BeyondNeoclassicalGrowth: Technology,HumanCapital,InstitutionsandWithin-CountryDierencesDaronAcemogluDepartmentofEconomics,MassachusettsInstituteofTechnologyMelissaDellDepartmentofEconomics,MassachusettsInstituteofTechnologyJanuary,2009.AbstractWedocumentsubstantial within-country(cross-municipality)dierencesinincomesforalargenumberof countriesintheWesternHemisphere. Asignicantfractionof thewithin-countrydierencescannotbeexplainedbyobservedhumancapital. Wearguethattheneo-classicalgrowthmodel, whichemphasizesdierencesanddynamicsinphysicalcapital, isnotuseful for thinking about these large within-country disparities. We propose a simple frameworkincorporatingbothdierences intechnological know-howacross countries anddierences inproductive eciency within countries, which provides a unied interpretation of both between-country and within-country patterns.PreparedfortheGrowthEmpiricssessionintheAmericanEconomicAssociationmeetings,SanFrancisco,2009. Wethanktheeditor,SteveDavis,andseminarparticipantsforusefulcomments. Anumberofindividualsprovidedinvaluableassistance inlocatingor accessingthe manydatasources usedinthis paper. We thankinparticularLuisBertola, Rafael MoreiraClaro, SebastianGaliani, BrettHuneycutt, AnaMaraIba nez, OscarLanderretche, Jose Antonio Mejia-Guerra, Ezequiel Molina, Glenn Graham Hyman, Daniel Ortega, Carmen Pages,PabloQuerubin,MarcosRobles,OscarRobaStuart,CarmenTaveras,JoseTesada,JimmyVasquez,andGastonYalonetzky. DaronAcemoglugratefullyacknowledgesnancialsupportfromtheNationalScienceFoundation.1 IntroductionLargedierencesinincomepercapitaacrosscountriesmotivatemuchof growththeoryanddevelopmenteconomics. Itisnowwellunderstoodthatthesecross-countrydierencesreectdivergent growth performances over the past 200 years. For example, while the United States andmost of Western Europe grew rapidly during almost the entire duration of the past two centuries,many Latin American countries have had a patchy growth experience and sub-Saharan Africastagnated during almost the entire period.The dominant approach for understanding dierences in income per capita starts with theneoclassical (Solow)growthmodel, wheregrowthandoutputlevelsareexplainedbyhumancapital, physicalcapitalandtechnology. Acountrygrowswhenitincreasestheseinputsandisricherthananotherwhenithasgreaterhumanandphysicalcapitalandbettertechnology.While the neoclassical growth approach recognizes the importance of technology, it focuses onthe accumulation of human and physical capital to explain dynamics. As is well-known, it doesnot oer a theory of technological dierences.The theoretical approach of the neoclassical growth model permeates the empirical growthliterature. Cross-country growth regressions are motivated by the slow dynamics resulting fromcapital accumulationinaclosed-economyneoclassical model. All factorsrelatedtotechnol-ogyandtheeciencyof productionaresubsumedinthereduced-formeectsof arangeof(potentially endogenous) covariates (e.g., Barro and Sala-i-Martin (1995)).Thereareatleasttwoimportantshortcomingsofcross-countrygrowthregressions. First,as documented and emphasized by Hall and Jones (1999), Klenow and Rodriguez-Clare (1997)and others, only a small portion of the large cross-country dierences in income can be explainedby dierences in human and physical capital. Technological dierences are key to understandingthe diverse fortunes of nations and their dierential growth trajectories. Second, dierences inhuman capital, physical capital and technology are proximate causes, and must be determinedby other, fundamental causes. The dierences in policies and institutions across countries thatinuence the incentives for investing in human and physical capital and technologies (e.g., Ace-moglu,JohnsonandRobinson,2005)areonenaturalcandidate.1Althoughtheshortcomingsof the neoclassical paradigm have long been recognized, the growth and development literatureshave not developed a benchmark framework comparable to the neoclassical growth model thathighlightstheroleof technological dierencesandclarieshowtheseareformedandchangeover time.Our objective in this paper is twofold. First, we document substantial within-country (cross-1Other factors that have beenemphasizedfor explaining cross-countrydierences include geographyandculture. Gallup, SachsandMellinger(1998)haveemphasizedtheimportanceof geographicfactorsinshapinglong-runeconomicdevelopment. Theeectsofculture(beliefsystems)andhowbeliefsinteractwithinstitutionshavealsobeenemphasizedrecentlyandareimportantandinterestingareasforfutureresearch.Dell, Jones, andOlken(forthcoming)documentastatisticallysignicantcross-sectional correlationbetweenclimateandincomewithinanumber of countries examinedinthis study. Thequantitativemagnitudes theyreport, thoughnottrivial, stronglysuggestthatclimatecannotexplainthefull regional variationinthedata.Thisleavessignicantscopefortheinstitutionalfactorsthatweexamine.1municipality)dierencesinoutputandstandardsoflivingforalargenumberofcountriesintheWesternHemisphere. Forexample, amongLatinAmericancountriesforwhichwehavemunicipalitylevel data, thebetween-municipalitydierencesinlaborincomeareabouttwicethe size of between-country dierence (when the United States is included, this ratio is reversed).About half of both between-country and between-municipality (or region) dierences are relatedto human capital, the remainder being due to residual factors. Disparities in physical capitalacrossregionsareunlikelytobeamajorfactorinexplainingthesedierencesbecauseoftherelatively free mobility of capital within national boundaries. Therefore, similar to the residualincross-countryexercises, theseregional residual dierencescanbeascribedtodierencesintheeciencyof productionacrosssub-national unitsi.e., totechnologydierences. Wearguethatasuccessful frameworkforthestudyof economicgrowthshouldnotonlyachievetheobjectivesstressedinthepreviousparagraphbutalsoactasavehicleforunderstandingtheemergenceandpersistenceof within-countrydierences. Inparticular, anygeneralizedexplanation of economic growth and development should be able to account for the large within-country dierences in income and eciency of production.2Second, weproposeasimplemodel thatprovidesauniedframeworkfortheanalysisofcross-countryandwithin-countrydierencesandemphasizestheimportanceof dierencesintheeciencyof productionandininstitutions. Productiveeciencyis determinedat thecountry level by the technology adoption decisions of prot-maximizing rms and by nationalinstitutions. Within countries, it is determined, among other things, by the availability of localpublic goods and security of property rights. Technological progress takes place at the world leveland results from the interaction of the technology choices of rms in all countries. Institutionsinuence both technology adoption decisions and the eciency with which technologies can beused in dierent regions of the country. Thus, our approach explicitly recognizes the dierencesbetween national and local institutions. The eects of institutions are further decomposed intoacomponentthatisperson-specic(inparticular, relatedtohumancapital)andonethatisregion-specic (for example, due to local enforcement of property rights, local entry barriers andavailability of public goods necessary for ecient production).It is often (explicitly or implicitly) assumed that even though institutional dierences may beimportant for understanding cross-country dierences in economic outcomes, they do not playa major role in explaining within-country dierences (e.g., Tabellini (2006)). This view is predi-cated on the notion that institutions are national in nature and cannot explain within-countrydierences. However,institutions vary greatly within countries,as evidenced by dierences in2Several previousstudieshaveexaminedtheextentofLatinAmericaninequality. Notably, Gasparini (2003)andLondo noandSzekely(2000)documenttheextentof incomeinequalityovertimewithinalargenumberofLatinAmericancountriesandprovidecross-countrycomparisons. Throughouruseof populationcensusesandlivingstandardsmeasurementsurveys, wehaveaccesstoahigher-qualitylaborincomecross-sectionandlargersamples than the existing literature, which tends to use lower quality sources in order to produce an income panel.Thus,weareabletoprovideamoresystematicandaccurateassessmentofinequalitypatterns. Wealsoconrmour ndings using household expenditure data, which are more reliable for several countries in our sample becauseahighfractionof thepopulationworksintheinformal sector. Finally, wedecomposewithinmunicipalityandacrossmunicipalityinequality.2thedegreeof lawenforcement, availabilityof schools, roadinfrastructureandmarketaccess.Moreover, national institutions may have dierential eects in dierent regions.Our perspective is that in the same way that national institutional dierences play a majorrole in accounting for cross-country income dierences, local institutional dierencesand thedierential regional eects of national institutionslikelyaccount for asubstantial fractionofwithin-countrydierences. Institutionsandpoliciesshapethecostsandbenetsandthefeasibilityof human capital investments in dierent areas and inuence major aspects of thelocal institutional environment.Theremainderofthepaperisorganizedasfollows. Inthenextsection,webrieyreviewcross-country evidence that highlights the importance of technological factors in understandingthe determinants of economic growth and prosperity. Section 3 describes the various micro datasetsweuseforinvestigatingwithin-countrydierences. Section4providesvariousdecompo-sitions of cross-country and within-country dierences in the Americas. Section 5 summarizesevidence on the extent of within-country dierences in institutional quality and availability ofpublic goods. Section 6 introduces our framework for interpreting dierences in prosperity anddynamicsof growthacrossandwithincountries. Section7concludes. Theonlineappendixcontains additional details on data sources, further results, and references.2 Cross-CountryDierencesandTechnologyThebaselineneoclassical(Solow)growthmodelstartswithanaggregateproductionfunctionfor a unique nal good that depends on physical capital, human capital and labor-augmentingtechnology. Let us write aggregate output in countryjasYj (t) = F (Kj (t) , Aj (t) hj (t) Lj (t)) , (1)whereKj (t)denotescapital, Lj (t)denoteslabor, hj (t)ishumancapital perworker, Aj (t)islabor-augmentingtechnology, andFisaconstantreturnstoscale(linearlyhomogeneous)production function. Dierences in income per capita across countries can be due to dierencesin physical capital, human capital or technology. The neoclassical growth model has no theoryof technology dierences, which are taken as given, and has a minimal theory of dierences inhuman capital. Much of its focus is on the dynamics of physical capital.Inaseminal contribution, Mankiw, RomerandWeil (1992)arguedthattheneoclassicalgrowth model, or essentially equation (1), provides a good account for cross-country dierencesin income per capita without signicant technology (Aj) dierences. While inuential, this viewhas subsequently been criticized by several authors. Most notably Klenow and Rodriguez (1997)andHall andJones(1999)havedocumentedthatwhenareasonableformisimposedontheaggregate production functionF, large technology dierences are necessary in order to accountfor the signicant cross-country dierences in income per capita or output per worker.This debate mostlyfocuses oncross-countryincome dierences. Acomplementaryfo-cusof empirical workstartingwiththeneoclassical growthmodel hasbeenconvergence(or3conditional convergence)acrosscountriesandthedynamicsof thegrowthrateof countries.Barro and Sala-i-Martin (1995) suggest that convergence across OECD countries,convergenceacross US regions and cross-country growth dynamics can be understood by the closed-economyneoclassical growth model. However, the evidence in Klenow and Rodriguez (1997) also suggeststhat growth dynamics must be understood in terms of changes in technology more than changesin the physical capital intensiveness of production. Moreover, particularly relevant for our focusin this paper, it is dicult to see how the closed-economy neoclassical growth model could havemuch to say about within-country dierences and dynamics.Motivated by these considerations, much of the modern literature focuses on technology dif-ferencesasthemajorfactorincross-countryincomedierencesgrowthdynamics. Similarly,belowweconsidertechnologydierencesasafactorinunderstandingcross-regiondisparities.But what is technology?The aggregate production function in (1) suggests a literal interpre-tationoftechnologyasadeterminantoftheproductionpossibilitiesfrontierfacingasociety,which we may refer to as technological know-how. This is, however, too narrow a denition.First, the eciency with which economies use the available factors of production is important.Various market imperfections will translate into dierences in the amount of output and whenlooked at through the lenses of an aggregate production function as in (1), they will appear asdierences in technology. Second,it is as implausible to presume that the available techno-logical know-how diers across regions within a country as it is to assume that capital does notfreely move to regions with higher (net) marginal product of capital within the same nationalborders. Thus a broad view of technology is particularly useful in thinking about within-countrydierences. Finally, such a broad conceptualization is also useful in emphasizing the role of in-stitutions, particularly local institutions. Once we recognize that societies dier in the eciencywithwhichtheyusetheavailablefactorsofproduction, anaturalnextstepistherealizationthat the local arrangement of markets, laws and governance will inuence this eciency. Thisview of technology highlights the importance of local institutions in shaping the eciency withwhich the available factors of production are used.Thisdiscussionthereforesuggestsathree-waydistinctionacrosspotentialdeterminantsofincome per capita in national and local economies:1. Technologywill potentiallyvaryatthenational level, thusinuencingcross-nationalincome dierences.2. Eciency of production will vary both at the national and the sub-national levels, inu-encing both cross-country and cross-municipality dierences.3. Human capital of the workforce will also dier both across countries and within countries,contributing to dierences at both levels.4. Institutions, including enforcement of property rights, entry barriers, and availability ofvariouspublicgoodsnecessaryforecientproductionandmarkettransactions,willinuencetheeciencyofproductionandthehumancapitaloftheworkforcebothatthenationalandsub-national levels.One reason for distinguishing 2 and 3 above is that human capital dierences are embedded in4workers and thus will be present even with free and costless migration within countries. On theother hand, local dierences in the eciency of production will cause between-region dierencesonly if mobility is costly.Thisdiscussionmotivatesourempirical focusinSection4. Westartwithmicrodataonindividual earnings fromseventeencountries intheWesternHemisphere(NorthandSouthAmerica). Microdataenableustodecomposelaborincomeinequalityintobetween-countryandwithin-countrycomponentsandprovideuswithasimplemethodologyforseparatingtheeect of humancapital fromother factors bycontrollingfor individual-level educationandexperience. This exercise enables us to undertake a preliminary decomposition of municipality-level economicdierencesbetweenthoseduetoeducation(proxyingforfactorsembeddedinworkers) and those related to the locality itself.3 DataWe use data on labor income, geo-referenced to the municipality, for 11 countries in the WesternHemisphere(seeTable1). Wealsoexaminelaborincomedatageo-referencedattheregionallevel foranadditional sixcountries,3anddataoneducational attainmentforanadditionaleightcountries. Ourdataaredrawnfromanumberof recentcensusesandlivingstandardsmeasurement surveys, all conductedsince2000. Alist of sources is providedinAppendixTableA1. Welimitourattentiontolaborincome, whichistypicallybetterreportedthantotal income.4Toincreasecomparabilityacrosscountries, weadjusteachcountrysincomedatasothatitaveragestoGDPperworkerinconstantinternationaldollars, takenfromthe2003 Penn World Tables. Population weights are constructed using 2000 GIS population data(Center for International Earth Science Information Network, 2004).Ourbaselineresultsdonotdeateincomesfordierencesinregional purchasingpower.Dierencesin costsoflivingareimportant forcomparisonsoflivingstandardsacrossregions.Because our focus is on productivity dierences rather than welfare, nationally-deated incomesare more informative. We also conrm the robustness of our qualitative conclusions to deatingincomes using the state median of a household-specic Paasche index constructed from a numberof household expenditure surveys. The additional data sources and the results are reported inAppendix Tables A1 and A2.4 Within-CountryandCross-CountryDierencesIn this section, we perform two exercises. First, we decompose inequality in labor income intothreecomponents: inequalitybetweencountries, inequalitybetweenmunicipalitiesorregions3Forthesesixcountries,weareunabletogeo-referencethedatatothemunicipalityeitherbecausethesurveyisnotgeo-referencedtothemunicipalityorbecausewedonothaveGISmunicipalityboundaries, whichweusetocalculatemunicipalitypopulationinordertoweightthedataappropriately.4ForLatinAmerica,thedataprovideinformationonmonthlylaborincome,soforthesecountriestheremaybegreatertransitoryvariabilitythanannuallaborincomenumbersfortheUnitedStatesandCanada.5(withincountries), andinequalitywithinmunicipalities/regions. Second, wedecomposelaborincome inequality at each level of geographic aggregation into two components: those explainedby observable human capital variables and the residual.As is well known,the set of additively decomposable inequality indicescorrespondsto theGeneral Entropy class of measures. We focus on two commonly used measures within the GeneralEntropy class: the Mean Log Deviation (MLD) index and the Theil index. The MLD index ofoverall inequality in the Western Hemisphere is:MLD = lny 1LJ

j=1Mj

m=1Ljm

i=1lnyjmi(2)whereyjmiis the labor income of individuali in municipalitym in countryj, yis mean laborincome in the Americas, andL is total population in the Americas.5Similarly, the Theil indexof overall Western Hemisphere inequality is:T=J

j=1Mj

m=1Ljm

i=1yjmiLyln_yjmiy_(3)Let us further deneyjmas mean labor income in municipalitym in countryj,Ljmas thenumberofindividualsinmunicipalitymincountryj, yjasmeanlaborincomeincountryj,andLjasthenumberof individualsincountryj. ThentheMLDandTheil indicescanbedecomposed into our three desired components of inequality as follows (see the Appendix):MLD =__lny J

j=1LjLlnyj__+J

j=1NjN____lnyj Mj

m=1LjmLjlnyjm__+Mj

m=1LjmLjMLDjm__(4)whereMLDjm=lnyjm

Ljmi=1lnyjmi/LjmistheMLDindexforinequalityinmunicipalitymincountryj. Thersttermin(4)measuresbetween-countryinequality. Thesecondandthirdtermsarebetween-municipality(within-country)andwithin-municipalityinequalityin-dices, respectively, weighted by countryjs population share. Similarly, the Theil index can bedecomposed asT=J

j=1LjLyjy_lnyjy_+J

j=1LjLyjy__Mj

m=1LjmLjyjmyjTjm +Mj

m=1LjmLjyjmyjln_yjmyj___(5)where Tjm= Ljmi=1yjmiLjmyjmln_yjmiyjm_is theTheil indexfor inequalityinmunicipalitymincountryj. These expressions show that the MLD index weights by population shares, whereasthe Theil index weights by income shares.5Sinceour focus islabor incomeinequality, wewouldhavepreferredtoweight thedatabythesizeof thelabor force rather than the size of the overall population. Unfortunately, data on labor force participation are notreadilyavailablefortheWesternHemisphereatthemunicipalitylevel.6We begin in Table 2 by examining the ratio between the 90th and 10th percentiles of the laborincomedistribution, aswell astheMLDandTheil indices, forall individualsinoursample.Weshow, bycountry, thedecompositionof inequalityintobetween-municipalityandwithin-municipality inequality, and also decompose overall inequality in the Western Hemisphere intoits three component parts:inequality across countries, inequality between municipalities/regions(withincountries), andinequalitywithinmunicipalities/regions. WhendecomposingWesternHemisphereinequality, weconsidertwopopulationweightingschemes. Therstusesactualpopulation, whereas the second assumes equal population in all countries, and thus reduces theinuence of large countries such as Brazil, Colombia, Mexico, and the United States. This latterschemeissimilarinspirittotheconventioninthegrowthliteraturewheredierentcountriesaregivenequalweight. Forcomparisonpurposes, wedecomposeoverallinequalityseparatelyforcountriesgeo-referencedtomunicipalitiesandforthosegeo-referencedtoregions. Atthebottom of the table, we decompose inequality for all countries included in our sample. We alsoreport cross-country inequality of 2000 GDP per worker and 2000 GDP per capita from the PennWorld Tables for all countries in the Americas for which data are available. This increases thesample size to 33 for GDP per worker and 37 for GDP per capita, primarily by adding Caribbeannations for which we do not have labor income data. The GDP data show that the cross-countryinequality pattern in our sample is similar to that for the entire Western Hemisphere.Table 2 highlights that inequality in labor income across countries is about one half to onethird of the magnitude of inequality within municipalities/regions and about two to three timesas large as inequality across municipalities/regions. For example, using the MLD index, equalpopulationweightsandfocusingoncountrieswithmunicipalitydata,overallbetween-countryinequalityis0.24, whilewithin-countryinequalityis0.66, 0.12of whichisduetobetween-municipalityinequality. Thecontributionofthebetween-municipalitiescomponentissmallerwiththeTheil index, whichgives greater weight tothetopof thedistribution, that is, totheUnitedStates(recall thattheTheil indexweightsbyincomeshareswhereasMLDusespopulation shares). To reduce the inuence of the United States (and Canada when we includecountries with data geo-referenced to regions), we also report the decomposition without thesetwocountries. Inthiscase, againfocusingonthedatawithmunicipalityreferencing, wendthatbetween-countrydierencesareabouthalf of thebetween-municipalitydierencesbothwiththeMLDandTheil indices. Whenwelookatall countrieswithouttheUnitedStatesandCanada, between-countryandbetween-regioncomponentsareofcomparablemagnitude,partly reecting the fact that for 6 of the 17 countries we only have data at the level of highlyaggregative regions.Appendix Table A2 shows a similar pattern when the data are deated using regional priceindices. AppendixTableA3documents inequalityineducational attainment for aslightlyextendedset of countries, 17of whicharegeo-referencedtomunicipalities and11of whichhave micro level census data. Appendix Table A4 documents inequality in equivalent householdexpenditure, which is likely measured with less error than labor income in countries with a largefractionoftheeconomicallyactivepopulationintheinformal sector. Weconcludefromthis7evidence that regional disparities are broadly of similar magnitude toor larger thanbetween-country dierences within Latin America.Table2alsoshowsthattheextentofinequalityacrosscountriesdependsonthemeasurebeingused,thoughEcuador,PeruandVenezuelaareamongthemostunequalcountrieswithall threemeasures. DispersionishighestinGuatemalaandPeru. Thisisnotsurprisinginview of the fact that both countries have large Amerindian populations concentrated in isolatedregions.6Tables 3 and 4 (as well as Appendix Table A5) limit our sample to males between the agesof 18 and 55 to compare incomes across a more homogenous labor force (particularly in termsof hours worked). Using this subsample, we perform the decomposition between predicted andresidual incomes. Recall thatyjmiis labor income of individuali in municipalitym in countryj. Let Xjmidenote the vector consisting of years of schooling,experience (calculated as age -education - 6), and experience squared. We then decompose labor income into predicted andresidual components by running the following regression by country for each countryj:lnyjmi = X

jmij +j +jmi, (6)wherejis a country-specic constant andjmi has zero mean and country-specic variance.Given estimates from (6),we examine inequality in overall labor income (yjmi),inequalityinpredictedlaborincome(exp(X

jmij)), andinequalityinresidual income(exp(j + jmi)).Notice that country-specic constants, which are unrelated to dierences in human capital, arepart of residual income.Table 3 reports several easy to interpret (though non-decomposable) summary measures ofinequalityforeachcountryandforouroverall sample. Thesearethe90-50and90-10ratiosand variances (for overall labor income, the predicted component and the residual component).Using Vto denote the variance, V (lnyjmi) = V (X

jmij)+V (j+ jmi). As with total income inTable 2, Peru is among the most unequal countries, though depending on the measure, Bolivia,Brazil, Colombia, El Salvador, Guatemala and Venezuela also appear highly unequal.Table 4 uses the Theil index to decompose each of the components of income (overall, pre-dicted, and residual) into inequality between countries, inequality between municipalities/regions(of countries), and inequality within municipalities. Appendix Table A5 performs the same ex-ercise using the MLD index. This decomposition shows large between-country predicted laborincome dierences, of about half the magnitude of between-country labor income inequality. Themagnitudeofbetween-countryresidual inequalityisbroadlysimilar, thoughalittlesmaller.7Thepatternforthedecompositionof between-municipalitydierencesisverysimilar. Table5alsoshowsthatthebulkof thewithin-municipalitydierencesareduetoresidual factors.This may reect the greater dispersion in unobserved skills or a greater extent of labor market6While the general patterns are similar in Appendix Table A4, the ranking of countries in terms of inequality isquitedierent. Forexample,Peruappearstoberelativelyequal. Thislikelyreectsdierencesinthevariabilityof monthly labor income relative to household expenditure and in the precise nature of the income questionnaires.7Noticethatthedecompositionbetweenpredictedandresidual incomesisnotadditive, sincewearetakingexponentialtransformationsofpredictedandresiduallogincomesbeforethedecomposition.8imperfections or discrimination in some countries than others. The ndings are similar when weuse the MLD index in Appendix Table A5.Overall, our evidence suggests that years of schooling and the experience of the labor force canexplain a large fraction of income disparities across and within countries, but residual factors arealso signicant and generally of comparable magnitude. Although these residuals undoubtedlyinclude a component of unmeasured human capital dierences within countries,8they also likelyreect the eects of local factors impacting productive eciency.5 Within-CountryDierences,InstitutionsandPublicGoodsThe large dierences in labor incomes, education and residual incomes documented in the pre-vious section are unlikely to be due to dierences in the physical capital intensity of production.Inthesamewaythattechnologydierencesplayanimportantroleinshapingcross-nationaleconomic dierences, they also likely play a major role in within-country dierentials. But whydotechnologyandthehumancapital endowmentoftheworkforcevaryacrossregionsandmunicipalities?One reaction might be that these dierences must be due geographic or culturalfactorsbecauseinstitutionsi.e., factorsinuencingtheeciencyof production, contractingopportunities, and the feasibility of adopting new technologiesvary primarily at the nationallevel. Thisargumentisnotconvincing,however,sincethepresumptionthatinstitutionsvarymainly at the national level receives little empirical or qualitative support.We nowbrieydiscuss the variationof local business conditions andaccess to(paved)roadsacrossmunicipalitiesinLatinAmerica. Theavailableevidencesuggeststhatbetween-municipality(within-country)dierencesintheseinstitutional andpublicgoodmeasuresarecomparable to or greater than between-country dierences in Latin America.The World Banks Doing Businesssub-national reports, which surveyed business conditionsin all 31 Mexican states (and the Federal District) and thirteen cities in Brazil, provide evidenceonwithin-countryvariationininstitutions(local businessconditions).9WithinMexico, theaverage time required to obtain clearance to start a business ranges from 12 days (Aguascalientes)to 69 days (Quintana Roo) and in Brazil from 19 days (Minas Gerais) to 152 days (Sao Paulo).This variation is of similar magnitude to that between Latin American countries. For example,within Latin America (excluding Mexico and Brazil) the same measure varies between 13 days(Panama) and 141 days (Venezuela). The World Bank report also shows that the time required toregister a property ranges from 18 days (Aguascalientes) to 154 days (Quintana Roo) in Mexicoand from 27 days (Marahao) to 85 days (Baha) in Brazil. The variation across countries rangesfrom 15 days (Ecuador) to 124 days (Nicaragua). Finally, enforcing a contract takes 581 daysinBajaCaliforniaSur,Mexico,thestatewiththemostdelays,whichistwo-and-a-halftimeslonger than the fastest state, Zacatecas. In Brazil, the range is from around one and a half years8Unmeasuredhumancapital dierencesmaybeparticularlyimportantincountriesthathaveattemptedtoincrease educational attainment without changing the amount andlocal compositionof educational funding(SaavedraandSuarez,2002).9Seehttp://www.doingbusiness.org/Subnational/forfurtherinformationanddocumentation.9(Sao Paulo) to over four years (Rio Grande do Sul). Once again, these are of similar magnitudeto the variation across Latin American countries, where the fastest country (excluding Mexico)isPeruat468daysandtheslowestisGuatemala,atapproximatelyfouryears. Thereisalsoevidencethatthecostsof performingtheseproceduresvarysignicantlywithincountriesaswell.10Moreover, Kauman et al. (2003) discuss a World Bank survey that documents the highvariability in the quality of public agencies across Bolivia regions.There are similarly sizable dierences in measures of public goods across sub-national areas.For example, our companion paper, Acemoglu and Dell (2009), shows substantial dierences inaverage distance of municipalities to the nearest intercity paved road. In Mexico, the population-weighted ratio of the 90th to the 10th percentile of municipality distance to the nearest pavedroad is 12.5 and in Brazil, it is 27.1.11The cross-country 90-10 ratio in distance to the nearestpaved road is quite a bit smaller, at 7.1.Providing detailed empirical support for the role of local institutions in shaping cross-regionandcross-municipalitydierencesisbeyondthescopeofthispaper. Nevertheless, weshouldnote that several recent studies provide such evidence for the countries examined in this paper.We discuss the strategies and the ndings of some of them in the online appendix. These papershighlight the importance of historical and institutional factors and provide suggestive evidenceon the role of local public goods.6 TowardsaFrameworkInthissection, weprovideasimpleframeworktointerpretcross-countryandwithin-countryincomedierences andtheir dynamics. Theframeworkbuilds onendogenous technologicalchangemodels(e.g., AghionandHowitt, 1992, GrossmanandHelpman, 1991, andRomer,1990) and on the model of international technological diusion presented in Acemoglu (2009).Motivated by the empirical results in Section 4 and the discussion in Section 5,we consider amodel thatexplicitlydistinguishescountriesaswell asregionswithincountries. Inaddition,wedelineateproductivitydierentialsresultingfromtechnology, humancapital embeddedinworkers, and local dierences in public goods and institutions. Physical capital dierences, whichare the main factor emphasized by the closed-economy neoclassical growth model, are omittedfrom this framework. For reasons discussed above, their contribution to regional dierences areunlikely to be large.6.1 EnvironmentWe consider an innite-horizon world economy in continuous time. There are J countries indexedbyj =1, 2, ..., J, andMjregions (municipalities) incountryj indexedbym=1, ..., Mj.10The time required to register collateral in Mexico ranges from 8 days (Michoacan) to 51 days (Quintana Roo)andinBrazil from2days(MinasGerais)to45days(Federal District). Thereisnocross-countrydatathatisdirectlycomparabletothis.11Itisalsousefultonotethatthesedierencesarenotpurelyduetogeographicfactorsandweshowthattheyhaveeectsonincomesaftercontrollingfordetailedgeographiccharacteristics.10Population in each country is normalized to 1 and there is no population growth. We assumethatall countriesandregionsproduceasinglenal gooddenotedbyY , andtheaggregateproduction function of regionm in economyjat timet isYj,m (t) =(j,m)1 __Nj(t)0xj,m(, t)1dv_(hj,mLj,m), (7)where Lj,m is labor input, which varies across regions, hj,m is the eciency of labor, determinedby education, public goods and other institutional factors, which can vary across regions, coun-tries and time, and j,m is a region-specic productivity term, which varies across regions withina country because of dierential availability of local public goods or because the enforcement ofproperty rights for businesses diers across regions.12In equation (7), the variable is raised tothe powerin order to simplify the expressions that follow. This is without any loss of gener-ality, sincehas no natural scale. Our population normalization implies that Mjm=1Lj,m = 1.For now, we also ignore migration across regions.Thefunctionalformin(7)issimilartothestandardDixit-Stiglitzaggregatorusedinen-dogenous technological change models. Similarly, Nj (t) denotes the number of machine varietiesavailable to country j at time t. This variable captures the technological know-how of country j.We assume that technology diuses slowly across countries and producers can only use the tech-nologies available in their country. This implies thatNjthus the available technologyvariesacross countries. However, once a country has a particular level of technology, this technologycanbeusedinall regionsinthecountry. Finally, xj,m (, t)isthetotal amountof machinevarietyused in regionm in countryjat timet. To simplify the analysis, let us suppose thatxs depreciate fully after use.Eachmachinevarietyineconomyj isownedbyatechnologymonopolist, whichwill sellmachines embodying this technology at the prot maximizing (rental) price pxj(, t) in all regionswithin thecountry. We assumethattherearenoregional taxesonmachines or dierencesintransportcosts, thusmachinepriceswill bethesameacrossregions.13Thismonopolistcanproduce each unit of the machine at a marginal cost of in terms of the nal good, and withoutany loss of generality, we normalize 1 .Weassumethatpreferencesofhouseholdswithineachcountrycanberepresentedbythepreferences of a constant relative risk aversion (CRRA) representative household, with the samedegreeof riskaversion(intertemporal elasticityof substitution) andthesamediscountrateacross countries. In particular, preferences at timet = 0 are given by_0exp(t) Cj (t)111 dt,12Inaddition, manydevelopingnations, particularlythoseinLatinAmerica, haveusednational policiesthatpromote cross-municipality inequality, including development strategies that favor urban areas or provide subsidiestoselectedregions(CardosoandFishlow,1989).13Dierencesintheavailabilityandqualityoflocalinfrastructure,emphasizedinthediscussionabove,aswellas in Williamson (1965) and Acemoglu and Dell (2009), would imply dierences in prices faced by rms in dierentlocalities. Weabstractfromtheseadditionaldierencestosimplifythediscussion.11with>0and 0. Althoughourempirical investigationsofarhasemphasizedincomeinequality, our focus here is on dierences in productivity, particularly across regions, and thusdierences inthesavingandconsumptionbehavior of households withinacountryarenotcentraltoourframeworkandtherepresentativehouseholdassumptionenablesustosuppressthese. In addition, there is no international trade in goods (all countries produce the same nalgood) or in assets (thus no international borrowing or lending). This implies that the followingresource constraint must hold for each countryjat each timet:Cj (t) +Xj (t) +jZj (t) Yj (t) , (8)where Xj (t) is investment or spending on inputs at time t and Zj (t) is expenditure on technologyadoption at time t, which may take the form of R&D or other expenditures, such as the purchaseorrental ofmachinesembodyingnewtechnologies. Theparameterjmeasurescountry-leveldistortionsorinstitutionalandpolicydierences,andwillbeadriverofpotentialtechnologydierences beyond those resulting from the availability of human capital.Technologyineachcountryj evolvesasaresultof thetechnologyadoptiondecisionsofprot-maximizingrms. Inparticular, theinnovationpossibilities frontier for eachcountrytakes the formNj (t) = j_N (t)Nj (t)_Zj (t) , (9)where N (t) isanindexof theworldtechnologyfrontier, j>0forall j, and>0andcommontoall economies. Thisformof theinnovationpossibilitiesfrontierimpliesthatthetechnological know-howofcountryjadvancesasaresultoftheR&Dandothertechnology-relatedexpendituresof rmsinthecountry. Theeectivenessof theseinvestmentsdependsonacountry-specicconstant, j>0, andmoreimportantly, onhowadvancedtheworldtechnology frontier is relative to this countrys technological know-how (captured by the termN (t) /Nj (t)). Each economy starts with some initial technology stockNj (0)> 0 and there isfree entry into research, so that any rm can invest in R&D (denoted by Zj (t)) and adopt newtechnologies according to the innovation possibilities frontier (9).Sinceworldgrowthis not thefocushere, supposethat theworldtechnologyfrontier ofvarieties expands at an exogenous rateg> 0, that is,N (t) = gN (t) . (10)Finally, we also assume that factor markets are competitive. The interest rate and the wagerate per unit of human capital in countryjare denoted, respectively, byrj (t) andwj (t).6.2 EquilibriumAnequilibriumisdenedintheusualfashion, assequencesoftechnologylevels, R&Dlevels,machine prices, interest rates and wage rates for each country and machine demands and outputlevels for each region, such that nal good rms and technology monopolists maximize prots,there is free entry into technology adoption, and the representative household in each country12maximizes its discounted utility. A balanced growth path equilibrium (BGP) refers to an equi-librium path in which each country grows at a constant rate. In thinking about the cross-countryandcross-regiondierences, theBGPisagoodstartingpoint. Nevertheless, wewillalsoseethatthisframeworkprovidesasimpleinterpretationofcross-countrygrowthdynamicswhenthe world economy is not in BGP.Itisstraightforwardtoverifythatinanyequilibrium, technologymonopolists, whofaceiso-elasticdemandcurvesfortheirmachines, will setaconstantmarkupovermarginal cost 1 , andtheequilibriumpriceofeverymachineineachcountryateachpointintimewill be pxj (, t) = 1. Given (7), this also implies that the demand for machines in each region ofeach country that will maximize the prots of the nal good producers will bexj,m (, t) = j,mhj,mLj,m. (11)This implies the intuitive result that there will be more intensive use of technologies when workershave greater human capital and when local conditions are more favorable for business. Conse-quently,a technology monopolist (for machine variety) in countryjwill make the followinglevel of prots at every point in time:j (, t) = Mj

m=1j,mhj,mLj,m, (12)where is the dierence between price and marginal cost (1 and 1), while the summationgives the total machine sales of this monopolist, which follows from (11).Let us start with the BGP. It is straightforward to verify that, given (9), all countries mustgrow at the same rate g as given in (10). The CRRA preferences of the representative householdimply the standard Euler equation, which gives the growth rate of consumption of each countryat each point in time asC (t)C (t)=1 (rj (t) ) . (13)In the BGP, output and thus consumption in each country grow at the rateg,so the interestrates must also be constant and equal tor = +g. (14)This equation together with (12) then implies that the value of a technology monopolist in BGPin countryjisVj=

Mjm=1j,mhj,mLj,mr.Combining this expression with the innovation possibilities frontier in (9), we obtain that countryjs relative technologyj Nj (t) /N (t) in BGP will bej=_j

Mjm=1j,mhj,mLj,mjr_1/, (15)13withrgivenby(14). Inaddition,itcanalsobeprovedthatthisBGPallocationisgloballysaddle-path stable, in the sense that starting with any strictly positive vector of initial technologylevels {Nj (0)}Jj=1 and the world technology frontier N (0) > 0, there exists a unique equilibriumpath converging to this BGP.14WhatdoesthisBGPallocationimplyforcross-countryandcross-regioninequality? Theabovederivationimmediatelyestablishesthatthelevel of incomepercapitainregionmincountryjin the BGP isyj,m =1/1 _j

Mjm=1j,mhj,mLj,mjr_1/(j,mhj,m) N (0) . (16)Similarly, summing across regions, total income in countryjin the BGP isyj=1/1 _jjr_1/__Mj

m=1j,mhj,mLj,m__(1+)/N (0)Lj, (17)where we introduced the division byLjexplicitly,even though until now population was nor-malized to 1, because this will be useful in the next section.Theseexpressions givethetheoretical counterparts of regional (municipal) andnationallabor incomes we computed and compared in Section 4. They highlight that countries that havebetterpossibilitiesforadoptingworldtechnologies(higher js), wherermsfacelessseverebarriersanddistortionstoadoptingtechnologies(lowerjs)andhaveonaveragebetterlocalinstitutions(higherj,ms)andworkerswithgreaterhuman capital(higherhj,ms)willtendtobe richer. Within a country, all regions share the same technology Nj, so it will be those regionsthat have better local institutions (higher j,ms) and those that have workers with higher humancapital(higherhj,ms)thatwillbericher. Itisalsoimportanttonotethattworegions(j, m)and (j

, m

) that have identical characteristics but are situated in dierent countries will havedierent income levels, because they will have access to dierent country-level technologies (NjandNj ).In light of the empirical results in Section 4, this framework therefore indicates that dier-encesinthehj,msandj,msarebothimportantforunderstandingregionalandcross-countrydierences. In particular, our decomposition suggests that the quantitative variation accountedfor by these two sources are broadly similar.6.3 MigrationThe framework presented above does not allow for migration across municipalities. A naturalquestion is whether migration would aect cross-municipality (or region) dierences in incomeand output. One may conjecture that dierences due to local business conditions, institutions14TheproofofthisresultfollowsthesimilarderivationinAcemoglu(2009)andisomittedheretosavespace.The only dierence is that in the model of world equilibrium considered in Acemoglu (2009),there are no within-countrydierences, butthisdoesnotaectthecharacterizationoftheequilibriumpathandtheproofofglobalstability.14and policies (thes in the model) would be arbitraged away when migration is possible. Thisis not necessarily the case, however.Avarietyof factorsmakemovementacrossmunicipalitiescostly. First, inpartsof LatinAmerica, there are explicit barriers to migration. In regions where a substantial portion of theland is held by indigenous communities, there are legal and traditional impediments to sellinglandtooutsiders, makinglargercities, whichoftenhavesignicant dis-amenities, themainviabledestinationsformigration. Secondandmoreimportantly, migrationwill arbitragealldierences due to thes only when there are no dierences in the costs of living and housingacross municipalities. In practice, both housing costs and the prices of other goods and servicesdier signicantly across regions.Recognizing that migration, even if it could take place without any impediments, would notleadtoanequalizationof non-humancapital incomes(whennotdeatedfully)impliesthatregional dierences will have two genuine components;those due to human capital dierences(andother factors mobilewithworkers) andthoseduetodierences inlocal business andeconomicconditions. Thesecorrespondtotheinuencesofthehsandsinthemodel. WecanthenmapthedierencesduetothehstothoserelatedtoeducationandthedierencesduetothestotheresidualdierencesobtainedinSection4afterweremovedtheinuenceof education and experience. A more systematic investigation of this distinction necessitates anexplicit modeling of dierences in costs of living across localities, which falls beyond the scopeof the current paper.7 ConcludingRemarksThisstudyusesanovel datasetof laborincomesintheWesternHemispheretodocumentwithin-country(cross-municipality)dierencesinincomesforalargenumberof countriesintheWesternHemisphere. WithinLatinAmerica, between-municipalitydierencesinincomesaregreaterthancross-countrydierences. Overallwithin-countrydierencesincludingbothbetween-municipality and within-municipality componentsare three to four times as large asthese other components. We also document that about half of the between-country and between-municipalitydierencescanbeaccountedforbydierencesinhumancapital, theremainderbeing due to residual factors.We propose a simple unied framework for the analysis of cross-country and within-countryincome dierences. Our framework emphasizes the importance of the eciency of production.Productive eciency is determined at the country level by the technology adoption decisions ofprot-maximizing rms and by national institutions, and within countries by local institutions,particularly the availability of local public goods and the security of property rights.In view of the importance of residual factors, captured by the s in the model, our frameworksuggests that dierences in local institutions are a crucial component of within-country (regional)dierences.Thesendingscallfornewtheoreticalworkmodelingtheimpactandendogenousdetermination of these local forces.15ReferencesAcemoglu,D.andM.Dell (2009): Roads and Economic Development: Evidence from Latin Amer-ica, mimeo.Acemoglu, D., S. Johnson, andJ. Robinson(2005): Institutionsasthefundamental causeoflong-growth, Handbook of Economic Growth.Aghion, P. andP. Howitt (1992): A Model of Growth Through Creative Destruction, Economet-rica, 60, 323351.Barro,R.andX.Sala-i-Martin (1995): Economic Growth, McGraw-Hill, New York.Cardoso, E. andA. Fishlow (1989): Latin American Economic Development: 1950-1980, NBERWorking Paper.CenterforInternationalEarthScienceInformationNetwork(2004): Global Rural-UrbanMappingProject, AlphaVersion: PopulationGrids, SocioeconomicDataandApplications Center(SEDAC), Columbia University, http://sedac.ciesin.columbia.edu/gpw (May 10th, 2007).Dell, M., B. Jones, andB. Olken(forthcoming): TemperatureandIncome: ReconcilingNewCross-Sectional and Panel Estimates, American Economic Association Papers and Proceedings.Gallup, J., J. Sachs, andA. Mellinger (1998): Geography and Economic Development, NBERWorking Paper.Gasparini, L.(2003): DierentLives: InequalityinLatinAmericaandtheCaribbean,inInequal-ityinLatinAmerica: BreakingwithHistory, ed. byDeFerranti etal., TheInternational BankforReconstruction and Development.Grossman,G.andE.Helpman (1991): Innovation and Growth in the Global Economy, MIT Press.Hall,R.andC.Jones (1999): Why Do Some Countries Produce So Much More Output Per WorkerThan Others?Quarterly Journal of Economics, 114, 83116.Kauffman,D.,M. Mastruzzi,and D. Zavaleta (2003): Sustained Macroeconomic Reforms, TepidGrowth: A Governance Puzzle in Bolivia? in InSearchofProsperity: AnalyticNarrativesonEco-nomic Growth, ed. by D. Rodrik, Princeton University Press.Klenow,P.andA.Rodriguez-Clare (1997): The Neoclassical Revival in Growth Economics: HasIt Gone Too Far?NBER Macroeconomics Annual 1997, 73103.Londo no,J.andM.Sz ekely (2000): Persistent Poverty and Excess Inequality, Journal of AppliedEconomics, 3, 93134.Mankiw,N.,D. Romer, and D. Weil (1992):A Contribution to the Empirics of Economic Growth,Quarterly Journal of Economics, 107, 407437.Romer,P. (1990): Endogenous Technological Change, Journal of Political Economy, 98, 71.Saavedra, J. andP. Su arez (2002): El nanciamiento de la educacion p ublica en el Per u: El rol delas familias, GRADE.Tabellini,G. (2006): Culture and Growth in the Regions of Europe, manuscript.Williamson,J.G. (1965): Regional Inequality and the Process of National Development: A Descrip-tion of the Patterns, Economic Development and Cultural Change, 13, 184.16Table1:SummaryStatisticsIncomeperWorkerEducationNo.MalesNo.MeanMun./Ref.toNo.No.MeanMun./Ref.toCountryObs.18-50Mun./Reg.Reg.Pop.Munic.Obs.Mun./Reg.Reg.Pop.Munic.Pop.(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)Argentina3,426,031298123,656yes37,497,728Bolivia8,1664,227106108,897yes21,707106108,897yes8,152,620Brazil3,481,6971,920,1491,51994,823yes8,831,9641,51994,823yes175,552,771Canada441,740196,208112,695,307no645,961112,695,307no30,689,040Chile14,8796,95227,551,517no1,398,106141107,114yes15,153,797Colombia18,4798,27694,665,758no3,842,76653378,898yes39,685,655CostaRica5,6996no343,6426165,547yes3,710,558Ecuador22,27510,58120628,822no976,648121103,938yes12,920,092ElSalvador22,93710,7966464906.51yes63,2256464906.51yes6,122,515Guatemala11,4405,70722641,901yes29,49422641,901yes12,820,296Honduras13,1605,9789844,973yes20,2099844,973yes6,200,898Mexico2,660,0161,562,0922,44241,390yes8,642,3112,44241,390yes100,087,900Nicaragua30,60613631630.57yes4,812,569Panama94,645550533040,776yes255,7553040,776yes2,836,298Paraguay6,8673,44117526,820yes32,85717526,820yes5,585,828Peru22,20711,33361030,619yes69,65761030,619yes27,012,899UnitedStates7,401,1563,272,0032,071126,211yes13,510,6452,071126,211yes284,153,700Uruguay8,0823,70719141,812no17,51919141,812no3,334,074Venezuela677,524380,797219110,118yes2,089,931219110,118yes23,542,649Total14,910,9697,457,3007,62744,249,0348,880799,871,887SeeAppendixTableA1forvariablesources.Table2:LaborIncomeInequality(AllIndividuals)MeanLogDeviationIndexTheilIndexMean90/10BetweenWithinBetweenWithinBetweenWithinBetweenWithinIncomeRatioCountryCountryMunic/RegMunic/RegCountryCountryMunic/RegMunic/Reg(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)Ref.toMunic.Bolivia7,25630.90.7200.1260.5940.6250.0990.526Brazil15,46213.00.6610.1110.5500.8380.1020.736ElSalvador10,95517.70.7200.0490.6710.5260.0490.477Guatemala10,19020.00.6920.2260.4660.6930.2360.457Honduras6,12125.70.7720.1450.6270.8560.1230.733Mexico18,6288.10.5090.1040.4060.7850.1110.674Panama19,49913.00.4950.0590.4360.4750.0560.420Paraguay12,2379.80.4160.1260.2900.3970.1120.285Peru11,08242.10.8870.2180.6690.7400.2150.525UnitedStates67,86515.70.5450.0380.5060.4670.0410.425Venezuela14,84815.70.8150.0280.7860.9910.0260.964Mun.(actual)31.30.2740.5970.0790.5180.2360.5420.0550.486Mun.(equal)29.20.2370.6570.1120.5460.2900.6120.0830.529NoUS(equal)20.80.0610.6690.1190.5500.0570.6910.1050.585Ref.toRegionCanada51,79621.00.5370.0050.5320.3600.0040.355Chile28,92910.00.5110.0030.5070.5450.0040.541Colombia13,75919.00.6860.0380.6480.6870.0400.648CostaRica20,94916.00.5590.0150.5430.5120.0150.498Ecuador10,70434.70.9660.0660.9001.6750.0641.611Uruguay19,49124.30.8100.0900.7200.8540.0810.773Reg.(actual)34.50.1810.6510.0280.6230.1800.5320.0160.516Reg.(equal)31.80.1300.6780.0360.6420.1350.6120.0240.588All(actual)32.30.2690.6050.0710.5340.2380.5410.0510.489All(equal)32.00.2110.6650.0850.5790.2350.6120.0570.555NoUS/CA(equal)22.10.0790.6810.0940.5880.0760.7170.0750.642GDPpwkr/pc-actualpop.0.263/0.3260.237/0.287GDPpwkr/pc-equalpop.0.178/0.2470.183/0.257SeeAppendixTableA1forsources.Column(2)givestheratioofthe90thpercentileofthelaborincomedistributiontothe10thpercentile,andcolumns(3)through(10)decomposeinequality.Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.Table3: InequalityDecomposition,SummaryMeasures(Males18-55)Labor Income Predicted Labor Income Residual Labor IncomeMean 90/10 90/50 90/10 90/50 90/10 90/50Income ratio ratio V [log(y)] ratio ratio V [X

] ratio ratio V [+ ](1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Ref. toMun.Bolivia 9,431 29.3 3.1 1.801 3.9 2.4 0.249 20.4 2.9 1.574Brazil 16,684 10.0 3.9 1.035 5.5 2.8 0.431 6.3 2.5 0.602El Salvador 10,024 14.5 3.0 2.879 3.1 1.8 0.191 10.5 2.3 2.692Guatemala 11,435 16.7 5.0 1.154 7.0 4.1 0.519 6.4 2.3 0.638Honduras 8,092 15.6 3.3 1.323 5.9 3.1 0.455 9.1 2.4 0.876Mexico 21,534 7.0 2.8 0.722 3.5 2.0 0.216 4.8 2.2 0.508Panama 22,853 11.3 2.7 1.021 4.3 2.4 0.318 7.2 2.2 0.707Paraguay 16,233 7.7 2.3 0.811 4.4 2.4 0.299 5.3 2.1 0.523Peru 10,148 31.2 3.1 1.980 6.5 2.5 0.463 16.3 2.7 1.494United States 84,127 10.7 2.5 1.138 5.1 1.8 0.329 6.8 2.2 0.806Venezuela 17,065 15.6 4.2 1.295 1.9 1.4 0.050 15.5 4.7 1.246Mun. (actual) 29.7 5.4 1.863 10.2 3.2 0.748 10.2 2.6 1.004Mun. (equal) 23.5 5.1 1.829 8.0 3.9 0.608 11.4 2.9 1.288No US (equal) 16.0 3.4 1.557 5.7 3.0 0.480 11.3 2.9 1.301Ref. to RegionCanada 62,086 12.3 2.2 1.381 4.4 1.7 0.297 8.6 2.2 1.084Chile 35,397 7.8 3.6 0.775 4.3 2.3 0.246 5.4 2.4 0.528Colombia 19,401 15.4 4.9 1.272 5.0 2.7 0.359 8.2 2.6 0.905Ecuador 12,343 15.7 3.1 1.385 4.1 2.4 0.303 10.6 2.4 1.079Uruguay 19,835 16.2 4.3 1.268 5.0 2.5 0.414 9.2 2.8 0.849Reg. (actual) 29.3 5.8 1.829 5.4 2.2 0.409 14.7 3.8 1.240Reg. (equal) 26.3 5.4 1.697 6.0 2.2 0.490 15.0 3.8 1.218All (actual) 29.7 5.6 1.876 9.5 3.1 0.700 10.8 2.8 1.047All (equal) 25.8 5.6 1.820 8.3 3.4 0.637 12.6 3.1 1.272No US/CA (equal) 16.5 3.7 1.532 7.1 3.2 0.582 11.5 2.9 1.225SeeAppendixTableA1forsources. Column(2)containstheratioofthe90thpercentileoflaborincometothe10thpercentile,andColumn(3)givestheratioofthe90thpercentiletothemedian. Column(4)givesthestandarddeviationofloglaborincome. Columns(5)through(7)providetheanalogousmeasuresforpredictedlaborincomeandcolumns(8)through(10)forresiduallaborincome. Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.Table4:InequalityDecomposition,TheilIndex(Males18-55)LaborIncomePredictedLaborIncomeResidualLaborIncomeBetw.With.Betw.With.Betw.With.Betw.With.Betw.With.Betw.With.CntryCntryMun/RegMun/RegCntryCntryMun/RegMun/RegCntryCntryMun/RegMun/Reg(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)Ref.toMun.Bolivia0.6100.1370.4740.1420.0220.1200.5170.0890.428Brazil0.8120.1050.7070.2620.0370.2250.4550.0330.422ElSalvador0.4870.0560.4310.0920.0140.0780.3410.0140.327Guatemala0.5940.2980.2960.3590.1990.1600.3760.0710.305Honduras0.9530.1420.8110.3170.0620.2560.4030.0420.361Mexico0.7470.1160.6310.1240.0190.1050.6430.0800.563Panama0.4570.0590.3980.1700.0160.1540.2860.0190.267Paraguay0.3740.1350.2390.1770.0560.1210.2030.0600.144Peru0.6290.2510.3780.2030.0630.1410.4060.0780.328UnitedStates0.4140.0510.3640.1200.0060.1140.3140.0170.297Venezuela0.9470.0320.9150.0250.0020.0230.9020.0250.877Mun.(actual)0.2510.4890.0640.4250.1450.1530.0160.1370.0860.4520.0360.416Mun.(equal)0.3000.5690.1000.4700.1730.1900.0440.1470.1230.5020.0440.458NoUS(equal)0.0450.6570.1280.5300.0940.2130.0560.1580.1320.5300.0480.482Ref.toRegionCanada0.3000.0050.2950.1120.0000.1110.2490.0040.245Chile0.5120.0020.5100.1330.0000.1330.2770.0000.277Colombia0.6900.0530.6370.1870.0160.1710.3660.0110.355Ecuador1.9810.0921.8890.1650.0040.1611.4040.0511.353Uruguay0.7750.0810.6940.2570.0270.2300.4240.0150.408Reg.(actual)0.2060.5120.0200.4920.0410.1530.0070.1460.1590.3900.0100.380Reg.(equal)0.1650.6240.0290.5950.0800.1810.0120.1690.1650.4870.0130.473All(actual)0.2550.4920.0600.4320.1360.1530.0150.1380.0980.4450.0330.412All(equal)0.2600.5900.0730.5180.1590.1860.0300.1560.1370.4980.0350.462NoUS/CA(equal)0.0660.7310.0980.6330.1470.2060.0380.1690.1330.5580.0430.515SeeAppendixTableA1forsources.Columns(1)through(4)decomposetheTheilindexforlaborincomeinequality,andcolumns(5)through(12)dothesameforpredictedlaborincomeinequalityandresiduallaborincomeinequality,respectively.Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.A.OnlineAppendicesNotforPublicationA1. Within-CountryDierences: ABriefReviewoftheRecentLiteratureRecent empirical work documents large dierences in public goods provision within countriesand sheds light on the institutional origins of these dierences. Dell (2008) utilizes a regressiondiscontinuity approach to examine the long-run impacts of the mita, an extensive forced mininglabor system in eect in Peru and Bolivia during the colonial era. The study estimates thata mitaeect lowers household consumption by around 32 percent in subjected districts todayandtraces channels of persistence. Mita districts historicallyhadfewer largelandownersandlowereducational attainment. Today, theyarelessintegratedintoroadnetworks, andtheirresidentsaresubstantiallymorelikelytobesubsistencefarmers. Acemoglu, Bautista,Querubin and Robinson (2008) nd a statistically signicant and robust association betweenpolitical inequality in the 19th century in Cundinamarca, Colombia (measured by the lack ofturnoverofmayorsinthemunicipalities)andeconomicoutcomestoday. Theyalsoprovideevidence, consistent withDells (2008) ndings, that theavailabilityof local publicgoodsmight be a particularly important intervening channel. Similar results are obtained for Brazilby Naritomi, Soares, and Assuncao.1In ongoing work, we investigate the inuence of regional road networks, an important localpublic good. Using the same sources of micro data as in the current paper, we look at the impactof roads on labor income, household expenditure and education. Since local roads are chosen bypoliticians and communities, they are clearly not exogenous to other economic characteristicsofmunicipalities. Asapotential strategyfordealingwiththeendogeneityofroads, weusehigh-resolution terrain data to predict the least cost paths between population centers in LatinAmerica that existed just prior to the construction of modern road networks in the 1960s. Wetheninstrumentforactual roadnetworkswiththispredictedgrid. Usingthisidenticationstrategy and controlling for a rich set of geographic factors,we nd that municipalities nearroads have signicantly higher labor incomes, largely but not entirely explained by the greatereducational attainment of their inhabitants.The recurring theme in these recent works is the importance of local public goods and localinstitutions for current economic outcomes, and empirical designs were constructed to isolateasourceofexogenousvariationinthesefactors. Forexample, inDell(2008)theregressiondiscontinuity strategy ensures that geographic and ethnic factors are held constant while theeects of historical institutions, in this case the mita, are estimated. In this light, we believethat a substantial portion of the within-country dierences documented in the previous sectionare also due to dierences in local institutions. Although, in most instances, the authors of the1Theimportanceoflocal institutionsisalsoapparentwhenfocusingsolelyonhistorical patterns. Babonis(2008)exploitsrainfallpatternsasasourceofvariationinthelocationofcoeeproductionwithinPuertoRicoandndslargeeectsonschool enrollmentandliteracyratesfollowingtheintroductionof coeeintoPuertoRicointhe19thcentury.A1studies discussed here do not (and cannot) systematically distinguish whether historical factorsinuencecurrenteconomicoutcomesthroughtechnologyorhumancapital,itisplausibletopresume that both factors acted as intervening variables.A2. DecomposingInequalityMeasuresIn this section, we derive the decompositions of the general entropy indices used in the maintext of this paper: the Mean Log Deviation (MLD) index and the Theil index.The mean log deviation index for total inequality in countryjis given by:MLDj = lnyj1LjMj

m=1Ljm

i=1lnyjmi, (A-1)whereyjmiis the income of individual i in municipalitym in countryj, yjis mean nationalincome andLjis total national population in countryj.Now adding and subtracting Mjm=1LjmLjlnyjm to both sides (whereyjm is mean income inmunicipalitym andLjm is the number of individuals in municipalitym), we obtain:MLDj =lnyjMj

m=1LjmLjlnyjm+Mj

m=1LjmLjMLDjm,whereMLDjm = lnyjm1LjmLjm

i=1lnyjmiistheMLDindexforinequalityinmunicipalitymincountryj. ThersttermistheMLDmeasureofacross-municipalityinequality, andthesecondtermisaweightedaverageoftheMLDs within each municipality, where the weights are municipality population shares.Now let us repeat the same exercise for decomposing the MLD index into inequality betweenversus within countries. The Western Hemisphere MLD index is then:MLD = lny 1LJ

j=1Mj

m=1Ljm

i=1lnyjmi,whereyjmiis the income of individuali in municipalitym in countryj,yis mean income inthe Western Hemisphere, andL is total population in the Western Hemisphere.Now add and subtract Jj=1LjLlnyj, whereyjis mean income in countryjandLjis thenumber of individuals in countryj. Again, basic algebra yields:MLD =lny J

j=1LjLlnyj+J

j=1LjL MLDj,A2whereMLDj = lnyj1LjMj

m=1Ljm

i=1lnyjmiis the MLD index for inequality in countryj.Plugging in from our decomposition ofMLDjabove yields:MLD =lny J

j=1LjLlnyj+J

j=1LjL lnyjMj

m=1LjmLjlnyjm+Mj

m=1LjmLjMLDjm,where,from above, MLDjm= lnyjm1Ljm

Ljmi=1lnyjmiis the MLD index for inequality inmunicipalitym in countryj. The rst term gives between country inequality, and the secondand third terms are between municipality (within country) and within-municipality inequality,respectively, weighted by countryjs population share.A similar exercise can be used to decompose the Theil index for countryj, given by:Tj =Mj

m=1Ljm

i=1yjmiLjyjln

yjmiyj

, (A-2)where yjmi, yj and Lj are dened as above. Again, adding and subtracting

Mjm=1LjmLjyjmyjlnyjmboth sides, we obtainTj =Mj

m=1LjmLjyjmyjTjm +Mj

m=1LjmLjyjmyjln

yjmyj

,whereTjm =Ljm

i=1yjmiLjmyjmln

yjmiyjm

is the Theil index for inequality in municipalitym in countryj.Now we decompose the Theil index for overall Western Hemisphere inequality, given by:T=J

j=1Mj

m=1Ljm

i=1yjmiLyln

yjmiy

, (A-3)whereyjmiis the income of individuali in municipalitym in countryj,yis mean income inthe Western Hemisphere, and Nis the total population in the Western Hemisphere. With thesame steps as before, we can also writeT=J

j=1LjLyjyTj +J

j=1LjLyjy

lnyjy

,A3whereTj =Mj

m=1Ljm

i=1yjmiLjyj

lnyjmiyj

is the Theil index for inequality in country j. Note that this is equal to (A-2). Plugging in thedecomposition from above yields:T=J

j=1LjLyjy

lnyjy

+J

j=1LjLyjyMj

m=1LjmLjyjmyjln

yjmyj

+Mj

m=1LjmLjyjmyjTjm,where againTjm =

Ljmi=1yjmiLjmyjmln

yjmiyjm

is the Theil index for inequality in municipalitymin country j. The rst component is cross-country inequality, the second component is betweenmunicipality inequality, and the third component is within-municipality inequality.AppendixTableA3reports educational inequalityusinghalf thesquaredcoecient ofvariation (SCV) index, which allows for zero values. Allowing for zero values is important, asover ve million individuals in our sample (slightly over 10%) have not received any schooling.The SCV index for the Western Hemisphere educational inequality is given by:SCV=121e21LJ

j=1Mj

m=1Lmj

i=1e2jmie2,wherethenotationisasabove, withejmistandingforyearsofschoolingforindividual iinmunicipality m in country j and e denotes mean years of schooling in the Western Hemisphere.Using similar steps to those above, it is straightforward to show that this can be decomposedas:SCV =12J

j=1LjL

eje

2e2+J

j=1LjL

eje

212Mj

m=1LjmLj

ejmej

2e2j+Mj

m=1LjmLj

ejmej

2SCVjm,whereSCVjm =121e2jm1LjmLmj

i=1e2jmie2jmis half the squared coecient of variation index for educational inequality in municipalitymincountryj. Asabove, thersttermgivescross-countryinequality, thesecondtermgivescross-municipality inequality, and the third term gives within-municipality inequality.A4A3. DataMethodologyandFurtherResultsWediscussedtheconstructionof ournationally(butnotregionally)deatedlaborincomedata in the text, so here our main focus is on the methodology used to create our householdexpenditure aggregate (examined in Appendix Table A4). We construct our measure of house-hold expenditure by aggregating expenditures on food and non-food items, durable goods, andhousing. Marriages, births, and funerals, which are lumpy and relatively infrequent expendi-tures, are excluded. We include expenditures on health, but patterns of household expenditureare very similar when these are instead excluded. Gifts and transfers made by the householdare not included, as these will be counted as they are spent by their recipients.Themethodweusetocalculatetheowexpenditureondurablegoods varies accord-ingtothedataavailableineachhouseholdsurvey. HouseholdsurveysforBolivia,Ecuador,Guatemala, Honduras, Nicaragua, Panama, Paraguay, andPeruincludethecurrent valueand age of durable goods held by each household. Following the recommendations ofDeaton and Zaidi (2002), wecalculatetheaverageageforeachdurablegoodt usingdataonthepurchasedatesof thegoodrecordedinthesurvey. Then, weestimatetheaveragelifetimeof eachgoodas2t, undertheassumptionthatpurchasesareuniformlydistributedthroughtime. Theremaininglifeof eachgoodisthencalculatedas2t t, wheretisthecurrentageofthegood. Aroughestimateoftheowofservicesisderivedbydividingthecurrentvaluebythegoodsexpectedremaininglife. Theinterestcomponentintheowofservices is ignored.Incontrast, thehouseholdsurveyforBrazilincludesalistofdurablegoodsheldbythehousehold and their ages, but does not contain estimates of their current values. We estimatethe purchase value of the good as the state median price for that good using data on purchasevaluesfromtheexpendituresectionofthesurvey. Thenwecalculatetheaveragelifeofthegood as 2t. To completetheestimate,we calculatethe average user cost of thegood asthemedian purchasevaluedividedby theaveragelifetimeofthegood. Finally,somehouseholdsurveys (Chile, Colombia, Costa Rica, El Salvador, Mexico, and Uruguay) do not include anyinformation about the stock of durables held by households. In these cases, we calculate thedurables sub-aggregate as expenditures by the household in the previous year on durable goods.Somesurveysaskmultiplequestionsonthesameexpenditureswithdierentreferenceperiodsi.e., the last two weeks versus a typical month. Following recommendations byDeaton and Zaidi, we use the latter. We calculate both per capita household expenditure andexpenditureperequivalentadult(inlocalprices, nationalprices, and2005international$).FollowingDeaton(1997), weassumethatchildrenaged0to4areequal to0.4adults, andchildren aged 5 to 14 are equal to 0.5 adults.Adjusting for dierences in purchasing power is important for making regional and inter-national comparisons of household welfare using expenditure data. Consider a Paasche priceindex comparing the price vector faced by the household, ph,and the reference price vector,A5p0:Php=phqhp0qh, (A-4)where qhis the consumption vector and ph qhdenotes the inner product of these two vectors.Rearranging yields:p0 qh=ph qhPhp=xhPhp(A-5)wherethevalueofthehouseholdsconsumptiondenedatreferenceprices, yhm=p0 qh, isour object of interest and xh is household expenditure. Note the convenient link with nationalincome accounting practice, in which real national product is the lefthand side of (A-5) summedover all households.To calculateyhm from our expenditure data, we rewrite (A-4) as:Php=1

k whkp0kphkwhere whk is the share of household hs budget devoted to good k and phk (and p0k) denotes the kthcomponentofthecorrespondingvector. Phpinvolvesnotonlythepricesfacedbyhouseholdhinrelationtothereferenceprices, butalsohouseholdhsexpenditurepattern. UsingaPaaschepriceindexwithhouseholdspecicweightsallowsustoaccountfordierencesinexpenditure patternsprevalent across regionswhen adjusting prices. We calculate Phpfromthe information about food quantities and expenditures available in our household surveys. Wetake the reference vector p0 to be the median of prices observed from individual households inthesurvey.Toreducetheinuenceofoutliers,wereplacetheindividual phkbytheirmediansover households in the same municipality.Calculatingprices fromour surveydatarequires convertingall quantities (i.e., pieces,bottles, bundles) to constant units (kilograms or liters) for each commodity, and then dividingtotal expenditure by quantity purchased. In some cases,national statistics oces performedthese calculations before releasing the data. In other cases, the survey documentation containsthe necessary conversion factorsby commodityto convert to constant units. In a couple ofcases(mostnotably,Bolivia),surveysreportsomequantitiesinpiecesbutdonotprovideconversion factors. In these instances, we use the conversion factors for similar goods providedby other surveys.Constructing a meaningful consumption aggregate also required checking the survey datafor obvious data entry errors and irregularities, most common in reporting food quantities. Insomebutnotallsurveys, nationalstatisticalocesdidathoroughjoboferrorchecking.Aftercarefullyexaminingindividualdatapoints,weusedthefollowingproceduretocorrectdata that were clearly recorded incorrectly. If the households annual expenditure on a goodwasmorethanfourstandarddeviationsabovethemeanexpenditureonthatgoodintheA6households municipality (or state if the municipality is not identied or had very few observa-tions), the observation was replaced by the municipality median. Less than 1% of the samplemeetsthiscuto. Theprocedureisusedforallsurveysinourdataset. Thedistributionofaggregate consumption is robust to instead dropping these items, or requiring the value to bemorethanvestandarddeviationsabovethemunicipalitymean. Resultsarealsorobusttousing deviations in logs rather than levels. We apply a similar procedure in calculating pricesfor the Paasche index, dropping quantities that are more than four standard deviations fromthe municipality mean.After deating regional prices to national prices, we further adjust for dierences in inter-national purchasing power by normalizing the data so that household expenditure within eachcountry aggregates to 2005 national per capita consumption in international dollars from theWorld Development Indicators.Income data are drawn from either population censuses or household surveys. The censusdatagiveasinglemeasureof laborincome, whereaslaborincomefromhouseholdsurveysisconstructedfromresponsestovariousquestionsaboutearnedincome(i.e. fromprimaryoccupation, secondaryoccupation, etc.) Themethodologyweusetoconstructtheincomeaggregateissimilartothatusedtoconstructtheconsumptionaggregate. Wecomparepercapita income from the census or household survey with the value of PPP adjusted GDP perworkerin2003, takenfromthePennWorldTables. ThisallowsustocalculateafactortoadjustincomesothatitaveragestoGDPperworkerinconstantinternational dollars. Toproduce the decompositions in Appendix Table A2, we also deate by the state median of thehouseholdspecicPaascheindexdiscussedabove. WhenaPaascheindexisunavailable,weuse data on regional purchasing power provided by National Statistics Oces.TableA1liststhedatasourcesusedinthispaper. TableA2examineslaborincomein-equality, wherelaborincomeshavebeendeatedbythestatelevel medianof thePaascheprice index described above. Table A3 documents inequality in years of schooling for nineteencountries,and Table 4 examines inequality in equivalent household expenditure,constructedaccording to the methodology described above. Finally, Table A5 decomposes labor income in-equality for males aged 18 to 55 into explained and residual inequality, reporting the Mean LogDeviation (MLD) index. Figures A1 through A3 provide maps showing mean (non-deated)labor income by municipality (or region) for North America, Mexico and Central America, andSouthAmerica,respectively.2NotethelargedierenceinscalebetweentheNorthAmericamap and the Latin America maps.2Particularlyforthecountrieswheredataaredrawnfromhouseholdsurveys, laborincomeisnotavailableforeverymunicipality. Inordertoprovideanapproximateoverall pictureof spatial patternsinincome, wereplacemissingmunicipalityvaluesbythemedianlaborincomeinthemunicipalitysrstadministrativeunit(i.e. stateordepartment).A7ReferencesAcemoglu, D., M. A. Bautista, P. Qeurubin, andJ. A. Robinson(2008): Economicandpoltical Inequality in Development: The Case of Cundinamarca, Colombia, NBER Working Paper.Acemoglu, D. andM. Dell(2009): RoadsandEconomicDevelopment: EvidencefromLatinAmerica, mimeo.Babonis, G. (2008): Political Institutions, Labor Coercion, and the Emergence of Public Schooling:Evidence from the 19th Century Coee Boom, manuscript.Deaton, A.(1997): TheAnalysisofHouseholdSurveys: AMicroeconometricApproachtoDevelop-ment Policy, Johns Hopkins University Press.Deaton, A. andS. Zaidi (2002): GuidelinesforConstructingConsumptionAggregatesforWelfareAnalysis, World Bank Publications.Dell, M.(2008): ThePersistentEectsofPerusMiningMita,manuscript, MITDepartmentofEconomics: http://econ-www.mit.edu/grad/mdell/papers.Naritomi, J., R. Soares, and J. Assunc ao(2007): Rent Seekingandthe UnveilingofDeFactoInstitutions: Development and Colonial Heritage Within Brazil, NBER Working Paper.A8TableA1:DataSourcesIncomeEducationExpenditurePricesCountrySourceYearSourceYearSourceYearSourceYearArgentinaPopulationCensus10%sample00BoliviaEncuestadeHogares02EncuestadeHogares02EncuestadeHogares02EncuestadeHogares02BrazilPopulationCensus6%sample00PopulationCensus6%sample00PesquisadeOrcamentosFamiliares02-03PesquisadeOrcamentosFamiliares02-03CanadaPopulationCensus2.5%sample01PopulationCensus2.5%sample01TheInter-cityIndexofPriceDierentials01ChileVIEncuestadePre-supuestosFamiliares06-07PopulationCensus10%sample02VIEncuestadePre-supuestosFamiliares06-07CalcsperformedbyNa-tionalStatisticsOce06-07ColombiaEncuestadeCalidaddeVida03PopulationCensus10%sample05EncuestadeCalidaddeVida03DANERegionalPriceIn-dex06-08CostaRicaEncuestaNacionaldeIn-gresosyGastosdelosHog-ares04PopulationCensus10%Sample00EncuestaNacionaldeIn-gresosyGastosdelosHog-ares04EncuestaNacionaldeIn-gresosyGastosdelosHog-ares04EcuadorEncuestadeCondicionesdeVida05-06PopulationCensus10%sample01EncuestadeCondicionesdeVida05/06EncuestadeCondicionesdeVida05-06ElSalvadorEncuestadePropositosMultiples06EncuestadePropositosMultiples06EncuestadePropositosMultiples06noneavailableGuatemalaEncuestaNacionaldeCondicionesdeVida00EncuestaNacionaldeCondicionesdeVida00EncuestaNacionaldeCondicionesdeVida00EncuestaNacionaldeCondicionesdeVida00HondurasEncuestadeCondicionesdeVida04EncuestadeCondicionesdeVida04EncuestadeCondicionesdeVida04EncuestadeCondicionesdeVida04MexicoPopulationCensus10.6%sample00PopulationCensus10.6%sample00EncuestaNacionaldeIn-gresosyGastosdelosHog-ares05EncuestaNacionaldeIn-gresosyGastosdelosHog-ares05NicaraguaEncuestaNacionaldeHog-aressobreMediciondeNiveldeVida05EncuestaNacionaldeHog-aressobreMediciondeNiveldeVida05EncuestaNacionaldeHog-aressobreMediciondeNiveldeVida05PanamaPopulationCensus10%sample00PopulationCensus10%sample00EncuestadeNivelesdeVida03EncuestadeNivelesdeVida03ParaguayEncuestaIntegradadeHogares01EncuestaIntegradadeHogares01EncuestaIntegradadeHogares01EncuestaIntegradadeHogares01PeruEncuestaNacionaldeHog-ares01EncuestaNacionaldeHog-ares01EncuestaNacionaldeHog-ares01EncuestaNacionaldeHog-ares01U.S.PopulationCensus5%sample00PopulationCensus5%sample00Munic.costoflivingindex(CouncilforCommunityandEconomicResearch)00UruguayEncuestadegastosyin-gresosdehogares05-06Encuestadegastosyin-gresosdehogares05-06Encuestadegastosyin-gresosdehogares05-06Encuestadegastosyin-gresosdehogares05-06VenezuelaPopulationCensus10%sample01PopulationCensus10%sample01noneavailableTableA2:LaborIncomeInequality(RegionallyDeated)MeanLogDeviationIndexTheilIndexMean90/10BetweenWithinBetweenWithinBetweenWithinBetweenWithinIncomeRatioCountryCountryMunic/RegMunic/RegCountryCountryMunic/RegMunic/Reg(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)Ref.toMunic.Bolivia7,25631.20.7200.1260.5940.6260.1000.526Brazil15,46212.10.6480.0980.5500.8290.0900.739ElSalvador10,95517.70.7200.0490.6710.5260.0490.477Guatemala10,19018.20.6780.2120.4660.6810.2230.458Honduras6,12124.90.7600.1330.6270.8420.1140.728Mexico18,6287.90.5030.0970.4060.7790.1040.675Panama19,4999.80.4160.1260.2900.3970.1120.285Paraguay12,23742.20.8740.2050.6690.7310.2030.528Peru11,08214.70.5300.0930.4360.5010.0840.417UnitedStates67,86516.40.5410.0340.5060.4620.0370.425Venezuela14,84815.70.8150.0290.7860.9910.0270.964Mun(actual)30.50.2820.5830.0700.5130.2370.5350.0500.485Mun(equal)29.20.2370.6550.1090.5460.2900.6100.0810.529NoUS(equal)20.50.0610.6660.1170.5500.0570.6900.1050.585Ref.toRegionCanada51,79619.70.5340.0020.5320.3570.0020.355Chile28,92910.00.5110.0030.5070.5450.0040.541Colombia13,75919.30.6890.0410.6480.6910.0420.649CostaRica20,94916.00.5580.0150.5430.5120.0140.498Ecuador10,70434.20.9550.0560.9001.6370.0541.583Uruguay19,49122.20.7870.0670.7200.8330.0610.772Reg(actual)35.10.1810.6500.0260.6230.1770.5320.0150.518Reg(equal)31.40.1300.6720.0310.6420.1320.6090.0200.589All(actual)32.50.2760.5930.0630.5300.2400.5350.0460.489All(equal)32.00.2140.6610.0820.5800.2380.6100.0550.555NoUS/CA(equal)22.10.0790.6780.0900.5880.0760.7130.0720.640SeeAppendixTableA1forsources.Allincomesaredeatedbythestatemedianofahousehold-specicPaascheindex.Column(2)givestheratioofthe90thpercentileoftheincomedistributiontothe10thpercentile,andcolumns(3)through(10)decomposeinequality.Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.TableA3:EducationalInequalityTheilIndexSCVIndex-NoZerosSCVIndex-ZerosMean90/50BtwW/inBtwW/inBtwW/inBtwW/inBtwW/inBtwW/inEduc.RatioV(ed)CntryCntryMun/RegMunic/RegCntryCntryMun/RegMun/RegCntryCntryMun/RegMun/Reg(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)Argentina7.42.021.80.1240.0080.1160.1150.0080.1070.2000.0130.186Bolivia5.43.019.80.3100.0280.2820.3360.0270.3090.3360.0270.309Brazil5.62.218.30.1800.0160.1630.1760.0160.1600.2890.0330.257Canada12.51.214.30.0370.0000.0360.0340.0000.0340.0460.0000.046Chile8.51.621.80.1210.0050.1150.1060.0050.1010.1500.0070.142Colombia6.22.623.10.1820.0200.1620.1770.0200.1570.3020.0350.268CostaRica6.32.018.70.1560.0110.1450.1560.0110.1450.2330.0160.217Ecuador6.32.223.20.1880.0130.1750.1860.0130.1730.2900.0230.267ElSalvador5.52.423.90.1930.0170.1760.1870.0170.1690.3920.0350.357Guatemala4.33.720.90.2360.0730.1630.2540.0750.1780.5710.1830.388Honduras5.42.018.30.1660.0260.1400.1720.0250.1470.3100.0520.258Mexico6.12.020.00.1680.0180.1500.1640.0180.1460.2700.0310.239Nicaragua4.62.818.40.1920.0220.1690.2010.0210.1800.4350.0630.372Panama5.72.418.30.1820.0300.1520.1850.0300.1560.2800.0410.238Paraguay6.82.122.60.1520.0220.1300.1380.0210.1170.2430.0350.208Peru7.32.524.70.1490.0150.1340.1420.0150.1270.2330.0270.206UnitedStates11.21.318.00.0510.0020.0490.0440.0020.0430.0720.0020.070Uruguay7.92.020.20.1330.0130.1200.1320.0130.1190.1620.0130.149Venezuela6.22.017.40.1240.0080.1170.1110.0080.1030.2280.0160.212All(actual)1.825.90.0300.1060.0090.0970.0300.0910.0070.0840.0480.1430.0120.131All(equal)2.024.30.0240.1480.0160.1320.0250.1380.0140.1230.0440.2180.0230.195NoUS/CA(equal)2.221.80.0090.1690.0190.1500.0080.1640.0180.1460.0150.2680.0310.237SeeAppendixTableA1forsources.Column(2)presentstheratioofthe90thpercentileoftheschoolingdistributiontothemedian,andcolumn(3)givesthevarianceofyearsofschooling.Bothincludeindividualswhohavenotreceivedschooling.Columns(4)through(15)decomposeinequality,usingtheTheilindex,halfthesquaredcoecientofvariation(excludingindividualswithnoschooling),andhalfthesquaredcoecientofvariation(includingindividualswithnoschooling),respectively.Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.TableA4:HouseholdEquivalentExpenditureInequalityMeanLogDeviationIndexTheilIndexMean90/1090/50BetweenWithinBetweenWithinBetweenWithinBetweenWithinExpend.RatioRatioV(log(c)CntryCntryMun/RegMunic/RegCntryCntryMun/RegMun/Reg(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)Ref.toMunic.Bolivia2,9535.22.20.4140.2000.0760.1230.1960.0670.129ElSalvador5,8905.62.40.4470.2220.0370.1850.2200.0370.183Guatemala5,2419.93.90.7820.4580.2730.1850.5740.3120.262Honduras3,0758.02.50.6290.2980.0960.2010.2840.0850.199Mexico8,6746.92.70.5900.3080.0820.2270.3280.0770.251Nicaragua2,8025.12.30.4000.2100.0450.1660.2230.0420.181Panama5,8509.22.90.7680.3790.1620.2180.3770.1450.232Paraguay3,2787.32.60.5980.2910.1180.1730.2840.1080.177Peru4,4425.72.30.4580.2260.1040.1220.2260.0960.129Mun.(actual)8.72.90.7200.0770.2950.0950.1990.0700.3230.0880.235Mun.(equal)8.22.90.6840.0700.2880.1100.1780.0700.3170.1120.205Ref.toReg.Brazil5,64911.53.60.9030.4660.0370.4290.4740.0350.439Chile7,8139.43.50.7470.3970.0060.3920.4140.0060.408Colombia4,0847.82.90.6590.3360.0650.2700.3440.0750.269CostaRica6,4699.33.10.8040.4020.0210.3820.4110.0220.389Ecuador5,0985.92.60.5040.2670.0190.2490.2850.0190.266Uruguay7,1927.52.80.5950.2980.0460.2530.2950.0430.253Reg.(actual)10.33.40.8380.0140.4220.0390.3830.0130.4360.0370.399Reg.(equal)8.93.10.7470.0230.3610.0320.3290.0220.3730.0300.343All(actual)9.93.30.8090.0480.3680.0630.3060.0490.3820.0620.320All(equal)8.73.00.7210.0590.3170.0790.2380.0560.3430.0740.269SeeAppendixTableA1forsources.Column(2)presentstheratioofthe90thpercentileofthehouseholdequivalent.expendituredistributiontothe10thpercentile,Column(3)givestheratioofthe90thpercentiletothemedian,andcolumn(4)presentsthevarianceofloghouseholdequivalentexpenditure.Columns(5)through(12)decomposeinequality,usingtheMeanLogDeviationindexandtheTheilindex,respectively.Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.TableA5:IncomeInequalityDecomposition,MeanLogDeviationIndex(Males18-55)LaborIncomePredictedLaborIncomeResidualLaborIncomeBetw.With.Betw.With.Betw.With.Betw.With.Betw.With.Betw.With.CntryCntryMun/RegMun/RegCntryCntryMun/RegMun/RegCntryCntryMun/RegMun/Reg(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)Ref.toMun.Bolivia0.6870.1680.5190.1340.0230.1110.5900.1100.479Brazil0.6350.1170.5180.2410.0390.2020.3460.0340.312ElSalvador0.6900.0560.6330.0940.0150.0790.5670.0140.553Guatemala0.5900.2880.3020.3160.1800.1360.3230.0700.253Honduras0.7010.1680.5340.2740.0640.2100.3890.0490.340Mexico0.4760.1110.3650.1160.0190.0970.3470.0650.282Panama0.4570.0630.3940.1660.0170.1490.3030.0200.283Paraguay0.3710.1590.2120.1640.0600.1040.2240.0670.156Peru0.7350.2520.4820.2180.0660.1520.5170.0880.429UnitedStates0.4550.0470.4080.1420.0070.1350.3300.0160.314Venezuela0.7890.0350.7540.0250.0020.0230.7570.0270.730Mun.(actual)0.2930.5350.0900.4460.1800.1640.0230.1410.1000.3690.0360.333Mun.(equal)0.2390.5990.1330.4660.1700.1720.0450.1270.1280.4270.0510.376NoUS(equal)0.0490.6130.1420.4710.1010.1750.0490.1260.1340.4360.0540.382Ref.toRegionCanada0.4320.0050.4270.1300.0000.1300.3430.0040.339Chile0.4430.0020.4410.1290.0000.1290.2620.0000.262Colombia0.6360.0520.5840.1860.0160.1710.3750.0100.365Ecuador0.8640.0950.7690.1590.0040.1550.6060.0520.554Uruguay0.6910.0910.6000.2330.0280.2050.4120.0170.396Reg.(actual)0.2120.5780.0370.5400.0410.1590.0080.1520.1550.3780.0120.366Reg.(equal)0.1620.6130.0490.5640.0850.1670.0100.1580.1590.4000.0170.383All(actual)0.2910.5420.0820.4600.1760.1620.0200.1420.1130.3680.0320.337All(equal)0.2270.6030.1070.4960.1560.1700.0340.1370.1410.4180.0400.378NoUS/CA(equal)0.0670.6260.1180.5080.1500.1750.0380.1370.1290.4300.0440.385SeeAppendixTableA1forsources.Columns(1)through(4)decomposetheMeanLogDeviationindexforlaborincomeinequality,andcolumns(5)through(12)dothesameforpredictedlaborincomeinequalityandresiduallaborincomeinequality,respectively.Actualreferstoweightingbyactualpopulation,whereasequalnormalizeseachcountryspopulationtobeofequalsize.FigureA1:LaborincomesinNorthAmericaMean Labor Income (PPP $)85,000FigureA2:LaborincomesinMexicoandCentralAmericaMean Labor Income (PPP $)15,000FigureA3: LaborincomesinSouthAmericaMean Labor Income (PPP $)15,000