Digital Economy, Intelligent Manufacturing, and Labor Mismatch

Shen, Yang; Zhang, Xiuwu

doi:10.20965/jaciii.2022.p0655

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…Yi and Yang [ 19 ] used the labour price per mu of rice and the daily wage of agricultural hired labour to reflect the price of rural labour. Shen and Zhang [ 20 ] used the total salary of employees in urban units to represent the labour price. Therefore, the average wage of labour is an effective indicator to measure the price of human resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality adjustment and analysis of human resource prices in China: Based on a hedonic price model

2024

PLoS ONE

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The competition in the world has shifted from natural resources and capital resources to human resources. The human resources have become the real power source of the economic growth. Firstly, the price of human resources in China is calculated. Secondly, in order to measure the human resources quality adjustment index, the indicators system is constructed. Third, the Hedonic method is used to calculate the human resources "pure price" of 31 provinces (autonomous regions and municipalities directly under the Central Government) in China. The "pure price" has no the factor of human resources quality. Lastly, comparing the price of human resources before and after quality adjustment. The results show that: first, the number of human resources in China increased continuously during 1995–2015 and decreased during 2016–2020. Second, the price of nominal human resources in China keeps rising. In 2020, the nominal price is 39,087 yuan per person which is 15.44 times as many as in 1995. Thirdly, after the quality adjustment, the price of human resources has decreased significantly. The multiple between the actual and nominal price of human resources is between 1.75 and 2.12. Fourthly, the province with high human resource quality adjustment index generally have high quality human resource level or quantity. Fifth, the top five provinces of actual human resource prices are Shanghai, Beijing, Guangdong, Tianjin, Zhejiang, the bottom five provinces are Guizhou, Yunnan, Henan, Xizang, Gansu. Finally, the paper puts forward some policy recommendations: Improving the data collection mechanism of human resources accounting to provide a basic guarantee for the accurate accounting of human resources. Improving the price of human resources in the central and western regions to attract the talents to transfer to the central and western regions. Enhancing the skills training of human resources to improve the quality of human resources in the western region.

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Section: Literature Reviewmentioning

confidence: 99%

Quality adjustment and analysis of human resource prices in China: Based on a hedonic price model

2024

PLoS ONE

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“…Currently, China's labor price is mainly manifested as low price distortion, the remuneration of labor suppliers is lower than the marginal contribution, and the price distortion in less developed areas is severe [56,57]. Underestimated labor remuneration will enable enterprises with low production efficiency to use many tangible low-cost factors to obtain more profits.…”

Section: The Intermediary Path Of Labor Price Distortionmentioning

confidence: 99%

“…Labor price distortion (LPD): Considering that factor price will affect resource allocation [73][74][75][76], the idea of factor price distortion continues. Assuming that labor mismatch exists in the form of price tax τ Li , the calculation process of LPD as follows:…”

Section: Mediating Variablementioning

confidence: 99%

Impact of Intelligent Manufacturing on Total-Factor Energy Efficiency: Mechanism and Improvement Path

2023

Self Cite

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Intelligent technology is the core driving force of the fourth industrial revolution, which has an important impact on high-quality economic development. In this paper, the panel data of 30 provinces from 2006 to 2019 were selected to construct a regression model to conduct an empirical analysis on the role and mechanism of intelligent manufacturing in improving total factor energy efficiency. The research results show that first, the productivity effect, scale effect and resource allocation effect of intelligent manufacturing can significantly improve the energy efficiency of the total factor, and the conclusion is still established after endogenous treatment and robustness testing. Second, the results of the action mechanism show that labor price distortion and carbon emission trading policy are important mechanisms for intelligent manufacturing to improve total-factor energy efficiency. Specifically, the corrected labor price can enhance the motivation of enterprise research and development and innovation and solve the dilemma of the low-end industrial structure, thus improving the efficiency of total-factor energy efficiency. The carbon emission trading policy strengthens the willingness of enterprises to improve the process, eliminate backward equipment and increase the research and development of green technology, and it has a positive regulatory role in the process of improving total-factor energy efficiency in intelligent manufacturing.

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“…Thirdly, the distribution law and dynamic evolution law are described using kernel density estimation. The fourth is to use spatial agglomeration analysis to investigate the spatial characteristics of the development of the digital economy (Shen&Zhang, 2022) [9] . These records can be consulted in the course of this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Studies on the influence of economic policy uncertainty on the growth of the digital economy

Cao¹

2023

IJMRGE

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This paper outlines a theoretical mechanism for how economic policy uncertainty impacts the growth of the digital economy and argues that this uncertainty will have an incentive effect as well as a selection effect. In light of this, this paper conducts empirical research using panel data of prefecture-level cities and the uncertainty index of China's economic policy created by Baker et al. (2016). (1) Economic policy uncertainty is having a positive impact on the growth of the digital economy, according to the study. (2) The effects of uncertain economic policy on the digital economy vary widely. The promotion of the digital economy is negatively impacted by the uncertainty of economic policy in the eastern and southern regions, as well as in regions with large cities and high levels of economic development, but this impact is minimal or nonexistent in other regions.

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Digital Economy, Intelligent Manufacturing, and Labor Mismatch

Cited by 16 publications

References 9 publications

Quality adjustment and analysis of human resource prices in China: Based on a hedonic price model

Quality adjustment and analysis of human resource prices in China: Based on a hedonic price model

Impact of Intelligent Manufacturing on Total-Factor Energy Efficiency: Mechanism and Improvement Path

Studies on the influence of economic policy uncertainty on the growth of the digital economy

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