Computing of the contribution rate of scientific and technological progress to economic growth in Chinese regions

doi:10.1016/j.eswa.2011.12.032

Expert Systems with Applications

Volume 39, Issue 10, August 2012, Pages 8514-8521

https://doi.org/10.1016/j.eswa.2011.12.032 Get rights and content

Abstract

According to the new economic growth theory, a new method of computing the contribution rate of scientific and technological (S&T) progress to economic growth based on the Cobb–Douglas production function and the Solow residual value method is proposed in this paper. This method includes three steps: Firstly, according to their levels of S&T progress, fuzzy soft clustering of thirty one Chinese regions is performed to obtain the membership degree of these places to the categories. Secondly, to calculate the contribution rates that different categories of levels of S&T progress contribute to economic growth. Thirdly, to multiply the obtained contribution rate of each category by the membership degree of the place belonging to this category, from which the contribution rate of S&T progress to economic growth in each place is obtained. Finally, this method is used to calculate the contribution rates of S&T progress to economic growth in thirty one Chinese regions during the period from 1998 to 2007. Last but not least, some reasonable suggestions and conclusions are proposed by analyzing the computing results.

Highlights

► Computes the contribution rate of scientific and technological(S&T) progress to economic growth in Chinese 31 regions. ► Adopt the method of soft computing fusion with hard computing. ► For contribution rate, Shanghai is the highest, the second highest is Beijing and the lowest is Sichuan.

Introduction

Economy is a product of the co-operation of science, techniques, labors and capital. Seeing from the strategic angle of the economic development, in order to realize the economic development in a continual, stable, and highly efficient way, we have to adjust the proportion of various input factors in the economy. It is important to obtain the contribution rate of each factor to economic growth, especially the contribution rate of S&T progress, which can help the leader make reasonable macro policies. The contribution rate of S&T progress to economic growth, in the broad sense, refers to the sum of contributions rate of other factors to output increase, excluding those of the increase in labor force and capital (Robert, 1996, Li, 1995, Sun, 1998). Mokhtarul Wadud (2004) and Lewis (1954) had proposed the total factor productivity (TFP). Since then, the idea that S&T progress has been an important factor in economic growth which attracted the attention of economists. In 1957, Solow (1957) adopted residual value method in computing the contribution rate of S&T progress, which created the measurement of S&T progress operational. After that economists have made continuous improvement in studying the contribution rate of S&T progress to economic growth (Hilbrink, 1989, Jorgenson, 2001, Sengupta, 2004, Shipley et al., 2004, Tallon and Kraemer, 1999), among which Denison’s research in the 1970s is relatively significant. Denison, when analyzing economic growth reasons from the year of 1929 to 1969 in the USA, divided the total input and total factors productivity into several small factors, and based on this classification he made a quantitative measurement of the effect of each individual factor on economic growth (Denison, 1962).

The study on S&T progress in China starts relatively late. In the 1980s, Jia (1997) initiated the potential analysis method with Chinese characteristics to estimate contribution rate of S&T progress to economic growth, which is based on the active decision theory. In 1983, the research group guided by Shi and Qin the first time completed the analysis of the effect of industrial technological progress in China (Shi & Qin, 1985). In 1998, the Department of Science and Technology of the former State Planning Committee in China launched research program on measuring the effect of S&T progress on economic growth (Jia, 1997). In 2000, Lu, Fan, Wei, and Xu (2000) had adopted such methods as the Solow function, the Denison’s analysis on economic growth factors, the Jogenson’s analysis of production efficiency and the production function to measure the effect of S&T progress on economic growth and had made empirical analysis. In 2002, Li (2003) had utilized the input and output method to measure the contribution rate of S&T progress to economic growth. Song (2003) had improved the measuring method of S&T progress by adopting the potential analysis theory. Zhou (2008) had applied the Cobb–Douglas production function in measuring the contribution rate of S&T progress to economic growth in 10 regions in Henan Province of China in the year of 2005. Liu (2006), by taking the Cobb–Douglas production function as the basic method, had measured the contribution rates of S&T progress, capital and labor force to economic growth respectively during the period of 1985–2002 and the period of 1995–2002 in Hebei province of China. Wu (2008) had measured the contribution rate of S&T progress in agriculture in Henan Province of China from the year of 1996 to 2005, and used the gray production function metabolizing model (Liu, 1997, Liu et al., 2004, Liu et al., 1999) in predicting the contribution rate of S&T progress in agriculture in Henan province of China from the year of 2006 to 2015.

It is obvious that Chinese and overseas scholars have made encouraging achievements in measuring the contribution of S&T progress to economic growth, but these researches are limited to use data in one specific country or region when at a given period of time. The limited data may have negative impact on the accuracy of parameter estimation in the production function. What’s more, the contribution rate of the same S&T progress in different regions may be significantly different and incomparable. In addition, the social and economic system is complicated and nonlinear, and the same S&T progress may make different contributions in different regions. So the new method proposed in this paper aims at measuring the contribution rates of S&T progress to economic growth in thirty one regions of China from the year of 1998 to 2007, by adopting the soft computing approach, the Cobb–Douglas production and the Solow residual value method. In the new method, we first make soft clustering of S&T progress in thirty one regions of China, then compute the contribution rates of S&T progress at various classifications to economic growth. And then calculate the contribution rate in each region by summing up the products of the contribution rate of each classification and the membership degree of this region to each classification. Finally, comparison of the rates in different regions is provided and rational suggestions to the economic development in various regions are presented.

Section snippets

The combined model of production function

The combined model of the extended Cobb–Douglas production function (C–D production function) and the Solow residual value method is employed in measuring the contribution rate of S&T progress to economic growth in various Chinese regions. The combined model is described as follows: $Y = {AK}^{α} L^{β} H^{γ}$ $a = y - α k - β l - γ h$

Eq. (1) is the C–D production function, in which Y represents the output, K represents the capital input, L represents the labor input, H represents the actual human capital, A represents the

The fuzzy soft clustering of 31 Chinese regions according to S&T progress

The Eq. (1): A = YK⁻^αL⁻^βH⁻^γ, indicates that S&T progress in one region is dependent to a certain extent on the output, the fixed assets stock, the labor input and the human capital in this region. In this paper per capita output, per capita fixed assets stock, the ratio of employers to the total population, and the actual human capital are taken into consideration which are shown in Table 2, GA-ISODATA (Zhu, Su, & Li, 2005) is used in fuzzy soft clustering of S&T progress in thirty one Chinese

The estimation of the model parameters of the four clusters and the calculation of the contribution rate of S&T progress

From the combined model, the key to calculate the contribution rate of S&T progress to economic growth is to estimate the parameters α, β and γ in Eq. (1), and to find the growth rate of four indexes y, k, l and h in Eq. (2). Next, we use the appropriate methods to estimate these parameters.

The calculation process and results

According to the membership degree of thirty one regions in Table 4, and the contribution rate of the factors to the total output growth in Table 7, the contribution rate of the factors of the thirty one regions to total output growth can be calculated as in Table 8. Fig. 2 shows the contribution rate comparison of the thirty one regions’ .

The contribution rate of S&T progress of each region to economic growth: $E_{Aj} = C_{A 1} μ_{1 j} + C_{A 2} μ_{2 j} + \dots + C_{Ai} μ_{ij}$

The contribution rate of fixed assets stock of each region

Conclusions

In 1970s, Denison analyzed the courses of economic growth of the United States through the accounting method of the sources of economic growth. Since then, many scholars have not only started to study the contribution rate of S&T progress to economic growth, but also proposed many measuring methods. In the late 1980s, China began to explore thoroughly and systematically the relationship between S&T progress and economic growth. Based on longitudinal data, however, current researches mostly

Acknowledgements

This work is supported by the National Natural Science Foundation of China No. 71103163 and 71103164, by Research Foundation of Humanities and Social Sciences of Ministry of Education of China No. 10YJC790071, by the Fundamental Research Founds for National University, China University of Geosciences (Wuhan) No. CUG090113 and CUG110411, by China Postdoctoral Science Foundation Grant No. 20090461293, by special grade of the financial support from China Postdoctoral Science Foundation Grant No.

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Computing of the contribution rate of scientific and technological progress to economic growth in Chinese regions

Abstract

Highlights

Introduction

Section snippets

The combined model of production function

The fuzzy soft clustering of 31 Chinese regions according to S&T progress

The estimation of the model parameters of the four clusters and the calculation of the contribution rate of S&T progress

The calculation process and results

Conclusions

Acknowledgements

Journal of Development Economics

Japan and the World Economy

The sources of economic growth in the U.S. and the alternative before

Soft computing of economic contribution rate of education in China and each area

China Soft Science

Economic impact and technical change

IEEE Transactions on Engineering Management

Capital stock estimates in Chinese manufacturing by perpetual inventory approach

China Economic Quarterly

On initiative decision theory: Non-ideal system decision

China Soft Science

Adjustment of coefficient of elasticity and improvement of calculation of contribution rate

The Journal of Quantitative and Technical Economics

Economic development with unlimited supplies of labor

The Manchester School

An inquiry into the technology nature and causes of the wealth of nations

Studies on the measurement method of technical change based on input–output table

The Journal of Quantitative and Technical Economics

The trap in the prediction of a shock disturbed system and the buffer operator

Journal of Huazhong University of Science and Technology

Examples analysis of technological advances on economic growth in Hebei Province

Teaching and Research of Shijiazhuang Vocational College of Law and Commerce

Grey model: Theory & application