Exploring he Causaliy Relaionship beween Trade Liberalizaion, Human Capial and Economic Growh: Empirical Evidence from Pakisan Dr. Imran Sharif Chaudhry Associae Professor of Economics a Bahauddin Zakariya Universiy Mulan, Pakisan E-mail: imranchaudhry@bzu.edu.pk Absrac There is a rapidly growing lieraure on he ineracion beween rade liberalizaion and economic growh bu few have analyzed he relaionship beween rade liberalizaion, human capial and economic growh. This paper aemps o invesigae empirically he causaliy relaionship beween rade liberalizaion, human capial and economic growh in Pakisan by applying coinegraion and Granger causaliy echniques of ime series economerics for he 1972-2007 period. The daa on rade liberalizaion and economic growh are aken from he World Developmen Indicaors, ESDS inernaional websie while human capial index is consruced based on he daa from Pakisan Economic Survey. The empirical resuls reveal ha here exis shor run and long run coinegraion and causaliy relaionships among variables in he growh model. I implies an educaion and rade openness policies may be feasible wih susained economic growh. I is also found ha causaliy runs from rade liberalizaion and human capial o economic growh. The resuls are also consisen wih he growh heories and economic lieraure. I. Inroducion Trade liberalizaion is ofen considered as a significan ool for increasing economic growh in he world economies. Expors of hose counries have increased who liberalized heir economies, and consequenly hese counries have also experienced he fases growh of GDP. Since he relaionship beween rade liberalizaion and economic growh has exensively been analyzed in he world, i remained conroversial among policy makers and economiss based on empirical findings (Chaudhry and Majeed, 2009). Many quesions were raised abou he relaionship beween rade and growh in developing counries [Kruger (1997)]. However, here is a grea consensus ha rade policy openness and higher raios of rade volume o GDP were posiively relaed wih economic growh. Many developing counries are liberalizing heir economies o become aracive desinaion for foreign
capial inflows. Openness of rade regime can increase he invesmen and efficiency of invesmen and also can increase he marke size in hese counries. Neverheless he impac of human capial on economic growh has also received a grea aenion in he recen years. There are wo major approaches o he quesion of how human capial affecs economic growh of an economy. The firs is o inroduce human capial as an inpu in he producion process [Uzawa (1965), Lucas (1988)]. This implies ha here exiss correlaion beween human capial and growh of oupu. The second approach follows Nelson and Phelps (1966); hey consider human capial as a source of produciviy growh. Since he sock of human capial deermines he capaciy of an economy o innovae or o implemen exising echnologies, hus favouring echnology diffusion and cach-up processes, he level of human capial is, in his view, relaed o he rae of growh. The concep of human capial is aained significan place in he heory of economic growh. However, he problem of is measuremen has no been addressed properly in he lieraure. Mankiw, Romer, and Weil (1992) used a naion s rae of secondary educaion enrolmen as a proxy for human capial. Sala-i-Marin, Doppelhofer and Miller (2004), have considered primary school enrolmens as a measure of human capial. Barro and Lee (1993) have considered he average years of schooling as a proxy variable for human capial. The heories of human capial [See Schulz (1961) and Becker (1962)] and heories of endogenous growh [See Lucas (1988), Romer (1990) and Rebelo (1991)] repored ha knowledge gained a cenral posiion in he growh process. In heories of human capial, educaion appears an essenial componen of economic growh as well as he link beween economic growh and human capial is explicily presened in he heories of endogenous growh. Therefore, here are many possible channels hrough which expors can promoe he human capial accumulaion and human capial can promoe growh rae of expors in developing counries. Since rade liberalizaion and human capial have been explained for economic growh separaely in he mos of he lieraure, he coinegraion analysis has been carried ou o examine he causaliy relaionship beween rade liberalizaion, human capial and economic growh in Pakisan. The paper is arranged as follows. Secion II addresses he issues of daa sources and mehodology employed. Resuls and discussion are produced in secion III. Finally secion IV saes he conclusion. II. Daa Sources and Mehodological Issues The sudy is based on secondary source of ime series daa covering he ime period 1972-2007. An increase in rade openness is ofen considered he increase in he size of he counry s rade. Generally 2
i is considered a proxy for rade liberalisaion. The higher level of rade openness reveals he success of rade liberalisaion policies. Therefore we use he rade openness (expors plus impors) as percenage of GDP (TRADE) as a measure of rade liberalizaion. The daa on rade liberalizaion and economic growh are aken from he World Developmen Indicaors (WDI) published by World Bank downloaded from ESDS inernaional websie while human capial index is consruced based on he daa from Pakisan Economic Survey. The variables Real Gross Domesic Produc (LRGDP), Employed Labour Force (LLABOUR), Gross Fixed Capial Formaion as Percen of GDP, Proxy for Capial (LCAPITAL), Real Expors as Percen of GDP (LEXPOR) and Human Capial Index (LHCAPT) are seleced for empirical analysis based on New Economic Growh Theory. This sudy buil weighed index of enrolmens a differen schooling levels o use i as a proxy variable for human capial sock. The sudy followed he mehod of Wang and Yao (2003) o consruc he human capial index for Pakisan. Wang and Yao (2003) consruced he series on human capial using he perpeual invenory mehod in a similar fashion as Barro and Lee (2000) for China. Wang and Yao (2003) use he flow variable as he number of graduaes compleing differen schooling levels raher han enrolmens a five-year inervals. The resul was a weighed index of educaional aainmen from five years levels of schooling: primary, junior secondary, senior secondary, special secondary and eriary. In his sudy, a slighly differen mehod from Wang and Yao (2003) in he consrucion of human capial index is employed because of he daa limiaions. The enrolmen raio of sudens a differen levels of schooling years is aken insead of compleed educaion because of some consrains in he availabiliy of daa. According o Pakisan s educaional sysem, he number of enrolmens a differen levels of schooling years is aken in hree caegories: primary sage, high school sage and universiy educaion. For hese hree caegories, numbers of schooling years are 5, 10 and 16 respecively. Number of schooling years is aken as a weigh for each corresponding level of enrolmens. A he naional level professional educaion ranges from 14 o 16 years, professional educaion is considered high qualiy human capial, so, 16 (maximum levels of schooling) years is aken as a weigh for professional educaion. The following formula is used o consruc he weighed index of enrolmens from hree levels of schooling: primary, higher secondary and universiy. H = (5H 1 + 10H 2 + 16H 3 ) / Populaion ()) Where 3
H = Human capial sock a year, Populaion () = Toal populaion a year, H 1, H 2 and H 3 represen he number of enrolmens a primary school, higher secondary school, and universiy levels respecively. Mos of he imes, empirical sudies of economic growh begin wih he neoclassical model, originally proposed by Solow (1956) and exended by Mankiw, Romer, and Weil (1992) o include human capial. This model appears in he general form as: α β 1 α β Y = A K H L ε1... (1) Where Y = Aggregae producion of he economy a ime. A = Toal facor produciviy a ime. K = Real capial sock a ime. L = Employed labour force a ime. H = Human capial sock a ime. = Usual error erm and independen from all he independen variable. Because his sudy aims o invesigae if and how rade openness affec economic growh via increases in produciviy, we assume ha oal facor produciviy can be expressed as a funcion of rade openness and oher exogenous facors C : A = f ( T, C, ε 2 )... (2) A = T Ψ, C ε 2... (3) Where T = Trade openness a ime. = usual error erm and independen from all he independen variables. We combine equaion (4.3) wih equaion (4.1) and obain: α β γ Ψ Y =, K H L T ε..... (4) Where C,,,, 3 = usual error erm and independen from all he independen variables. α = Elasiciy of producion wih respec o K. β = Elasiciy of producion wih respec o capial. γ = Elasiciy of producion wih respec o human capial. δ = Elasiciy of producion wih respec o labour force paricipaion. Ψ = Elasiciy of producion wih respec o rade openness. 4
Taking naural logs (Ln) on boh sides of equaion (4) gives an esimable linear funcion: Y = C LnK LnH LnL LnT..... (5) Ln 1 + α + β + γ + Ψ + ε 4 Where all coefficiens are consan elasiciies, C 1 = Ln C is a consan parameer, is usual error erm and is independen of all he independen variables, which reflecs he influence of all oher facor. According o equaion (5), an economeric model of he seleced variables used in his sudy is given as: LGDP = β 1 + β 2 LTRADE + β 3 LHCAPT + β 4 LCAPITAL + β 5 LLABOUR + u (6) To find ou he long and shor run dynamics beween rade liberalizaion, human capial and economic growh, his sudy employs ime series economerics, such as analysis of co-inegraion, error-correcion models and Granger causaliy analysis. According o Granger and Newbold (1974), he OLS esimaion of regressions in he presence of non-saionary variables yields spurious regressions. Therefore, esing ime series daa for saionary is of grea imporance for reliable resuls. The saionary of he variables is deermined by performing he Augmened Dickey-Fuller (ADF) es. Dickey and Fuller (1979, 1981) developed a procedure o formally es for non-saionariy. The significan par of heir es is ha esing for non-saionariy is equivalen o esing for he of a uni roo. As he error erm is unlikely o be whie noise, Dickey and Fuller exended heir es procedure suggesing an augmened version of he es which includes exra lagged erms of he dependen variable in order o eliminae auocorrelaion. The lag lengh on he exra erms is eiher deermined by he Akaike Informaion Crierion (AIC) or Schwarz Bayesian Crierion (SBC) or more usefully by he lag lengh necessary o whien he residuals. The ADF es can be performed wih inercep, rend and inercep, and none of hem. If he economic series have become non-saionary a level and have he same inegraion order hen co-inegraion becomes an over-riding requiremen for any economic model. The coinegraion in muliple equaions can be examined only by he Johansen approach. When he variables or series are having coinegraed relaionships hen he linear combinaion of hese series would be saionary and gives long relaionship beween he variables. For shor-run relaionship, Error Correcion Model (ECM) is employed. The VECM is a convenien model measuring he correcion from disequilibrium of he previous period which has very good economic implicaions. Neverheless, Granger Causaliy Tes has been applied for empirical invesigaion for causaliy of he variables in his sudy. 5
III. Resuls and Discussions Before going o he ime series economeric analysis, a deailed descripive analysis is carried ou. Our complee daa se consiss of hiry six years of annual observaions from 1972 o 2007 on he seleced variables. The descripive saisics is repored in able 1 and saes ha he average of Real Gross Domesic Produc for our sudy period is 2660.44 billion rupees wih sandard deviaion of 1318. The average for rade liberalizaion index is 33.81 while 3.18 is he sandard deviaion. Human capial index is 0.012 on an average and wih deviaion 0.006. The variaion in labour force paricipaion is more han he capial. Skewness is a measure of symmery, or more precisely, he lack of symmery. Therefore, as far as skewness (lack of symmery) of he variables is concerned GDP, HCAPT and LABOUR are righ skewed and res of he wo variables are lef skewed. All variables are lile skewed o lef or righ. Table 1 Descripive Saisics Analysis, 1972-2007 GDP TRADE CAPITAL HCAPT LABOUR Mean 2660.444 33.814 16.583 0.012 29.832 Median 2545.000 34.348 17.002 0.011 28.715 Maximum 5550.000 38.910 21.410 0.031 48.070 Minimum 906.000 27.720 11.435 0.005 17.780 Sd. Dev. 1318.686 3.180 2.027 0.006 8.284 Skewness 0.416-0.298-0.427 1.677 0.533 Kurosis 2.168 2.185 3.836 5.584 2.390 Jarque-Bera 2.074 1.529 2.145 26.900 2.261 Probabiliy 0.355 0.466 0.342 0.000 0.323 Observaions 36.000 36.000 36.000 36.000 36.000 Table 2 Correlaion Marix GDP TRADE CAPITAL HCAPT LABOUR GDP 1.00 0.29 0.34 0.89 0.99 TRADE 0.29 1.00 0.49 0.19 0.24 CAPITAL 0.34 0.49 1.00 0.45 0.35 HCAPT 0.89 0.19 0.45 1.00 0.91 LABOUR 0.99 0.24 0.35 0.91 1.00 Kurosis is a measure of wheher he daa are peaked or fla relaive o a normal disribuion. Kurosis saisic of he variables shows ha only HCAPT and CAPITAL are lepokuric (long-ailed or higher peak) and all oher variables are playkuric (fa or shor-ailed). These measures of skewness and kurosis can be combined o deermine wheher a random variable follows a normal disribuion. A Jarque-Bera (JB) es for normaliy suggess ha residuals are no normally disribued for HCAPT as is value of probabiliy is 0.00. For all oher variables included in his sudy i is concluded ha residuals for hese variables are normally disribued. The srengh of he relaionship of variables is also esimaed and repored in able 2. All variables are posiively correlaed wih each oher. The resuls sae ha labour force paricipaion and 6
human capial index are highly correlaed, capial is moderaely correlaed and rade openness is weakly correlaed wih economic growh. Table 3 Resuls of Augmened Dickey-Fuller Tes (ADF) for Uni Roo Resuls of uni roo es wih inercep Resuls of uni roo es wih rend and inercep variables Level 1 s difference Conclusion Level 1 s difference Conclusion LGDP -1.26-4.60 I(1) -1.12-4.59 I(1) LLABOUR 0.32-6.74 I(1) -1.74-6.75 I(1) LCAPITAL -1.83-4.12 I(1) -1.87-4.07 I(1) LHCAPT 0.048-5.76 I(1) -1.42-5.72 I(1) LTRADE -2.94-5.95 I(1) -2.91-5.87 I(1) Noe: The null hypohesis is ha he series is non-saionary, or conains a uni roo. The rejecion of null hypohesis for ADF es is based on he MacKinnon criical values 1 percen. The resuls of he regression equaion (6) conclude ha i is spurious regression as R 2 >d (0.98> 0.95). Higher R 2 and significan raios are also supporing he argumen. So our analysis is shifing owards he applicaion of Time Series economeric echniques. The objecive of sudy is o examine he long run and shor run relaionships of variables and for his purpose Johansen (1988, 1991) and Johansen-Juselius (1990) ess are being applied. The sudy invesigaes he long run dynamic ineracion among real gross domesic produc and oher variables. Neverheless firs sep in ime series economerics afer finding spurious resuls is o examine he saionary of he variables o deermine he order of inegraion of he variables. For his purpose, he ADF es for uni roo has been used a level and he firs difference of each series. Table 3 exhibis he resuls of he Augmened Dickey Fuller (ADF) es which clearly shows ha ime series are no saionary a level bu ha he firs differences of he logarihmic ransformaions of he series are saionary. When he ADF es is conduced a firs difference of each variable, he null hypohesis of non-saionary is easily rejeced a 1% significance level as shown in he able. This is consisen wih some previous sudies of order one, I (1). Table repors he uni roo resuls using ADF ess boh wih and wihou rend. Boh models indicae ha he null of he uni roo canno be rejeced for all variables as he absolue values of ADF saisics are well below he 99% criical value of he es saisic. Thus, we conclude ha all he variables series are non-saionary; daa becomes saionary afer he firs 7
difference as absolue values of he ADF saisic are now greaer han 99% criical value of he es saisic. The second sep is o deermine he opimal lag lengh. We esimaed he model for a large number of lags and hen reduce down o check for he opimal value of Akaike Informaion Crierion (AIC) and Schwarz crierion (SBC). By doing his we found ha he opimal lag lengh is 2 lags. Having me hese requiremens, his sudy performs coinegraion analysis. As described before he maximum likelihood based Johansen (1988, 1991) es and Johansen-Juselius (1990) procedures are used o deermine he presence of coinegraing equaions in a se of ime series daa. A race saisic has been used o es he null hypohesis of r co inegraing vecors. The race saisics of all hree models is esimaed o choose which one model is appropriae. I sars wih he smaller number of co inegraing vecors r=0 and checks wheher he race saisics for model 2 rejecs he null, if yes hen proceed o he righ and so on hence model 3 suggess ha he race saisic is smaller han he 5% and 1% criical values a r=5. So his model does no show coinegraion and we sop our analysis a his poin. Model 2 (Co inegraion wih resriced inerceps and no deerminisic rend in he daa) was found o be he mos appropriae. Table 4 Unresriced Coinegraion Rank Tes (Maximum Eigenvalue) Eigenvalue Likelihood Raio 5 Percen Criical Value 1 Percen Criical Value Hypohesize No. of CE(s) 0.870 128.351 76.070 84.450 None ** 0.637 60.984 53.120 60.160 A mos 1 ** 0.325 27.541 34.910 41.070 A mos 2 0.235 14.551 19.960 24.600 A mos 3 0.159 5.702 9.240 12.970 A mos 4 Noe: *(**) denoes rejecion of he hypohesis a 5% (1%) significance level L.R. es indicaes 2 coinegraing equaion (s) a 1% significance level Table 5 Normalized Coinegraing Coefficiens: 1 Coinegraing Equaion(s) LGDP LTRADE LHCAPT LCAPITAL LLABOUR C 1.00-3.06 0.54 2.21-2.64 8.23 0.89 0.34 0.93 0.47 3.71 (-3.44) (1.59) (2.38) (-5.62) (2.21) Noe: Figures in parenheses are he raios To deermine he sign and magniude of he long run relaionship and elasiciies in above equaions he co inegraing vecors have been normalized on LGDP. The resuls for he coinegraion 8
ess are presened in able 4. I is concluded ha here exiss wo coinegraing relaionships. Table 5 repors he resuls regarding he coefficiens of β marices in erms of normalized coinegraing coefficiens of Is equaion. There exiss long run relaionship among he variables. All he variables are saisically significan and all coefficiens excep human capial have more elasic relaionship wih economic growh. Trade liberalizaion has correc sign i.e. 1 percen increase in rade openness leads o 3.06 percen rise in he real gross domesic produc and sands more elasic. Human capial and capial boh have inverse relaion wih GDP in long run while labour force paricipaion has he posiive impac on GDP. Since long run associaion has been observed among differen variables, we can also explore he possibiliy of a shor run relaionship by using an error correcion model (ECM) framework. ECM permis he inroducion of pas disequilibrium as explanaory variables in he dynamic behaviour of exising variables and hus faciliaes in capuring boh he shor run dynamics and long run relaionships among variables. The vecor error correcion (VEC) for real gross domesic produc and is deerminans ake he form ρ z = Г i z -i + z -1 + ( χ D ) + u i= 1 Where Z = Se of all variables. The variable u represens random disurbance. D is a vecor of exogenous variables and χ,, Г i are vecors of parameers. Table 6 gives he shor run dynamic relaionship and he se of shor run coefficiens in he VECM, which relaes he changes in LGDP o changes in oher variables and he error erm in he lagged periods. Hence he lagged difference erms capure he shor run changes in he corresponding level variables. In he ECM specificaions, several feaures of he regression resuls are shown in he able 6. The coefficien ECT -1 is significan and does have he correc sign (negaive). The coefficien of ECT -1 indicaes he speed of adjusmen and in his case, 7 percen adjusmen is observed. In oher words abou 7 percen of disequilibrium is correced each year. Trade openness has posiive impac on growh bu i is no significan, while human capial and physical capial have reverse signs wih GDP. Labour force paricipaion is a significan variable and especially has posiive impac wih wo years lag on GDP. 9
Table 6 Resuls of ECM for Shor Run Dynamics Dependen Variable = LGDP Independen Variables Coefficien -Saisic Consan 0.07-2.69 D(LGDP(-1)) -0.31-1.25 D(LGDP(-2)) -0.03-0.16 D(LTRADE(-1)) 0.01 0.23 D(LTRADE(-2)) 0.04 0.84 D(LHCAPT(-1)) -0.01-0.27 D(LHCAPT(-2)) -0.059-1.75 D(LCAPITAL(-1)) -0.02-0.50 D(LCAPITAL(-2)) 0.02 0.45 D(LLABOUR(-1)) -0.29-1.85 D(LLABOUR(-2)) 0.34 1.72 EC -1-0.07-1.91 R-squared 0.64 Adj. R-squared 0.45 F-saisic 3.41 According o Granger, he presence of coinegraing vecor indicaes ha Granger causaliy mus exiss in a leas one direcion. A variable Granger causes he oher variable if i helps forecas is fuure values. The opimum lag lengh of VAR is k=2 based on AIC. The resuls of Granger Causaliy es are repored in able 7 and sae ha here is unidirecional causaliy beween real gross domesic produc and rade openness. Human capial and labour force paricipaion are causing he real GDP and resuls in unidirecional causaliy. 10
Table 7 Resuls of Granger Causaliy Tes Pair wise Granger Causaliy Tes Sample: 1972-2007 Lags: 2 Null Hypohesis: Observaions F-Saisic Probabiliy LTRADE does no Granger Cause LGDP 34 0.33 0.72 LGDP does no Granger Cause LTRADE 3.01 0.06 LHCAPT does no Granger Cause LGDP 34 8.08 0.00 LGDP does no Granger Cause LHCAPT 0.52 0.60 LCAPITAL does no Granger Cause LGDP 34 0.61 0.55 LGDP does no Granger Cause LCAPITAL 2.79 0.08 LLABOUR does no Granger Cause LGDP 34 5.44 0.01 LGDP does no Granger Cause LLABOUR 0.21 0.81 LHCAPT does no Granger Cause LTRADE 34 1.05 0.36 LTRADE does no Granger Cause LHCAPT 0.62 0.54 LCAPITAL does no Granger Cause LTRADE 34 1.31 0.29 LTRADE does no Granger Cause LCAPITAL 0.51 0.60 LLABOUR does no Granger Cause LTRADE 34 0.77 0.47 LTRADE does no Granger Cause LLABOUR 0.87 0.43 LCAPITAL does no Granger Cause LHCAPT 34 0.87 0.43 LHCAPT does no Granger Cause LCAPITAL 0.72 0.50 LLABOUR does no Granger Cause LHCAPT 34 2.29 0.12 LHCAPT does no Granger Cause LLABOUR 0.32 0.73 LLABOUR does no Granger Cause LCAPITAL 34 0.14 0.87 LCAPITAL does no Granger Cause LLABOUR 2.61 0.09 IV. Conclusion The sudy has aemped o provide empirical evidence concerning he relaionship beween rade liberalizaion, human capial and economic growh. The empirical analysis was based on Johanson s coinegraion, parsimonious ECM and causaliy for Pakisani ime series daa from 1972 o 2007. i. The Uni Roo Tes based on ADF indicaes ha all variables are non-saionary a heir level form and become saionary a heir firs difference, since he variables are inegraed of same order, I(1). ii. Johanson s coinegraion es indicaes ha here exiss a long-run relaionship beween economic growh, physical capial, employed labor force, human capial, and rade openness. 11
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