Statistical Analysis On The Differences Of Provincial Economic Development In China

According to the report of the 19 th National Congress of the Communist Party of China,the principal contradiction facing Chinese society has evolved.What Chinese now face is the contradiction between unbalanced and inadequate development and the people’s ever-growing needs for a better life.Unbalanced development of regional economy is also an important manifestation of imbalance.Since the 21 st century,national strategies of “China’s Western Development Policy”,“The Reform and Resurgence of the Old Industrial Base of the Northeast”,“The Rise of Central China” and “The Belt and Road” have been put forward one after another.It is showed that the Communist Party of China and the state attach great importance to narrowing the regional economic gap and promoting the construction of harmonious society.Theories and methods of regional economics,statistics,spatial econometrics and other fields were comprehensively used in this paper.Refer to theoretical and practical researches on provincial economic differences,macroeconomic characteristics were combined to analyze the variation trend and influencing factors on China’s provincial economic differences.From the perspective of time,the research period was from 2010 to 2019.Dynamic analysis methods were used to analyze the time evolution characteristics of China’s provincial economic differences.In terms of spatial level,2010,2013,2016 and 2019 were selected as research sections to explore the spatial structure of China’s provincial economic differences by using static analysis method.At the same time,taking 2019 as the study section,based on the principal component index system,the economic development status of China’s provinces was ranked.Then areas of economic prosperity and depression were divided by cluster analysis.And a spatial lag model was established to explore the influencing factors of China’s provincial economic differences.The research conclusions were as follows:First,the evolution of time and space was based on the provincial economic development in China.From the perspective of time,China’s per capita gross regional product was increasing from 2010 to2019,but the growth rate slowed down.From the spatial level,the east is rich,the west is poor and the middle part is in the middle level,which preliminarily proved that there was a spatial agglomeration effect in China’s provincial economic development.Second,the score and the ranking of China’s provincial economic development level were based on the principal component index system.10 economic indicators included gross regional product were selected.Then comprehensive scores and the rank of each province were obtained by principal component analysis.There were 10 provincial units with positive scores and 21 provincial units with negative scores.The number of provincial units whose development level lower than the national average reached two-thirds.The highest score from Beijing(F= 4.49)and the lowest score from Tibet(F =-2.58)indicated that there was a large gap between these provinces.Third,differences on provincial economic development in China were based on the cluster analysis.Using the comprehensive score of China’s provincial economic development obtained by the principal component index system,areas of economic prosperity and depression were divided into three categories by hierarchical clustering method based on the sum of deviation square.Beijing and Shanghai belonged to the first category.The second category included Tianjin,Zhejiang,Jiangsu,Shandong,Guangdong and Fujian.The remaining provinces belonged to the third category.Overall,China’s provincial economic development was extremely imbalance.Beijing and Shanghai led the country’s economic development.The economic development level of some eastern coastal provinces was high.And there was a large gap between the economy development on eastern,central and western regions.Fourth,exploratory spatial data analysis(ESDA)was used to describe the difference analysis of provincial economic development in China.Based on global indication of spatial autocorrelation analysis,the positive correlation of provincial economic development was found.Based on the local indication of spatial autocorrelation analysis,the Moran scatter plot showed that the economy of Chongqing and Hubei is getting more prosperous,while the economy of Heilongjiang,Jilin and Liaoning was getting more depressed.And the driving effect to neighboring provinces from Shandong was increasing,while that of Beijing was weakening.On the whole,the diffusion trickle-down effect and transition characteristics were weakened,while the negative low-speed growth and the polarization effect was enhanced,which should be paid more attention to.According to the LISA cluster map,the most common cluster type of provincial economic development level in China was Low-Low,mainly concentrated in the west,especially in the southwest.While the High-High cluster areas mainly concentrated in the Yangtze River Delta Economic Zone.Fifth,the spatial lag model was used for the provincial economic development in China.Gross regional product was selected as dependent variable.And 10 independent variables included total investment of foreign-invested enterprises were also selected.The spatial lag model was established according to the test results.In this model,the order of the positive influencing factors by the regression coefficient was as follows: transaction value in technical markets,total retail sales of consumer goods,industrial structure,average wage from employed persons for urban non-private units and per capita gross regional product in neighbouring provinces.Ultimately,according to research results,measures and suggestions are put forward to advance the coordinated provincial economic development in China: adhere to regional coordinated development and strengthen the diffusion trickle-down effect;build China into scientific and technological powerhouse;promote consumption;optimize and upgrade the industrial structure;increase wage and income.