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2018 | OriginalPaper | Buchkapitel

12. Factor Price Distortion, Technological Innovation Pattern and the Biased Technological Progress of Industry in China: An Empirical Analysis Based on Mediating Effect Model

verfasst von : Xuejie Bai, Shuang Li

Erschienen in: Energy, Environment and Transitional Green Growth in China

Verlag: Springer Singapore

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Abstract

This paper constructs the influencing factors system of industrial technological progress bias in China using the mediating effect model, through introducing the two indexes of factor price distortion and technological innovation pattern. The result shows that at present, the direction of technological progress of China’s industry is capital biased, which will adversely affect the income distribution and industrial upgrading. In recent years, the factor price of China’s industry has a negative distortion, and the degree of distortion has not been effectively curbed. Technological progress bias and the distortion degree of factor price are both affected by industry factor intensity. The distortion of factor price leads to the bias of technological progress towards the capital through direct or indirect effects, in which the mediating effect of technology innovation accounts for 23% of the total effect. The introduction of technology innovation mode will increase the capital biased technological progress. Therefore, it is necessary to actively promote the reform of marketization of factors and enhance the ability of independent innovation to optimize the direction of China’s industrial technological progress.

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Vorheriges Kapitel Evaluating Performance of New Energy—Evidence from OECD

Nächstes Kapitel Environmental Innovation and Green Transformation of Economic Growth Pattern: Evidence from China

When comparing with capital, labor and other factors, land resources factor has its special features and it is difficult to acquire data, therefore, this article discusses the distortions of capital and labor only.

Due to limited space, the detailed derivation process has been omitted.

Capital-intensive industries include (according to per capita capital in descending order): the production and supply of electricity, steam hot and water; petroleum and natural gas exploitation; petroleum refining and coking; coal gas production and supply; tap water production and supply; chemical fiber manufacturing; tobacco processing industry; ferrous metal smelting and rolling processing; non-ferrous metal smelting and rolling processing; chemical raw materials and products manufacturing; food manufacturing; paper and paper products; beverage manufacturing; ferrous metal mining; transportation equipment manufacturing; non-metallic mineral products; non-ferrous metal mining industry; pharmaceutical manufacturing; labor-intensive industries include (according to per capita capital in ascending order): leather, fur, down and relative products; clothing and other fiber products manufacturing; cultural, educational and sporting goods manufacturing; furniture manufacturing; instrument, meter, cultural and office machinery; wood processing and bamboo, rattan, palm products; general equipment manufacturing industry; metal products industry; electrical machinery and equipment manufacturing; special equipment manufacturing; plastic products industry; textile industry; food processing industry; rubber products industry; electronic and communication equipment manufacturing; record media reproduction of printing industry; non-metallic mining industry; coal mining industry.

It can be proved that, CD production function is the special form of CES production function when ρ → −∞ or ρ → 0 and its factor replacement elasticity is kept at 1.

In order to testify the robustness of estimated results derived by CES function, this paper adopts CD production function to predict α, σ simultaneously, and estimations derived from both production functions have little difference.

Due to space limitation, the specific steps of the Kmenta approximation are omitted. See above for the derivation of technological progress bias, or refer to Wang et al. (2006).

The modified heteroscedasticity Wald test and Wooldridge test were used to test the panel data. It was found that there were intergroup heteroskedasticity and first order autocorrelation.

According to the measured results of factor price distortion, the distortion degrees in capital-intensive industries are lower than that in labor-intensive industries, but the gap is very narrow. So the direct effects on the technological progress bias in these two types of industries should not differ tremendously. The empirical results hereinafter validate this hypothesis.

Due to space limitation, this paper omits the estimated results of the control variables which were introduced gradually, and only lists the estimated results of the model after introducing all the control variables. Readers interested in the estimated results in the process of estimations can contact the author.

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Titel: Factor Price Distortion, Technological Innovation Pattern and the Biased Technological Progress of Industry in China: An Empirical Analysis Based on Mediating Effect Model
verfasst von: Xuejie Bai
Shuang Li
Verlag: Springer Singapore
Buch: Energy, Environment and Transitional Green Growth in China
Print ISBN: 978-981-10-7918-4

Electronic ISBN: 978-981-10-7919-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-10-7919-1_12

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