AI Revolution: Job Creation Surges in China's Manufacturing Sector

By Soham NandiReviewed by Susha Cheriyedath, M.Sc.Jan 26 2024

In an article published in the journal Nature, researchers examined the impact of artificial intelligence (AI) on employment in China from 2006 to 2020. Contrary to common beliefs, the introduction of AI, particularly industrial robots, increased jobs by enhancing labor productivity and refining the division of labor in enterprises. The positive effects of AI on employment varied, with benefits seen in the job share for women and workers in labor-intensive industries.

Background

The pursuit of Sustainable Development Goal 8, advocating for full and productive employment, requires navigating the evolving landscape of technological advancements. Since the Industrial Revolution, each technological wave has sparked debates on the balance between efficiency and employment. In the middle of the fourth industrial revolution propelled by AI and automation, the repercussions on employment dynamics became a pivotal focus. China, with its extensive population and substantial labor force, stands out as an intriguing case for delving into the ramifications of AI on the job market.

The current body of literature offers diverse viewpoints on the correlation between AI and employment, spanning from optimistic scenarios of job generation to apprehensions about the potential for widespread unemployment. This study addressed the gaps in understanding by focusing on the deployment of industrial robots as a measure of AI in China's manufacturing sector.

The researchers sought to conduct an empirical analysis, delving into the influence of AI on employment. Their focus encompassed inquiries into the integration of industrial robots, shifts in labor productivity, and the role of AI in shaping employment trends specific to gender and industry. Notably, the authors introduced the concept of virtual agglomeration (VA), highlighting the transformative influence of the Internet, big data, and the industrial Internet of Things (IoT) on organizational structures.

Through a comprehensive strategy that incorporated machine learning (ML) models and advanced causal identification techniques, the researchers provided fresh perspectives on the impacts of AI on employment. This illuminated potential avenues for fostering inclusive and sustainable development in the digital era. 

Theoretical Mechanism and Research Hypotheses

The researchers delved into the impact of AI on employment by examining the direct influence of AI, particularly through the deployment of intelligent robots capable of handling routine and complex tasks. Contrary to concerns about job substitution, the research proposed that AI, by creating new tasks and demands, fostered employment growth. The transition to a technology-oriented economy emphasized the shift from coded to non-programmed complex labor, deepening the division of labor and accelerating employment trends in modern service industries.

• Hypothesis 1 (H1) posited that AI increased employment, challenging pessimistic views on technological unemployment.
• Hypothesis 2 (H2) suggested that AI's promotion of employment was achieved through enhancing labor productivity, deepening capital, and refining the division of labor.

Additionally, the authors introduced the concept of VA, a digital and platform-driven transformation that transcended geographical limitations. Hypothesis 3 (H3) proposed that AI promoted employment by improving the VA of enterprises. VA was seen as a catalyst for knowledge spillover, decentralized management models, and the amplification of market scale effects beyond traditional geographical boundaries. This comprehensive theoretical framework aimed to provide insights into the nuanced dynamics of AI's impact on employment, accounting for both direct effects and the role of VA in the digital era.

Study Design and Data Sources

The authors investigated the impact of AI on employment, focusing on the industrial sector's employment scale (ES) in manufacturing cities and towns in China from 2006 to 2020. The core explanatory variable was AI, measured by the installation density of industrial robots sourced from the International Federation of Robotics. Mediating variables include labor productivity, capital deepening (CD), division of labor refinement (DLR), and VA.

Control variables encompassed road accessibility, industrial structure, research and development (R&D) investment, wage cost, marketization, urbanization, and macrocontrol. The econometric model employed a linear regression framework, examining the relationship between AI and ES, while a second model explored the mediating effects of mechanism variables.

Data from various Chinese statistical sources covered 30 provinces, municipalities, and autonomous regions. The researchers addressed potential endogeneity issues by incorporating control variables. The research aimed to provide insights into the nuanced dynamics of AI's influence on employment, examining both direct effects and mediating mechanisms within the Chinese context.

Empirical and Extensibility Analysis

The study's empirical analysis employed a two-way fixed-effects model to examine the impact of AI on employment in China's manufacturing sector. The results consistently showed a significantly positive relationship between AI and ES, supporting the hypothesis that AI contributed to job creation. The researchers found that the positive effect of AI on employment was greater than any substitution effect, affirming the employment creation mechanism of technological progress.

Robustness tests using alternative specifications and additional control variables confirmed the robustness of the baseline regression results. To address potential endogeneity issues, a two-stage least squares method was applied, and the results continued to support the conclusion that robots created more jobs and increased labor demand in enterprises.

The analysis further explored gender bias, revealing that AI positively impacted both male and female employment, with a more significant promotion effect on female employees. The authors also investigated how AI affected employment, finding that AI promoted employment through mechanisms such as CD, improved labor productivity, and DLR. Additionally, AI contributed to employment by enhancing VA in the cloud and network, supporting the idea that AI and digital technology facilitated employment growth by promoting enterprise agglomeration.

Conclusion

In conclusion, researchers investigated the impact of AI on employment in China, revealing a consistently positive effect on job creation. The research emphasized the need for increased investment in AI research, developing domestic robots, and accelerating intelligent infrastructure. Policy recommendations included enhancing the social security system, improving public services, and addressing the changing demand for jobs. While the findings were limited to China during the sample period, the study provided valuable insights and policy implications for managing the influence of AI on employment globally.