The Contribution of Economic Geography to GDP per Capita

Transcription

1 ISSN OECD Journl: Economic Sudies Volume 2008 OECD 2008 The Conriuion of Economic Geogrphy o GDP per Cpi y Hervé Boulhol, Alin de Serres nd Mrgi Molnr Inroducion nd min findings Generl empiricl frmework The sic deerminns of GDP per cpi Benchmrk specificion nd empiricl resuls Economic disnce Why proximiy mers The disnce of OECD counries o world mrkes Empiricl nlysis: Augmened Solow model nd proximiy Trnspor coss Evoluion of rnspor nd elecommunicions cos indices Impc of rnspor coss on openness o rde nd GDP per cpi. 21 Overll economic impc nd policy implicions Overll impc Policy implicions Conclusions Noes Biliogrphy Annex: The Augmened Solow Model The uhors would like o hnk numerous OECD collegues, in priculr Sveinjörn Blöndl, Sen Doughery, Jørgen Elmeskov, Chrisin Ginell, Dvid Hugh, Peer Hoeller, Nick Johnsone, Vincen Koen, Dirk Pil, Jen-Luc Schneider nd Andres Woergoeer, for heir vlule commens s well s Philippe Brird nd Mrine Levsseur for echnicl ssisnce nd Croline Aen for edioril suppor. The pper hs lso enefied from commens y memers of he Working pry No. 1 of he OECD Economic Policy Commiee, s well s he pricipns o he The Grviy Model Conference, Groningen, Ocoer

2 Inroducion nd min findings Over he ps severl yers, he OECD hs qunified he impc of srucurl policies on employmen, produciviy nd GDP per cpi e.g. OECD, 2003, The resuls from hese sudies, which hve uil on vs cdemic lierure, hve conriued o eer undersnding of he min chnnels linking policies o lour nd produc mrke oucomes in OECD counries. In doing so, hey hve lso underscored he limis o he undersnding of economic growh: only limied pr of he cross-counry dispersion in GDP levels nd growh res cn e explined y qunifile policy levers, les on he sis of sndrd mcro-growh regression nlysis. This pper exmines how much of he cross-counry dispersion in economic performnce cn e ccouned for y economic geogrphy fcors. To do so, n ugmened Solow model is used s enchmrk. The choice is moived y he fc h his model hs served s he sic frmework in previous work on he deerminns of growh, herey ensuring some coninuiy. I hs long een recognised, however, h while providing useful enchmrk o ssess he conriuions of fcor ccumulion s source of differences in GDP per cpi, he sic Solow growh model ignores poenilly imporn deerminns. For insnce, i leves lrge porion of growh o e explined y he level of echnology, which is ssumed o grow re se exogenously. In order o ridge some of he gps, exensions of he model in he lierure hve generlly ken four ypes of prly reled direcions: i R&D nd innovion, ii goods mrke inegrion nd openness o inernionl rde, iii quliy of insiuions, nd iv economic geogrphy. The focus of his pper is on economic geogrphy, lhough his is no olly independen from he oher fcors, in priculr inernionl rde. More specificlly, for he purpose of his sudy, he concep of economic geogrphy is exmined hrough he proximiy o res of dense economic civiy. The key poin of his spec of geogrphy is he recogniion h proximiy my hve fvourle impc on produciviy, hrough vrious chnnels opering vi produc nd lour mrkes. In he cse of produc mrkes, one of he key chnnels is h proximiy induces sronger compeiion eween producers, hus encourging efficien use of resources nd innovion civiy. Anoher is h n esy ccess o lrge mrke for consumers nd suppliers of inermedie goods llows for he exploiion of incresing reurns o scle. Furhermore, he presence of lrge mrkes llows for hese scle effecs o e relised wihou dversely ffecing compeiion. The scope for exploiing higher reurns o scle is hmpered y disnce o mjor mrkes, oh wihin nd cross counries, due o rnsporion coss. Trnsporion coss lso reduce he scope for specilision ccording o comprive dvnge, noher imporn driver of gins from rde long wih he iliy o rep scle economies. While he economic geogrphy lierure focuses minly on rde linkges, prllel lierure on urn nd spil economics pus more emphsis on gglomerion exernliies s enefi from opering in n re of dense economic civiy. Such 2

3 exernliies my include economies of scle reled o infrsrucure nd oher pulic services, s well s he poenil gins ssocied wih he ccess o lrge pool of workers, nd loclised knowledge spillovers. In principle, i is possile o provide some qunificion of hese enefis, using sndrd mesures of economic densiy, such s he shre of populion living in ciies. In prcice, such mesures re highly endogenous o economic developmen nd finding pproprie insrumens o ddress he endogeneiy prolem is eyond he scope of his pper. As resul, his spec is only exmined in very enive wy in he finl secion of he pper. The empiricl sregy pursued in he pper is s follows. In he nex secion, he ugmened Solow model, which is used s he sic frmework, is firs riefly descried nd esimed oh in level nd in error-correcion forms, over smple of 21 OECD counries over he period The influence of proximiy o mjor mrkes on GDP per cpi is hen invesiged in he following secion, inroducing in he enchmrk model vrious indicors of disnce o mrkes, such s mesures of mrke poenil, mrke nd supplier ccess, s well s he sum of disnces o world mrkes nd populion densiy. The vrious mesures of disnce o mrkes re ll found o hve sisiclly significn effec on GDP per cpi, wih he excepion of populion densiy. The esimed economic impc vries somewh cross specificions, u i is fr from negligile. For insnce, he lower ccess o mrkes relive o he OECD verge could conriue negively o GDP per cpi y s much s 11% in Ausrli nd New Zelnd. Conversely, he enefi from fvourle locion could e s high s 6-7% of GDP in he cse of Belgium nd he Neherlnds. Ler in he ex, he impc of disnce is lernively exmined vi he more specific chnnel of rnsporion nd elecommunicion coss. To his end, rod indicors of weigh-sed rnsporion coss covering mriime, ir nd rod shipping hve een consruced for 21 OECD counries over he period , long wih n indicor of he cos of inernionl elecommunicions. Bsed on hese indicors, here is lile evidence h he impornce of disnce in he rnsporion of goods hs diminished during he ps wo or hree decdes hough rnspor coss my hve fllen relive o he vlue of rnspored goods. In conrs, he cos of inernionl elecommunicions hs fllen in ll counries o he poin where i is siclly no longer significn nywhere. Overll, rnsporion coss re found o hve negive nd significn effec on GDP per cpi hrough heir effec on inernionl rde. Bsed on hese esimes, differences in rnspor coss relive o he OECD verge conriue o reduce GDP per cpi y eween 1.0% nd 4.5% in Ausrli nd New Zelnd. A he oher end, he lower rnspor coss for Cnd nd he Unied Ses conriue o rise GDP per cpi relive o he verge OECD counry, u only y smll mrgin vrying eween 0.5% nd 2.5%. The quniively smller effecs hn hose found on he sis of mesures of economic disnce re consisen wih rnsporion coss eing only one spec of coss reled o disnce. Mos of he geogrphy fcors discussed in his pper cnno e influenced y policy or re only ffeced y policy in indirec wys. Neverheless, numer of policy issues re ddressed in he penulime secion, which lso provides summry of he comined economic impc of he geogrphic vriles used in he empiricl nlysis. 3

4 Generl empiricl frmework A sic empiricl frmework is required in order o ssess he impornce of economic geogrphy in deermining GDP per cpi. Agins he ckground of erlier OECD nlysis in his re, his secion riefly reviews he sic deerminns of GDP per cpi, discusses lernive specificions in erms of levels nd chnges over ime, nd repors he resuls of n empiricl nlysis using only he sic deerminns. The reminder of he pper will hen exmine wheher economic geogrphy vriles cn ccoun for some of he vrince in GDP per cpi lef unexplined y he sic deerminns. The sic deerminns of GDP per cpi The empiricl frmework used o ssess he influence of economic geogrphy deerminns is he Solow 1956 model ugmened wih humn cpil. The model hs een widely used in he empiricl growh lierure, owing lrgely o is simpliciy nd flexiiliy. For insnce, despie eing derived from specific frmework, he empiricl version of model is sufficienly generl o e consisen wih some endogenous growh models Arnold e l., The Solow model hs een widely used s heoreicl frmework o explin differences cross counries in income levels nd growh perns. The model is sed on simple producion funcion wih consn reurns-o-scle echnology. In he ugmened version of he model Mnkiw, Romer nd Weil, 1992, oupu is funcion of humn nd physicl cpil, s well s lour working-ge populion nd he level of echnology. Under numer of ssumpions ou he evoluion of fcors of producion over ime, he model cn e solved for is long-run sedy-se equilirium wherey he ph of oupu per cpi is deermined y he res of invesmen in physicl nd humn cpil, he level of echnology, nd he growh re of populion see Annex for deiled derivion. In he sedy-se, he growh of GDP per cpi is driven solely y echnology, which is ssumed o grow consn re se exogenously in he sic model. The long-run relionship derived from he ugmened Solow model cn e esimed eiher direcly in is level form, or hrough specificion h explicily kes ino ccoun he dynmic djusmen o he sedy se. Esimes of he long-run relionship in sic form hve een used in he lierure e.g. Mnkiw, Romer nd Weil, 1992; Hll nd Jones, 1999; Bernnke nd Gürkynk, 2001, in priculr in sudies focusing on income level differenils cross counries. However, since he model hs ofen een used in he empiricl growh lierure o exmine issues of convergence, some form of dynmic specificion hs een more common. The wo ypes of specificion sic or dynmic cn e expeced o yield similr resuls if counries re no oo fr from heir sedy ses or if deviions from he ler re no oo persisen. In principle, dynmic specificion is preferle, even when he ineres is minly on he idenificion of long-run deerminns. This is ecuse persisen deviions from sedy se re more likely o led o ised esimes of he long-run prmeers in sic regressions, especilly when he ime-series dimension of he smple is relively shor. In prcice, esiming dynmic pnel equions is lso frugh wih economeric prolems Durluf nd Quh, Furhermore, mjor drwck wih he mos common echniques sed on dynmic fixed-effec esimors is h only he inerceps re llowed o vry cross counries, implying h ll counries converge o heir sedy-se he sme speed, n ssumpion unlikely o hold even mong developed counries. 1 4

5 To ddress he ler issue, previous sudies hve relied on he Pooled Men Group PMG esimor, which llows for shor-run coefficiens nd he speed of djusmen o vry cross counries, while imposing homogeneiy on long-run coefficiens OECD, However, even hough he PMG esimion echnique is inuiively ppeling nd perhps he mos suile under some condiions, i is no wihou limiions especilly when such condiions re no me. For insnce, due o he lrge numer of prmeers nd he non-liner consrins, he mximum likelihood esimion echnique is prone o prolems of convergence on locl opim. And, experience suggess h prmeer esimes cn e priculrly sensiive in presence of muli-collineriy mong regressors, wih some prmeer vlues eing in such cses oo lrge nd unsle o e plusile. For he purpose of his sudy, he model is firs re-esimed wih only he sic deerminns included in he specificion, i.e. proxies for invesmen in physicl nd humn cpil, populion growh nd echnicl progress. Then, numer of deerminns re dded o he enchmrk specificion hroughou he res of he pper, u he se of ddiionl vriles is limied o hose reled o economic geogrphy fcors. One excepion is he mesure of exposure o inernionl rde which, given he impornce of geogrphy on rde, is used o ssess he impc of rnsporion coss on GDP per cpi see ler in he ex. The reson for leving oher poenil vriles ou is essenilly one of prsimony, i.e. o limi he numer of specificions, which quickly runs up s ech ddiionl deerminn is considered. 2 However, his implies h poenilly significn conrol vriles re no included, wih he risk h his enils in erms of ises nd rousness of he resuls s regrds he deerminns of economic geogrphy. In order o minimise hose risks, ll specificions include vrious cominions of counry nd yer fixed-effecs nd/or liner ime rends, ll of which re inroduced in pr o cpure omied vriles. Benchmrk specificion nd empiricl resuls The empiricl version of he ugmened-solow model is re-esimed over pnel d se comprising 21 counries nd 35 yers of oservions In wh will serve s he reference model for he res of he pper, he level of GDP per working-ge person in counry i nd yer y i is regressed on he re of invesmen in he ol economy s K,i, he verge numer of yers of schooling of he populion ged 25-64, which is used s proxy for he sock of humn cpil hc i 3 nd he growh re of populion n i ugmened y consn fcor inroduced s proxy for he sum of he rend growh re of echnology nd he re of cpil depreciion g +d, wih ll vriles expressed in logs. 4 Technologicl progress is cpured lernively y liner ime rend or ime dummies. The resuls presened in his pper re sed on oh level specificion, using les-squre esimor h correcs for heeroskedsiciy nd conemporneous correlions, nd n error correcion specificion, using he pooled men group PMG esimor. Due o persisence in he series, conrol for firs-order seril correlion is sysemiclly mde when he level specificion is esimed. The funcionl forms of he 5

6 equions esimed in level nd error-correcion forms re respecively specified s follows see Annex for derivion: Level specificion AR1 Log yi = α. Log sk, i + β. Log h ci + ϕ. ΔLog h ci + γ. Log ni + g + d + ςi + ei + e + ui ui = ρ. ui 1 + εi, εi i. i. d. 1 Error-correcion specificion Pooled Men Group ΔLog y i.[ Log y α. Log s + β. Log h c + γ. Log n + g + d ] = λi +. ΔLog s 0i i 1 K, i 1i K, i i 2i where e i nd e re counry nd yer fixed-effecs, respecively, nd is liner ime rend. The prmeers,,, nd re he long-run prmeers on he hree sic deerminns nd he ime rend. The prmeer is he firs-order uocorrelion coefficien used in he level specificion. 5 The oher prmeers cpure shor-run dynmics nd will no e repored in he le of resuls. Finlly, u i nd i re he residuls. The resuls from re-esiming he empiricl version of he ugmened-solow model re presened in Tle 1. The firs hree columns refer o he level specificion nd he ls wo re sed on he error-correcion specificion. Focusing on he level specificion, he i +. ΔLog h c +. ΔLog n + g + d + e + ς. + ε i i i i i 2 Tle 1. Bsic frmework: Regression resuls Augmened-Solow model 1 Dependn vrile GDP per cpi Level AR1 Level AR1 Level AR1 Error correcion PMG Error correcion PMG Common prmeers Physicl cpil 0.184*** 0.156*** 0.199*** 0.292*** 0.572*** Humn cpil 0.334*** 0.792*** *** Populion growh *** 0.661*** Time rend 0.015*** Rho Counry-specific prmeers Lmd *** 0.086*** Time rend No No Yes Yes No Fixed effecs Counry Yes No Yes Yes Yes Yer Yes Yes Yes No No Smple size Tol numer of oservions Numer of counries Noe: Sndrd errors re in prenheses. *: significn 10% level; ** 5% level; *** 1% level. 1. The funcionl forms corresponding o he level nd error-correcion specificions re repored erlier in he ex. In he level specificion, sndrd errors re rous o heeroscedsiciy nd o conemporneous correlion cross pnels. In he error-correcion specificion, only long erm prmeers re repored. 2. The populion growh vrile is ugmened y consn fcor g + d designed o cpure rend growh in echnology nd cpil depreciion. This consn fcor is se 0.05 for ll counries. 3. Rho is he firs-order uo-correlion prmeer. 4. The prmeer lmd is he verge of he counry-specific speed djusmen prmeer, i. 6

7 coefficien on humn cpil is quie sensiive o he conrol for fixed effecs nd or ime rends. In priculr, i comes ou significnly higher when counry fixed effecs re excluded column 2, suggesing h n imporn pr of he informion conined in he verge numer of yers of schooling is reled o differences in verge levels cross counries. Moreover, i compleely drops ou when counry-specific ime rends re included in he regression in ddiion o counry- nd yer-fixed effecs column 3. Turning o he error-correcion specificion, he resuls shown in he fourh column re similr o hose oined in he erlier OECD nlysis sed on n lmos idenicl specificion wih counry fixed effecs nd counry-specific prmeers on he ime rend nd he sme esimion mehod PMG. 6 The speed of djusmen prmeer suggess rpid convergence o he sedy-se, resul which is influenced y he inroducion of counry-specific ime rend prmeers. 7 Also, he prmeer esime on humn cpil suggess srong effec, wih one exr yer of schooling leding o n increse in GDP per cpi y round 8% in he long run for he verge OECD counry. However, here gin, he significnce of he humn cpil coefficien depends on wheher or no he rend is ssumed o e common or counry specific column 5. 8 Figure 1 presens he conriuion of physicl cpil, humn cpil nd fixed effecs o he gp in GDP per cpi relive o he verge OECD counry nd on verge over he period. 9 The resuls presened in he wo pnels re sed on he specificions shown in columns 1 nd 4, respecively. No surprisingly, he conriuion of physicl nd humn cpil is smll relive o h of he fixed effecs. Indeed, he ler ccoun for 72% nd 87% of he GDP per cpi vrince over his verge period for he level nd he error correcion specificion respecively. Some of he highes fixed effecs re in oh specificions recorded for Norwy nd, o lesser exen, he Unied Ses nd Sweden. Porugl, Greece, New Zelnd nd Jpn hve he lrges negive effecs. The posiion of Irelnd nd Swizerlnd is priculrly sensiive o wheher common or counry-specific ime rends re inroduced. The res of he pper invesiges wheher some of hese lrge fixed effecs cn e ccouned for y indicors of economic geogrphy nd, more generlly, he exen o which such indicors cn explin pr of income levels which is no explined y he usul deerminns. Economic disnce In his secion, differen mesures of proximiy o mrkes or cenrliy re inroduced nd esed in he empiricl nlysis s poenil deerminns of GDP per cpi. Some of hem re simple mesures sed on GDP, counry size, populion nd disnces vis-à-vis oher counries. The ohers re model-sed mesures derived from ilerl rde flows. Why proximiy mers The role of geogrphic disnce nd he influence of neighouring counries hve lrgely een negleced in rdiionl growh heory which relies essenilly on nionl chrcerisics, e.g. fcor endowmens nd echnologicl progress. Ye, he clusering of economic civiies is well-known phenomenon h rises quesions ou he exen o which he proximiy o high-income neighours mers for counry s own income. The developmen process migh indeed e hindered in counries h re disn from cenres of economic civiies. 7

8 Figure 1. Bsic frmework: Conriuions of explnory vriles 1 Difference o verge counry, Fixed effecs Humn cpil Physicl cpil Acul GDP per cpi Esimed GDP per cpi Percenge poins A. Level specificion Tle 1, column NOR USA IRL CHE SWE DNK CAN AUT AUS NLD FRA BEL FIN GBR ITA JPN ESP NZL GRC PRT Percenge poins B. Error correcion specificion Tle 1, column NOR USA IRL CHE SWE DNK CAN AUT AUS NLD FRA BEL FIN GBR ITA JPN ESP NZL GRC PRT 1. These chrs show he conriuion of ech explnory vrile o GDP per cpi sed on Tle 1. The conriuions re compued s differences o he verge counry nd on verge over he period The conriuion of fixed effecs is he sum of counry nd yer fixed effecs in Pnel A, nd he sum of counry fixed effecs nd counry specific ime rends in he Pnel B. For Norwy nd Pnel A, s n exmple, he chr reds s following: On verge eween , Norwy hd GDP per cpi which ws 36% ove he verge cross counries, wheres he esimed difference o he verge is 23% sed on Tle 1, column 1. These 23% re roken down ccording o he conriuion of fixed effecs 23%, physicl cpil 3% nd humn cpil 3%. Becuse of rek in he series due o he reunificion, d for Germny were used only for he period Therefore, Germny is no included in he figure. Disnce cn ffec produciviy nd income levels hrough vrious chnnels, including rde, foreign invesmen nd echnology diffusion. There is mple evidence showing he impornce of disnce for rde nd FDI flows e.g. Nicolei e l., 2003, s well s for echnology spillovers Keller, Furhermore, rde nd FDI re ovious chnnels of knowledge spillovers Eon nd Korum, 1994 nd 1996, which reinforces he impc of disnce on produciviy. Focusing on he rde chnnel, disnce direcly rises rnspor nd oher rde coss nd is n oscle o oh domesic nd foreign rde. There re numer of iner-reled wys hrough which his chnnel ffecs produciviy. Greer proximiy o world mrkes 8

9 increses he opporuniy o concenre resources in civiies of comprive dvnge. I lso encourges specilision of firms h cn in efficien scle nd more generlly exploi incresing reurns in specific fields of producion. Moreover, sronger compeiion pressures force compnies o use ville inpus efficienly nd encourge hem o innove nd minin compeiive dvnge. In ddiion o influencing GDP per cpi vi is impc on echnicl efficiency, disnce cn lso ffec exernl erms of rde. A relively remoe nd sprsely populed counry hs o inernlise rnspor coss ino producer prices of rdele goods in order o remin compeiive in world mrkes or oherwise suffer lower sles. Becuse, y definiion, he fcor prices of moile fcors end o e equlised cross locions, he coss of remoeness re orn y he immoile fcors, i.e. mosly lour in n inernionl perspecive. Indeed, even if echnologies re he sme everywhere, firms in more remoe counries cn only fford o py relively lower wges Redding nd Venles, In ddiion o is direc impc on incomes, geogrphy migh hve n influence hrough oher fcors such s physicl or humn cpil. Reurns o physicl nd humn cpil migh e higher in counries hving eer ccess o lrge mrkes Redding nd Sco, In urn, high reurn o skills increses he incenive o inves. As regrds humn cpil, Redding nd Sco provide some evidence h he world s mos peripherl counries hve relively low levels of educion, feure found lso in he cse of Europen regions Breinlich, The disnce of OECD counries o world mrkes In his secion, four mesures of proximiy o mrkes or cenrliy re consruced nd compred. The firs one is populion densiy. The second one depends solely on disnces eween counries. The hird one is simple mesure sed on disnces vis-à-vis oher counries nd he size of heir GDPs, nd he ls one is model-sed mesure derived from ilerl rde flows. The nex secion is specificlly dediced o he effecs of economic disnce mesured y rnspor coss. Populion densiy, sum of disnces nd mrke poenil Populion densiy, defined s he rio of populion o surfce re, is n indicor of proximiy o he domesic mrke. The higher he densiy he lower he ggreged domesic rnspor coss. However, he criicl shorcoming of his mesure is is filure o ke ino ccoun he effecive ccess o foreign mrkes. A simple mesure of disnce o mrkes h does so is one sed on ilerl disnces. From he perspecive of empiricl nlysis, his mesure is rcive ecuse i is sed on exogenous chrcerisics of geogrphy. Alhough he sum of he disnces of ech counry o Tokyo, Brussels nd New York hs een commonly used in he empiricl lierure, he choice of hese hree locions is rirry nd crees issues of endogeneiy. Hence, eer lernive is o sum he disnces o ll counries Hed nd Myer, 2007: Dissum i = d i j j In order o compue Dissum, he world ws divided in 32 res: Afric, Ausrli, Ausri, Belgium, Brzil, Cnd, Chin, CIS counries, Denmrk, Esern Europe, Finlnd, Frnce, 3 9

10 Germny, Greece, Irelnd, Ily, Jpn, Kore, Lin Americ oher hn Brzil nd Mexico, Mexico, he Middle Es, he Neherlnds, New Zelnd, Norwy, Porugl, Spin, Sweden, Swizerlnd, Turkey, he Unied Kingdom, he Unied Ses nd Asi oher h he counries lredy included. Pure disnce mesures, however, fil o ke ino ccoun he size of mrkes. Moreover, his mesure depends on how geogrphic res re consruced. For exmple, differen picure would e oined if he Europen Union ws considered s one eniy or, lernively, he Norh Americ ws disggreged ino ses/provinces. Therefore, more refined mesure of proximiy o mrkes is mrke poenil, which is defined s he sum of ll counries GDP weighed y he inverse of he ilerl disnce Hrris, 1954: Mrke Poenil i = j GDP d i j j The mrke poenil mesure mus ke ino ccoun, for given counry, he domesic mrke nd include is own GDP weighed y he inverse of inernl disnce. Becuse he inernl disnce is generlly smller hn exernl disnces, i is ssocied wih greer weigh nd is herefore sensiive prmeer for mesures of cenrliy. The mos commonly used disnce indicors comine geodesic cpil-o-cpil disnces eween counries nd inernl disnces sed on surfce res. 10 I follows h mrke poenil is likely o e posiively correled wih populion densiy due o he domesic componen. Mrke nd supplier ccess Alhough i is n inuiive indicor of cenrliy, mrke poenil is n d-hoc wy of cpuring he influence of disnce o mrkes. In priculr, he weighing of foreign mrkes in he mrke poenil compuion is sed solely on disnces, regrdless of he rue ccessiiliy of hese mrkes. In h respec, mrke poenil is very crude mesure of mrke ccess. Indeed, ccessiiliy depends, in ddiion o disnce, on rde policy nd culurl relionships, mong oher deerminns. A eer pproch consiss in looking no only he poenil, u rher he cul ccessiiliy o counries mrkes. A mesure sed on such n pproch hs een proposed in he new economic geogrphy lierure, which hs revived he concep of proximiy o mrkes nd formlised he role of economic geogrphy in deermining income. Using he mehodology proposed y Redding nd Venles 2004 nd descried in Box 1, mesures of mrke nd supplier ccess hve een derived from ilerl rde equions esimed over he period 1970 nd 2005 for he 32 counries/res covering 98.5% of world rde flows in goods see Boulhol nd de Serres, 2008, for deils. Comprison of he differen mesures The vrious mesures of cenrliy discussed in he previous su-secion hve een compued for mos OECD counries nd Tle 2 repors he compued vlues for 2005, plus he verge of he counry rnking over he differen mesures. To fcilie he comprison, ech of hese mesures is scled such h he verge cross counries is 100 for ech yer. The cross-counry pern is resonly close cross indicors. Liner correlion is especilly high, round 95%, eween mrke poenil, mrke ccess nd supplier ccess nd he verge rnking. Rnking he counries enles o disinguish five 4 10

11 Box 1. Consrucion of mrke ccess nd supplier ccess mesures Mrke nd supplier ccess mesures re derived from he esimion of grviy-like relionship. As is common in he lierure, rde coss in he ilerl rde specificion re ssumed o depend on hree vriles: ilerl disnce, common order nd common lnguge. Noing X i j s he expor from counry i o counry j nd d ij he ilerl disnce, he following equion is esimed for ech yer : Log X Lnguge + m + v i j, = si +. Log d i j +. Border + c. where he so-clled freeness of rde, which is inversely reled o rde coss, is given y Log ij =.Log d ij +.Border + c.lnguge. The esimes of inr-counry freeness of rde, ii, re compued sed on he sme formul pplied o inernl disnce, common order nd common lnguge. s i nd m j re unoserved exporer nd imporer chrcerisics, respecively. For ech yer, hey re proxied y counry fixed effecs. According o he model see Boulhol nd de Serres, 2008, for deils, hese effecs cpure some chrcerisics of he counries reled o he numer of vrieies, expendiures on mnufcures, price indices, ec. Mrke nd supplier ccess, respecively MA nd SA re hen consruced from he esimed prmeers of he ilerl equion ccording o: M Ai = m k φik ; S Ai = s k φik k k For ll he counries, mrke ccess supplier ccess respecively is compued s weighed sum of unoserved imporer chrcerisics m j exporer chrcerisics s i respecively of ll counries. Only he weighs pu on ech prner chnge cross counries, wih hese weighs eing funcion of esimed rde coss. If given counry k hs lrge mrke cpciy m k, counries hving low rde coss wih counry k, i.e. high freeness of rde, pu high weigh on m k nd end o hve high mrke ccess. A similr rgumen pplies o supplier ccess for counries hving low rde coss wih prners hving lrge expor cpciy. Noe h his is he sme principle s h pplied o mrke poenil, whose compuion oils down o weighing ll counries GDP y he inverse of he ilerl disnces. j ij groups, in scending order nd Figure 2 represens his clusering using mrke poenil for illusrion purposes: The remoe nd sprsely populed counries: Ausrli nd New Zelnd. Low-income peripherl counries. High-income peripherl counries, Kore nd Norh Americ. Coninenl Europe, he Unied Kingdom nd Jpn. The cenrlly loced nd dense economies of Belgium nd he Neherlnds. As expeced, ccess mesures re negively correled o he sum of disnces nd posiively correled o populion densiy, suggesing h mrke nd supplier ccess encompsses hese differen geogrphicl dimensions. Besides, populion densiy is n imporn fcor explining he posiion of Jpn nd Kore or ove wh could e expeced from he pure sum-of-disnces mesure. 11 Given he size of is own mrke, he relive posiion of he Unied Ses in erms of mrke poenil or mrke ccess migh look surprising. As shown y he firs column in Tle 2 which gives he simples mesure of proximiy, one reson is h he Unied Ses is much furher from mrkes hn Europen counries. Anoher reson is h he size of 11

12 Tle 2. Mesures of proximiy/disnce o mrkes, 2005 Sum of disnces Dissum Averge cross counries = 100 for ech indicor Mrke poenil Mrke ccess Supplier ccess Populion densiy Averge rnking 1 Ausrli Ausri Belgium Cnd Denmrk Finlnd Frnce Germny Greece Irelnd Ily Jpn Kore Mexico Neherlnds New Zelnd Norwy Porugl Spin Sweden Swizerlnd Turkey Unied Kingdom Unied Ses Liner correlion coefficien Sum of disnces Mrke poenil Mrke ccess Supplier ccess Densiy All he counries re rnked sed on ech of he five indicors, 1 snding for he mos remoe counry nd 24 for he mos cenrl one. The verge rnking is he verge of hese five rnkings. he domesic mrke is no in iself n deque indicor of mrke poenil or ccess o mrkes. To see his more closely, Tle 3 reks down mrke poenil nd mrke ccess ino heir domesic nd foreign componens, respecively. Looking for exmple mrke poenil, i is rue h he domesic componen represens wo hirds of he ol for he Unied Ses wheres h shre is only 22% for he Neherlnds nd 4.5% for Cnd. Sill, he domesic mrke poenil for he Unied Ses is only 30% greer hn h for he Neherlnds, even hough is GDP is 20 imes igger. This is ecuse he inernl disnce of he Unied Ses is 15 imes igger. Wh mers is no he size of he ol domesic mrke, cpured here y he GDP, u h size relive o inernl disnce. 12 In ny cse, hese considerions hve very limied consequences for he economeric nlysis h follows, since hey refer essenilly o he levels of he proximiy mesures nd mos of he regressions include counry fixed effecs. 12

13 200 Figure 2. Mrke poenil, Averge cross counries = BELNLDGBRFRADEUCHEDNKJPN AUTITA CAN IRLNORSWEESPKORUSA FIN PRTGRCTURMEXAUSNZL 1. Mrke poenil is defined in equion 4. Tle 3. Domesic nd foreign componens of mrke poenil nd mrke ccess, 2005 Bse: World = 100 Mrke poenil Mrke ccess Inernl disnce 1 Tol Domesic Foreign Tol Domesic Foreign Km Ausrli Ausri Belgium Cnd Denmrk Finlnd Frnce Germny Greece Irelnd Ily Jpn Kore Mexico Neherlnds New Zelnd Norwy Porugl Spin Sweden Swizerlnd Turkey Unied Kingdom Unied Ses The underlying ssumpion ehind he inernl disnce d i i = 2 / 3 rei /π is h counry is disk where ll suppliers re loced in he cenre nd consumers re loced uniformly over he re. Empiricl nlysis: Augmened Solow model nd proximiy The impc of ccess o mrkes on GDP per cpi hs een esed in differen conexs nd ll hese sudies find h proximiy hs n imporn impc on GDP per 13

14 cpi. 13 However, none of hem hs focused on developed counries despie heir widely vrying ccess o mrkes. In rod smple covering oh les nd mos developed counries, Ausrli nd New Zelnd generlly pper o hve overcome he yrnny of disnce Dolmn, Prhm nd Zheng, However, his inference migh e misleding if he d do no enle o ccoun for imporn counry specificiies. Focusing on more homogenous group over lrge period using pnel echniques should herefore led o more relile esime. This su-secion ssesses he impc of he differen mesures of proximiy/disnce on GDP per cpi when dded o he usul explnory vriles in he ugmened Solow frmework. 14 Tle 4 presens firs se of resuls oined from he GDP per cpi level specificion. In order o idenify he sum-of-disnces nd populion densiy mesures, counry fixed effecs hve o e removed nd, herefore, he firs wo columns include counry effecs, wheres he ls wo do no. 15 This firs se of resuls indices h he effec of proximiy is rous o he vrious mesures. Mrke poenil, he weighed sum of mrke nd supplier ccess, nd he sum of disnces re ll highly significn wih he expeced sign, wih only populion densiy no hving ny srong link o GDP per cpi. 16 This confirms h, s expeced from he previous secion, populion densiy is much weker indicor of proximiy o mrkes hn he oher hree. Bsed on he esimes reled o he sum of disnces which do no conrol for counry fixed effecs, n increse of 10% in he disnces o ll counries riggers decrese of 2.1% in GDP per cpi. 17 Tle 4. Bsic frmework wih proximiy vriles 1 Dependn vrile GDP per cpi Level AR Physicl cpil 0.178*** 0.174*** 0.178*** 0.156*** Humn cpil 0.313*** 0.317*** 0.928*** 0.813*** Populion growh Mrke poenil 0.086*** Weighed sum mrke nd supplier ccess 0.056*** Sum of disnces 0.210*** Populion densiy Rho Fixed effecs Counry Yes Yes No No Yer Yes Yes Yes Yes Smple size Tol numer of oservions Numer of counries Noe: Sndrd errors re in rckes. *: significn 10% level; ** 5% level; *** 1% level. 1. The funcionl form corresponding o he level specificion is repored erlier in he ex. Sndrd errors re rous o heeroscedsiciy nd o conemporneous correlion cross pnels. 2. The populion growh vrile is ugmened y consn fcor g +d designed o cpure rend growh in echnology nd cpil depreciion. This consn fcor is se 0.05 for ll counries. 3. Rho is he firs-order uo-correlion prmeer. 14

15 In order o es he rousness of he proximiy effecs cross specificions, he following resuls focus on he indicor h ress more firmly on sound heoreicl grounds, i.e. mrke nd supplier ccess. The firs hree columns of Tle 5 dd he weighed sum of mrke nd supplier ccess o he specificions shown in columns 1 o 3 of Tle 1, respecively. Mrke nd supplier ccess is lwys highly significn, eing rous o he inclusion of counry nd yer dummies, s well s counry specific ime rends. Moreover, he esime for he ccess vrile is round in ll cses, while he prmeers for humn nd physicl cpil re mosly unchnged compred wih Tle This resul suggess hn he impc of cenrliy o mrkes cs on op of hese usul deerminns. Also, he fc h excluding he counry effecs does no ler he prmeer significnly mens h he ccess effec is idenified y he vriion hrough ime s well s cross counries. The esimed effec of ccess is firly rous o he remen of physicl cpil, humn cpil nd he ccess vriles s eing poenilly endogenous column Finlly, in he ls column, he error correcion specificion is esed using he pooled men group esimor. Here gin, he impc of cenrliy seems o e orhogonl o he oher dimensions, lhough he level of he prmeer is somewh higher. Figure 3 presens he conriuion of mrke nd supplier ccess o GDP per cpi for he period, sed on he esimes in columns 1 nd 5, which re represenive of he level nd error-correcion specificions respecively. Unsurprisingly, Ausrli nd New Zelnd re he ig losers from heir geogrphic posiion. To lesser exen, Greece, Porugl nd Finlnd suffer compred wih he verge counry. The eneficiries re core Europen counries, especilly Belgium nd he Neherlnds. As noed ove, he order of mgniude of he geogrphy effecs vries susnilly depending on he specificions. For exmple, mrke nd supplier ccess is esimed o penlise Ausrli nd New Zelnd y round 11% of GDP in he level specificion. The effec would e lmos hree imes s lrge sed on he error-correcion specificion, which is hrdly plusile. Conversely, Belgium nd he Neherlnds enefi y round 6-7% compred wih he verge counry in he level frmework nd y 16-18% in he error correcion one. Trnspor coss In his secion, he influence of proximiy o lrge mrkes on GDP per cpi is exmined hrough he working of direc chnnel: rnsporion coss. The cos of rnsporing goods is oviously closely linked o disnce. However, shifs in modes of rnspor, echnologicl improvemens in long-disnce shipping nd chnges in fuel coss hve influenced he relionship eween geogrphic disnce nd economic disnce. To some exen, he impc of rnspor coss ws implicily cpured in he mesures of mrke nd supplier ccess derived in he previous secion. Neverheless, he developmen of indicors of rnspor coss llows for ssessing direcly heir impc on rde nd GDP per cpi, seprely from oher fcors ffecing mrke ccess, such s vriions in he degree of openness o rde cross vrious foreign mrkes s well s over ime. Trnspor coss consiue only one source of ol rde coss, lei n imporn one. According o recen esimes, rodly defined rde coss of represenive goods expressed in d vlorem x-equivlen erms cn e s high s 170% in indusrilised counries Anderson nd vn Wincoop, 2004 wih rnspor coss mouning o 21%, he res eing ccouned for y order-reled rde rriers 44% nd reil nd wholesle 15

16 Tle 5. Sensiiviy of proximiy effecs cross specificions 1 Dependn vrile GDP per cpi Level AR1 Level AR1 Level AR1 Level AR1 Error correcion model Common prmeers Physicl cpil 0.174*** 0.166*** 0.188*** 0.171*** 0.307*** Humn cpil 0.317*** 0.750*** *** 0.902*** Populion growh *** Weighed sum of mrke 0.056*** 0.066*** 0.064*** 0.091** 0.131** nd supplier ccess Rho Counry-specific prmeers Lmd *** Time rend No No Yes No Yes Fixed effecs Counry Yes No Yes Yes Yes Yer Yes Yes Yes Yes No Smple size Tol numer of oservions Numer of counries Firs sge regressions 5 Husmn es 2 4 = 12.4 P = Hnsen J-s 2 29 = 5.87 P vlue = 1.00 Physicl cpil She R 2 = P vlue = Humn cpil She R 2 = Weighed sum of mrke nd supplier ccess P vlue = She R 2 = P vlue = Noe: Sndrd errors re in prenheses. *: significn 10% level; ** 5% level; *** 1% level. 1. The funcionl forms corresponding o he level nd error-correcion specificions re repored erlier in he ex. In he level specificion, sndrd errors re rous o heeroscedsiciy nd o conemporneous correlion cross pnels. In he error-correcion specificion, only long erm prmeers re repored. 2. The populion growh vrile is ugmened y consn fcor g +d designed o cpure rend growh in echnology nd cpil depreciion. This consn fcor is se 0.05 for ll counries. 3. Rho is he firs-order uo-correlion prmeer. 4. The prmeer lmd is he verge of he counry-specific speed djusmen prmeer, i. 5. The insrumens used in column 4 re Z i =Dissum i.h where he h re ime dummies. The ess repored for he Insrumenl Vriles esimor red s following. The Husmn es is join es of exogeneiy of physicl cpil, humn cpil nd mrke nd supplier ccess. Exogeneiy is rejeced nd his is due o humn cpil only his is seen when including residuls from he firs-sge regressions in he min equion. The over-idenificion es is he Hnsen es. I is compued wihou he AR1 process for he residuls. For firs-sge regressions, She pril R 2 i.e. sed on he excluded insrumens only re repored for ech poenilly endogenous regressor, long wih he P-vlue of he F-es. These sisics revel h wek insrumens could e n issue for physicl cpil only. disriuion coss 55%. 20 Excluding disriuion, rnspor coss would on he sis on hese esimes ccoun for ou one-hird of inernionl rde coss. This covers he conriuion of oh direc freigh chrges including insurnce nd indirec holding cos for rnsi, invenory coss, ec. rnspor coss. The empiricl nlysis presened in his secion is sed on esimes of freigh chrges for ir, mriime nd rod rnsporion 16

17 Figure 3. Esimed impc of mrke nd supplier ccess on GDP per cpi 1 Deviion from verge OECD counry in Per cen 20 Level specificion Tle 5, column 1 Error correcion specificion Tle 5, column BEL NLD JPN GBR CHE FRA DNK AUT CAN ITA IRL USA SWE NOR ESP FIN PRT GRC NZL AUS 1. Conriuions of mrke nd supplier ccess o GDP per cpi re sed on Tle 5. They re compued s differences o he verge counry nd on verge over he period For exmple, sed on he esime from he level specificion, he fvourle ccess o world mrkes h Belgium enefis from compred wih he verge counry would conriue o s much s 6.7% of is GDP. Becuse of rek in he series due o he reunificion, d for Germny were used only for he period Therefore, Germny is no included in he figure. of merchndise. Indirec coss, which re usully inferred from rde flow regressions rher hn direcly oserved, re no covered. In ddiion, he cos of inernionl elecommunicions is considered insofr s i ffecs rde in services nd, o lesser exen, rde in goods vi is impc on ck-office operion, finncing, ec. The res of he secion provides some deils on he consrucion of n index of overll rnspor coss nd is hree min componens, s well s he cos of inernionl elecommunicions, for he 21 OECD counries included in he empiricl nlysis repored in he previous secions. Given he limied vililiy of d covering oh he ime-series nd cross-secion dimensions in consisen nd comprle fshion, numer of key ssumpions re required in order o uild comprehensive dse. The impc of rnspor coss on GDP per cpi is hen exmined oh vi is impc on exposure o cross-order rde nd direcly s n dded deerminn in he sic frmework used in erlier secions. Evoluion of rnspor nd elecommunicions cos indices Mehodology nd d sources The consrucion of n ggrege index of rnsporion coss covering ir, mriime nd rod componens requires informion ou he coss for shipping goods eween ilerl locions for ech mode of rnspor, wih he respecive coss mesured in he sme unis o llow for ggregion. In ddiion, he consrucion of counry-specific indices requires h he respecive coss e weighed so s o reflec he relive impornce of ech rding prner s well s of ech mode of rnspor. In principle, rde flow d could e used o consruc weighs h re consisen wih he cul disriuion of goods shipped ccording o he mode of rnspor nd ilerl desinions. Doing so, however, would mke he ggrege index endogenous o he individul coss nd is herefore voided. The indicors of rnsporion coss used in 17

18 his pper re ken direcly from Golu nd Tomsik 2008, which provides deils regrding rw d vililiy, sources, ssumpions mde nd resuls. The min feures cn e summrised s follows: The sic cos of ech mode of rnsporion eween ny wo locions is mesured in US dollrs per kilogrmme shipped, nd he cos of mriime shipping is ssumed o e he sme for counries wihin rod region e.g. for ll EU counries vis-à-vis oher rod regions. For ech counry, he coss of shipping goods o ech ilerl desinion re ggreged on he sis of GDP weighs of prner counries including counry s own GDP, s ws he cse for he indicor of mrke poenil discussed in he previous secion. The min reson for preferring GDP weighs s opposed o cul rde weighs is o void he endogeneiy of rde perns wih respec o rde coss. The relive impornce of ech mode of rnspor in moving goods cross locions is sed on mixure of ssumpions nd hrd d h re ville for few counries. The key ssumpion mde in his conex is h ll rde eween neighours is ssumed o ke plce vi rod rnsporion. The nominl ggrege index of rnspor cos, expressed in dollrs per kilogrmme, is defled using eiher he US GDP deflor or he US price index of mnufcuring goods. Resuls The overll indicor of rnspor coss over he period is shown in Figure 4 for 21 OECD counries. The figure lso provides he conriuion of ech of he hree min su-componens o he overll cos. Individul counries cn e regrouped ino four locks on he sis of heir overll coss. No surprisingly, rnspor cos is highes for Ausrli nd New Zelnd wih cos over 2½ imes h oserved in Norh Americ. This is followed y Jpn which forms group on is own, u level h is susnilly lower hn oserved for he firs group. The indicor shows similr coss for Europen counries, wih only slighly higher vlues oserved in peripherl counries, reflecing higher rod rnspor coss. A he oher end, rnspor coss re lowes in Cnd nd he Unied Ses, owing lrgely o lower conriuion from mriime freigh chrges. In fc, he Figure 4. Overll rnspor coss nd conriuion from hree su-componens Defled y US GDP deflor 2000 = 1, verge $/kg 0.40 Rod Air Mriime NZL AUS JPN GRC PRT FIN ESP NOR SWE IRL ITA AUT DNK GBR FRA CHE DEU NLD BEL USA CAN Source: Golu nd Tomsik

19 mriime componen ccouns for he lrges porion of he vriion in he overll coss cross he four groups of counries. As regrds he evoluion of overll rnspor coss over ime, differen picure emerges depending on wheher he series re defled y he US GDP deflor or y he US price index of mnufcuring goods. On he ler sis, here is cler upwrd rend in he four groups of counries hroughou he smple period hough wih somewh differen slopes wheres no cler rend ppers for he series sed on he roder deflor, les no since he 1970s Figure 5. In oh cses, he profile reflecs o lrge exen he conriuion from mriime shipping coss Figure 6. Looking more closely he profile of mriime rnspor coss wh snds ou is he widening discrepncy since he mid-1990s eween he cos for shipping goods from Asi, which hve gone up in rel erms, nd hose for goods shipped from Europe or Norh Americ, which hve fllen. The rek from he erlier pern which sw he coss in he hree zones moving roughly ogeher coincides wih he emergence of lrge rde imlnces. The shrp rise in expors from Es Asi hs led o cpciy olenecks in he mjor pors of h region while coniners re reurned o Asi hlf empy. Figure 5. Tol verge rnspor cos Europe USA nd Cnd Ausrli nd New Zelnd Jpn $/kg 0.45 A. Expors, defled y US GDP deflor, 2000 = $/kg 0.45 B. Expors, defled y US mnufcuring goods deflor, 2000 = Source: Golu nd Tomsik

20 Figure 6. Averge mriime rnspor cos Europe USA nd Cnd Ausrli nd New Zelnd Jpn $/kg 0.36 A. Defled y US GDP deflor, 2000 = $/kg 0.36 B. Defled y US mnufcuring goods deflor, 2000 = Source: Golu nd Tomsik To summrise, he percepion h he relive influence of coss reled o disnce is fs diminishing is no suppored, les no y recen rends in inernionl shipping coss. 21 This ppren puzzle ws lredy noed in erlier sudies in priculr Hummels, In he cse of mriime rnspor, specil fcors such s rising fuel prices nd por chrges my hve plyed role in offseing he gins from echnologicl improvemens. Moreover, sudies sed on micro d Blonigen nd Wilson, 2006 h compre prices for shipping similr goods nd similr mriime roues u vi differen modes i.e. using coniners or no, sugges h he enefi from coninerision my no e s lrge s presumed ll else eing equl. In ny cse, firm conclusions in his re need o e qulified due o limiions of d vililiy nd mesuremen. I is no cler how d on rod rnspor, for insnce, reflec he gins in quliy erms such s hose from he use of glol posiioning sysems which llows for precise rcking of he meril in rnsi. In similr vein, mesured price indices for ocen shipping my no dequely reflec improvemen in he service provided, for insnce ime svings rough ou y coninerision. And, he impornce of ime s rde rrier hs een sressed in erlier sudies Hummels, 2001; Nordås, 2006; Nordås e l., 20

21 2006. More generlly, ll rnsporion modes hve enefied from progress in informion nd communicion echnology s well s from eer inegrion vi inermodl sysems. Tken fce vlue, he sence of decline in he weigh-sed mesures of rel cos of rnspor i.e. nominl coss defled y he mnufcuring price index suggess h here my hve een less echnologicl progress in rnsporion hn in mnufcuring. However, due o innovions ouside he rnspor secor, he composiion of rded goods hs chnged significnly over he ps decdes, nd mny vlule goods re now relively ligh, e.g. elecronic chips. Consequenly, rnspor coss my well hve fllen relive o he vlue of rnspored goods. 22 One re where he presumed deh of disnce does no seem o e ll exggered is inernionl elecommunicions since coss in his re hve fllen in ll counries o he poin where hey re no longer significn nywhere Figure 7. In fc, hisoricl d indice h he susnil cross-counry vriions h sill previled in he erly 1970s hd lrgely disppered y he le 1980s, nd since hen he downwrd rend hs coninued, ringing coss o siclly zero during he erly 2000s. I should e noed, however, h his indicor only cpures one ype of elecommunicions nd herefore he remen of his spec of disnce is covered oo nrrowly for firm conclusions o e drwn. 23 Noneheless, his resul would sugges h counries h re priculrly ffeced y heir disnce o mrke my wish o ensure h heir ICT neworks re priculrly well developed no les y geing heir regulory frmeworks righ so s o fully exploi he enefis from rding in he ypes of services where physicl disnce mers lile. Index, USA = 100 in Figure 7. Rel cos of one minue inernionl elephone cll from seleced origin counries Defled y US GDP deflor Ausrli Frnce Germny Jpn Unied Kingdom Unied Ses Source: Golu nd Tomsik Impc of rnspor coss on openness o rde nd GDP per cpi The impc of rnspor coss on GDP per cpi is ssessed oh indirecly vi heir effecs on individul counries exposure o inernionl rde nd more direcly s n ddiionl deerminn in he sic growh equion. The firs se of regressions exmines he impc of rnsporion coss hrough heir effec on inernionl rde openness Tle 6. This pproch is sed on he presumpion h rnsporion coss mer for 21