Robot adoption and urban total factor productivity: evidence from China
Abstract
Industrial robots are having a profound and lasting impact on China’s economy. This research examines the deployment of industrial robots and their effects on urban total factor production from theoretical and empirical angles. It is created using panel data from 286 cities at the prefecture level between 2003 and 2017. It is found that: First, robot adoption promotes urban total factor productivity. Second, adopting robots has a more positive influence on urban total factor productivity development in western, underdeveloped, and less market-oriented areas compared to the developed and market-oriented areas in the east. Third, adopting robots could enhance urban innovation vitality, increase total factor productivity, boost industrial agglomeration, and improve technological progress or technical efficiency. Policy enlightenment provided by these findings can guide future technological advancements and promote high-quality city development.
First published online 07 June 2024
Keyword : industrial robot, urban total factor productivity, technological progress, technical efficiency
How to Cite
Li, B., & Zhou, C. (2024). Robot adoption and urban total factor productivity: evidence from China. Technological and Economic Development of Economy, 30(5), 1330–1351. https://doi.org/10.3846/tede.2024.21102
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Petralia, S. (2020). Mapping general purpose technologies with patent data. Research Policy, 49(7), Article 104013. https://doi.org/10.1016/j.respol.2020.104013
Ramachandran, R., Reddy, K., & Sasidharan, S. (2020). Agglomeration and productivity: Evidence from Indian manufactuaring. Studies in Microeconomics, 8(1), 75–94. https://doi.org/10.1177/2321022220923211
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Wei, W., Zhang, W. L., Wen, J., & Wang, J. S. (2020). TFP growth in Chinese cities: The role of factor-intensity and industrial agglomeration. Economic Modelling, 91, 534–549. https://doi.org/10.1016/j.econmod.2019.12.022
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Wu, H., Hao, Y., & Ren, S. (2020). How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China. Energy Economics, 91, Article 104880. https://doi.org/10.1016/j.eneco.2020.104880
Zhang, D. (2021). Marketization, environmental regulation, and eco-friendly productivity: A Malmquist-Luenberger index for pollution emissions of large Chinese firms. Journal of Asian Economics, 76, Article 101342. https://doi.org/10.1016/j.asieco.2021.101342
Zhang, J. (2008). Estimation of China’s provincial capital stock (1952–2004) with applications. Journal of Chinese Economic and Business Studies, 6(2), 177–196. https://doi.org/10.1080/14765280802028302
Zhao, X., Nakonieczny, J., Jabeen, F., Shahzad, U., & Jia, W. (2022). Does green innovation induce green total factor productivity? Novel findings from Chinese city level data. Technological Forecasting and Social Change, 185, Article 122021. https://doi.org/10.1016/j.techfore.2022.122021
Zhou, C., & Li, B. (2023). How does e-commerce demonstration city improve urban innovation? Evidence from China. Economics of Transition and Institutional Change, 31(4), 915–940. https://doi.org/10.1111/ecot.12361
Acemoglu, D., & Restrepo, P. (2018b). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
Aghion, P., David, P. A., & Foray, D. (2009). Science, technology and innovation for economic growth: Linking policy research and practice in ‘STIG Systems’. Research Policy, 38(4), 681–693. https://doi.org/10.1016/j.respol.2009.01.016
Aleksandrova, E., Behrens, K., & Kuznetsova, M. (2020). Manufacturing (co) agglomeration in a transition country: Evidence from Russia. Journal of Regional Science, 60(1), 88–128. https://doi.org/10.1111/jors.12436
Amri, F., Zaied, Y. B., & Lahouel, B. B. (2019). ICT, total factor productivity, and carbon dioxide emissions in Tunisia. Technological Forecasting and Social Change, 146, 212–217. https://doi.org/10.1016/j.techfore.2019.05.028
Bárány, Z. L., & Siegel, C. (2018). Job polarization and structural change. American Economic Journal: Macroeconomics, 10(1), 57–89. https://doi.org/10.1257/mac.20150258
Beugelsdijk, S., Klasing, M. J., & Milionis, P. (2018). Regional economic development in Europe: The role of total factor productivity. Regional Studies, 52(4), 461–476. https://doi.org/10.1080/00343404.2017.1334118
Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 23–57). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0001
Caragliu, A., & Del Bo, C. F. (2019). Smart innovative cities: The impact of smart city policies on urban innovation. Technological Forecasting and Social Change, 142, 373–383. https://doi.org/10.1016/j.techfore.2018.07.022
Cui, C., Yu, S., & Huang, Y. (2023). His house, her house? Gender inequality and homeownership among married couples in urban China. Cities, 134, Article 104187. https://doi.org/10.1016/j.cities.2022.104187
Dakpo, K. H., Desjeux, Y., Jeanneaux, P., & Latruffe, L. (2019). Productivity, technical efficiency and technological change in French agriculture during 2002–2015: a Färe-Primont index decomposition using group frontiers and meta-frontier. Applied Economics, 51(11), 1166–1182. https://doi.org/10.1080/00036846.2018.1524982
Du, J., Liang, L., & Zhu, J. (2010). A slacks-based measure of super-efficiency in data envelopment analysis: A comment. European Journal of Operational Research, 204(3), 694–697. https://doi.org/10.1016/j.ejor.2009.12.007
Du, L., & Lin, W. (2022). Does the application of industrial robots overcome the Solow paradox? Evidence from China. Technology in Society, 68, Article 101932. https://doi.org/10.1016/j.techsoc.2022.101932
Fan, G., Wang, X. L., & Zhu, H. P. (2011). China marketization index: Report on the relative progress of marketization in various regions in 2011. Economic Science Press.
Fan, G., Wang, X., & Ma, G. (2012). The contribution of marketization to China’s economic growth. China Economist, 7(2), 4–14.
Ge, Y., & Chang, F. H. (2021). Productivity growth in Chinese cities: The agglomeration effect for cross-regional industrial structures. Theoretical & Applied Economics, 29(4), 91–104. http://www.ebsco.ectap.ro/Theoretical_&_Applied_Economics_2021_Winter.pdf#page=91
Huang, G., He, L. Y., & Lin, X. (2022). Robot adoption and energy performance: Evidence from Chinese industrial firms. Energy Economics, 107, Article 105837. https://doi.org/10.1016/j.eneco.2022.105837
Huang, J., Cai, X., Huang, S., Tian, S., & Lei, H. (2019). Technological factors and total factor productivity in China: Evidence based on a panel threshold model. China Economic Review, 54, 271–285. https://doi.org/10.1016/j.chieco.2018.12.001
International Federation of Robotics. (2020). IFR presents World Robotics Report 2020. https://ifr.org/ifr-press-releases/news/record-2.7-million-robots-work-in-factories-around-the-globe
International Federation of Robotics. (n.d.). https://ifr.org/free-downloads/
Kou, Z., & Liu, X. (2017). FIND report on city and industrial innovation in China. Fudan Institute of Industrial Development, School of Economics, Fudan University, Shanghai, China. https://fddi.fudan.edu.cn/fddien/main.htm
Krüger, J. J. (2008). Productivity and structural change: A review of the literature. Journal of Economic Surveys, 22(2), 330–363. https://doi.org/10.1111/j.1467-6419.2007.00539.x
Lan, X., Hu, Z., & Wen, C. (2023). Does the opening of high-speed rail enhance urban entrepreneurial activity? Evidence from China. Socio-Economic Planning Sciences, 88, Article 101604. https://doi.org/10.1016/j.seps.2023.101604
Li, Y., Zhang, Y., Pan, A., Han, M., & Veglianti, E. (2022). Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms. Technology in Society, 70, Article 102034. https://doi.org/10.1016/j.techsoc.2022.102034
Liu, J., Chang, H., Forrest, J. Y. L., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of China’s manufacturing sectors. Technological Forecasting and Social Change, 158, Article 120142. https://doi.org/10.1016/j.techfore.2020.120142
Luo, S., Sun, Y., Yang, F., & Zhou, G. (2022). Does fintech innovation promote enterprise transformation? Evidence from China. Technology in Society, 68, Article 101821. https://doi.org/10.1016/j.techsoc.2021.101821
National Bureau of Statistics in China. (n.d.). Chinese Urban Statistical Yearbooks. https://www.stats.gov.cn/english/
New First-tier Cities Research Institute. (n.d.). 2021 China City Business Charm Ranking List. https://www.datayicai.com/report/detail/268
Nguyen, T. A., & Nguyen, D. A. (2018). The determinants of TFP at firm-level in Vietnam. Journal of International Economics and Management, 111, 36–53. https://jiem.ftu.edu.vn/index.php/jiem/article/view/195
Pan, W., Xie, T., Wang, Z., & Ma, L. (2022). Digital economy: An innovation driver for total factor productivity. Journal of Business Research, 139, 303–311. https://doi.org/10.1016/j.jbusres.2021.09.061
Petralia, S. (2020). Mapping general purpose technologies with patent data. Research Policy, 49(7), Article 104013. https://doi.org/10.1016/j.respol.2020.104013
Ramachandran, R., Reddy, K., & Sasidharan, S. (2020). Agglomeration and productivity: Evidence from Indian manufactuaring. Studies in Microeconomics, 8(1), 75–94. https://doi.org/10.1177/2321022220923211
Rawat, P. S., & Sharma, S. (2021). TFP growth, technical efficiency and catch-up dynamics: Evidence from Indian manufacturing. Economic Modelling, 103, Article 105622. https://doi.org/10.1016/j.econmod.2021.105622
Roszko-Wójtowicz, E., Grzelak, M. M., & Laskowska, I. (2019). The impact of research and development activity on the TFP level in manufacturing in Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 14(4), 711–737. https://doi.org/10.24136/eq.2019.033
Saleem, H., Shahzad, M., Khan, M. B., & Khilji, B. A. (2019). Innovation, total factor productivity and economic growth in Pakistan: A policy perspective. Journal of Economic Structures, 8(1), 1–18. https://doi.org/10.1186/s40008-019-0134-6
Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 39(3), 312–320. https://doi.org/10.2307/1926047
Tao, C. Q., Yi, M. Y., & Wang, C. S. (2023). Coupling coordination analysis and spatiotemporal heterogeneity between data elements and green development in China. Economic Analysis and Policy, 77, 1–15. https://doi.org/10.1016/j.eap.2022.10.014
Van Neuss, L. (2019). The drivers of structural change. Journal of Economic Surveys, 33(1), 309–349. https://doi.org/10.1111/joes.12266
Wang, J., Sun, F., Lv, K., & Wang, L. (2022). Industrial agglomeration and firm energy intensity: How important is spatial proximity? Energy Economics, 112, Article 106155. https://doi.org/10.1016/j.eneco.2022.106155
Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37. https://doi.org/10.2478/jagi-2019-0002
Wang, R., & Feng, Y. (2021). Research on China’s agricultural carbon emission efficiency evaluation and regional differentiation based on DEA and Theil models. International Journal of Environmental Science and Technology, 18, 1453–1464. https://doi.org/10.1007/s13762-020-02903-w
Wang, S. L., Tuan, F., Gale, F., Somwaru, A., & Hansen, J. (2013). China’s regional agricultural productivity growth in 1985–2007: A multilateral comparison. Agricultural Economics, 44(2), 241–251. https://doi.org/10.1111/agec.12008
Wei, W., Zhang, W. L., Wen, J., & Wang, J. S. (2020). TFP growth in Chinese cities: The role of factor-intensity and industrial agglomeration. Economic Modelling, 91, 534–549. https://doi.org/10.1016/j.econmod.2019.12.022
While, A. H., Marvin, S., & Kovacic, M. (2021). Urban robotic experimentation: San Francisco, Tokyo and Dubai. Urban Studies, 58(4), 769–786. https://doi.org/10.1177/0042098020917790
Wu, H., Hao, Y., & Ren, S. (2020). How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China. Energy Economics, 91, Article 104880. https://doi.org/10.1016/j.eneco.2020.104880
Zhang, D. (2021). Marketization, environmental regulation, and eco-friendly productivity: A Malmquist-Luenberger index for pollution emissions of large Chinese firms. Journal of Asian Economics, 76, Article 101342. https://doi.org/10.1016/j.asieco.2021.101342
Zhang, J. (2008). Estimation of China’s provincial capital stock (1952–2004) with applications. Journal of Chinese Economic and Business Studies, 6(2), 177–196. https://doi.org/10.1080/14765280802028302
Zhao, X., Nakonieczny, J., Jabeen, F., Shahzad, U., & Jia, W. (2022). Does green innovation induce green total factor productivity? Novel findings from Chinese city level data. Technological Forecasting and Social Change, 185, Article 122021. https://doi.org/10.1016/j.techfore.2022.122021
Zhou, C., & Li, B. (2023). How does e-commerce demonstration city improve urban innovation? Evidence from China. Economics of Transition and Institutional Change, 31(4), 915–940. https://doi.org/10.1111/ecot.12361