772.06 2,626.53 2,882.50 2,923.28 2,826.53 2,717.92

CATALONIA 3,180.41 3,137.10 3,549.84 3,664.44 3,573.37 3,436.15

VALENCIA 3,455.48 3,165.39 3,419.23 3,401.65 3,267.92 3,125.40

EXTREMADURA 2,942.53 2,969.35 3,217.75 3,280.05 3,169.53 2,985.09

GALICIA 2,817.26 2,829.97 3,081.35 3,199.09 3,193.36 3,011.17

MADRID 3,377.43 3,220.62 3,378.90 3,536.19 3,484.91 3,361.77

MURCIA 3,349.53 3,011.22 3,562.04 3,469.82 3,361.54 3,222.82

NAVARRA 3,384.91 3,315.27 3,590.28 3,711.17 3,702.11 3,605.04

BASQUECOUNTRY 3,407.74 3,415.26 3,730.86 3,866.54 3,794.65 3,641.46

LARIOJA 3,424.19 2,970.43 3,301.45 3,351.10 3,323.35 3,186.53

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Table2.CostofLivingIndexwiththeutilitylevelofMadrid‐2011forSpanishRegions(2006‐2011).

2006 2007 2008 2009 2010 2011

ANDALUSIA 0.8941 0.8543 0.9186 0.9273 0.9199 0.8813

ARAGON 0.9221 0.8973 0.9837 1.0244 0.9922 0.9538

ASTURIAS 0.8837 0.8544 0.9562 0.9463 0.9526 0.9193

BALEARICISLANDS 0.9816 0.8595 0.9624 0.9863 0.9422 0.8851

CANARYISLANDS 0.9820 0.9532 1.0367 1.0501 1.0143 0.9816

CANTABRIA 0.9502 0.8688 0.9741 1.0130 0.9671 0.9562

CASTILELEON 0.8588 0.8318 0.8931 0.9370 0.9130 0.8692

CASTILELAMANCHA 0.8246 0.7813 0.8574 0.8696 0.8408 0.8085

CATALONIA 0.9461 0.9332 1.0559 1.0900 1.0629 1.0221

VALENCIA 1.0279 0.9416 1.0171 1.0119 0.9721 0.9297

EXTREMADURA 0.8753 0.8833 0.9572 0.9757 0.9428 0.8880

GALICIA 0.8380 0.8418 0.9166 0.9516 0.9499 0.8957

MADRID 1.0047 0.9580 1.0051 1.0519 1.0366 1.0000

MURCIA 0.9964 0.8957 1.0596 1.0321 0.9999 0.9587

NAVARRA 1.0069 0.9862 1.0680 1.1039 1.1012 1.0724

BASQUECOUNTRY 1.0137 1.0159 1.1098 1.1502 1.1288 1.0832

LARIOJA 1.0186 0.8836 0.9821 0.9968 0.9886 0.9479

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Figure1.ConsumerPriceIndexbyregionsfor2006‐2011.

‐0,10

‐0,05

0,00

0,05

0,10

0,15

0,20

2007 2008 2009 2010 2011

ANDALUCIA

CATALUÑA

EXTREMADURA

NAVARRA

PAIS VASCO

CASTILLA LAMANCHA

23

Figure2.CostofLivinginEurosbyregionsfor2006‐2011.

2500

2700

2900

3100

3300

3500

3700

3900

2006 2007 2008 2009 2010 2011

ANDALUCIA

CATALUÑA

EXTREMADURA

PAIS VASCO

CASTILLA LAMANCHA

NAVARRA

24

Figure3.FoodCPIgrowthratefor2007‐2011,Base2006.

‐0,10

‐0,05

0,00

0,05

0,10

0,15

0,20

2007 2008 2009 2010 2011

ANDALUCIA

CATALUÑA

EXTREMADURA

PAIS VASCO

GALICIA

NAVARRA

25

Figure4.CostofLivingIndexgrowthratefor2007‐2011,Base2006.

‐0,10

‐0,05

0,00

0,05

0,10

0,15

0,20

2007 2008 2009 2010 2011

ANDALUCIA

CATALUÑA

EXTREMADURA

NAVARRA

PAIS VASCO

CASTILLA LAMANCHA

26

Table3.CostofLivingIndexoftheutilitylevelMadrid‐2011byregionsandforthe1stdecile(2006‐2011).

2006 2007 2008 2009 2010 2011

ANDALUSIA 0.8941 0.8543 0.9186 0.9273 0.9199 0.8813

ARAGON 0.9221 0.8973 0.9837 1.0244 0.9922 0.9538

ASTURIAS 0.8837 0.8544 0.9562 0.9463 0.9526 0.9193

BALEARICISLANDS 0.9816 0.8595 0.9624 0.9863 0.9422 0.8851

CANARYISLANDS 0.9820 0.9532 1.0367 1.0501 1.0143 0.9816

CANTABRIA 0.9502 0.8688 0.9741 1.0130 0.9671 0.9562

CASTILELEON 0.8588 0.8318 0.8931 0.9370 0.9130 0.8692

CASTILELAMANCHA 0.8246 0.7813 0.8574 0.8696 0.8408 0.8085

CATALONIA 0.9461 0.9332 1.0559 1.0900 1.0629 1.0221

VALENCIA 1.0279 0.9416 1.0171 1.0119 0.9721 0.9297

EXTREMADURA 0.8753 0.8833 0.9572 0.9757 0.9428 0.8880

GALICIA 0.8380 0.8418 0.9166 0.9516 0.9499 0.8957

MADRID 1.0047 0.9580 1.0051 1.0519 1.0366 1.0000

MURCIA 0.9964 0.8957 1.0596 1.0321 0.9999 0.9587

NAVARRA 1.0069 0.9862 1.0680 1.1039 1.1012 1.0724

BASQUECOUNTRY 1.0137 1.0159 1.1098 1.1502 1.1288 1.0832

LARIOJA 1.0186 0.8836 0.9821 0.9968 0.9886 0.9479

27

Table4.CostofLivingIndexoftheutilitylevelMadrid‐2011byregionsandforthe9thdecile(2006‐2011).

2006 2007 2008 2009 2010 2011

ANDALUSIA 0.8556 0.8305 0.8980 0.9265 0.8958 0.8469

ARAGON 0.9157 0.8709 0.9712 1.0003 0.9921 0.8980

ASTURIAS 0.8771 0.8645 0.9581 0.9576 0.9932 0.9232

BALEARICISLANDS 1.0476 0.9142 0.9923 1.0393 0.9857 0.9285

CANARYISLANDS 0.9775 0.8956 0.9864 0.9814 0.9460 0.9190

CANTABRIA 0.9771 0.9127 1.0096 1.0193 1.0204 0.9350

CASTILELEON 0.8591 0.8095 0.8859 0.9291 0.8964 0.8340

CASTILELAMANCHA 0.8382 0.8007 0.8658 0.8865 0.8357 0.8141

CATALONIA 0.9810 0.9511 1.0837 1.1094 1.0847 1.0174

VALENCIANA 0.9786 0.9304 0.9941 1.0070 0.9708 0.9156

EXTREMADURA 0.8745 0.8778 0.9528 0.9574 0.9157 0.8646

GALICIA 0.8264 0.8283 0.9179 0.9443 0.9451 0.8778

MADRID 0.9979 0.9658 1.0398 1.0658 1.0703 1.0000

MURCIA 1.0333 0.8468 1.0042 0.9992 0.9602 0.8797

NAVARRA 0.9600 0.9346 0.9915 1.0385 1.0153 0.9236

BASQUECOUNTRY 0.9842 0.9861 1.0869 1.1385 1.1177 1.0601

RIOJA 0.9137 0.8478 0.9416 0.9553 0.9374 0.8491

28

Figure5.EvolutionoftheCostofLivingIndexbetween1stand9thdecilesoftherichest

regionsfor2006‐2011,Base2006.

‐0,15

‐0,1

‐0,05

0

0,05

0,1

0,15

0,2

2006 2007 2008 2009 2010 2011

CATALUÑA10 NAVARRA10 PAIS VASCO10

CATALUÑA90 NAVARRA90 PAIS VASCO90

29

Figure6.EvolutionoftheCostofLivingIndexbetween1stand9thdecilesofthepoorest

regionsfor2006‐2011.Base2006.

‐0,15

‐0,10

‐0,05

0,00

0,05

0,10

0,15

0,20

2006 2007 2008 2009 2010 2011

ANDALUCIA10 CASTILLA LAMANCHA10

EXTREMADURA10 ANDALUCIA90

CASTILLA LAMANCHA90 EXTREMADURA90