Download Research on the Relationship Between Financial Structure and

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Research on the Relationship Between Financial Structure and
Regional Economic Growth in Open Economy
SUN Ke
School of Business, Jiaxing University, P.R.China, 314001
[email protected]
Abstract: The relationship between economic growth and financial system is always a hot topic. This
paper does an empirical research on this spot in open economy and makes a comparison between eastern
and western regions. The results show financial structure is important for Shanhai, Zhejiang and
Shannxi, while its effect on Jiangsu is statistically insignificant. Bank-based financial system seems
more important for economic growth of Shanghai and Shannxi, and market-based financial system is
more important for Zhejiang.
Keywords: Financial structure, Regional economic growth, Open economy
1 Introduction
The relationship between economic growth and financial system is always a hot topic not only in the
economic research area but also in the financial research area. Discussions focusing on the functions of
financial sectors on economic growth have never end. Robinson (1952) and Lucas (1988) think that the
influences of finance on economic growth are over emphasized. On the other hand, McKinnon (1973)
stress the importance of analyzing the relationship between financial development and economic growth
in seeking the source of economic development. The economists pay much attention to the relative
importance of bank-based and market-based financial systems (Goldsmith,1969; Boot and Thakor, 1997;
Kul et al, 2008). The debate on the relative merits of bank-based versus market-based financial systems
has a long history of over a century (Levine, 2002; Allen et al, 2006). Nonetheless, there is hardly any
consensus at the theoretical level. Competing theoretical models posit the superiority of one type of
financial system over the other or they simply relegate financial structure as irrelevant. A large body of
empirical literature has attempted to evaluate this debate (Arestis et al., 2001; Beck et al, 2004). With
the development of Chinese economy, the financial structure is changing at the same time. Whether
current financial system adapts to real economy is still an important question need to be answered. As
far as we considered, different regions may have different performance, so we make a comparison in this
paper to see the differences between eastern and western regions in China.
2 Model Specifications and Data Description
2.1 Model specification
Here we extend traditional Cobb-Douglas production function, where financial structure and financial
development account for TFP (total factor productivity). Our basic specification is shown in Eq. (1).
1
Y = F ( K , L, F S , F D , E ) = BK α Lβ ( F S ) λ ( F D ) δ e ε
Where, Y is output, K is physical capital stock, L is labor, FS and FD respectively are measures of
financial structure and financial development, B is TFP. A high value of FS means a system that is more
of a market-based variety; while a lower FS means more of a bank-based system. eτ is the error term. α, β,
λ and δ is capital output elastic coefficient, labor output elastic coefficient, financial structure’s
contribution to economic growth and financial development’s contribution to economic growth,
respectively. According to classical theory of economic growth, contributions of other factors are all
represented by B. Express equation (1) in logarithmic form, we can get equation (2) which is our
benchmark empirical model.
2
ln Y = ln B + α ln K + β ln L + λ ln( F S ) + δ ln( F D ) + ε
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By integrating international trade situation and FDI into equation (2), we can analyze the long run
relationship between output, real capital stock, labor, financial structure and financial development in
open economy. This way we can get equation (3).
ln(Y ) t = a 0 + a1 ln( K ) t + a 2 ln( L ) t + a 3 ln( F S ) t + a 4 ln( F D ) t + a 5 ln( EXIM ) + a 6 ln( FDI ) + et 3
The standard econometric specification of growth model regresses real per capita GDP growth on a
number of growth determinants. Our approach is time series. We estimate the relationship between
output, labor, physical capital stock, financial development and financial structure in open economy.
（）
2.2 Data description
Our sample consists of four regions in China, i.e. Shanghai, Jiangsu, Zhejiang and Shannxi. All data are
obtained from the related statistical yearbook dataset. Nominal GDP and GFI variables are deflated by
the GDP deflator. Measures of financial structures and financial development are computed following
Beck and Levine (2002) and Levine (2002). Two measures of financial structure employed are: (i)
Structure-Activity (SA), which is computed as the log of the ratio of Stock Market Total Value Traded
to Private Credit, and (ii) Structure-Size (SZ), measured as the log of the ratio of Stock Market
Capitalization to Private Credit. The Structure-Activity measures the activity of stock market relative to
banks and other financial institutions. The Structure-Size measures the size of stock market relative to
the rest of the financial sector (bank and non-bank institutions). The aggregate measure of financial
structure (FS) is the weighted sum of all the principal components of the two variables SA and SZ,
which captures their total variation. The two underlying measures of financial development are: (i)
Finance-Size (FZ), computed as the log of the product of Private Credit Ratio and Stock Market
Capitalization Ratio; and (ii) Finance-Activity (FA), which is the log of the product of Private Credit
Ratio and Stock Market Value Traded Ratio. Finance-Size measures the overall size of stock market,
banks and non-bank financial institutions whereas Finance-Activity measures their total activities. The
aggregate measure of financial development (FD) is the weighted sum of all the principal components of
FZ and FA. A consistent time series of total physical capital stock for the whole sample period is not
available, so we construct it for each region from the respective real gross fixed investment series using
the perpetual inventory method. Following Luintel and Khan (1999, 2004), a depreciation rate of 8%
and the sample-average growth rate of real investment, are used to compute the initial capital stock.
3 Empirical Results
3.1 Descriptive Statistics
Table 1 reports some descriptive statistics of our dataset. It is evident that our sample consists of regions
with varying growth experiences. In our sample, Shannxi has the lowest real average GDP and Jiangsu
has the highest. In terms of growth, all of the regions represent high level of growth. Zhejing is the
fastest growing economy (13.259% average annual per capita real income growth) while Shannxi shows
the lowest but not low average annual growth rates of 11.325% during the sample period. A striking
feature, however, is that all provinces have evolved towards a more market-based financial system over
the last years. The levels of Private Credit Ratio have gone up in all but Shannxi is not so significant. On
average, the Private Credit Ratio is higher in the last five years compared with the first five years of the
sample, especially in Zhejiang province where the Private Credit Ratio is 1.984 times higher. However,
the rise in capitalization ratios and value-traded ratios are by far greater. They have shot up, respectively,
by 1.485, 2.807, 3.231 and 1.851 folds during the same period. Stock Market Capitalization and Stock
Market Value Traded both show positive average annual growth rates for all regions, although their
magnitudes vary across the provinces. The last ratio of Table 1 shows that the financial systems of all
regions grew towards a more market-oriented system; the average annual conversion rate is 19.239% for
Shanghai (lowest) and 65.006% for Shannxi (highest).
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Table 1 Descriptive Statistics 1993-2007
YM
YG(%)
PCS
PCE
PCM
PCG(%)
SMS
SME
SMM
SMG(%)
2721.22
12.293
1.588
1.846
1.703
1.425
1.020 1.515
1.310 19.888
SH
SAS
SAE
SAM
SAG(%)
SZS
SZE
SZM
SZG(%)
0.643
0.937
0.750
37.411
0.643
0.831
0.773 19.239
YM
YG(%)
PCS
PCE
PCM
PCG(%)
SMS
SME
SMM
SMG(%)
6097.58
13.011
0.601
0.872
0.731
2.900
0.057 0.160
0.143 40.119
JS
SAS
SAE
SAM
SAG(%)
SZS
SZE
SZM
SZG(%)
0.146
0.319
0.243
59.547
0.090
0.184
0.189 36.483
YM
YG(%)
PCS
PCE
PCM
PCG(%)
SMS
SME
SMM
SMG(%)
4248.16
13.259
0.632
1.254
0.930
5.217
0.052 0.168
0.148 40.717
ZJ
SAS
SAE
SAM
SAG(%)
SZS
SZE
SZM
SZG(%)
0.159
0.281
0.230
72.035
0.080
0.133
0.154 34.440
YM
YG(%)
PCS
PCE
PCM
PCG(%)
SMS
SME
SMM
SMG(%)
1187.77
11.325
1.114
1.115
1.168
-0.823
0.067 0.124
0.143 63.401
SX
SAS
SAE
SAM
SAG(%)
SZS
SZE
SZM
SZG(%)
0.190
0.294
0.251
272.53
0.059
0.116
0.121 65.006
YA—average of GDP YG—average annual growth rate of GDP PC—private credit/GDP SM—stock market
capitalization/GDP SA—stock market total value traded/private credit SZ—stock market capitalization /private
credit Subscripts S、E、M、G are the average value of the first five years, the average value of the last five years, the
mean value of the sample period and the average annual growth rate, respectively. SH = Shanghai; JS = Jiangsu; ZJ
= Zhejiang; SX = Shannxi.
；
；
；
；
；
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3.2 Empirical Results
From table 2, we can see that in open economy financial structure is important for Shanghai, Zhejing
and Shannxi, but has no statistically significant effect on Jiangsu. The coefficient of financial structure
for both Shanghai and Shannxi is negative, which means that in open economy bank-based financial
system seems more important for the economic growth of Shanghai and Shannxi. The coefficient for
Zhejing is positive, which means that in open economy market-based financial system seems more
important for the economic growth of Zhejiang. The coefficient of both FS and FD for Jiangsu is
statistically insignificant, so we cannot tell the differences of FS and FD for Jiangsu. From the statistical
results, we can also see that the coefficient a5 is significantly positive and a6 is significantly negative,
which mean that in open economy the change of international trade volume has significantly positive
influence on economic growth of Shanghai. In fact, in these years the ratio of EXIM and GDP has been
as high as 170%. In comparison, FDI has significantly negative effect on GDP. For Jiangsu, the
coefficient of FS and FD is statistically insignificant, while the coefficient of EXIM is statistically
significant which means that the extraverted economy in Jiangsu play a great role in economic
development. For Zhejing, the situation is different with Shanghai. The coefficient a5 is significantly
negative and a6 is significantly positive, which mean that in open economy the change of international
trade volume has significantly negative influence on economic growth of Zhejing and FDI has
significantly positive effect on it. For Shannxi, both a5 and a6 are statistically significant which means
that the effects of open economy on the economic growth of Shannxi have not represented.
SH
Coef.
t Sta.
Sig.
F Sta.
JS
a0
18.249***
16.630
.000
19843.873***
a0
Table 2 Results of Specification (3)
a1
a2
a3
a4
.631***
7.697***
-.062***
.064***
43.034
15.393
-3.564
3.550
.000
.000
.007
.008
Sig.
.000
Adj. R2
a1
a2
a3
a4
a5
260
a5
.018**
1.095
.035
D-W
a6
a6
-.026**
-2.874
.021
2.604
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Coef.
t Sta.
Sig.
F Sta.
ZJ
Coef.
t Sta.
Sig.
F Sta.
SX
Coef.
t Sta.
Sig.
F Sta.
-.579
.694***
.648
.017
-.010
.078**
.003
-.137
15.096
1.184
.738
-.398
3.239
.202
.895
.000
.275
.484
.702
.014
.846
8008.407***
Sig.
.000
Adj. R2
.99975
D-W
1.973
a0
a1
a2
a3
a4
a5
a6
dum1
1.287
.819*** .289**
.079*** -.067*** -.059** .047** -.069***
1.457
26.290
2.685
7.509
-5.693
-2.523
2.902
-4.372
.196
.000
.036
.000
.001
.045
.027
.005
18188.686***
Sig.
.000
Adj. R2
.9999
D-W
2.532
a0
a1
a2
a3
a4
a5
-1.978
.964***
.335
-.192**
.211***
-.035
-.345
9.920
.359
-2.956
3.526
-.305
.739
.000
.729
.018
.008
.768
524.548***
Sig.
.000
Adj. R2
.996
D-W
-.027**
-2.990
.020
dum2
-.053***
-3.941
.008
a6
-.013
-.254
.806
1.585
4 Conclusion
The relationship between economic growth and financial system is always a hot topic. This paper
studies this spot in open economy and makes a comparison between eastern and western regions. The
results show that financial structure is important for Shanhai, Zhejiang and Shannxi, while its effect on
Jiangsu is not statistically significant. For the statistically significant coefficients, the coefficient for
both Shanghai and Shannxi is negative, so bank-based financial system seems more important for
economic growth in these two provinces, and the coefficient for Zhejiang is positive, so market-based
financial system is more important. The coefficient of both FS and FD for Jiangsu is statistically
insignificant which seems in accordance with the financial service view and needs further deep research.
：
Acknowledgment
This work is supported by Zhejiang Social Sciences Fund (10CGYD85YBX)
References
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markets. Journal of Money, Credit and Banking, 2001, 33 (1), 16-41.
[3]. Beck T., Levine R. Stock markets, banks and growth: panel evidence. Journal of Banking and
Finance, 2004(28): 423-442.
[4]. Kul B. Luintel, Mosahid Khan, et al. Financial structure and economic growth. Journal of
Development Economics, 2008 (86): 181-200.
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