中国及其31个地区科技进步对经济增长的贡献率研究
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摘要
“广义科技进步对经济增长的贡献率”是指产出增长中扣除劳动力和资金投入数量增长的贡献份额后,所有“其它生产要素”贡献份额之和。自上世纪40、50年代,丁伯根和希朗戴维斯提出全要素生产率后,科技进步作为经济增长的重要因素之一引起经济学家们的重视。中国对科技进步与经济增长关系的研究始于20世纪70年代末,在国外已有的理论基础上,中国的研究主要集中在以柯布—道格拉斯生产函数和索罗余值法为主的定量研究方面,出现了以中国社会科学院研究组和高校学者为代表的一系列研究成果,另外中国各地方政府和科研单位也广泛地测算地方科技进步对经济增长的贡献率。
但是社会经济系统是一个复杂、庞大的非线性系统,科技进步对经济增长的作用几乎渗透到与经济增长相关的方方面面,因此准确测量科技进步对经济增长的贡献率存在很多问题。现有的测算方法都离不开两个关键问题：第一,生产要素指标的选取；第二,生产要素弹性系数的估计。生产要素衡量指标的选取直接影响弹性系数的估计,因为目前应用最广泛的弹性系数估计方法就是回归法,该方法不仅要求样本数据达到一定的数量,而且对样本数据反应敏感。一旦生产要素指标选取不当,弹性系数的估计就会出现很大偏差,从而直接影响测算结果。另外,目前对科技进步贡献率的研究大多是采用某一国家或某一地区的时间序列数据,测算某地区在某个时间段的科技进步对经济增长的贡献率,这样由于数据样本有限,很可能会影响到生产函数中参数估计的精确性,而且地区之间的科技进步贡献率没有可比性。
本论文依据新经济增长理论,提出了一种基于C-D生产函数和索罗余值法测算科技进步对经济增长贡献率的新方法,该方法包括4个步骤：首先,对中国31个地区按照科技进步水平进行软分类,得到各地区隶属于各类别的隶属度；第二,分别计算不同科技水平类别的科技进步对经济增长的贡献率；第三,把得到的各个类别的贡献率与各个地区隶属于各类的隶属度相乘,即得到各个地区科技进步对经济增长的贡献率；最后,以各个地区的贡献率乘以该地区所占全国GDP的比重,进行加权计算,得到中国科技进步对经济增长的贡献率。并运用该方法计算了中国及其31个地区1998-2007年间的科技进步贡献率,比较各类地区的科技进步贡献率,分析其原因,得出相应结论,为各地区经济发展提出合理建议。该测算方法不仅克服了数据样本少的局限性,而且使得各地区的科技进步贡献率具有可比性。本论文最后还基于Matlab语言进行系统开发,实现整个计算过程的系统化、智能化和界面化,使计算易于操作。
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 Davis 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 have ermerged. Meanwhile, Chinese local governments and research institutes have 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 small 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.
但是社会经济系统是一个复杂、庞大的非线性系统,科技进步对经济增长的作用几乎渗透到与经济增长相关的方方面面,因此准确测量科技进步对经济增长的贡献率存在很多问题。现有的测算方法都离不开两个关键问题：第一,生产要素指标的选取；第二,生产要素弹性系数的估计。生产要素衡量指标的选取直接影响弹性系数的估计,因为目前应用最广泛的弹性系数估计方法就是回归法,该方法不仅要求样本数据达到一定的数量,而且对样本数据反应敏感。一旦生产要素指标选取不当,弹性系数的估计就会出现很大偏差,从而直接影响测算结果。另外,目前对科技进步贡献率的研究大多是采用某一国家或某一地区的时间序列数据,测算某地区在某个时间段的科技进步对经济增长的贡献率,这样由于数据样本有限,很可能会影响到生产函数中参数估计的精确性,而且地区之间的科技进步贡献率没有可比性。
本论文依据新经济增长理论,提出了一种基于C-D生产函数和索罗余值法测算科技进步对经济增长贡献率的新方法,该方法包括4个步骤：首先,对中国31个地区按照科技进步水平进行软分类,得到各地区隶属于各类别的隶属度；第二,分别计算不同科技水平类别的科技进步对经济增长的贡献率；第三,把得到的各个类别的贡献率与各个地区隶属于各类的隶属度相乘,即得到各个地区科技进步对经济增长的贡献率；最后,以各个地区的贡献率乘以该地区所占全国GDP的比重,进行加权计算,得到中国科技进步对经济增长的贡献率。并运用该方法计算了中国及其31个地区1998-2007年间的科技进步贡献率,比较各类地区的科技进步贡献率,分析其原因,得出相应结论,为各地区经济发展提出合理建议。该测算方法不仅克服了数据样本少的局限性,而且使得各地区的科技进步贡献率具有可比性。本论文最后还基于Matlab语言进行系统开发,实现整个计算过程的系统化、智能化和界面化,使计算易于操作。
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 Davis 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 have ermerged. Meanwhile, Chinese local governments and research institutes have 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 small 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.
引文
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