Download Shandong Information Industry and Economic Growth: An

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Shandong Information Industry and Economic Growth: An
Empirical Analysis Based on Co-integration Theory∗
CAI Yanbing1, LIU Xueni2
1. School of Economics, Shandong Institute of Business and Technology, China, 264005
2. College of Population, Resources and Environment, Shandong Normal University, China, 250014
[email protected]
Abstract: Based on the co-integration theory, the paper makes an empirical analysis on the short-term
and long-term relationship between the information industry and economic growth in Shandong
province, by using data from 1995 to 2009. The main steps are: the unit root test, description
co-integration theory, error correction modeling, Granger causality tests and the analysis of generalized
impulse response function. The results suggest: there is a co-integration between the information
industry and economic growth in Shandong province; information industry has significant influence on
economic growth; information industry has individual Granger causality relation to economic growth;
impulse response analysis shows that there is a positive response to the interaction in a long run.
Keywords: Information industry, Economic growth, Co-integration analysis, Granger causality test,
Impulse response function
1 Introduction
Information industry is defined as a general term for industrial sector specializing in the developing
information technology, developing and producing equipment and product and providing information
services in social and economic activities, and industrial clusters including information collection,
production, testing, conversion, storage, transmission, processing, distribution, applications and so
numerous sectors. Information industry is mainly made up of information industry, information services
and information development industry [1].Currently, information industry has been regarded as a leading
industry which measures a country’s core competitiveness. During 1996~2004, the GDP increased from
16.09 percent to 16.43 percent, and stimulated economic growth by 0.35 percentage points in China,
while the information industry growth rate increased by 15.86 percentage points over the same period.
Obviously information industry has a great pulling function to GDP. Domestic and foreign scholars have
analyzed and made empirical research on the relationship of information industry and economic growth.
Dale W. Jorgenson, Khuong Vu (2005) described seven regions and 14 major economies and world
economic growth during the period 1989~2003 by comparing the growth of world output and the growth
of productivity input. As expected, the result showed that the information technology investment exert a
tremendous influence in the world economic growth and recovery, and differences in per capital output
is caused by different per capital input rather than changes in productivity [2]. R.Dholakia and
B.Harlam(1994) analyzed the correlation between the infrastructure and economic growth in United
States by means of econometric methods, and the results showed that there is a maximum correlation
between the telecommunications infrastructure and economic growth in 1990[3]. Xu Shenghua and Mao
Xiaobing measured information industry’s contribution to economic growth by using the index of
information abundance. Results indicated that in China, information industry’s contribution to economic
growth rate is 25.27 percent during 1998~2001, which showed that the information technology had a
strong impact on economic development [4]. Chen Xiaohua analyzed the information industry’s
contribution to economic development in Guangxi Province during 1996~2006 by means of empirical
measurement. He found that information technology’s indirect transformation effect to traditional
∗ Social Sciences and Humanities Plan of Ministry of Education: The study of information transmission problem on
quality and safety of agricultural products based on the perspective of supply chain.
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industry was strengthening over time, while the contribution of GDP was not significant [5].
Existing research mainly focused on the qualitative and policy research of information industry to
economic growth, but there are fewer empirical researches. Although a small amount of scholars have
used empirical analysis, basically they study from country’s macro perspective, and less of regional
micro-study. Based on the time series data of added value of information industry in Shandong Province
during 1995~2009, and after conducting unit root test and co-integration test, the paper establishes error
correction model of added value of information industry and economic growth. What’s more, it analyzes
Granger causality and impulse response between them, and exquisitely describes the relationship
between information industry and economic development in Shandong Province, with a view to make
recommendations to the development of information industry.
2 Co-integration Theory Review
Assuming a number of economic indicators are linked to some economic system, in the long-run these
variables should have a balanced relationship, which is the basic starting point in model establishing. In
the short term, because of random interference, these variables may deviate from the mean. If the
deviation is temporary, it will return to equilibrium over time; If it is persistent, there is not a balanced
relationship between these variables. Co-integration can be seen as the statistical nature of this balanced
relationship [6]. Engle and Granger pointed out that the linear combination of two or more non-stationary
time series may be stationary. If the stationary linear combination exists, these non-stationary time series
would have co-integration relationship which describes the long-standing “regularity” of them.
Co-integration theory can solve the spurious regression problem arisen by the traditional econometric
methods estimate for non-stationary time series as a new technology about dynamic model specification,
estimation and testing .The steps of empirical analysis using the co-integration theory are: Firstly, test
the stability of time series and its first-order differential variables; Secondly, test the co-integration
relationship between variables; Thirdly, establish error correction model between co-integration
variables and equilibrium; Finally, check and analyze the cause and effect relationship between
variables. In addition, the impulse response can be used to describe the delicate relationship between
time series variables.
3 Dynamic Empirical Analysis of Relationship Between Shandong Information
Industry and Economic Growth
Because Shandong Statistical Yearbook is based on the statistical division of the three industries, there is
no information industry statistics, it uses the added value of electronics and communications equipment
manufacturing (ELC) and telecommunications industries (COM) to replace approximately the
information industry (IT, unit: 100 million) increased value during 1995~2009. Using the gross domestic
product (GDP, unit: million) at current prices to represent the economy development of Shandong
during the same period. These data are obtained from the “Shandong statistical Yearbook” (1996~2010)
and “Shandong statistical Bulletin”. Because logarithmic processing of variables can not only avoid data
volatility, but also eliminate heteroscadasticity, and does not affect the co-integration relationship
between variables, it takes the natural logarithm for each variable. The variables become: LIT and LGDP,
whose time series (Figure 1) show that a linear correlation exists between them.
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10.8
10.4
L
G
D
P
10.0
9.6
9.2
8.8
8.4
4
5
6
7
8
L IT
Figure 1 LIT and GDP scatter graph
Test results in this article are complete with the help of metrology software Eviews5.0.
3.1 Variable unit root test
Before analyzing the time series, the stability of variables must be tested, and the standard test method is
unit root test. This paper uses the ADF method to test the stability of variables. Hypothesis testing
is:  H 0 : γ = 0 , that is to say the null hypothesis is the sequence that includes a unit root and the
H1 :γ < 0
alternative hypothesis does not include a unit root which may also include constant term and time trend
∧
term. We should first judge which hypothesis γ is to accept, and then determine whether or not the
time series includes unit roots. If the sequence includes a unit root, it is non-stationary, and it is
necessary to test its first-order differential stability. If the first-order difference is stable, the sequence is
a stable first-order integration which be expressed as I (1) . If not, continue to test until the sequence is
stable. The results are shown in table 1:
Table 1 ADF unit root test results
Variables
LIT
ADF test statistic
Test form (C,T,L)
1 percent critical values
Stability
△LIT
-3.0589
(C,T,2)
-4.9923
non-stationary
-6.1215
(C,0,3)
-2.7719
stationary
LGDP
-3.4177
(C,T,2)
-3.7700
non-stationary
△LGDP
-4.2719
(C,0,3)
-2.9372
stationary
①test form is (C,T,L)，C,T,L represent constant, linear time trend and lagged differences respectively in the
②△ indicates first order difference；③The choice criteria of lag phase are based on the minimum value of
Note:
model .
SIC.
In table 1, the ADF test values of both LIT and LGDP in original sequence are greater than the critical
values at 1 percent level of significance, so the null hypothesis is accepted, which means original
sequences are non-stationary. While the ADF values of their first-order differential sequences are less
than critical values at 1 percent level of significance, so the sequence I (1) is stationary.
3.2 Co-integration test between the variables
Currently, Engle-Granger two-step method and Johansen maximum likelihood method are the main
methods of the co-integration test. In order to avoid the inconvenience led by E-G method, and have
good small sample properties, the paper uses Johansen test method [7].This approach is used to test the
regression coefficient based on the VAR model, which suggested by Johansen and Juselius during 1988
and 1990. Johansen likelihood method tests co-integration relationship and the rank of co-integration
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vector by testing the number of non-zero latent roots of equation. Characteristic root test include two
types: trace feature value test and the maximum feature value test, as shown in table 2, 3.
In table 2, the trace statistic value is 19.373, greater than the critical value 15.495 at 5 percent level of
significance, therefore, the null hypothesis of zero co-integration vector is rejected and the alternative
hypothesis of one co-integration vector is accepted. Homogeneously, in table 3, max statistic test gets
the same conclusion that there is one co-integration relationship between IT and GDP in Shandong
Province.
Co-integration vector (LGDP, LIT) can be written as (1.000 000,-0.590 910) after being normalized, and
the long-run equilibrium relationship expression between them can be expressed as:
(1)
µ t = LGDP − 0 . 590 910 LI T
(0.028
87 )
The value in parentheses is standard error.
In the formula (1), the elasticity of LIT for LGDP is 0.591, which indicates that the logarithm of
information industry increases per 1 percent, accordingly the logarithm of GDP increases 0.591 percent.
The effect of information industry on economic growth in Shandong Province is quite significant.
Table 2 Johansen co-integration test(trace test)
Null hypothesis
Feature values
Trace statistic
5 percent critical value
P-Value
None *
0.748 2
19.372 6
15.494 7
0.012 4
At most 1
0.105 1
1.443 7
3.841 5
0.229 5
Table 3 Johansen co-integration test (Max statistic)
Null hypothesis
Feature values
Max statistic
5 percent critical value
P-Value
None *
0.748 2
17.928 8
14.264 6
0.012 6
At most 1
0.105 1
1.443 7
3.841 5
0.229 5
3.3 Error correction model
Error correction model (abbreviated as ECM) uses the dynamic non-equilibrium process of data to
approach the long-run equilibrium process of economic theory, and the specific process is as follows:
(2 )
∆ y t = β 0 + a ( y t −1 − k1 x t −1 ) + β 2 ∆ x t + u t
Error correction term ( yt −1 − k1 xt −1 ) can be replaced by ecmt −1 , which reflects the short-term
deviation of yt from xt at t time point. a is adjustment coefficient indicating the degree of regulation
of deviation and color tone between yt yt -1 and (k 0 + k1 xt −1 ) at t-1 time, and a < 0 . The model can be
changed as:
∆yt = β 0 + aecmt −1 + β 2 ∆xt + ut
(3)
Its parameters can be estimated by using the least squares method.
The co-integration test of time series variable suggests that a long-run equilibrium relationship exists
between information industry and economic growth in Shandong Province. In order to describe the
degree which changed from short-term fluctuations to long-run equilibrium between them, the error
correction model is needed to reflect the degree, and the result is as follows:
∆LGDP = 0 .029 1 + 0 .889 8 * ∆ LGDPt −1 + 0.094 8 * ∆ LITt −1 − 0 .279 1 * ECM t −1 (4 )
∆ L IT = 0 . 296 7 + 0 .051 4 * ∆ L ITt −1 − 0 .147 8 * ∆ LGDP t −1 + 0 . 270 3 * ECM t −1 (5 )
In the formula (4): error correction coefficient is -0.279, which means the GDP is in state of imbalance
in the short term, and it is approaching to the long-run equilibrium with the force of 27.91 percent.
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What’s more, in the short term, the effect of economic growth lagged one year on itself is very
significant(0.890), while the effect of information industry lagged one period on the GDP is not
significant(0.095). This is because information industry is an intelligence-intensive and capital-intensive
industry, and its capital accumulation and technology improvement need a process, in the short term, the
impact of information industry is not very significant, but in the long run the role of the information
industry will be very powerful.
In the formula (5): error correction coefficient is 0.270, which means the adjust force of information
industry to the deviation from long-run equilibrium is still very large. In the short term, the effect
coefficient of information industry lagged one year on itself is 0.051, while the effect of GDP lagged one
period on the information industry is negative, indicating that a negative correlation exists between
them.
3.4 Granger causality test
Granger causality test is proposed by Granger (1969) and promoted by Sims (1972),which is used to test
the causal relationship between variables.The method solves the problem of whether x causes y, which
mainly depends on how far the current y can be interpreted by the past x, and whether x can explain
higher levels after adding its lagged values. If x is helpful to y in the forecast, or the correlation
coefficient of both x and y is statistically significant, it can be said “x Granger cause y”, otherwise not. It
is shown from co-integration test of time series variables that a long-run equilibrium relationship exists
between information industry and economic growth in Shandong Province. However, whether a causal
relationship exists between them, the method of Granger causality test is needed to test for further, and
the test results are shown in table 4:
Table 4 The results of Granger causality test between Shandong information industry and the GDP
Null hypothesis
LIT does not Granger Cause LGDP
LGDP does not Granger Cause LIT
LIT does not Granger Cause LGDP
LGDP does not Granger Cause LIT
LIT does not Granger Cause LGDP
LGDP does not Granger Cause LIT
Samples
Lagged differences
14
1
13
2
12
3
11
4
LIT does not Granger Cause LGDP
LGDP does not Granger Cause LIT
F-statistic
Probability
0.165 17
0.692 23
0.497 55
0.475 24
3.024 15
0.105 16
0.609 92
0.566 85
0.976 92
0.473 29
0.501 64
0.697 49
1.307 53
0.476 72
0.285 16
0.868 09
The following conclusions can be drawn from table 4:(1) Overall, in the short term the effect of
information industry on the GDP is not significant in Shandong Province.(2) At the confidence level of
85 percent, information industry is the Granger cause of economic growth when the lag period is 2;
when the lag period is 3 or 4, information industry is the Granger cause of economic growth, but not
significant; when the lag period is 1, information industry does not Granger cause economic growth.(3)
When the lag period is 1, economic growth is the Granger cause of information industry in Shandong
Province, but not significant; when the lag period is 2, 3 or 4,economic growth does not Granger cause
information industry. To sum up, in the long run the promoting effect of information industry on the
GDP is significant, while the promoting effect of the GDP on information industry is not significant.
3.5 Analysis of impulse response between variables
Granger test describes statistically more causal relationship based on the lag structure of VAR system,
but excludes spot causal flow between variables. However, the impulse response function not only
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includes corresponding period relationship but also reflects the lag effect, beyond the observed ability of
Granger causality [8]. Impulse response function is used to analyze how the effect of disturbance of time
series model spread to each variable.
Impulse response has given a rich and delicate description on the issue of how the Shandong
information industry influences the GDP in the short and long term. Results of impulse response are
shown in figure 2 and 3. In figures, the solid line is the calculated value of the response function, and the
dotted line is the confidence interval of response function plus or minus twice the standard deviation.
In the figure 2, when given a positive pulse to IT at current period, the Shandong GDP will reach the
highest point in the seventh period after minor fluctuating in the first two periods, and followed by
steady growth. It indicates that the information industry is shocked by external conditions first, after that
the shock passes through the market to other industries, and finally brings same shock upon the GDP.
The shock has a significant promoting effect and longer continued effectiveness. Therefore, the
contribution of information industry to economic growth will become increasingly apparent over time.
In the figure 3, when given a positive pulse to the GDP at current period, Shandong IT will reach the
max in the fifth period after minor fluctuating in the first three periods, and followed by slow growth.
This shows that the shock of Shandong GDP will bring homonymous shock to IT too, that is, the
stimulating effect of the added value of Shandong GDP on its IT will change smooth in ten year, and
continues to show a positive response. In a word, Shandong GDP can also bring the development of
information industry, that is, they affect each other.
Response value
Response value
.15
.10
.05
.00
.3
.2
.1
.0
-.1
-.05
2
4
6
8
10
12
14
16
18
-.2
20
Lags
Figure 2 Response of Shandong GDP to the IT pulse
2
4
6
8
10
12
14
16
18
Figure 3 Response of Shandong IT to the GDP pulse
4 Conclusions and Policy Implications
Through empirical analysis of the relationship between information industry and economic growth in
Shandong Province, following conclusions can be drawn:
(1) A long-term stable relationship exists between information industry and economic growth during
1995~2009 year in Shandong Province, by showing that the information industry has a huge
contribution rate to economic growth, that is, the logarithm of information industry in Shandong
Province increases 1 percent, correspondingly the logarithm of GDP increases 0.591 percent. The
long-term cause and effect relationship between them performs that information industry is the
individual Granger causality relation to GDP, which fully confirms that the information industry is the
multiplier of economic development in Shandong Province.
(2) Error correction model shows that in the short term, the adjustment of information industry to the
deviation from long-term economic equilibrium is relatively small and Granger causality between them
is not significant in Shandong Province. The result means that information industry has an active role in
promoting economic growth after a lag period, and the reason may be that information industry has a
direct or indirect contribution to economic growth and the indirect contribution to traditional industries
transforming to GDP growth need a process.
(3) Impulse response analysis shows that information industry’s contribution to economic growth would
fluctuate in the short term, however, it has a stable and positive response to economic growth in the long
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ran, so the government should take long-term stable industrial policy.
(4) At present, the level of information-based is not enough high in Shandong Province, and the
information industry development is inconsistent with economic growth. In order to achieve GDP
organic growth, Shandong should accelerate the development of information industry. Firstly, it should
rely on the characteristics of regional resources to develop strong areas, establish developing program
and have clear direction. Secondly, they should make long-term industrial policy to guide enterprises to
orient technical innovation. What’s more, Shandong should establish monitoring mechanism of
information goods market, through the market adjustment mechanisms to promote the rational flow of
product elements so that achieve the optimal allocation of information resources. They can also select
effective information management industry to create a healthy and harmonious environment for the
information technology market. Finally, Shandong should adjust department structure and improve the
softening fraction of information industry, lead the sector structure to develop towards advanced and
rational direction, and ultimately develop by leaps and bounds.
Author in brief or Acknowledgment:
1. First Author: Cai Yanbing, Shandong Yantai, Shandong Institute of Business and Economics.
Yan-Bing Cai (1969 - ), male, Taian City, Shandomg Province, professor, vice president of School of
peninsula economy research, management PhD. Research Interests: regional economy.
Address: Binhai Road 191, Laishan Yantai City Zip Code: 264005
Tel: 13589776778 (M).
E-mail: [email protected]
2. The second author: Liu Xueni, Shandong Normal University.
Liu Xueni (1985 - ), female, Baoji City, Shanxi Province, China, Master, research direction: the regional
economy.
References
[1]. Jing Jipeng. Economics of information. Beijing: Science Press, 2007: 324 (in Chinese)
[2]. Dale W, Jorgenson, Khuong Vu. Information Technology and the World Economy. Scandinavian
Journal of Economics, 2005 (107): 631~650
[3]. R.Dholakia, B.Harlam. Telecommunications and economic development. Telecommunications
Policy, 1994 (6): 35~38
[4]. Xu Xianhua, Mao Xiaobin.The analysis of information industry's contribution to economic growth.
Management World, 2004 (08): 75~80 (in Chinese)
[5]. Chen xiaohua.Developing level of information industry in Guangxi and its contribution to
economic growth. Journal of Industrial Technology and Economy, 2009 (28): 123~126 (in
Chinese)
[6]. Gao Tiemei.Econometric analysis and modeling. Beijing: Tsing Hua University Press, 2006:
272~273 (in Chinese)
[7]. Zhang Xinli, Tian Ye.Empirical study of relationship between information industry and economic
growth in China. Journal of Dalian Maritime University (Social Sciences Edition), 2009 (05):
30~33 (in Chinese)
[8]. Johansen S. Statistical analysis of co-integration vectors. Journal of Economic Dynamics and
Control, 1988, 12 (2-3): 231~254
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