Development of financial intermediation and econom

economic growth:The Chinese experience

Chen HAO ⁎

CERDI,Universitéd'Auvergne,65boulevard François-Mitterrand,63000Clermont-Ferrand,France

Received 1September 2004;received in revised form 9January 2006;accepted 9January 2006

Abstract

Using Chinese provincial data from 1985to 1999and applying recent GMM techniques developed for dynamic panels,this paper examines how the development of financial intermediation influences China's economic growth during the post-1978reform period.Our econometric results show that China's financial intermediation development contributes to its rapid economic growth through two channels:first,the substitution of loans for state budget appropriation and second,the mobilization of household savings.Loan expansion,however,does not contribute to growth since loan distribution by financial intermediaries is inefficient.Deep financial sector reform aimed at correcting this inefficiency is desirable,and is expected to sustain China's economic development in the future.

©2006Elsevier Inc.All rights reserved.

JEL classification:N15;O16;053

Keywords:Financial intermediation;Financial development;Economic growth;China

1.Introduction

The relationship between financial intermediation and economic growth has attracted a lot of attention among economists for a long time,particularly since the emergence of the new theories of endogenous economic growth.Although different economists attach different degrees of importance to financial intermediation,its role in economic growth can be theoretically postulated and has been supported by more and more empirical evidence.

Theoretically,financial intermediation,by reducing information and transaction costs,can affect economic growth through two channels:(i)productivity;and (ii)capital formation.

With China Economic Review 17(2006)347–

362

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doi:10.1016/j.chieco.2006.01.001

regard to the first channel,it is generally argued that financial intermediaries can promote efficient capital allocation by facilitating risk management,identifying promising projects,monitoring management,and facilitating the exchange of goods and services,which in turn leads to an improvement in total factor productivity(Levine,1997).For example,Greenwood and Jovanovic (1990)show that financial intermediation provides a vehicle for diversifying and sharing risks, inducing a shift in capital allocation towards risky but“high expected return”projects.This shift then spurs productivity improvement and economic growth.Bencivenga and Smith(1991)argue that financial intermediaries,by pooling the idiosyncratic liquidity risks,channel households' financial savings into illiquid but high-return projects and avoid the premature liquidation of profitable investments,which favors the efficient use of capital and promotes economic growth.

The impact of financial intermediation on growth through the second channel–capital formation–is ambiguous.Financial intermediation may raise or reduce the savings rate.For example,the development of financial intermediation can raise the returns to savings,which has an ambiguous effect on savings due to the well-known income and substitution effects.However, McKinnon(1973)states that,if all economic units are restricted to self-finance,they need to accumulate funds in preparation for undertaking lumpy investments.Money and physical capital are,therefore,complements rather than substitutes.Thus,financial repression,by imposing interest rate ceilings to keep interest rates at artificially low(even negative)levels,harms domestic savings,capital formation and economic growth,while financial liberalization,which causes interest rates to rise towards market-clearing levels and makes bank deposits more attractive,will raise the demand for money and lead to a higher level of investment and economic growth.

Most of the empirical research based on cross-country data suggests a positive relationship between financial intermediation and economic growth.King and Levine(1993)identify a positive correlation between the level of a country's financial intermediation and the growth rate of its real per capita GDP.However,the relevance of the finding is compromised by the problematic issue of causality and the potential bias arising from the joint determination of financial development and growth.Levine(1998,1999)improve upon King and Levine(1993), by using legal factors as instrumental variables for financial intermediation indicators to control for simultaneity bias.They find that the exogenous component of banking development is positively correlated with per capita income growth,productivity improvement and capital accumulation.Furthermore,Levine,Loayza,and Beck(2000)and Beck,Levine,and Loayza (2000)apply recent GMM techniques developed for dynamic panels,and provide more evidence that the development of financial intermediation has a strong and causal effect on economic growth.

Since the beginning of economic reform in1978,China's performance in terms of economic growth and financial sector expansion has been impressive.Over the period1978–2001,the Chinese economy saw an annual growth rate of9.4%in real terms,while loans outstanding relative to GDP increased from51.1%to117.1%(China Statistical Yearbook,various years).1It appears that fast economic growth and development of financial intermediation go hand in hand. Is this phenomenon only coincidental,or does it confirm the conclusion of numerous theoretical and empirical research papers that financial development plays an important role in fostering economic growth?Unfortunately,although a lot of studies have been conducted searching for explanations of China's economic miracle,few have considered financial development as a potential determinant of such miraculous growth.Furthermore,most case studies of the finance-1Henceforth,unless otherwise indicated,all data cited are from China Statistical Yearbook(State Statistical Bureau, various years).

growth nexus on China use traditional “loan-to-GDP ratio ”-type indicators to measure the development of financial intermediation,and fail to detect a positive relationship between finance and growth.2This paper shows that for the Chinese economy the ratio of loans to GDP reflects only one aspect of the development of financial intermediation,and may be the least significant channel through which the development of financial intermediation affects economic growth.There exist two important channels –the substitution of loans for state budget appropriation and the mobilization of household savings –through which the development of financial intermediation spurs China's economic growth.This paper uses recent Generalized Method of Moment (GMM)estimators in the empirical analysis,and so prevents potential biases induced by simultaneity and unobserved individual-specific effects from compromising the relevance of our findings.

The remainder of the paper is organized as follows.Section 2outlines China's development in financial intermediation during the post-1978period.It presents three aspects of this development and analyzes their respective impacts on economic growth.Section 3introduces the economic methodology and our growth regression model,presents the indicators of the development of financial intermediation,and shows the main empirical results.Section 4concludes and draws out some policy implications.

2.Financial intermediation in China

2.1.The development of financial intermediation in China

Before the reform,China's financial system was characterized by an all-inclusive mono-bank system.The People's Bank of China,the only financial institution,performed the functions of both a central bank and commercial banks.However,under the control of the central government,it played the role of the economy's accounting and settlement center,rather than that of a financial intermediary.Its role was extremely limited on resource allocation.It was only allowed to distribute working capital loans to enterprises,while most fixed asset investments were financed by the state budget.

Since the onset of economic reform in 1978,changes have taken hold in China's economy.These changes –particularly the evolution of the national income distribution between government and households –have fostered financial sector development.During the pre-reform period,the government –making use of price policy,state sector labor remuneration policy and the profit transfers regime –controlled an important part of national income,while household income did not exceed the subsistence level.The savings capacity of households was so limited that their share in bank deposits was negligible.Between 1952and 1977,government revenues never fell below 20%of GDP,peaking in 1960at 39.3%of GDP.A major portion of this revenue came from state-owned enterprises (SOEs)profit transfers,averaging 50.5%for this period.For the same period,households'share in total bank deposits never exceeded 17%,with a low point of

7.7%in 1962.In 1977,households'total financial savings amounted to only 5.7%of GDP (China Financial Statistics:1952–1991).With so little savings at that time,China's economy didn't need a sophisticated financial system to channel the households'surplus income into productive investments.

The economic reform –begun in 1978–has resulted in a material change in the national income distribution through the liberation of prices,the rapid development of non-state 2See Aziz and Duenwald (2002),Boyreau-Debray (2003),Shan,Morris,and Sun (2001),and Shan and Morris (2002).349

C.Hao./China Economic Review 17(2006)347–362

enterprises and the granting of greater autonomy to SOEs(Zhang,1999).First,as a result of the liberation of prices,agricultural products'prices increased sharply,raising rural households' earnings,while also raising the input costs of the SOEs-dominated industry,consequently eroded its profit base.Second,the emergence and rapid expansion of the non-state sector intensified competition on the product and factor markets,which broke the SOEs'monopoly,further reduced their profit margins and raised the labor remuneration.Finally,SOEs,having been conferred more and more autonomy,distributed a greater proportion of their revenue to their employees.As a result,government revenue declined from31.2%of GDP in1978to17.1%GDP in2001as its main source,the flow of SOEs profit transfers,gradually diminished.On the other hand,rural and urban households saw their share in national income increasing continuously.

This evolution of the national income distribution,on the one hand,limits the government's capacity to finance capital investments through the state budget,3on the other hand,it reinforces the households'savings potential.In2001,households'financial savings added up to7376.2 billion yuan,or76.9%of GDP.There is more and more demand for financial intermediaries to engage in the savings–investment process,channeling households'savings into productive investments in the enterprise sector.Financial intermediation is developing rapidly and plays a critical role in resource allocation.

A large number of financial institutions have been established,and the pre-reform mono-bank system has been transformed into a more sophisticated and diversified financial system.Financial intermediaries,especially four state-owned banks,dominate this new system,while the financial market is still at an early stage of development and the scale of direct finance is limited.In2001, the total loans of financial institutions reached11,231.5billion yuan,accounting for117.1%of GDP,while stock market capitalization reached only15.1%of GDP despite impressive growth since1994.4The bond market is essentially reserved for the central government to raise funds. The corporate sector's access to this market is extremely limited.In2001,the total accumulated value of corporate bonds amounted to only100.9billion yuan,accounting for4.0%of the value of total outstanding bonds and1.1%of GDP.Therefore,corporations regard loans as their primary source of external funding;direct finance through bond and stock markets plays only a marginal role.

Financial deepening is impressive.Real monetary balances expand at a rate faster than the real economy.Financial depth measured by the ratio of M2to GDP has increased from24.6%in1978 to194.7%in2002,which is among the highest in the world(International Monetary Fund,2003). This striking financial deepening is mainly due to two factors:the monetization of the economy and especially the expansion of households'financial savings.In2001,households'deposits accounted for77.9%of quasi-money and47.2%of M2.

Finally,domestic loans,taking the place of state budget appropriations,become the primary external source for financing capital investments.Fig.1shows the changes in financial sources of fixed asset investment.In1981,state budgetary appropriation,as the most important external source,financed28.1%of total fixed asset investment,while the share of domestic loans was only 12.7%.By2001,this situation had completely changed.State budget lost its importance,while loans became the primary means of external finance.Furthermore,some researchers argue that in recent years about half of the funds titled“self fundraising and others”actually comes from loans, 3Moreover,since1985,the central government has been forced to subsidize loss-making SOEs,which limits furthermore its ability to finance capital investments directly.

4Stock market capitalization is calculated as the ratio of market value of tradable stocks to GDP.The stocks owned by the state are non-tradable and excluded in the calculation.

because some types of loans which are not authorized by the regulations are actually extended to enterprises and enterprises put them in the category “self fundraising and others ”.If this statement is correct,loans actually financed more than one half of the total fixed asset investment in 2001.

In conclusion,the development of financial intermediation in China has three main aspects:(i)loan expansion;(ii)mobilization of households'savings;and (iii)substitution of loans for state budget appropriation as the primary source of external funding.

2.2.The impact on growth of the development of financial intermediation in China

In this section,we analyze the impact on growth of the three aspects of the development of financial intermediation in China,respectively.

China's bank-dominated financial sector is famous for its inefficiency and misallocation of capital.Its distribution of loans,both between the state and the non-state sector and among provinces,is far from rational based on purely economic considerations.

The state sector,while contributing less and less to economic growth,continues to absorb a disproportionately large share of bank loans.Under the government's pressure,most household savings are channeled into the inefficient state sector,even into loss-making SOEs by financial intermediaries,particularly the four state-owned banks.As a result of that,the non-state sector,being perceived as the more efficient and dynamic sector,has extremely limited access to debt finance.

Furthermore,the distribution of loans among provinces is also seriously affected by the central government's political considerations.The central government regards loans as a means for achieving regional equality.It uses the financial system,especially the state banking sector,to implicitly tax rich provinces and subsidize poor provinces:financial resources are channeled into poor provinces to support their credit expansion (Park &Sehrt,2001).Statistics confirm this argument.Fig.2plots the local state banking sector's loan/deposit ratio against local real per capita GDP.It appears that these two variables are negatively associated with a correlation coefficient of −0.17.This means that provinces with a lower level of economic development receive preferential credit treatment from the central government.Since the actual loan distribution is influenced by political concerns,it may differ from the optimum distribution according to economic fundamentals.Financial intermediaries may have not directed financial resources to their most efficient use.

Therefore,the efficiency of loans is questionable.Loan expansion may not be an effective channel through which the development of financial intermediation can promote economic

a. 1981

3,8%55,4%28,1%

State bugetary

appropriation

investment

b. 200169,6%

4,6%State budgetary appopriation Fig.1.Financial sources of fixed asset investment.Source:State Statistical Bureau (2002).

351

C.Hao./China Economic Review 17(2006)347–362

Loan/Deposit ratio

2.00

1.75

1.50

1.25

1.00

0.75

6.0 6.2 6.4 6.6 6.8

7.07.27.47.67.8

8.0 8.2

Log(real per capita GDP)

Fig.2.Local loan/deposit ratio and local real per capita GDP(1978–1999,average values).Source:Comprehensive Statistical Data and Materials on50Years of New China(1999)and the Research and Statistics Department of People's Bank of China(2000).

growth(see the findings of Aziz&Duenwald,2002;Boyreau-Debray,2003;Shan&Morris, 2002;Shan,Morris,&Sun,2001).

However,although loan distribution is not totally efficient according to purely commercial considerations,loans are generally considered a more efficient means than state budget appropriation for allocating financial resources.Unlike budget appropriation,loans call for payments of interest and principals.So they help to harden the enterprises'budget constraints,and may promote more efficient use of the provided capital.Moreover,bank employees have more incentives to allocate financial resources toward profitable projects than government bureaucrats, because bank employee compensation is linked to the quality of the lending portfolio.The main consideration of government bureaucrats,however is social stability(Cull&Xu,2003). Empirically,Liu and Li(2001)find a significant relationship between output growth and financial sources of fixed asset investments:compared to state appropriation,domestic loans are used more efficiently and have a larger impact on output growth.

The impact of the development of financial intermediation on economic growth may also run through the mobilization of households'financial savings.

McKinnon(1973)argues that,when all economic units are confined to self-finance,money balance has to be accumulated before costly and indivisible investment projects can be undertaken,and so money and physical capital are complements.Thus,a rise in interest rate levels stimulates domestic savings and facilitates the process of accumulation and self-finance,which will have a favorable impact on investments and lead to a higher growth rate.This argument seems to be applicable to China.Chinese non-state enterprises and households have actually extremely limited access to bank loans.They rely largely on their own accumulation of funds for financing their investments.So their demand for money and their investments may be positively associated.5

5In China,the savings deposits of individual enterprises in financial intermediaries are classified into the category “households'savings deposits”.

In addition,in China there exist effective informal financing channels that convert household savings into productive investments of non-state enterprises.So the expansion of household savings may increase the availability of financial resources for non-state enterprises and favor their investments.

If we review Fig.1,we can find that until now self-finance remains the principal source of investment for the Chinese corporate sector,even when the state sector,the main beneficiary of domestic banking system,is included.For the non-state sector,the category“self fundraising and others”accounts for76.4%of total financial resources of fixed asset investments.Even if half of the funds in this category actually come from bank loans,a considerable part of total funds might come from the accumulation of the non-state enterprises and the savings of their employees or other households.

Therefore,we conjecture that in China the non-state sector's investments are positively associated with household savings.Since the non-state sector constitutes China's growth engine and its investments are much more efficient than those of the state sector,the expansion of its investment may have a favorable impact on growth performance and productivity improvement.So the mobilization of household savings may constitute an effective channel through which the development of financial intermediation positively affects economic growth.

In summary,since the beginning of economic reform,China's financial intermediation took off and became an essential means of allocating financial resources.Financial intermediaries channel more and more household savings into productive investment.However,the distribution of loans is far from socially optimal.But loans are more efficient than state budgetary appropriation for allocating financial resources.The substitution of loans for state budgetary appropriation may improve the efficiency of capital use.So we argue that,in the specific context of the Chinese economy,the development of financial intermediation may favor economic growth through the mobilization of household savings and the substitution of loans for state budget appropriation,but possibly not through loan expansion.

3.Financial intermediation and growth:Evidence from China's provincial data

To empirically assess the impact of the development of financial intermediation on China's economic growth,this study uses provincial data for the following reasons:

1.Both the level of economic and financial development vary so significantly among provinces

that provincial data may contain interesting information that can be exploited.

2.Boyreau-Debray(2003)argues that the degree of inter-provincial capital mobility is low in

China,which makes the analysis of local financial intermediation's impact on local economic growth meaningful.

3.The time series of many financial variables at the national level are not long enough to allow

econometric analysis.The use of provincial data not only increases our choices of financial variables,but also expands the sample size significantly.

In the following section,we first introduce the first-differenced GMM estimator developed by Arellano and Bond(1991)and the GMM-System estimator suggested by Arellano and Bover (1995)and Blundell and Bond(1998).Then we construct a set of indicators to measure the development of financial intermediation,describe the data,present our model,and finally show the main results.3.1.Methodology

Let us consider the following growth equation:

D Y i;t¼aþðb−1ÞY i;t−1þg X i;tþg iþe i;tð1Þwhere Y is the logarithm of real per capita GDP,X is the set of explanatory variables,ηis the time

invariant individual-specific effect,εis the error term,with the subscripts i and t representing an

individual and time,respectively.

Clearly,Eq.(1)can be rewritten as:

Y i;t¼aþb Y i;t−1þg X i;tþg iþe i;tð2ÞIn estimating Eq.(2),we are confronted with two main econometric issues.The first one

results from the introduction of both a lagged dependent variable and an unobserved time

invariant individual-specific effects in the equation.Hsiao(1986)shows that omitting the

individual fixed effects in a dynamic panel data model will render the ordinary least squares

(OLS)levels estimates biased and inconsistent.For example,the likely positive correlation

between lagged dependent variable Y i,t−1and omitted fixed effectsηi can make the OLS-

coefficient estimate bˆbiased upwards.On the other hand,Nickell(1981)shows that the Within groups estimator,an alternative estimation technique which takes into account the fixed effects,

gives an estimate of bˆthat is biased downwards in short panels.Thus a consistent and unbiased estimate of bˆis expected to lie in between the OLS levels estimate and the Within groups estimate. The second issue results from the potential endogeneity of explanatory variables.With regard to Eq.(2),a growth regression,the right-hand-side variables are endogenous to some degree.So we must control for the endogeneity of the explanatory variables to avoid potential biases induced by simultaneity.

To address these problems,Arellano and Bond(1991)propose the first-differenced GMM

estimator.It consists in eliminating the time invariant individual-specific effectsηi by taking the

first difference of Eq.(2).Doing that we obtain

Y i;t−Y i;t−1¼bðY i;t−1−Y i;t−2ÞþgðX i;t−X i;t−1Þþðe i;t−e i;t−1Þð3ÞBy construction,(Y i,t−1−Y i,t−2)and(εi,t−εi,t−1)are correlated.OLS estimation of Eq.(3)will not give an unbiased and consistent estimate ofβ.Hence,we must find valid instruments for (Y i,t−1−Y i,t−2).

Assuming that(a)the error terms are not serially correlated,

E½e i;t e i;s ¼0for i¼1;⋯;N and s p t

and that(b)the initial conditions Y i,1are predetermined,

E½Y i1e i;t ¼0for i¼1;⋯;N and t z2

Arellano and Bond(1991)proposes the following moment restrictions

E½Y i;t−sðe i;t−e i;t−1Þ ¼0for t¼3;⋯;T and s z2

Since the values of Y i,t lagged two periods or more are correlated with(Y i,t−1−Y i,t−2),but not with(εi,t−εi,t−1),they are valid instruments for Eq.(3).With regard to the explanatory variables X i,t,there are three possible situations:

If the explanatory variables X i,t are strictly exogenous(i.e.,the explanatory variables are assumed to be uncorrelated with all past,present and future values of the error term),then all the past,present and future values of X i,t are valid instruments for Eq.(3).

If the explanatory variables X i,t are predetermined(i.e.,the explanatory variables are assumed to be correlated with past values of the error term,but uncorrelated with current and future values of the error term),then the values of X i,t lagged one period or more are valid instruments for Eq.(3).

If the explanatory variables X i,t are endogenous(i.e.,the explanatory variables are assumed to be correlated with past and present values of the error term,but uncorrelated with future values of the error term),then the values of X i,t lagged two periods or more are valid instruments for Eq.(3).

However,Blundell and Bond(1998)argue that when the lagged dependent and the explanatory variables are persistent over time,lagged values of these variables are only weak instruments for the first-differenced equation.And the first-differenced GMM estimator is expected to have a large finite sample bias and poor precision in simulation studies.Blundell and Bond(2000)confirm this statement by showing that in the case of weak instruments,the first-differenced GMM estimator will be biased towards the Within groups estimator.To reduce the potential biases and imprecision,Arellano and Bover(1995)and Blundell and Bond(1998) suggest estimating a system that combines the set of equations in first-differences(Eq.(3))with the additional set of equations in levels(Eq.(2)).For the regression in differences,the instruments are the same as above.For the regression in levels,the instruments are the suitably lagged differences of corresponding variables.Assuming that(a)the differences of the explanatory variables are uncorrelated with the individual-specific effects,

E½D X i;t g i ¼0for i¼1;⋯;N and t¼2;N;T

and(b)ΔY i2are uncorrelated with the individual-specific effects,

E½D Y i2g i ¼0for i¼1;⋯;N;

then,if the X i,t are strictly exogenous or predetermined,ΔY i,t−1andΔX i,t are valid instruments for the levels equations;if the X i,t are endogenous,ΔY i,t−1andΔX i,t−1are valid instruments for the levels equations.

The consistency of the GMM-System estimator depends on the validity of the assumption of no serial correlation of the error term,and on the validity of the instruments.This can be tested by two specification tests proposed by Arellano and Bond(1991),Arellano and Bover(1995),and Blundell and Bond(1998).One is a Sargen test of over-identifying restrictions,which can test the overall validity of the instruments.Another is the M2statistic,which tests the presence of second-order serial correlation in the first-differenced error term.Failure to reject the null hypotheses of both tests provides evidence to suggest that the no serial correlation assumption and the instruments are valid.

3.2.Indicators of the development of financial intermediation

In the empirical literature on finance-growth nexus,there are two important issues related to the indicators of the development of financial intermediation:(i)the appropriate measurementof the development of financial intermediation;(ii)the potential endogeneity of financial variables.

As for the first issue,in the empirical literature,the value of credits issued to the private sector divided by GDP is used extensively to measure the development of financial intermediation. However,China's statistical data do not provide any information about credit allocation between state and non-state sector.We only have banking sector's aggregate lending at our disposal,but not the lending to private sector.So most case studies about China use the ratio of state banking sector's loan outstanding relative to GDP to measure China's development in financial intermediation and fail to detect a positive relationship between financial development and economic growth.However,as we have shown in Section2,the loan-to-GDP ratio shows only one aspect of the development of financial intermediation,and may be the least possible channel through which the development of financial intermediation affects economic growth.Thus the findings based purely on this indicator may be misleading.We believe that the development of financial intermediation contributes to economic growth mostly through the substitution of loans for state budget appropriation and the McKinnon“conduit”effect.In the empirical study,we use three indicators to capture the three aspects of the development of financial intermediation:(i)the ratio of the state banking sector's loans outstanding relative to GDP(bank)6;(ii)the ratio of household savings deposits in financial intermediaries relative to GDP(savings),and(iii)the share of fixed asset investment financed by domestic loans relative to that financed by state budgetary appropriation(loan/budget).Following the analysis of Section2,we expect that savings and loan/budget enter growth regressions positively and significantly,while bank does not.

As for the second issue,all these three financial variables are considered to be endogenous.We use GMM-System estimator to control for endogeneity and our econometric results do not suffer from the potential bias arising from the joint determination of finance and growth.

3.3.Data and model

The panel consists of data for28Chinese provinces over the period1985–1999.7Data are averaged over five3-year periods.This choice is due to two main reasons.First,the use of3-year averages rather than annual data allows us to dampen the influence of short-term shocks and business cycles and focus on the long-run relationship between financial intermediation and growth.Second,data are averaged over3-year intervals rather than over5-years intervals or a 15-year interval in order to allow us to keep enough observations to exploit the time series dimension of the data.Moreover,conscious of potential pitfalls of this arbitrary choice,we check the robustness of our empirical results by using data averaged over5-year intervals and data averaged over the entire1985–1999period.

The data on education are drawn from Démurger(2001),all other data come from the China Statistical Yearbook(various years),the Comprehensive Statistical Data and Materials on50 Years of New China(1999),the Almanac of China Finance and Banking(various years)and the China Regional Economy:A Profile of17Years of Reform and Opening-up(1996).

6At the provincial level,data on total loans outstanding of the financial system are available on a consistent basis only after19.However,at the national level,the state banking sector accounts for more than75%of total loans outstanding of the financial system during the post-1978reform period.So we use the aggregate lending of the state banking sector as the indicator showing the loan expansion aspect of financial intermediation development.

7Due to data unavailability,Tibet and Hainan are excluded from the sample.Table1

Descriptive statistics and correlations

Economic growth Bank Savings Loan/budget Descriptive statistics

Mean0.1780.8280.445 4.3 Maximum0.488 1.597 1.15417.201 Minimum−0.0960.3950.1440.447 Std.dev.0.1120.2250.190 3.315 Observations140139139124 Correlations

Economic growth 1.000

Bank−0.222(0.009) 1.000

Savings0.125(0.144)0.497(0.000)

Loan/budget0.369(0.000)−0.066(0.470)0.411(0.000) 1.000

p-values are reported in parentheses.

To assess the impact of the development of financial intermediation on economic growth, we introduce financial variables into the traditional growth regression framework.Our analysis consists of estimating the following growth equation:

D Y i;t¼aþðb−1ÞY i;t−1þg X i;tþy F i;tþg iþe i;tð4Þwhere Y is the logarithm of real per capita GDP,X is the set of traditional growth determinants (population growth,education and infrastructure),F is the indicator of the development of financial intermediation(bank,savings and loan/budget),ηis the unobserved province-specific effect,εis the error term,and the subscripts i and t represent province and time, respectively.

Regarding the set of control variables,we introduce the average years of schooling to control for human capital accumulation(education),the density of roads as a proxy for infrastructure (infrastructure)and the population growth rate(population growth).All these control variables are assumed weakly exogenous.8Besides,all financial variables–bank,savings,and loan/budget–are assumed to be endogenous,since some theorists argue that the relationship between finance and growth is reciprocal:finance favors growth and growth in turn spurs financial development.9 Hence we must control for the endogeneity of financial variables to avoid potential biases induced by simultaneity.

Table1presents descriptive statistics and correlations for the dependent variable and the financial variables.All these variables exhibit a large variation.Savings and loan/budget are positively correlated with the growth rate,while the correlation between bank and the growth rate is negative and significant.Savings is positively and significantly correlated with bank and loan/ budget.However,bank and loan/budget do not move in the same direction.They are negatively correlated,although this correlation is not significant.A high ratio of local loans outstanding to GDP does not mean that local enterprises are more dependent on bank loans to finance investment than the enterprises of other provinces.The provinces whose local banks extend a large sum of loans may also rely largely on state budgetary appropriation to allocate financial resources.

8The empirical results are similar when these control variables are assumed strictly exogenous.

9See,for example,Greenwood and Smith(1997).3.4.Results

Arellano and Bond(1991)and Blundell and Bond(1998)argue that,although a two-step estimator is more efficient than a one-step estimator,Monte Carlo studies show that the efficiency gain is small while the asymptotic errors associated with the two-step estimators may be seriously biased downwards.Thus asymptotic inference from one-step standard errors may be more reliable.We therefore report the one-step parameter estimates for the GMM-System estimator(in Table2).

In columns1,2and3,we introduce the three variables of financial intermediation separately. As we expect,savings and loan/budget enter the growth regression positively and significantly. Since we have controlled for the endogeneity of these two variables,the results suggest that the development of financial intermediation has a causal and positive impact on growth through the channels of the mobilization of household savings and the substitution of loans for state budget appropriation.However,the coefficient on bank is negative and significant.It seems that loan distribution by state banking sector is far from efficient and affects negatively economic growth. Another possible explanation is that those provinces which grow relatively fast rely less on bank loans for financing,and more on self fundraising.Furthermore,the positive impact on growth of the mobilization of household savings and the substitution of loans for state budget appropriation is economically large.For example,over the period1997–1999,Hubei province's value of loan/ budget was2.34,while the mean value for the whole country was5.52.Therefore if exogenous factors had pushed Hubei province's value of loan/budget to the country's mean,Hubei province would have witnessed its annual growth rate increased by2.29percentage points during this period.Similarly,if its value of savings had been at the mean value for the whole country(0.66) instead of the actual0.43,it would have grown2.19%points faster per year during this period.In column4,we introduce all these three financial intermediation variables simultaneously.We obtain similar results.Bank enters the regression negatively and significantly,while savings and loan/budget enter the regression positively with a p-value below0.05and0.10respectively.With regard to other variables,a lower population growth rate favors economic growth.However,the Table2

Development of financial intermediation and economic growth(GMM-System estimator,data averaged over3-year intervals from1985to1999)

1234

Constant−0.261(0.150)0.405⁎(0.086)−0.198(0.226)0.161(0.396) Initial GDP per capita−0.012(0.775)−0.132⁎⁎(0.025)−0.001(0.971)−0.038(0.274) Population growth−0.142⁎⁎⁎(0.000)−0.084⁎⁎⁎(0.007)−0.049⁎⁎(0.021)−0.051⁎⁎⁎(0.003) Education−0.053(0.449)−0.116(0.409)−0.0(0.544)0.003(0.971) Infrastructure−0.016(0.614)0.115⁎⁎⁎(0.001)0.026(0.293)0.009(0.604) Bank−0.172⁎⁎⁎(0.004)−0.173⁎⁎⁎(0.000) Savings0.152⁎⁎⁎(0.000)0.128⁎⁎⁎(0.001) Loan/budget0.082⁎⁎⁎(0.000)0.035⁎(0.087) Sargen test0.9700.9780.983 1.000

M20.90.2130.6570.762 Observations83837373

Provinces28282727

In the regression,the right-hand-side variables are included as log(variable);p-values in parentheses,⁎(⁎⁎)(⁎⁎⁎)indicate statistical significance at the10(5)(1)percent level;for the regressions including the variables loan/budget,Fujian province is excluded from the sample due to missing data.

coefficient on the human capital variable(education)is always insignificant and the infrastructure variable(infrastructure)enters only one of the four regressions significantly.Finally,turning to the test statistics,neither the Sargen test nor the M2statistics provide evidence that rejects the validity of the instruments and the no serial correlation assumption.

For comparative purposes,we also present the results using the OLS levels estimator and the Within groups estimator in Table3.In comparison with Table2,the main difference consists in the coefficient on the lagged dependent variable.It seems that the OLS levels estimator gives an estimate biased upwards while the Within groups estimator is biased downwards,which conforms with the theoretical arguments of Hsiao(1986)and Nickell(1981).The GMM-System estimate of this coefficient lies comfortably above the corresponding Within Groups estimate,and below the corresponding OLS levels estimate,which can be regarded as a signal that the GMM-System estimator is probably preferable.Moreover,with regard to the financial intermediation variables, bank enters the regression negatively and significantly,loan/budget always has a positive and significant coefficient,and savings enters three of four regressions positively and significantly. The use of alternative estimators does not change our conclusion concerning the role of financial intermediation in the process of economic growth in China.

Finally,we evaluate the sensitivity of our results to data frequency by using data averaged over5-year intervals and data averaged over the entire1985–1999period.Due to lack of exogenous factors to instrument the financial variables,we use initial values of bank,savings and loan/budget to control for possible simultaneity.Table4and Table5report the results.In comparison with Table2,both the OLS estimator and the Within estimator using data averaged over5-year intervals produce very consistent results for the financial variables,except that loan/ budget does not enter the regression significantly when all financial variables are introduced Table3

Development of financial intermediation and economic growth(OLS estimator and Within estimator,data averaged over 3-year intervals from1985to1999)

OLS OLS OLS OLS Within Within Within Within

Constant−0.218

(0.136)

0.071

(0.745)

−0.132

(0.252)

−0.020

(0.841)

Initial GDP per capita

0.016

(0.617)

−0.014

(0.708)

0.002

(0.946)

0.011

(0.538)

−0.324⁎⁎⁎

(0.000)

−0.271⁎⁎⁎

(0.000)

−0.280⁎⁎⁎

(0.001)

−0.339⁎⁎⁎

(0.000)

Population growth −0.076⁎⁎⁎

(0.000)

−0.078⁎⁎⁎

(0.001)

−0.036⁎

(0.100)

−0.037⁎⁎

(0.022)

−0.139⁎⁎⁎

(0.000)

−0.126⁎⁎⁎

(0.000)

−0.115⁎⁎⁎

(0.000)

−0.095⁎⁎⁎

(0.000)

Education−0.054

(0.442)−0.144

(0.128)

−0.022

(0.763)

−0.029

(0.650)

1.145⁎⁎⁎

(0.000)

0.630⁎

(0.090)

0.742⁎⁎

(0.029)

0.740⁎⁎

(0.012)

Infrastructure−0.003

(0.809)

0.023

(0.190)

0.013

(0.329)

−0.005

(0.514)

0.205⁎⁎

(0.021)

0.143⁎

(0.074)

0.198⁎⁎

(0.028)

0.156⁎⁎

(0.034)

Bank−0.140⁎⁎⁎

(0.000)−0.167⁎⁎⁎

(0.000)

−0.166⁎⁎

(0.012)

−0.263⁎⁎⁎

(0.000)

Savings0.071⁎⁎

(0.038)

0.101⁎⁎⁎

(0.000)

0.073

(0.177)

0.140⁎⁎⁎

(0.000)

Loan/budget0.063⁎⁎⁎

(0.000)

0.035⁎⁎⁎

(0.002)

0.037⁎⁎

(0.033)

0.022⁎⁎

(0.050)

Observations111111100100111111100100 Provinces2828272728282727

R20.330.260.430.580.610.570.580.70

In the regression,the right-hand-side variables are included as log(variable);p-values in parentheses,standard errors are corrected for heteroskedasticity,⁎(⁎⁎)(⁎⁎⁎)indicate statistical significance at the10(5)(1)percent level;for the regressions including the variables loan/budget,Fujian province is excluded from the sample due to missing data.

simultaneously.The pure cross-section regressions using data averaged over the entire 1985–1999period also produce coefficients with the same signs as Table 2for financial variables.However the significance levels of these coefficients are much lower,which might result from the loss of information due to the use of initial values of financial variables and pure cross-sectional data.

Table 4

Development of financial intermediation and economic growth:(OLS estimator and Within estimator,data averaged over 5-year intervals from 1985to 1999)

OLS

OLS OLS OLS Within

Within

Within

Within

Constant −0.048(0.351)0.085⁎(0.086)0.005(0.883)0.072(0.117)Initial GDP per capita 0.001(0.970)−0.009(0.367)−0.006(0.427)−0.008(0.327)−0.056⁎(0.096)−0.061⁎⁎⁎(0.002)−0.128⁎⁎⁎(0.000)−0.117⁎⁎⁎(0.000)Population growth −0.025⁎⁎(0.018)−0.016⁎(0.085)−0.009(0.337)−0.011(0.178)−0.028⁎⁎(0.028)−0.012(0.196)−0.035⁎⁎(0.011)−0.008(0.529)Education −0.026(0.292)−0.034(0.198)0.012(0.582)−0.001(0.955)0.220(0.150)−0.0(0.428)0.346⁎⁎⁎(0.002)0.134(0.268)Infrastructure 0.007(0.196)0.011⁎⁎(0.032)0.004(0.182)

0.006⁎⁎(0.046)0.074⁎(0.067)0.004(0.900)0.034(0.361)

−0.011(0.767)Bank 0.002(0.855)

−0.035⁎⁎⁎(0.000)−0.038(0.143)

−0.055⁎⁎(0.012)Savings 0.037⁎⁎⁎(0.000)

0.042⁎⁎⁎(0.000)0.075⁎⁎⁎(0.000)

0.080⁎⁎⁎(0.000)Loan/budget 0.015⁎⁎⁎(0.000)0.001(0.7)0.017⁎⁎(0.012)0.003(0.594)Observations 8484757584847373Provinces 2828272728282525R 2

0.14

0.38

0.32

0.47

0.430.69

0.49

0.73

In the regression,the right-hand-side variables are included as log(variable);p -values in parentheses,standard errors are corrected for heteroskedasticity,⁎(⁎⁎)(⁎⁎⁎)indicate statistical significance at the 10(5)(1)percent level;for the regressions including the variables loan/budget ,Fujian province is excluded from the sample due to missing data.

Table 5

Development of financial intermediation and economic growth:(OLS estimator,data averaged over the entire 1985–1999period)

1

2

3

4

Constant

0.071(0.310)0.070(0.304)0.118⁎(0.083)0.086(0.223)Initial GDP per capita −0.014(0.233)−0.014(0.226)−0.021⁎⁎(0.047)−0.016(0.142)Population growth −0.001(0.979)−0.016(0.375)0.001(0.948)−0.008(0.677)Education 0.019(0.603)−0.006(0.867)0.040(0.202)0.025(0.4)Infrastructure 0.010⁎(0.057)0.012⁎⁎(0.033)0.005(0.232)0.009⁎(0.074)Bank −0.013(0.593)

−0.024(0.292)Savings 0.022(0.285)

0.029(0.1)Loan/budget 0.009⁎(0.100)0.006(0.313)Observations 28282727Provinces 28282727R 2

0.21

0.24

0.29

0.37

In the regression,the right-hand-side variables are included as log(variable);p -values in parentheses,⁎(⁎⁎)(⁎⁎⁎)indicate statistical significance at the 10(5)(1)percent level;for the regressions including the variables loan/budget ,Fujian province is excluded from the sample due to missing data.

Conforming to the findings of most cross-country studies,this paper finds that the development of financial intermediation exerts a positive,causal and economically large impact on China's economic growth.This impact runs through two channels–the substitution of loans for state budget appropriation and the mobilization of household savings–but not through the channel of loan expansion because of the inefficiency of loan distribution.The failure of several previous case studies about China to identify a significant relationship between financial development and growth may be due to the fact that these case studies focus their attention only on one aspect of financial intermediation development–loan expansion–but ignore other aspects.

Based on our empirical results,we argue that,loan distribution in China is not highly efficient based on commercial criteria,but loans are more efficient than state budgetary appropriation.The fundamental change of the means of resource allocation,from state budget appropriation to bank loans,improves the efficiency of capital use and promotes growth.It appears that due to incentives distorted by the political process,the government performs poorly as a distributor of financial resources.It would be desirable that governments limit their role to that of a regulator and supervisor,and refrain from intervening in the lending decision process of financial intermediaries.

Moreover,the McKinnon“conduit”effect seems to work in China.It would be desirable that the Chinese monetary authority always maintains real interest rates at positive levels to induce households to hold claims on the domestic banking system.

Finally,our results also suggest that it should be very important and urgent to improve the efficiency of China's financial intermediation.Deep reform needs to be implemented for transforming China's financial sector into a more efficient engine of growth.It would be desirable that the four state-owned banks can be transformed into independent commercial banks and that all financial intermediaries make their lending decisions based on purely commercial criteria.It's also very crucial to improve the non-state sector's access to bank loans.This requires that the legal system should be strengthened to provide investors with strong protection,and also that non-state enterprise should make corporate governance, beneficial ownership and financial reporting more transparent.

Acknowledgements

The author wishes to thank Sylviane Guillaumont Jeanneney and Jean-Louis Combes for helpful comments and suggestions as well as the participants of the European Development Research Network(EUDN)First Academic Annual Conference14th–15th November2003in Agence Française de Développement(AFD),Paris.

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