Download China Region Economic Growth Convergence and Its Mechanism

China Region Economic Growth Convergence and Its Mechanism
1
Xu Hongfan 1
Liu Duan2
School of Management, China University of Geosciences (Wuhan), Wuhan, Hubei, 430074, P.R. China
2 Management School, Hunan University, Changsha, Hunan, 410082, P.R. China
Abstract We make cluster analysis on twenty-nine provinces or municipalities directly under the
Central Government, divide them into three regions according to their economic structure, and then we
make Barro regression to test China regional economic growth convergence. It indicates that there is no
absolute β convergence but a significant club convergence, namely there is a conditional convergence
in the poorer region group and the richer group but not between them, even not in the Region II of
middle economic level. This is different from the previous results about the east, the west and the middle
regions. Convergence mechanisms of the neo-classical growth theory and the new growth theory are
both found in the Region I and the Region III. The convergence resulting from technology transfer in
Region I is slower than that in the Region III due to the differences between learning methods of these
two regions in the process of learning technology.
Keywords National economics, Club convergence, Cluster analysis, Barro regression, Panel data
1 Introduction
According to the neo-classical growth theory, the economics of the countries of lower income will
grow faster, so the income of poor countries will get close to the rich countries, which is called
economic growth convergence. Borro and Salai(1992) put forward absolute convergence and
conditional convergence under different circumstances[1]. The absolute convergence means that the per
person economic growth ratio of the poor countries are higher than the rich countries, given that national
economic characteristics are not considered. The conditional convergence means that the economic
growth ratio is in proportion to the degree of national economics departing from its equilibrium, where
national economic characteristics are considered. On this base, Durlauf and Johnson (1996) bring
forward Club Convergence, meaning that the economic growth ratio of the club members of similar
economic structure are convergent in a long-run if they have similar initial conditions[2]. With the
development of the new growth theory, the differences of national technology advancement induce
different national economic growth ratio, and the convergence mechanism put more attentions to
technical proliferation and transfer than capital marginal return degression. Convergence can be
explained by these two determinants.
The economic growths of many regions are accelerating during China reform, and the differences in
regions are more significant since 1990. The differences are more and more discussed by many scholars.
Cai Fang and Du Yang (2000), Shen Kunrong and Ma Jun (2002) divide China into three regions, the
east, the west and the middle, and survey conditional convergence in these regions[3,4]. Liu Qiang(2001)
find that there is convergence among different provinces as the neo-classical growth theory assumes[5].
Lin Yifu (2003) discovers that there is no absolute convergence but a conditional convergence in
China[6]. Xu Xianxiang (2004) investigates city economics and finds that there are convergence
mechanisms of both the neo-classical growth theory and the new growth theory in China cities[7].
As a whole, the economics of the east coastal area is better than the west and the middle in the past
twenty years, so many literatures divide China into the east, the west and the middle to survey regional
convergence. However, there are three obstacles. Firstly, this kind of division has no consentaneous
criterion. More important, this kind of division cannot define the similar economic structure, so the
results may have some flaw. In addition, previous researches used cross-sectional data, but the estimate
of cross-sectional analysis is biased. The panel analysis is not only consistent with the results of
neo-classical growth theory, but also can resolve the biased estimate. Besides this, the reason and the
mechanism of convergence are also worth studying. Therefore, we make cluster analysis on twenty-nine
provinces or municipalities directly under the Central Government, divide them into three regions
1
according to their economic structure 1 , and make panel analysis on regional economic growth
convergence and its mechanism.
2 Division of Regions
Accoding to Cai Fang (2000), Wang Xiaolu, Fan Gang (2004), Xu Helian, Lai Mingyong (2003), we
select marketability level, city and countryside structure, industrial structure, city open level, human
capital investment, material capital investment, and growth rate of population to measure area economic
stricture[3,8,9], shown in Table 1.
Index
Marketability level
City and countryside structure
Industrial structure
City open level
Human capital investment
Material capital investment
Growth rate of population
Province
Beijing
Szechwan
Shanxi
Neimenggu
Anhwei
Sinkiang
Sort
1
1
2
2
2
3
Province
Tientsin
Liaoning
Gansu
Hebei
Jiangxi
Qinghai
Table 1 Economic Structure Indices
Symbol
Definition
SC
The government expends and disbursement to GDP
CX
The urban population to the total population
CY
The second, three industries to GDP
KF
The area imports and exports to GDP
RZ
The student in above middle school to the total population
WZ
The entire society fixed assets investment to GDP
RK
The natural population growth rate
Sort
1
1
2
2
3
3
Table 2 Region Cluster
Province
Sort
Province
Shanghai
1
Jiangsu
Jilin
1
Heilongjiang
Chekiang
2
Shandong
Shanxi
2
Fujian
Hainan
3
Guizhou
Yunnan
3
Sort
1
1
2
2
3
Province
Guangdong
Hubei
Hunan
Henan
Guangxi
Sort
1
1
2
2
3
We make cluster analysis on the averages of the economic structure index from 1989 to 2004. Table 2
shows the results. Sort I includes Beijing, Tientsin, Liaoning, Heilongjiang, Jilin, Shanghai, Jiangsu,
Hubei, Guangdong, and Szechwan. They have the highest marketability level and city open level, and
the proportion of the second, three industries in their GDP is the largest. Sort II includes Hebei, Shanxi,
Neimenggu, Chekiang, Anhwei, Fujian, Shandong, Henan, Hunan, Shanxi, and Gansu. Sort III includes
Jiangxi, Guangxi, Hainan, Guizhou, Yunnan, Qinghai, and Sinking. They have the lowest marketability
level and city open level, and the proportion of the second, three industries in their GDP is the smallest.
Our division is different from others in previous literatures, for they divide China into the east, the west,
and the middle. In our division, the economic structures in a region seem more similar, and may more
satisfy the precondition of club convergence.
3 Regional Convergence and Its Mechanism
3.1 Model and data
Barro regression is applied in many studies on convergence, but most of them adopt cross-sectional
data. Islam(1995) prefers panel analysis to cross-sectional analysis, because the estimates in the former
are biased. The panel analysis is not only consistent with the results of the neo-classical growth theory,
which assumes the long-run growth ratios of the economic entities are equal to each other and the rate of
technology advancement is exogenous, but also can resolve the problem of the biased estimate.
Moreover, Islam finds that panel regression equation can be developed based on Barro regression and
MRW analysis frame consequently [10]. As a result, we make Barro regression on panel data to analyze
the regional convergence in China. The Barro regression is g i , t , t + T = α + β l n ( y i t ) + ε i , t .
Our data come from China Statistical Yearbook (1989 to 2005) and the Assembly of Statistical Data
of New China in Past 50 Years. Per person GDP is the real per person GDP, namely the nominal per
1
Chongqing became a municipalitiy directly under the Central Government in 1997, and its statistic method has
changed a lot, so it is excluded from our data. Because of the unavailability of data in the capital storages of Tibet
and Ningxia, they are also excluded.
2
person GDP revised according to the national price index in the same year. The capital storage is
simulated according to the perpetual inventory. Firstly, we simulate the nominal capital storage based on
the fixed assets current capacity. Then we get the real capital storage according to price index.
3.2 Descriptive statistics
Firstly, we measure δ convergence with the dispersion of region per person GDP logarithm, which
can test whether there is an absolute convergence. If the δ becomes smaller time-dependently, the
δ convergence exists. It means that the difference of regional economics becomes more insignificant. If
the δ becomes larger time-dependently, the δ dissimilation exists, which means that the difference
of regional economics becomes more significant.
Figure 1 to Figure 4 show that the national regional difference, the regional difference of Region I
and Region II are all increasing in past ten years, meaning that there is a δ dissimilation. The
difference of Region III is decreasing in past ten years, except in 1993, meaning that there is a δ
convergence.
0.3
0.3
0.25
0. 25
0.2
0.2
0.15
0. 15
0.1
0.1
0.05
0. 05
03
20
01
20
99
19
97
19
95
19
93
19
19
89
20
19
03
01
20
99
19
97
95
19
19
19
19
93
91
89
19
91
0
0
Figure 2: Difference of per person GDP of Region
Figure 1: Difference of national per person GDP
0.2
Ⅰ
0. 25
0.2
0. 15
0. 15
0.1
0.1
0. 05
0. 05
Figure 3: Difference of per person GDP of Region
03
20
01
20
99
19
97
19
95
19
91
93
19
19
Ⅱ
19
89
03
20
01
20
99
19
97
19
95
19
93
19
91
0
19
19
89
0
Figure 4: Difference of per person GDP of Region
Ⅲ
3.3 Regional convergence analysis
We make Barro regression to test absolute β convergence, conditional convergence and club
convergence in regional economic growth, and the results are shown in Table 3. Regression 1, 2, 3 and 4
respectively tests convergence in the entire country, Region I, II, and III. Regression 1 shows that the
coefficient of initial per person GDP is not insignificantly negative, which means that the absolute β
convergence is not significant in the entire country. However, when we divide China into three regions,
we find that a significant conditional convergence exists in Region I and III, for the coefficients of initial
per person GDP in regression 1and 3 are significantly negative. The coefficient of initial per person
GDP in regression 2 is significant positive, so there is no conditional convergence in Region II, this is
different from others’ results. Club convergence is distinguished from conditional convergence, and it
means that the economic entities with similar initiate economic structure in a group will convergent,
namely that there is a conditional convergence in the poorer region group and the richer group but not
between them, even not in the Region II of middle economic level. Our results about the three regions
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nicely describe the club convergence, which is different from others’ results based on the division of the
east, the west, and the middle.
Table 3 Regional Absolute Convergence and Club Convergence
Regression 1
Regression 2
Regression 3
Regression 4
Fixed effect
Yes
Yes
Yes
Yes
α
0.501
0.171
-0.261
1.440
β
-0.041 (-0.841)
-0.002** (-2.312)
0.059*** (3.515)
-0.184*** (-7.712)
R2
0.095
0.198
0.153
0.251
Note: ***, **, and * mean that the coefficients are different from zero at significance of 1%, 5%, and 10%.
T-statistics are in the parenthesis.
3.4 Convergence mechanism
The frame of Dowrick and Rogers (2002) study can survey the convergence mechanism in both the
neo-classic growth theory and the new growth theory, the former resulting from capital marginal return
degression and the latter resulting from technical popularization and transfer[11]. Therefore, we use this
regression, g i ,t = c + β ln( y i ,0 ) + χ k i ,t .Here, g , y , and k is respectively assumed as the growth
rate of per person GDP, the initial per person GDP, and the growth rate of per person capital storage.
This regression includes capital variable, which directly describe the effect of capital accumulation on
convergence. The initial per person GDP is explained as technological gap, so we can survey the effects
of capital accumulation and technical transfer on convergence at the same time. If χ is significant and
χ <1, the convergence in the neo-classic growth theory exists, and if β is significant and β <0, the
convergence in the new growth theory exists.
Table 4: The Effects of Capital Accumulation and Technical Transfer on Convergence
Regression 5
Regression 6
C
0.198
1.146
β
-0.013** (-2.085)
-0.185 (-7.790)
χ
0.568*** (4.544)
0.370* (1.356)
R2
0.342
0.361
Speed of Capital Convergence
4.76
4.81
Speed of Technologic Convergence
1.62
1.84
Note: ***, **, and * mean that the coefficients are different from zero at significance of 1%, 5%, and 10%.
T-statistics are in the parenthesis. Speed of Capital Convergence is (1- α )(n+g+ δ ), and Speed of Technologic
Convergence is -[ln(1+t β )]/t.
Capital variable is respectively introduced into regression 2 and 4 to get regression 5 and 6, to study
the convergence mechanism in Region I and III. The goodness of fit of regression 5 and 6 (34.2% and
36.1%) are better than regression 2 and 4(19.8% and 25.1%), and the coefficients, α and β , are both
significant, and α <1 and β <0, so both capital marginal return degression and technical popularization
and transfer affect the convergences in Region and III.
The speeds of capital convergence in Region and III is respectively 4.76% and 4.81%, very close to
the speed of 5% in Dowrick and Rogers (2002). The speeds of technologic convergence in Region
and III is respectively 1.62% and 1.84%. It means that as a developing country, China is the receiver
of technology, the open level, the marketability level, and the proportion of the second and third
industries to GDP of Region are higher, and the provinces in this region learn foreign technology
earlier than other regions. During the process of learning, they explore the economic growth mode by
applying and doing. Region III has the lowest open level and marketability level, and it learns the
successful experiences from Region and grows by observing, so the process of digesting technology
is shorten. Therefore, the speed of technologic convergence in Region
is lower than that in Region
III.
Ⅰ
Ⅰ
Ⅰ
Ⅰ
Ⅰ
Ⅰ
4. Conclusion
We make cluster analysis on twenty-nine provinces or municipalities directly under the Central
Government, divide them into three regions according to their economic structure. The panel analysises
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find that there is no absolute β convergence but a significant club convergence in China. Convergence
mechanisms of the neo-classical growth theory and the new growth theory are both found in the Region
I and the Region III. The convergence resulting from technology transfer in Region I is slower than that
in Region III due to the differences between learning methods of these two regions in the process of
learning technology.
Reference
[1] Barro, R., Sala-i-Martin, X. Convergence. Journal of Political Economy, 1992, 100(2): 223-251.
[2] Durlauf, Steven N., Johnson, Paul A. Multiple Regimes and Cross-Country Growth Behavior.
Journal of Applied Econometrics, 1995, 10: 365-384.
[3] Cai Fang, Du yang. Convergence and Divergence of Regional Economic Growth in China.
Economic Research, 2000,10: 30-37.
[4] Shen Kunrong, Ma Jun. The Characteristics of Club Convergence of China Economic Growth and
Its Cause. Economic Research, 2002,1:19-27.
[5] Liu Qiang. Convergence in China. Economic Research, 2001,6:15-19.
[6] Lin Yifu, Liu Peilin. Chinese Development Strategy and Economic Convergence. Economic
Research, 2003, 3:35-42.
[7] Xu Xianxiang. Convergence in Chinese Cities. Economic Research, 2004, 5:40-48.
[8] Wang Xiaolu, Fan Gang. Analysis on the Regional Disparity in China and the Influential Factors.
Economic Research, 2004, 1:33-44.
[9] Xu Helian, Lai Mingyong, Xu Qingsong. GRPLS Regression on Influencing Factors on China
Economic Growth. Journal of Management Sciences in China, 2003,6:8-12.
[10] Isiam, N.. Growth Empirics: A Panel Data Approach. Quarterly Journal of Economics, 1995, 110:
1127-1170.
[11] Dowrick,S., M. Rogers. Classical and Technological Convergence: Beyond the Solow-Swan
Growth Model. Oxford Economic papers, 2002, 54: 369-385.
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