Modern Economy, 2011, 2, 868-873
doi:10.4236/me.2011.25097 Published Online November 2011 (http://www.SciRP.org/journal/me)
Financial Intermediation Development and Total Factor
Productiv ity Growth: Eviden ce from Chinese Mainland
Provincial Panel Data
Yaojun Yao
School of Finance, Zhejiang Gongshang University, Hangzhou, China
Received April 27, 2011; revised June 5, 2011; accepted July 1, 2011
Abstract
Modern financial development theories suggest that, financial development can promote technological pro-
gress and long-term economic growth. Based on the Chinese mainland provincial panel data, the paper tests
empirically the relationship between financial intermediation development and total factor productivity
growth. In terms of the degree-of-freedom of bank loan decision-making, the ratio of loans of private enter-
prises and individuals to total loans is used to measure the development of Chinese financial intermediation.
This paper finds that financial intermediation development significantly promotes total factor productivity
growth when controlling for other variables, such as capital formation rate, foreign direct investment, gov-
ernment intervention and the urbanization level.
Keywords: Financial Intermediation Development, Technological Progress, Total Factor Productivity
1. Introduction
Is financial development important to long-term eco-
nomic growth? Before the emergence of endogenous
economic growth theory, a universally recognized an-
swer hadn’t been reached. Some pioneering researches
pay attention to the relationship between money and
growth rather than between finance and growth [1,2].
Endogenous economic growth theory provides a good
theoretical framework for analyzing the relationship be-
tween finance and growth. In this framework, financial
development promotes technological progress and exerts
positive influence on long-term economic growth [3].
Too many literatures study Chinese finance-growth
nexus in China, but few researches focus on the impacts
of financial intermediation development on technological
progress. Based on the Chinese mainland provincial
panel data, this paper tests empirically whether total fac-
tor productivity growth, which is used to measure tech-
nological progress, has a positive relationship with fi-
nancial intermediation development. The rest of this pa-
per is organized as follows. Section 2 reviews some lit-
eratures. Section 3 discusses the level of Chinese finan-
cial intermediation d evelopment and the measurement of
total factor productivity. Section 4 presen ts the main em-
pirical results. Section 5 concludes the paper.
2. Literature Review
In the framework of AK model, financial development
improves long-time economic growth through three
channels: increasing the marginal productivity of gener-
alized capital, raising saving rate [1,2] and efficiently
converting savings to investment [4]. The mechanisms of
the first channel include that, financial development will
make more finance supp ort available to the efficient pro-
jects confronted with liquidity constraints [3], financial
marke ts diversify r isk and enco urage enterpr ises to mak e
use of more professional technology [5], and financial
intermediations make capital flow into the projects with
high social return [6]. Because technical knowledge falls
into generalized capital in the models, AK model
framework ignores the essential differences between
technological progresses (innovation) and capital accu-
mulation. In addition, AK models assume that production
activities are always efficient so that technological pro-
gress is automatic. Compared with AK models, new
Schumpeter models [7] emphasize that technological
innovation is the engine of economic growth and believe
that technological progress comes from purposeful R &

869 Y. J. YAO
D activities. In the framework of n ew Schumpeter model,
King & Levine [8] suggest that financial development
lowers agency cost (due to the economics of scale), and
then promotes technological innovation and economic
growth. Their study also indicates that financial system
diversifies the risk of innovation activities, which will
also improve technological innovation. De la Fuente &
Martin [9] assume that the probability of successful in-
novation depends on the degree of entrepreneurs’ efforts
which only can be supervised incompletely with a certain
cost. This kind of information friction leads to the emer-
gence of financial intermediaries as agents’ supervisors.
The contracts between financial intermediaries and en-
trepreneurs make the entrepreneurs pay optimal level of
efforts. With lower supervision costs, the entrepreneurs
can get more favorable loan terms to encourage
higher-level innovation activities. Blackburn & Hung [10]
have the same conclusion with de la Fuente & Martin [9],
but in their model, the achievements of R & D are as-
sumed to be private information and only enterprises
know whether t he inn o vat i o n projects are successful.
Among empirical studies, Beck, et al. [11] find that
financial intermediation development significantly pro-
motes total factor productivity (TFP) productivity growth
but has a weak link with capital accumulation. However,
Rioja & Valev [12] find that the promotion effect on TFP
of the financial intermediation development only exists
in developed countries. Tadesse [13] makes use of cross-
country industrial data to find that between the industrial
technological progress and the development of bank ex-
ists a significantly positive relationship, but the influence
of stock markets on industrial technological progress is
weak. Inklaar & Koetter [14] suggest that the relation-
ships between some traditional indicators of financial
development and productivity are unsignificant, but the
efficiency of financial intermediaries has a significantly
positive influence on productivity.
Zhang & Jin [15] focus on the impacts of Chinese fi-
nancial intermediation development on technological
progress. They find that Chinese financial intermediation
development promotes the growth rate of TFP signifi-
cantly. In the study, the ratio of loan to state-owned en-
terprises (SOEs) to GDP is used to measure the level of
financial deepening . However, the official loans statistics
are not categorized by the type of property right of the
loanee; therefore, certain estimation methods have to be
used to solve problems relating to data acquisition [16],
which is likely to lead to unreliable empirical results.
Guariglia & Poncet [17] use the ratio of bank loans to
government appropriation in fixed assets investment fi-
nance to measure Chinese financial intermediation de-
velopment, and with this index, they arrive the same
conclusion with Zhang & Jin [15].
3. Measuring Financi al In t e rm e diation
Development and TFP
3.1. Measuring Financial Intermediation
Development
China’s banking sector, in which state-owned banks are
dominant, is criticized for inefficien t credit allo catio n [18,
19]. On the one hand, bank loans mostly flow into SOEs,
with less than 20 percent being lent to non-SOEs. How-
ever, non-SOEs contributed to approximately 65 percent
of GDP an d 7 0 - 80 p er cen t of GDP gr ow th in 2 007 [ 20 ].
On the other hand, although the aim of financial reform
in China is to transform the government-co ntrolled banks
into independent financial institutions [18], the central
government regards credit as an instrument for narrow-
ing the disparities among provincial economies, and
provinces with low economic development levels are
much more easily able to acquire bank credit [19,21].
Therefore, it is doubtfu l th at loans to GDP can be us ed as
a measure of the development of China’s financial in-
termediation because of inefficient credit distribution.
The question now is what index we should choose to
measure the true state of Chinese financial intermedia-
tion development. In a country with financial repression,
the process of financial intermediation deepening could
be defined as the results of the system reforms, which
include letting banks operate independently for them-
selves, reducing or even eliminating mandatory loans,
and making financial decision-making more market-
oriented [1,2,8]. In view of this piont, we believe that
some structural indicators could be used to measure the
development of financial intermediation in China.
To develop a structural indicator to measure the real
state of the development of financial intermediation in
China, we firstly review the structure of bank loans. In
China, bank loans are categorized as short-term, me-
dium-term and long-term loans, trust and entrust loans
and other loans, among which short-term loans have the
most freedom in terms of decision-making, whereas
other loan decisions are subject to many external con-
straints. For example, according to the official guide for
loans, which has been executed since 1998, the amount
available for medium-term and long-term loans for fixed
assets depends on a set of indices, such as the ratio of
medium-term and long-term loans to medium-term and
long-term deposits and the ratio of fixed asset loans to
total loans. The direction of credit allocation is also re-
quired to be consistent with national industrial and mac-
roeconomic policies. There are eight classes of short-
term loans, including: industrial lo ans, commercial lo ans,
construction industrial loans, agricultural loans, loans to
township and village enterprises (TVEs), loans to three

Y. J. YAO
870
kinds of foreign-invested enterprises, loans to private
enterprises and individuals, and other short-term loans.
According to the research conducted by Lin and Li [22],
industrial loans, commercial loans and construction in-
dustrial loans are designated as loans for SOEs by the
government. Agricultural loans are also required by the
government to promote the development of agriculture,
rural areas and farmers. Loans for TVEs are influenced
by local governments with strong motivation to stimulate
local economic growth under the background of fiscal
decentralization. In addition, three kinds of foreign-in-
vested enterprises have enjoyed much supernational
treatment, and banks are often pressed to provide loans
to them. Banks are more independent in the decision-
making relating to loans to private enterprises and indi-
viduals. The increase in the ratio of loans for private en-
terprises and individuals to total loans reflects the en-
hancement of the degree-of-freedom of banks making
loan decisions. Therefore, we argue that it is reasonable
to measure the development of financial intermediation
in China using the ratio of loans of private enterprises
and individuals to total loans. This index has positive
correlation with economic growth; see Figure 1, which
is consistent with the prediction of the mainstream theory
of financial development.

E
H
eL

 (2)
where E is per capita education years of labor force, and


E  is the weighed sum of the return rates of educa-
tion. According to Psacharopoulos & Patrinos [23], in
China the return rate of edu cation at the stage of primary
education is 0.18, at the stage of secondary education is
0.134 and at the stage of higher education is 0.151. For
example, if E is 14, then


E  6 0.1806 0.1342 0.1512.186  .
By (1) and (2), we have
1
1()
()
a
aaaE a
YY
AKHK eL


1


 (3)
According to Fu & Wu [24], 1a
A
 is TFP. By the
data sources that are showed in Section 4, we can calcu-
late out the values of TFP that are presented in Table 1.
4. Empirical Analysis
4.1. The Econometric Model and Variables
The econometric model in this paper is:
itiitjjit it
fp afindControl



 
 (4)
where tfp is the growth rate of TFP; find is the level of
financial intermediation development, which is measured
with the ratio of loans of private enterprises and indi-
viduals to total loans; Control represents other control
variables; a is time-constant prov incial effects;

is the
idiosyncratic error;

and

represent the coeffi-
cients to be estimated; The subscripts i and t represent
the provinces and time, respectively; j indicates other
control variables. Following Zhang & Jin [15], other
control variables include capital formation rate (inv),
foreign direct investment (fdi) which is measured by the
ratio of actually utilized foreign direct investment to
3.2. Measuring TFP
We suppose that the form of production function is
Cobb-Dougl as form:

1a
a
YKAH

 (1)
where y is output, k is physical capital, A is technology
level, H is human capital, and

is the output elasticity
of capital. Suppose that the relationship between H and
the number of labor force L is
Figure 1. Ratio of loans of private enterprises and individuals to total loans and the growth rate of per capita real GDP in
each province, 2002-2006. Sources: Drawn by the author according to Financial statistics and analy sis for each year and Statisti-
al yearbook for each province. c

871 Y. J. YAO
Table 1. Chinese mainland provincial TFP (2001-2005).
2001 2002 2003 20042005
Beijing 0.438 0.404 0.402 0.4130.418
Tianjin 0.454 0.480 0.498 0.5220.553
Hebei 0.251 0.259 0.267 0.2800.298
Shanxi 0.231 0.241 0.250 0.2640.272
Inner Mongolia 0.244 0.256 0.274 0.2820.303
Liaoning 0.441 0.480 0.507 0.5480.523
Jilin 0.249 0.243 0.251 0.2440.275
Heilongjiang 0.311 0.325 0.34 0.3580.378
Shanghai 0.704 0.710 0.718 0.7490.763
Jiangsu 0.435 0.465 0.479 0.4990.504
Zhejiang 0.353 0.361 0.365 0.3680.384
Anhui 0.231 0.241 0.239 0.2550.280
Fujian 0.307 0.319 0.328 0.340.349
Jiangxi 0.238 0.247 0.243 0.2570.276
Shandong 0.292 0.296 0.313 0.3270.343
Henan 0.159 0.164 0.174 0.1820.196
Hubei 0.290 0.305 0.302 0.3130.333
Hunan 0.189 0.195 0.201 0.2100.225
Guangdong 0.397 0.419 0.444 0.4610.461
Guangxi 0.128 0.134 0.139 0.1440.156
Sichuan 0.265 0.278 0.281 0.3000.322
Guizhou 0.125 0.128 0.124 0.1290.140
Yunnan 0.283 0.310 0.293 0.2920.307
Shaixi 0.200 0.210 0.197 0.2090.242
Gansu 0.288 0.290 0.283 0.2910.331
Qinghai 0.159 0.157 0.170 0.1760.201
Ningxia 0.175 0.173 0.188 0.1870.212
Xinjiang 0.209 0.214 0.207 0.2130.242
Sources: Calculate by the author according to the data sources that are
showed in Section 4.
nominal GDP), government intervention (gov, which is
measured by the local fiscal expenditures net of the ex-
penditures for culture, education , science and health), the
urbanization level (town, which is measured by the pro-
portion of non-agricultural population in the total popu-
lation).
tfp is calculated by Equation (3), where the data for y
and L are available from Chinese statistical yearbook;
the data for physical capital (at the price of 1952) are
obtained by the method of Zhang, et al. [25]; the data for
per capita education years are obtained by the method of
Tian [26]. Following Tian [26], the output elasticity of
capital

is 0.483. find is calculated by the data from
Financial statistics and analysis. The data for other con-
trol variables are available from Chinese statistical year-
book. The data for loans of private enterprises and indi-
viduals in most provinces are published from 2002, and
also in order to isolate the effect of the global financial
crisis, so the empirical study uses the Chinese mainland
provincial panel data in years 2002-2005. Tibet Autono-
mous Region is not included in the samples for the diffi-
culties in data extraction. In add ition, H ainan and Chong -
qing are included in Guangdong and Sichuan. The de-
scriptive statistics of the indexes are presented in Table
2.
4.2. Results of Estimation and Discussion
Because the samples are not selected randomly, this pa-
per uses fixed effect method rather than random effect
method to estimate the model. The results of the full-
sample estimation are presented in column 2 of Table 3.
If the idiosyncratic error doesn’t meet the standard as-
sumption, the common standard error is always bias and
so th e in f er en ce s o f s ig ni f icance of estimated coefficients
are incorrect. Wooldridge test [27] suggests that we can’t
reject the null hypothesis that the within-group error
terms are first-order serially uncorrelated even at the
10% significance level; the result of Pesaran test sug-
gests that the null hypothesis that the between-group
error terms are uncorrelated is rejected at the 5% signifi-
cance level; Adjusted Wald test provides the powerful
evidence for the groupwise heteroskedasticity of error
terms. With these test results, we decide to deduce sig-
nificance of each coefficient under cluster-robust stan-
dard er ror.
According to full-sample estimation, we find that fi-
nancial intermediation development has a positive effect
on TFP growth at the 5% significance level, which is
consistent with the prediction of the mainstream theory
of financial development. The coefficient of capital for-
mation rate (inv) is negative and significant at the 10%
significance level, which indicates that the increase of
investment-to-GDP ratio continually will prevent the
improvement of Chinese economic growth efficiency.
The sign of foreign direct investment (fdi) is negative
and significant at the 10% significance level. A potential
explanation for the result is that, the inflow of foreign
capital slows the process of domestic R & D and innova
Table 2. Descriptive statistics of variables.
Variables (unit)ObservationsMean Standard
deviation Minimum Maximum
tfp (%) 112 3.967 4.118 –7.76315.789
find (‰) 112 8.721 6.717 0.885 43.209
inv 112 0.492 0.109 0.309 0.849
fdi (US $/RMB)112 0.487 0.469 0.103 2.581
gov 112 0.120 0.047 0.059 0.294
town 112 0.325 0.127 0.149 0.646

Y. J. YAO
872
Table 3. Results of estimation.
Full-sam ple e s tim ati on Partial- sam p le e s tim ati on
find 0.2403**
(0.2152)
[0.1161]
0.2271**
(0.2553)
[0.1211]
inv –23.3540*
(13.9601)
[13.1491]
–36.6899**
(16.6429)
[16.5993]
fdi –4.4952*
(3.0697)
[2.4164]
–14.9033**
(6.2348)
[7.0309]
gov –35.2687 (53.0785)
[51.4383]
–7.2685
(56.8894)
[60.2504]
town 34.5628 (26.8882)
[25.5632]
10.5967
(30.5926)
[23.2099]
interception 8.5348 (13.8613)
[13.4438]
25.1383*
(15.8655)
[13.1622]
Wooldridge test F(1,27) = 0.441
Prob = 0.512 F(1,24) = 0.031 Prob = 0.863
Pesaran test CD = 1.781
Prob = 0.075 CD=2.416 Prob = 0.016
Adjusted Wald test

228 11814.16
 
Prob = 0.000

225 1488 9.48
 
Prob = 0.000
Notes: *, **and ** * indicate significance at the 10, 5 and 1 percent levels,
respectively. Figures in parentheses are common standard errors while those
in square brackets are cluster-robust standard error. The deductions of sig-
nificance of each coefficient go under cluster-robust standard error.
tion development and leads to high technological de-
pendence, and as a result, the policy to promote domestic
industrial technological progress through the in troduction
of foreign capital hasn’t achieved great accomplishments.
The sign of government intervention (gov) is negative,
which is consistent with the theoretical expectations, but
not significant. This paper doesn’t find that urbanization
can promote TFP growth. According to Zhang & Jin [15],
if without sustainable economies of scale, urbanization
has a weak impact on TFP growth, even though high-
level urbanization might promote the initial level of pro-
ductivity.
In empirical studies the robustness of statistical infer-
ence is a noteworthy problem. Tusi [28] finds that, when
studying China’s economic growth, whether the sample
includes Beijing, Tianjin and Shanghai or not might lead
to very different empirical conclusions. Boyreau-Debray
[29] points out that it is debatable whether the above
three municipalities should be included when studying
the relationship between China’s economic growth and
finance development, because there is always a high de-
gree of capital mobility b etween these municipalities and
the surrounding provinces. Therefore, to verify the ro-
bustness of estimation results, we re-estimate the model
with a sample excluding the above three municipalities
(see column 3 of Table 3). The results of partial-sample
estimation still indicate that the positive relationship be-
tween financial intermediation development and economic
growth is robust.
5. Conclusions and Policy Implications
For policy-making to fuel economic growth, it is very
important that the mechanism through which financial
development promotes economic growth is identified
[30]. By new Schumpeter models in endogenous eco-
nomic growth theory, financial development can promote
technological progress and long-term economic growth.
Using Chinese mainland provincial p anel data, this paper
examines the relationship between financial intermedia-
tion development and TFP growth. In terms of the de-
gree-of-freedom of bank loan decision-making, the ratio
of loans of private enterprises and individuals to total
loans is used to measure the development of Chinese
financial intermediation. When controlling for other
variables, such as capital formation rate, foreign direct
investment, government intervention and the urbaniza-
tion level, this paper finds that financial intermediation
development significantly promotes TFP growth, re-
gardless of whether the sample includes Beijing, Tianjin
and Shanghai.
The empirical results in the present paper also provide
some interesting policy implications for China’s eco-
nomic growth and financial development. The financial
intermediation development has had a positive and sta-
tistically significant effect on TFP growth, suggesting
that a sound financial sector is indispensable for eco-
nomic growth. To reach a higher stage of financial de-
velopment, it is most important and urgent that allocativ e
efficiency of financial resources be improved. Taking
this into consideration, financial system reforms, includ-
ing encouraging banks to operate independently, reduc-
ing or eliminating mandatory loans and making financial
decision-making more market-oriented, should be further
pursued.
6. Acknowledgements
The project is supported by Zhejiang Provincial Natural
Science Foundation of China (No. Y7100670) and Key
Research Basement of Humanities of Social Sciences
(Finance) of Zhejiang Provincial Higher Education In-
stitutions.
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