Finance-growth nexus in China revisited - Eastern Mediterranean ... [PDF]

Oct 13, 2009 - 2000; Pagano & Volpin, 2001; Levine et al., 2000; Wang, 2000; Hung, 2003; Christopoulos & Tsionas

2 downloads 18 Views 454KB Size

Recommend Stories


China in the Mediterranean
The best time to plant a tree was 20 years ago. The second best time is now. Chinese Proverb

Organic aerosols in Eastern Mediterranean
Pretending to not be afraid is as good as actually not being afraid. David Letterman

Eastern Mediterranean University
Pretending to not be afraid is as good as actually not being afraid. David Letterman

eastern mediterranean natural gas
So many books, so little time. Frank Zappa

Eastern Mediterranean, Turkey
If you feel beautiful, then you are. Even if you don't, you still are. Terri Guillemets

Shifting Eastern Mediterranean Geometry
Don’t grieve. Anything you lose comes round in another form. Rumi

Navigating the Eastern Mediterranean
Your big opportunity may be right where you are now. Napoleon Hill

Energy Discoveries in the Eastern Mediterranean
You can never cross the ocean unless you have the courage to lose sight of the shore. Andrè Gide

Adolescent health in the Eastern Mediterranean Region
And you? When will you begin that long journey into yourself? Rumi

(Annelida, Onuphidae) in the eastern Mediterranean
The greatest of richness is the richness of the soul. Prophet Muhammad (Peace be upon him)

Idea Transcript


This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright

Author's personal copy International Review of Economics and Finance 19 (2010) 189–195

Contents lists available at ScienceDirect

International Review of Economics and Finance j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / i r e f

Finance-growth nexus in China revisited: New evidence from principal components and ARDL bounds tests Abdul Jalil a, Mete Feridun b,⁎, Ying Ma a a b

School of Management, Wuhan University, Wuhan, Hubei, PR China 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

Available online 13 October 2009 JEL classification: O18 O16 C31

a b s t r a c t This article re-examines the finance-growth nexus in China using principal components analysis and ARDL bounds testing approach to cointegration. The results suggest that principal components have an effective role in examining the links between growth and financial development and, that financial development fosters economic growth. © 2009 Elsevier Inc. All rights reserved.

Keywords: Financial development Economic growth ARDL PCA

1. Introduction There exists a broad literature on the finance-growth nexus. Most studies have documented a positive relationship between financial 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 economic growth (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 relationship between 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's transition from a centrally-planned economy into a more market-oriented one has been phenomenally successful1. Although there exists a plethora of theoretical and empirical studies investigating the sources of economic growth in China (see, for example, 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 principal component analysis (PCA) to combine three conventional measures of financial development. This composite indicator is then

⁎ Corresponding author. Tel.: +90 392 630 2127; fax: +90 392 365 1017. E-mail address: [email protected] (M. Feridun). 1 See Chan et al. (2007) for an in-depth review of the Chinese economy. 1059-0560/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2009.10.005

Author's personal copy 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 in China 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 and explains the methodology, Section 4 presents the empirical results, and Section 5 points out the conclusions and the policy implications 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 then used by Pagano (1993), where output growth depends on total factor productivity, the efficiency of financial intermediation, and the saving rate: ð1Þ

Y = AKt

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 lending rates, transaction fees and so on, which are the resources absorbed in producing intermediation services. Only the fraction (λ) of total savings can be used to finance investment. The smaller the spreads, the more efficient is the financial system. Therefore, the saving–investment relationship can be written as It = λSt. The economic growth rate gy can be expressed as: ð2Þ

gy = gA + gK where gK =

Kt

+ 1 −Kt

=

Kt

It + ð1−δÞKt −Kt λSt = −δ = Aλst −δ Kt Kt

and 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 financial development. In the long-run, gK approaches a permanently positive and exogenous value which is determined by the difference between Aλst and δ. For a positive growth rate in the long-run, the Aλst > δ must hold. The level of (λ) is determined by the level of development 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 of manufactured goods, which in turn, imply higher economic development. Hence, trade openness is included to the model as China is an open economy. Translating the theory into an empirical specification following Christopoulos and Tsionas (2004) and Khan et al. (2005), the following equation is obtained: Yt = α0 + α1 IFDt + α2 Kt + α3 Rt + α4 TRt + 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 of real 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 three measures of financial development. It follows Creane et al. (2003) who consider that a comprehensive index or a principal component better represents “what is broadly meant by financial development” (Creane et al., 2003). The article uses three measures: liquidity liabilities (LLY), the ratio of credit to private sector to nominal GDP (PRIVO), and the ratio of commercial bank assets to the sum of commercial bank and central bank assets (BTOT). The economic growth is proxied by the real per capita GDP, which is measured as a ratio of real GDP to total population. The real GDP is measured as nominal GDP divided by GDP deflator (2000 = 100). The time series data spans from 1977 to 2006, is in annual frequency and is obtained from the World Bank's World Development Indicators (2007) and the IMF's International Financial Statistics (2007). The selection of annual frequency is determined by data availability. Additionally, Hakkio and Rush (1991) proved that increasing the number of observation by using the quarterly and monthly data will not improve the robustness 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 and Levine (1993) use broad money (M2) ratio to nominal GDP. Theoretically, the increase in ratio means the increase in financial depth. But in developing countries, M2 contains a large portion of currency. The implication of rising M2 is monetization instead of

Author's personal copy A. Jalil et al. / International Review of Economics and Finance 19 (2010) 189–195

191

Table 1 Correlation matrix.

PRIVO LLY BTOT Y K R TR

PRIVO

LLY

BTOT

Y

K

R

TR

1.000 0.956 0.650 0.934 0.936 0.034 0.866

1.000 0.726 0.985 0.992 0.041 0.929

1.000 0.651 0.693 0.234 0.593

1.000 0.997 − 0.069 0.957

1.000 − 0.033 0.960

1.000 − 0.148

1.000

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 financial intermediary sector because it includes central bank, deposit money banks and other financial institutions. But, this is an indicator of 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 can increase private saving by increasing the reserve requirement. The supply of credit to private sector is important for the quality and quantity of investment (Demetriades & Hussein, 1996). So, the ratio of credit to private sector to nominal GDP (PRIVO) is our second 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 proxy for the advantage of financial intermediaries in channeling savings to investment, monitoring firms influencing corporate governance 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 associated with either economic growth or financial development. In this regard, real interest rate, capital stock and trade ratio are used. The real interest rate, R is the deposit rate minus the inflation rates, while the trade ratio TR is the total value of exports and imports as share of nominal GDP. The capital series ‘K’ is constructed from the investment flows. The perpetual inventory method is used following Khan (2005) and using a 5% rate of geometric decay following Perkins (1988) and Wang and Yao (2003). The capital series 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, LLY and PRIVO are quite high. Hence, if all variables are used simultaneously in the model then there is a high possibility of multicollinearity, which may lead to incorrect inferences. In order to overcome this problem, the principal components of the selected financial development variables are estimated following Creane et al. (2003). Principal components analysis (PCA) is a statistical method used to transform a number of correlated variables into a smaller number of uncorrelated variables called principal components, while retaining most of the original variability in the data (see Feridun & Sezgin, 2008). Table 2 reports the results of the PCA. As can be seen in the table, the eigenvalues indicate that the first principal component explains about 85% of the standardized variance. Therefore, only information related to the first principal component is presented. The factor scores suggest that the individual contributions of PRIVO, LLY and BTOT to the standardized variance of the first principal component are 37.0, 38.0, and 33.0%. We use these as the basis of weighting to construct a financial depth index, denoted as IFD.

Table 2 Principal component analysis. Principal component

Eigenvalues

% of variance

Cumulative %

1 2 3

2.562 0.400 0.038

0.854 0.133 0.013

0.854 0.987 1.000

Variable

Factor loadings

Communalities

Factor scores

PRIVO LLY BTOT

0.592 0.608 0.529

0.898 0.947 0.717

0.370 0.380 0.330

Table 3 ADF unit root tests.

Y K IFD R TR

ADF

k

− 1.43 2.28 0.07 − 4.09 − 0.48

3 2 0 1 0

ADF

k

ΔY ΔK ΔIFD

− 4.87 − 3.76 − 3.68

2 1 0

ΔTR

− 4.87

0

Author's personal copy 192

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

Table 4 Granger causality tests. Null hypothesis:

F-statistic

Prob.

Y does not Granger cause IFD IFD does not Granger cause Y

0.356 2.843

0.892 0.058

3.1. Methodology The present article employs the autoregressive distributed lag model (ARDL), introduced by Pesaran et al. (2001) as it can be applied 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 modeling framework (Laurenceson & Chai, 2003). Also, the error correction model (ECM) can be derived from ARDL through a simple linear transformation (Banerjee et al., 1993). ECM integrates short-run adjustments with long-run equilibrium without losing long-run information (Pesaran & Shin, 1999). Moreover, small sample properties of the ARDL approach are far superior to that of the Johansen and Juselius cointegration technique (Pesaran & Shin, 1999). The ARDL approach to cointegration involves the estimation of the following model: p

p

p

p

p

i=1

i=1

i=1

i=1

i=1

ΔYi = β0 + ∑ ψi ΔYt−i + ∑ ϕi ΔIFDt−i + ∑ ϖi ΔKt−i + ∑ γi ΔRt−i + ∑ ηi ΔTRt−i

ð4Þ

+ θ1 Yt−1 + θ2 FDt−1 + θ3 Kt−1 + θ4 Rt−1 + θ5 TRt−1 + Ut where β0 is drift component, the variables are as explained before and Ut denotes the white noise. Table 5 ARDL model: long run results. Dependent variable: Y Regressor

Coefficient

t-values

IFD K R TR Intercept

0.101 2.496 0.006 0.013 0.042

2.510 34.861 1.083 2.258 4.581

Test-stats

p-values

Diagnostic test statistics

2

χ sc(1) χ2ff(1) χ2nor(1) χ2het(1)

0.019 2.432 1.461 0.012

0.851 0.213 0.397 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 form, normality of errors and heteroskedasticity, respectively. Table 6 ARDL model ECM results. Dependent variable: Y Regressor

Coefficients

t-values

ΔIFD ΔK ΔK1 ΔR ΔR1 ΔTR ΔTR1 Intercept ECM(− 1)

0.002 14.446 0.426 − 0.001 NA 0.001 0.001 0.001 − 0.138

1.112 64.460 1.9316 − 0.991 NA 1.752 1.468 1.731 − 2.327

Diagnostic test statistics R-squared F (7, 21) DW

0.7881 13.349 2.0053

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

Author's personal copy A. Jalil et al. / International Review of Economics and Finance 19 (2010) 189–195

193

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

The first step in the ARDL bounds test approach is to test for a long-run relationship among the variables using F-tests. The null hypothesis in the equation is H0: θ1 = θ2 = θ3 = θ4 = θ5 = 0, which implies the non existence of long-run relationship. On the other hand, 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 compared with two sets of critical values estimated by Pesaran et al. (2001). One set assumes that all variables are I(0) and other assumes they are I(1). If the calculated F-statistics exceeds the upper critical value, the null hypothesis 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 ARDL method estimates (p + 1)k number of regressions, where p is the maximum number of lags and k is the number of variables in the equation. The model is selected on the basis of the Schwartz-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: p

p

p

p

p

i=1

i=1

i=1

i=1

i=1

ΔYi = β0 + ∑ ψi ΔYt−i + ∑ ϕi ΔFDt−i + ∑ ϖi ΔKt−i + ∑ γi ΔRt−i + ∑ ηi ΔTRt−i

ð5Þ

+ αECMt−1 + Ut : 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 integrated of order 2 or above. ADF is applied to test the stationary hypothesis for all series under consideration. The results shown in Table 3 suggest 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. Hence Eq. (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 Akaike information 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 and 0.0136% increases, respectively, in the real per capita GDP in the long-run. These results indicate that China's economic growth can be attributed to financial development, as well as to an increase in capital and international trade. As evident from Table 5, the estimated model passes the diagnostic tests of serial correlation, functional form specification, normality and heteroskedasticity. 2

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

Author's personal copy 194

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

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

Next, the short-run dynamics are estimated. As can be seen in Table 6, ΔIFD is insignificant and TR is significant at 10% level of significance. The coefficient ECMt−1 has the correct sign and suggests that nearly 13% of the disequilibria in GDP growth of the previous quarter's shock adjust back to the long-run equilibrium in the current quarter. Also, R2 indicates that the estimated model has 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 and plotted against the break points. If the plots of CUSUM and CUSUMSQ statistics stay within the critical bounds of 5% level of significance, the null hypothesis of all coefficients in the given regression are stable and cannot be rejected. As can be seen in Figs. 1 and 2, the estimated CUSUM and CUSUMSQ stay within the critical bonds indicating that all coefficients in the ARDL error correction model are stable. 5. Conclusions This article has re-examined the finance-growth nexus in China using principal components analysis and ARDL bounds testing approach to cointegration. The results suggest that financial development indeed fosters economic growth in China. As the results of this article have shown, the growth of the Chinese economy is, among other factors, driven by financial development. 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 inefficient state sector, large-scale rural–urban migration in reaction to a growing urban–rural income inequality, and significant amounts of non-performing loans held by state-owned banks, to name a few. In the last few decades, the culmination of policy loans, soft budget constraints for state-owned enterprises and the decentralisation of local state-owned commercial banks in China have resulted in a considerable stock of non-performing loans estimated at 2 trillion RMB, which constitutes roughly 20% of the total national income. In this respect, the non-performing loans problem constitutes one of the most significant challenges confronting China at present. Indeed, the Chinese policy-makers have begun to tackle the non-performing loans problem. The results obtained in this article suggest that the policy-makers should continue following this path. The results also emphasize the importance of the continuation of reforms of banking and financial services and the anticipated move toward some privatization in both state-owned enterprises and banks in China. References Banerjee, A., Dolado, J., Galbraith, J. W., & Hendry, D. F. (1993). Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data Oxford: Oxford University Press. Beck, T. (2002). Financial development and international trade: Is there a link? Journal of International Economic, 57, 107−131. Borensztein, E., & Ostry, D. J. (1996). Accounting for China's growth performance. American Economic Review, 86, 224−228.

Author's personal copy A. Jalil et al. / International Review of Economics and Finance 19 (2010) 189–195

195

Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relations over time. Journal of the Royal Statistical Society, 37, 149−163. Chan, K. C., Fung, Hung-Gay, & Thapa, Samanta (2007). China financial research: A review and synthesis. International Review of Economics & Finance, 16(3), 416−428. Chow, G. C. (1993). Capital formation and economic growth in china. Quarterly Journal of Economics, 108, 809−842. Chow, G. C., & Li, K. W. (2002). China's economic growth: 1952–2010. Economic Development and Cultural Change, 51, 247−256. Christopoulos, D. K., & Tsionas, E. G. (2004). Financial development and economic growth: Evidence from panel unit root and cointegration tests. Journal of Development Economics, 73, 55−74. Creane, S., Goya, l . R., Mobarak, M., & Sab, R. (2003). Financial development and economic growth in the Middle East and North Africa Finance and Development. 4 No.1. A quarterly magazine of the IMF. Demetriades, P. O., & Hussein, K. A. (1996). Does financial development cause economic growth? Time series evidence from 16 countries. Journal of Development Economics, 51, 387−411. Easterly, W. (1993). How much do distortions affect growth? Journal of Monetary Economics, 32, 187−212. Ergungor, O. E. (2008). Financial system structure and economic growth: Structure matters. International Review of Economics & Finance, 17(2), 292−305. Feridun, M., & Sezgin, S. (2008). Regional underdevelopment and terrorism: The case of south eastern Turkey. Defence and Peace Economics, 19(3), 225−233. Friedman, M., & Schwartz, A. J. (1963). A Monetary History of the United States. Princeton: Princeton University Press. Fry, M. J. (1997). In favour of financial liberalisation. Economic Journal, 107, 754−770. Gelb, A. H. (1989). Financial policies, growth, and efficiency, Policy Planning, and Research Working Papers, No. 202. World Bank. Goldsmith, R. W. (1969). Financial Structure and Development. New Haven: Yale University Press. Hao, C. (2006). Development of financial intermediation and economic growth: The Chinese experience. China Economic Review, 17(4), 347−362. Hakkio, C. S., & Rush, M. (1991). Cointegration: How short is the long-run? Journal of International Money and Finance December, 571−581. Huang, Y. (2005). Will political liberalization bring about financial development? Bristol Economics Discussion Paper no. 05/578. Hicks, J. (1969). A Theory of Economic History. Oxford: Claredon Press. Hung, F. (2003). Inflation, financial development, and economic growth. International Review of Economics & Finance, 12(Issue 1), 45−67. Khan, S. U. (2005). Macro determinants of total factor productivity in Pakistan. SBP Research Bulletin, 2(2), 383−401. Khan, S. M., & Senhadji, A. S. (2000). Financial Development and Economic Growth: An Overview. Washington, D. C: International Monetary Fund IMF Working Paper 00/209. Khan, A., Qayyum, A., & Sheikh, S. (2005). Financial development and economics growth: The case of Pakistan. The Pakistan Development Review, 44, 819−837. King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. Quarterly Journal of Economics, 108, 717−737. Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 45, 1−28. Laurenceson, J., & Chai, C. H. J. (2003). Financial Reform and Economic Development in China. Cheltenham, UK: Edward Elgar. Liang, Qi, & Teng, J. Z. (2006). Financial development an economic growth: Evidence from China. China Economic Review, 17, 395−411. Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46, 31−77. Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22, 3−42. McKinnon, R. I. (1973). Money and Capital in Economic Development Washington D.C: Brookings Institution. Pagano, M. (1993). Financial markets and growth: An overview. European Economic Review, 37, 613−622. Pagano, M., & Volpin, P. (2001). The political economy of finance. Oxford Review of Economic Policy, 17, 502−519. Perkins, D. H. (1988). Reforming China's economic system. Journal of Economic Literature, 26, 601−645. Pesaran, M. H., & Pesaran, B. (1997). Working with Microfit 4.0: Interactive Econometric Analysis Oxford: Oxford University Press. Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and Economic Theory in 20th Century: The Ragnar Frisch Centennial Symposium, Chapter 11. Cambridge University Press. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289−326. Rebelo, S. (1991). Long-run policy analysis and long-run growth. Journal of Political Economy, 99, 500−521. Rioja, F., & Valev, N. (2004). Does one size fit all? A reexamination of the finance and growth relationship. Journal of Development Economics, 74, 429−447. Robinson, J. (1952). The Rate of Interest and Other Essays. London: Macmillan. Roubini, N., & Sala-i-Martin, X. (1992). Financial repression and economic growth. Journal of Development Economics, 39, 5−30. Rousseau, P. L., & Wachtel, P. (2000). Equity markets and growth: Cross-country evidence on timing and outcomes, 1980–1995. Journal of Banking & Finance, 24 (12), 1933−1957. Rousseau, P. L., & Vuthipadadorn, D. (2005). Finance, investment, and growth: Time series evidence from 10 Asian economies. Journal of Macroeconomics, 27, 87−106. Schumpeter, J. A. (1911). The Theory of Economic Development Cambridge: MA7 Harvard University. Press. Shan, Z. J., Morris, A. G., & Sun, F. (2001). Financial development and economic growth: An egg-and-chicken problem. Review of International Economics, 9(3), 443−454. Shaw, E. S. (1973). Financial Deepening in Economic Development. New York: Oxford University Press. Wang, E. C. (2000). A dynamic two-sector model for analyzing the interrelation between financial development and industrial growth. International Review of Economics and Finance, 9, 223−241. Wang, Y., & Yao, Y. (2003). Sources of China's economic growth 1952–1999: Incorporating human capital accumulation. China Economic Review, 14, 32−52. Wu, Y. R. (2000). Is China's economic growth sustainable? A productivity analysis. China Economic Review, 11, 278−296. Yu, Q. (1998). Capital investment, international trade and economic growth in China: Evidence in the 1980–90s. China Economic Review, 9, 73−84.

Smile Life

When life gives you a hundred reasons to cry, show life that you have a thousand reasons to smile

Get in touch

© Copyright 2015 - 2024 PDFFOX.COM - All rights reserved.