Factor allocation structure and green-biased technological progress in Chinese agriculture

Abstract

In this paper, a nonparametric method is used to measure the total factor productivity (TFP) growth index in Chinese agriculture from 1981 to 2017, and the factor bias of technological progress is identified based on the theory of induced technological progress. Then, according to the degree of dependence of technological progress on fertiliser, biased technological progress is divided into green-biased technological progress and pollution-biased technological progress, and then empirical test the factors allocation structure that induce and promote green-biased technological progress. The results show that China's agricultural TFP has undergone three stages of accelerated growth, negative growth and fluctuation, and the growth momentum has undergone three transformations, which are jointly driven by technological efficiency and technological progress, dominated by technological progress and dominated by technological efficiency. Biased technological progress has contributed to the long-term growth of agricultural TFP in most regions of China, but it is mainly biased towards capital-using and fertiliser-using. The labour/capital ratio and the capital/fertiliser ratio are increased, reducing the capital/soil ratio, which can induce and promote green-biased technological progress while suppressing pollution-biased technological progress. The mechanism test results show that increasing labour input can indirectly promote green technological progress by reducing mechanisation.

Keywords:

• Factor allocation structure
• biased technological progress
• total factor productivity
• sequential-DEA
• agricultural

JEL CODES:

• O33; O47; Q16; Q56

Acknowledgements

The authors are grateful to the anonymous referees who provided valuable comments and suggestions to significantly improve the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Table 1. Input mix and input bias towards technological progress.

Table 2. Descriptive statistics of variables.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China (71473295), the Foundation of Southwest University (SWU1909516).