Industrial Robots and Regional Economic Outcomes: Evidence From China’s Provinces
Year: 2025
Type: article
Abstract: The deployment of robots across various sectors has become a defining feature of technological modernization and economic transformation. This study investigates the economic impact of industrial robot adoption on productivity, employment, and wage dynamics using panel data from Chinese provinces between 2000 to 2023. This study adopts a quantitative research design employing panel data econometrics to investigate the impact of industrial robot adoption on economic productivity and labor market outcomes across countries and sectors. as well as median wage growth. Specifically, the paper applied the fixed effects, difference-in-differences (DiD), and instrumental variable (IV) regressions, the analysis reveals a significant positive relationship between robot density and total factor productivity (TFP) However, robot adoption is associated with modest employment declines, particularly in regions with lower human capital levels. Interaction models further indicate that the positive effects of robots are amplified in areas with a higher share of skilled labor, supporting the theory of labor market polarization. These findings underscore the importance of complementary investments in education, R&D, and reskilling programs to fully realize the benefits of automation while mitigating its social costs. We recommend that policymakers must craft holistic responses that mitigate displacement risks while amplifying the productivity and wage-enhancing potentials of automation. Moreso, policy makers should scale up investments in technical and vocational education to ensure that workers can transition into complementary roles as technology evolves. The results offer valuable insights for policymakers aiming to foster inclusive growth in the age of intelligent automation.
Author: Kamaldeen Ibraheem Nageri
Institution: Walter Sisulu University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7105507864
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