Technology differences, institutions and economic growth : a conditional conditional convergence

Transcription

1 No Technology dfferences, nsuons and economc growh : a condonal condonal convergence Hervé Boulhol

2 Technology dfferences, nsuons and economc growh : a condonal condonal convergence Hervé Boulhol No February

3 Technology dfferences, nsuons and economc growh : a condonal condonal convergence TABLE OF CONTENTS SUMMARY...4 ABSTRACT...5 RÉSUMÉ...6 RÉSUMÉ COURT INTRODUCTION TECHNOLOGY DIFFERENCES AND DIFFUSION GROWTH EQUATIONS AND INSTITUTIONS DATA FROM THE INSTITUTIONAL DATABASE TO THE ECONOMETRIC SPECIFICATION RESULTS CONCLUSION...24 BIBLIOGRAPHY...25 APPENDIX APPENDIX APPENDIX APPENDIX LIST OF WORKING PAPERS RELEASED BY CEPII

4 CEPII, Workng Paper No TECHNOLOGY DIFFERENCES, INSTITUTIONS AND ECONOMIC GROWTH : A CONDITIONAL CONDITIONAL CONVERGENCE SUMMARY The augmened Solow model by Mankw, Romer and Wel (1992) exhbed he role of human capal for long erm growh pah and led s auhors o accep eher he assumpon of dencal echnology across counres or he reamen of echnology dfferences as resduals n he growh equaon. However, Hall and Jones (1996, 1999) and Klenow and Rodrguez-Clare (1997) have shown ha producvy levels and oupu per worker are hghly correlaed, whch cass doubs on he condonal convergence scenaro. Ye he cross secon leraure has no drawn he necessary mplcaons. Acknowledgng he mporance of akng no accoun producvy dfferences, we break down producvy no wo componens: a pure echnologcal par and s complemen called effcency. From a smple model of echnology dffuson, we focus on he neracons beween nsuons and echnology dfferences and denfy hree complemenary channels hrough whch nsuons mpac growh: effcency n he use of echnology, long erm TFPgrowh and echnology dffuson. To shed lgh on how growh and nsuons nerplay, our framework s esed from a new and dealed daabase on nsuons developed by he French Mnsry of Economy, Fnance and Indusry (MINEFI). Daa was colleced hrough a quesonnare by he Economc Mssons of he MINEFI n 51 counres represenng 80% of world GDP. The daabase consss of 330 ems on nsuons n a broad sense, each recevng a rankng from 0 o 4 for each counry. The robusness of he daabase has been esablshed by Berheler, Desdogs and Ould-Aouda (2003) hrough a comparave sudy wh oher nsuonal daabases used n varous economc sudes. We fnd ha echnology dffuson subsanally mpacs economc performance and ha cachng-up s condonal o he qualy of he approprae nsuon a mx of R&D, nnovaon and capal-rsk suppor -, wh he annual rae of convergence o he echnologcal froner varyng from 0% o 12.4% dependng on he counry. Insuons also maer for echnologcal effcency, as our non-corrupon varable, for nsance, conrbues as much as he sock of human capal o he producvy level heerogeney. Moreover, long run TFP-growh dfferences are sgnfcanly deermned by such nsuons lke he ones reflecng a compeve produc marke or a favourable nnovaon envronmen. Havng conrolled for nsuons, nernaonal rade measured, by he openness rae, s nsgnfcan o explan neher TFP-growh dfferences nor echnology dffuson. However, when we ake he manufacurng share n expors no accoun, we fnd a sgnfcan mpac of rade on TFP-growh, comng solely from he rcher counres n he sample, whch clearly pons o a non-lneary. Ths suggess ha he conrbuon of rade s posve only f he specalsaon s approprae and he developmen farly advanced. Ths las resul s enave because of poenal endogeney bases. Includng human capal flows as a deermnan of he seady-sae reveals ha MRW s approach and ours are 4

5 Technology dfferences, nsuons and economc growh : a condonal condonal convergence complemens raher han subsues. Condonal convergence here s also condonal o sharng he same echnology and qualy of nsuons, whch renders recen observed dvergence well accouned for. ABSTRACT Hghlghng ha echnology s only a componen of producvy, hs sudy focuses on he neracons beween nsuons and echnology dfferences o explan cross-counry growh paern. Three complemenary channels hrough whch nsuons mpac growh are denfed: effcency n he use of echnology, long erm TFP-growh and echnology dffuson. From a new and dealed daabase on nsuons developed by he French Mnsry of Economy, Fnance and Indusry, poor nsuonal qualy, beyond human capal, s esmaed o be he source of an annual growh-rae loss of beween 2.4 and 6.1 percenage pon for half of he counres. Technology dffuson speed s nsuonally relaed and he dsance o he echnology froner s reduced from 0% o 12.4% annually dependng on he counry. Trade has a non lnear nfluence on growh, beng sgnfcan only for counres already advanced n he developmen phase. Condonal convergence here s also condonal o sharng he same echnology and qualy of nsuons, renderng recen observed dvergence well accouned for. J.E.L. classfcaon: Keywords: O11; O33; O47 Technology dffuson; Insuons; Producvy; Growh 5

6 CEPII, Workng Paper No ECARTS TECHNOLOGIQUES, INSTITUTIONS ET CROISSANCE ÉCONOMIQUE: UNE CONVERGENCE CONDITIONNELLE CONDITIONNELLE RÉSUMÉ L exenson du modèle de Solow par Mankw, Romer e Wel (1992) a ms en évdence le rôle du capal human pour le sener de crossance à long erme e a condu ses aueurs à acceper l hypohèse d une echnologe denque pour l ensemble des pays ou le raemen des dfférences echnologques comme résdus des équaons de crossance. Hall e Jones (1996, 1999) e Klenow e Rodrguez-Clare (1997) on cependan monré que les nveaux de producvé e de producon par êe éaen foremen corrélés, remean en cause le scenaro de convergence condonnelle, sans que la léraure en a ré oues les mplcaons qu s mposen. Reconnassan l enjeu de la prse en compe des dfférences de producvé, nous décomposons la producvé en deux élémens : une composane puremen echnologque e son complémen que nous appelons «effcacé». A parr d un modèle smple de dffuson echnologque, nous nous concenrons sur les neracons enre les nsuons e les dfférences de echnologes pour explquer l évoluon comparée de la crossance enre pays. Tros canaux complémenares par lesquels les nsuons on une nfluence sur la crossance son denfés: l effcacé dans l ulsaon des echnologes, la crossance de la producvé à long erme e la dffuson des echnologes. Pour clarfer les neracons enre les nsuons e la crossance économque, nore modèle es esé à parr d une nouvelle base de données nsuonnelles déallée, développée par le MINEFI. Les données on éé collecées par les Mssons Economques du MINEFI dans 51 pays représenan 80% du PIB mondal. La base de donnée es composée de 330 ems sur les nsuons, noon prse dans son sens large, chacun d enre eux recevan une noe enre 0 e 4 pour chaque pays. Berheler, Desdogs e Ould-Aouda (2003) on éabl la robusesse de la base en la rapprochan d aures bases de données nsuonnelles ulsées dans les ravaux économques. Nous rouvons que la dffuson echnologque a un mpac subsanel sur la performance économque e que le rarapage es condonnelle à la qualé de l nsuon adéquae un mélange de R&D, d nnovaon e de suppor au capal-rsque -, avec une vesse annuelle de convergence vers la fronère echnologque varan de 0% à 12,4% selon les pays. Les nsuons mporen auss pour l effcacé dans l ulsaon des echnologes, e nore mesure de la non-corrupon, par exemple, conrbue auan que le sock de capal human à la dsperson de la producvé. De plus, les dfférences de crossance de la producvé oale des faceurs (PTF) à long erme son sgnfcavemen déermnées par des nsuons elles que celles refléan un marché des produs concurrenel ou un envronnemen favorable à l nnovaon. En conrôlan les dfférences nsuonnelles, le commerce nernaonal, mesuré par le aux d ouverure, n es sgnfcaf n pour explquer les dfférences de crossance de la PTF n pour la dffuson des echnologes. Cependan, lorsque l on prend en compe la par des bens manufacurés dans les exporaons, alors nous rouvons un mpac sgnfcaf du commerce sur la crossance de la PTF, provenan 6

7 Technology dfferences, nsuons and economc growh : a condonal condonal convergence seulemen des pays les plus rches dans l échanllon, ce qu ndque une non-lnéaré. Cela suggère que la conrbuon du commerce es posve seulemen s la spécalsaon es approprée e le développemen déjà avancé. Ce derner résula es fragle en rason d évenuels bas d endogénéé. De plus, la prse en compe de l mpac du flux de capal human pour l éa réguler révèle que l approche de MRW e la nore son des complémens pluô que des subsus. La convergence condonnelle es c condonnelle auss au fa de dsposer des mêmes echnologes e des mêmes nsuons, e rend compe de la dvergence récemmen observée. RÉSUMÉ COURT Inssan sur la dsncon enre producvé e echnologe, cee éude se concenre sur les neracons enre les nsuons e les écars de echnologques pour explquer l évoluon comparée de la crossance enre pays. Tros canaux complémenares par lesquels les nsuons on une nfluence sur la crossance son denfés: l effcacé dans l ulsaon des echnologes, la crossance de la producvé à long erme e la dffuson des echnologes. A parr d une nouvelle base de données nsuonnelles déallée, développée par le MINEFI, nous esmons que l nsuffsane qualé des nsuons, au-delà du capal human, es la cause d un défc de aux de crossance annuelle enre 2,4 e 6,1 pon de pourcenage pour la moé des pays. La vesse annuelle de dffuson des echnologes vare de 0% à 12,4% selon les pays en foncon de leur nveau nsuonnel. Le commerce a un mpac non lnéare sur la crossance pusqu l es sgnfcaf seulemen pour les pays déjà avancés dans la phase de développemen. La convergence condonnelle es c condonnelle auss au fa de dsposer des mêmes echnologes e de la même qualé des nsuons, e rend compe de la dvergence récemmen observée. J.E.L. classfcaon: Mos-clés: O11 ; O33 ; O47 Dffuson des echnologes ; Insuons ; Producvé ; Crossance 7

8 CEPII, Workng Paper No TECHNOLOGY DIFFERENCES, INSTITUTIONS AND ECONOMIC GROWTH : A CONDITIONAL CONDITIONAL CONVERGENCE (*) Hervé Boulhol 1. INTRODUCTION The semnal arcle by Mankw, Romer and Wel (1992), subsequenly denoed MRW, shed lgh on he conrbuon of human capal n reconclng he measured low speed of condonal convergence beween counres wh a physcal capal share of around onehrd. Ther man concluson s ha, when human capal s added, he hen augmened Solow model s well sued o analyse growh across counres. I follows ha, conrary o endogenous growh heory, he growh process s solely drven by facor accumulaon (ncludng human capal), s conssen wh a rae of convergence of around 2% a year (raher han he 4%-5% expeced from he Solow exbook model) and valdaes underlyng assumpons of consan reurns o scale and dencal echnology. The MRW framework has been crcsed on dfferen grounds. Frs, he embedded human capal heory reas human capal jus as anoher accumulang facor. Ths mples ha human capal should ener no growh equaon hrough s growh rae, bu here s confuson n wheher he sock of human capal maers raher han he flow as n MRW. Benhabb and Spegel (1994) convncngly suppor he vew ha he sock of human capal s a deermnan of he magnude of a counry s Solow resdual. Second, as mos of he cross-counry leraure, he MRW approach s subjec o he bas comng from he dencal echnology assumpon. The condonal convergence predcons has been more and more dffcul o reconcle wh he facs ponng o global dvergence as oulned by Prche (1997). Promped by J.Temple s fourh queson abou he causes of ncome dfferences (Temple, 1999, p.113), we sar from he nference ha f poor counres are poor no only because of a lack of npus ha wll accumulae faser foserng convergence, mus be ha hey are poor also because of overall effcency and echnology dfferenals - whaever hese mean - ha may perss or aggravae over me especally f hey are due o nsuonal dfferences. 1 The role of nsuons as a deermnan of long erm growh s ncreasngly recognsed, ye more research s needed o dsenangle whch nsuons maer for economc performance and above all o ncorporae hem properly n economc heory. Even hough Rodrk (2002, 2003) s convncng n argung ha he same nsuons may have dfferen economc (*): I would lke o express my graude o Lonel Fonagné, Jacques Ould-Aouda and Parck Arus for havng made hs sudy possble. I would parcularly lke o hank Agnès Bénassy-Quéré, Gullaume Gauler, Roman Rancère and he parcpans of he Cep semnar for her valuable commens. Fnally I very much apprecaed he help I receved from Mayls Coupe and Davd Galvn, as well as he warm welcome I receved from Cep employees. 1 (Q4) Are poor counres poor manly because hey lack npus, or because of echnology dfferences? 8

9 Technology dfferences, nsuons and economc growh : a condonal condonal convergence mpacs based on a counry s dosyncrases, we sugges ha he qualy of some nsuons does nfluence economc performance overall. In hs sudy, we show ha echnology dfferences play an mporan role n he cross-counry growh paern and ha nsuons maer for oal facor producvy (TFP) level and growh raes, and for echnology dffuson. Many reasons may explan why echnology dffers sgnfcanly beween counres. Paen proecon, learnng by dong, knowledge dfferences derac echnology from s publcgood preenson (Casell, Esquvel and Lefor, 1996). Moreover, akng a broader vew of producvy, nsuons lnked o he socal, polcal or legal aspecs of effcency conrbue o producvy dfferenals. Surprsngly, he growh leraure does no pay enough aenon o heerogeney n echnology. When does, hs heerogeney s reaed n panel esmaes and as a fxed effec, he deals of whch are rarely avalable, makng an assessmen of wheher hey do represen wha hey should dffcul. Excepons are Hall and Jones (1996, 1999), Klenow and Rodrguez-Clare (1997) who precsely esmae producvy dfferences and Bloom, Cannng and Sevlla (2002) whose concerns are close o ours. We hope our conrbuon o be heorecal and emprcal. Theorecally we denfy hree channels hrough whch nsuons may mpac producvy. Frs, a sac conrbuon hrough effcency n he use of echnology, second a perssen dynamc one hrough long run TFP-growh raes and hrd a emporary dynamc one hrough echnology dffuson. Emprcally we es our framework usng a new and dealed daabase on nsuons ha was developed by he French Mnsry of Economy, Fnance and Indusry (MINEFI). Daa from a quesonnare was colleced by he Economc Mssons of he MINEFI n 51 counres represenng 80% of world GDP. The daabase consss of 330 ems on nsuons n a broad sense, each recevng a rankng from 0 o 4 for each counry. We fnd ha echnology dffuson subsanally mpacs economc performance and ha cachng-up s condonal o he qualy of he approprae nsuon a mx of R&D, nnovaon and capal-rsk suppor -, wh he annual rae of convergence o he echnologcal froner varyng from 0% o 12.4% dependng on he counry. Insuons also maer for echnologcal effcency, as our non-corrupon varable, for nsance, conrbues as much as he sock of human capal o he producvy level heerogeney. Moreover, long run TFP-growh dfferences are sgnfcanly deermned by such nsuons lke he ones reflecng a compeve produc marke or a favourable nnovaon envronmen. In addon, ncludng human capal flows as a deermnan of he seadysae reveals ha MRW s approach and ours are complemens raher han subsues. Havng conrolled for nsuons, nernaonal rade measured, by he openness rae, has a non-lnear conrbuon o TFP-growh, beng sgnfcan and posve only f he specalsaon s approprae and he developmen farly advanced. Ths las resul s enave because of poenal endogeney bases. The sudy s organsed as follows. Secon 2 hghlghs he mporance of akng no accoun echnology dfferences and nroduces our approach regardng he echnologcal process. Secon 3 deals how nsuons and growh nerplay n he model. Secon 4 descrbes he daa and he selecon of he nsuonal varables, leadng o he economerc 9

10 CEPII, Workng Paper No specfcaon n Secon 5. Resuls are presened n Secon 6 where economerc ssues are also dscussed. Fnally, Secon 7 concludes. 2. TECHNOLOGY DIFFERENCES AND DIFFUSION The mos dspuable crcal assumpon n he cross-secon growh leraure suggess ha eher all counres share he same level of echnology and echnologcal progress or ha he dfferences n hese levels are reaed as resduals, mplyng ha hey are beng consdered ndependen from oher explanaory varables. Ths s an exreme conjecure snce means ha echnologcal change spreads nsananeously and compleely o every counry, whaever he level of openness or nsuonal profle, and leads o havng only he dfferences of capal per un of labour o explan dfferences of oupu per capa. Assumng a Cobb-Douglas producon funcon and wh sandard noaons, Y = K a b 1 a b ). H ( A L (1) where Y s oupu, K and H are socks of physcal and human capal respecvely, A s he producvy level and L he number of workers. Oupu per worker y for he counry s herefore gven by 1 a b a b 1 a b.( K / L ).( H / L ) A KH y Y / L = A.( Z / L ) a (2) wh Z KH b/ a = K.( H / L) defnng a capal aggregae bul from he physcal capal sock and he human capal sock per capa. If we assume ha he producvy level A s ndependen of he counry ( = A, ), hen he rao of oupu per capa n 1980 A beween he USA and Uganda of 48 o 1, beng he wo exremes n our daa, ranslaes no KH a hghly unrealsc capal aggregae, Z, per capa rao of 110,700 o 1 usng a physcal capal share of one-hrd. Moreover, recognsng he producvy dfferences, s apparen from equaon (2), vald a each me, ha boh he nal producvy level and he nal oupu per capa are closely lnked, whch renders growh equaon esmaes assumng dencal producvy serously based. Whle hs nconvenence s wellacknowledged, he growh leraure has no drawn ye all he necessary mplcaons. Over he las ffeen ears, economc research has made some noable advances n he undersandng of wha producvy s. However, he essence of s conens remans unknown, and he parameer A s ofen ndsncly desgnaed as eher he producvy or he echnology level. Ths creaes confuson n denfyng he role of echnology and herefore, adopng a dfferen posure, we nss here on he dsncon beween he wo noons and call he complemen of echnology n producvy: effcency. Inspred by Bassann and Scarpea (2001), we break down he oal level of producvy A no wo componens: a pure echnology level B and he degree of effcency n usng hs echnology X so ha A equals B. X. As he benchmark, he counry wh he hghes GDP per capa n 1980, he USA, has been chosen. We denoe b = B / B, an nverse 10 ben

11 Technology dfferences, nsuons and economc growh : a condonal condonal convergence ndcaor of he dsance o he froner, x = X / X, he rao of he relave effcency o he benchmark, and a = A / Aben = b. x, he relave producvy level. Insuonal qualy s consdered as mpacng he echnologcal effcency X and possbly he echnology dffuson whch process s mos smply governed by: 2 ben b& ( ) = v.(1 b ( )) (3) where sands for me: n he long run echnologes converge o he froner a a pace represened by v, whch wll be esed as beng consan across counres or nsuonallyrelaed. Noe s only he pure echnology componen ha s assumed o converge (or dverge f v s negave), and oal producvy dscrepances may perss as a resul of dfferences n nsuonally-relaed effcency X. 3. GROWTH EQUATION AND INSTITUTIONS We suppose ha nsuons ener no growh equaons hrough hree dfferen channels: he level of echnologcal effcency, he progress of echnologcal effcency and possbly he speed of echnology dffuson. Nocng ha he producvy level A can be wren as A. x. b, we can spl TFP-growh no hree componens: ben A & A = A & A ben ben x& x b& b (4) The frs erm on he rgh s smply he benchmark TFP-growh, denoed by g, he second, denoed c, s he long run TFP-growh defc o he benchmark and he hrd s he echnologcal cach-up componen derved from resolvng he dfferenal equaon (3). A & A = g c e v.(1 b (0)) v. (1 b (0)) (5) Insuons wll have an mpac hrough X (0), hrough c, and poenally hrough v. We now need o negrae equaon (5) no he growh equaon. The growh model we hen develop s he augmened Solow model enrched o ake no accoun he heerogeney of echnologes and he conrbuon of nsuons. Wh n denong he populaon growh, d K H he physcal and human capal deprecaon rae, s and s he fracon of oal ncome nvesed n physcal and human capal respecvely, Appendx 1 esablshes he followng growh equaon: 2 In a recen paper, Benhabb and Spegel (2003) refers o hs dffuson process, orgnang n he Nelson- Phelps model, as he confned exponenal dffuson process. 11

12 CEPII, Workng Paper No Log y ( ) Log y (0) 1 e = β. g c f y (0) 1 e. Log a (0). A (0) v, ( b (0)) ben β. K a H b ( s ).( s ). Log a ( n d g c ) b 1/(1 a b) where β = 1 a b).( n d g c ) s he usual speed of condonal convergence and he las erm ( v. 1 1 (1 b (0)). e 1 f = v, ( b (0)). Log 1. v (7) b (0) b (0) s he conrbuon of echnology dffuson o growh: s posvely relaed o he speed v and o he dsance o he echnologcal froner. Equaon (6) s o be compared o he augmened-solow growh equaon whch s exacly he same as f we assume ha nal oal producvy level A (0) s dencal across counres, ha every counry s a he froner ( b = 1) and ha here s no long erm TFP-growh dfferences ( c = 0). The growh process s herefore he resul of four dsnc forces: he adjused absolue convergence hs source of convergence s lessened here because of overall producvy level dfferences, herefrom he adjecve adjused -, a second convergence componen comng from echnologcal cach-up, he usual non-convergence semmng from dfferences n long erm pahs due o dfferen nvesmen raes and an addonal dvergence force comng from long erm TFP-growh dfferences. The man reason for consderng ha all counres share he same echnology les n he dffculy o observe relave echnology levels. Wha s only needed here, as shown by equaon (6), s he relave level of nal producvy and here s one pece of nformaon from whch we can esmae. Indeed equaon (2) llusraes ha nal oupu per worker s ceranly lnked o nal producvy. We wll assume ha a par η of nal ncome raos can be explaned by nal producvy raos, he complemen 1 η beng explaned by capal dfferences, and herefore we formally wre: (6) a (0) A (0) A ben (0) y = y ben (0) (0) η (8a) Hence η s characersed by: Cov η = ( Log a (0), Log y (0)) Var ( Log y (0)) (8b) Klenow and Rodrguez-Clare (1997) use esmaes of socks of physcal and human capal o nfer producvy levels and assess ha producvy dfferences accoun for half or more of level dfferences n 1985 GDP per worker (p.75). For nsance accordng o 12

13 Technology dfferences, nsuons and economc growh : a condonal condonal convergence equaon (2), wh η = and a = b = 1/ 3, he oupu per capa rao beween he USA and Uganda of 48 n 1980 s explaned by a conrbuon from he producvy level rao KH of 2.7 and a capal aggregae, Z, per capa rao of 6,100 nsead of 110,700. For sure, he exreme smplfcaon n equaon (8a), whch has hough he mer of hghlghng he poenal correlaon beween nal producvy and nal oupu per worker, and of avodng he man pfall of he cross-secon approach, s a srong ad hoc assumpon, bu akng η = 0, as s done n mos of he cross-secon leraure, s as srong and ceranly a far more naccurae ad hoc assumpon. 4. DATA 4.1. General daa The me frame s he perod from 1980 o For daa enerng he radonal Solow model, we use Penn World Table The per capa oupu s he real GDP per capa a Purchasng Power Pary, chan seres. For he nvesmen rae K s, we use he average over he perod of he nvesmen share of real GDP (varable k n he daabase). As n MRW, he proxy for he rae of human capal accumulaon s he percenage of he workng-age populaon n second-level educaon. Ths percenage s consruced by mulplyng he gross secondary enrollmen rae (World Developmen Indcaors) by he percenage of he workng-aged populaon aged 15 o 19. The average of hs varable over he years 1980, 1990 and 2000 s named HCFLOW Insuons daabase We use an orgnal daabase on nsuons developed by he MINEFI, well descrbed and analysed n Berheler, Desdogs and Ould-Aouda (2003). Ths daabase focuses prmarly on emergng counres as 44 of he 51 counres are developng counres and also ncludes a conrol group of developed counres. Daa was gahered from a very dealed quesonnare: for each counry, 330 ems aggregaed n 115 ndcaors were made avalable. As an example he ndcaor relaed o he effcency of publc polcy lnked o he qualy of he ax sysem s bul from four ems assessng he mporance of he black marke, he mporance of fraud n he formal economy, n cusoms and he capacy of he Admnsraon o mplemen ax measures. Imporanly, Berheler e al have esablshed he robusness of he daabase by hghlghng s convergence wh varous nsuonal daabases (World Bank, Fraser Insue, Economs Inellgence Un, Polcal Economc Rsk Consulancy, IMF, Transparency Inernaonal among ohers) all coverng 30% of he sock varables from he quesonnare. The lmaons of he daabase are wofold: a farly small number of counres and a me perod lmed o a sngle pon n or close o year We wll consder ha he nsuonal profle for a gven counry s sable over he 3 Alan Heson, Rober Summers and Bena Aen, Penn World Table Verson 6.1, Cener for Inernaonal Comparsons a he Unversy of Pennsylvana (CICUP), Ocober

14 CEPII, Workng Paper No perod of he growh analyss. Ths rases posonous quesons of endogeney snce a counry experencng a favourable economc developmen ncreases s chances of developng beer nsuons and he causaly beween growh and nsuons mgh be reversed. Therefore, hose ndcaors ha are oo suspcous n hs respec are excluded and hs endogeney ssue s economercally addressed n secon 6.4. For he purpose of he sudy, we cluser he remanng 94 ndcaors no fve groups represenng dsnc aspecs of he nsuonal profle. These fve nsuonal domans cover produc marke, labour marke, fnancal sysem, nnovaon and a general headng for all oher ndcaors. Each doman s analysed hrough a facor analyss whch aggregaes he orgnal ndcaors and provdes robus nsuonal varables synhessng mos of he nformaon n he daabase Facor Analyss The chosen approach s very close o ha of Ncole, Scarpea and Boylaud (1999) developed o buld produc marke regulaon ndcaors. I dffers only n he facors aggregaon mehodology and deals are found n Appendx 2. The dea s smple: for each doman, we run a facor analyss and selec he number of relevan facors accordng o usual ess. The axes are hen roaed n order o enhance nerpreaon of facors and an aggregaed ndex s bul for each doman. Unsurprsngly hese aggregaed ndces are exremely correlaed wh each oher so ha valuable dealed nformaon s los n he aggregaon process. As a consequence we preferred o use, as explanaory varables for he role of nsuons on growh, he facors ha conaned enough nformaon and were easly denfed. Therefore we wll focus on varables whch rean more han 25% of her doman varance. Table 1 summarses he varables whch passed hese ess. 4 In addon o he aggregaed ndces, fve oher varables are seleced: CORRUPTION, an ndcaor of he non-corrupon level, CONTRACT, a varable referrng o he conracual mporance n he labour marke, BANKRULES, assessng he qualy of bank regulaon and prudenal rules, R&D-CAPRISK, an ndcaor of R&D/nnovaon effor and favourable capal rsk sysem and INTPROP, a varable lnked o he proecon of nellecual propery rghs (IPRs). 5 If we lower our hreshold o 20% of he varance, hree of he sx hen added varables are of parcular neres snce hey represen a very dsnc aspec of he produc marke nsuons: TRADECOMPET, an ndcaor of he nernaonal and domesc procompeve envronmen, LARGECO, he share of large companes n he dsrbuon secor, whch may be an ndcaor of effcen scale, and NEWENTRY, represenng low barrers o new enry. Chars 1 and 2 represen he counres n a wo-dmensonal plan for he nsuonal varables ha wll be of parcular neres below: CORRUPTION, R&D- CAPRISK and PRODINDEX. Char 1, for nsance, ndcaes a srong posve correlaon 4 As always wh facor analyss, he advanage of such a mehodology s ha he fnal ndcaors are compued as objecvely as possble based on he daa. The man nconvenence s almos dencal. Indcaors are daa based whch means ha hey depend upon he specfc sample and f we add new counres, ndcaors for counres n he orgnal sample wll be alered. 5 For he labour marke and fnancal sysem domans, because he general ndex s farly nangble, he facors only wll be used (see Appendx 2, $ 3.3 and 3.4). 14

15 Technology dfferences, nsuons and economc growh : a condonal condonal convergence beween he non-corrupon varable and he nal GDP per capa, bu also shows ha he corrupon ndex adds valuable nformaon, as a gven GDP per capa level may be assocaed wh a wde range of corrupon levels. < Table I, Chars 1 and 2 > 5. FROM THE INSTITUTIONAL DATABASE TO THE ECONOMETRIC SPECIFICATION 5.1. Insuonal varables The ndcaors n Table 1 are he canddaes for he compuaon of our nsuonal varables. These ndcaors are ndfferen o a lnear ransformaon and we lnearly normalse hem so ha hey mee he consrans embedded n he model presened n c X (0) dff secons 2 and 3 as follows. We defne I, I, I he nsuonal ndcaors ha are respecvely lnked o long erm TFP-growh, nal effcency n he use of echnology and echnology dffuson. As by defnon c ben equals 0, he long erm TFP-growh defc o he benchmark, c, s defned by: c c ben c = c.( I I ) (INST 1) where c s a parameer o esmae and whch measures he mpac of hs parcular c nsuon, I, on long erm TFP-growh. As regards he nal use of echnology, equaon (8a) descrbes how we nfer he relave levels of nal producvy a (0) whch s broken down no b 0). x (0). So, by makng he furher assumpon ha he lowes effcency ( level n he sample, x (0), equals half (n logarhm) he mnmum producvy level (0) mn mn a (n logarhm), we deduce x (0) for each counry and hen b (0). 6 7 Formally, b (0) = a X(0) ben (0) / x (0) X(0) x (0) = 1 ζ.( I I ), wh ζ such ha x mn (0) = a mn (0) 1/ 2 (INST 2) Fnally o es wheher nsuons mpac he speed of echnology dffuson, we wre: v dff dff = v.( I I mn ) w (INST 3) 6 For a varable z, we defne zmn = mn z. 7 Ex pos, we assess ha he bes f s obaned beween 50% and 60% 15

16 CEPII, Workng Paper No where v s a parameer o esmae and measures he mpac of hs nsuon, dffuson speed, and w s a consan Economerc specfcaon dff I, on he The specfcaon s drecly derved from equaon (6) by lnearsng n c. Wh he ~ addonal noaons for he speed of convergence β = (1 a b).( n d g) and ~ β ρ = 1/.(1 e ), we oban: Log y ( ) Log y (0) y (0) = ρ. Log a (0). A c. Z f v, ben ( b (0)) ρ. Log (0) u K a H ( s ).( s ) ( n d g) b a b 1/(1 a b) g (9) where K a H y (0) a b ρ ( s ).( s ) Z = 1 (1 a b).(1 ρ. ). Log. (1 ρ. ). Log a (0). A (0) 1 a b n d g ( n d g) ben b a b and u s he resdual. The reamen of demographc growh s somemes confusng. Ceran auhors have been nconssen, reang ρ as a consan bu keepng, he demographc varable, n counry-dependen elsewhere n he equaon. We chose o run he esmaes eher by conssenly keepng n counry dependen everywhere or by consderng ( n d g) a consan everywhere because of he smplfcaons enals bu a he expense of neglecng any demographc mpac. Takng equaon (INST 1) no equaon (9) leads o: Log y ( ) Log y(0) y (0) = ρ. Log a (0). A c.( I c ben I c ). Z ben K a ( s ).( s ρ. Log (0) ( n d g) f v, ( b (0)) u H b ) a b 1/(1 a b) g (10) wh b (0) and v gven by (INST2) and (INST3). We recall ha nsuons nervene hrough x (0) from whch we deduc b (0), hrough c and poenally hrough v. The consan ( d g) s fxed o a realsc 0.06 and resuls are no much mpaced f s n he (0.05,0.08) range. When consdered as a consan, ( n d g) wll be fxed a

17 Technology dfferences, nsuons and economc growh : a condonal condonal convergence 6. RESULTS Ths secon sars wh he esmaes of he radonal Solow model and s MRW exenson (6.1). In order o faclae he undersandng of he conrbuons of he nsuons whn he specfcaon of equaon (10) and o denfy he role of educaon separaely, he resuls are successvely presened whou human capal (6.2) and ncludng human capal (6.3). Then, addresses economerc ssues (6.4), provdes more quanfcaon of he role of nsuons (6.5) and fnally dscusses he mpac of nernaonal rade (6.6) Sarng pon esmaes Because of mssng daa, our sample s lmed o 44 counres. As a sarng pon, applyng MRW approach o our daa ( a ( 0) = b (0) = 1, c = 0 n equaon (10) ), we es he followng specfcaon: Log y( ) Log y(0) a s b s = ρ. Log y (0). ρ. Log. ρ. Log ce u 1 a b n g d 1 a b n g d K H An esmaed b sgnfcanly dfferen from zero dsngushes he augmened Solow model from he exbook verson. The speed of convergence mpled by he nal ncome ~ coeffcen ρ s here β = (1 a b).( n d g), lower han n he Solow model. Table II presens he resuls for he Solow model n he frs wo columns and for he augmened verson n he las wo. Columns (2) and (4) dffer from columns (1) and (3) respecvely by negang he populaon growh dfferences across counres. Dsapponngly, boh specfcaons have poor explanaory power when we nclude each counry s demographc evoluon because of he resrcon mposed lnkng he speed of convergence o ndvdual demographc growh. 8 If we lm ourselves o columns (2) and (4), we fnd agan he man resuls of MRW, hanks economercally o he posve correlaon beween nal oupu and he human capal varable: he esmaed physcal capal share s closer o he expeced ( ) range, human capal accumulaon plays a sgnfcan role, he common emprcally esmaed 2% speed of convergence s conssen wh he model. < Table II > 8 Durlauf and Johnson (1995, Table II) showed ha MRW sample ressed o he approprae resrcon. 17

18 CEPII, Workng Paper No Model esmaes whou human capal To ncorporae he role of nsuons, we sar wh he nal effcency level x (0) whch 18 X(0) we mos smply derve from he general doman of nsuons, usng as I eher he aggregaed ndex GENINDEX or he frs facor CORRUPTION. We hen nfer he nal producvy level and he pure echnology componen followng equaons (8) and (INST2). Table III gves hese esmaed levels, usng η = The mpled nal dsance o he echnologcal froner s very close wheher we use one ndcaor or he oher, confrmng ha mos of he nformaon n hs doman s ncluded n he non-corrupon varable. Because of he sraghforward reason why corrupon may nduce weak effcency, we wll lm ourselves o I X (0) = CORRUPTION from now on. < Table III > Insuons ha mos lkely explan long erm producvy dfferenals have now o be c chosen. The core equaon (10) s esed wh I beng deermned accordng o (INST1) by he global ndex of he produc and nnovaon domans, and he man ndcaor n he labour marke and fnancal sysem domans successvely, keepng he rae of echnology dffuson v consan across counres. We assess he qualy of he resuls, summarsed n Table IV, accordng o hree crera: sgnfcance of he parameer esmaes, explanaory power, physcal capal share esmae closer o heorecal predcon. Along hese lnes, he resuls are very close o one anoher excep wh BANKRULES where he esmaes are less precse (remember ha varable defnons are found n Table I). The labour marke varable CONTRACT s relaed o boh lmed chld labour and small nformal economy. Because of he very lkely endogeney of hs varable, wll be dropped for furher analyss. The man prelmnary nferences are: frs, sgnfcan esmaes excep for he echnologcal cach-up annual speed whch s esmaed a around 5% bu s weakly sgnfcan, casng doub on uncondonal echnology dffuson; second, nsuons maer for long erm TFP-growh, n parcular he global qualy of he compeve produc marke and he nnovaon-frendly envronmens have a sgnfcan mpac on long erm growh enalng poenal dvergence; hrd, he explanaory power s very encouragng especally compared o prevous resuls shown n Table II; fourh, despe havng aken no accoun echnology heerogeney, capal share esmaes are oo hgh rasng he lkelhood of msspecfcaon and bases; sxh, and lnked o he ffh, here effecvely s condonal convergence (here s also condonal o sharng he same echnology and effcency) a a speed of around 3%, bu oo low o be n lne wh he underlyng Solow model. < Table IV > As suggesed n sub-secon 5.1, one channel hrough whch nsuons may nfluence growh s by promong or hnderng echnology dffuson. To es wheher he dffuson s dff condonal o nsuons, we use equaon (INST3) akng as I he nsuons mos lkely o do he ask n he nnovaon doman, R&D-CAPRISK and INTPROP, and resrcng ourselves o usng PRODINDEX or INNINDEX for he nsuon mpacng

19 Technology dfferences, nsuons and economc growh : a condonal condonal convergence long erm TFP-growh (Table V). The resuls are very sensve o he nsuon o whch we condon he cach-up. Proecon of IPRs for nsance (columns (2) and (4)) does no speed up echnologcal cach-up: s mpac s nsgnfcan and f anyhng negave, harmng convergence o he froner of counres whch ry o proec IPRs. On he conrary, nsuons whch favour R&D, nnovaon and capal-rsk do have a sgnfcan and srongly posve nfluence on cach-up speed whch hghlghs he condonal naure of echnology convergence. Columns (1) and (3) also exhb a capal share esmae of around 0.50, whch mples a Solow-ype condonal convergence speed slghly above 4%, and dmnshed he rsks of based esmaes. Unforunaely, a hgher speed of convergence does no necessarly mean ha a poor counry converges faser han wha s usually esmaed, bu ha would, had he same overall producvy (A) as he benchmark counry. Moreover, specfcaon n columns (1) and (3) have very sasfyng explanaory power as assessed by he adjused R-square above As he consan par of he dffuson speed w s nsgnfcan, we dscard and re-esmae he specfcaon of column (1) o reach column (5), whch ends up beng he base equaon of hs sudy whou human capal, and column (6) by furher relaxng he consrans on he demographc growh varables n. Resuls n hese las wo columns are very smlar, excep ha akng each counry s demographc growh no accoun leads o a less precse esmae of he long erm TFP-growh heerogeney parameer c bu o a capal share even closer o he one-hrd sandard. Focusng on column (5), he cenral esmae of for v means ha he annual dffuson speed ranges from 0% for Zmbabwe o 12.4% for Tawan. Usng a dfferen mehodology, Bloom e al. (2002) found an uncondonal echnologcal convergence of 2% a year. Wh he esmaed value of c n column (5) and (3), he wors esmaed long erm TFP-growh performance n he sample s Syra wh an annual spread of respecvely 3.3% and 4.0% compared o he USA. We develop he quanfcaon of he mpac nsuons have on economc performance furher n subsecon 6.5. < Table V > The general ndex PRODINDEX comes from he aggregaon of facor scores n he produc marke doman, he hree mos prevalen beng TRADECOMPET, LARGECO and NEWENTRY. Based on he core esmae of column (5), Table V, whch of hese hree aspecs of produc marke compeon sgnfcanly conrbues o long run TFP-growh? The frs wo varables are no found sgnfcan whereas NEWENTRY leads o smlar esmaes: eher low barrers o enry alone or s combnaon wh domesc and nernaonal compeon (TRADECOMPET) and large sze of frms (LARGECO) explan he nfluence of a more compeve produc marke on TFP-growh. The esmaons have been conduced so far by consderng ha hree quarers of he dfferences n he nal oupu per capa (n logarhm) could be explaned by dfferences n he oal level of nal producvy, meanng n oher words ha η was fxed a We now wan o es how our resuls are sensve o he choce of a gven value for η (he lower he η he closer he nal producvy levels beween counres) and Table VI provdes some comparave esmaes,. Based on he hree crera defned above o assess 19

20 CEPII, Workng Paper No he qualy of our esmaes sgnfcance, explanaory power, conssen capal share -, he conclusons are clear-cu: allowng for producvy heerogeney defnely mproves he resuls as he esmaes wh he lower η s, by far, less good whaever he crera. Acually, columns (3 o 5) srongly suppor our approach, by suggesng ha he share of dfferences n nal ncome per capa due o he dfferences n oal nal producvy levels s ceranly greaer han one-half, whch s conssen wh Klenow and Rodrguez- Clare s analyss. Moreover, hese resuls mply ha nsuons maer for he effcency n he use of echnology, approxmaed by a non corrupon ndex, and omng hs mpac by consderng dencal effcency and echnology s msplaced and leads o based esmaes Human capal < Table VI > Resuls of he prevous sub-secon are subjec o poenal bases due o omng poenally mporan varables lke human capal. There are many ways n whch he educaon level can nfluence growh. Educaonal achevemen may have an mpac on he effcency n he use of a gven echnology (our X varable), on long erm TFP-growh or on he rae of human capal accumulaon. In he frs wo cases, he sock of human capal, HCSTOCK, s probably he varable of neres and we use he average years of second-level schoolng for he populaon aged 25 and over n 1980 from he Barro-Lee daabase. In he hrd case, we wll use he human capal flow varable à la MRW called HCFLOW. To es wheher educaon has an nfluence over he effcency level n our framework, X equaon (10) s smply esmaed by usng I = HCSTOCK. The resuls ndeed sugges ha he sock of human capal a he begnnng of he perod s sgnfcan n explanng nal effcency (Table VII, column (2)). In fac, boh he nsuonal varable CORRUPTION, a proxy for he non-corrupon level, and he sock of educaon HCSTOCK probably maer for effcency. These wo varables exhb a posve lnear correlaon coeffcen of 0.55 and by dchoomy we show n column (3) ha he opmal combnaon s acheved wh I X (0) (0) = CORRUPTION 4. HCSTOCK. MRW assers ha akng no accoun human capal as anoher accumulang facor enables one o valdae he Solow assumpon of dencal producvy across counres. 9 We now show ha he role of human capal hey pu forward does no conflc wh our framework bu raher complemens. Table VIII reproduces he resuls where we nfer ha human capal accumulaon adds valuable nformaon n he growh process whou much alerng he basc parameers esmae of secon 6.1. The physcal capal share esmae s now very close o he common sense value, he jon effec of emphassng he role of human capal accumulaon and producvy heerogeney. 9 Or a leas uncorrelaed wh he regressors. 20

21 Technology dfferences, nsuons and economc growh : a condonal condonal convergence < Tables VII VIII > Fnally, deparng from he reamen of human capal as jus anoher facor and neglecng complex ssues of accumulaon, we smply wonder f he sock of human capal has an nfluence on long erm TFP-growh and herefore we nclude he sock measure HCSTOCK n addon o he nsuonal varable PRODINDEX n our c varable. The economercs do show a posve conrbuon of human capal sock o producvy-growh and weaken somewha he sgnfcance of our nsuonal varable. However, hese resuls are doubful because he esmaed physcal capal share s rased back o around 70%. To summarse our resuls on he role of educaon, we fnd ha he sock of human capal plays an mporan par n explanng effcency, quanavely equvalen o he one comng from oher nsuonal varables lke non-corrupon (see below 6.5), and ha he flow of human capal has explanaory power n he deermnaon of long-erm growh pah whch complemens he analyss we have run so far. However he lnk beween he sock of human capal and he long erm TFP-growh s confusng Economerc ssues To reach he above resuls we made wo srong assumpons. The frs s ha he nsuonal qualy ha we measure has been deemed o be sable over he perod under sudy. There s no much we can do here as he nformaon s only avalable for around year 2000, oher han beng on he robus srucural dmenson of hese nsuons. Obvously, hs s an approxmaon as he nsuonal envronmen mgh have changed sgnfcanly for some counres beween 1980 and The second ssue refers o he poenal endogeney of he nsuons, and reverse causaly from growh o nsuons ha may ensue. To llusrae why hese concerns may no be oo problemac, he R&D- CAPRISK varable, for nsance, can be expeced o capure an exogenous srucural aspec of nnovaon faclang echnology ransfers. Indeed, s manly deermned by he conrbuon of fve ems: suppor o R&D and nnovaon from he Mnsry of Research, from he publc or prvae research cenres and from he echnopoles (hree ems), he qualy of he relaonshps beween unverses, research cenres and companes, and fnally he mporance of a fnancal scheme favourng capal rsk. We address hese pons and check he robusness of our resuls n hree seps. Frsly, smple descrpve sascs reveal ha our nsuonal varables capure deep and lasng srucural componens of he counres characerscs. The eases check s our non-corrupon varable snce oher comparable measures exs. CORRUPTION exhbs a Pearson lnear correlaon coeffcen of 82% wh Knack and Keefer s measure for he perod, called here KK Along hose lnes and wh he dea of nsrumenng he nsuonal varables ha ener our core specfcaon, CORRUPTION, PRODINDEX and R&D-CAPRISK, we regress hese hree varables wh poenal nsrumens: he Knack and Keefer s non-corrupon measure (KK80-89), he logarhm of oupu per capa n 1980 (LOGY80), he annual 10 The source s Easerly and Levne daabase: hp:// 21

22 CEPII, Workng Paper No average growh rae of oupu per capa beween 1970 and 1980 (LAGDLOGY), he ferly rae n 1980 (World Developmen Indcaors, FERT80) and n addon he K logarhm of he nvesmen rae s (LOGSK) for he PRODINDEX regresson and he R&D compose ndcaor for (World Developmen Indcaors, R&D80-83) for he R&D-CAPRISK regresson. Despe hs lmed se of regressors, he hgh levels of 2 adjused- R obaned (76%, 62%, 70% respecvely for CORRUPTION, PRODINDEX and R&D-CAPRISK) sugges ha consderng hese nsuonal varables exogenous n he equaon explanng growh beween 1980 and 2000 should no be problemac. Secondly, we formally assess he endogeney of he nsuonal varables for our wo core equaons, wh and whou human capal, hrough a Hausman es. For he specfcaon whou human capal, as we are here concerned wh he endogeney of our varables and no wh he bas comng from omed varables (human capal), one precauon has o be aken n he choce of he nsrumens: human capal measures are no vald nsrumens as hey are mos lkely correlaed wh he resduals. Table IX presens he resuls wh columns (1) and (4) beng our base OLS esmaes whou and wh human capal respecvely columns (1) and (4) of Table VIII. Unsurprsngly, gven he economerc relaons denfed above, nsrumenal varable esmaes (IV) and OLS do no dffer sgnfcanly, even when nsrumenng he nvesmen rae varables. Hausman es sascs mply ha we should lm ourselves o he OLS. In oher words, our nsuonal varables reflec robus srucural componens of he counres n Obvously, one can hnk of cases, lke Chna or Ireland, where rejecng endogeney of nsuons qualy for growh esmaes over he las 20 years seem dubous. However, he ess show ha, n he sample overall, he deep and lasng characerscs of he nsuons seleced oversep endogeney concerns. Fnally, anoher way o es he robusness of our assumpons s o compare our esmaes wh esmaes usng a dfferen me frame, he dea beng ha f nsuons have grealy evolved over he perod, hs wll nduce very dfferen esmaes for a longer perod. The resuls, no presened here, ndcae ha esmaes for he perod prove o be very close o our base resuls for he perod, whch renforces he prospecs of our nsuonal varables capurng srucural parameers, bu non-endogeney s no rejeced as clearly as before snce he Hausman es χ -square probably s only 0.16 whou human capal. < Table IX> The presence of oulers has also been esed. Ther excluson does no aler he man resuls and deals wll be provded upon requess Quanfyng he mpac of nsuons furher Ths s beyond he scope of hs sudy o sele he debae concernng he conrbuon of human capal o growh as jus anoher accumulang facor. Therefore, he followng resuls are presened wh he specfcaon eher whou or wh human capal. From he specfcaon whou human capal, he esmaed annual growh can be broken down no four componens as dealed n Appendx 3: adjused (o accoun for nal producvy level dfferences) absolue convergence, nvesmen rae conrbuon, long-erm TFP- 22

23 Technology dfferences, nsuons and economc growh : a condonal condonal convergence growh and echnology dffuson. 11 When one akes he dfferences o he mean n he sample, Table X s obaned: for nsance, Chna s annual growh-rae over he perod was 4.2% above he average, he esmaon of he model s 4.6%, broken down no 1.0% comng from adjused absolue convergence due o decreasng capal reurns, 0.5% due o dfferences n nvesmen rae, -0.8% semmng from long-erm producvy-growh dfferences explaned here by dfferences n produc marke effcency dermenal o Chna and 4.0% due o echnology dffuson, he man drver of Chna s over-performance. 12 < Table X > Furhermore, n order o measure he conrbuon of each componen globally, s sandard devaon s compued and repored n Table XI. In ha sense, physcal capal nvesmen rae and echnology dffuson conrbue mos o he dfferences n annual growh beween counres havng a dsperson wce as grea as he one of he adjused absolue convergence componen and almos hree mes he one of long erm TFP-growh. When human capal s ncluded, he conrbuon of s accumulaon rae comes hrd n explanng growh dsperson across counres n beween physcal capal nvesmen rae and echnology dffuson on he one hand and long erm TFP-growh and adjused absolue convergence on he oher. Based on our esmaes, we can calculae wha coss a counry n erms of annual growh rae no o have he bes nsuons n he sample. Table XII ndcaes ha Ngera loses he mos from he poor qualy of nsuons wh a counerfacual growh loss of 6.1% annually, broken down no 2.3% due o poor effcency (CORRUPTION), 2.7% because of a weak echnology dffuson (R&D-CAPRISK) and 1.1% n long erm TFP-growh loss (PRODINDEX). For a comparson, Arad and Sala--Marn (2003) denfy egh deermnans of Afrca s economc underperformance o OECD counres, whch oals up o an annual growh defc of around 8%. In erms of effcency, he loss comng from a weak sock of human capal compared o he USA averages 1.3% annually, o be compared o 0.9% for he non-corrupon varable. However, he dspersons are comparable and hs s due o he over-performance of he USA n erms of educaon: he counry ha comes n second, Japan, loses 0.6% annually o he USA because of human capal sock dfferences. The medan annual loss over he sample due o he hree major nsuonal aspecs denfed oals up o 2.4%. Because he sample s very heerogeneous wh boh counres among he rches and among he poores, he nsuons measures, resulng from he daa based facor analyss, canno dfferenae much beween he mos developed counres, and 11 The producvy-growh componen s no jus c as calculaed a he end of secon % for Syra bu raher as apparen from equaon (10) c. Z. 12 For he USA, 1.7 percenage pon of annual growh s no accouned for by he model whou human capal. Wh he base esmaon wh human capal, USA s growh s correcly esmaed, bu he USA do no appear a he echnologcal froner n 1980, (where Argenna and Germany sand): s nal oupu per capa s explaned by he srong conrbuon of he sock of human capal o he effcency raher han by s hen echnologcal performance. USA s economc growh over-performance comes hen from he conrbuon of human capal o he seady-sae and from echnologcal cach-up. 23