International Review of Economics and Finance 19 (2010) 189–195

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International Review of Economics and Finance

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Finance-growth nexus in China revisited: New evidence from principalcomponents and ARDL bounds tests

Abdul Jalil a, Mete Feridun b,⁎, Ying Ma a

a School of Management, Wuhan University, Wuhan, Hubei, PR Chinab Department of Banking and Finance, Faculty of Business and Economics, Eastern Mediterranean University, Gazi Magosa, Turkey

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +90 392 630 2127; faE-mail address: mete.feridun@emu.edu.tr (M. Feri

1 See Chan et al. (2007) for an in-depth review of t

1059-0560/$ – see front matter © 2009 Elsevier Inc.doi:10.1016/j.iref.2009.10.005

a b s t r a c t

Available online 13 October 2009

This article re-examines the finance-growth nexus in China using principal componentsanalysis and ARDL bounds testing approach to cointegration. The results suggest that principalcomponents have an effective role in examining the links between growth and financialdevelopment and, that financial development fosters economic growth.

© 2009 Elsevier Inc. All rights reserved.

JEL classification:O18O16C31

Keywords:Financial developmentEconomic growthARDLPCA

1. Introduction

There exists a broad literature on the finance-growth nexus. Most studies have documented a positive relationship betweenfinancial development and economic growth (see, for example, Schumpeter, 1911; Hicks, 1969; Goldsmith, 1969; Mckinnon,1973; Shaw, 1973; Gelb, 1989; Roubini & Sala-i-Martin, 1992; King & Levine, 1993; Easterly, 1993; Fry, 1997; Khan & Sehadji,2000; Pagano & Volpin, 2001; Levine et al., 2000; Wang, 2000; Hung, 2003; Christopoulos & Tsionas, 2004 and Ergungor, 2008).Several other studies, on the other hand, have documented a negative relationship between financial development and economicgrowth (see, for example, Robinson, 1952; Kuznets, 1955; Friedman & Schwartz, 1963; and Lucas, 1988). On the other hand,Demetriades and Hussein (1996) and Rousseau and Vuthipadadorn (2005) have documented a bi-directional relationshipbetween financial development and economic growth.

The purpose of this article is to investigate the impact of financial sector development on the macroeconomic activity in China,one of the greatest transition economies whose financial sector has been going through various reforms since 1979. China'stransition from a centrally-planned economy into a more market-oriented one has been phenomenally successful1. Althoughthere exists a plethora of theoretical and empirical studies investigating the sources of economic growth in China (see, forexample, Chow, 1993; Borensztein & Ostry, 1996; Yu, 1998; Wu, 2000; Shan et al., 2001; Chow & Li, 2002; Hao, 2006, and Liang &Teng, 2006), the role of financial development has not been explored thoroughly.

The present article offers a contribution to literature through introducing a novel financial depth indicator using principalcomponent analysis (PCA) to combine three conventional measures of financial development. This composite indicator is then

x: +90 392 365 1017.dun).he Chinese economy.

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190 A. Jalil et al. / International Review of Economics and Finance 19 (2010) 189–195

used in the autoregressive distributed lag (ARDL) bounds testing approach to cointegration to explore the finance-growth nexus inChina using annual time series data that spans from 1977 to 2006.

The rest of the article is structured as follows: Section 2 sets up the analytical framework, Section 3 defines the variables andexplains the methodology, Section 4 presents the empirical results, and Section 5 points out the conclusions and the policyimplications that emerge from the article.

2. Analytical framework

The empirical investigation carried out in the present article is based on the ‘AK’model introduced by Rebelo (1991) and thenused by Pagano (1993), where output growth depends on total factor productivity, the efficiency of financial intermediation, andthe saving rate:

where

and

Y = AKt ð1Þ

where, Y, A and Kt denote the output, total factor productivity and capital, respectively. A certain proportion of savings, the size of(1−λ) with o<λ<1, is the cost of financial intermediation per unit of savings, i.e. the spread between borrowing and lendingrates, transaction fees and so on, which are the resources absorbed in producing intermediation services. Only the fraction (λ) oftotal savings can be used to finance investment. The smaller the spreads, the more efficient is the financial system. Therefore, thesaving–investment relationship can be written as It=λSt. The economic growth rate gy can be expressed as:

gy = gA + gK ð2Þ

gK =Kt + 1−Kt

Kt=

It + ð1−δÞKt−Kt

Kt=

λStKt

−δ = Aλst−δ

st =St.

Yt= St

.AKt

:

Eq. (2) expresses that economic growth depends on the total factor productivity (A), the efficiency of financial intermediation(λ), and the rate of savings (s). When the rate of depreciation δ is assumed to be constant, economic growth depends on financialdevelopment. In the long-run, gK approaches a permanently positive and exogenous value which is determined by the differencebetween Aλst and δ. For a positive growth rate in the long-run, the Aλst>δmust hold. The level of (λ) is determined by the level ofdevelopment of the financial service sector.

Since this model represents a closed economy, it does not take into account the capital flows. To overcome this shortcoming,trade openness is included. As Beck (2002) explains, financial development results in higher level of exports and trade balance ofmanufactured goods, which in turn, imply higher economic development. Hence, trade openness is included to themodel as Chinais an open economy. Translating the theory into an empirical specification following Christopoulos and Tsionas (2004) and Khanet al. (2005), the following equation is obtained:

Yt = α0 + α1IFDt + α2Kt + α3Rt + α4TRt + ut ð3Þ

where Y denotes the natural log of real per capita GDP, IFD denotes a proxy for financial development, K denotes the natural log ofreal per capita capital, R denotes real deposit rate and TR denotes the total trade to GDP ratio.

3. Construction of variables and data

A novel feature of this article is to use a principal component that combines threemeasures of financial development. It followsCreane et al. (2003) who consider that a comprehensive index or a principal component better represents “what is broadly meantby financial development” (Creane et al., 2003). The article uses three measures: liquidity liabilities (LLY), the ratio of credit toprivate sector to nominal GDP (PRIVO), and the ratio of commercial bank assets to the sum of commercial bank and central bankassets (BTOT). The economic growth is proxied by the real per capita GDP, which is measured as a ratio of real GDP to totalpopulation. The real GDP is measured as nominal GDP divided by GDP deflator (2000=100). The time series data spans from 1977to 2006, is in annual frequency and is obtained from the World Bank's World Development Indicators (2007) and the IMF'sInternational Financial Statistics (2007). The selection of annual frequency is determined by data availability. Additionally, Hakkioand Rush (1991) proved that increasing the number of observation by using the quarterly and monthly data will not improve therobustness of the result in the cointegration analysis and the time frame used is of higher importance.

Different measures of financial development have been suggested in the literature. For example, Gelb (1989) and King andLevine (1993) use broad money (M2) ratio to nominal GDP. Theoretically, the increase in ratio means the increase in financialdepth. But in developing countries, M2 contains a large portion of currency. The implication of rising M2 ismonetization instead of

Table 1Correlation matrix.

PRIVO LLY BTOT Y K R TR

PRIVO 1.000LLY 0.956 1.000BTOT 0.650 0.726 1.000Y 0.934 0.985 0.651 1.000K 0.936 0.992 0.693 0.997 1.000R 0.034 0.041 0.234 −0.069 −0.033 1.000TR 0.866 0.929 0.593 0.957 0.960 −0.148 1.000

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financial depth (Demetriades & Hussein, 1996). Hence, liquid liabilities (LLY) is more relevant indicator of financial development(Rousseau & Wachtel, 2000; Rioja & Valev, 2004; and Levine et al. 2000). This indicator measures the overall size of the financialintermediary sector because it includes central bank, deposit money banks and other financial institutions. But, this is an indicatorof size and ignores the allocation of capital.

However, it is possible that credit to private sector remain stagnant even if the deposits are increasing. The government canincrease private saving by increasing the reserve requirement. The supply of credit to private sector is important for the qualityand quantity of investment (Demetriades & Hussein, 1996). So, the ratio of credit to private sector to nominal GDP (PRIVO) is oursecond variable of financial depth.

On the other hand, the ratio of commercial bank assets to the sum of commercial bank and central bank assets (BTOT) is a proxyfor the advantage of financial intermediaries in channeling savings to investment, monitoring firms influencing corporategovernance and undertaking risk management relative to the central bank (Huang 2005).

In addition to the measure of financial development and economic growth, this study uses several control variables associatedwith either economic growth or financial development. In this regard, real interest rate, capital stock and trade ratio are used. Thereal interest rate, R is the deposit rate minus the inflation rates, while the trade ratio TR is the total value of exports and imports asshare of nominal GDP. The capital series ‘K’ is constructed from the investment flows. The perpetual inventory method is usedfollowing Khan (2005) and using a 5% rate of geometric decay following Perkins (1988) and Wang and Yao (2003). The capitalseries is also converted into real terms (2000=100).

Table 1 shows the correlation coefficients among the pair of variables. The correlations between, Y and the level of BTOT, LLYand PRIVO are quite high. Hence, if all variables are used simultaneously in the model then there is a high possibility ofmulticollinearity, which may lead to incorrect inferences. In order to overcome this problem, the principal components of theselected financial development variables are estimated following Creane et al. (2003). Principal components analysis (PCA) is astatistical method used to transform a number of correlated variables into a smaller number of uncorrelated variables calledprincipal components, while retaining most of the original variability in the data (see Feridun & Sezgin, 2008). Table 2 reports theresults of the PCA.

As can be seen in the table, the eigenvalues indicate that the first principal component explains about 85% of the standardizedvariance. Therefore, only information related to the first principal component is presented. The factor scores suggest that theindividual contributions of PRIVO, LLY and BTOT to the standardized variance of the first principal component are 37.0, 38.0, and33.0%. We use these as the basis of weighting to construct a financial depth index, denoted as IFD.

Table 2Principal component analysis.

Principal component Eigenvalues % of variance Cumulative %

1 2.562 0.854 0.8542 0.400 0.133 0.9873 0.038 0.013 1.000

Variable Factor loadings Communalities Factor scores

PRIVO 0.592 0.898 0.370LLY 0.608 0.947 0.380BTOT 0.529 0.717 0.330

Table 3ADF unit root tests.

ADF k ADF k

Y −1.43 3 ΔY −4.87 2K 2.28 2 ΔK −3.76 1IFD 0.07 0 ΔIFD −3.68 0R −4.09 1TR −0.48 0 ΔTR −4.87 0

Table 4Granger causality tests.

Null hypothesis: F-statistic Prob.

Y does not Granger cause IFD 0.356 0.892IFD does not Granger cause Y 2.843 0.058

Table 5ARDL model: long run results.

Dependent variable: Y

Regressor Coefficient t-values

IFD 0.101 2.510K 2.496 34.861R 0.006 1.083TR 0.013 2.258Intercept 0.042 4.581

Diagnostic test statistics

Test-stats p-values

χ2sc(1) 0.019 0.851χ2ff(1) 2.432 0.213χ2nor(1) 1.461 0.397χ2het(1) 0.012 0.731

Notes: ARDL (1,1,2,2,0) selected on the basis of AIC.χ2sc(1), χ2ff(1), χ2nor(1) and χ2het(1) denote the test statistics for serial correlation, functional formnormality of errors and heteroskedasticity, respectively.

Table 6ARDL model ECM results.

Dependent variable: Y

Regressor Coefficients t-values

ΔIFD 0.002 1.112ΔK 14.446 64.460ΔK1 0.426 1.9316ΔR −0.001 −0.991ΔR1 NA NAΔTR 0.001 1.752ΔTR1 0.001 1.468Intercept 0.001 1.731ECM(−1) −0.138 −2.327

Diagnostic test statistics

R-squared 0.7881F (7, 21) 13.349DW 2.0053

ECM=Y−0.1011⁎IFD−2.4961⁎K−0.0026357⁎R−0.013586⁎TR −0.042705⁎Intercept.

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3.1. Methodology

The present article employs the autoregressive distributed lag model (ARDL), introduced by Pesaran et al. (2001) as it can beapplied irrespective of whether the underlying variables are I(0), I(1) or a combination of both (Pesaran & Pesaran, 1997). Besides,the ARDL model takes a sufficient number of lags to capture the data generating process in a general-to-specific modelingframework (Laurenceson & Chai, 2003). Also, the error correction model (ECM) can be derived from ARDL through a simple lineartransformation (Banerjee et al., 1993). ECM integrates short-run adjustments with long-run equilibrium without losing long-runinformation (Pesaran & Shin, 1999). Moreover, small sample properties of the ARDL approach are far superior to that of theJohansen and Juselius cointegration technique (Pesaran & Shin, 1999).

The ARDL approach to cointegration involves the estimation of the following model:

ΔYi = β0 + ∑p

i=1ψiΔYt−i + ∑

p

i=1ϕiΔIFDt−i + ∑

p

i=1ϖiΔKt−i + ∑

p

i=1γiΔRt−i + ∑

p

i=1ηiΔTRt−i

+ θ1Yt−1 + θ2FDt−1 + θ3Kt−1 + θ4Rt−1 + θ5TRt−1 + Ut

ð4Þ

where β0 is drift component, the variables are as explained before and Ut denotes the white noise.

,

Fig. 1. Plot of cummulative sum of recursive residuals for IFD.

193A. Jalil et al. / International Review of Economics and Finance 19 (2010) 189–195

The first step in the ARDL bounds test approach is to test for a long-run relationship among the variables using F-tests. The nullhypothesis in the equation is H0: θ1=θ2=θ3=θ4=θ5=0, which implies the non existence of long-run relationship. On the otherhand, the alternative hypothesis is H1:θ1≠0, θ2≠0, θ3≠0, θ4≠0, θ5≠0. The test which normalizes on Y is represented as: FY(Y /IFD,K,R,TR).

The calculated F-statistics value is comparedwith two sets of critical values estimated by Pesaran et al. (2001). One set assumesthat all variables are I(0) and other assumes they are I(1). If the calculated F-statistics exceeds the upper critical value, the nullhypothesis of no cointegration is rejected irrespective of whether the variable are I(0) or I(1). If it is below the lower critical value,the null hypothesis of no cointegration cannot be rejected. If it falls inside the critical value bands, the test is inconclusive.

In order to choose the optimal lag length for each variable, the ARDLmethod estimates (p+1)k number of regressions, where pis the maximum number of lags and k is the number of variables in the equation. The model is selected on the basis of theSchwartz-Bayesian Criteria (SBC) and Akaike's Information Criteria (AIC).

If a long-run relationship exists among the variables, the following error correction model is estimated:

2 The

ΔYi = β0 + ∑p

i=1ψiΔYt−i + ∑

p

i=1ϕiΔFDt−i + ∑

p

i=1ϖiΔKt−i + ∑

p

i=1γiΔRt−i + ∑

p

i=1ηiΔTRt−i

+ αECMt−1 + Ut :

ð5Þ

The error correction model result indicates the speed of adjustment back to long-run equilibrium after a short-run shock.

4. Empirical results

The first step is to investigate the time series properties of the variables in order to ensure that none of the variable is integratedof order 2 or above. ADF is applied to test the stationary hypothesis for all series under consideration. The results shown in Table 3suggest that none of the variables is integrated of order 2 or above.

Therefore, the presence of the long-run relationship can be investigated using the ARDL bounds testing procedure. HenceEq. (4) is estimated through the OLS procedure. Since the calculated F-statistic (7.254) is higher than the upper critical values,there is strong evidence of a long-run relationship among the underlying variables2. In order to learn the direction of casualty,Granger casualty tests are conducted. F-statistic and probability values are constructed under the null hypothesis of non causality.As can be seen in Table 4, there exists a unidirectional causality running from financial development to economic growth.

Next, Eq. (4) is estimated through ARDL methodology. The total number of regression estimated (2+1)5=243. The Akaikeinformation criterion (AIC) is used for the selection of order of ARDL as (1,1,2,2,0). The IFD, K and TR are, respectively, 0.1011,2.4961 and 0.0136 and statistically significant, which implies that a 1% increase in IFD, K and TR will lead to 0.1011, 2.4961 and0.0136% increases, respectively, in the real per capita GDP in the long-run. These results indicate that China's economic growth canbe attributed to financial development, as well as to an increase in capital and international trade. As evident from Table 5, theestimated model passes the diagnostic tests of serial correlation, functional form specification, normality and heteroskedasticity.

critical values are I(0) =3.76 and I(1)=5.06 at the 1% level of significance (see Pesaran et al (2001)).

Fig. 2. Plot of cummulative sum of squares of recursive residuals for IFD.

194 A. Jalil et al. / International Review of Economics and Finance 19 (2010) 189–195

Next, the short-run dynamics are estimated. As can be seen in Table 6, ΔIFD is insignificant and TR is significant at 10% level ofsignificance. The coefficient ECMt−1 has the correct sign and suggests that nearly 13% of the disequilibria in GDP growth of theprevious quarter's shock adjust back to the long-run equilibrium in the current quarter. Also, R2 indicates that the estimatedmodelhas a reasonably good fit.

In order to check the stability of the long-run coefficients, the cumulative sum (CUSUM) and the cumulative sum of squares(CUSUMSQ) tests suggested by Brown et al. (1975) are used. The CUSUM and CUSUMSQ statistics are updated recursively andplotted against the break points. If the plots of CUSUM and CUSUMSQ statistics stay within the critical bounds of 5% level ofsignificance, the null hypothesis of all coefficients in the given regression are stable and cannot be rejected. As can be seen in Figs. 1and 2, the estimated CUSUM and CUSUMSQ stay within the critical bonds indicating that all coefficients in the ARDL errorcorrection model are stable.

5. Conclusions

This article has re-examined the finance-growth nexus in China using principal components analysis and ARDL boundstesting approach to cointegration. The results suggest that financial development indeed fosters economic growth in China. Asthe results of this article have shown, the growth of the Chinese economy is, among other factors, driven by financialdevelopment. Therefore the Chinese policy-makers are advised to take necessary actions to ascertain financial development.China has been phenomenally successful in transitioning from a centrally-planned economy into a more market-oriented,trillion dollar economy. However, the economy still faces some challenges such as rising urban unemployment, the inefficientstate sector, large-scale rural–urban migration in reaction to a growing urban–rural income inequality, and significant amountsof non-performing loans held by state-owned banks, to name a few. In the last few decades, the culmination of policy loans, softbudget constraints for state-owned enterprises and the decentralisation of local state-owned commercial banks in China haveresulted in a considerable stock of non-performing loans estimated at 2 trillion RMB, which constitutes roughly 20% of the totalnational income. In this respect, the non-performing loans problem constitutes one of the most significant challengesconfronting China at present. Indeed, the Chinese policy-makers have begun to tackle the non-performing loans problem. Theresults obtained in this article suggest that the policy-makers should continue following this path. The results also emphasizethe importance of the continuation of reforms of banking and financial services and the anticipated move toward someprivatization in both state-owned enterprises and banks in China.

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