Productive cities Evidence from the OECD · Labour Productivity is measured as GDP (Millions of US$...
Transcript of Productive cities Evidence from the OECD · Labour Productivity is measured as GDP (Millions of US$...
AlexanderC.LembckeOECD/GOV,RegionalDevelopmentPolicyDivision
DivergentCitiesConferenceCambridge,16July2015
Productive citiesEvidence from the OECD
Motivation
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• Acountry’sproductivityis,inlargepart,determinedbytheproductivityofitscities;understandinghowtoincreasetheproductivityofthesecitiesisthereforeanurgentpolicyquestion.
• Largeurbanagglomerationsaccountforover50%oftotalGDPwhiletakinguplessthan5%oftotalsurfacearea.
• Itiswellknownthat,inmanycountries,economicproductivityincreaseswithcitysize.Thisisinpartaresultofsorting,asbettereducatedindividualshaveatendencytoliveandworkinlargercities.However,productivityincreasesarepresentevenwhencontrollingforsorting‐ aswedoforthisproject.
• Usingcitydefinitionsbasedonfunctionsratherthanadministrativeboundaries(FUA),thepapercontraststhecomplexityofadministrativeboundariesbyFUAandhenceexaminestheroleofurbangovernanceintheeconomicproductivityofacity.
Evidencefor5+2OECDcountries:• Germany,Mexico,Spain,UKandUS• Netherlands andJapaninprogress
Coherentandcomparativemethodology• Usewagemicrodata &OECDMetropolitanAreasDatabase:Citydefinitionbasedoneconomiclinks(commutingflows),ratherthanadministrativeboundaries
• Two‐stepeconometricframework accountforindividualcharacteristicsthatdetermineproductivity
• Estimateagglomerationbenefits,examinetheroleofurbangovernanceandothercharacteristicsforcityproductivityand
Background for the presentation
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• Sizematters:Estimatedelasticityforagglomerationbenefitsaround0.02‐0.06(roughly:2‐6%higherproductivityforadoublinginpopulation)
• Productivityishigherincitieswithmoreskilledworkers; withlarger employmentsharesinhigh‐techmanufacturing,financeandbusinessservices; largerneighbouringcitiesandlowerlevelsofgovernmentalfragmentation.
• Citieswithfragmentedgovernancestructuresexhibitlowerlevelsofproductivity:Doublingthenumberofmunicipalitieswithinametropolitanareaisassociatedwith6%lowerproductivity
• Thepresenceofagovernancebodyhalvesthispenalty
City productivity: Results from 5 (+1) OECD countries
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• DefinitionofFunctionalUrbanAreasbasedonpopulation densityin1km2 cellsthatarematchedtomunicipalboundariesandconnectedviacommutingpatterns.
• Urbancentresareidentifiedbyaggregatingdenselypopulated1km2 cells.Urbancentreswithatleast50,000inhabitantsarekept.
• Theyarematchedwiththeboundariesofthelowestadministrativelevelforwhichstatisticaldataistypicallyavailable(NUTS5/LAU2)
• UrbancentresandthelessdenselypopulatedmunicipalitiesinthecommutingzonearecombinedintoFunctionalUrbanAreasbasedoncommutingflows(>15%).
• Moreinfo:OECD(2012)RedefiningUrban
A functional definition for cities
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OECD metropolitan areashttp://measuringurban.oecd.org
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Larger Metropolitan Areas are more productive (higher GDP per worker)
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Source:OECDMetropolitanExplorer.LabourProductivityismeasuredasGDP(MillionsofUS$constantPPP,constantprices,referenceyear 2005)dividedbythetotalnumberofemployeesinaFunctionalUrbanArea.Datareferto2010ortheclosestavailableyear.
• ResultsfromAhrend,Farchy,Kaplanis andLembcke (2014).EstimationfollowsCombes,Duranton andGobillon(JEconGeo,2011)
• Usemicrodataandfollowa2‐stepapproach:i.applyindividualwageregressionsinordertoestimatethedifferentialproductivitylevelsofcities,controllingforsorting(onobservables)ii.explainthedifferentialcityproductivitylevelsfoundinthefirststepbyregressingthemonanumberofcityexplanatoryvariables
• Accountsforbiasarisingfromnon‐randomsortingofmoreproductive(e.g.skilled)individualsacrosscities
Empirical strategy: Two step approach
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• Firststep:
yiat:wage(ln)Xiat:gender,education,experience,occupation,part‐timingdiat:city‐yearFE
• Secondstep:
• Allowsforflexiblecountrytimetrendsdct• Samplelimitsampletoyearsavailableforallcountries(2005‐2007)
Empirical strategy: Two step estimation
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• ExplainfirststageestimateswithasetofrelevantFUAlevelcontrols( ; )– agglomeration:population;densityandarea(logged)– humancapital:shareofuniversitygraduates– industrialstructure:specialisation,compositionandtechnologicalintensity
– urbangovernance:administrativefragmentation(lnnumberofmunicipalities;governancebody)
– location“fundamentals”:port;capitalcity;popincitieswithin300kmradius
Empirical strategy: Explaining productivity differentials
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• Microdata withinfoonwagesandworkplacelocation• UK:ASHEfirststage(1%sample);city‐yearcontrolsfromQLFS;2003‐2010
• Spain:MCVL;administrativedata(4%sample);2005‐2011• Germany:EmploymentpaneloftheGermanFederalEmploymentAgency(2%sample);German weakly anonymous BA-Employment Panel (version 1998-2007)
• US:IPUMSdata;Census1990,2000andAmericanCommunitySurvey2005‐2007
• Mexico:ENE2000‐2004;ENOE2005‐2010
Data
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City productivity increases with city size- even after controlling for sorting
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Pooled regression results, 2005-2007
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(I) (II) (III) (IV)ln(population) 0.038***
(0.005)ln(density) 0.038*** 0.045*** 0.045***
(0.006) (0.006) (0.006)ln(area) 0.038*** 0.070*** 0.070***
(0.006) (0.009) (0.009)ln(municipalit.) -0.032*** -0.035***
(0.006) (0.006)Log (popul. in 0.017**catchment area) (0.008)R-Squared 0.76 0.76 0.78 0.78Observations 1,290 1,290 1,290 1,290FUAs 430 430 430 430
‐ Effectisdisaggregatedintoapuresizeeffectmeasuredby(log)areacoveredbyFUAandagglomerationbenefitscapturedby(log)populationdensity
‐ Analysisonthepooleddataestimateselasticityofaround0.04
• SourcesofagglomerationfromMarshall(1890);reviewsbyRosenthalandStrange(2004),Puga (2010)
• Thickerlabourmarkets:labourmarketpooling;bettermatching• gainfromreducedlabouracquisitionandtrainingcostsinthicklocal
labourmarketswithabundantspecialisedlabourforce
• Sharing facilities,inputs,gainsfromspecialisation• firms may face lower costs for specialised non-traded inputs that are
shared locally in a geographical cluster.
• Knowledgespillovers• face-to-face contact can enable tacit knowledge spillovers through
increases in the intensity of the interactions with other firms or individuals
Sources of agglomeration benefits
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Heterogeneity: bigger is better
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Spain
UnitedStates
Heterogeneity: borders matter(ed)
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Germany
Mexico
Heterogeneity: distance matters
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Netherlands
UnitedKingdom
Heterogeneity: distance matters
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Netherlands
UnitedKingdom
excludingFUAsthatborderametropolitanarea(lightblue)
Distance matters
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ProductivitydifferentialsanddistancetoLondon
AnnualpercapitaGDPgrowthrates(1995‐2010)anddrivingtimetotheclosestmetroareaof2millionormoreinhabitants
Spillovers of large cities: Economic growth in EU regions
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• Inmanycountries,thegovernancestructureofurbanareasisinadequate,asadministrativebordersdonotcorrespondtotheeconomicarea.
• Thisdiscrepancymightleadtoconflictsofinterestamongthevariousagents nottherightscaleforpolicy(lackofpoliciesorduplication)(CheshireandGordon,1996;CheshireandMagrini,2009)
• Someareasaffectedbycoordinationproblems:– transport;stymietransportinfrastructureinvestmentscongestion
– landuse;inadequatelanduseplanning urbansprawl– easeofdoingbusiness;additionalbureaucracyunderinvestment
The role of governance
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Berlin
Madrid
Administrative fragmentation in two OECD metropolitan areas
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City productivity decreases with fragmentation
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All FUAs Metro areas Metro areas Metro areas
ln(density) 0.048*** 0.064*** 0.065*** 0.047***
(0.006) (0.012) (0.012) (0.012)
ln(area) 0.064*** 0.082*** 0.085*** 0.087***
(0.008) (0.012) (0.012) (0.013)
ln(municipalit.) -0.032*** -0.032*** -0.057*** -0.066***(0.006) (0.010) (0.016) (0.017)
ln(municipalit.) 0.031** 0.036**x govern. body (0.014) (0.015)
governance body -0.079** -0.092**
(0.034) (0.038)
Add.controls no no no yesR-Squared 0.779 0.847 0.855 0.880Observations 1,290 420 420 420FUAs 430 140 140 140
Fragmentation and governance bodies
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• Theempiricalanalysisconfirmsthattheproductivityofresidentsincreaseswithcitysize.Butitalsoshowsheterogeneityacrosscountries
• Humancapital,high‐techandknowledgeintensiveservices orientedcitiesmaketheirresidentsmoreproductive
• Smallercitiescan“borrow”agglomerationbenefits.• Importantly,theanalysisshowsthatadministrativefragmentationreducesproductivity,apenaltythatisalleviated(butnotnegated)bygovernancebodies.
Concluding remarks
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Thepresentationdrawsfrom:Ahrend,Farchy,Kaplanis andLembcke (2014)“WhatMakesCitiesMoreProductive?Agglomerationeconomiesandtheroleofurbangovernance:Evidencefrom5OECDCountries”Ahrend andSchumann(2014)“DoesregionaleconomicGgowthdependonproximitytourbancentres?”OECD(2015)TheMetropolitanCentury:UnderstandingUrbanisationanditsConsequencesOECD(2015)GoverningtheCityOECD(forthcoming)MetropolitanReview:Rotterdam‐TheHagueOECD(2012)RedefiningUrban:anewwaytomeasuremetropolitanareas
Thank you
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Variables USA MEX DEU GBR ESPpopulation 2.47 0.88 0.47 0.46 0.39
(3.115) (2.211) (.615) (1.169) (.810)
area 11.2 4.3 1.2 0.7 1.6(12.907) (6.989) (1.136) (.878) (2.205)
pop.density 0.35 0.41 0.51 0.74 0.63(.379) (.542) (.513) (.518) (.913)
% university 0.28 0.16 0.14 0.19 0.12graduates (.056) (.051) (.041) (.07) (.054)
Capital 0.01 0.01 0.01 0.01 0.01(.12) (.115) (.096) (.1) (.115)
Port 0.48 0.19 0.17 0.16 0.32(.503) (.392) (.381) (.367) (.468)
Herfindahl 0.06 0.06 0.05 0.05 0.07index (.006) (.018) (.013) (.009) (.025)
# local governments 5.0 5.4 44.3 4.3 31.8per 1,000 inh. (3.8) (10.1) (50.4) (7.0) (52.2)
% of pop.in 0.67 0.81 0.52 0.72 0.72largest municip. (.233) (.232) (.183) (.221) (.165)
Governance 0.87 0.81 0.83 0.29 0.13body (.341) (.402) (.381) (.469) (.354)
Summary statistics:Regression controls
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UK Productivity differentials and latitude
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