HIGHER EDUCATION AND ECONOMIC GROWTH: AN EMPIRICAL INVESTIGATION OF COINTEGRATION AND CAUSALITY FOR TURKISH ECONOMY

Transcription

1 HIGHER EDUCATION AND ECONOMIC GROWTH: AN EMPIRICAL INVESTIGATION OF COINTEGRATION AND CAUSALITY FOR TURKISH ECONOMY Ekrem ERDEM * Can Tansel TUĞCU ** ABSTRACT Recenly, sudies abou he effecs of higher educaion on economic growh in develoing counries are aking more aenion. In his aer, long-run and causal relaionshis beween higher educaion and economic growh in Turkish economy over eriod were analysed. For his urose, wo higher educaion indicaors were regressed over real GDP using ARDL (Auoregressive Disribued Lag) bounds esing aroach and he causaliy beween hese variables was invesigaed by Dolado and Lükeohl s syle Granger causaliy es. Resuls showed ha, higher educaion is coinegraed o economic growh and eiher higher educaion or economic growh has significan causal effecs on each oher. Keywords: Higher Educaion, Economic Growh, Coinegraion, Causaliy. YÜKSEKÖĞRETİM VE EKONOMİK BÜYÜME: TÜRK EKONOMİSİ İÇİN BİR EŞBÜTÜNLEŞME VE NEDENSELLİK ARAŞTIRMASI ÖZ Son yıllarda gelişmeke olan ülkelerde yüksek öğreimin ekonomik büyüme üzerindeki ekilerini konu alan çalışmalar daha fazla ilgi çekmekedir. Bu çalışmada Türk ekonomisinde döneminde yüksek öğreim ve ekonomik büyüme arasındaki uzun dönemli ve nedensel ilişkiler analiz edilmişir. Söz konusu amaç için iki yüksek öğreim gösergesi ARDL sınır esi yaklaşımı kullanılarak reel GSYİH üzerine regrese edilmiş ve değişkenler arasındaki nedensellik ilişkisi Dolado ve Lükeohl arzı Granger nedensellik esi ile araşırılmışır. Sonuçlar Türk ekonomisinde yüksek öğreim ile ekonomik büyümenin eşbüünleşik ve hem yüksek öğreimin hem de ekonomik büyümenin birbirleri üzerinde anlamlı nedensel ekileri olduğunu gösermişir. Anahar Kavramlar: Yüksek öğreim, Ekonomik Büyüme, Eşbüünleşme, Nedensellik. * Prof. Dr., Erciyes Üniversiesi, İ.İ.B.F., İkisa Bölümü. ** Dr., (Corresonding auhor), Nevşehir Üniversiesi, İİBF, İkisa Bölümü. Makalenin kabul arihi: Mayıs 2011

2 2 Erciyes Üniversiesi İkisadi ve İdari Bilimler Fakülesi Dergisi, Sayı: 36, Temmuz-Aralık 2010 ss.1-14 INTRODUCTION In his sudy, he long-run and causal relaionshis beween higher educaion he inegral ar of human caial and economic growh in Turkish economy over he eriod were invesigaed using recenly develoed coinegraion and causaliy echniques. The relaionshi beween human caial and economic growh was seriously aken ino accoun by Adam Smih (1776) a firs, followed by Schulz (1962) who brough a sysemaic ersecive o human caial, and Denison (1967) who argued ha invesing in human caial is vial for economic growh. Aferwards, he rising imorance of new growh heories (e.g. Lucas, 1988; Romer, 1990; Rebelo, 1991; Mankiw e al., 1992) made a huge conribuion o he value of human caial in he economic growh rocess of he economies. Following hese basic heories, his aer focuses on human caial as a deerminan of economic growh. Alhough human caial includes educaion, healh and asecs of social caial, he main focus of he resen sudy is on educaion, aricularly higher educaion. Educaion is widely acceed as a leading insrumen for romoing economic growh a all levels. I conribues o economic growh hrough imaring general aiudes, disciline and secific skills necessary for a variey of worklaces. If one look a he researches which ake educaion ino accoun as a deerminan of economic growh, i can easily be seen ha huge ar of he lieraure is comosed of coinegraion, causaliy and simle regression analysis which ry o find ou he long-run and causal relaionshis beween educaion and economic growh. This lieraure can also be devided ino wo grous. Firs grou of researches consiss of sudies which examine he ineracion beween overall educaion and economic growh. Using Johansen coinegraion and Granger causaliy analysis, while Babaunde and Adefabi (2005), Sari and Soyas (2006), Taban and Kar (2006) found coinegraion and bi-direcional causaliy beween educaion and economic growh; Aseriou and Agiomirganakis (2001) and Pradhan (2009) found educaion is coinegraed wih economic growh, bu here exiss uni-direcional causaliy form economic growh o educaion. In addiion, using Engle-Granger mehodology, Francis and Iyare (2006) and Kui (2006) roved he coinegraion relaion and uni-direcional causaliy from educaion o economic growh oo. On he oher hand, emloying simle regression analysis, while Barro and Salai-Marin (1995) and Keller (2006), showed educaion has significan and osiive growh effecs; using VAR, Deniz and Dogruel (2008) found overall educaion simulaes economic growh in MENA counries, bu only rimary and secondary educaion levels have longrun osiive effecs on economic growh in Turkey. Second grou of researches consiss of sudies which invesigae he ineracion beween each levels of educaion and economic growh searaely. Since

3 Higher Educaion and Economic Growh: An Emirical Invesigaion Of Coinegraion And Causaliy For Turkish 3 he aim of his aer is o find ou he long-run and causal relaionshis beween higher educaion and economic growh, brief lieraure will be given aroriaely for his urose. Meulemeeser and Rocha (1995), examined he causal relaionshis beween higher educaion and economic growh using Engle- Granger coinegraion and Granger causaliy analysis. They found ha srong uni-direcional causaliy exiss from higher educaion o economic growh in Jaan, France, UK and Sweden, bu no causaliy in Ialy and Ausralia. Jaoul (2002), searched for he causal relaionshis beween higher educaion and economic growh in France using Johansen coinegraion and Granger causaliy analysis and showed ha higher educaion and economic growh are coinegraed and bi-direcional causaliy exiss beween hem. Narayan and Smyh (2006), examined he long-run and causal relaionshis beween higher educaion, real income and real invesmen in China emloying ARDL aroach and Granger causaliy analysis. They found ha when real invesmen is deended variable, all of hem are coinegraed. Bu here is no coinegraion if deenden variable changes. According o causaliy analysis, boh higher educaion and real income cause real invesmen. They concluded ha higher educaion has indirec effecs on economic growh via is effecs on real invesmen. Khorasgani (2008), examined he long-run relaionshi beween higher educaion and economic growh in Iran using ARDL mehodology. He saed ha higher educaion has osiive and significan effecs on economic growh boh in he shor and he long-run. In addiion, while Lin (2004) and Bloom e al. (2005) invesigaed he effecs of higher educaion curricula on economic growh using simle regression analysis and roved ha higher educaion has osiive and significan growh effecs; Sanders e al. (2003) and Bhandari and Curs (2007) examined he imac of higher educaion exendiures on economic growh using simle regression analysis in U.S. and found no significan relaionshi from higher educaion exendiures o economic growh. On he conrary, economic growh fosers higher educaion exendiures. This sudy differs from he revious sudies in several resecs. Firs, mos of he revious sudies used Johansen or Engle-Granger echniques for invesigaing he long-run relaionshis beween educaion and economic growh. In his sudy, recenly develoed ARDL aroach o coinegraion was emloyed for his urose. Second, sudies which used ARDL aroach o coinegraion for deerminaion of long-run relaionshis, also used he simle Granger analysis for esing he causaliy beween educaion and economic growh. In his aer, because of some imerfecions of simle Granger mehodology, a new causaliy echnique which was develoed by Dolado and Luhkeohl (1996) was uilized. Third, he ineracion beween higher educaion and economic growh has never been sudied in Turkey, searaely. This aer aims o arially fulfill his ga and conribue he emirical lieraure. The final difference of he resen sudy sems from is caabiliy of checking he sabiliy of he

4 4 Erciyes Üniversiesi İkisadi ve İdari Bilimler Fakülesi Dergisi, Sayı: 36, Temmuz-Aralık 2010 ss.1-14 arameers in order o avoid from invalid oliical imlicaions. For his urose sabiliy ess which were ioneered by Brown e al. (1975) were used. The aer is organized as follows. In he nex secion, conceual framework is summarized. In secion 3, model secificaion and he daa are resened. In secion 4, mehods and findings are exlained. Finally, secion 5 concludes. I. CONCEPTUAL FRAMEWORK Mos of he recen sudies showed ha higher educaion is an imoran simulus for economic develomen and has bi-direcional causaliy wih economic growh. According o Bloom e al. (2005), his ineracion can be reresened as shown below: Figure 1: Conceual Framework Of Higher Educaion And Economic Growh According o Bloom e al. (2005), higher educaion can lead o economic growh hrough boh rivae and ublic channels. The rivae benefis for individuals are well esablished and include beer emloymen rosecs, higher salaries and a greaer abiliy o save and inves. These benefis may resul in beer healh and imroved qualiy of life, hus seing off a viruous siral in

5 Higher Educaion and Economic Growh: An Emirical Invesigaion Of Coinegraion And Causaliy For Turkish 5 which life execancy imrovemens enable individuals o work more roducively over a longer ime furher boosing lifeime earnings. Public benefis are less widely recognized, which exlain many governmens neglec of higher educaion as a vehicle for ublic invesmen. Bu individual gains can also benefi sociey as a whole. Higher earnings for welleducaed individuals raise ax revenues for governmens and ease demands on sae finances. Higher educaion can also imrove a naion s healh, conribue o reduced oulaion growh, imrove echnology and srenghen governance. I also ranslaes ino greaer consumion, which benefis roducers from all educaional backgrounds (Bloom e al., 2005: 16). II. MODEL AND THE DATA Since i is relaively easy o be augmened by adding various dynamics, he exising economic growh lieraure usually emloys Cobb Douglas roducion funcion. In his aer he relaionshi beween higher educaion and economic growh was examined by using an augmened Cobb-Douglas roducion funcion which was linearized by Lin (2004: 357): lny = ln A + β ln I + δ ln L + γ ln HE + ε (1) where Y reresens he real income, A reresens he combinaion of echnology and knowledge, I reresens hysical caial, L reresens labor, HE reresens he higher educaion indicaors, є reresens he error erm and reresens ime rend. For he analysis, while GDP in consan local currency uni wih 1998 rices is he roxy for real income, he hysical caial is roxied by gross fixed caial formaion which was measured in consan local currency uni wih 1998 rices and he labor is roxied by he oal workforce who is 15 years old and older; HE conains wo differen higher educaion indicaors. One of hem is oal higher educaion sock (STOCK) which is he sum of he sudens who enrolled in one of he higher educaion insiuions. The second one is higher educaion graduae (GRAD) which reresens he eole who graduaed from a higher educaion insiuion. While selecing hese indicaors, revious sudies (e.g. Sanders e al., 2003; Lin, 2004; Narayan and Smyh, 2006; Khorasgani, 2008) were referenced. The sudy is based on annual daa covering he ime eriod The daa for real income, hysical caial and labor were downloaded from OECD Sa daabase. The daa for higher educaion indicaors were derived from Saisical Indicaors of Turkish Saisical Insiue.

6 6 Erciyes Üniversiesi İkisadi ve İdari Bilimler Fakülesi Dergisi, Sayı: 36, Temmuz-Aralık 2010 ss.1-14 III. METHODS AND FINDINGS A. STATIONARY ANALYSIS In order o avoid surious regression resuls and achieve consisen and efficien coefficiens, i is necessary o check he saionary of he variables and find wheher he variables in quesion have uni roo or no. Since mos of he macroeconomic variables are driven by non-saionary rocess, he analysis should sar wih invesigaing he uni roo roeries. The resen sudy emloys wo differen uni roo ess (i.e. augmened Dickey-Fuller (ADF) and he Phillis-Perron (PP)). According o findings which were exhibied in Table 1, while real income (lny), hysical caial (lni) and labor (lnl) have uni roo a he level (i.e. difference saionary), higher educaion indicaors (lnstock and lngrad) do no (i.e. level saionary). Table 1: Resuls For Uni Roo Tess Tes Level Variables ADF PP Consan lny lni lnl -2, lnstock lngrad Consan and Trend lny lni lnl lnstock * lngrad Firs-Difference Consan lny lni lnl lnstock ** lngrad Consan and Trend lny lni lnl lnstock ** lngrad * significan a 1% level; ** significan a 5% level; * significan a 10% level. * ** * The findings also suor he fac ha emloying coinegraion analysis is a good choice for invesigaing he long-run relaionshis beween real income and indeenden variables. Because under he non-saionary condiions, OLS esimaion may have some inconveniences for he considered urose.

7 Higher Educaion and Economic Growh: An Emirical Invesigaion Of Coinegraion And Causaliy For Turkish 7 B. COINTEGRATION ANALYSIS There are several aroaches which are used for invesigaing he long-run relaions (coinegraion) among ime-series variables (e.g. Engle and Granger (1987), Johansen and Juselius (1990)). However hese mehods concenrae on he cases in which underlying variables are inegraed of order one. Since he findings of saionary analysis showed ha he considered variables have differen inegraion orders, i is decided o emloy a new coinegraion mehod known as he Auoregressive Disribued Lag (ARDL) aroach (i.e., he bounds esing aroach) o coinegraion develoed by Pesaran e al. (2001). The imorance of he ARDL aroach sems from is alicabiliy wihou checking he inegraion order of he variables. Bu if he order of inegraion is I(2), his mehod will no be suiable for esing coinegraion relaionshi. The uni roo es resuls suor he fac ha he considered variables are I(0) and I(1) and his mehod is hereby aroriae for he analysis. The ARDL reresenaion of equaion (1) which should be esimaed for he bounds esing aroach is as follows: lny = a 0 + a lny + ln I 1i i 2i i i= 1 i= 0 i= 0 a + a ln L 3i i + i= 0 a ln HE 5i i (2) + θ lny θ ln I θ ln L θ ln HE u where Δ is he difference oeraor, is he lag lengh, and u is serially uncorrelaed error erm. There are wo sages ha he ARDL rocedure has. In he firs one, he null hyohesis of no-coinegraion H 0 :θ 1 =θ 2 =θ 3 =θ 4 =0 is esed agains H 1 :θ 1 0, θ 2 0, θ 3 0, θ 4 0. Tesing coinegraion relaionshi is based on F- saisic which is non-sandard irresecive of wheher he variables are I(0) or I(1) and he criical values for his es were abulaed by Pesaran e al. (2001). These criical values creae a bound which covers all ossible classificaions of he variables. If he calculaed F-saisic lies above he uer level of he bound, he H 0 is rejeced, suoring coinegraion relaionshi in he long-run. If he calculaed F-saisic lies below he lower level of he bound, he H 0 canno be rejeced, indicaing no coinegraion. If he calculaed F-saisic falls beween he bounds, he error-correcion erm in his case is used o deermine he exisence of coinegraion. If a negaive and significan error-correcion erm is obained, he variables are said o be coinegraed. If he long-run relaionshi is suored, he error-correcion model (ECM) from equaion (2) is esimaed as he second sage of he ARDL rocedure. The ECM funcion is as follows:

8 8 Erciyes Üniversiesi İkisadi ve İdari Bilimler Fakülesi Dergisi, Sayı: 36, Temmuz-Aralık 2010 ss.1-14 lny + ψec k i k i k i i= 1 i= 0 i= 0 i= u = α + ω lny + λ ln I + φ ln L + γ k ln HE i (3) where ψ is he error-correcion arameer and EC is he residual obained from equaion (2). In order o sar he ARDL rocedure, i is necessary o aly F-es on he seleced models considering aroriae lag lenghs. To his end, maximum six lags were imosed on he level of variables and Schwarz Bayesian Crierion (SBC) was referenced for deciding he oimum number of lags. Panel A a Table 2 shows he F-saisics for coinegraion analysis based on he seleced ARDL models. Findings reveal ha he calculaed F-saisics reresen coinegraion for boh models. In addiion, significan negaive error-correcion arameers also suor he exisence of coinegraion relaionshi which was defined as an alernaive and an efficien way of esing coinegraion by Bahmani-Oskooee and Brooks (1999). The meaning of he esimaed coinegraion relaionshi for he considered variables is ha he Turkish economy is caable of correcing any dearures from he long-run seady sae equilibrium and here is no facor which has disoring effecs over he long-run growh ah of he economy. Panel B a Table 2 exhibis he long-run coinegraion esimaes. Due o he aim of he sudy, he exlanaions are concenraed on he higher educaion indicaors. I is clearly seen ha eiher higher educaion sock (STOCK) or higher educaion graduae (GRAD) has saisically significan and osiive coefficiens and one ercen increase in higher educaion sock and higher educaion graduae increase he real income by 0.15 and 0.05 ercen, resecively. Findings of he long-run analysis of higher educaion mean ha eriary enrollmen and he graduaes who could be reaed as he ouu of higher educaion sysem are one of he mos imoran inus for he economic growh ah of Turkish economy. Thus oliicians should ake hese imlicaions ino accoun and go furher ino he higher educaion as an imoran growh dynamic of Turkish economy. The ARDL rocedure emloys OLS esimaor for esimaing he coinegraion vecor, for his reason one should rovide ha he assumions of he OLS mehodology are suored. According o he diagnosic checking whose findings were resened in anel D a Table 2, boh wo models ass he assumions of no-serial correlaion, homoscedasiciy, normaliy and regression secificaion error even a he 10% significance level. Moreover, since he sabiliy of he arameers is no guaraneed by he coinegraion relaionshi, one should

9 Higher Educaion and Economic Growh: An Emirical Invesigaion Of Coinegraion And Causaliy For Turkish 9 rovide he sabiliy of he coinegraion arameers. In his aer he sabiliy of he long-run arameers was esed by alying cumulaive sum (CUSUM) and cumulaive sum of squares (CUSUMSQ) ess which were develoed by Brown e al. (1975). The sabiliy es resuls summarized in anel D a Table 2 reveal ha boh wo models have he sable arameers over he ime, ha is long-run coefficiens do no have arameer insabiliies.. Table 2: Resuls For Coinegraion Analysis (Deenden Variable: lny) Higher Educaion Indicaors STOCK GRAD Panel A: Coinegraion F-sa Error-correcion Parameer [0.006] [0.005] Panel B: Long-run Coefficiens Consan 4.21 [0.346] 2.05 [0.543] lni 0.41 [0.003] 0.47 [0.000] lnl 0.10 [0.861] 0.41 [0.335] lnhe 0.15 [0.012] 0.05 [0.076] Panel C: Diagnosic Checking Adjused-R Serial Correlaion a [0.061] [0.041] Heeroscedasiciy b [0.708] [0.554] Normaliy c [0.960] [0.921] Funcional Form d [0.158] [0.540] Panel D: Sabiliy Checking CUSUM Sable Sable CUSUMQ Sable Sable The criical values for F-saisic are ( ) for 10 ercen, ( ) for 5 ercen, and ( ) for 1 ercen level of significance, obained from Table CI(iii) Case III in Peseran e al. (2001: 300). a: The Breusch Godfrey LM es saisic for no serial correlaion. b: The Jarque Bera saisic for normaliy, c: The Whie s es saisic for homoscedasiciy. d: The Ramsey s Rese es saisic for regression secificaion error. Numbers in brackes are -values. C. CAUSALITY ANALYSIS Once a coinegraion relaion beween variables is rovided, causaliy could be searched a leas for one direcion (Engle and Granger, 1987). To his end, modified Granger causaliy es which was develoed by Dolado and Lükeohl (henceforh, DL) (1996) was uilized. The main advanage of his aroach is ha he esimaed model is robus o he ye of inegraion and coinegraion roeries exhibied by daa (Booh and Ciner, 2005). In addiion his

10 10 Erciyes Üniversiesi İkisadi ve İdari Bilimler Fakülesi Dergisi, Sayı: 36, Temmuz-Aralık 2010 ss.1-14 es overcomes he singulariy roblem, which is creaed by he zero resricions of classical Granger causaliy es on VAR coefficiens, by adding an addiional lag o he rue order of he VAR model. The esing rocedure has wo ses. In he firs one, a VAR() is deermined by a model selecion crierion such as SBC. And in he second one, VAR(+1) model is esimaed by OLS and he sandard zero resricion is u on he deermined lags in he model. The obained saisic which is asymoically disribued as chi-square was defined as Modified Wald (MWALD) by Dolado and Lükeohl (1996). The findings from causaliy analysis were reored in Table 3. Resuls show ha higher educaion is eiher an inu or an ouu for economic growh in Turkish economy. According o his, boh he higher educaion sock (STOCK) and graduae (GRAD) have uni-direcional causaliy wih real income. The direcions of causaliy are from STOCK o Y and from Y o GRAD. These resuls indicae ha he larger eriary enrollmen is ensured by he economy, he more real income i generaes; and he more real income i generaes, he sronger he higher educaion sysem will be. Table 3: Resuls For Causaliy Analysis Hyohesis +1 MWALD Causal STOCK does no cause Y [0.037] Yes Y does no cause STOCK [0.647] No GRAD does no cause Y [0.168] No Y does no cause GRAD [0.002] Yes The SBC was used o deermine he aroriae lag orders. Numbers in brackes are -values. CONCLUSION In his sudy, coinegraion and causaliy beween higher educaion and economic growh in Turkey were invesigaed using annual daa covering he eriod The sudy uilized boh he ARDL aroach o coinegraion and he Dolado and Lükeohl s Granger causaliy es. For hese analyses, wo differen higher educaion indicaors were used in order o avoid non-robus conclusions. Alhough he covered eriod has resriced he analysis no o emloy more han wo indicaors, invesigaion of coinegraion relaionshi beween higher educaion and economic growh revealed ha Turkey should inves in higher educaion in order o imrove growh erformance of he economy in he longrun. According o he analysis, eiher higher educaion sock or higher educaion graduae are good simulus for rising Turkish GDP. As menioned, suoring he coinegraion relaion reflecs he exisence of causaliy beween variables. Thus, afer roving he coinegraion relaion,

11 Higher Educaion and Economic Growh: An Emirical Invesigaion Of Coinegraion And Causaliy For Turkish 11 he causaliy beween higher educaion and economic growh was analyzed. Deending o he seleced indicaor, findings showed ha here exiss unidirecional causaliy eiher from higher educaion o economic growh or from economic growh o higher educaion. This resul is consisen wih he siuaion which was figured by Bloom e al. (2005). Accordingly, he causaliy from higher educaion sock o real GDP reflecs he firs wo channels (e.g. rivae and ublic channels) which suor he higher educaion o lead economic growh. And he causaliy from real GDP o higher educaion graduae reresens he feedback mechanism which in urn srenghens he higher educaion sysem. Finally, combining he findings from coinegraion and causaliy analysis summarizes ha in Turkey higher educaion is beneficial for economic growh boh in he shor and in he long-run. Furhermore, he conribuion of higher educaion sysem o economic growh in urn imroves he efficiency of iself.

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