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Can industrial robot applications help cross the middle-income trap? — Empirical evidence based on crossed-country panel data

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  • Yang, Wenju
  • Wang, Dongmei

Abstract

What middle-income countries can draw from the experience of the countries that have successfully crossed the middle-income trap has become a theoretical and practical issue where an in-depth study is demanded as these countries account for a large proportion of the world's economy. There are many research studies on crossing the middle-income trap and artificial intelligence, but few studies have combined the two. This paper investigates the impact of industrial robot applications on the middle-income trap crossing at both theoretical and empirical levels. In theory, the scale effect and pricing behavior of robots are introduced into the task model proposed by Acemoglu and Restrepo (2018a) in this paper, which proves that robots can affect economic growth directly, and the greater the robot application, the greater the total output. In terms of empirical analysis, this paper draws on the classification criteria of Felipe et al. (2014) and adopts the absolute value method to classify economies into five groups: trapped, crossed, high-income, middle-income, and low-income group. Based on the panel data of 24 trapped and 37 leapfrogged economies from 1993 to 2019, we have empirically examined the effect of industrial robot application on crossing the middle-income trap. The results indicate that industrial robot applications facilitate economies to cross the middle-income trap through technological innovation, industrial structure and FDI, of which industrial structure includes both output structure and employment structure. This paper provides empirical facts and policy implications for developing economies to cross the middle-income trap through industrial applications.

Suggested Citation

  • Yang, Wenju & Wang, Dongmei, 2023. "Can industrial robot applications help cross the middle-income trap? — Empirical evidence based on crossed-country panel data," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002688
    DOI: 10.1016/j.techfore.2023.122583
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    Cited by:

    1. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    2. Chen, Wei & Song, Hongti, 2024. "National innovation system: Measurement of overall effectiveness and analysis of influencing factors," Technology in Society, Elsevier, vol. 77(C).
    3. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    4. Ding, Tao & Li, Hao & Liu, Li & Feng, Kui, 2024. "An inquiry into the nexus between artificial intelligence and energy poverty in the light of global evidence," Energy Economics, Elsevier, vol. 136(C).
    5. Manal Elhaj & Jihen Bousrih & Hind Alofaysan, 2024. "Can Technological Advancement Empower the Future of Renewable Energy? A Panel Autoregressive Distributed Lag Approach," Energies, MDPI, vol. 17(20), pages 1-18, October.

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