Applied Econometrics and International Development. AEID. Vol. 4-3 (2004)

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1 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) HUMAN CAPITAL, TECHNOLOGY DIFFUSION AND ECONOMIC GROWTH IN LOW-TO-MIDDLE INCOME COUNTRY: A TIME SERIES PERSPECTIVE OF GUATEMALA, LOENING, Josef Ludger Absrac This paper invesigaes he impac of human capial on economic growh in Guaemala for hrough he applicaion of an error-correcion mehodology. Two channels are analyzed by which human capial is expeced o influence growh. A beer-educaed labor force appears o have a significan impac on economic growh boh via facor accumulaion as well as on he evoluion of oal facor produciviy. The resuls have been found robus concerning daa issues and parameer sabiliy. JEL Classificaion Codes: I20, C22, C5, 054. Keywords: Educaion, Growh, Economerics, Guaemala.. Lack of case sudy evidence Guaemala has enjoyed relaive macro-economic sabiliy during he pas decades wih average annual growh raes of abou 3.9 percen. However, due o rapid populaion growh, per capia growh has averaged only abou.3 percen per year. A coninuaion of his growh rae would imply ha he average Guaemalan would need approximaely 53 years o double his real income. According o The World Bank, Washingon DC, USA, and Ibero-America Insiue for Economic Research, Universiy of Goeingen, Germany. [email protected] Acknowledgemens: I would like o hank María-Carmen Guisan, Hermann Sauer, Armando Morales, Dierk Herzer and Felicias Nowak-Lehmann for valuable discussions and commens. Mario Salguero, Esuardo Morán and Sergio Recinos from Banco de Guaemala provided grea assisance in he compilaion of daa. The resuls and opinions presened here are my own, and should no be aribued o he insiuions I am affiliaed wih. 07

2 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) World Bank (2003) esimaes, abou 56 percen of Guaemala s populaion live in povery. Economic growh is regarded as an essenial ingredien for expanding economic opporuniies for poor people depending on innumerable facors, including he accumulaion of human capial. While here is a raher srong heoreical suppor for a key role of human capial on growh, he empirical evidence is no clear-cu. In conras o microeconomic sudies which generally sugges significan reurns o educaion on individual earnings, growh regressions on he macro level have ofen failed o find a significan and posiive conribuion of human capial o economic growh. Moreover, he relaionship beween mos measures of human capial and oupu growh has frequenly been found surprisingly weak. The evidence comes almos enirely from cross-counry regressions, and here is very lile empirical analysis for individual counries (see Loening 2004). For he case of Guaemala here is no single sudy ha assesses he direc impac of educaion on economic growh. The aim of his aricle is o fill his gap. For he case of Guaemala, he main findings sugges ha a beer-educaed labor force appears o have a significan impac on economic growh boh via facor accumulaion as well as on he evoluion of oal facor produciviy. The reminder of his paper is organized ino four secions. Secion 2 discusses he economeric mehodology. Secion 3 presens he empirical resuls. Secion 4 concludes. 2. Economeric approach for Guaemala The amoun of empirical lieraure on economic growh is enormous. Among innumerable conribuions here are wo imporan empirical approaches which model he impac of human capial on economic growh. One way is o incorporae human capial as an addiional facor wihin he producion funcion, for example by adaping he Solow (956) model. Up o now, his approach has remained he workhorse of empirical research. Mankiw, Romer and Weil (992) show ha radiional growh heory can accommodae human capial and may provide a reasonable approximaion of crosscounry daa. 08

3 Loening, J. Human Capial, Technology Diffusion and Economic Growh Sill, one of he key insighs of he Solow model is ha he facor accumulaion per se is insufficien o achieve long-run growh, and ha long-run growh paricularly depends on growh in oal facor produciviy. Human capial accumulaion may herefore have only a shor-erm impac on he rae of growh. However, raes of accumulaion are expeced o have explanaory power for growh raes during he ransiion o an evenual balanced growh pah. Consideraion of ransiion could herefore open up he possibiliy of assessing he macroeconomic role of educaion for economic growh wihin his framework. In addiion, since he shor run in he conex of growh heory is ofen hough of in erms of decades, even shor-run effecs could be worhwhile policy objecives. An alernaive way, o some exen associaed wih endogenous growh, is o model explicily echnological progress as a funcion of he level of human capial and oher variables. In a raher influenial sudy, Benhabib and Spiegel (994) firs use he srucural form of he human capial augmened producion funcion o esimae he role of educaion for a sample of indusrialized and developing counries. In heir analysis, he regression coefficien on he change in average schooling years urns ou o be saisically insignifican and someimes even eners wih a negaive sign. Benhabib and Spiegel hen propose an empirical growh model in which human capial exernaliies can be considered o be embodied in subsequen advances in educaion and in new physical capial via echnology impor as proposed in he models of Lucas (988) and Romer (990). Their empirical resuls sugges ha he level of schooling, which eners wih a posiive coefficien, indeed faciliaes he adopion of echnology from abroad and he creaion of domesic echnologies. In a similar manner, Morales (998) shows ha he compleion of secondary educaion proxied by enrollmen raios appears o have a significan and posiive impac on oal facor produciviy wihin a ime-series conex for El Salvador. I is imporan o noe ha in such cases he esimaed increase in produciviy is no simply a phenomenon in he ransiional period since an increase in he flow of educaion leads o a gradual increase in he human capial sock. Implici in his concep is he claim ha by increasing he average level of educaion he rae of economic growh will be permanenly increased over ime. -09-

4 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) Taking he above menioned sudies ino accoun and given he fac ha cross-counry growh regressions have ofen led o disappoining resuls, he nex paragraphs provide he empirical specificaion for he wo differen channels hrough which educaion is assumed o influence economic growh. Model reas human capial as an addiional facor of producion, while Model 2 hypohesizes ha human capial levels direcly affec he aggregae echnology parameer. 2. Human Capial as a Facor of Producion (Model ) The human capial augmened growh model considers human capial as an independen facor of producion and can be represened in a Cobb-Douglas producion funcion wih consan reurns o scale: α ß ( α β ) () Y = A K H L where Y represens oupu and A is he level of echnology or oal facor produciviy. K, H and L are physical capial, human capial and labor. Mulicollineariy beween capial and labor is avoided by sandardizing oupu and he capial sock by labor unis, which also impose he resricion ha he scale elasiciy of he producion facors is equal o uniy. Convered ino a logarihmic expression, he producion funcion can be esimaed in is srucural form: (2) ln y = ln A + α ln k + β ln h + u where he lower case variables y = Y/L and k = K/L are oupu and physical capial in inensive erms and h = H/L sands for average human capial. A firs glance, he formula already appears suiable for esimaion. However, some problems arise since i is well known ha mos macroeconomic ime-series conain uni roos and ha regression of one non-saionary series on anoher is likely o yield spurious resuls. As repored in Table, he daa for he case of Guaemala is no excepion. By ransforming he ime-series o saionariy by firs differencing, he esimaion bias will be removed. However, in any case his will creae is own problems, noably because of he risk of losing informaion on he long-run relaionships of he variables. 0

5 Loening, J. Human Capial, Technology Diffusion and Economic Growh Table. Guaemala: Saionariy of he Time Series, Variables ADF es saisic Resul lny non-saionary lnk -2.0 non-saionary lnh.7 non-saionary IM/I -2.86* saionary lny -4.79** saionary lnk -4.26** saionary lnh -2.62* saionary IM/I -7.22** saionary ** (*) Rejecs he hypohesis of a uni roo a he (0) percen level assuming one lag and a consan in he es equaion. The lag lengh was deermined using he Schwarz crierion. One approach o dealing wih his dilemma is o employ an errorcorrecion model ha combines long-run informaion wih a shorrun adjusmen mechanism. This mehodology has also been used successfully in alernaive growh sudies. Examples of his are Nehru and Dareshwar (994), Morales (998) and Bassanini and Scarpea (200). The error-correcion model may be esimaed in wo ways. Banerjee e al. (993) show ha he generalized one-sep error-correcion model is a ransformaion of an auoregressive disribued lag model. As such, i can be used o esimae relaionships among non-saionary processes. In order o esimae he human capial augmened producion funcion, he errorcorrecion model may be wrien as follows: (3) ln y = γ ln k 2 ln h γ 3 (ln y α ln k β lnh ln A ) + u As i sands, his equaion canno be esimaed by Ordinary Leas Squares since he variables in parenhesis canno be formed wihou knowledge of α and β. However, one can esimae he reparameerized form: (4) ln y = ln A ln k 2 ln h 3 ln y 4 ln k 5 lnh 6 Dummy + u --

6 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) Esimaes of he parameer γ 3 can be used o calculae he required elasiciies α and β. The coefficien γ 3 conains addiional informaion because i can be inerpreed as a measure of he speed of adjusmen in which he sysem moves owards is equilibrium on he average. In he case of Guaemala, i was found useful o include a dummy variable ino he error-correcion model in order o es and evenually correc for he deviaions of he long-run rend on oupu growh semming from he civil srife (he violen conflic dummy was coded as for and for 2000). Once he overall model fi has been found saisfacory, equaion (3) is reformulaed in order o incorporae an error-correcion erm. Engle and Granger (987) sugges a wo-sep procedure, in which he error-correcion erm EC - is derived from he lagged residuals u of he levels regression in equaion (2) ha can be used o esimae he model: (5) ln y = ln A ln k 2 lnh 3 EC 4 Dummy + u Equaions (4) and (5) should in principle produce similar resuls, because boh formulaions can be undersood as a ransformaion of each oher. They may herefore yield informaion abou he robusness of he esimaed coefficiens. 2.2 Human Capial Affecing he Technology Parameer (Model 2) The basic framework for he second specificaion is a sandard Cobb-Douglas producion funcion wih consan reurns o scale: α ( α) (6) Y = A K L which is sandardized by labor unis in order o avoid mulicollineariy beween capial and labor. Convered ino a logarihmic expression, he equaion becomes: (7) ln y = ln A + α ln k + u Combining he long-run informaion of he variables wih a shorrun adjusmen mechanism, he equaion can be represened in is error-correcion form: (8) ln y = γ ln k γ 2 (ln y α ln k ln A ) 2

7 Loening, J. Human Capial, Technology Diffusion and Economic Growh In conras o he human capial augmened growh model, however, oal facor produciviy is considered o be a funcion of exogenous variables, namely educaion and foreign inpus. Benhabib and Spiegel (994) posulae ha an educaed labor force may play a key role in deermining produciviy raher han enering on is own as a producion facor. In he ineres of simpliciy, hey assume ha human capial is exogenously given and ha higher levels of human capial cause increased produciviy. Benhabib and Spiegel follow Romer (990) and Nelson and Phelps (966). In heir empirical growh model human capial affecs oal facor produciviy hrough wo channels. Firs, higher levels of human capial direcly influence produciviy via is impac on domesic innovaion. Second, higher levels of human capial cause improvemens in oal facor produciviy by faciliaing he adopion and implemenaion of foreign echnology and herefore reducing he knowledge gap beween he echnologically leading naions and he developing world. In addiion, along wih many oher auhors, Lee (995) emphasizes ha relaively cheaper foreign inpus are imporan deerminans of growh since hey provide a wider range of inermediae inpus (which in urn migh enhance echnological progress) and affec he efficiency of capial accumulaion. Using cross-counry daa, Lee shows ha he raio of impors in invesmen has a significan posiive effec on economic growh. Taking ino accoun hese sudies, he echnology parameer is reaed as a non-consan and is allowed o change over ime: IM (9) ln A = c 4 ln h 5 6 Dummy I where c is a consan or exogenous echnological progress, h represens he level of human capial proxied by average years of schooling, and IM/I is he raio of oal impors o gross domesic invesmen. Moreover, he effecs of civil srife and periods of high violence, assumed o have a negaive impac on produciviy and oupu growh, are esed by he dummy variable. Combining equaions (8) and (9) yields he one sep error-correcion model in is re-parameerized form: -3-

8 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) (0) ln y = c ln k 2 ln y 3 ln k IM ln h Dummy + u I γ In analogy o he firs empirical model, one can also apply a wo sep procedure using he lagged residuals of he level regression from equaion (7) and incorporae an error-correcion erm ino he specificaion: () ln y = c ln k 2 EC IM ln h Dummy + u I γ Noice ha he final equaions are quie similar when compared wih he human capial augmened model. Therefore, i may be difficul o disinguish empirically beween he wo approaches. However, he implicaion of he alernaive Model 2 is ha he level of human capial raher han he growh raes now play a role in deermining he growh of oupu per worker. Growh of oupu per worker now depends posiively upon he average level of human capial hrough is impac on produciviy. As Loening (2004) poins ou, despie some effors in increasing average years of schooling, Guaemala s human capial base sill remains far behind he Lain American average. If hese equaions are significan, hey could yield informaion abou he low performance of Guaemala s economy in erms of annual per capia growh. 3. Resuls of error-correcion regressions The following paragraphs presen he economeric resuls of he wo models; he original ime series daa and a deailed descripion of he procedures o come up wih an esimae of he human capial sock for Guaemala are available in Loening (2005). Overall, he adjused R 2 in all specificaions of he error-correcion model is raher high indicaing a good fiing of he respecive model o he daa. Tes saisics do no poin ou any evidence of serial correlaion nor misspecificaion a convenional levels (e.g., a Breusch-Godfrey 4

9 Loening, J. Human Capial, Technology Diffusion and Economic Growh es finds no evidence for he presence of firs, second or hird order serial correlaion, and he residuals have been found normally disribued following saionary paerns). Boh specificaions of he error-correcion model lead o similar resuls alhough he one sep procedure is he preferred one. Considering he disorions caused by he inernal miliary conflic and he limied choice of explanaory variables, he resuls have been found accepable. Figure. Guaemala: Acual versus Fied Growh of GDP per Worker, Model, ACTUAL FITTED Figures and 2 show he finess of equaions (4) and (0). Gradually, he empirical specificaion ha hypohesizes ha human capial affecs he echnology parameer (Model 2) performs slighly beer and is resuls have been found more robus concerning parameer sabiliy han he human capial augmened producion funcion (Model ). The error-correcion coefficien in all specificaions is saisically significan and suggess a moderae speed of adjusmen owards he long-run growh pah, equal o abou 3 o 6 percen of he deviaions per year. Afer a cerain shock o he economy i would ake on he average approximaely 20 years o reach he level of oupu consisen wih long-run growh (wih differences o be less han 5 percen). The esimaed capial share in oupu is approximaely /2 o 3/5 and was found slighly oo large while compared wih he empirical evidence for developing counries. The -5-

10 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) mos sriking resul for Guaemala is, however, ha in boh empirical models he average years of schooling appear o be srongly correlaed wih per capia growh. Figure 2. Guaemala: Acual versus Fied Growh of GDP per Worker, Model 2, ACTUAL FITTED 3. Human Capial as a Facor of Producion (Model ) Human capial as a producion facor, measured by average years of schooling, appears o have a posiive and significan impac on he growh of oupu per worker (Table 2). The esimaed long-run effec of a percen increase of he average years schooling on GDP per uni of labor is approximaely 0.6 percen. The schooling coefficien has been found robus concerning alernaive assumpions abou he physical and human capial sock. Employing alernaive daa would no change he magniude or he significance of he variable. The shor-run elasiciy of schooling is more difficul o explain. I is quesionable wheher or no ha educaion has shor-erm effecs on growh. A possible inerpreaion of his correlaion could be ha an increase in he average years of schooling parly behaves as a proxy for improved expecaions, as emphasized by Morales (998). Anoher possibiliy for he increase could be reverse causaliy effecs. In oher words, periods of increased enrollmen in educaion are more favorable o higher raes of shor-run growh. However, he shor- 6

11 Loening, J. Human Capial, Technology Diffusion and Economic Growh erm schooling coefficien was found o be sensiive o daa issues. Consequenly, is magniude mus be inerpreed wih care. Table 2. Producion Funcion for Guaemala: Human Capial as Facor Inpu, Dependen variable: Explanaory Variables percen change of GDP/worker Equaion (4) Equaion (5) Consan * (-2.2) (-0.26) Percen change of capial/worker 0.900** (22.2) 0.904** (23.9) Percen change of schooling/worker (.57) (.62) ln GDP/worker [-] -0.32** (-2.90) ln capial/worker [-] 0.072* (2.02) ln average schooling [-] 0.022* (2.0) Violence conflic dummy ** (-4.95) ** (-5.34) Error erm [-] -0.3** (-2.95) Long-run elasiciy of capial Long-run elasiciy of schooling Adjused R F-saisic Durbin-Wason S.E. of regression Observaions saisics in parenhesis. ** Significan a %, * significan a 5%. The long-run relaionship of oupu wih respec o is explanaory variables can be derived from equaion (4) in Table 2. The resuls in -7-

12 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) erms of he human capial augmened Cobb-Douglas producion funcion are he following: (2) Y = A K H L Overall, wihin he chosen framework, one can conclude ha over he medium-erm human capial accumulaion plays an imporan role for economic growh in Guaemala. However, faser long-erm growh would depend crucially on Guaemala s abiliy o increase produciviy. In his respec, he resuls of he following empirical specificaion may provide useful insighs Human Capial Affecing he Technology Parameer (Model 2) The second empirical model emphasizes ha he average level of schooling should no be reaed as an exra inpu ino he producion funcion bu may direcly affec oal facor produciviy. Based on he regression resuls of equaion (0) in Table 3, he following formulas in erms of he Cobb-Douglas producion funcion can be obained: (3) Y = A K L ln y = ln A k IM (4) ln A = ln h Dummy I (5) ln y = 0.97 ln k 0.64 (ln y ln k ln A ) Equaion (25) expresses he producion funcion in he long run, equaion (26) displays he variables ha are hough o explain he evoluion of oal facor produciviy and equaion (27) shows he shor-erm dynamics of growh per labor uni. Noice ha he esimaed producion elasiciy of physical capial in he long-run equaion is now larger han is facor share (as esimaed in he human capial augmened producion funcion) reflecing is correlaion wih human capial. Taken a face value, Model 2 provides wo mechanisms ha govern he evoluion of oal facor produciviy. Firs, he level of human

13 Loening, J. Human Capial, Technology Diffusion and Economic Growh capial, as measured by average years of schooling, appears o have a highly significan and posiive impac on produciviy growh in Guaemala. Table 3. Producion Funcion for Guaemala: Human Capial Affecing he Technology Parameer, Dependen variable: Explanaory Variables percen change of GDP/worker Equaion (0) Equaion () Consan -0.7** (-3.83) ** (-3.2) Percen change of capial/worker 0.97** (25.8) 0.940** (28.0) ln GDP/worker [-] -0.64** (-3.86) ln capial/worker [-] 0.096** (2.89) ln average schooling 0.029** (2.92) 0.07* (2.30) Raio of impors/gross domesic invesmen 0.058** (3.45) 0.057** (3.28) Violence conflic dummy -0.09** (-3.0) ** (-3.25) Error erm [-] -0.30** (-3.37) Long-run elasiciy of capial Adjused R F-saisic Durbin-Wason S.E. of regression Observaions saisics in parenhesis. ** Significan a %, * significan a 5%. Second, he empirical resuls imply ha foreign echnology inpus are imporan deerminans for produciviy growh (he raio of oal impors o domesic invesmen may hold as an indicaor for he -9-

14 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) qualiy of invesmen). Almos obvious is he finding ha periods of high violence or poliical insabiliy, as proxied by he dummy variable, influence negaively he efficien use of facor inpus and economic growh. Ineresingly, he schooling variable and he raio of impors o invesmen proved o have some join effecs. Tha is, he empirical specificaion works bes when boh variables are included wihin he equaion. Employing he variables on heir own would slighly reduce heir significance. This effec could imply ha here is an addiional role for educaion in order o arac physical capial. Lucas (990) suggesed an alernaive channel for human capial o growh. One reason why physical capial does no flow o poor counries may be he fac ha hese counries are ypically poorly endowed wih facors complemenary o physical capial, hereby reducing is rae of reurn. 4. Conclusions This paper analyzed wo channels by which human capial is hypohesized o influence growh. Firs, a beer-educaed labor force appears o have a posiive and significan impac on economic growh via facor accumulaion. Over he medium run, a percen increase of he average years of schooling would raise oupu per worker by abou 0.6 percen. Second, he average level of human capial appears o have a srong impac on he evoluion of oal facor produciviy. One reason for he low performance of he economy in erms of per capia growh may herefore be aribued o Guaemala s poorly developed human capial base, lagging far behind he Lain American average. However, i is empirically challenging o separae boh approaches, and fuure research may conrol for poenial endogeneiy of schooling, he effec of he civil war and changes in he condiioning se of he variables. The empirical resuls in his sudy have some policy implicaions. In paricular, hey underscore he need for furher effors in Guaemala o increase is level of human capial. Given he incomplee characer of he average-years-of-schooling measure and he poenial exisence of hreshold levels in educaion, as well as numerous non-moneary benefis of educaion, he conribuion of human capial may be underesimaed in is quaniaive form. An 20

15 Loening, J. Human Capial, Technology Diffusion and Economic Growh addiional finding is ha he composiion of invesmen appears o be an imporan facor behind produciviy growh. References Banerjee, A., J. Dolado, J. Galbraih and D. F. Hendry (993), Coinegraion, Error Correcion, and he Economeric Analysis of Non-saionary Daa, Oxford: Oxford Universiy Press. Bassanini, A. and S. Scarpea (200), Does Human Capial Maers for Growh in OECD Counries? Evidence from Pooled Meangroup Esimaes OECD Working Paper ECO/WKP 200/8. Benhabib, J. and M. Spiegel (994), The role of human capial in economic developmen: evidence from aggregae cross-counry daa, Journal of Moneary Economics, Vol. 34, No. 2, pp Engle, Rober F. and C. W. J. Granger (987), Coinegraion and Error Correcion: Represenaion, Esimaion, and Tesing, Economerica, Vol. 55, No. 2, pp Lee, J.-W. (995), Capial Goods Impor and Long-run Growh, Journal of Developmen Economics, Vol. 48, No., pp Loening, L. J. (2004), Economic Growh, Biodiversiy Conservaion, and he Formaion of Human Capial in a Developing Counry: The Case of Guaemala, Goeingen Sudies in Developmen Economics, Vol. 3, Frankfur: Peer Lang. Loening, J. L. (2005). Esimaing Human and Physical Capial Socks in a Daa Scarce Environmen: An Applicaion o Guaemala, , Inernaional Journal of Applied Economerics and Quaniaive Sudies, Vol.5-, forhcoming. 2 2 Preliminary resuls for are displayed in The Impac of Educaion on Economic Growh in Guaemala, Discussion Paper 87, Ibero-America Insiue for Economic Research, Universiy of Goeingen, June

16 Applied Economerics and Inernaional Developmen. AEID. Vol. 4-3 (2004) Lucas, R. E. (988), On he Mechanics of Economic Developmen, Journal of Moneary Economics, Vol. 22, No., pp Lucas, R. E. (990), Why Doesn' Capial Flow from Rich o Poor Counries? American Economic Review, Vol. 80, No. 2, pp Mankiw, N. G., D. Romer and D. N. Weil (992), A Conribuion o he Empirics of Economic Growh, Quarerly Journal of Economics, Vol. 07, No. 2, pp Morales, R. A. (998), Deerminans of Growh in an Errorcorrecion Model for El Salvador, IMF Working Paper 98/04. Nehru, V. and A. Dhareshwar (994), New Esimaes of Toal Facor Produciviy Growh for Developing and Indusrial Counries, World Bank Policy Research Working Paper 33. Nelson, R. and E. Phelps (966), Invesmens in Humans, Technological Diffusion, and Economic Growh, American Economic Review, Vol. 56, No. -2, pp Romer, P. M. (990), Endogenous Technological Change, Journal of Poliical Economy, Vol. 98, No. 5, pp. S7-S02. Solow, R. M. (956), A Conribuion o he Theory of Economic Growh, Quarerly Journal of Economics, Vol. 70, No., pp World Bank (2003), Povery in Guaemala, Repor No GU, Washingon D.C. Journal published by he Euro-American Associaion of Economic Developmen. hp:// 22