The impact of industrial labour productivity on regional income differences in China

Влияние производительности труда в промышленности на региональные различия в доходах в Китае

Shi Yongjing (Ши Юнцзин)
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Shi Yongjing (Ши Юнцзин): Southern Federal University

State and Municipal Management Scholar Notes, 2023, vol. 4, 290-297

Abstract: Since China’s reform and opening up, the eastern coastal areas have taken the lead in development, and formed significant regional development differences. One of the major reasons for the differences is the significant differences in industrial labour productivity between different regions. The purpose of this report is to examine the impact of industrial labour productivity on regional income differences, clarify its impact mechanism, and provide recommendations for regional coordinated development. This report adopted a combination of quantitative and qualitative analysis. It used a regression model to examine the impact of industrial labour productivity on regional income differences in China, and made qualitative analysis to explain the impact mechanism. Through relevant analysis, this report mainly concluded three findings. Firstly, there are significant differences in industrial labour productivity between regions in China. Secondly, industrial labour productivity is proved to be an important factor affecting regional income differences in China. Thirdly, the Chinese government needs to narrow the gap in regional industrial labour productivity from multiple aspects, such as increas-ing human capital, strengthening technological innovation, optimising the market and institutional environment, improving the incentive mechanism, etc. The research significance of this report lies in verifying the impact of China’s industrial labour productivity on regional income differences, and deeply analysing its impact mechanism. This has important policy implications for narrowing regional income differences in China.

Keywords: China’s industrial labour productivity; regional income differences; regression analysis; impact mechanism; recommendations (search for similar items in EconPapers)
Date: 2023
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