Substitution or creation? Identifying the role of artificial intelligence in employment
Abstract
Recognising the significant role of artificial intelligence in the labour market is essential for China to develop sustainably. The research utilises the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China. Through comparison, the MF-VAR method has a more substantial explanatory power than the low-frequency VAR (LF-VAR) model, the impulse responses of the former reveal that AII exerts favourable and adverse influences on UR. Among them, the positive effect occurs on the AII in the first and second months. In contrast, the negative one appears on the AII in the third month, highlighting that artificial intelligence has both stimulating and inhibiting effects on the labour market in China. By analysing UR’s predictive error variance decomposition, the total impact of China’s artificial intelligence technology on employment is a substitution; this outcome is accordant with the theoretical dis¬cussion. In the new round of scientific and technological revolution and industrial transformation, meaningful recommendations for China would be put forward to avert the wave of unemployment brought by the development of artificial intelligence technology.
First published online 09 September 2024
Keyword : artificial intelligence, employment, mixed frequency data, China
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alenizi, F. A., Abbasi, S., Mohammed, A. H., & Rahmani, A. M. (2023). The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions. Computers & Industrial Engineering, 185, Article 109662. https://doi.org/10.1016/j.cie.2023.109662
Ali, S., Akhlaq, F., Imran, A. S., Kastrati, Z., Daudpota, S. M., & Moosa, M. (2023). The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review. Computers in Biology and Medicine, 166, Article 107555. https://doi.org/10.1016/j.compbiomed.2023.107555
Amagasa, S., & Moriya, T. (2022). Emergency consultation system with automatic response software using artificial intelligence. Health Policy and Technology, 11(3), Article 100629. https://doi.org/10.1016/j.hlpt.2022.100629
Attfield, C. L. F., & Silverstone, B. (1998). Okun’s law, cointegration and gap variables. Journal of Macroeconomics, 20(3), 625–637. https://doi.org/10.1016/S0164-0704(98)00076-7
Benos, N., & Stavrakoudis, A. (2022). Okun’s law: Copula-based evidence from G7 countries. The Quarterly Review of Economics and Finance, 84, 478–491. https://doi.org/10.1016/j.qref.2020.10.004
Boubtane, E., Coulibaly, D., & Rault, C. (2013). Immigration, unemployment and GDP in the host country: Bootstrap panel Granger causality analysis on OECD countries. Economic Modelling, 33, 261–269. https://doi.org/10.1016/j.econmod.2013.04.017
Cai, D. S., Aziz, G., Sarwar, S., Alsaggaf, M. I., & Sinha, A. (2023). Applicability of denoising-based artificial intelligence to forecast the environmental externalities. Geoscience Frontiers, 15(3), Article 101740. https://doi.org/10.1016/j.gsf.2023.101740
Chang, T., Hsu, C.-M., Chen, S.-T., Wang, M.-C., & Wu, C.-F., (2023). Revisiting economic growth and CO2 emissions nexus in Taiwan using a mixed-frequency VAR model. Economic Analysis and Policy, 79, 319–342. https://doi.org/10.1016/j.eap.2023.05.022
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118
Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L., & Zemmel, R. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company. https://threeoaksadvisory.com/staging1/wp-content/uploads/2024/03/the_economic-of-generative-ai.pdf
Czarnitzki, D., Fernández, G. P., & Rammer, C. (2023). Artificial intelligence and firm-level productivity. Journal of Economic Behavior & Organization, 211, 188–205. https://doi.org/10.1016/j.jebo.2023.05.008
Deng, Y., Jiang, W. Y., & Wang, Z. Y. (2023). Economic resilience assessment and policy interaction of coal resource oriented cities for the low carbon economy based on AI. Resources Policy, 82, Article 103522. https://doi.org/10.1016/j.resourpol.2023.103522
Donglin, S., Linhui, W., & Zhiqing, D. (2012). Capital-embodied technological change and its contribution to economic growth: 1981–2007. Social Sciences in China, 33(4), 108–130. https://doi.org/10.1080/02529203.2012.731803
Elhorst, J. P., & Emili, S. (2022). A spatial econometric multivariate model of Okun’s law. Regional Science and Urban Economics, 93, Article 103756. https://doi.org/10.1016/j.regsciurbeco.2021.103756
Feng, S., Hu, Y., & Moffitt, R. (2017). Long run trends in unemployment and labor force participation in urban China. Journal of Comparative Economics, 45(2), 304–324. https://doi.org/10.1016/j.jce.2017.02.004
Ghysels, E., Hill, J. B., & Motegi, K. (2016). Testing for Granger causality with mixed frequency data. Journal of Econometrics, 192(1), 207–230. https://doi.org/10.1016/j.jeconom.2015.07.007
Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS touch: Mixed data sampling regression models (CIRANO Working Paper No. 2004s-20). University of California. https://escholarship.org/uc/item/9mf223rs
Götz, T. B., Hecq, A., & Smeekes, S. (2016). Testing for Granger causality in large mixed-frequency VARs. Journal of Econometrics, 193, 418–432. https://doi.org/10.1016/j.jeconom.2016.04.015
Goyal, A., & Aneja, R. (2020). Artificial intelligence and income inequality: Do technological changes and worker’s position matter? Journal of Public Affairs, 20(4), Article e2326. https://doi.org/10.1002/pa.2326
Gravina, A. F., & Pappalardo, M. R. (2022). Are robots in rich countries a threat for employment in emerging economies? Economics Letters, 221, Article 110888. https://doi.org/10.1016/j.econlet.2022.110888
Guliyev, H. (2023). Artificial intelligence and unemployment in high-tech developed countries: New insights from dynamic panel data model. Research in Globalization, 7, Article 100140. https://doi.org/10.1016/j.resglo.2023.100140
Guliyev, H., Huseynov, N., & Nuriyev, N. (2023). The relationship between artificial intelligence, big data, and unemployment in G7 countries: New insights from dynamic panel data model. World Development Sustainability, 3, Article 100107. https://doi.org/10.1016/j.wds.2023.100107
Hang, H., & Chen, Z. (2022). How to realize the full potentials of artificial intelligence (AI) in digital economy? A literature review. Journal of Digital Economy, 1(3), 180–191. https://doi.org/10.1016/j.jdec.2022.11.003
Hu, J., Wang, K.-H., Su, C. W., & Umar, M. (2022). Oil price, green innovation and institutional pressure: A China’s perspective. Resources Policy, 78, Article 102788. https://doi.org/10.1016/j.resourpol.2022.102788
Huang, H., Li, T., Ding, Y., Li, B., & Liu, A. (2023a). An artificial immunity based intrusion detection system for unknown cyberattacks. Applied Soft Computing, 148, Article 110875. https://doi.org/10.1016/j.asoc.2023.110875
Huang, X., Wu, X., Cao, X., & Wu, J. (2023b). The effect of medical artificial intelligence innovation locus on consumer adoption of new products. Technological Forecasting Social Change, 197, Article 122902. https://doi.org/10.1016/j.techfore.2023.122902
Javed, M. (2023). Robots, natives and immigrants in US local labor markets. Labour Economics, 85, Article 102456. https://doi.org/10.1016/j.labeco.2023.102456
Jiang, W., & Yu, Q. (2023). Carbon emissions and economic growth in China: Based on mixed frequency VAR analysis. Renewable and Sustainable Energy Reviews, 183, Article 113500. https://doi.org/10.1016/j.rser.2023.113500
Jung, J. H., & Lim, D.-G. (2020). Industrial robots, employment growth, and labor cost: A simultaneous equation analysis. Technological Forecasting Social Change, 159, Article 120202. https://doi.org/10.1016/j.techfore.2020.120202
Kelishomi, A. M., & Nisticò, R. (2022). Employment effects of economic sanctions in Iran. World Development, 151, Article 105760. https://doi.org/10.1016/j.worlddev.2021.105760
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, Article 106848. https://doi.org/10.1016/j.compbiomed.2023.106848
Kuzin, V., Marcellino, M., & Schumacher, C. (2011). MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area. International Journal of Forecasting, 27(2), 529–542. https://doi.org/10.1016/j.ijforecast.2010.02.006
Lei, Y., Liang, Z., & Ruan, P. (2023). Evaluation on the impact of digital transformation on the economic resilience of the energy industry in the context of artificial intelligence. Energy Reports, 9, 785–792. https://doi.org/10.1016/j.egyr.2022.12.019
Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023a). Methods and applications for artificial intelligence, big data, internet of things, and blockchain in smart energy management. Energy and AI, 11, Article 100208. https://doi.org/10.1016/j.egyai.2022.100208
Li, T., Barwick, P. J., Deng, Y., Huang, X., & Li, S. (2023b). The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China. Journal of Urban Economics, 135, Article 103543. https://doi.org/10.1016/j.jue.2023.103543
Li, Y.-p., & Qi, A.-q. (2022). Replace or create: Analysis of the relationship between the artificial intelligence and youth employment in post epidemic era. Procedia Computer Science, 202, 217–222. https://doi.org/10.1016/j.procs.2022.04.029
Ma, B., Yang, J., Wong, F. K. Y., Wong, A. K. C., Ma, T., Meng, J., Zhao, Y., Wang, Y., & Lu, Q. (2023). Artificial intelligence in elderly healthcare: A scoping review. Ageing Research Reviews, 83, Article 101808. https://doi.org/10.1016/j.arr.2022.101808
Ma, H., Gao, Q., Li, X., & Zhang, Y. (2022). AI development and employment skill structure: A case study of China. Economic Analysis Policy, 73, 242–254. https://doi.org/10.1016/j.eap.2021.11.007
McMillan, L., & Varga, L. (2022). A review of the use of artificial intelligence methods in infrastructure systems. Engineering Applications of Artificial Intelligence, 116, Article 105472. https://doi.org/10.1016/j.engappai.2022.105472
Miller, J. I. (2014). Mixed-frequency cointegrating regressions with parsimonious distributed lag structures. Journal of Financial Econometrics, 12(3), 584–614. https://doi.org/10.1093/jjfinec/nbt010
Mo, F., Rehman, H. U., Monetti, F. M., Chaplin, J. C., Sanderson, D., Popov, A., Maffei, A., & Ratchev, S. (2023). A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Robotics and Computer-Integrated Manufacturing, 82, Article 102524. https://doi.org/10.1016/j.rcim.2022.102524
Motegi, K., & Sadahiro, A. (2018). Sluggish private investment in Japan’s lost decade: Mixed frequency vector autoregression approach. The North American Journal of Economics and Finance, 43, 118–128. https://doi.org/10.1016/j.najef.2017.10.009
Mutascu, M. (2021). Artificial intelligence and unemployment: New insights. Economic Analysis and Policy, 69, 653–667. https://doi.org/10.1016/j.eap.2021.01.012
Nguyen, Q. P., & Vo, D. H. (2022). Artificial intelligence and unemployment: An international evidence. Structural Change and Economic Dynamics, 63, 40–55. https://doi.org/10.1016/j.strueco.2022.09.003
Ni, B., & Obashi, A. (2021). Robotics technology and firm-level employment adjustment in Japan. Japan and the World Economy, 57, Article 101054. https://doi.org/10.1016/j.japwor.2021.101054
Parteka, A., & Kordalska, A. (2023). Artificial intelligence and productivity: Global evidence from AI patent and bibliometric data. Technovation, 125, Article 102764. https://doi.org/10.1016/j.technovation.2023.102764
Prentice, C., Wong, I. A., & Lin, Z. W. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance. Journal of Retailing and Consumer Services, 73, Article 103376. https://doi.org/10.1016/j.jretconser.2023.103376
Qin, M., Su, C.-W., Lobonţ, O.-R., & Umar, M. (2023a). Blockchain: A carbon-neutral facilitator or an environmental destroyer? International Review of Economics & Finance, 86, 604–615. https://doi.org/10.1016/j.iref.2023.04.004
Qin, M., Su, Y. H., Zhao, Z., & Mirza, N. (2023b). The politics of climate: Does factionalism impede U.S. carbon neutrality? Economic Analysis and Policy, 78, 954–966. https://doi.org/10.1016/j.eap.2023.04.039
Qin, M., Mirza, N., Su, C.-W., & Umar, M. (2023c). Exploring bubbles in the digital economy: The case of China. Global Finance Journal, 57, Article 100871. https://doi.org/10.1016/j.gfj.2023.100871
Qin, M., Su, A. S., Li, R., & Su, C. W. (2024). Speculation, climate or pandemic: Who drives the Chinese herbal medicine bubbles? China Economic Review, 87, Article 102213. https://doi.org/10.1016/j.chieco.2024.102213
Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence. Journal of Business Research, 116, 68–74. https://doi.org/10.1016/j.jbusres.2020.05.019
Rebelo, A. D., Verboom, D. E., dos Santos, N. R., & de Graaf, J. W. (2023). The impact of artificial intelligence on the tasks of mental healthcare workers: A scoping review. Computers in Human Behavior, 1(2), Article 100008. https://doi.org/10.1016/j.chbah.2023.100008
Said, N., Potinteu, A. E., Brich, I., Buder, J., Schumm, H., & Huff, M. (2023). An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception. Computers in Human Behavior, 149, Article 107855. https://doi.org/10.1016/j.chb.2023.107855
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Assessing behavioral data science privacy issues in government artificial intelligence deployment. Government Information Quarterly, 39(4), Article 101679. https://doi.org/10.1016/j.giq.2022.101679
Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with artificial intelligence (AI)-enabled malware and intrusion detection. Journal of Industrial Information Integration, 36, Article 100520. https://doi.org/10.1016/j.jii.2023.100520
Schramm, S., Wehner, C., & Schmid, U. (2023). Comprehensible artificial intelligence on knowledge graphs: A survey. Journal of Web Semantics, 79, Article 100806. https://doi.org/10.1016/j.websem.2023.100806
Sequeira, T. N., Garrido, S., & Santos, M. (2021). Robots are not always bad for employment and wages. International Economics, 167, 108–119. https://doi.org/10.1016/j.inteco.2021.06.001
Silvestrini, A., & Veredas, D. (2008). Temporal aggregation of univariate and multivariate time series models: A survey. Journal of Economic Surveys, 22(3), 458–497. https://doi.org/10.1111/j.1467-6419.2007.00538.x
Soler, D., Sanz, M. T., Caselles, A., & Micó, J. C. (2018). A stochastic dynamic model to evaluate the influence of economy and well-being on unemployment control. Journal of Computational Applied Mathematics, 330, 1063–1080. https://doi.org/10.1016/j.cam.2017.04.033
Su, C.-W., Song, Y., Chang, H.-L., Zhang, W., & Qin, M. (2023). Could cryptocurrency policy uncertainty facilitate U.S. carbon neutrality? Sustainability, 15(9), Article 7479. https://doi.org/10.3390/su15097479
Su, C.-W., Yang, S., Peculea, A. D., Biţoiu, T. I., & Qin, M. (2024). Energy imports in turbulent eras: Evidence from China. Energy, 306, Article 132586. https://doi.org/10.1016/j.energy.2024.132586
Sun, W., Zhang, Z., Chen, Y., & Luan, F. (2023). Heterogeneous effects of robots on employment in agriculture, industry, and services sectors. Technology in Society, 75, Article 102371. https://doi.org/10.1016/j.techsoc.2023.102371
Thapa, A., Nishad, S., Biswas, D., & Roy, S. (2023). A comprehensive review on artificial intelligence assisted technologies in food industry. Food Bioscience, 56, Article 103231. https://doi.org/10.1016/j.fbio.2023.103231
Tian, H., Zhao, L., Li, Y., & Wang, W. (2023). Can enterprise green technology innovation performance achieve “corner overtaking” by using artificial intelligence? Evidence from Chinese manufacturing enterprises. Technological Forecasting and Social Change, 194, Article 122732. https://doi.org/10.1016/j.techfore.2023.122732
Wang, C., Zheng, M., Bai, X., Li, Y., & Shen, W. (2023a). Future of jobs in China under the impact of artificial intelligence. Finance Research Letters, 55, Article 103798. https://doi.org/10.1016/j.frl.2023.103798
Wang, Z., Liu, Y., & Niu, X. (2023b). Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Seminars in Cancer Biology, 93, 83–96. https://doi.org/10.1016/j.semcancer.2023.04.009
Wang, Y., Su, C.-W., Zhang, Y., Lobonţ, O.-R., & Meng, Q. (2023c). Effectiveness of principal component-based mixed-frequency error correction model in predicting gross domestic product. Mathematics, 11(9), Article 4144. https://doi.org/10.3390/math11194144
Wang, H., Ding, L., Guan, R., & Xia, Y. (2020). Effects of advancing internet technology on Chinese employment: A spatial study of inter-industry spillovers. Technological Forecasting and Social Change, 161, Article 120259. https://doi.org/10.1016/j.techfore.2020.120259
World Bank Group. (2016). World development report 2016: Digital dividends. World Bank. https://doi.org/10.1596/978-1-4648-0671-1
Wu, J., Wang, X., Dang, Y., & Lv, Z. (2022). Digital twins and artificial intelligence in transportation infrastructure: Classification, application, and future research directions. Computers and Electrical Engineering, 101, Article 107983. https://doi.org/10.1016/j.compeleceng.2022.107983
Yang, C.-H. (2022). How artificial intelligence technology affects productivity and employment: Firm-level evidence from Taiwan. Research Policy, 51(6), Article 104536. https://doi.org/10.1016/j.respol.2022.104536
Yu, L., Zhang, X., & Yin, H. (2022). An extreme learning machine based virtual sample generation method with feature engineering for credit risk assessment with data scarcity. Expert Systems with Applications, 202, Article 117363. https://doi.org/10.1016/j.eswa.2022.117363
Zeba, G., Dabić, M., Čičak, M., Daim, T., & Yalcin, H. (2021). Technology mining: Artificial intelligence in manufacturing. Technological Forecasting and Social Change, 171, Article 120971. https://doi.org/10.1016/j.techfore.2021.120971
Zhai, S., & Liu, Z. (2023). Artificial intelligence technology innovation and firm productivity: Evidence from China. Finance Research Letters, 58, Article 104437. https://doi.org/10.1016/j.frl.2023.104437
Zhang, X.-X., & Liu, L. (2020). The time-varying causal relationship between oil price and unemployment: Evidence from the U.S. and China (EGY 118745). Energy, 212, Article 118745. https://doi.org/10.1016/j.energy.2020.118745
Zhang, Y., Geng, P., Sivaparthipan, C. B., & Muthu, B. A. (2021). Big data and artificial intelligence based early risk warning system of fire hazard for smart cities. Sustainable Energy Technologies Assessments, 45, Article 100986. https://doi.org/10.1016/j.seta.2020.100986