The contribution of robots to productivity growth in 30 OECD countries over 1975–2019

Cette, Gilbert; Devillard, Aurélien; Spiezia, Vincenzo

doi:10.1016/j.econlet.2021.109762

Cited by 33 publications

(14 citation statements)

References 12 publications

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“…In Table 4 , the regression results of the main explanatory variable m shows that the coefficient is 0.0290 and significant at the level of 1%, indicating that an increase in RAMOC will promote technical change with bias toward automated machines. The results are consistent with those of Acemoglu (2002) , Weiss and Garloff (2011) , Acemoglu and Restrepo (2019) , and Cette et al (2021) . On one hand, the enterprises’ investment behavior tends to match the production factors related to automated machines.…”

Section: Discussionsupporting

confidence: 89%

Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China

et al. 2022

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This paper studies the impact of a recent increase in the ratio of automated machines to ordinary capital (RAMOC) on the bias of technical change in the manufacturing industry and the mechanism influencing this. Using panel data of A-share listed manufacturing companies on the Shanghai and Shenzhen stock exchanges from 2012 to 2019, combined with the Xtfrontier model and trans-log production function, we measure the index of the bias of technical change of the manufacturing industry in China. Furthermore, we adopt a fixed effects model to test the impact of an increase of investment in automated machines on the bias of technical change. We also use an intermediary effect model to examine the intermediate mechanism from the perspectives of capital and skill matching. The results show that technical change in the manufacturing industry is biased toward automated machine capital. An incremental increase in RAMOC leads to technical change in the manufacturing industry becoming biased toward automated machine capital, wherein the intermediary mechanism is the labor structure effect. Based on industrial linkage, the investment in automated machines in the upstream (downstream) manufacturing industry increases, the technical change of the downstream (upstream) manufacturing industry is biased toward automated machine capital, and the forward linkage effect is greater than the backward linkage effect. This research enhances understanding of (1) the direction and characteristics of technical change in China, (2) how to improve the output efficiency of automated machines, (3) differences in factor revenue distribution, and (4) how new growth points in the economy can be cultivated. They show that we should encourage and support investment in automated machines, vigorously promote technical change to bias toward automated machine capital, improve the skill level of the labor force, and strengthen the match between automated machines and labor.

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Section: Discussionsupporting

confidence: 89%

Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China

et al. 2022

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“…People and businesses turned to online platforms to make online purchases and pursue communication, education, and work. Digital 1 Some studies have argued that while digital technologies offer a vast potential to boost productivity growth (Acemoglu and Restrepo 2020;Cette, Devillard, and Spiezia 2021;Mosiashvili and Pareliussen 2020), their full potential is yet to be realized. 2 For instance, China accounted for less than 1 percent of global e-commerce retail transaction value about a decade ago, but that share has grown to more than 40 percent on the eve of the pandemic, and the penetration of e-commerce (as a share of total retail sales) stood at 15 percent, compared to 10 percent in the United States.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Innovation and Digitalization in Asia to Boost Productivity

Kinda¹,

Sollaci²,

Chai³

et al. 2023

Departmental Papers

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“…At present, the research on the traditional determinants of total factor productivity (TFP) is relatively mature and is mostly from the perspectives of R&D, foreign direct investment, and urbanization (Ashraf et al, 2016;Kumar & Kober, 2012;Tsamadias et al, 2019). Some studies have investigated the impact of technological innovation on TFP from the perspectives of Internet technology, information and communications technology (ICT), and industrial robots (Brynjolfsson & Hitt, 1996;Cette et al, 2021;Kallal et al, 2021) but their conclusions are not uniform. Some scholars believe that technological innovation has improved TFP (Venturini, 2022), while others have concluded the opposite (Jorgenson et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Does Intelligence Improve the Total Factor Productivity of China’s Manufacturing Industry?

Qin

2023

Asian Economics Letters

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This paper empirically tests the impact of intelligence on the total factor productivity of China’s manufacturing industry through a double fixed-effect model. The results show that the current process of intelligentization can significantly improve total factor productivity in China’s manufacturing industry. Specifically, intelligence significantly promotes total factor productivity in the eastern region, but does not significantly impact total factor productivity in central and western regions.

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The contribution of robots to productivity growth in 30 OECD countries over 1975–2019

Cited by 33 publications

References 12 publications

Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China

Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China

Accelerating Innovation and Digitalization in Asia to Boost Productivity

Does Intelligence Improve the Total Factor Productivity of China’s Manufacturing Industry?

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