The Impact of Urbanization and Factor Inputs on China s Economic Growth A Spatial Econometrics Approach

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1 Economic Management Journal December 2018, Volume 7 Issue 2, PP The Impact of Urbanization and Factor Inputs on China s Economic Growth A Spatial Econometrics Approach Yajie Bai SHU-UTS SILC Business School, Shanghai University Abstract This paper utilizes a panel data of 31 provinces in China spanning from 2007 to Spatial econometrics is applied to carry out regression analysis of the impact of urbanization and factor inputs on China s economic growth. By comparing differences among regions, this paper finds that in the regional level, the positive effect of urbanization in the Eastern region and the Western region is significant, and the positive effect of the proportion of input factors in the Central region is also significant but to a lesser extent. In general, there exists spatial spill-over effect between urbanization and factor inputs structure and economic growth, i.e., both are capable of producing positive effect, but the input role played by the scale factor has diminishing marginal effect. Urbanization is more likely to become the driving force of economic growth and to stimulate economic growth. Keywords: Urbanization; Capital-Labor Ratio; Economic Growth; Spatial Econometrics 1. INTRODUCTION In the long run, China s economic development under the new normal is in need of structural adjustment, i.e., it is imperative to change the way of economic growth and break macroeconomic unbalance. On the path to become a high-income country, China s economy faces challenges to its labor-intensive and capital-intensive economic growth mechanism. The decline of the demographic dividend and the decrease of the efficiency of capital cause China s economic growth to lack intrinsic motivation. From the perspective of industrialization and urbanization, the growth of China s economy urgently needs upgrading of its industrial structure. It also needs to further promote urbanization, reasonable allocation of resources in industrial sectors and among different regions, improve the efficiency of utilizing resources, and make urbanization the new driving force of economic growth. By doing so can China break the lock-in effect of its original economic growth mechanism and implement stable development that further stimulates economic growth. Policy on China s economic development more and more focuses on how to achieve sustainable economic growth. Since 2012, China s GDP growth rate has been below 8%. To be specific, the real economic growth rate has been about 7.5%. The actual economic growth rate would be even lower than its current growth rated had it not been the governments direct interfere. The general view is that the slowdown in China s economic growth will lead to changes and decline in income, employment, industrial structure and fluctuations in many areas. East Asia Economic Development Report issued by the World Bank in 2006 indicates that the economic growth pattern of a country growing from a low-income economy to a middle-income economy cannot be applied to construct a high-income economy. Following the old economic growth model makes it difficult to break through the GDP per capita ceiling of $10,000. Economic growth will fall into a period of stagnancy. In 2013, China s GDP per capita reached $6,700. According to the World Bank s classification standard, China has become a middle-income country. With its economic growth slowing in recent years, China s economic growth rate has become a hot issue of concern. The remaining part of this paper is organized as follows. Section 2 reviews related literature. Section 3 introduces the data. Section 4 presents empirical analysis. Section 5 concludes the paper and provides policy implication

2 2. RELATED LITERATURE A great deal of research pertains to obstacles of economic growth. Felipe et al. (2012) analyzes the economic development of different countries and argues that industrial restructuring, export, and economic diversification are the causes of stagnant economic growth. Ohno (2009) indicates that the key for Vietnam to overcome the middle income trap is to develop an active industrial structure policy. Malkin and Spiegel (2012) investigates the economic development of different regions of China and finds that narrowing down the unbalanced development of regional economies can help China overcome vast regional disparities. Ma (2010) shows that the evolution of the demand structure and the adjustment of the industrial structure is the key to stimulate economic growth. Zheng (2011) extensively reviews the economic development of developing countries and argues that improving efficiency is the strategy that China must adopt. It also emphasizes that institution, policy, and infrastructure are the inherent forces of improving efficiency. Rao and Wei (2012) utilizes a two-stage theory and investigates the economic development of emerging countries taking into account of the actual conditions in China, and argues that technology progress is the key to economic development. Technology growth is the reasonable economic development model that China should pursue. Tian and Zheng (2013) refers to the development experience of Korea and indicates that China needs to rely on the development of total factor productivity for its economic growth. Zhan, Wang and Shi (2014) conduct both theoretical and empirical analysis and conclude that the heterogeneity of sector investment leads to slowing down of China s economic growth. Liu and Cai (2014) argues that the income and expenditure of people of upper-middle income countries contribute to those countries leap into high- income countries. Yang and Ru (2011) indicates that urbanization is an important strategy to maintain sustainable economic development for China, and proposes that promoting urbanization requires the cooperation of government, the market and social divisions. Zhang (2014) argues that zero mistakes in new urbanization strategy is the priority strategy of China s urban transformation and upgrading based on the background of the current economic and social development. Ma (2009) compares the development experience of countries all over the world. Taking into account of China s conditions, it indicates that urbanization and industrialization are the two engines that guide the path of China s economic growth. Wang and Qi (2013) thoroughly investigate the economic efficiency of urbanization and the effect of economic development. It proposes that a new way of urbanization facilitates rapid and sustainable economic development of China. Wang and Ma (2014) utilize factor decomposition method and propose that in the future the main way to reduce poverty in China depends on the allocation and alleviation effect on urban poverty. Wang and Yang (2014) puts forward that China s future urbanization should focus on innovation-driven development strategy. Wang and Wu (2015) investigates the threshold effect of China s economic fluctuations on economic growth and illustrates that stable government policies can help the national economy avoid falling into a serious crisis under economic shocks. To eliminate hindrance of economic growth, it is essential to investigate structural factors, which include changes in the industrial structure, changes in the income gap, regional development imbalances and other structural changes in many aspects. Using panel spatial econometrics model, this paper considers the imbalance in regional economic development in China and investigates the impact of urbanization on economic growth, from two perspectives of urbanization and micro capital -labor input. In the dual economy in long-run development, China s urbanization has made rapid development. The relation between urbanization and economic growth can be divided into three stages. The first stage is from 1987 to 1995, when urbanization was less than 30% and average annual growth rate was 2%. Growth rate was the lowest in this period, but the correlation between urbanization and economic growth was the highest, i.e., urbanization and economic growth promoted each other mostly in this period. The second stage is from 1996 to 2010, when the average level of urbanization reached 40%, and average annual growth rate was close to 4%. The process of urbanization, after a period of rapid development, tends to be stable in this stage. The correlation between urbanization and economic growth intensified. The third stage started in 2011 and it goes on to the present. Chinese society began to enter an urbanized society with urbanization level having exceeded 50%. However, the correlation between urbanization and economic growth has exhibited a sign of weakening. With relaxation of policy and institution, urbanization is showing a trend of accelerating while the economic growth rate is slowing down

3 TABLE 1 LONGITUDE AND LATITUTE Region Province Longitude Latitude Beijing Tianjin Hebei Liaoning Shanghai East Jiangsu Zhejiang Fujian Shandong Guangdong Guangxi Hainan Shanxi Inner Mongolia Jilin Heilongjiang Centre Anhui Jiangxi Henan Hubei Hunan Chongqing Sichuan Guizhou Yunnan West Tibet Sha anxi Gansu Qinghai Ningxia Xinjiang From the point of view of economic growth, over the past 30 years, China s economic growth mainly depends on investment and exports. The traditional labor-intensive and capital-intensive economic model was an important driving force of China s economic growth. This development pattern also determines the misallocation of resources. This industrial structure also poses constraints for future economic growth and potential changes in the demand. GDP per capita and capital-labor ratio both exhibit a growing trend. Nevertheless, since the 2008 financial crisis, the growth rate of GDP per capita has been far less than that of capital-labor ratio, which indicates that economic growth began to exhibit marginal diminishing trend. Besides, demographic dividend has gradually decreased while labor costs continue to rise. Endogenous growth driving force is insufficient, and the traditional labor-intensive and capital-intensive economic model has been unable to bring further economic growth. Therefore, to maintain the economic growth rate, it is vital to activate micro driving forces of economic growth. Based on the discussion above, a preliminary conclusion can be reached that in the current period of slowing economic growth, the mutual promotion effect of urbanization and economic growth has weakened. Traditional driving forces of economic growth, i.e., input-intensive economic growth mode, leads to lack of inherent driving force. From the practical point of view, there is still potential for urbanization. What is the correlation between urbanization and economic growth? Can urbanization become an efficient way to break the bottleneck of economic growth? Does the way production factors are used constitute an important factor in stagnant economic growth? This paper attempts to answer the abovementioned questions by utilizing panel spatial econometric model and investigating the relation between urbanization, factor inputs, and economic growth. It discusses what urbanization can do to promote economic growth and examines the relation between input factors and sustainable economic growth from microscopic mechanism

4 3. DATA The data are mainly gathered from China Statistical Yearbook, Wind Database, and China City Statistical Yearbook. Data on GDP per capita, urbanization, capital labor ratio spanning from 2007 to 2014 from 31 provincial-level administrative divisions are gathered. Among all data, data of the year 2007 follow the calculation of Zhang, Wu and Zhang (2004). GDP per capita of each provincial-level administrative division represents changes of income. Although GDP per capita cannot accurately reveal changes in structural index such as industrial structure, household income disparity etc., it can illustrate the results of economic growth. Urbanization is calculated as the ratio of population living in cities to population of each provincial-level administrative division. Capital-labor ratio is calculated as the net value of fixed capital divided by the number of the employed. It illustrates the proportional relation between physical capital and labor in production. Dependent variable is GDP per capita, explanatory variables are urbanization, capital labor ratio, and fixed assets per capita and public expenditure per capita. The unit is 100 million RMB. Descriptive statistics of longitudes and latitudes of 31 provinces are provided in Table 2. TABLE2 DESCRIPTIVE STATISTICS OF LONGITUDE AND LATITUTE Variable Observation Mean Standard Deviation Min Max Longitude Latitude Euclidean distance is calculated as follows. D = (X 1 X 2 ) 2 + (Y 1 Y 2 ) 2 where X 1, X 2 are maximum of latitude and minimum of latitude, respectively, and Y 1, Y 2 are maximum of longitude and minimum of longitude. Correlation matrix is constructed based on Euclidean distance. Moran test is conducted afterwards. Table 3 provides Moran s I Spatial Autocorrelation Test of GDP Per Capita ( ). TABLE3 GDP PER CAPITA, MORAN S I SPATIAL AUTOCORRELATION TEST ( ) Year Moran I Value Year Moran I Value ** 0.020** 2011 (2.113) (2.051) ** 0.024** 2012 (2.045) (1.978) ** 0.025** 2013 (2.082) (1.962) ** 0.025** 2014 (2.102) (1.963) Note: *** denotes significance at 1% significance level, ** denotes significance at 5% significance level, * denotes significance at 10% significance level. z values are in brackets. Test results in Table 3 indicate that Moran s I values ( ) are all positive and significant at 5% significance level. It reveals that provincial GDP per capita has spatial autocorrelation. Therefore, spatial econometric approach is conducted to investigate the impact of urbanization and capital labor ratio on GDP per capita. Figure 1 shows GDP per capita of 2014 on the map of China. Different colors represent different levels of GDP per capita. 4. EMPIRICAL ANALYSIS Due to huge economic disparities among regions of China, regions have significant spatial heterogeneity. Nevertheless, traditional econometric techniques neglect spatial effect. Therefore, this paper utilizes spatial econometrics to investigate the impact of urbanization and capital-labor ratio on economic growth

5 FIG. 1 GDP PER CAPITA (2014) Before running spatial econometric regression, it is imperative to identify applicability of different spatial econometric models. In other words, if an inappropriate spatial econometric model is utilized, it might lead to spurious results. LeSage and Pace (2009) argues that if the spatial autocorrelation between dependent variable and independent variables are neglected, the result is even worse than neglecting the spatial autocorrelation of error terms. The main reason is that the former one neglects explanatory variables, causing bias and inconsistence, while the latter one only loses some efficiency. Therefore, this paper tests the applicability of spatial econometric models before running regressions. The spatial econometric model, ideally, would be of the following form: n lny it = α + τlny it 1 + ρ W ij lny jt + X itk β K + W ij X jtk θ K + μ i + γ i + φ it, j=1 K K=1 n K n K=1 j=1 φ it = λ j=1 m ij φ it + ϵ it, (1) i=1,..., n t=1,..., T in which lny it 1 is lagged lny it,w ij is the corresponding value of spatial weighted matrix of I and j. θ K is spatial lagged variable. X represents all explanatory variables, including Urbanization, Capital-Labor Ratio, Public Goods, Defense, Public Security, Education, Science and Technology, Culture, Sports, and Media, Social Security and Employment, Health and Medicare, Environment Protection, Community Affairs, Agriculture, Forestry, Water Conservancy, Transportation. a) If λ is 0, it is Spatial Durbin Model (SDM), b) If ρ and θ are 0, it is Spatial Error Model (SEM), c) If λ and θ are 0, it is Spatial Lagged Model (SLM), d) If θ is 0, it is spatial autocorrelation Model (SAC). Differentiate Equation 1 with respect to X yields Equation

6 β K W 12 θ K W 1n θ K ] = (1 ρw) 1 W [ 21 θ K β K W 2n θ K ] (2) X nk W n1 θ K W n2 θ K β K [ Y If λ=0, then Equation 2 in SDM can be shown as β K W 12 θ K W 1n θ K (1 ρw) 1 W [ 21 θ K β K W 2n θ K ], W n1 θ K W n2 θ K β K If ρ and θ are both 0, then Equation 2 in SDM can be shown as β K β [ K 0 ] 0 0 β K If θ is 0 or λ and θ are 0, then Equation 2 in SAC or SLM can be shown as β K 0 0 (1 ρw) 1 0 β [ K 0 ] 0 0 β K Elhorst (2009) draws similar conclusion with LeSage and Pace (2009) in terms of model selection. It argues that the model should first take into account of spatial autocorrelation between dependent variable and independent variable, and eliminating spatial autocorrelation in the error term is a more robust method, i.e., first tests Spatial Durbin Model (SDM), and then conducts Lagrange Multiplier Test and Wald Test. Two hypotheses: H 0 : θ + ρβ = 0 and H 0 : θ = 0 are tested to verify if SDM can be simplified into SEM or SLM. The process is as follows: (1) LM Test: If θ + ρβ = 0, Based on SDM: Y = ρwy + Xβ + WXθ + ε, Let ε = μ(1 ρw) Y = ρwy + Xβ ρwxβ + ε Y(1 ρw) = Xβ(1 ρw) + ε { Y = Xβ + μ μ = ρwμ + ε, SEM can be derived. (2) Wald Test: If θ = 0, Based on SDM: SLM can be derived. Y = ρwy + Xβ + WXθ + ε Y = ρwy + Xβ + ε, As an initial step, all explanatory variables are included in SDM in order to calculate spatial lag θ. Afterwards LM test and Wald test are conducted. LM test finds that SEM model is valid. Wald test finds that SLM model is not applicable. Therefore, SEM model and SDM model are utilized. Estimation results are shown in Table 4. Fixed effect in panel

7 data analysis is first conducted. The result is compared with spatial fixed effect, time fixed effect, and both fixed effect of SEM model and SDM model. TABLE4 EMPIRICAL RESULTS Variable SEM Model SDM Model Fixed Spatial Fixed Time Fixed Both Fixed Spatial Fixed Time Fixed Both Fixed Effect Effect Effect Effect Effect Effect Effect Urbanization *** *** (0.25) (1.71) (4.59) (1.61) (1.61) (5.35) (0.02) Capital-Labor Ratio *** (8.32) *** (15.01) *** (22.94) *** (15.19) *** (13.20) *** (17.52) *** (13.18) Public Goods (-1.76) (-1.85) (-0.54) (-1.93) (-1.89) (-0.76) (-1.11) Defense ** * * * *(2.63) (3.12) (-0.23) (2.42) (2.30) (0.61) (2.17) Public Security (1.48) *** (5.78) *** (11.53) *** (5.82) *** (5.99) *** (9.26) *** (3.74) Education *** (5.19) *** (4.19) *** (5.37) *** (4.35) *** (4.09) ** (3.29) *** (4.58) Science and * *** *** *** *** *** *** Technology (2.57) (6.26) (5.95) (7.39) (5.36) (4.83) (5.37) Culture, Sports, and Media (-0.72) (-0.42) (-1.32) (-0.79) (-1.13) (-1.18) (-0.65) Social Security and Employment (-1.31) (-1.42) *** (-6.42) (-1.29) (0.09) *** (-6.22) (-0.90) Health and *** *** Medicare (0.07) (0.99) (-5.54) (0.80) (1.16) (-3.93) (1.55) Environment * ** * ** * *** Protection (1.33) (2.47) (3.15) (2.32) (2.93) (2.19) (3.45) Community *** *** * ** Affairs (0.77) (3.72) (0.19) (3.77) (2.37) (1.24) (3.16) Agriculture, Forestry, Water Conservancy (-3.31) *** (-3.45) *** (-5.69) ** (-3.04) *** (-3.52) *** (-4.20) *** (-4.60) Transportation (1.60) (1.34) (0.33) (1.51) (1.47) (0.16) (2.65) R Log-likelihood In order to avoid inconsistency and inefficiencies in assessing spatial econometric models, this paper utilizes Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) to select the most appropriate estimation method. Log maximum likelihood has been estimated in the previous regression. Therefore, AIC and BIC can be calculated accordingly. Estimation results are presented in Table 5. TABLE5 INFORMATION CRITERIA ESTIMATION RESULTS Fixed Effect Spatial Econometric Model AIC BIC Spatial Fixed SEM SDM * * Time Fixed SEM SDM * * Both Fixed SEM SDM * * Note: * denotes the most efficient estimation method in each information criteria. Table 5 indicates that among all results of spatial fixed effect, SDM is the most efficient according to AIC and BIC. The same result is found in time fixed effect and both fixed effect. Therefore, SDM time fixed effect is utilized to

8 conduct regional cumulative effect analysis. China is divided into three regions according to Table 1. Direct effect, indirect effect (spill-over effect) and overall effect are estimated in each region. The estimation result is shown in Table 6. TABLE6 REGIONAL CUMULATIVE EFFECT Variable Effect Nationwide East Centre West Direct Effect Urbanization Indirect Effect * * Overall Effect Direct Effect *** *** *** Capital-Labor Ratio Indirect Effect Overall Effect Direct Effect * Public Goods Indirect Effect Overall Effect Direct Effect * Defense Indirect Effect *** Public Security Education Science and Technology Culture, Sports, and Media Social Security and Employment Health and Medicare Environment Protection Community Affairs Agriculture, Forestry, Water Conservancy Transportation Overall Effect Direct Effect *** * *** Indirect Effect *** *** Overall Effect Direct Effect *** *** *** *** Indirect Effect Overall Effect Direct Effect *** ** * Indirect Effect *** * *** Overall Effect Direct Effect * Indirect Effect ** *** Overall Effect Direct Effect Indirect Effect Overall Effect Direct Effect * Indirect Effect *** Overall Effect Direct Effect *** *** *** Indirect Effect ** Overall Effect Direct Effect ** ** * *** Indirect Effect Overall Effect Direct Effect *** * Indirect Effect * *** Overall Effect Direct Effect * * *** Indirect Effect ** Overall Effect Note: *** denotes significance at 1% significance level, ** denotes significance at 5% significance level, * denotes significance at 10% significance level. Table 6 indicates that overall, 1 percent increase in urbanization is associated with an increase of percent in GDP per capita. This means that urbanization can promote China s economic growth in the long term. Besides, 1 percent increase in capital-labor ratio leads GDP per capita to increase by percent. It reveals that the current capital-labor input structure does not bring effective economic growth. Reliance on the traditional capital-labor input mode of economic growth no longer suits the requirements of sustained economic growth. It is also the major cause of shortage of intrinsic motivation in China s economic growth. In terms of regional development, compared with Central China, 1 percent increase in urbanization rate increases GDP per capita by percent in the East and

9 percent in the West, while 1 percent increases in capital-labor ratio increases GDP per capita by % in the East and % in the West. The impact of urbanization on regional economic development is significant. On the contrary, capital-labor input structure has little influence on economic growth, and the impact tends to be similar across different 5. CONCLUSION AND POLICY IMPLICATION This paper utilizes a panel data of 31 provinces in China spanning from 2007 to It analyzes the impact of urbanization and the input structure of production factors on China s economic growth. By further promoting urbanization can China avoid falling into the middle income trap. The following conclusions can be drawn. First and for most, there exists spatial spill-over effect between urbanization and GDP per capita. China s GDP growth has slowed down since 2011, and the correlation between urbanization and economic growth has weakened. As indicated by empirical results, urbanization significantly increases GDP per capita. Therefore, urbanization should be further promoted. Besides, due to the uneven development among regions in China, the impact of urbanization differs a lot among different regions. Due to the fact that East China is the most urbanized region, urbanization has a positive effect on the economic growth in provinces in East China. Nevertheless, if no higher urbanization rate can be achieved, then economic growth will come to a half. Therefore, in order to sustain economic growth in East China, it is necessary to pursue structural adjustment of production mode and development in new industries instead of relying exclusively on urbanization. Urbanization also has significant and positive impact on economic growth in West China. However, since urbanization in West China is relatively backward, there is a great potential in urbanization per se and its impact on economic growth. Therefore, by promoting urbanization can West China effectively avoid suffering slow economic growth. Moreover, despite that the capital-labor ratio has a positive stimulating effect on economic growth; the marginal diminishing effect has become increasingly evident. The current mode of economic growth can no longer remain the inherent motive force of the sustainable economic growth. Regional difference is also small. This conclusion complies with what can be observed in Figure 1. In terms of regional disparities, economic growth mode in East China and West China has little impact on economic growth. To be specific, the relation is even negative in Zhejiang and Shaanxi. While in Central China the input structure still remains conducive to economic growth. To sum up, development in East China is more in line with China s general situation and the role of urbanization is very obvious, due to the urban agglomeration effect caused by polymerization of capital, talent, technology and other important resources towards the eastern part of China. Upgrading of the industrial structure in East China is the fastest among all regions of China. Consumption and investment demand are also strong. The region s economic growth has always been in a leading position, suffering from the minimum middle-income trap risk. Therefore, the focus of East China s economic growth is new urbanization, i.e., improve the urban function and prevent urban poverty. Meanwhile accelerate the optimization and upgrade of the industrial structure in the region. Similarly, in West China, urbanization has more positive influence on economic growth than capital-labor composition. Nevertheless, in West China, urbanization and economic growth are relatively backward, with a large number of economic values of resources yet to be developed. The rapid urbanization in the West can effectively stimulate the inherent vitality of these resources, so as to achieve the goal of economic growth. Therefore, urbanization in West China has great potential in promoting economic growth. Urbanization has a relatively small positive impact on economic growth in Central China, while the capital-labor composition is conducive to economic growth, despite of diminishing marginal effect. Therefore, it is advised to promote urbanization and economic growth pattern in Central China. Urbanization and capital-labor ratio play an important role in promoting economic growth. Their contribution to economic growth is much higher than the traditional elements of investment driven economic growth. Therefore, urbanization can break the bottlenecks of economic growth

10 REFERENCE [1] Gill Indermit, Kharas Homi. An East Asian Renaissance: Ideas for Economic Growth. World Bank Publications, [2] Jesus Felipe, Arnelyn Abdon, Utsav Kumar (2012). Tracking the Middle-Income Trap: What Is It, Who Is in It, and Why?. Levy Economics Institute of Bard College Working Paper, 1-4. [3] Kenichi Ohno (2009). Avoiding the Middle-Income Trap-Renovating Industrial Policy Formulation in Vietnam. ASEAN Economic Bulletin, [4] Israel Malkin, Mark M. Spiegel (2012). Is China Due for a Slowdown?. FRBSF Economic Letter, 1-5. [5] Pedroni, Peter. Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors [J]. Oxford Bulletion of Economics and Statistics, 1999, [6] Pedroni, Peter. Purchasing Power Parity Tests in Co-integrated Panels [J]. Review of Economics and Statistics, 2001, [7] Kao C. Spurious Regression and Residual- based Tests for Cointegration in Panel Data [J]. Journal of Econometrics, 1999,