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Robotics applications, inclusive employment and income disparity

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  • Xin, Baogui
  • Ye, Xiaopu

Abstract

Robots are outstanding representatives of contemporary high-end intelligent equipment and high-tech. Widespread industrial robot adoption has profound implications for employment quality now and in the future. This study utilizes international and Chinese data to estimate provincial robot density. Fixed-effects models reveal industrial robot adoption improves labor conditions, especially for low-skilled and agricultural workers, indicating inclusive employment gains. However, quantile regression demonstrates an inverted U-shaped relationship between robot adoption and employment quality, suggesting diminishing marginal returns. Further analysis uncovers rising income inequality from robot adoption, shown by increased labor income variation. The study aims to inform policies promoting quality employment in the digital era while optimizing income distribution as automation reshapes work. Understanding robotics' nuanced impact on employment and inequality is critical for evidence-based policymaking. Key findings show robots presently boost job quality for vulnerable workers but may also widen income gaps. More research is needed on sustainable policies maximizing the benefits of automation while ensuring broadly shared prosperity. As automation diffuses through the economy, policymakers must carefully balance productivity with equity. This study contributes robust data analysis illuminating the complex interactions between robot adoption, employment gains, and income distribution. The results highlight the need for calibrated policies targeting inclusive growth as automation transforms the labor market. Further research should explore these dynamics across countries and industries. With thoughtful governance, robotics can raise productivity and living standards for all.

Suggested Citation

  • Xin, Baogui & Ye, Xiaopu, 2024. "Robotics applications, inclusive employment and income disparity," Technology in Society, Elsevier, vol. 78(C).
  • Handle: RePEc:eee:teinso:v:78:y:2024:i:c:s0160791x24001696
    DOI: 10.1016/j.techsoc.2024.102621
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