This paper develops a biennial Malmquist-Luenberger productivity index, in which it takes resources and the environment into account, and use a spatial econometric analysis to measure the Chinese provincial spatial convergence of the total factor productivity ( TFP ) to conclude its decomposition. The empirical results show that: 1) China’s TFP increase significantly in recent years, mainly driven by technical improvement; 2) there is nationwide conditional convergence of productivity except for diffusion in the northeast and east regions. Because of the large spatial differences amongst various areas in China, the convergence of different region is affected by different factors; 3) we expect that “resource curse” would present in the regions in China excluding east regions. “Pollution haven” exists in the Central and western areas, suggesting that the perspective of China’s industrial environment is not optimistic; 4) the current ownership structure does not facilitate TFP growth, and industrial structure of inland areas limits local TFP growth. In general, if policy makers intend to converge the development gap between regions, assisting the developing areas to catch up with the relatively developed regions, it is crucial to improve the system of state-owned enterprise and the industrial structure, and government also needs to evaluate and test the effect of FDI rationally.
Since the reform and opening-up policy from 1978, China’s economic performs amazingly, and the economic society changes greatly, this phenomenon is called as “Chinese miracle” by scholars. Along with the rapid economic development, however, China’s economic space layout changes greatly. In particular, the growth gap between regions has the trend to be deeper. Regional differences expanding not merely reduce the economic efficiency, but affect social stability. At present, China’s economy is into the transformation stage with slow development, and regional economic development is entering a new period. In order to implement “the Belt and Road” strategy, and promote the regional economic innovation from theory to practice, how to identify the influencing factors of regional development difference, narrow the gap in development among regions and realize the regional coordinated development, has become a great hurdle to the government and academia.
As one of the hot topics of academic research, regional disparity is ubiquitous in the economic development. Academic circles generally believed that the keys of the dynamics of economic growth mainly include inputs, TFP and technological change and etc. In early 1990’s most researchers thought that inputs play an important role to drive the economy growth. As the Neoclassical growth theory, the point of diminishing marginal returns is accepted by scholars, which makes researchers begin to pay close attention to the TFP. And a large number of empirical studies have shown that TFP, technological changes and efficiency variations are the main reasons for the differences in regional economic growth. The disparity of regional economic growth is mainly from two aspects. On the one hand, the parity of growth speed on the TFP, technological changes and efficiency variations. On the other hand, the disparity of the factors which influences on TFP, technological changes and efficiency variations. Due to the existence of spatial effect on the growth of TFP and the influencing elements, we need to consider such spatial impact when we study the regional economic growth difference. For this reason, this article mainly discusses the TFP convergence on a spatial framework.
To measure the TFP gap in China it is customary to evaluate the China’s economic growth pattern. In fact, China’s economic growth, to a large extent, depends on the high energy consumption, high emission and high pollution.
shows that with the increase of human activities greenhouse gas emissions increased significantly, carbon dioxide emissions of 90 years in the world is 1.5 times to the emissions at the beginning 80 years.
The traditional economic growth theory, however, only focus on GDP and other “good” output rather than the “bad” output such as the carbon dioxide produced by enterprise production activities, and only use economic interest to evaluate the enterprise’s production efficiency, resulting in bias when we measure the regional economic development. Now, China’s economy is in the stage of economic transition and the slowdown in growth, which means that the economic development should depend on “quality and efficiency” rather than “quantity”. Therefore, to evaluate China’s economic growth, it is necessary to take energy inputs and factors of environmental control into the production function, in which the energy structure, industrial structure and other factors should be considered also, thus on point of the quality growth we are able to investigate the spatial effect of China’s economic growth.
The rest of this paper is organized as follows. The next section features a brief review of the relevant literature about the sources of economic growth. The theoretical methods, which contain the Biennial Malmquist-Luenberger (BML) productivity index, Spatial Durbin Model (SDM) and its relevant test are introduced in Section 3. Section 4 discusses the data and empirical results, and analyzes the spatial difference containing the dynamics of economic growth and convergence. Finally, Section 5 presents the conclusions and policy implications derived from the study.
Researching the related factors which affect China’s economic growth and convergence is crucial to identify the developmental difference of different region, and has important significance to regional development balance in China. On the discussion of China’s economic high-speed growth, Chow et al. [
Accordingly, many scholars do a lot of empirical research in the convergence problem of economy. Mankiw [
For the computational development of the Malmquist-Luenberger (ML) index, Caves et al. [
Through the above literature, we can find that: Part of the literatures assume that the efficiency of different areas in China will tend to the same level, ignoring the regional heterogeneity (see e.g., Zou et al. [
Contra posing the limitations of above study, this paper tries to lucubrate from the following three aspects: 1) combine the data envelopment analysis (DEA) and directional distance function, and use the BML index to measure Chinese TFP, considering the carbon emission constraints and energy inputs; 2) introduce the spatial econometrics to index decomposition theory, analyze the total effect, direct effect and indirect effect of influential elements in different regions; 3) utilize Spatial Durbin Model to study the development convergence in different region, and try to explain the effect of different factors to the TFP convergence.
In this section, we use the data envelopment analysis (DEA), a non-parametric estimation method, to decompose the TFP. DEA method is proposed by Charnes [
The regional production technology level is reflected in the productivity and the production frontier of the input-output function. That is the set of the maximum of the expected output and the minimum of not-expected output by the given inputs. Taking into account the importance of energy and environment in the production stage of enterprises, it is assumed that the production set in period t is:
where the variables
where
The standard measure to a region’s efficiency is the max improvement range for the reduction of inputs and increase of outputs. Chambers et al. [
where
Equation (4) shows that the BML index is decomposed into two components: technical efficiency change (EFF) and technological change (TC). The first component, EFF, which indicates a catching-up effect and measures the change in the distance towards the best practice frontier by given the technology. The second component, TC, which measures the technological progress, captures the extent, to which the production frontier shifts from period t to
Where
s.t.
Conditional convergence (Barro [
where
In this equation, if the explanatory coefficient is significant we can achieve two objectives. On the one hand, the variable
Anselinet et al. [
where
Based on the view of Pace and LeSage [
Among them,
Equation (14) provides a way to measure the spatial impact of regions.
This study considers 30 provinces in China as study subjects. Data for 15 years between 1999 and 2012 are collected in the empirical analysis. In the productivity measure, the inputs variables (labor, capital stock and energy consumption), and outputs variables (GRP and CO2 emissions) are considered in each region. We also use the independent variable (technology patents, energy structure, ownership structure, industrial structure, urbanization level, foreign direct investment) to measure the spatial effect on productivity.
The data on labor input and GRP are obtained directly from the China Statistical Yearbook [
And
Therefore, the capital stock of t can be obtained by the following formula:
Using the Equation (16), we adopt different depreciation rates and capital stocks for each province.
The actual provincial CO2 emissions cannot be obtained directly from the official data. CO2 emissions mainly result from fossil energy consumption. The publication Guidelines for National Greenhouse Gas Inventories [
where j represents the type of energy; E represents a variety of energy consumption; and NCV, CEF and COF represent the average low calorific values of energy, carbon emission coefficients and the carbon oxidation factor, respectively. For control variables, the level of technology is represented by the number of patent applications, the energy structure of coal consumption accounts for the proportion of total energy consumption. The industrial output value accounts of the proportion of industrial structure and the proportion of urban population accounts for the second total industrial output.
We also study the influence of the innovation activity, energy structure, ownership structure, industrial structure, regional openness, and urban scale on economic growth. The innovation activity is represented by the number of patent applications, and the energy structure is expressed as the proportion of raw-coal consumption in total energy consumption. Besides, the ownership structure is presented by the ratio of the state- owned factories production to factories production; The industrial structure is denoted by the percentage of value-added of industry in total value-added. The level of urbanization is expressed by the proportion of the city population in total population. The degree of economic openness, in addition, is represented by FDI.
By decomposing the growth of the 30 provinces of China, we get the ratio of the total factor productivity, technical efficiency and technological progress of the provinces in 1999 to 2012.
Mean | S.D. | Max | Min | Obs | |
---|---|---|---|---|---|
Gross regional product (100 million CNY) | 6620.05 | 6671.37 | 42,860.33 | 223.88 | 450 |
Carbon dioxide emissions (10,000 tonnes) | 23,104.14 | 18,408.16 | 10,667.02 | 892.85 | 450 |
Labor (10,000 persons) | 2304.32 | 1525.40 | 6288.00 | 230.4 | 450 |
Capital stock (100 million CNY) | 18,642.28 | 17,905.50 | 110,064.98 | 953.54 | 450 |
Energy consumption (10,000 tones) | 8797.00 | 6970.46 | 40631.00 | 384.48 | 450 |
Technology (item) | 10,845.60 | 25,738.52 | 269,944.00 | 62 | 450 |
Energy structure (%) | 71.23 | 19.63 | 158.57 | 24.22 | 450 |
Ownership structure (%) | 44.18 | 21.81 | 116.08 | 7.78 | 450 |
Industrial structure (%) | 40.26 | 11.38 | 65.99 | 12.66 | 450 |
Urbanization level (%) | 34.61 | 16.14 | 89.76 | 14.25 | 450 |
FDI (100 million) | 216.1275 | 292.95 | 1511.1828 | 1.0621 | 450 |
Provinces | EFF | TC | TFP | Provinces | EFF | TC | TFP |
---|---|---|---|---|---|---|---|
Ningxia | 0.9047 | 2.5458 | 2.3031 | Guizhou | 1.0347 | 1.5719 | 1.6264 |
Jiangsu | 1.0020 | 2.1309 | 2.1350 | Gansu | 0.9955 | 1.6217 | 1.6143 |
Shaanxi | 0.9723 | 2.0326 | 1.9763 | Hainan | 0.8851 | 1.8102 | 1.6022 |
Xingjiang | 0.8783 | 2.1757 | 1.9110 | Henan | 0.9486 | 1.6786 | 1.5923 |
Fujian | 0.8745 | 2.1730 | 1.9004 | Guangxi | 0.8394 | 1.8913 | 1.5877 |
Qinghai | 0.9781 | 1.9329 | 1.8905 | Hunan | 1.0049 | 1.5712 | 1.5790 |
Zhejiang | 0.9246 | 2.0366 | 1.8832 | Hebei | 0.8814 | 1.7824 | 1.5710 |
Yunnan | 0.9384 | 1.8763 | 1.7607 | Tianjin | 1.2052 | 1.3008 | 1.5677 |
Shandong | 0.9022 | 1.9403 | 1.7505 | Shanghai | 1.0000 | 1.5420 | 1.5420 |
Chongqing | 1.0582 | 1.6083 | 1.7019 | Guangdong | 1.0000 | 1.5111 | 1.5111 |
Sichuan | 1.0736 | 1.5745 | 1.6904 | Jilin | 0.9097 | 1.3635 | 1.2403 |
Anhui | 0.9878 | 1.7076 | 1.6868 | Liaoning | 0.9141 | 0.8152 | 0.7451 |
Beijing | 1.0247 | 1.6299 | 1.6701 | Heilongjiang | 1.4271 | 0.3764 | 0.5371 |
Jiangxi | 0.9794 | 1.6900 | 1.6551 | Inner Mongolia | 1.2146 | 0.0740 | 0.0898 |
Hubei | 1.0131 | 1.6127 | 1.6338 | Shanxi | 0.5903 | 0.1342 | 0.0792 |
Countrywide | 0.9787 | 1.5904 | 1.5345 | Central China | 0.9811 | 1.7263 | 1.6462 |
Northeast China | 1.0836 | 0.8517 | 0.8408 | West China | 0.9207 | 1.3990 | 1.3710 |
East China | 0.9700 | 1.7857 | 1.7133 |
Notes: The relative changes of TFP, TC and EFF were reported in the table in 2012 to 1999, and Regional EFF, TFP, TC change is the average value.
EFF has improved in the northeast while showed a downward trend in the eastern, central, western and other regions; From provincial aspect, the EFF of Beijing, Tianjin, Inner Mongolia, Heilongjiang, Jiangsu, Hubei, Chongqing, Sichuan, Guizhou and Hunan (ten provinces) has improved and Heilongjiang’s EFF made fastest progress in the period of 1999-2012; Guangdong and Shanghai’s EFF, besides, have not change in the period. Except the above provinces, the EFF of other provinces has descended especially the Shanxi.
TC has a rising trend in the country. From regional level, TC has declined in the northeast which exists the technical retrogression, but has a rising trend in the eastern, central and western areas; From the province level, Liaoning, Heilongjiang, Shanxi and Inner Mongolia (four provinces) appear technology retrogression phenomenon; Among them, Shanxi and Inner Mongolia’s technology retrogress most seriously; Jiangsu, Zhejiang, Fujian, Shaanxi, Ningxia and Xinjiang make a great progress in technology, mainly concentrating in the Eastern region.
The TFP has an upward trend which is consistent with the TC in the regional aspect. Similarly, only Liaoning, Heilongjiang, Shanxi and Inner Mongolia, from the province level, have a phenomenon of productivity degradation. EFF and TC show the fact that Shanxi now has the lowest productivity in China. From 1999 to 2012, the technical efficiency of our country had been decreasing. But because the positive effect of the technology improvement is greater than the negative effect of the technical efficiency decline, the total factor productivity in China is increasing, and we can draw a conclusion that the total factor productivity improvement in the period of our country is mainly due to the improvement of technology.
This paper determines the type of fixed effects by the LR test of the joint significance of space and time, and then make LM test of spatial lag and spatial error (including robust LM inspection) basing the results of LR test. Having compared with the results of Wald test, we use the spatial model (SDM) to carry out the empirical analysis. The relevant test results are shown in Appendix Tables A1-A3. Lesage and Pace [
Innovation activity has significantly positive effect to EFF in central, eastern regions and nationwide, showing that the innovation of the technology has promoted the convergence of EFF; For the whole country, central and northeast region, the promotion of the innovation activity is concentrated in the spillover effect. In addition, the total effect
Variable | Countrywide | Northeast | East | West | Central |
---|---|---|---|---|---|
EFFt−1 | −0.0635** | −0.3022*** | −0.1351** | −0.0122 | −0.0648*** |
(0.0260) | (0.0350) | (0.0610) | (0.0560) | (0.0140) | |
Patent | 0.0367** | 0.0018 | 0.0147*** | 0.0169 | 0.2158** |
(0.0150) | (0.0080) | (0.0030) | (0.0160) | (0.1070) | |
Energy | 0.0434 | −0.0771 | 0.0138 | −0.002 | 0.2788*** |
(0.0280) | (0.1770) | (0.0200) | (0.0510) | (0.0850) | |
Ownership | −0.0794 | −0.0791 | 0.1358 | −0.0778 | −0.1882** |
(0.0720) | (0.0750) | (0.1070) | (0.1670) | (0.0790) | |
Industrial | −0.8811** | −0.1739** | −0.0458 | −0.8161* | −4.8184* |
(0.4200) | (0.0770) | (0.3600) | (0.4610) | (2.5200) | |
Urbanization | 0.0984 | −1.2804* | −0.0709 | 0.3428 | 0.9735 |
(0.3860) | (0.7370) | (0.1400) | (0.4170) | (0.7700) | |
FDI | −0.0091 | 0.0153 | −0.0091 | 0.013 | 0.0491 |
(0.0280) | (0.0120) | (0.0160) | (0.0310) | (0.0430) | |
N | 390 | 39 | 130 | 143 | 78 |
R2 | 0.0488 | 0.5857 | 0.3595 | 0.0857 | 0.1509 |
Variable | Countrywide | Northeast | East | West | Central |
---|---|---|---|---|---|
TCt−1 | 0.3349*** | −0.066 | −0.2548*** | 0.1803 | 0.1940*** |
(0.0620) | (0.1400) | (0.0900) | (0.1140) | (0.0660) | |
Patent | −0.0368*** | −0.0049 | −0.0462*** | −0.0035 | −0.0029 |
(0.0120) | (0.0170) | (0.0110) | (0.0260) | (0.0550) | |
Energy | −0.1188 | −0.1677*** | 0.1050*** | −0.0323 | −0.6073*** |
(0.0750) | (0.0390) | (0.0380) | (0.1180) | (0.0920) | |
Ownership | 0.0842 | −0.1751*** | −0.0152 | −0.1185 | −0.0793 |
(0.0970) | (0.0460) | (0.1790) | (0.2460) | (0.0970) | |
Industrial | 0.9226 | −1.6138*** | 0.9090** | 0.3999 | 0.1287 |
(0.5660) | (0.1270) | (0.4260) | (0.9110) | (1.2610) | |
Urbanization | 0.158 | 1.5460* | 0.3889 | −0.5331 | 0.169 |
(0.4640) | (0.8210) | (0.4510) | (0.6920) | (0.5650) | |
FDI | −0.022 | −0.0085 | −0.018 | −0.0462 | −0.0108 |
(0.0320) | (0.0100) | (0.0130) | (0.0470) | (0.0310) | |
N | 390 | 39 | 130 | 143 | 78 |
R2 | 0.4135 | 0.9644 | 0.3024 | 0.4711 | 0.929 |
Notes: *, **, *** denote p < 0.1, p < 0.05 and p < 0.01 respectively; Here only report the total effect of the regression results, the direct effect and spillover effect on EFF is shown in Appendix
to the central region is biggest, showing strong positive externality. It means that the innovation activity has the largest promotion on the convergence of EFF to the central region. In the aspect of TC, the total effect of innovation activity in the nationwide and the eastern region is positive, which indicates that the innovation activity has made the TC divergence in east, and further made TC divergence in nationwide. And the coefficient in the TC indicates that the positive impact of innovation activity is based on positive externalities in middle region and northeast while is based on direct effect on western region. This also indicates that the innovation activity can promote the improvement of the TC in the west, but it will not promote the regional convergence.
In the aspect of EFF, the energy structure only has a positive effect on the central convergence by the direct effect. This shows that central region can use coal to achieve the “catch-up” effect in technical efficiency while the energy consumption structure adjustment has little such effect in other regions. Energy structure is not significant on the total effect of TC in west, which shows that the energy structure does not promote technological progress in the West. Besides, the energy structure has positive total effect on east and negative total effect on northeast and middle area, which means that the energy structure not merely promote the convergence of the eastern technology, but also expand the difference in northeast and central region. It can be indicated that there is a heterogeneity of the influence from energy structure in China. From the spillover effect, we can know that the energy structure can promote the TC in the East, but it is unfavorable to the TC in the northeast and the middle region.
The structure of ownership has increased the regional difference on northeast TC and central EFF. And we can know from spillover effect that the structure of ownership hinders the development of EFF in northeast and TC in east China.
From regional level, the rise in industrial proportion will increase the difference of the regional EFF except for the east; From regional level, the total effect, direct effect and spillover effect of industrial structure are negative in nationwide. This phenomenon illustrates that the structure of industry not merely expands the divergence of EFF, but also inhibits the growth of EFF. This negative effect in the middle is more obvious. The central region of the industry relies heavily on local resources, which is unfavorable to the development of local high and new technology industries. This “resource curse” (Sachs et al. [
The level of urbanization only has significant effect on the TC and EFF in northeast. The total effect and spillover effect of EFF in Northeast China is negative, which indicates that the improvement of urbanization level occurs in relatively developed provinces, and thus, the urbanization increases the technical efficiency gap between developed provinces and backward provinces; The total effect and spillover effect on northeast TC are positive, which means that the urbanization can promote the industrial upgrading of other provinces in the technological innovation, and then narrow the technological gap in northeast.
The empirical results show that the FDI isn’t the dynamic to promote the convergence of EFF and TC in China. The regression results, moreover, are not significant in the middle and western regions, it is shows that the impact of FDI on technical efficiency has not been shown in the middle area and west, consisting with the conclusion of Guo [
Technology has a negative gross effect in the east but a positive in the west. It indicates that innovation activities accelerate the diffusion of TFP in the East, while the convergence of TFP in the West. Although the effect of innovation activities is not significant in some certain areas, we can know from the coefficient that it does indeed improve the productivity. For the whole country, technology promotes the growth of EFF and TC. Equation (4) proves empirically that innovation-activities promote the growth of TFP. We can get the consistent results from other regions. It’s worth mentioning that different regions have their own characteristics. The promotion on TFP derived from eastern innovation activities is not balanced in this region; Innovation activities show strong positive externalities in the northeast; In the west, innovation activities stimulates TFP
Variable | Countrywide | Northeast | East | West | Central |
---|---|---|---|---|---|
TEPt−1 | 0.7059*** | 0.1086 | 0.1693 | 0.3484*** | 0.3750*** |
(0.0860) | (0.1650) | (0.1190) | (0.1320) | (0.0790) | |
Patent | 0.0005 | 0.0295 | −0.0205** | 0.0082 | 0.1927** |
(0.0140) | (0.0520) | (0.0090) | (0.0160) | (0.0960) | |
Energy | −0.125 | 0.0241 | 0.1138*** | −0.0408 | −0.4858*** |
(0.1020) | (0.1310) | (0.0270) | (0.0640) | (0.0240) | |
Ownership | −0.0353 | −0.1851** | 0.2157 | −0.1315 | −0.2294** |
(0.0870) | (0.0720) | (0.1760) | (0.1030) | (0.1140) | |
Industrial | −0.172 | −2.4337*** | 0.5612 | −0.2237 | −4.3257* |
(0.4970) | (0.8290) | (0.3940) | (0.4010) | (2.2400) | |
Urbanization | −0.0231 | 0.5024 | 0.3402 | −0.31 | 1.3046** |
(0.4020) | (1.0250) | (0.3560) | (0.6390) | (0.6140) | |
FDI | −0.0025 | 0.0419* | −0.018 | −0.018 | 0.0387 |
(0.0250) | (0.0220) | (0.0140) | (0.0230) | (0.0350) | |
N | 390 | 39 | 130 | 143 | 78 |
R2 | 0.8923 | 0.8059 | 0.2822 | 0.6599 | 0.8484 |
Notes: *, **, *** denote p < 0.1, p < 0.05 and p < 0.01 respectively; Here only report the total effect of the regression results, the direct effect and spillover effect on TFP is shown in Appendix
growth by promoting TC, which direct effect is obvious; Similar to the northeast, innovation activities in the central region also presents positive externalities, but it can promote the convergence of TFP in this area differently.
The energy structure in the eastern region has a positive effect on TFP, which indicates that the energy structure can promote the TFP convergence. In the northeast, both the total effect and direct effect are negative, indicating that the increasing proportion of raw coal expenditure goes against the TFP improvement. This consists with the above analysis of EFF and TC in the northeast; Similar to the northeast, the energy structure in the central region performs to expand the TFP difference, and inhibits the growth of TFP, which is due to the specific industrial structure; In addition, the effect of energy structure is not significant on the TFP convergence and growth in the western region. Because of the heterogeneity of different regions, the role of energy structure on convergence is not significant in the whole China.
State-owned enterprises accelerate the TFP diffusion in the northeast and central regions. The structure of ownership has a negative direct effect on the TFP improvement in the northeast and east regions. In the east of China, the more perfect mechanism of market economy is more efficient. Especially in the northeast, the loss of state owned enterprises is serious, which hinders the economic development. In addition, the ownership structures in the west and the central region show negative spillover effects, which may be due to the imperfect management of state-owned ingredients. Local protectionism in the central region blocks the circulation of factors such as capital and technology, further influencing the productivity improvement. The analysis of the EFF in the central region also confirms this statement. The economy in the west is mainly driven by government investments. However, the government-oriented economic mode is unitary and can’t support economic growth. Therefore, the rise of the proportion of the state-owned structure in the western region is not assistant to the TFP.
The industrial structure has a significant negative total effect in the northeast and central regions, which shows that the industrial structure has increased the diffusion of TFP in these areas. The declining industrial environment and disordered market competition restrain the TFP in northeast. The resource-dependent industry structure in the central region also inhibits the growth of TFP. Industrial clusters in the east, besides, are conducive to the growth of TFP.
Urbanization level is significantly positive in central region, showing the positive spillover effect. It declares urbanization can promote central TFP growth, and further narrows the development gap; In the west, there is no such spillover effect, but the direct effect is positive on the local provinces; Although the urbanization level is not significant on northeast, it really promote the northeast TFP because of the effect on TC and EFF.
FDI only promotes the convergence of TFP in northeast, and shows the positive spillover effect, which indicates that the introduction of FDI can stimulate the improvement of TFP in the surrounding areas, and further narrow the gap of regional development; FDI shows negative spillover effect in the east, due to the provinces which introduced the FDI are more developed provinces, FDI has increased the gap between the developed regions and the relatively backward regions; For the central and western regions, FDI showed a negative direct effect, reflecting that the introduction of foreign capital is not conducive to the improvement of TFP. It supports the “Pollution Haven” Hypothesis in a certain.
In the current critical situation of resources and environment, how should Chinese economy achieve sustainable growth and narrow the development gap among regions? Based on this question, this paper uses the DEA model to measure the total factor productivity of China provinces and its decomposition in the period of 1999-2012, and analyzes the spatial convergence of economic growth in China using the spatial Durbin model. The study finds as follow:
From 1999 to 2012, Chinese total factor productivity has a rising trend, which is mainly derived by technical improvement; Technological-backward provinces are mainly in the northeast, while the provinces with the fast technology progress are mainly in the eastern region, and shows that the development of China is not balanced. Productivity has conditional convergence nationwide. It means that the gap in productivity growth among the regions of China is shrinking. There are “club” convergences in the central and western regions, but in the northeast and east the convergence of different regions is affected by different factors, which shows that there is a huge difference in the economic growth of various regions in China.
The innovation activity has promoted the efficiency of all regions. This confirms that the “innovation-driven” strategy, which is implemented to guide the regional economic innovation development, is reasonable and prospective. The energy structure promotes the growth of productivity in the east, which shows that the industrial structure of the eastern region can utilize energy better and get greater economic benefits by lower down the pollution cost. However, due to the fact that the industrial structure of northeast and central region over relies on energy, the uses of raw coal aren’t conducive to productivity growth. “Resource curse” exists from northeast and central region.
The industrial structure promotes the productivity growth in the northeast and central China, and inhibits the growth of productivity in the northeast, central and western regions. While in the east, it is shown as a driving effect on TFP. This shows that the industrial structure of the northeast, central and western regions should be adjusted. The central and western region can learn from the industrial development mode of east, giving full play to the regional characteristics such as endowment advantages of resource, and further narrow its development gap with the eastern developed region.
The state-owned enterprises are the key to TFP growth in all regions. The government should adjust the guidance to strengthen the economic cooperation among regions and jointly promote the long-term economic growth regionally; The injection of FDI makes the northeast accumulate some capital and achieve further technology improvement, which contributes to the northeast economic growth, but there is a “pollution heaven” in the central and western region; Moreover, urbanization promotes the productivity of hinterland, showing the importance of urbanization in inland areas.
The authors are grateful to Jinan University as this work was funded by case study projects of Enterprise transformation development of the institute for enterprise development, Jinan University (No. 12).
Mei, L.H. and Chen, Z.H. (2016) The Convergence Analysis of Regional Growth Differences in China: The Perspective of the Quality of Economic Growth. Journal of Service Science and Management, 9, 453-476. http://dx.doi.org/10.4236/jssm.2016.96049
Nationwide | Northeast | East | West | Central | ||
---|---|---|---|---|---|---|
LR test | Spatial fixed | 30.9523 | 9.5288** | 8.8076** | 4.4938*** | 5.6171 |
Time fixed | 27.0802** | 17.1082 | 32.0296*** | 11.9144 | 34.0069*** | |
LM test (robust) | spatial lag | 7.8009*** | 9.4342*** | 8.7216*** | 1.7081 | 2.0128*** |
spatial error | 11.8502** | 13.0144** | 12.1879*** | 5.8694** | 4.1030** | |
Wald test | spatial lag | 25.9386*** | 15.1201** | 18.5121*** | 14.0831* | 12.1278* |
spatial error | 14.4481** | 14.4200** | 16.3334** | 3.2752 | 19.4052*** |
Nationwide | Northeast | East | West | Central | ||
---|---|---|---|---|---|---|
LR test | Spatial fixed | 78.3506*** | 24.6279*** | 21.1314** | 23.7641*** | 49.2122*** |
Time fixed | 14.2177 | 18.694 | 38.3150*** | 13.022 | 17.0377 | |
LM test (robust) | spatial lag | 8.2761*** | 6.2268** | 8.2053*** | 8.4072*** | 10.4722*** |
spatial error | 12.5335** | 5.6751** | 11.8276*** | 11.9061*** | 14.3289*** | |
Wald test | spatial lag | 22.9030*** | 15.7858** | 17.2399** | 13.8574* | 14.6735** |
spatial error | 20.9467*** | 21.8075*** | 15.3271** | 14.2594** | 14.2867** |
Nationwide | Northeast | East | West | Central | ||
---|---|---|---|---|---|---|
LR test | Spatial fixed | 59.7511*** | 24.6279*** | 9.2095 | 24.5453*** | 50.5407*** |
Time fixed | 28.1792*** | 18.694 | 59.6224*** | 20.0697*** | 15.4246 | |
LM test (robust) | spatial lag | 7.8719*** | 2.2268 | 2.0829 | 1.2221 | 6.9778*** |
spatial error | 11.8492** | 4.6751** | 6.044** | 15.3614*** | 6.2578** | |
Wald test | spatial lag | 13.3461* | 39.0109*** | 12.9337* | 33.7675*** | 13.6889* |
spatial error | 15.1170** | 25.5371*** | 3.8885 | 1.7259 | 15.035** |
Variable | Nationwide | Northeast | East | West | Central |
---|---|---|---|---|---|
EFFt−1 | −0.0589** | −0.3595*** | −0.1528** | −0.0084 | −0.0839*** |
(0.0240) | (0.0550) | (0.0710) | (0.0520) | (0.0180) | |
Patent | 0.0015 | −0.0382*** | 0.002 | −0.0015 | 0.0595 |
(0.0050) | (0.0100) | (0.0080) | (0.0060) | (0.0480) | |
Energy | 0.0413 | −0.0011 | 0.0159 | −0.0018 | 0.3608*** |
(0.0270) | (0.0960) | (0.0230) | (0.0490) | (0.1080) | |
Ownership | −0.0148 | −0.3130*** | 0.0192 | 0.0296 | 0.0405 |
(0.0250) | (0.0900) | (0.0380) | (0.0530) | (0.0580) | |
---|---|---|---|---|---|
Industrial | −0.0956* | −0.0091 | 0.068 | −0.1361* | −0.6726** |
(0.0490) | (0.2460) | (0.0940) | (0.0800) | (0.3310) | |
Urbanization | 0.0574 | −0.709 | 0.041 | 0.2034* | 0.3126 |
(0.0630) | (0.7450) | (0.0640) | (0.1060) | (0.1970) | |
FDI | 0.0017 | 0.0184** | −0.0068 | 0.0062 | −0.0286 |
(0.0040) | (0.0080) | (0.0060) | (0.0050) | (0.0260) |
Variable | Nationwide | Northeast | East | West | Central |
---|---|---|---|---|---|
EFFt−1 | −0.0046 | 0.0573** | 0.0177 | −0.0039 | 0.0191*** |
(0.0050) | (0.0260) | (0.0140) | (0.0090) | (0.0040) | |
Patent | 0.0352** | 0.0400** | 0.0126 | 0.0184 | 0.1562** |
(0.0150) | (0.0180) | (0.0080) | (0.0180) | (0.0620) | |
Energy | 0.0021 | −0.076 | −0.0021 | −0.0002 | −0.0820*** |
(0.0030) | (0.1010) | (0.0030) | (0.0070) | (0.0250) | |
Ownership | −0.0646 | 0.2339*** | 0.1166 | −0.1074 | −0.2287*** |
(0.0840) | (0.0370) | (0.0940) | (0.1750) | (0.0770) | |
Industrial | −0.7854** | −0.1648 | −0.1138 | −0.68 | −4.1458* |
(0.3920) | (0.3130) | (0.2750) | (0.4520) | (2.2040) | |
Urbanization | 0.041 | −0.5713 | −0.1119 | 0.1394 | 0.6609 |
(0.3440) | (0.4330) | (0.1400) | (0.3660) | (0.6060) | |
FDI | −0.0108 | −0.0031 | −0.0023 | 0.0067 | 0.0777 |
(0.0270) | (0.0040) | (0.0190) | (0.0300) | (0.0660) |
Variable | Nationwide | Northeast | East | West | Central |
---|---|---|---|---|---|
TCt−1 | 0.3827*** | −0.082 | −0.2120*** | 0.1827 | 0.2637*** |
(0.0860) | (0.1940) | (0.0740) | (0.1130) | (0.0750) | |
Patent | 0.0116 | −0.0580** | 0.0039 | 0.0269*** | −0.0347* |
(0.0110) | (0.0250) | (0.0110) | (0.0100) | (0.0210) | |
Energy | −0.1376 | −0.3558** | 0.0878*** | −0.042 | −0.8486*** |
(0.0870) | (0.1790) | (0.0320) | (0.1200) | (0.1820) | |
Ownership | 0.0246 | 0.0598 | −0.1414** | 0.0592 | 0.0335 |
(0.0450) | (0.1450) | (0.0600) | (0.1130) | (0.1100) | |
Industrial | 0.1113 | 0.4097 | 0.1869*** | −0.1034 | −0.4052 |
(0.1170) | (0.3360) | (0.0600) | (0.1830) | (0.5970) | |
---|---|---|---|---|---|
Urbanization | −0.07 | −0.3158 | −0.0417 | 0.0154 | −0.2202 |
(0.1140) | (0.8120) | (0.0810) | (0.1960) | (0.1750) | |
FDI | −0.0058 | −0.0003 | 0.0155* | −0.0155 | 0.028 |
(0.0060) | (0.0100) | (0.0090) | (0.0100) | (0.0290) |
Variable | Nationwide | Northeast | East | West | Central |
---|---|---|---|---|---|
TCt−1 | 0.3827*** | −0.082 | −0.2120*** | 0.1827 | 0.2637*** |
(0.0860) | (0.1940) | (0.0740) | (0.1130) | (0.0750) | |
Patent | 0.0116 | −0.0580** | 0.0039 | 0.0269*** | −0.0347* |
(0.0110) | (0.0250) | (0.0110) | (0.0100) | (0.0210) | |
Energy | −0.1376 | −0.3558** | 0.0878*** | −0.042 | −0.8486*** |
(0.0870) | (0.1790) | (0.0320) | (0.1200) | (0.1820) | |
Ownership | 0.0246 | 0.0598 | −0.1414** | 0.0592 | 0.0335 |
(0.0450) | (0.1450) | (0.0600) | (0.1130) | (0.1100) | |
Industrial | 0.1113 | 0.4097 | 0.1869*** | −0.1034 | −0.4052 |
(0.1170) | (0.3360) | (0.0600) | (0.1830) | (0.5970) | |
Urbanization | −0.07 | −0.3158 | −0.0417 | 0.0154 | −0.2202 |
(0.1140) | (0.8120) | (0.0810) | (0.1960) | (0.1750) | |
FDI | −0.0058 | −0.0003 | 0.0155* | −0.0155 | 0.028 |
(0.0060) | (0.0100) | (0.0090) | (0.0100) | (0.0290) |
Variable | Nationwide | Northeast | East | West | Central |
---|---|---|---|---|---|
TEPt−1 | 0.6109*** | 0.111 | 0.1083 | 0.3697*** | 0.4959*** |
(0.0460) | (0.1690) | (0.0760) | (0.1270) | (0.0670) | |
Patent | 0.0064 | −0.0724*** | 0.0062 | 0.0171** | 0.0318 |
(0.0100) | (0.0180) | (0.0100) | (0.0070) | (0.0340) | |
Energy | −0.1108 | −0.2666** | 0.0724*** | −0.0467 | −0.6542*** |
(0.0910) | (0.1280) | (0.0150) | (0.0700) | (0.0780) | |
Ownership | −0.0023 | −0.2626** | −0.0841** | 0.0622 | 0.0959 |
(0.0320) | (0.1310) | (0.0370) | (0.0680) | (0.1100) | |
Industrial | 0.0029 | −0.0383 | 0.2081*** | −0.1655* | −1.0171 |
(0.0880) | (0.2730) | (0.0540) | (0.1010) | (0.6460) | |
Urbanization | −0.0405 | −0.3626* | 0.0237 | 0.1455* | −0.2276 |
(0.1020) | (0.2090) | (0.0920) | (0.0830) | (0.1770) | |
FDI | −0.0050* | 0.0218 | 0.0063 | −0.0089* | −0.01 |
(0.0030) | (0.0180) | (0.0040) | (0.0050) | (0.0100) |
Variable | Nationwide | Northeast | East | West | Central |
---|---|---|---|---|---|
TEPt−1 | 0.095 | −0.0024 | 0.0609 | −0.0213 | −0.1209*** |
(0.0830) | (0.0050) | (0.0450) | (0.0370) | (0.0440) | |
Patent | −0.0059 | 0.1019** | −0.0267*** | −0.0089 | 0.1610** |
(0.0140) | (0.0450) | (0.0100) | (0.0150) | (0.0640) | |
Energy | −0.0142 | 0.2908** | 0.0414*** | 0.0059 | 0.1685** |
(0.0220) | (0.1200) | (0.0130) | (0.0100) | (0.0760) | |
Ownership | −0.0329 | 0.0775 | 0.2997** | −0.1937** | −0.3252*** |
(0.0810) | (0.1740) | (0.1470) | (0.0820) | (0.1150) | |
Industrial | −0.1749 | −2.3954*** | 0.3532 | −0.0582 | −3.3086** |
(0.4610) | (0.6580) | (0.3580) | (0.4300) | (1.6820) | |
Urbanization | 0.0174 | 0.8649 | 0.3166 | −0.4555 | 1.5321** |
(0.3330) | (1.2270) | (0.2770) | (0.6930) | (0.6690) | |
FDI | 0.0025 | 0.0200*** | −0.0243* | −0.0091 | 0.0487 |
(0.0250) | (0.0040) | (0.0140) | (0.0210) | (0.0370) |
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