Download Empirical Research on the Relationship between Financial

Empirical Research on the Relationship between Financial
Development and Economic Growth: Based on the Time Series
Analysis of Hubei Province
SUN Yachao
School of Economics, Wuhan University of Technology, P.R.China, 430070
Abstract It is a relatively mature theory that Finance should make mutual development with economy.
Based on this, the paper using the methods of Johansen cointegration-test and Granger-causality-test
investigates the relationship between financial development and economic growth of Hubei province for
the first time, and hopes to provide policy support for the local government’s decision-making. And that,
the research has far more meaning for the balancing of the regional development.
Key words Hubei Province, Johansen-cointegration-test, Granger-causality-test,Financial Development,
Economic Growth
Since the reform and opening, the economic and financial industries in Hubei province have made
rapid progress. GDP has increased from 82.438 billion yuan in 1990 to 749.70 billion yuan in 2006, and
maintained a high annual growth rate. Particularly, finance becomes an important tool in macro
economic control and an essential lever in the distribution of social resources. The financialization trend
of economy is becoming increasingly obvious. Therefore, it’s of far reaching importance to make
research on the relationship between finance and economy in Hubei province, and it’s also an important
factor that cannot be ignored in the implementation of Central Rise Strategy. In order to provide an
experience support for the strategy, this paper using the methods of Johansen cointegration-test and
Granger-causality-test investigates the relationship between financial development and economic growth
of Hubei province for the first time.
1 Introduction
In recent years, researches about the affect of financial development to economic growth have
attracted universal attention in the world. Many studies indicate that a sound financial support system
may reduce the cost of information and transaction; and that affect the saving rate, invest
decision-making, technology innovation and economic growth rate (Levine 1997). But it is
unambiguous that different economic developing stage calls for the different financial structure, that is,
financial structure tends to exert influence on stable and rapid economic growth via waking on capital
formation. For example, when dwelling on capital accumulation, people prefer to fling more energy into
capital externalities or stable returns than non-renewable elements to weight the growth rate of per
capital GDP (P. Romer, 1986; R. Lucas, 1988; S. Rebelo, 1991). Here is two ways for the financial
system to affect the capital accumulation: one is changing saving rate, the other is saving reallocation;
furthermore, it can also reach the goal via altering the technology innovation rate, namely, only probing
into the creation of the new technics or the new products (Romer, 1990; Grossman, G. &E. Helpman,
1991; Aghion P& Howitt P, 1992).
Based on the initial work by Goldsmith (1969), empirical research on the relationship between
financial development and economical growth has been deepened. Levine’s research (1997), data rooted
in the year of 1960 1989 from 80 countries, indicates that financial deepening and economic growth
have the positive relationship. Kul B. Luintel & Mosahid Khan (1999) using the methods of Var and
cointegration test got the conclusion that financial development and economical growth have the mutual
causality. Moshin & Abdelhak (2000) using the method of 2SLS got the conclusion that financial
development and economical growth have the positive relationship. By the time series data, Adolfo
Sachsida (2001) using the method of Granger causality test got the conclusion that financial
development and economical growth have the mutual causality. By the panel data, Beck & Levine (2002)
，
－
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and Noman Loayza & Romain Ranciere (2002) using the method of GMM got the conclusion that
financial deepening accelerates the economic growth at some degree, etc… It is obvious that macro
layer analysis makes up the mainstream, yet ignoring the regional layer analysis of the relationship
between financial development and economical growth. Hereby, the paper using the methods of
Johansen cointegration-test and Granger-causality-test investigates the relationship between financial
development and economic growth of Hubei province for the first time, and hopes to provide policy
support for the local government’s decision-making. And that, the research has far more meaning for the
balancing of the regional development.
2 Variable Description
This paper is planning to use the methods of Johansen cointegration-test and Granger-causality-test to
investigate the relationship between finance and economic growth of Hubei province, in order to
uncover the relationship entirely and realistically and provide a theory and experience support for the
policy decision in Hubei financial development and economic growth.
2.1 Data selection
The selection of data bases on following three principles: one is the availability, namely, data like
GDP, CPI index and etc can be collected from China Financial Yearbook, China Statistic Yearbook,
China economic Yearbook and relating statistic websites; another is the continuity, namely it allows of
no absence of data in any year of the period; the last is the realistic meaning, namely considering the
realities of the country and the province, for example, the stock market in China is now paying more
attention to investors than capital chaser, yet it still cannot reach the goal and is now deviating from the
national economy growth, which greatly weakens the realistic meaning of the data.
2.2 Index selection
Taking the home and abroad scholars’ studies on the relationship between finance and economic
growth for references, we use the following empirical indexes:
Index of Deposit
This index is defined as deposit in saving bank over GDP, using the amount of deposit to reflect the
state of financial operation. In undeveloped countries, it’s to a great deal supply-oriented not
demand-oriented that contributes to the growth of economy, and capital market is small, so this index
can reflect the running of economy indirectly.
Index of Loan
This index is defined as credit amount over GDP, which represents an important capital source in
undeveloped countries. So it’s often regarded as an essential service to push the economic growth which
has a close relationship with economy.
Index of RPI (Retail Price Index)
We select this index based on the fact that the price index often greatly influences the GDP growth
rate. By this index, the influence can be eliminated.
⑴
⑵
⑶
3 Cointegration Test
The purpose of empirical test is to make regression analysis on the historical data and find out a
suitable model, to perform its function of forecasting. However, there is a premise for regression
analysis that the time series selected should be stable, or it’ll become the so-called spurious regression.
Cointegration Test can combine the virtues in the short-term dynamic model and the long-term
equilibrium model in time series analysis, and so provides a good solution for the modeling of unstable
time series. And that is the reason this methods be selected.
3.1 Unit Root Process
Prior to testing for cointegration, the time series properties of the variables need to be examined. In
testing for stationarity, the augmented Dickey-Fuller [1979] (ADF) test was implemented. Here the
variables include GDP, Deposit, Loan and RPI, and the result is given in table 1.
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Variable
ADF test
result
GDP
-2.3371
Deposit
3.2485
Loan
-2.0572
RPI
-2.6746
Note:
Table 1 The Result of ADF Test on the Original Sequence
Critical
Testing type
value
Conclusion
D.W.
R2
c, t, k
(5% level)
（ ）
（c, t, 1）
（c, t, 1）
（c, t, 1）
（c, t, 1）
F-test
-3.8288
unstable
0.5470
1.4343
3.6225
-3.8288
unstable
0.8735
1.9408 20. 7103
-3.8288
unstable
0.6115
2.1626
4.7219
-3.8288
unstable
0.5776
2.1189
4.1024
⑴ c and t represent constant term and trend term, and k represents the number of lagging phase.
⑵ R is the determination coefficient, F tests whether there exists heteroscedasticity.
⑶ The critical value in the table is figured out by the data given by Mackinnon.
⑷ We use the software of EViews 3.1 for data processing and calculating.
2
For the variables of
△GDP, △Deposit, △Loan and △RPI, the test result is given in table 3.
Table 2 ADF Test Result on the First-order Difference Sequence
Variable
ADF test
result
Testing type
c, t, k
（ ）
（c, t, 1）
（c, t, 2）
（c, t, 0）
（c, t, 1）
△GDP －3.4998
△Deposit -4.2047
△Loan -3.5698
△RPI -4.1921
Note: △ represents difference operator.
Critical value
(5% level)
Conclusion
-3.1222
stable
-3.8288
stable
-3.1222
stable
-3.8288
stable
R2
D.W.
F-test
1.1341
1.2096
0.3028
2.1870
2.1720
0.0833
1.6733
0.9995
1.7058
1.8359
0.0991
0.2686
Table 1 and table 2 show that the sequences of original variables are unstable but their first-order
difference sequences are stable, namely they are I(1) sequences. According to the theory of cointegration,
if the variable sequences are all integrated of the first order, and some certain linear combination of
these sequences is stable, then there exists a cointegration relationship between these sequences, which
fulfills the requirement of building a cointegrating equations system.
3.2 Cointegration Test on Variables
Generally, there are three common methods for cointegration test: first is the augmented
Engle-Granger (AEG) test, developed by Engle and Granger, basing on the error term of cointegration
regression; second is the Durbin-Watson test, namely conducting hypothesis test by DW value; third is
the cointegration test based on VAR, advanced by Johansen and Juselius. In this paper we use the
method of Johansen maximum likelihood (Johansen (1988), Johansen and Juselius (1990, 1995)) to test
the relationship.
The result of cointegration test is given in Table 3.
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Variable
Table 3 Johansen Cointegration Test Result
Critical
Null
Alternative
Trace Test
Value
hypothesis
hypothesis
Value
(1% level)
Feature
Conclusion
There are 2
cointegration
0.3929
r≤1
r=2
6.49
*3.76
relationships.
There are 1
0.6276
r=0
r=1
14.37
20.04
GDP
cointegration
Loan
0.1111
r≤1
r=2
1.53
6.65
relationships.
There are 1
0.5965
r=0
r=1
13.32
20.04
GDP
cointegration
RPI
0.1101
r≤1
r=2
1.52
6.65
relationships.
Note: * represents the critical value under 5% level of significance, others represent the critical value
under 1% level of significance.
r represents the vector number of cointegration sequences.
GDP
Deposit
0.7437
r=0
r=1
24.19
*15.41
⑴
⑵
The result in table4 shows that GDP has a certain cointegration relationship with Deposit, Lo
an and RPI. Their OLS estimators are respectively given as follows:
⑴
GDP = 1100.22653 + 0.8674359496 ∗ Deposit
R2
R2
R2
（228.4321） （0.0747）
[4.8164]
[11.6176]
= 0.9121， Adj.R = 0.9054 ，DW=0.2671，S.D.＝1746.304，F＝134.968 ，A.I.C=15.5340
⑵
GDP = 77.14478541 + 1.221600931 ∗ Loan
（124.7262） （0.0429）
[0.6185]
[28.4701]
= 0.9842 ， Adj.R = 0.9830 ，DW=0.9130，S.D.＝1746.304，F＝810.548 ，A.I.C=13.8174
⑶
GDP = 13226.46485 − 95.7760933 ∗ RPI
（5396.537） （51.4421）
[2.4509]
[-1.8618]
= 0.2105 ， Adj.R = 0.1498 ，DW=0.1964，S.D.＝1746.304，F＝3.4664，A.I.C=17.7297
2
2
2
Note: The figures in parenthesis represent the coefficient S.D. dependent var, those in square brackets represent
t-statistical amount.
⑴⑶
Equation - suggest,
The credibility of the above regression equations is not sufficient, so it’s necessary to process them
respectively. The processing way is as follows:
First, for GDP and deposit
To eliminate the autocorrelation in the error term, we construct a model as follows:
Yt = α + β X t + µ t
µ t = ρµ t −1 + ε t
，δ
, ε t ~ IID(0
2
),ρ ≠ 0
⑷
If ρ can be determined, the autocorrelation will be eliminated. Here, we using the Cohrane-Orcutt
Iteration Method estimate the value of ρ , and correct the coefficient in equation
until the error term
converges. The corrected equation
⑴ can be rewritten as:
GDP = 3508.4079845 + 0.481034 ∗ Deposit
⑴
⑸
Here, we have reason to believe that the unit variation of deposit will result in a weak increase of
GDP.
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⑵
Secondly, for GDP and Loan
The error term in equation has an uncertain autocorrelation, but the equation itself is perfect, so we
can reckon on that the unit variation of credit loan will result in a strong increase of GDP.
Thirdly, for GDP and RPI
The preliminary analysis indicates that the credibility of equation is poor. However, combined with
the results of the foregoing stability test and cointegration test, we can trust that the poor credibility
results from the small sample size. So, the equation still conveys some economic meaning, namely there
is negative correlation between RPI and GDP.
⑶
4 Causality Test
Granger causality test is often used for the causal relationship research on time series variables. This
paper use the method to test whether financial development contributes to economic growth in Hubei
province, and the testing result is given in table 4.
Table 4: Granger causality test
No.
Null hypothesis
Lags.
F-Statistic
Prob.
Decision
Deposit is not the Granger
7.01275
0.12868
reject
4
reason of GDP
GDP is not the Granger reason
4
4.34989
0.19556
accept
of Deposit
Loan is not the Granger reason
4
4.98589
0.17398
reject
of GDP
GDP is not the Granger reason
4
2.42002
0.31314
accept
of Loan
RPI is not the Granger reason
1
11.5135
0.00600
* reject
of GDP
GDP is not the Granger reason
1
2.15993
0.16966
*accept
of RPI
Note: * means the decision is made under the significant level of 1%, and the others are made
under 5%.
When lag.=2, there is bilateral reason relations between Deposit and GDP under the
significant level of 5%. Under 1% significant level, GDP is the single direct reason of deposit.
When lag.=1, RPI is still the single direct reason of GDP under 5% significant level.
①
②
③
⑴
⑵
⑶
Table5 suggests that,
First, there is a mutual-stimulating relationship between households saving deposit and GDP in Hubei
province in short run (e.g., 2 years), namely deposit can result in the increase of GDP, and vice versa.
But in a long run (e.g., 4 years), deposit can promote the increase of GDP to some extent, yet the
increase of GDP may not result in the increase of deposit in financial institutes. Maybe there are two
reasons: one is the development of financial market broadens the investment field and people are no
longer limited their income to traditional field like deposit; the other is the change of people’s
consuming view, like consumption in advance, brings the decrease of bank deposit.
Second, bank loans still promote GDP growth in Hubei province. It conforms to the overall
conditions in China and most of developing countries, namely supply-oriented economic growth pattern.
In addition, as can be seen from the above long-term relationship between deposit and GDP, Hubei
province is now undergoing a transformation period from supply-oriented to demand-oriented.
Third, time lag of influence that Retail Price Index (RPI) exerts on GDP is relatively short, so price
rising will bring some shock on the increase of GDP. This conclusion ties in very well with the effect
that inflation exerts on economic growth.
5 Summary and Conclusion
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Based on the total production of finance and GDP, and the annual data from 1999-2006, this paper
using the methods of Johansen cointegration-test and Granger-causality-test investigates the relationship
between financial development and economic growth of Hubei province, without regard for the effect of
retail price index. The result shows that financial development does promote the increase of GDP; yet
the increase of GDP has relatively limited influence on financial development in Hubei province. In
addition, it needs to be noticed that the decrease of deposit really foreshows the coming of the period of
economic transition, but on the other hand, the increasing ratio of credit loan to GDP indicates the
continuation of extensive economy. Therefore, what we need to do in solving the problem of Hubei
economic development is One Transition and One Transformation, that is, one transition from supply
oriented to demand-oriented and one transformation from extensive economy to intensive economy.
References
[1]Rohinson Joan(1952), “The Generalization of the General Theory”, in: The Rate of Interest and Other
Essays, Macmillan, London:67-142
[2]R.Lucus(1988), “On the mechanics of economic development”, Journal of Monetary Economics
22(Jan.):3-42.
[3]E.S.Shaw(1973), Financial Deepening in Economic Development[M], Oxford University Press.
[4]R.G.King&R.Levine(1993a),“Finance, entrepreneurship and economic growth: Theory and evidence”, Journal of Monetary Economics 35:513-542.
[5]Romer, Paul M. (1986), “Increasing returns and long-run growth”, Journal of Political Economy,
94(May): 1002-1037.
[6]Mankiw, Gregg, David Romer and David Weil(1992), “A contribution to the empirics of economic
growth”, Quarterly Journal of Economics 107(May):402-437.
[7]Grossman, G. and E. Helpman (1991), “Quality Ladders in the Theory of Growth”, Review of
Economic Studies, 58(Vol.): 43-61.
[8]Aghion P& Howitt P(1992), “A model of growth through creative destruction”, Econometrica,
60(Feb.):323-351.
[9]T.Beck& R.Levine(2002), “Industry growth and capital allocation”, Journal of Finance Economics
64:147-180.
[10]R.Levine(1997), “Financial development and economic growth: views and agenda”, Journal of
Economic Lecture 35(June):688-726.
[11]Kul B. Luintel, & Mosahid Khan(1999), “A quantitative reassessment of the finance-growth nexus:
evidence from a multivariate VAR”, Journal of Development Economics, 60(Vol.): 381-405.
[12]Kahn, Moshin and Senhadji, Abdelhak(2000), “Threshold effects in the relationship between
inflation and growth”, Manuscript.
[13]Adolfo Sachsida(2001), “A causality test between financial deepening and growth”, Working paper
of Social Science Network Electronic Paper Collection.
[14]Norman loayza, Romain ranciere(2002), “Financial development financial fragility and growth”,
CESifo Working paper, 684(5).
The Author can be contacted from Email: [email protected]
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