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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 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 2 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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).
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 3 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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)
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 4 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 5 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 6 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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).
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 7 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 8 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 10 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 11 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 12 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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,
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 13 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 14 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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).
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 15 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 16 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 17 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 18 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 19 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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;
For the regressions including the variables loan/budget, Fujian province is
excluded from the sample due to missing data.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 20 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 21 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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;
For the regressions including the variables loan/budget, Fujian province is
excluded from the sample due to missing data.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 22 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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;
For the regressions including the variables loan/budget, Fujian province is
excluded from the sample due to missing data.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 23 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(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.
Chen, H., ‘Development of Financial Intermediation and Economic Growth: Chinese Experience’. - 24 -
Proceedings of the 15th Annual Conference of the Association for Chinese Economics Australia
(ACESA)
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