development of financial intermediation and economic ...mams.rmit.edu.au/vau5brg8hx2sz.pdf · of...

Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia

(ACESA)

Development of Financial Intermediation and

Economic Growth Chinese Experience

CHEN Hao1

CERDI, Université d’Auvergne

65, boulevard François-Mitterrand

63000 Clermont-Ferrand, France

Tel: 0033 660189655

E-mail: [email protected]

Abstract Using Chinese provincial data from 1985 to 1998 and 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-1978

reform period. Our econometric results show that China’s financial intermediation

development contributes to its rapid economic growth through two channels: the

substitution of loans for state budget appropriation and the mobilization of

households savings, but not through loan expansion since loan distribution by

financial intermediaries is inefficient. Deep financial sector reform aiming at

correcting this inefficiency is desirable, and is expected to sustain China’s economic

development in the future.

Key Words: financial intermediation; financial development; economic growth;

China.

1 Correspondence to: CHEN Hao

CERDI, Université d’Auvergne

65, boulevard François-Mitterrand

63000 Clermont-Ferrand, FRANCE

E-mail: [email protected]

Tel: 0033 660189655

Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 1 -


(ACESA)

I. Introduction

The relationship between financial intermediation and economic growth has attracted

much attention of economists since 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 regard to the first channel, it is generally argued that financial

intermediaries, by facilitating risk management, identifying promising projects,

monitoring management, and facilitating the exchange of goods and services, can

promote efficient capital allocation leading to a total factor productivity improvement

(Levine, 1997). For example, Greenwood and Jovanovic (1990) shows that financial

intermediation provides a vehicle for diversifying and sharing risks, inducing capital

allocation shift toward risky but “high expected return” projects. This shift then spurs

productivity improvement and economic growth. Bencivenga and Smith (1991)

argues 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 efficient use of

capital and promotes economic growth. However, the impact of financial

intermediation on growth through the second channel—capital formation—is

ambiguous. Financial intermediation may raise or reduce the savings rate.

Most of the empirical research based on cross-country data suggests a positive

relationship between financial intermediation and economic growth. King and Levine

(1993) identifies 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 its 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



(ACESA)

instrumental variables for financial intermediation indicators to control for

simultaneity bias, and 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 in 1978, China’s performance in economic

growth and the financial sector expansion has been impressive. Over the period 1978-

2001, the Chinese economy saw an annual growth rate of 9.4% in real terms, while

loans outstanding relative to GDP increased from 51.1% to 117.1% (China Statistical

Yearbook, various years)2. It 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 done searching for the explanations

of China’s economic miracle, few has considered financial development as a potential

determinant of such miraculous growth. Furthermore, most case studies of finance-

growth nexus about 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 growth3. This paper shows that for Chinese

economy the loan-to-GDP ratio reflects 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. There exist

however two other channels—the substitution of loans for state budget appropriation

and the mobilization of households savings—through which the development of

financial intermediation spurs China’s economic growth. Furthermore, this paper uses

recent Generalized Method of Moment (GMM) estimators in the empirical analysis,

2 Henceforth, unless otherwise indicated, all data cited are from China Statistical Yearbook (State

Statistical Bureau, various years). 3 See Aziz and Duenwald (2002); Boyreau-Debray (2003); Shan, Morris and Sun (2001) and Shan and

Morris (2002).



(ACESA)

and so prevents potential biases induced by simultaneity and unobserved individual-

specific effects from compromising the relevance of its findings.

The remainder of the paper is organized as follows. Section 2 outlines China’s

development in financial intermediation during the post-1978 period, presents three

aspects of this development and analyzes their respective impacts on economic

growth. Section 3 introduces the economic methodology and our growth regression

model, presents the indicators of the development of financial intermediation, and

shows the main empirical results. Section 4 concludes and draws out some policy

implications.

II. Financial Intermediation in China: Development and

Weakness

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 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, a set of changes have taken place 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’s labor remuneration policy and profit transfers regime—

controlled an important part of national income. On the other hand, 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 1952 and 1977,

government revenues never fell below 20% of GDP, peaking in 1960 at 39.3% of

GDP. A major portion of this revenue came from state-owned enterprises (SOEs)



(ACESA)

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). Therefore, 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 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, which raised the rural households’ earning, and

also raised the input costs of the SOEs-dominated industry and consequently eroded

the profit base of SOEs. Second, the emergence and rapid expansion of the non-state

sector intensified competition on the product and factor markets, which broke the

SOEs’ monopoly, 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 from 31.2% of GDP in 1978 to 17.1% GDP in 2001 since its main source,

the flow of SOEs profit transfers, gradually declined in importance. 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

government’s capacity to finance capital investments through the state budget4, on the

other hand, it reinforces the households’ savings potential. In 2001, households

financial savings added up to 7376.2 billion yuan, or 76.9% 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.

4 Moreover, since 1985, the central government has been forced to subsidize loss-making SOEs, which

limits furthermore its ability to finance capital investments directly.



(ACESA)

A large number of financial institutions have been established, and the pre-reform

mono-bank system has been transformed to 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. In 2001, the total loans of

financial institutions reached 11231.5 billion yuan, accounting for 117.1% of GDP,

while stock market capitalization reached only 15.1% of GDP despite impressive

growth since 19945. The bond market is essentially reserved for the central

government to raise funds. The corporate sector’s access to this market is extremely

limited. In 2001, the total accumulated value of corporate bonds amounted to only

100.9 billion yuan, accounting for 4.0% of the value of total outstanding bonds and

1.1% 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 expands at a rate faster

than the real economy. Financial depth measured by the ratio of M2 to GDP has

increased from 24.6 percent in 1978 to 194.7 percent in 2002, which is among the

highest in the world (IFS, 2003). This striking financial deepening is mainly due to

two factors: the monetization of the economy and the expansion of households

financial savings, especially the latter. In 2001, households deposits accounted for

77.9% of quasi-money and 47.2% of M2.

Finally, domestic loans, taking the place of state budget appropriations, become the

primary external source for financing capital investments. Figure 1 shows the changes

in financial sources of fixed asset investment. In 1981, state budgetary appropriation,

as the most important external source, financed 28.1% of total fixed asset investment,

while the share of domestic loans was only 12.7%. By 2001, this situation had

completely changed. State budget lost its importance, while loans became the primary

means of external finance. Furthermore, Some informed researchers argue that in

recent years about half of the funds titled “self fundraising and others” actually comes

from loans, because some types of loans which are not authorized by the regulations

5 Stock 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.



(ACESA)

are actually extended to enterprises and enterprises put them in the category “self

fundraising and others”. If this statement is correct, loans financed actually more than

one half of the total fixed asset investment in 2001.

Figure 1: Financial sources of fixed asset investment

a. 1981

12.7%

3.8%

55.4%

28.1%Self fundraisingand Others

State bugetaryappropriation

Domesticloans

Foreigninvestment

b.2001

69.6%

19.1%

6.7%

4.6%

Self fundraisingand Others

Domestic loans

State budgetaryappropriation

Foreigninvestment

(Source: China Statistical Yearbook 2002).



(ACESA)

In conclusion, the development of financial intermediation in China have three main

aspects: (i) loan expansion; (ii) the mobilization of households savings; and (iii) the

substitution of loans for state budget appropriation as the primary source of external

funding. In the following, we analyze respectively the impact on growth of these three

aspects.

China’s bank-dominated financial sector is famous for its inefficiency and

misallocation of capital. Its distribution of loans, both between state and 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 by financial intermediaries,

particularly the four state-owned banks, into the inefficient state sector, even into loss-

making SOEs. On the other hand, 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 use financial system, especially

the state banking sector, for implicitly taxing rich provinces and subsidizing poor

provinces: financial resources are channeled into poor provinces for supporting their

credit expansion (Park and Sehrt, 2001). Statistics confirm this argument. Figure 2

plots 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 –0.16. This means that those provinces with a lower level of economic

development receive preferential credit treatment from the central government. Since

it is influenced by political concerns, the actual loan distribution may differ from the

optimum distribution according to economic fundamentals. Financial intermediaries

may have not directed financial resources to their most efficient use.



(ACESA)

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 growth.

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 enterprises’ budget constraint, and may promote more efficient use of capitals.

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 lending portfolio and the main consideration

of government bureaucrats consists in social stability (Cull and Xu, 2003).

Empirically, Liu and Li (2001) finds 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.

o

S

(1

C

Loan/Deposit rati

0 .7 5

1 .0 0

1 .2 5

1 .5 0

1 .7 5

2 .0 0

ource: Com

999)

hen, H., ‘Deve

Figure 2: Local loan/deposit ratio and local real per capita GDP

(1978-1998, average values)

6 .0 0 6 .2 5 6 .5 0 6 .7 5 7 .0 0 7 .2 5 7 .5 0 7 .75 8 .0 0

Log(real per capita GDP)

prehensive Statistical Data and Materials on 50 Years of New China

lopment of Financial Intermediation and Economic Growth: Chinese Experience’. - 9 -


(ACESA)

Furthermore, the impact of the development of financial intermediation on economic

growth may also run through the mobilization of households financial savings.

First, McKinnon (1973) argues that, when all economic units are confined to self-

finance, money balance have to be accumulated before costly and indivisible

investment projects can be undertaken, and so money and physical capital are

complements. This argument seems to be applicable to China. Chinese non-state

enterprises and households have actually extremely limited access to bank loans.

In addition, in China there exist effective informal financing channels that convert

households savings into productive investments of non-state enterprises. The non-

state sector is more efficient than state sector and constitutes effectively China’s

growth engine. So the mobilization of households savings may favor economic

growth and productivity amelioration through supporting non-state sector’s

investments.

In summary, since the beginning of economic reform, China’s financial

intermediation took off and became an essential means of resources allocation.

Financial intermediaries channel more and more household savings into productive

investment. The distribution of loans may be 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 Chinese economy, the

development of financial intermediation may favor economic growth through the

mobilization of households savings and the substitution of loans for state budget

appropriation, but may not through loan expansion.



(ACESA)

III. 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

obviously 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 of this section, we introduce at first 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.

A. Methodology

Let us consider the following growth equation:

Yi,t = α + βYi,t-1 + γXi,t + ηi + εi,t

(1)

where Y is the logarithm of real per capita GDP, X is the set of explanatory variables,

η is the time invariant individual-specific effect, ε is the error term, with the

subscripts i and t representing an individual and time, respectively.



(ACESA)

In estimating equation 1, we are often confronted with two mains 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 Yi,t-1 and

omitted fixed effects ηi can make the coefficient estimate by OLS biased 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 that is biased downwards in short panels. Thus a consistent and

unbiased estimate of is 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 equation 1, 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.

^β

^β

^β

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

GMM estimator. It consists in eliminating the time invariant individual-specific

effects ηi by taking the first difference of equation 1. Doing that we obtain

Yi,t - Yi,t-1 = β(Yi,t-1 - Yi,t-2) + γ(Xi,t - Xi,t-1) + (εi,t - εi,t-1)

(2)

By construction, (Yi,t-1 - Yi,t-2) and (εi,t - εi,t-1) are correlated. OLS estimation of

equation 2 will not give an unbiased and consistent estimate of β. Hence, we must

find valid instruments for (Yi,t-1 - Yi,t-2).

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

E[εi,t εi,s] = 0 for i = 1, …, N and s ≠ t

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

E[Yi1 εi,t] = 0 for i = 1, …, N and t ≥ 2

Arellano and Bond (1991) proposes the following moment restrictions

E[Yi,t-s (εi,t - εi,t-1)] = 0 for t = 3, …, T and s ≥ 2



(ACESA)

Since the values of Yi,t lagged two periods or more are correlated with (Yi,t-1 - Yi,t-2),

but not with (εi,t - εi,t-1), they are valid instruments for equation 2.

With regard to the explanatory variables Xi,t, there are three possible situations:

If the explanatory variables Xi,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 Xi,t are valid instruments for

equation 2.

If the explanatory variables Xi,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 Xi,t lagged one period

or more are valid instruments for equation 2.

If the explanatory variables Xi,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 Xi,t lagged two

periods or more are valid instruments for equation 2.

However, Blundell and Bond (1998) argues 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) confirms 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 (equation 2) with the additional

set of equations in levels (equation 1). 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,



(ACESA)

E[∆Xi,t ηi] = 0 for i = 1, …, N and t = 2, …,T

and (b) ∆Yi2 are uncorrelated with the individual-specific effects,

E[∆Yi2 ηi] = 0 for i = 1, …, N,

then, if the Xi,t are strictly exogenous or predetermined, ∆Yi,t-1 and ∆Xi,t are valid

instruments for the levels equations; if the Xi,t are endogenous, ∆Yi,t-1 and ∆Xi,t-1 are

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 m2 statistic, 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.

B. Indicators of the development of Financial intermediation

We use three indicators to measure the three aspects of the development of financial

intermediation: (i) the ratio of state banking sector’s loans outstanding relative to

GDP (bank)6; (ii) the ratio of households 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 Section 2, savings and loan/budget are

expected to enter growth regressions positively and significantly, while bank not

significantly.

6 In China, the statistics concerning credit to the private sector are not available. Moreover, at the

provincial level, total loans outstanding of financial system is available on a consistent basis only after

1989. However, at the national level, the state banking sector accounts for more than 75% of total loans

outstanding of financial system during the post-1978 reform period. So we use the aggregate lending of

state banking sector as the indicator showing the loan expansion aspect of financial intermediation

development.



(ACESA)

C. Data and Model

The panel consists of data for 28 Chinese provinces over the period 1985-19987. 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 on 50 Years of New China (1999), the Almanac of China Finance and

Banking (various years) and the China Regional Economy: A Profile of 17 Years of

Reform and Opening-up (1996).

To assess the impact of the development of financial intermediation on economic

growth, we introduce the financial variables into the traditional growth regression

framework. Our analysis consists in estimating the following growth equation:

Yi,t = α + βYi,t-1 + γXi,t + δFi,t + ηi + εi,t

(3)

where Y is the logarithm of real per capita GDP, X is the set of traditional growth

determinants (investment, population growth, education and infrastructure), F is the

indicators of the development of financial intermediation (bank, savings and

loan/budget), η is the unobserved province-specific effect, ε is the error term, and the

subscripts i and t represent province and time respectively.

Regarding the set of control variables, we introduce the ratio of fixed asset investment

to GDP as a proxy for physical capital (investment), the share of population with at

least secondary schooling as a proxy for human capital (education), the density of

roads as a proxy for infrastructure (infrastructure) and the annual population growth

rate (population growth). All these control variables are assumed weakly exogenous8.

Besides, 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

development9. Hence we must control for the endogeneity of financial variables to

avoid potential biases induced by simultaneity.

7 Due to the data unavailability, Tibet and Hainan are excluded from the sample. 8 The empirical results are similar when these control variables are assumed strictly exogenous. 9 See Greenwood and Smith (1997).



(ACESA)

Table 1 presents descriptive statistics and correlations for the dependent variable and

financial variables. All these variables exhibit a large variation. Savings and

loan/budget are positively and significantly, but bank is negatively and significantly

correlated with the growth rate. Savings is positively and significantly correlated with

bank and loan/budget, and bank and loan/budget are negatively and insignificantly

correlated.

Table 1: Descriptive Statistics and Correlations

Descriptive Statistics

Economic growth Bank Savings Loan/budget

Mean 0.06 0.83 0.43 4.31

Maximum 0.31 1.63 1.14 19.48

Minimum -0.11 0.38 0.12 0.36

Std. Dev. 0.06 0.22 0.18 3.57

Observations 406 403 403 346

Table 1 (continued)

Correlations

Economic growth Bank Savings Loan/budget

Economic growth 1.000

Bank -0.188 1.000

(0.000)

Savings 0.103 0.476

(0.042) (0.000)

Loan/budget 0.273 -0.074 0.423 1.000

(0.000) (0.177) (0.000)

p-values are reported in parentheses



(ACESA)

D. 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 GMM-System estimator (Table 2 and Table 3).

In order to identify the global impact of the development of financial intermediation

on economic growth, we exclude at first the variable investment from the regression.

Table 2 reports the results. In the columns 1, 2 and 3, we introduce respectively the

three variables of financial intermediation. As we expect, bank does not enter the

growth regression significantly, while the coefficients on the other two financial

variables, savings and loan/budget, are positive and strongly significant. Since we

have controlled for the endogeneity of these three 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 households savings and the

substitution of loans for state budget appropriation. Furthermore, this impact is

economically large. For example, Inner Mongolia province’s value of loan/budget

over the period 1985-1998 was 1.77, while the mean value for the whole country was

4.30. Therefore if exogenous factors had pushed Inner Mongolia province’s value of

loan/budget to the country’s mean, Inner Mongolia province would have witnessed its

annual growth rate increased by 2.13 percentage points. With regard to other

variables, a lower population growth rate and a more developed infrastructure favor

economic growth, which confirms our expectations. However, the coefficient on the

human capital variable (education) is not significant. Finally, turning to the test

statistics, neither the Sargen test nor the m2 statistics provide evidence that rejects the

validity of the instruments and the no serial correlation assumption.



(ACESA)

Table 2: Development of Financial Intermediation and Economic Growth:

Without Investment (GMM-System, dependent variable: logarithm of

real GDP per capita)

1 2 3

Constant 0.131 0.417 0.409

(0.742) (0.078) (0.196)

Initial GDP per capita 0.899*** 0.926*** 0.852***

(0.000) (0.000) (0.000)

Population growth -0.042*** -0.035*** -0.042***

(0.000) (0.000) (0.000)

Education 0.143 -0.001 0.104

(0.086) (0.988) (0.132)

Infrastructure 0.097*** 0.015 0.109**

(0.004) (0.734) (0.020)

Bank 0.023

(0.328)

Savings 0.061***

(0.004)

Loan/Budget 0.024***

(0.000)

Sargen Test 1.000 1.000 1.000

M2 0.116 0.130 0.097

Observations 333 333 287

Provinces 28 28 27

Note: In the regression, the right-hand-side variables are included as log(variable);

p-values in parentheses, ** (***) indicates statistical significance at the 5 (1)

percent level;

For the regressions including the variables loan/budget, Fujian province is

excluded from the sample due to missing data.



(ACESA)

To separate the productivity effects of financial intermediation from its investment

effects, we now introduce the variable investment into the regression. Table 3 shows

the results. Investment has a positive and significant coefficient, which supports the

theoretical prediction that physical capital formation contributes to economic growth.

Bank is still insignificant while the coefficients on savings and loan/budget remain

significant and decline only slightly, from 0.061 to 0.051 and from 0.024 to 0.018,

respectively. It appears that the impact of financial intermediation on growth runs

through its impact on investment and productivity, but mainly through the latter. With

regard to other variables, the results are similar to those of Table 2, except that

education enters two of three regressions significantly (see columns 1 and 3). The

human capital spurs growth through its impact on total factor productivity, but may

hamper physical capital formation since education demands economic resources and

reduces resources available for investments in physical projects. As a result, its global

effects are ambiguous as shown in Table 2. Finally, both the Sargen test and the m2

statistics give support to our model.



(ACESA)

Table 3: Development of Financial Intermediation and Economic Growth: With

Investment (GMM-System, dependent variable: logarithm of real GDP

per capita)

1 2 3

Constant 0.927*** 0.772*** 0.856***

(0.004) (0.006) (0.007)

Initial GDP per capita 0.835*** 0.884*** 0.804***

(0.000) (0.000) (0.000)

Investment 0.075*** 0.067*** 0.074***

(0.001) (0.005) (0.003)

Population growth -0.032*** -0.029*** -0.039***

(0.000) (0.000) (0.000)

Education 0.283*** 0.073 0.184**

(0.000) (0.488) (0.019)

Infrastructure 0.086** 0.031 0.120**

(0.023) (0.545) (0.016)

Bank -0.035

(0.177)

Savings 0.051**

(0.036)

Loan/Budget 0.018***

(0.003)

Sargen Test 1.000 1.000 1.000

M2 0.112 0.082 0.077

Observations 333 333 287

Provinces 28 28 27

Note: In the regression, the right-hand-side variables are included as log(variable);

p-values in parentheses, ** (***) indicates statistical significance at the 5 (1)

percent level;





(ACESA)

For comparative purposes, we present the results using OLS levels estimator and

Within groups estimator in Table 4 and Table 5. In comparison with Table 2 and

Table 3, 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, the use of

OLS levels estimator and Within groups estimator makes the variable bank enter the

regressions with a negative coefficient, which is significant in seven of eight

regressions. In contrast, the GMM-System estimator always gives the variable bank

an insignificant coefficient. As we have shown in Section 2, the central government

considers financial intermediation as a means to tax rich and dynamic regions and to

subsidize poor and stagnant regions, which may lead to an artificially imposed

causality from economic development to financial intermediation. It seems that OLS

levels estimator and Within groups estimator suffer from the bias induced by the

endogeneity of the variable bank, while the GMM-System estimator manages to avoid

this bias. Finally, with regard to the other financial intermediation variables, savings

and loan/budget always has a positive and strongly significant coefficient. The use of

alternative estimators does not change our conclusion concerning the role of financial

intermediation in the process of economic growth in China.



(ACESA)

Table 4: Development of Financial Intermediation and Economic Growth:

Without Investment (OLS and Within)

OLS OLS OLS Within Within Within

Constant -0.154 -0.217** -0.036

(0.203) (0.027) (0.630)

Initial GDP per capita 1.006*** 1.011*** 0.995*** 0.839*** 0.890*** 0.827***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Population growth

-

0.028*** -0.026*** -0.016**

-

0.041*** -0.036*** -0.041***

(0.000) (0.000) (0.029) (0.000) (0.000) (0.000)

Education -0.029 -0.073*** -0.008 0.314*** 0.107 0.205***

(0.297) (0.002) (0.674) (0.000) (0.215) (0.004)

Infrastructure 0.002 0.005 0.003 0.090** 0.043 0.089**

(0.672) (0.314) (0.544) (0.016) (0.272) (0.029)

Bank -0.023** -0.038

(0.017) (0.175)

Savings 0.038*** 0.046**

(0.000) (0.011)

Loan/Budget 0.020*** 0.020***

(0.000) (0.001)

Observations 333 333 287 333 333 287

Provinces 28 28 27 28 28 27

R2 0.990 0.991 0.990 0.960 0.961 0.955

Note: Dependent variable is the logarithm of real per capital GDP;

In the regression, the right-hand-side variables are included as log(variable);

p-values in parentheses, standard errors are corrected for heteroskedasticity, **

(***) indicates statistical significance at the 5 (1) percent level;





(ACESA)

Table 5: Development of Financial Intermediation and Economic Growth: With

Investment (OLS and Within)

OLS OLS OLS Within Within Within

Constant -0.001 -0.185 0.046

(0.996) (0.107) (0.685)

Initial GDP per capita 0.989*** 1.007*** 0.985*** 0.798*** 0.860*** 0.797***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Investment 0.040** 0.010 0.025 0.068*** 0.058** 0.066***

(0.011) (0.583) (0.143) (0.002) (0.018) (0.006)

Population growth -0.029***

-

0.026*** -0.018** -0.036***

-

0.033*** -0.039***

(0.000) (0.000) (0.028) (0.000) (0.000) (0.000)

Education -0.021

-

0.071*** -0.008 0.358*** 0.153 0.234***

(0.483) (0.003) (0.678) (0.000) (0.150) (0.004)

Infrastructure 0.005 0.006 0.006 0.096** 0.054 0.098**

(0.235) (0.270) (0.309) (0.014) (0.238) (0.028)

Bank -0.034*** -0.055**

(0.001) (0.050)

Savings 0.035*** 0.035**

(0.000) (0.011)

Loan/Budget 0.019*** 0.015**

(0.000) (0.012)

Observations 333 333 287 333 333 287

Provinces 28 28 27 28 28 27

R2 0.990 0.991 0.990 0.962 0.962 0.957

Note: Dependent variable is the logarithm of real per capital GDP;

In the regression, the right-hand-side variables are included as log(variable);

p-values in parentheses, standard errors are corrected for heteroskedasticity, **

(***) indicates statistical significance at the 5 (1) percent level;





(ACESA)

IV. Conclusion

Conforming with the findings of most cross-sectional 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

households savings—but not through the channel of loan expansion. 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 cases

studies concentrate their attention on only one aspect of financial intermediation

development—loan expansion—but ignore other aspects.

Based on our empirical results, we argue that, in China loan distribution is not totally

efficient according to 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, governments 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.

The efficiency improvement of China’s financial intermediation has great potential.

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 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 strong protection,

and non-state enterprise should make corporate governance, beneficial ownership and

financial reporting more transparent.



(ACESA)

References

Arellano, M. and S. Bond (1991), “Some Tests of Specification for Panel Data: Monte

Carlo Evidence and An application to Employment Equations”, Review of Economic

Studies, Vol.58, No.2, pp.277-297.

Arellano, M. and O. Bover (1995), “Another Look at the Instrument Variable

Estimation of Error-Components Models”, Journal of Econometrics, Vol.68, No.1,

pp.29-52.

Aziz, J. and C. Duenwald (2002), “Growth-Financial Intermediation Nexus in China”,

IMF Working Paper 02/194, International Monetary Fund, Washington, D.C.

Beck, T., R. Levine and N. Loayza (2000), “Finance and the Sources of Growth”,

Journal of Financial Economics, Vol.58, No.1-2, pp.261-300.

Bencivenga, V.R. and B.D. Smith (1991), “Financial Intermediation and Endogenous

Growth”, Review of Economic Studies, Vol.58, No.2, pp.195-209.

Blundell, R. and S. Bond (1998), “Initial Conditions and Moment Restrictions in

Dynamic Panel Data Models”, Journal of Econometrics, Vol.87, No.1, pp.115-143.

Blundell, R. and S. Bond (2000), “GMM Estimation with Persistent Panel Data: An

Application to Production Functions”, Econometric Reviews, Vol.19, No.3, pp.321-

340.

Boyreau-Debray, G. (2003), “Financial Intermediation and Growth: Chinese Style”,

Policy Research Working Paper 3027, World Bank, Washington, D.C.



(ACESA)

Caballero, R.J. (1990), “Consumption Puzzles and Precautionary Savings”, Journal of

Monetary Economics, Vol.25, No.1, pp.113-136.

Cull, R. and L.C. Xu (2003), “Who gets credit? The behavior of bureaucrats and state

banks in allocating credit to Chinese state-owned enterprises”, Journal of

Development Economics, Vol.71, No.2, pp.533-559.

Démurger, S. (2001), “Infrastructure Development and Economic Growth: An

Explanation for Regional Disparities in China?”, Journal of Comparative Economics,

Vol.29, No.1, pp.95-117.

Devereux, M.B. and G.W. Smith (1994), “International Risk Sharing and Economic

Growth”, International Economic Review, Vol.35, No.4, pp.535-550.

Diamond, D. and P.H. Dybvig (1983), “Bank Runs, Deposit Insurance, and

Liquidity”, Journal of Political Economy, Vol.91, No.3, pp.401-419.

Goldsmith, R.W. (1969), Financial Structure and Development, Yale University

Press, New Haven.

Greenwood, J. and B. Jovanovic (1990), “Financial Development, Growth and the

Distribution of Income”, Journal of Political Economy, Vol.98, No.5, pp.1076-1107.

Greenwood, J. and B.D. Smith (1997), “Financial Markets in Development, and the

Development of Financial Markets”, Journal of Economic Dynamics and Control,

Vol.21, No.1, pp.145-181.



(ACESA)

Hsiao, C (1986), Analysis of Panel Data, Cambridge University Press, Cambridge,

MA.

Jappelli, T. and M. Pagano (1994), “Saving, Growth and Liquidity Constraints”,

Quarterly Journal of Economics, Vol.109, No.1, pp.83-109.

Kimball, M.S. (1990), “Precautionary Saving in the Small and in the Large”,

Econometrica, Vol.58, No.1, pp.53-73.

King, R.G. and R. Levine (1993), “Finance and Growth: Schumpeter Might Be

Right.” Quarterly Journal of Economics, 108(3pp.717-738.

Levhari, D. and T.N, Srinivasan (1969), “Optimal Savings under Uncertainty”,

Review of Economic Studies, Vol.36, No.1, pp.153-163

Levine, R. (1997), “Financial Development and Economic Growth: Views and

Agenda”, Journal of Economic Literature, Vol.35, No.2, pp.688-726.

Levine, R. (1998), “The legal Environment, Banks and Long-run Economic Growth”,

Journal of Money, Credit and Banking, Vol.30, No.3, pp.596-613.

Levine, R. (1999), “Law, Finance and Economic Growth”, Journal of Financial

Intermediation, Vol.8, No.1-2, pp.36-67.

Levine, R., N. Loayza and T. Beck (2000), “Financial Intermediation and Growth:

Causality and Causes”, Journal of Monetary Economics, Vol.46, No.1, pp.31-77.



(ACESA)

Liu, T. and K-W Li (2001), “Impact of Financial Resources Liberalization in China’s

Economic Growth: Provincial Evidence”, Journal of Asian Economics, Vol.12, No.2,

pp.245-262.

McKinnon, R.I. (1973), Money and Capital Market in Economic Development,

Brookings Institution, Washington D.C.

National Bureau of Statistics (1999), Comprehensive Statistical Data and Materials

on 50 Years of New China, China Statistics Press, Beijing.

Nickell, S. (1981), “Biases in Dynamic Models with Fixed Effects”, Econometrica,

Vol.49, No.6, pp.1417-1426.

Park, A. and K. Sehrt (2001), “Tests of Financial Intermediation and Banking Reform

in China”, Journal of Comparative Economics, Vol.29, No.4, pp.608-644.

Research and Statistics Department of People’s Bank of China (1992), China

Financial Statistics: 1952-1991, China Financial Publishing House, Beijing.

Research and Statistics Department of People’s Bank of China (various years),

Almanac of China Finance and Banking, People’s China Publishing House, Beijing.

Shan, J.Z., A.G. Morris and F. Sun (2001) “Financial Development and Economic

Growth: An Egg-and Chicken Problem?”, Review of International Economics, Vol.9,

No.3, pp.443-454.

Shan, J.Z. and A.G. Morris (2002), “Does Financial Development ‘Lead’ Economic

Growth?”, International Review of Applied Economics, Vol.16, No.2, pp.153-168.



(ACESA)

State Statistical Bureau (1996), China Regional Economy: A Profile of 17 Years of

Reform and Opening-up, China Statistical Bureau, Beijing.

State Statistical Bureau (various years), China Statistical Yearbook, China Statistical

Publishing House, Beijing.

Tsuru K. (2000), “Finance and Growth: Some Theoretical Considerations, and A

Review of the Empirical Literature”, Economic Department Working Paper 228,

Organisation for Economic Co-operation and Development, Paris.

Wu X. (eds) (1997), Research Reports on Debt Restructuring of China’s State Sector,

[Zhongguo guoyou jingji zhaiwu chongzu yanjiu baogao], China Financial Press,

Beijing.

Zhang, W. (1999), “Development in Chinese Corporate Finance and Its Implication

for Ownership Reform”, Asia Pacific Financial Deregulation, pp.183-205, Routledge,

London.


development of financial intermediation and economic ...mams.rmit.edu.au/vau5brg8hx2sz.pdf · of...

Documents