The Corresponding Supporting Effect Of Chinese Eastern And Western Areas

On July 6^th, 1996,the General Office of the State Council of the People's Republic of China had transponded "the report on organizing more developed regions and undeveloped regions to develop cooperative poverty alleviation ".It officially deployed Beijing supporting Neimenggu, Tianjin supporting Gansu, Shanghai supporting Yunnan, Guangdong supporting Guangxi, Jiangsu supporting Shanxi, Zhejiang supporting Sichuan, Shandong supporting Xinjiang, Liaoning supporting Qinghai, Fujian supporting Ningxia, Dalian,Qingdao,Shenzhen,Ningbo supporting Guizhou. This policy signed that the national poverty alleviation launched all-round. In 2002, the State Council of the People's Republic of China made Zhuhai, Xiamen supporting Chongqing decision. So far, there are 15 eastern developed provinces and municipalities to support the west 11 provinces (Tibet is a poor area which enjoyed key supporting policy), things have been involved in poverty alleviation collaboration 26 provinces (autonomous regions and municipalities). All relevant provinces'(autonomous regions and municipalities) governments attached great importance to poverty alleviation works, did a lot of successful works which promoted the coordinate development of the economic society and common progress, and made positive contribution to the construction of a socialist harmonious society. This dissertation explores the supporting effect between eastern and western provinces. The main contents and conclusions in the dissertation are:1.This dissertation detailedly describes the Data Envelopment Analysis based on the output orientation.Then we estimate the Output-Oriented Malmquist Productivity Indexes of all the provinces in the sample and their decompositions which include Technical Change and Efficiency Change, and analyze on the factors that impact them. Through empirical analysis, this chapter draw: the spilled internal spending stock of the science and technology activity from supporting provinces to supported provinces promotes the latter's total factor productivity and technical change, but causes the latter's efficiency change drops.Thereinto, the effects on the total factor productivity and technical change of supported provinces are significant, the influences on the efficiency change of supported provinces are not significant.2.We interprets Stochastic Frontier Analysis in detail, and then points out that 1992 model of Coelli et al and 1995 model of Coelli et al is not homologous, which means that these two kinds of models can not test the hypothesis one another. In this method, based on the assumption that the production function is translog production function in the provinces, we estimated 1995 model of Coelli et al by using the sample of this paper. Through the estimated results, we can calculate the annual frontier technical progress change,relative technical efficiency and scale efficiency in the provinces, and then get their annual total factor productivities. Then we analyzed on the factors that impacted them.The results show that: the spilled internal spending stock of the science and technology activity from supporting provinces to supported provinces promotes the latter's total factor productivity and its decomposition.Thereinto, the effects on the total factor productivity of supported provinces are significant, but the technical change and efficiency change are not.3.The dissertation analyzed the differences and relations between the Data Envelopment Analysis and Stochastic Frontier Analysis, then we apply the two methods to calculate the growth of total factor productivity in the provinces and their decompositions in the sample, and then we studied the corresponding supporting effect between eastern and western provinces. Our empirical results indicate that the spilled internal spending stock of the science and technology activity from supporting provinces to supported provinces promotes the latter's total factor productivity and its decomposition by using the two methods, but these effects some are significant and some not.4. Considering Data Envelopment Analysis does not strip the influences of environment variables and random noise in measuring total factor productivity of the provinces and their decompositions; and Stochastic Frontier Analysis can strip random noise,but it has definite functional form.Based on a combination of the two methods, this chapter apply three-stage DEA to calculate the total factor productivity in the provinces and their decompositions in the sample. The benefits of this method are that it can peel environment variables,random noise and management inefficiency to effect inputs(or output) , and do not require to set the form of production function (or cost function).Then we analyze the factors which effected the total factor productivity of the provinces and their decompositions, we can conclude that the spilled internal spending stock of the science and technology activity from supporting provinces to supported provinces promotes the latter's total factor productivity and technical change,but causes the latter's efficiency change droped, and all of the effects are significant.Through an inclusive analysis on certain important issues concerned with the corresponding supporting effect of Chinese eastern and western areas, this dissertation had brought forth some innovative ideas andconclusions:1. We analyzed the differences and relations between the Data Envelopment Analysis and Stochastic Frontier Analysis in detail.2. This dissertation illustrates the theoretical sources of three-stage Data Envelopment Analysis in detail.3.This dissertation introduces the corresponding supporting relationship between provinces which includes Beijing supporting Neimenggu, Tianjin supporting Gansu, Shanghai supporting Yunnan, Guangdong supporting Guangxi, Jiangsu supporting Shanxi, Zhejiang supporting Sichuan, Shandong supporting Xinjiang, Liaoning supporting Qinghai, Fujian supporting Ningxia, Dalian,Qingdao,Shenzhen and Ningbo supporting Guizhou, Zhuhai and Xiamen supporting Chongqing into the spatial correlation matrix, thus makes the spatial correlation matrix about the correlation between the supporting provinces and the supported provinces more accord with the actual situation. Such spatial correlation matrix has no leading card so far.4. This dissertation has found that the spilled internal spending stock of the science and technology activity from supporting provinces to supported provinces promotes the latter's total factor productivity and technical change,and these effects are significant.And the effects of the spilled internal spending stock of the science and technology activity from supporting provinces to supported provinces are uncertain.