中国与其31个地区科技进步对经济增长贡献率

【Abstract】wWw.shuoshilunwen.com This paper is supported by China Postdoctoral Science Foundation funded project "Estimating Contribution Rate of Scientific and Technological Progress to Economic Growth by Soft Computing"(Grant No.20090461293)."Scientific and technological progress contribution rate to economic growth in broad sense" refers to the sum share of all the "factors of production," deducting the contributions of the increased labor force and capital. Since Tinbergen and shirang Dis proposed the total factor productivity in 1940s and 1950s, technological progress as an important factor in economic growth has caused economists’ attention.Research on relationship between scientific and technological progress and economic growth can be dated back to1970s. Based on the existed theories proposed by foreign researchers, domestic researches mainly focus on quantitative studies which take Cobb-Douglas production function and the Solow method as their basis. And series of research findings represented by Chinese Academy of Social Sciences and scholars in universities he ermerged. Meanwhile, Chinese local governments and research institutes he also calculated contribution rate of local scientific and technological progress contribution rate to economic growth.Since the socio-economic system is a complex and huge nonlinear system, and the impact of scientific and technological progress on the economic growth is involved in almost every aspect, problems still existed in accurately calculating of scientific and technological progress contribution rate to economic growth. Two key points can not be neglected referring to the existed computing methods:one is the selection of indicators of production factors; the other is the ways of estimating of elasticity of production factors. The selection of indicators of production factors directly influences the estimation of elasticity of production factors. The most widely used method at present for estimation of elasticity is the regression method, which not only requires a certain amount of sample data, but also is sensitive to the sample data. The improper selection of indicators of production factors will cause a big deviation in the estimation of elasticity, which directly affects the results. In addition, the current researches on contribution rate of scientific and technological progress are mostly based on time series data of a country or a region in a certain period of time. If the sample data is limited, it may affect the estimation accuracy of parameters in production function, and the contribution rates of scientific and technological progress among different areas are not comparable.According to the new economic growth theory, a new method of computing contribution rate of scientific and technological progress based on C-D production function and Solow method is proposed in this paper. This method includes three steps:Firstly, fuzzy soft clustering of 31 Chinese regions is performed to obtain the degree of membership of categories that these places belong to, according to the level of technological improvement. Secondly, calculate the contribution rate in different categories of levels of technological improvement that contribute to the growth of economics. Thirdly, multiply the obtained contribution rate of each category by the degree of membership of this category which the region belong to, from which the contribution rate of technological improvement in each place is obtained. Finally, this method is used to calculate the contribution rates of technological improvement in 31 Chinese regions during 1998 to 2007. Last but not least, some reasonable suggestions are proposed through computing result analysis and the derived corresponding conclusions. The calculation method can not only overcome the limitations of all data samples, but also makes the contribution rate of science and technology between different regions comparable. At last, this dissertation also developed an information system based on Matlab language to make the whole calculation process more systematized, intelligent and easier for operation.