Regional variation in health care utilization and mortality

ContentslistsavailableatScienceDirect

Journal of Health Economics

jo u rn al h om ep age :w w w . e l s e vi e r . c o m / l o c a t e / e c o n b a s e

Regional variation in health care utilization and mortality ^夽

Anna Godøy

, Ingrid Huitfeldt

aInstituteforResearchonLaborandEmployment,UniversityofCalifornia,Berkeley,UnitedStates

bInstituteforSocialResearch,Norway

cStatisticsNorway,Norway

dFrischCentre,Norway

a rt i c l e i n f o

Articlehistory:

Received17April2019

Receivedinrevisedform25October2019 Accepted27October2019

Availableonline19February2020

JELclassification:

H51 I1 I11 I13 Keywords:

Healthcaresupply Healthcaredemand Healthcarespending Regionalvariation Healthoutcomes

a b s t ra c t

Geographicvariationinhealthcareutilizationhasraisedconcernsofpossibleinefficiencies inhealthcaresupply,asdifferencesareoftennotreflectedinhealthoutcomes.Usingcom- prehensiveNorwegianmicrodata,weexploitcross-regionmigrationtoanalyzeregional variationinhealthcareutilization.Ourresultsindicatethatplacefactorsaccountforhalfof thedifferenceinutilizationbetweenhighandlowutilizationregions,whiletherestreflects patientdemand.Wefurtherdocumentheterogeneousimpactsofplaceacrosssocioeco- nomicgroups.Placefactorsaccountfor75%oftheregionalutilizationdifferenceforhigh schooldropouts,and40%forhighschoolgraduates;forpatientswithacollegedegree,the impactofplaceisnegligible.Wefindnostatisticallysignificantassociationbetweentheesti- matedplaceeffectsandoverallmortality.However,wedocumentanegativeassociation betweenplaceeffectsandutilization-intensivecausesofdeathsuchascancer,suggesting high-supplyregionsmayachievemodestlyimprovedhealthoutcomes.

©2020TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCC BYlicense(http://creativecommons.org/licenses/by/4.0/).

夽 ThisresearchhasreceivedsupportfromtheResearchCouncilof Norway(grants#214338,#227117and#256678).Datamadeavailableby StatisticsNorwayandtheNorwegianPatientRegistryhavebeenessential forthisproject.Wearegratefultotheeditor,twoanonymousreferees, AnnaAizer,AmyFinkelstein,SimenGaure,SverreA.C.Kittelsen,Samuel KleinerandEdwinLeuvenforhelpfuldiscussions,suggestionsandcom- mentsatvariousstagesofthisproject.Commentsfromconferenceand seminarparticipantsatASHEcon2016,EALE2016,studentmicrolunchat theUniversityofChicago,2016,andESOPlunchseminarattheUniversity ofOslo,2016aregratefullyacknowledged.

∗ Correspondingauthorat:StatisticsNorway,Akersveien26,0177Oslo, Norway.

E-mailaddresses:anna.godoy@berkeley.edu(A.Godøy), ingrid.huitfeldt@ssb.no(I.Huitfeldt).

1. Introduction

Geographic variation in health care utilization has raisedconcernsofpossibleinefficienciesinthesupplyof healthcare.Inparticular,wemaybeconcernedthatsome regionsarespendingtoomuchonhealthcare,giventhat highutilizationregionstendnottoachievebetterhealth outcomes(Finkelsteinetal.,2016;Skinner,2011).Inthis paper, we leverage detailed microdata from Norway to answer two questions. First, to what extent is regional variationinhealthcareutilizationdrivenbyplace-specific factors, as opposed to variation in underlying patient health?Second,is higherregionalsupply ofhealth care associatedwithbetterhealthoutcomes?

Wearguethatbothquestionsarecentraltopolicymak- ersseekingtounderstandregionalvariationinhealthcare utilization.Inprinciple,regionalvariationinhealth care

https://doi.org/10.1016/j.jhealeco.2019.102254

0167-6296/©2020TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/

4.0/).

utilizationcanbedrivenbyvariationindemandfactors, suchaspatienthealth,aswellassupplyfactors,suchas physicians’practicestyles.Generally,demand-drivenvari- ationisseenaslessproblematic–regionsmayhavehigher orloweraverageutilizationratesdependingonwhether theinhabitantsrequiremoreorlesscare.Supplydriven variationonthecontrary,typicallysignalsinefficiencies.

Ontheone hand,variation inhospital regioneffects could indicate inefficiently high utilization if higher regionalsupply doesnottranslatetobetterhealth out- comes.Inthiscase,reducinghealthcareutilizationinhigh supplyregionscanleadtoefficiencygains.If,ontheother hand,highsupplyregionsdohavebetterhealthoutcomes, wemayinsteadbeconcernedwithutilizationbeingtoo lowinlowutilizationregions,andtheprescribedpolicy responsemayinvolveraisingutilizationratesinselected regions.Inotherwords,policyrecommendationsarelikely todependontheanswertothesecondquestion,thatis,the impactofhospitalregioneffectsonhealthoutcomes.

PreviousresearchfromtheU.S.hasuncoveredsubstan- tialregionalvariationinhealthcareutilization(Finkelstein etal.,2016;Songetal.,2010;Baickeretal.,2004;Fisher etal., 2009,2003a,b).Finkelsteinet al. (2016)findthat 40–50%ofthisvariationisattributabletopatientdemand factors, while the rest is explained by supply factors.

Themajorityofexistingpapers,however,concludesthat regional variation in health care spending is primarily driven by thesupply side (see, e.g. Cutler et al., 2018;

Chandraetal.,2012;Anthonyetal.,2009).

Meanwhile,itisnotaprioriclearifthesefindingswould translatetoanationalizedsinglepayerhealthcaresystem, wherehospitalsaresimilarintermsofpaymentschemes andphysicianincentives,andpatientsfacenotonegligi- blecopayments.Furthermore,existingliteraturefromthe U.S.ismainlybased ontheMedicarepopulation, which includesonlypatientsaged65yearsorolder.Thepresent paperdrawsondatafromtheentireNorwegianpopula- tionandincludesallmajorhospitalsinthecountryover theperiod2008–2013,removingconcernsaboutselection intothesample.¹

Thepresent paperisalsorelatedtoalargeliterature studyingthelinkbetweeneducationandhealthcare.There is a well-documentedsocioeconomicgradient in health outcomes(see,e.g.Cutleretal.,2008,forareview).Edu- cationisassociatedwithbetterself-reportedhealth,lower riskofbeingdiagnosedwithseveralconditionsandlower mortalityrates.Evidence suggeststhere mightalsobea socioeconomicgradientinhealthcareutilization.Papers fromEuropeancountriesandtheU.S.findthathighincome groupsaremorelikelytoaccessspecialisthealthservices comparedtolowerincomegroupswho are,ifanything, morelikelytousegeneralpractitionercare(VanDoorslaer etal.,2000;d’UvaandJones,2009).Similarpatternsare foundinNorway:Vikumetal.(2012)findthathighincome andmoreeducatedpatientsaremorelikelytoseeamed-

1 Datacontainallpublichospitalsaswellasprivateproviderscontract- ingwiththehealthauthorities.Veryfewhealthcareinstitutionsoperate asfor-profitinstitutionswithoutanycontractwithpublichealthauthor- ities.

icalspecialistorreceiveoutpatienttreatmentathospitals, butnorelationshipisfoundforgeneralpractitionervis- its(orinpatienthospitalcare).Moreover,Fivaetal.(2014) documentthathighlyeducatedindividualsutilizecentral- izedspecializedtreatmenttoagreaterextentthandoless educatedpatients.Thesefindingsareconsistentwithapat- ternwherethelocalavailabilityofhospitalservicesareless bindingformoreeducatedpatients,leadingustoexpect asmallerimpactofplaceforthisgroupcomparedtoless educatedpatients.

Identifyingandestimatinghospitalregioneffectsinthe presenceofpatientheterogeneityiscomplicatedbythefact thatpatientdemandforhealthcareislargelyunobserv- able.Individualdemographicvariablessuchasage,gender andeducation,areadmittedlycrudeproxiesforunderlying healthstatus.Toidentifyhospitalregioneffects,wefollow closelytheapproachofFinkelsteinetal.(2016),exploit- ingmigrationofpatientsacrosshospitalreferralregions.

Specifically,weestimatepanelmodelsofloghealthcare utilizationwithplaceandpatientfixedeffects,controlling fullyfortime invariantindividualheterogeneity.Similar modelswithtwo-wayfixedeffectshavebeenusedprevi- ouslyinresearchseparatingtheimpactsofworkersand firmsonwageinequality(e.g.Abowdetal.,1999,2002;

Combesetal.,2008;Cardetal.,2013;Gibbonsetal.,2014), aswellasinpapersstudyingexposuretoneighborhoods onintergenerationalmobility,schoolingandmortality(e.g.

ChettyandHendren,2018a,b;Chettyetal.,2016),teacher quality(e.g.Rothstein,2010;Jackson,2013;Chettyetal., 2014a,b;Mansfield, 2015),and physicianpracticestyles (Molitor,2018).

Themodelallowsformoversandstayerstohavesys- tematicallydifferentutilization,andforutilizationtobe correlatedwiththemovers’originordestinationchoices.

Thekeyidentifyingassumptionisthatconditionalonper- sonand place,mobilitypatternsare asgoodasrandom with respect to health. Our model thus mirrors a dif- ferencein differencesdesign,whichrequiresthat trends inlatenthealthdemanddonotvarysystematicallywith themovers’originordestination.Totestthisassumption empirically,weimplementaneventstudyapproach,esti- matingpatternsofhealthcareutilizationaroundthetime ofmigration.

By observing patterns of individual utilization when patientsmovebetweenregions,thetwo-wayfixedeffects model is able to credibly identify the relative impacts of each region on healthcare utilization. However, the estimatedregionfixedeffectsarenotbythemselvessuf- ficient todrawconclusionsonpolicyrecommendations.

First,whileweusethetermssupplyanddemandfactors throughoutthepaper,weacknowledgethattheresearch designofthispaperisnotidealfordistinguishingbetween thetwo.Under theassumptions ofourmodel,thetwo- wayfixedeffectsmodelallowsustoidentifyanaggregate placeeffect.Thisaggregatecomprisesanumberoffactors, includinghospitalpracticestyles,physicianpracticestyles, peereffectsandgeographiccharacteristicsoftheregion.

Second,unlessthesefixedeffectsareanchoredtoresult- inghealthoutcomes,wecannotknowifregionswithhigh fixedeffectshaveaninefficientlyhighsupplyofhealthcare,

orwhetheritisthelowutilizationregionsthatprovidetoo fewservices.

However, while the two-way fixed effects model is well suited tostudy utilization,the model maybe less wellsuitedtostudytheseresultinghealthoutcomes.One reasonisthatanumberofpotentiallyobservablehealth outcomes,includingmortality,bydefinitionareonce in a lifetime events. These outcomes are not possible to modeldirectlyinthetwo-wayfixedeffectsmodel.More- over,whilehealthcareutilizationpatternscanchangevery quickly,resultinghealthoutcomesmaybethoughtofas aslowerprocess,wherethequalityandquantityofcare affectoutcomeswithsignificantlags.Thetwo-wayfixed effectsmodelidentifiesshortruneffectsfromthewithin person variation, precluding the study of such delayed impacts.

In the second part of the paper, we address these shortcomingsby estimatingpanelmodelsof causespe- cificmortalityratesasfunctionsoftheestimatedhospital regioneffects.Thisanalysisrelatestoanunsettledliter- ature,mainlyfromtheU.S.,ontherelationshipbetween spendingandhealth(see,e.g.Doyleetal.,2015;Joyntand Jha,2012;Doyle,2011;Cutleretal.,2018).Ourmortality analysismakestwodistinctcontributionstothisfield.First, we linkmortalitytotheestimated patientand hospital regioneffectsratherthanaverageutilization.Second,we mergeinformationoncauseofdeathtoindividualutiliza- tiondatainordertoshedfurtherlightonthelinkbetween spendingandmortality.

Interpreting thecorrelationbetweenregionalutiliza- tion and mortalityrate is complicated by thefact that regions withsickerindividuals willtendtohave higher demandforhealthcare,drivingupaverageutilizationrates.

This form of omitted variable bias will lead toa posi- tivecorrelation betweenutilizationrates andmortality.

Meanwhile,ourempiricalstrategyexploitinginterregional migration yieldsasetofhospitalregioneffects thatare effectivelypurgedofpatientdemandfactors.Tobeclear, theestimatedhospitalregioneffectsmayreflectbothlocal variationinthesupplyofhealthcare,aswellasanum- berofotherfactorssuchasenvironmentalorsocialfactors.

This can in turncomplicate the analysisof health out- comes,aswecannotdistinguishbetweentheimpactsof health caresupplyperse andunobservedplacecharac- teristics. To addressthis issue,we leverage variation in utilizationintensityacrosscausesofdeath.Ifregionalsup- plyofhealthcareshiftsmortalityrates,wemightexpect moresignificantcorrelations betweenregioneffects and mortalityforconditionswherepatientstendtousemore hospitalservicesinthetimeleadinguptodeath,suchas cancer. Meanwhile,theseassociationsshouldbeweaker forcausesassociatedwithloweraverageutilizationrates, likedeathsfromexternalcauses.

Ourlinkingofestimatedhospitalregioneffectstomor- talityalsorelatesmoregenerallytotheliteraturethatlinks schoolorteachervalueaddedestimatestolongruneffects (see,e.g.Chettyetal.,2014a;Rothstein,2010).Asinthislit- erature,acausalinterpretationofthefixedeffectsonlong runoutcomesrequiresstrongassumptionsontheselection onobservables.Inparticular,unobserveddeterminantsof mortality,suchasunobservedhealth,mustbeunrelatedto

theestimatedplacefixedeffectsconditionalontheobserv- ablecharacteristics.Tobe clear,weare notclaiming to estimatetruecausaleffectsofspending,rather,themodels shouldbeseenaspredictive.²

Ourresultsshowthatplacefactorsaccountforroughly halfof thegapinaverageutilizationbetweenhighand lowutilizationregions.Thisresultisrobusttoanumber ofsensitivitychecks,includingalternativehospitalmarket definitions, using balanced samples to avoid composi- tionalbias,andrichermodelspecifications.Disaggregating resultsbyeducationalattainment,extendedmodelsdoc- umentthatplace-specificfactorsaremore importantto people with low education compared to people with highereducation.Estimatedeventstudymodelsindicate thatplace factorsaccountfor approximately75%of the differenceinaverageutilizationbetweenhighandlowuti- lizationregionsforhighschooldropouts,comparedto50%

forpatientswithahighschooldiploma.Forpatientswitha collegedegree,theeventstudymodelsfailtodetectaclear shiftinutilizationatthetimeofmove,suggestingnegligible impactsofplaceforthisgroup.

Theheterogeneouseffectsacrosseducationgroupsare notlikelytobeduetovariationinpatients’directcostsof treatment,ascopaymentsaresmallanddonotvaryacross regions.Existing evidenceindicatesthat moreeducated patientsmaybebetterequippedtosearchoutinformation onrisksandbenefitsofdifferenttreatmentoptions.Thus,it mayappearthatevenincountrieswithlowfinancialbar- riersinaccessingspecialistcare,moreeducatedpatients maymaintainanadvantageinaccessingspecialisthealth care.

Thesefindingsarealsorelatedtothebroaderliterature ontheeffects ofplace,inparticularChyn(2018)’sstudy of neighborhood effects, which documents differential impacts of placeby backgroundcharacteristics. Follow- ingthedemolitionofpublichousinginChicago,affected childrenwereforcedtomoveoutofdisadvantagedneigh- borhoods,leadingtosignificantlyimprovedoutcomeslater in life: Chyn’ssubsample analysistreatmenteffects are largerforchildrenfromfamilieswherenoadultsarework- ing,aswellasforchildrenfromhousingprojectswiththe highestpovertyrates.Ourfindingthatplacehasagreater impactonutilizationratesoflesseducatedadultssuggests thatthispatterncouldholdmoregenerally,eveninavery differentcontext.

Themortalityanalysisfindsnosignificantassociation of hospitalregion effects and all-causemortality.How- ever,thepicturechangessomewhatwhenwedistinguish betweenmajorcausesof death. In particular,themod- elsfindthathigherhospitalregioneffectsareassociated withastatisticallysignificantreductionincancerdeaths.

Moregenerally,higherhospitalregioneffectstendtopre- dictlowermortalityfromrelativelyutilization-intensive causesofdeath,suggestingthathighsupplyregionsmay infactachievemodestlyimprovedhealthoutcomes.

2 Ourapproachestimatingimpactsbycauseofdeathcanonlybeinter- pretedcausallyunderanarrowsetofassumptions,includingthestrong assumptionthatcauseofdeath(butnotdeathalone)shouldbeuncorre- latedwithotherplacecharacteristics.

Therestofthepaperisstructuredasfollows.Section 2describesthe institutional settingand data. Section3 presentstheeconometricmodelsanddiscussesidentify- ingassumptions.ResultsarepresentedinSection4.Section 5presentsestimatedmodelsofcausespecificmortality.

Finally,Section6concludes.

2. Institutionsanddata 2.1. Institutionalsetting

Health care expenses in Norway are mainly subsi- dizedbynationalinsuranceschemes.Hospitalservicesare rationedbywaittime,aimingatprioritizingpatientsbased ontheirmedicalneedsforhealthcare.Someservices,such asoutpatientadmissionsandvisitstoGPsaresubjectto smallcopaymentrates.In2015,theout-of-pocketpayment rateforanoutpatientprocedurewas320NOK(∼40USD).

However,onceapatient’syearlytotalout-of-pockethealth careexpendituresexceedabout2100NOK(∼260USD)all furtherexpenseswithinthatcalendaryeararereimbursed.

Health trusts, which are independent administra- tiveenterprisescomprisingseveralinstitutions,havethe responsibilitytodeliverhospital careservicestoinhab- itants residing in defined catchment areas. The health trustsaregovernedasasingleadministrativeunit with a centralized management group, i.e. they have a CEO anda board ofdirectors,and are themselvesownedby fourstate-ownedregionalhealthauthoritieswhohavethe overarchingresponsibilityforprovidingspecialisthealth care.³Patientswhoarereferredtospecialisthealthcare arefreetochoosetreatmentatanyhospital,butinpractice, veryfewendupatahospitalinanotherhealthregion.⁴

In this paper, we focus on care delivered by hospi- tals,which doesnot include theprimarycare sectoror specialistsoperatingoutsideofthehospitals.Therearesig- nificantinstitutionaldifferencesintheprovisionofprimary andspecialistcare.Hospitalsarefundedbytheregional healthauthorities,followingguidelinessetbythenational government.Inparticular,theactivity-basedpartof the hospitalreimbursement,whichisoursourceforcalculating individualutilization,containsnogeographiccomponents.

Reimbursements are made based on diagnosis-specific priceswhichreflecttheaveragecostoftreatinganypatient withthatspecificdiagnosis.Thismeansthatanydiagnosis willtriggerthesamehospitalreimbursementregardlessof thelocationofthepatientorthehospital,andregardlessof theactualcostsincurredintreatingthepatient.

Themanagementofprimarycareservicesismuchless centralized.Primarycarephysicians typicallyoperatein privatepracticewithreimbursementsfromthe govern- ment;they will have contracts with the municipalities butnotwiththeregionalhealthauthorities.Inaddition,

3 Therearefourregionalhealthauthorities,and31healthtrustsper 1.1.2018(24healthtrustsper1.1.2012).Reimbursementfromthestate totheregionalhealthauthoritiesentailsacombinationoffixedbudget andactivity-basedfinancing.

4 90%ofelectivesurgeriesareperformedwithinthepatients’own region,and22%choosesahospitalatanotherhealthtrust,butstillwithin thehealthregion(Huitfeldt,2016).

patientsarefreetochoosetheirownprimarycareprovider atanytime(uptotwotimesperyear);thisincludespeo- plewhoswitchprimarydoctorsfollowingamovebetween regions,butitalsoincludespatientswhoswitchdoctorfor otherreasons.Ontheonehand,thisyieldsagreatervari- ationinprimarycareprovidersbetweenpatients,onthe otherhand,weworrythat thechoiceofprimarydoctor is likelytobeendogenous tohealth, makingtheidenti- fying assumptions lesslikely to holdfor thesechanges.

Meanwhile,variationintheprovisionofprimarycareisa potentialdriverofutilizationdifferencesinspecialistcare.

ThesepatternswillbediscussedinmoredetailinSection 4.5.Tosummarize,theNorwegianhospitalsystemischar- acterizedbyuniversalcoverage, lowcopayments, and a highdegreeofcentralization.Hospitalsfacethesamefinan- cialincentives,andphysiciansathospitalsareemployed onfixedsalaryratherthanonafee-for-serviceorcapita- tionfeebasis.Thismayleavelessscopeforsupply-driven demand,andsimilarmoralhazardproblems.

2.2. Data,sampleandsummarystatistics

Theempiricalanalysisis basedondatathatcombine severaladministrativeregistersfromStatisticsNorway,the NorwegianPatientRegistry(NPR),andtheCauseofDeath Registry. Aunique personal identifieris provided every Norwegianresidentatbirthoruponimmigration,enabling ustomatchthehealthrecordswithadministrativedataof theentireresidentpopulationofNorway.

DataprovidedbyStatisticsNorwaycontainbirthand death dates, sex,district and municipalityof residence, countryoforigin,education,occupation,annualearnings and welfarebenefitsreceipt.These dataare linkedwith patientdatafromNPR,containingcompletepatientlevel observationsforallsomaticpublichospitalsandprivate hospitalscontracting withregionalhealth authorities in Norwayfrom2008onward.Recordsincludemainandsec- ondarydiagnoses(ICD10),surgicalandmedicalprocedures (NCSP/NCMP),timeofdeathsin/outofhospital,exacttime, dateandinstitutionofadmissionsanddischarges,dateof referral,diagnosisgroupsanddiagnosiscostweight.Each patientdischargedatasomatichospitalisassignedadiag- nosisgroupthatuniquelydeterminesthereimbursement rate.

Health care utilization is defined as an individual’s yearly total hospital care expenditures, calculated by applyingthediagnosisgroupsystemandprices(foryear 2012)oneachyear.

Our sample covers a period of six years, from2008 to 2013.For the baseline estimation sample, two addi- tionalrestrictionsareimposed.First,weretainonlypeople agedbetween30and75.Theassumptionsunderlyingour empiricalapproachmaybelesslikelytoholdforyounger andolderpersons.Foryoungerpeople,wenotethatindi- vidualsareexemptfromthelegalrequirementtoregister changeofaddresswhileenrolledineducation.Thiscould potentiallymakemobilitydatalessaccurateforteenagers andyoungeradults,whomaydelaychangingtheiraddress until after they complete schooling. Meanwhile, older adultsaremorelikelytomoveforhealthrelatedreasons.

Inaddition,weexcludepeoplewhomovebetweenhos-

Table1

Descriptivestatisticsofestimationsample.

Stayers Movers

Female 0.49 0.46

Norwegian-born 0.86 0.74

Enrolledineducation 0.08 0.15

Educationalattainment

Highschooldropout 0.42 0.32

Highschoolgraduate 0.27 0.23

College 0.30 0.45

Agefirstobserved

30–44 0.44 0.69

45–59 0.33 0.22

60–75 0.24 0.09

Firstobservedresidence

North 0.10 0.10

Mid 0.14 0.10

West 0.21 0.13

SouthEast 0.56 0.68

Annualhealthcareutilization(USD)

Mean 1184.6 906.3

Standarddeviation 5636.8 5296.6

Shareofpatient-yearswithzero 0.66 0.68 Averagenumberofyearsobserved 5.40 5.45 Sharewhodiesduringstudy 0.025 0.0049 Numberofpatient-years 15,080,854 634,012

Numberofpatients 2,792,692 116,367

Notes:Tableshowsdescriptivestatisticsformoversvs.stayersaged30–75 basedondatafortheperiod2008-2013.

pital referralregionsmore thanonce duringthe6 year period.Thisrestrictioneasestheeventstudyapproachas allmoverswillhaveonewell-definedmoveyear.Inthe robustnesssectionwerelaxthisassumption,andestimate thetwo-wayfixedeffectmodelwithnorestrictiononthe numberofmoves.Notethatboththerestrictiononageand numberofmovesareappliedonlytothebaselineestima- tionsampleusedtoestimatehospitalregionandpatient fixedeffects.Inthesubsequentanalysisof mortality,all agesareretainedintheanalysissample.

The resultingestimation sample contains 15,570,065 person-yearobservations.⁵Inourempiricalmodels,identi- ficationofhospitalregioneffectsisobtainedbyindividuals who move between regions. Table 1 shows descriptive statisticsforstayersandmoversseparately.Comparedto stayers,moversaremore likelytobemale andforeign- born.Moversarealsomorelikelytobeinschool–roughly 15%ofthemoversareenrolledineducationatthefirstyear ofobservation,comparedto8%inthestayersample.The averageeducationalattainmentlevelishigherformovers– 45%haveacollegedegree–comparedto30%ofthestayers.

SeeonlineAppendixTablesA2andA3formoredescriptive statisticsbyeducationalgroup.

On average, movers are younger than stayers; the majorityofmoversarebetween30and44yearsold.Resi- dentialoriginsarequiteevenlydistributedamongmovers and stayers,although slightlymore ofthemoverscom-

5Weadditionallyexcludeindividualswhomoveinthefirstorlastyear ofoursample,asthesedonotprovideanyusefulvariation.

pared to stayers originate from the South East region (capitalarea).

Theaveragepersonisfollowedfor5.4yearsinthestayer group,and 5.45in themoving group.Thereareseveral entryandexitroutesfromthesample:asmallsharedies duringthestudyperiod,2.5%inthestayergroupand0.5%

inthemovinggroup.Individualswillalsoenterandexit theagegroupsunderstudy(aged30–75),andtheremay bebothimmigrationandemigration;weonlyobserveres- idents.Thereare116,367uniquemovers,and2,792,692 uniquestayers(i.e.roughly4%movers).

The moving populationhas a slightly lower average annualutilization,whichagainislikelyduetothelower shareofelderlyamongthisgroup.Asmanyas67%ofthe moversnevervisitthehospitalduringthestudyperiod;the shareisonlyslightlylowerinthestayerpopulation.The distributionofutilizationisright-skewedforbothmovers andstayers.InonlineAppendixFig.A1weshowthefull distributionofutilizationinlogsandlevels.

Notethattheobserveddifferencebetweenstayersand moversdoesnotinitselfposeathreattotheinternalvalid- ityof thetwo-wayfixedeffects model, asourmodelis identifiedexclusivelyfromwithinindividualvariationin outcomes.Thedifferencesmay,however,beinformative abouttheexternalvalidityofourfindings,andifonewishes toextrapolatetheresultstothefullpopulation.Wediscuss thisfurtherinSection4.4.

Themaingeographicunitofanalysisisahospitalrefer- ral region (HRR). We will define these regions in two differentways:(i)28localhospitalsconditionalonthem havingbothmaternitywardandemergencyroom;(ii)19 healthtrustswithdefinedcatchmentregions.Somehealth trustsdonotservetheirowncatchmentregion;thesemay havedifferentfunctionsorbehighlyspecialized.Forboth definitions,thehospitalreferralregionsaredefinedbased onresidentialmunicipality.Wewillapplydefinition(i)of hospitalreferralregionsinourbaselineestimations;def- inition(ii)willbeused intherobustnesssection.Using definition(i),thereareonaverageabout1.9institutions withineachHRR.

Asdiscussed above,patients may seekmedical care outsidetheirownregionofresidence.Inoursample,we calculateaverageutilizationratesfortheHRRsbasedsolely onpatients’residenceregion,regardlessofwherecarewas actually provided. Aboutone fifthof total expenditures occuroutsideofapatient’sHRRofresidence.⁶

Fig.1showsthedistributionofyearlyaveragepatient utilizationacrossHRRs.⁷TheaverageHRRhasanaverage utilizationof1412USDperpatientperyear(standarddevi-

6 Thisisasubstantialshare.Ourregressionmodelwillidentifyandesti- mateasetofaggregateplaceeffects,theseestimatedeffectscouldreflect anumberoffactorsincludingvariationinhoweasyitistoaccesscare inotherregions,e.g.throughvariationintraveltimesorGPscultureof referringpatientstoout-of-regionprovidersofspecialistcare.However, ouranalysisfailstofindanysystematicrelationshipbetweentheshareof utilizationthatoccursoutsidepatients’regionofresidenceandtheesti- matedplaceeffects.Thatis,itdoesnotappeartobethecasethatplaces withmoreout-of-regionutilizationhavesystematicallyhigherestimated placeeffects.SeeonlineAppendixFig.A3.

7 Fig.1indicatesthattheremaybeanoutlierhospitalregion.Thisregion issmall,andaccountsforonly0.45%ofthesample,hence,theestimated

Fig.2. MapofNorway.Utilization(inUSD)byhospitalreferralregion.Figureshowsthegeographicdistributionofyearlyaverageutilizationperpatient inthe28hospitalregions,dividedintoquintiles.ThicksolidlinesmarkHRRborders;thinsolidlinesmarkmunicipalityborders.(Forinterpretationofthe referencestocolorinthisfigurecitation,thereaderisreferredtothewebversionofthisarticle.)

ation184USD).InonlineAppendixFig.A1weshowthatthe spreadissubstantialevenafterpurgingutilizationforsex,

placeshareisvirtuallyunchanged(47.6%comparedtobaseline49%)when excludingthisregion.

ageand educationaldifferences.Thegeographic pattern ofutilizationcanbeseeninFig.2,wherecolorsillustrate quintilesofhealthcareutilization.

Fig.1.Distributionofutilization(inUSD).Figureshowsthedistribution ofyearlyaverageutilization(inUSD)perpatientinthe28hospitalreferral regions.

3. Empiricalmodels

We begin our empirical analysis by disentangling the components of utilization attributable to place- specific heterogeneity,e.g. hospital qualityor physician knowledge;andpatient-specificheterogeneity,e.g.health endowment or preferences.Next,we usetheestimated placeandpatientcomponentstoshedlightontheirrela- tiveimportanceinexplainingdifferencesinaveragepatient utilizationacrosshospitalregions.⁸

3.1. Fixedeffectsmodels

TheempiricalspecificationcloselyfollowsAbowdetal.

(2002,1999andFinkelsteinetal.(2016)wherethedepen- dentvariabley_it,personi’slogofutilizationofhealthcare (plus1)inyeart,isexpressedasafunctionofindividualhet- erogeneity,hospitalregionheterogeneity,andmeasured timevaryingcharacteristics:

y_it=˛_i+_j(i,t)+X_it+ε_it, (1)

where i=1,...,N,t ∈

n_i1,...,n_iT

, and the function j(i,t)indicatesthehospitalregionjofindividualiinyear t, wherej=1,...,J.Thereare T_i observations perindi- vidualandN^∗=

iT_itotalobservations.⁹Thecomponent

˛_iistheindividualeffect,and_j(i,t)isthehospitalregion effect.TimevaryingcovariatesareincludedasX_it,andinthe baselinespecificationthisincludesfixedeffectsforcalen-

8Notethat,theaimofthissectionisnottoestimatetheindividual healthproductionfunction,nortoevaluatetheimpactofplaceonindi- vidualutilization.Rather,weaimatexploringsourcesofdifferencesin averagepatientutilizationbetweenhospitals.Wereturntothepotential implicationsofthisvariationinSection5.

9Inestimationofmodel(1)wedroptheyearofmove,aswedonothave informationontheexactdateofmove.Thisexclusionavoidsattributing utilizationtothewronghospitalregion.

daryearandage(in5-yearbins)only.¹⁰Weexplorericher versionsofEq.(1)intherobustnesssection.

Identificationof individual and hospital region fixed effectshingesonthepresenceofmovers,i.e.itrequires thepossibilitytoobservethesameindividualatdifferent hospitalregions(atdifferentpointsintime).

Asdiscussedin,e.g.Bonhommeetal.(2017),Lamadon etal.(2017)andFinkelsteinetal.(2016),causalinterpre- tationoftheparametersinEq.(1)restsontwoimportant assumptions. First, mobility needs to be exogenous to the utilization residual,which would follow if, e.g. the assignmentofindividuals tohospitalregions is random conditionalonallobservablecontrolsandtimeinvariant unobservables.Second,weassumealogadditivefunctional form.Thisimpliesthatindividualswhomovefromhospi- talregionj tojwillonaverageexperienceanaverage utilizationchangeof_j−_j,whereasthosewhomovein theoppositedirectionwillexperienceanaveragechange of_j−_j.

Theseassumptions flexiblyallowfor richpatternsof sorting,as themovingdecision mayberelated to˛_ior _j.For example,themodel allowsfor moversandnon- moverstohavesystematicallydifferentutilizationlevels, andforutilizationlevelstobecorrelatedwiththemovers’

originordestination.Moreover,mobilitymayberelated tocharacteristicsofhospitalsunrelatedtoutilization,such asgeographiclocation,andoftheindividual,suchasher earningspotential.Wereturntoathoroughdiscussionof thevalidityoftheidentifyingassumptionsbelow.

Tostudythesources ofutilizationdifferences across hospitalregions,weusetheestimatedpatientandhospital regionfixedeffectsinadecompositionexercise.Precisely we ask how much of the difference in average utiliza- tionbetweenhighutilizationregionsandlowutilization regionscanbeexplainedbythetypeofpatientstheyhave, and howmuch is duetoplace-specific factors(see,e.g.

Finkelsteinetal.,2016;Combesetal.,2008).¹¹

Asastartingpointforthedecompositionexercise,we usetheestimatesfromEq.(1),andaverageoverhospital referralregions:

y_j=cˆ_j+ˆ_j, (2)

wherey_jistheaverageutilizationathospitalregionj,ˆ_j aretheestimatedhospitalregioneffects,andwelabel ˆc_j asanaveragepatientcompoundeffect,comprisingfixed effectsforpatient,ageandyear.Hospitalreferralregions arethensplitintotwogroupsdependingontheaverage patientutilizationy_jatthehospitals;averageutilizationis abovemedianathospitalsingroupAandbelowmedian

10 Notethat,astheindividualfixedeffectsabsorbthecohorteffect,age andyearareperfectlycollinear.InTable3weshowthatourspecification isrobusttoalternativewaysofincludingageinthemodel.Inprinciple,our modelcouldalsoincludefixedeffectsforrelativeyearofmoving(where relativeyearfornon-moversarenormalizedtozero).Thisallowsthepos- sibilitythatthedecisiontomoveiscorrelatedwithhealthshocks.Inour baselinemodelwefocusonthesimplestmodelformulatedinEq.(1),but therobustnesssectionshowsthatinclusionofsuchrelativeyeardummies doesnotaffectourresults.

11 SeealsoonlineAppendixCforavariancedecompositionexercise.

ingroupB.Wenextcalculatethedifferencebetweenthe averagehospitalregioneffectestimatesinthetwogroups, andfinallydividebythedifferenceinaverageutilization.

Thisrendersahospitalregionshare _y^ˆA−ˆB

A−yB.Wesimilarly constructthepatientcompoundshareascˆA−ˆcB

yA−yB.

The hospital regionshare tells us how much of the differenceinutilizationbetweenhighandlowutilization regionscanbeexplainedbycharacteristicsofthehospi- talregionnetofpatientcharacteristics,whilethepatient compoundsharetellsushowmuchofthedifferenceinuti- lizationbetweenhighandlowutilizationregionscanbe attributedtopatientcharacteristicsalone.

3.2. Identifyingassumptions

Theestimatedhospitalregioneffectscanonlybeinter- pretedcausally ifmobility is conditionally independent oflatenthealthoutcomes.Tostructurethediscussionon endogenous mobility, we follow Card et al. (2013) and assumethattheerrortermε_itinEq.(1)consistsofthree separaterandomeffects:amatchcomponent_j(i,t),aunit rootcomponent_it,andatransitoryerrorω_it:

ε_it=_ij(i,t)+_it+ω_it. (3)

The match effect _ij(i,t) represents an idiosyncraticuti- lizationpremiumorreductionobtainedbyindividualiat hospitalj,relativetothebaselinelevel˛_i+_j.Matcheffects ariseif,e.g.somehospitalsarehighlyspecializedintreating certaintypesofpatients.Theunitrootcomponent_itcap- turespotentialdriftintheindividual’sutilizationovertime, suchashealthdeterioration.Thetransitorycomponentω_it representsanyleft-outmean-revertingfactors.Weassume that_ij(i,t)hasmeanzeroforalliandforallj;andboth_it andω_ithavemeanzeroforeachpersoninthesample.

Sortingonmatcheffects:Biascanariseifindividualssort tohospitalsbasedontheidiosyncraticmatchcomponent _ij(i,t).Thisformofsortingchangestheinterpretationofthe estimatedhospitalregioneffectssincedifferentindividuals havedifferentutilizationpremiumsatanygivenhospital, dependingontheirmatchcomponent.In thelimit,ifall movesareduetothematchcomponent,wecouldexpect allmovestoleadtoincreasedutilization.

Drift:Endogenousmobilitymayariseifpatientswith graduallydeclininghealthsystematicallymovetodifferent typesofhospitals.Ifindividualswithdeterioratinghealth systematicallymovetohighutilizationregions,wemight overestimatetheimportanceofhospitalregioneffects,as thedriftcomponent_it willbepositivelycorrelatedwith thechangeinthehospitalregioneffects.Inotherwords,Eq.

(1)willbebiasediftrendsinutilizationvarysystematically withthemovers’originordestination.

Transitoryerror: Shocksor fluctuationsin thetransi- toryerrorω_it maybeassociatedwithsystematicmoves betweenhigherandlowerutilizationregions.Forexample, ifindividualswhoexperienceanegativehealthshockare morelikelytomovetohigherutilizationregions,estimated hospitalregioneffectsmightbeamplified.

Fig. 3.Distribution ofdestination-origin differenceinlog utilization (ıi).Figureplotsthedistributionofıi,i.e.thedifferenceinaveragelog utilizationinthedestinationandoriginregions.Sampleisallmovers (N=707,464person-years).

3.3. Eventstudyframework

Wenowdiscusswhethertheidentifyingassumptions are likely to hold in our data. Although we can never entirelyruleoutthatchangesinunobservedhealthpredicts mobilitypatterns,henceviolatingtheidentifyingassump- tions, we canlook for patterns in our datato mitigate potentialconcerns.Wethereforeintroduceaneventstudy frameworktrackingindividuals’utilizationbeforeandafter theymove.Themodelservesadualpurpose:havingshown thatendogenousmobilitydoesnotseemtobeaconcern, theeventstudymodel’sestimateswillgiveafirstindication oftherelativeimportanceofpatientandhospitalregion effectsinexplainingvariationinaverageutilization.

Ifeveryonemovedfromlow-utilizationhospitalregion jtohigh-utilizationhospitalregionj,wecouldplotaver- ageutilizationbyrelativeyearof move,and thenstudy whetherthemoversincrease theirutilization.However, inthedataweobservepeoplemovinginbothdirections:

from hightolow utilizationregions and theotherway around.Thesemovescouldcanceleachotheroutandpro- duceaflateventstudyfigure.Moreover,the“magnitude”of themovesvariesconsiderably:whilesomepersonsmove fromregionsthatarefairlysimilar,othermovesarechar- acterizedbymuchlargerdifferencesinaveragehealthcare utilizationintheoriginanddestinationregions.Toaccount forthis,wefollowFinkelsteinetal.(2016)andaugmentthe standardeventstudymodeltoconsiderboththedirection andmagnitudeofthemove.Withthisinmind,wedefine

ı_i=y_j(i)−y_j(i)

as thedifferenceinaveragelogutilizationin thedesti- nation(y_j(i))andorigin(y_j(i))hospitalregions.ı_icanbe interpretedasascalingfactor,capturingthedirectionand magnitudeofi’smove.Fig.3showsthedistributionofı_i. Thedistributionisfairlysymmetricalwithmeanjustabove zerowhichmeansthatslightlymorepeoplemovefromlow tohighutilizationhospitalregionsthantherearepeople movingfromhightolowutilizationhospitalregions.

Havingdefinedtherelevantparameters,weformulate thefollowingeventstudyequation,wherethescalingfac- torı_iisinteractedwithasetofdummyvariablesindicating eventtimek:

y_it=˛_i+

k=−4

ˇ_k

ı_i×1(t−t_i^∗=k)

+X_it+ε_it (4)

wheret^∗_i denotestheyearofmoveofindividuali.Here, asbefore,˛_iarefixedeffectscapturinganytimeinvariant characteristicsofindividuali,includingunobservedchar- acteristicsthatarecorrelatedwiththechoiceoforiginor destinationregion,andX_itisavectorofage(in5-yearbins) andcalendaryeardummies.¹²

Theprimarycoefficientsofinterestaretheˇ_k,capturing theeffectsoftheeventtimecoefficientsmultipliedbythe scalingfactorı_i.Ourdataallowestimationofˇ_k fork=

∈[−4,4].Thecoefficients{ˇ₋4,...,ˇ4}areonlyidentified relativetoeachother;weusethenormalizationthatˇ₋₁= 0.

InOnlineAppendixBweshowthatiftheassumptions underlyingthetwo-wayfixedeffectsmodelhold,thecoef- ficientsˇ_kfromEq.(4)canberelatedtotheparametersin Eq.(1)asfollows:

ˇ_k=

_j(i)−_j(i)

y_j(i)−y_j(i)

if k>0 (5)

Since we donot have fullycontinuous data,in thecal- endaryearofthemove(k=0),thecoefficientshouldbe a positive number between these two values, i.e. ˇ₀ ∈

0,_y^j(i)−j(i)

j(i)−yj(i)

.Theeventstudymodelalsoservestogivea firstindicationoftherelativeimportanceofhospitalregion effects.Intuitively,ifdifferencesinutilizationaredriven entirelybydifferencesinpatientfactors,individualutiliza- tionisnotexpectedtochangearoundtheyearofmove.

Ontheotherhand,ifthevariationinaverageutilization isdrivenentirelybyhospitalregioneffects,individualuti- lizationshouldrespondwithaone-to-onechangewiththe magnitudeofthemove,i.e.coefficientsof1fork>0.

4. Results

4.1. Eventstudyresults

Fig.4plotstheestimatedcoefficientsˇ_ktogetherwith 95%confidenceintervals.Recallthatweidentified three forms of potentially problematic endogenous mobility:

drift,sortingonmatchingeffects,andcorrelatedfluctua- tionsinthetransitoryerror.First,thepatternofestimated ˇ_kbeforeandafterthemovegivesadirectindicationof thepresenceofproblematicdrift.Thefigureshowsnoclear

12Notethattheindividualfixedeffects˛_ialsoabsorbmoveyeareffects, aswerestrictthemodeltoindividualswhomoveexactlyonce.Aswiththe mainmodel,theeventstudyspecificationcouldadditionallyincludeevent yearfixedeffects.Thiswouldcontrolfordifferencesinthepropensityto move,butwouldnotaccountfordifferencesinthechoiceofdestination.

However,eventstudyestimatesarenotsensitivetotheinclusionofevent yearfixedeffects.

Fig.4.Eventstudyfigure.FigureshowspointestimatesofˇkfromEq.(4).

Fig.5. Eventstudybydirectionofmove.Figureshowspointestimates ofˇkfromEq.(4)whentheeventstudyisestimatedseparatelybythe directionofmove.Soliddots/redareadisplaysutilizationforindividu- alsmovingfromhigh-tolowutilizationregions(ıi<0),whilehollow dots/greenplotsutilizationforindividualsmovingfromlow-tohighuti- lizationregions(ıi>0).(Forinterpretationofthereferencestocolorin thisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)

systematictrendsinutilizationpriortomove,suggesting thatdriftinindividuals’utilizationisuncorrelatedwiththe movers’originordestination.Theeventstudyalsogivesan indicationofwhetherfluctuationsinthetransitoryerror ω_itsystematicallycorrelatewithmobilitypatterns.Gener- ally,wewouldexpectanysystematicmovinginresponseto gradualchangesinhealthstatustogiverisetoanupward trendintheestimatedˇ_kintheyearsleadinguptothe move.Theeventstudymodeldoesnotfindanyclearevi- denceofthis.

Therearealsonosignsofanytrendspostmove.Apos- itiveslopingpost-trendcouldbethecaseinpresenceof habitformation,wheretoday’spatientpreferencesarea functionofhistoric utilization.Ifthiswerethecase,we wouldexpectthatpeoplemovingfromhightolow uti- lizationregionsexperiencedmorepersistencecompared tooppositemoves(Finkelsteinetal.,2016).Toinvestigate thismoreclosely,wehaveestimatedaneventstudymodel whereweseparatebetweenpeoplemovingfromhighto lowutilizationregions,andpeoplemovingfromlowtohigh

utilizationregions.Fig.5indicatesthatboththesizeofthe jumpandthepost-trendaresimilarinthetwocases.

Similarly,migration due tolatentdemandfor health carerepresentsapotentialthreattoouridentificationstrat- egy.Considerapatientwhoexperiencesanegativehealth shockthatrequiresaspecifictypeoftreatmentthatisnot easilyavailableinherregionoforigin.Thiscouldcauseher tomovetoahighsupplyregionmotivatedbyaneedfor thisservice.Ifthis healthshockhappens graduallyover time,itwouldshowupaspre-trendsintheevent-study figures.However,amovewhichisprecipitatedbyasudden shiftinlatentdemandforhealthwillbiasourresults,sim- ilarlytootherunobservedconcurrenthealthshocks.We believethisisunlikely;ifahealthshockinducesindividu- alstomovetohigh-utilizationregions,onewouldexpect toseeelevatedutilizationinthefirstyearaftermovefor patientsmoving fromlow-tohigh-utilizationregions - Fig.5displaysnosuchpatterns.

Fig.5canalsobeusedtoevaluatetheassumptionofno sortingonmatcheffects.Toseethis,considerthecasewith systematicpositivesortingonmatcheffects.Inthelimit, allmoves mayleadtoincreasedutilization.Inthiscase, patientswho movefromhightolowutilizationregions wouldstillseeincreasedutilization.Estimatingtheevent studymodelonthissubsamplecouldyieldeventstudyesti- matesthatwerenegative.Meanwhile,ifthereisnosorting onmatcheffects,thechangeinutilizationaroundthetime ofmoveshouldbesymmetrical.InFig.5,individualsmov- ingfromlowtohighutilizationregionsseemtoexperience utilizationchangesthatarerelativelyequalinmagnitude (butofdifferentsign)toindividualsmovingfromhighto lowutilizationregions:i.e.whilethepostmovepointesti- matesmayappeartodifferbythedirectionofmove,the differencesarenon-significantinallyears.¹³Thisprovides suggestiveevidenceagainstthepossibilityofsorting on matcheffects.

Tofurtherassesstheimportanceofmatcheffects,we followCard et al. (2013)and estimatea fullysaturated modelthatincludesadummyforeachindividual-hospital regionpair.Ifmatcheffectsareimportant,thesaturated modelwillfit thedata much betterthantheadditively separable baseline model. Adjusted R² increases only marginally in the saturated model, implying that the improvementinfitismodest.¹⁴

Theabsence ofmatch effects also providesjustifica- tionforourlogadditivemodel.Asafurtherjustification forlogadditivity,wefollowFinkelsteinetal.(2016)and additionallyplotthechangeuponmoveinindividuallog utilizationagainst theaverage destination-origindiffer- enceinlogutilization(ı).Tothisend,wedividetheaverage differencebetweendestinationand origin intoventiles, andplottheaverageindividualchangeinutilizationupon moveforeachventile.OnlineAppendixFig.A4showsthat thisrelationshipislinear,andsymmetricaboveandbelow

13 Recallthateventtimeisscaledbyboththemagnitudeanddirection ofmove.Hence,utilizationforindividualsmovingfromhigh-tolowuti- lizationhospitalregionsdisplaysapositivejumpuponmovealthough individualsdecreasetheirutilization.

14 Baselinemodel:R²=0.4657,Adj.R²=0.3478.SaturatedmodelR²= 0.4693,Adj.R²=0.3494.

zero, lending further support to ourassumption of log additivity.¹⁵

As discussed in the previous section, if fluctuations in thetransitoryerrorω_it systematicallyaffect mobility patternsthroughgradualhealthdeterioration,wewould expecttoseeanincreasingtrendintheestimatedevent timecoefficientsˇ_k fork<0.Theestimatedcoefficients plotted inFig.3 do notexhibit acleartrend, indicating thatchangesinhealththathappenovertimedonotsys- tematicallycorrelatewithmobilitypatterns.Inabsenceof suchanincreasingtrend,theonlyremainingthreatwould beahealthshockthatinducessystematicmovingwithin thesameyear.Thoughthisisingeneraldifficulttover- ify,alikelyimplicationisthatsuchacuteconditionswould induceintensetreatmentimmediatelyfollowingthemove.

Ifso,thiswouldhavegeneratedaspikeinthefirstyear afterthemove,andperhapsbemoreprominentforper- sonsmovingfromlowtohighutilizationhospitalregions;

weobservenosuchpatternsinoureventstudygraphs.

Tosummarize,theestimatedeventstudymodellends supporttoourkeyidentifyingassumptionsofcondition- allyexogenousmobilityandlogadditivity.Fig.3alsogives a first indicationof therelative importance of hospital regioneffects.Theestimatedrelativeyearcoefficientsˇ_k exhibitapositivejumpatthetimeofthemove,from0to approximately0.4.Wecaninterpretthisastheplacefac- tors’shareofutilization,orviceversa,thatapproximately 1−0.4=0.6isthepatientshare.Next,wepresentresults fromthebaselinetwo-wayfixedeffectsmodel.

4.2. Fixedeffectsestimates

EstimationofEq.(1)byordinaryleastsquaresproduces coefficientestimates˛ˆ_i,ˆ_j(i,t), ˆ,and ˆε_it.Figure6plotsthe estimatedhospitalregioneffectsagainstaverageloguti- lization.Thefigureshowsanupwardsloping,fairlylinear relationshipbetweenthetwovariables:Hospitalregions withhigheraverageutilizationtendtohavehigheresti- matedfixedeffects.Lookingattheestimatedlinearslope coefficientgivesanestimateofthequantitativeimportance ofhospitalregioneffectsindeterminingaveragehospital regionutilization.Toillustrate,ifthegeographicalvaria- tioninaverageutilizationwasdrivenentirelybypatient effects,theestimatedhospitalregioneffectsshouldnotbe correlatedwithaveragehospitalregionutilization,yielding aslopecoefficientofzero.Intheoppositescenario,where geographicalvariationisentirelydrivenbyplacespecific factors,themodelshouldyieldaslopecoefficientof1.The estimatedslopecoefficientof0.49thusindicatesthatvari- ationinhospitalregioneffectsaccountsforroughlyhalf ofthedifferenceinaverageutilizationbetweenhospital referralregions.

Weproceedbypresentingresultsfromthedecompo- sitionexercise.Table2showsthatplacefactorsaccount for39–59%ofthedifferenceinutilizationbetweenhospi-

15Notethatlogadditivitydoesnotcompletelyruleoutcomplementar- ities,aspatientandhospitalregioneffectsaffectthelevelofutilization multiplicatively.Thismeans,thatthelevelutilizationwillvarymore acrossplacesforsickerindividualscomparedtothatforhealthyindivid-