Handbook of Regional and Urban Economics

1630 Pages·2015·23.698 MB·English

by  Gilles Duranton, J. Vernon Henderson and William C. Strange (Eds.)

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INTRODUCTION TO THE SERIES
The aim of the Handbooks in Economics series is to produce Handbooks for various
branchesofeconomics,eachofwhichisadefinitivesource,reference,andteachingsup-
plementforusebyprofessionalresearchersandadvancedgraduatestudents.EachHand-
bookprovidesself-containedsurveysofthecurrentstateofabranchofeconomicsinthe
formofchapterspreparedbyleadingspecialistsonvariousaspectsofthisbranchofeco-
nomics.Thesesurveyssummarizenotonlyreceivedresultsbutalsonewerdevelopments
fromrecentjournalarticlesanddiscussionpapers.Someoriginalmaterialisalsoincluded,
but the main goal is to provide comprehensive and accessible surveys. The Handbooks
areintendedtoprovidenotonlyusefulreferencevolumesforprofessionalcollectionsbut
also possible supplementary readings for advanced courses for graduate students in
economics.
Kenneth J. Arrow and Michael D. Intriligator
v
FOREWORD
ThefieldsofRegionalandUrbanEconomicshaveevolvedremarkablysince2004whenthe
last volume of the Handbook series (Volume 4) was published.
TheemphasisofVolume4wasverymuchonagglomerationatvariousspatialscales
(neighborhood,urban,andregional).Muchofthecontentwastheoretical,withalarge
proportion of theoretical chapters and a clear separation between theory and empirics.
Volume4alsoarrivedasKrugman’sNewEconomicGeographyhadreacheditspeak.This
emphasisonagglomerationmeantthatmanytraditionalurbanissueswerenotcovered.As
such,policydiscussionswerelimitedtoagglomerationissues,suchasregionalinequalities
andtheeffectofmarketintegration(followingworriesassociatedwith“globalization”and
deeper economic integration within Europe and North America). The decade since
Volume4hasseencontinuedprogressonagglomerationandrelatedareas,butithasalso
seenasignificantbroadeninginboththeareasofstudyandthemethodsofinquiry.
Thisvolumeisinpartareturntomoretraditionalurbantopicsthatwerecoveredin
Volumes 1–3 of the Handbook series. One example of this is housing, a research topic
whichhasseenmajoradvancesinthelast10years.AmajorhousingcrisisintheUnited
Statesandmuchofthedevelopedworldiscertainlypartoftheexplanationforrevivalof
researchonhousing.Inaddition,therearealsoimportant ongoingdebatesabouturban
sprawlanditseffectsandhowlanduseregulationsareshapingcitiesintheUnitedStates
andelsewhere.Technologyandsometimeslegislationarealsochangingthewaywebuy
and sell houses. This raises some interesting questions about the microstructure of the
housing market. Thus, Volume 5 of the Handbook of Regional and Urban Economics has
a significant emphasis on housing and property markets.
Housingisnottheonlynewfocusforurbanresearch.Thereisalsorenewedinterest
intheeffectsoftransportationoncities,neighborhoodandcitydynamics,urbanameni-
ties, urban environmental issues, urban crime, urban costs, land use, migration, and a
range of other topics. These issues are considered in both developed and developing
world settings. Volume 5 reflects this intellectual broadening as well.
Another important shift in urban and regional economics is in methods. For the
first time in the Handbook of Regional and Urban Economics series, explicit chapters on
methodology are included. The greater availability of data and the gradual adaption of
“modern”methodologieshaveprofoundlychangedthenatureofempiricalwork.These
approaches(structuralandquasi-experimental)arebecomingmorewidelyadopted.The
chapters in this volume acknowledge this, but they also point out that a lot urban and
regionalresearchremainsinneedofamethodologicalupgrade.Inaddition,thechapters
pointtoarangeofuniquemethodologicalchallengesarisingfromthespatialdatathatis
xv
xvi Foreword
usedinurbanandregionalresearch.Thedirectapplicationofmethodologiesborrowed
fromlaboreconomicsorindustrialorganizationis,thus,oftennotenough.Fortunately,
boththechaptersfocusingprimarilyonmethodsandthosethatconsiderindividualtopics
offer numerous suggestions of how to move forward. In most instances, this involves
forging closer links between theory and empirical research.
Allof theseissueshave significantimplications forpublicpolicy.Volume5includes
chaptersfocusingonpolicytopicsthathavehadlittlecoverageinpreviousvolumes,such
as mortgages,place-basedpolicies,and urbancrime.Thevolumealsoincludes chapters
on more traditional issues such as tax competition, neighborhood effects, and housing
policy.Thesetraditionalissuesarestillextremelyimportantbutarenowexploredusing
more credible empirical approaches. And although these chapters are particularly ori-
ented toward policy, the applied nature of Urban and Regional Economics means that
most chapters are policy relevant at least to some degree.
Ultimately, we see the chapters included in the volume as making a strong case for
research that appropriately combines theory and empirics, that embraces the many ele-
mentsofurbaneconomies,andthatispolicyrelevant.Ofcourse,asthevolumehascome
together,ithasbecomeapparentthattherearegapsinthevolumejustastherearegapsin
thefieldsofregionalandurbaneconomics.Forinstance,toomuchoftheempiricalevi-
denceonurbanissuescomesfromAmericancities.Whilethevolumedoescontaintwo
chaptersfocusedonissuesindevelopingcountries,moreworkonurbanphenomenain
developing countries is needed. As another example, while there is a chapter on trans-
portation focused on evaluation of major inter regional transport networks, there is no
coverageoftraditionalandevolvingtopicssuchasmodalchoice,peakpricing,theuseof
incurred transport costs to value urban amenities, and the like. We hope that these and
other gaps will motivate young (and less young) researchers to expand our knowledge.
We are grateful to many people and organizations for helping to make this project
happen.Thecontributionoftheauthorsisobvious.Thesecontributionsweresharpened
bytheparticipantsatconferencessponsoredbytheWhartonRealEstateDepartmentand
the Centre for Real Estate at the Rotman School of Management at the University of
Toronto.SeveralpaperswerealsopresentedattheUrbanEconomicsAssociationsessions
attheNorthAmericanRegionalScienceCouncilmeetingsandattheNationalMeetings
of the American Real Estate and Urban Economics Association. We are grateful to the
peopleandorganizationswhohavemadetheseinteractionspossible.Wealsoaregrateful
to various people at Elsevier for their helpfulness and professionalism, especially Joslyn
Chaiprasert-Paguio and Scott Bentley. Finally, we are all grateful to all those who are
close to us for their patience and support.
Gilles Duranton
Vernon Henderson
William Strange
November 4, 2014
CONTRIBUTORS
NathanielBaum-Snow
Departmentof Economics, Brown University,Providence, RI, USA
Kristian Behrens
(cid:1)
DepartmentofEconomics;CIRPEE,Universite´duQue´beca`Montre´al,Montre´al,QC,Canada;
National ResearchUniversity, Higher School of Economics, Moscow, Russia,and CEPR,
London,UK
Marius Bru€lhart
Universityof Lausanne, Lausanne,Switzerland, and Centre for Economic Policy Research
(CEPR), London,UK
JanK.Brueckner
Departmentof Economics, Universityof California, Irvine,CA, USA
Sam Bucovetsky
YorkUniversity, Toronto, ON, Canada
Gerald Carlino
Federal Reserve Bank of Philadelphia,Philadelphia,PA, USA
Sewin Chan
Robert F. Wagner School of Public Service, New York University,NY, USA
Pierre-Philippe Combes
Aix-Marseille University(Aix-MarseilleSchool of Economics), CNRS &EHESS,Marseille;
EconomicsDepartment, Sciences Po, Paris, France,and Centre for Economic Policy
Research(CEPR),London, UK
Morris A.Davis
DepartmentofFinanceandEconomics,RutgersBusinessSchool,RutgersUniversity,Newark,
NJ,USA
Klaus Desmet
Departmentof Economics, Southern Methodist University,Dallas, TX, USA
Gilles Duranton
WhartonSchool,UniversityofPennsylvania,Philadelphia,PA,USA,andCEPR,London,UK
FernandoFerreira
TheWharton School,University of Pennsylvania, Philadelphia, PA, USA
Steve Gibbons
London School of Economics, London, UK
Edward L.Glaeser
Harvard University and NBER, Cambridge, MA, USA
xvii
xviii Contributors
Laurent Gobillon
Centrefor Economic Policy Research (CEPR), London,UK;InstitutNational d’Etudes
De´mographiques; ParisSchool of Economics, Paris,France,and TheInstitute for the Study
of Labor(IZA),Bonn,Germany
Joseph Gyourko
The Wharton School, Universityof Pennsylvania, Philadelphia,PA,and NBER, Cambridge,
MA, USA
Lu Han
Rotman Schoolof Management, Universityof Toronto,Toronto, ON, Canada
Andrew Haughwout
Federal ReserveBankof New York, NY, USA
J. Vernon Henderson
Department of Geography, London School of Economics,London, UK
ThomasJ.Holmes
University of Minnesota and Federal Reserve Bank of Minneapolis, Minneapolis, MN, USA
Matthew E.Kahn
Department of Economics,UCLA and NBER and IZA, LosAngeles, CA, USA
William R.Kerr
Harvard University,Bank of Finland,and NBER, Boston, MA, USA
SomikV.Lall
Urban Developmentand Resilience Unit, SustainableDevelopmentNetwork,World Bank,
USA
Ethan Lewis
Dartmouth College, Hanover,NH,and NBER, Cambridge, MA, USA
Raven Molloy
Boardof Governors, Federal Reserve System,Washington,DC,USA
Charles G.Nathanson
Northwestern University,Evanston, IL, USA
David Neumark
UCI,NBER, and IZA,Irvine,CA, USA
Brendan O’Flaherty
Department of Economics,Columbia University,NY,USA
EdgarO. Olsen
Department of Economics,University of Virginia, Charlottesville, VA,USA
HenryG. Overman
London Schoolof Economics, London,UK
Eleonora Patacchini
Cornell University,Ithaca,NY, USA
Giovanni Peri
University of California-Davis, CA, and NBER,Cambridge, MA, USA
Contributors xix
Diego Puga
CEPR,London,UK,andCentrodeEstudiosMonetariosyFinancieros(CEMFI),Madrid,Spain
Stephen J. Redding
EconomicsDepartment and WWS, Princeton UniversityFisherHall,Princeton, NJ, USA
Frédéric Robert-Nicoud
CEPR; SERC,The London School of Economics and Political Science, London, UK, and
Geneva School of Economics and Management, Universite´ de Gene`ve, Gene`ve, Switzerland
Stuart S.Rosenthal
Maxwell Advisory Board Professor of Economics, Department of Economics, Syracuse
University,Syracuse, NY, USA
Stephen L. Ross
Departmentof Economics, Universityof Connecticut,Storrs, CT, USA
Kurt Schmidheiny
Centre for Economic Policy Research(CEPR),London, UK; Universityof Basel, Basel,
Switzerland, and CESifo, Munich,Germany
Rajiv Sethi
Departmentof Economics, Barnard College, Columbia University, NY, USA, and
Santa FeInstitute, Santa Fe, NM, USA
Holger Sieg
Universityof Pennsylvania, Philadelphia,PA, USA
Helen Simpson
Universityof Bristol, CMPO, OUCBT and CEPR, Bristol, UK
William C.Strange
Rotman School of Management, University of Toronto,Toronto, ON, Canada
Giorgio Topa
Federal Reserve Bank of New York and IZA, NY, USA
Joseph Tracy
Federal Reserve Bank of New York, NY, USA
MatthewA. Turner
EconomicsDepartment, BrownUniversity, Providence, RI, USA
Stijn Van Nieuwerburgh
Departmentof Finance, Stern School of Business,New York University, NY, USA
Randall Walsh
Departmentof Economics, Universityof Pittsburgh and NBER, Pittsburgh, PA, USA
Jeffrey E. Zabel
Departmentof Economics, Tufts University, Medford,MA, USA
Yves Zenou
Stockholm University,IFN,and CEPR, Stockholm, Sweden
CHAPTER 1
Causal Inference in Urban and Regional
Economics
Nathaniel Baum-Snow*, Fernando Ferreira†
*DepartmentofEconomics,BrownUniversity,Providence,RI,USA
†TheWhartonSchool,UniversityofPennsylvania,Philadelphia,PA,USA
Contents
1.1. Introduction 4
1.2. AFrameworkforEmpiricalInvestigation 6
1.2.1 Abinarytreatmentenvironment 9
1.2.2 Ataxonomyoftreatmenteffects 11
1.2.3 Continuoustreatments 15
1.2.4 Randomization 15
1.3. SpatialAggregation 20
1.4. SelectiononObservables 23
1.4.1 Fixedeffectsmethods 24
1.4.2 Differenceindifferencesmethods 30
1.4.3 Matchingmethods 37
1.5. IVEstimators 43
1.5.1 Foundations 45
1.5.2 ExamplesofIVinurbaneconomics 47
1.6. RegressionDiscontinuity 53
1.6.1 Basicframeworkandinterpretation 54
1.6.2 Implementation 56
1.6.3 ExamplesofRDinurbaneconomics 59
1.7. Conclusion 62
References 63
Abstract
Recoveryofcausalrelationshipsindataisanessentialpartofscholarlyinquiryinthesocialsciences.
Thischapterdiscussesstrategiesthathavebeensuccessfullyusedinurbanandregionaleconomicsfor
recoveringsuchcausalrelationships.Essentialtoanysuccessfulempiricalinquiryiscarefulconsider-
ation of the sources of variation in the data that identify parameters of interest. Interpretation of
suchparametersshouldtakeintoaccountthepotentialfortheirheterogeneityasafunctionofboth
observablesandunobservables.
Keywords
Casual inference, Urban economics, Regional economics, Research design, Empirical methods,
Treatmenteffects
HandbookofRegionalandUrbanEconomics,Volume5A ©2015ElsevierB.V.
ISSN1574-0080,http://dx.doi.org/10.1016/B978-0-444-59517-1.00001-5 Allrightsreserved. 3
4 HandbookofRegionalandUrbanEconomics
JEL Classification Code
R1
1.1. INTRODUCTION
Thefieldofurbanandregionaleconomicshasbecomemuchmoreempiricallyoriented
overrecentdecades.In1990,49%ofpublicationsintheJournalofUrbanEconomicswere
empirical,growingto71%in2010.Moreover,thesetofempiricalstrategiesthataremost
commonlyemployedhaschanged.Whilemostempiricalpapersin1990onlyusedcross-
sectionalregressions,articlesin2010weremorelikelytouseinstrumentalvariables(IV),
panel data, and nonlinear models. Furthermore, special attention is now paid to the
employmentofresearchdesignsthatcanplausiblyhandlestandardomittedvariablebias
problems.Whileonlyahandfulofpapersattemptedtodealwiththeseproblemsin1990,
morethanhalfoftheempiricalpublicationsin2010usedatleastoneresearchdesignthat
ismoresophisticatedthansimpleordinaryleastsquares(OLS),suchasdifferenceindif-
ferences(DD),matching,andIV,torecovercausalparameters.However,thecredibility
of estimates generated with these more sophisticated techniques still varies. While, in
general, the credibility of empirical work in urban economics has improved markedly
since 1990, many studies continue to mechanically apply empirical techniques while
omitting important discussions of the sources of identifying variation in the data and
ofwhichtreatmenteffects,ifany,arebeingrecovered.Table1.1detailsthepercentages
ofpublicationsintheJournalofUrbanEconomicsthatwereempiricalandthedistributionof
empirical methods used for the years 1980, 1990, 2000, and 2010.
This chapter discusses the ways that researchers have successfully implemented
empirical strategies that deliver the most credible treatment effect estimates from data
setsthatdescribeurbanandregionalphenomena.Ourtreatmentemphasizestheimpor-
tance of randomization, which has been more broadly recognized in other fields, most
notably development economics. Randomized trials are an important tool to recover
treatment effects, especially those of interest for policy evaluation (Duflo et al.,
2008). However, it is typically more challenging and expensive to implement field
Table1.1 PrevalenceofempiricalmethodsintheJournalofUrbanEconomics,1980–2010
Aspercentagesofempiricalpapers
Logit/ Panel Differencein
Year Empirical OLS IV probit data differences Randomization Matching
1980 57% 87% 10% 3% 0% 0% 0% 0%
1990 49% 79% 17% 13% 4% 0% 0% 0%
2000 62% 64% 32% 36% 14% 4% 0% 0%
2010 71% 77% 46% 26% 62% 8% 3% 5%
Notes:AuthorscalculationsfromallpublishedarticlesintheJournalofUrbanEconomicsintheindicatedyears.
CausalInferenceinUrbanandRegionalEconomics 5
experiments in settings of interest to urban and regional economists, as it is in other
fields such as labor economics. General equilibrium effects, which contaminate control
groups with influences of treatment, are more likely to arise in urban settings.
Moreover,thenatureofsuchgeneralequilibriumeffectsismorelikelytobetheobject
of inquiry by urban and regional researchers. Labor economists have typically adopted
higherstandardsfor evaluating thecredibilityofestimated causaleffectsin researchthat
usesnonexperimentaldata.Hereweexploreidentificationstrategiesthathavebeensuc-
cessfully used to recover credible estimates of treatment effects, typically in the absence
ofexperimentalvariation. TheseincludeDD, variousfixed effectsmethods, propensity
scorematching,IV,andregressiondiscontinuity(RD)identificationstrategies.Wealso
discusstreatmenteffectheterogeneityandhowdifferencesinresultsacrossidentification
strategies may simply reflect different causal relationships in the data. We emphasize
that especially without experimental variation (and even often with experimental var-
iation), no one identification strategy is ever perfect. Moreover, when considering
causal effects of treatments, it is useful to think in the context of a world in which a
distribution of treatment effects exists. Selection into treatment (on both observable
and unobservable characteristics) and treatment effect heterogeneity makes empirical
work complicated.
One recurring theme of this chapter is the following principle, which applies to all
empiricalstrategies:itiscrucialtoconsiderthesourcesofvariationinthetreatmentvari-
ables that are used to recover parameters of interest. Distinguishing this “identifying
variation” allows the researcher to consider two central questions. First, could there
be unobserved variables that both influence the outcome and are correlated with this
identifyingvariationinthetreatmentvariable?Ifsuchomittedvariablesexist,coefficients
on the treatments are estimated as biased and inconsistent. We typically label such situ-
ationsasthosewithan“endogeneityproblem.”Second,howrepresentativeofthepop-
ulation is the subset of the data for which such identifying variation exists? If clean
identificationexistsonlyinasmallunrepresentativesubsetofthepopulation,coefficients
on treatment variables apply only narrowly and are unlikely to generalize to other
populations.
Throughoutthechapter,wediscussthekeypropertiesofvariousidentificationstrat-
egies mostly assuming a simple linear data-generating process which allows for hetero-
geneous treatment effects. Each section cites articles from the literature for readers
interested in thedetails of more complex applications. This structure allows us to easily
explain the relationships between different empirical strategies while leaving space to
cover applications in urban and regional economics. In each section, we illustrate best
practices when implementing the research design by discussing several recent examples
from the literature.
Given the importance of the use of economic models to aid in the specification
of empirical models and interpret treatment effect estimates, we view the material on
structural empirical modeling in Chapter 2 as complementary to the material discussed