Artificial Intelligence and Enterprise Export Price Markup in China’s Economy: Based on Large Language Models
DOI:
https://doi.org/10.31181/ijes1512026255Keywords:
Artificial intelligence, Price markup, Economic upgrading, Staggered difference-in-differences, Large language modelsAbstract
China’s exports face long-standing challenges of low quality, low price, and low profit margins. Amid intensifying global economic competition and deeper value chains, breaking the ‘low-value-added lock-in’ to boost export enterprises’ profit margins is urgent for addressing foreign trade bottlenecks. This study systematically examines artificial intelligence (AI)’s effect and mechanism on export enterprises’ price markup. It integrates 2007–2016 data from China’s Customs Database and Shanghai and Shenzhen A-share manufacturing listed companies, combined with AI indicators built by extracting annual report text via large language models. Results show AI significantly raises export enterprises’ price markup, with the ‘intelligent empowerment’ model having a stronger promotional effect than ‘machine replacing human’. Mechanism analysis reveals AI’s dual impacts: positive effects from efficiency improvement and technological innovation, and negative effects from intensified market competition and higher information transparency. Heterogeneity analysis finds AI benefits state-owned enterprises, high-productivity enterprises, quality-competitive enterprises, and those exporting to developed countries more. Additionally, it reduces markup dispersion and improves resource allocation efficiency. Overall, AI drives export enterprises to break low-value-added dilemmas, optimise resource allocation, and further boost China’s export economy.
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Sheng, D., & Liu, Z. Q. (2017). RMB Exchange Rate, Processing Trade and Markups of Chinese Firms. World Economy, 40(1), 3–24. https://doi.org/10.19985/j.cnki.cassjwe.2017.08.005
An, G., & Wang, J. (2023). Local Market Size and Firm Price Markup——Micro Evidence from Chinese Enterprises. Journal of International Trade, 8, 88–105. https://doi.org/10.13510/j.cnki.jit.2023.08.006
Popescu, A. I. (2024). The Evolution of On-demand Platforms: Conceptual Framework, Regulatory Challenges, and Policy Implications in the Digital Economy. International Journal of Economic Sciences, 13(2), 55-86. https://doi.org/10.52950/ES.2024.13.2.004 DOI: https://doi.org/10.52950/ES.2024.13.2.004
Alkhudary, R., Queiroz, M. M., & Féniès, P. (2024). Mitigating the Risk of Specific Supply Chain Disruptions Through Blockchain Technology. Supply Chain Forum: An International Journal, 25(1), 1–11. https://doi.org/10.1080/16258312.2022.2090273 DOI: https://doi.org/10.1080/16258312.2022.2090273
Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, Firm Growth, and Product Innovation. Journal of Financial Economics, 151, 103745. https://doi.org/10.1016/j.jfineco.2023.103745 DOI: https://doi.org/10.1016/j.jfineco.2023.103745
Lerner, A. P. (1934). The Concept of Monopoly and the Measurement of Monopoly Power. Review of Economic Studies, 1, 157–175. https://doi.org/10.2307/2967480 DOI: https://doi.org/10.2307/2967480
Domowitz, I., Hubbard, R. G., & Petersen, B. C. (1986). Business Cycles and the Relationship between Concentration and Price-cost Margins. Rand Journal of Economics, 17(1), 1–17. https://doi.org/10.2307/2555624 DOI: https://doi.org/10.2307/2555624
Siotis, G. (2003). Competitive pressure and Economic Integration: An Illustration from Spain, 1983–1996. International Journal of Industrial Organization, 21, 1435–1459. https://doi.org/10.1016/S0167-7187(03)00051-1 DOI: https://doi.org/10.1016/S0167-7187(03)00051-1
Edmond, C., Midrigan, V., & Xu, D. Y. (2015). Competition, Markups, and the Gains from International Trade. American Economic Review, 105(10), 3183–3221. https://doi.org/10.1257/aer.20120549 DOI: https://doi.org/10.1257/aer.20120549
De Loecker, J., & Warzynski, F. (2012). Markups and Firm–Level Export Status. The American Economic Review, 102(6), 2437–2471. https://doi.org/10.1257/aer.102.6.2437 DOI: https://doi.org/10.1257/aer.102.6.2437
Bai, P. W., & Yu, L. (2021). Digital Economy Development and Firms’ Markup: Theoretical Mechanisms and Empirical Facts. China Industrial Economy, 11, 59–77. https://doi.org/10.19581/j.cnki.ciejournal.2021.11.003
Fang, P. P., & Liao, H. (2022). Intermediate Input Import Competition, Supply Chain Linkages and Markup of Supply Enterprise. World Economy Study, 10, 55–71+136. https://doi.org/10.13516/j.cnki.wes.2022.10.005
Myers, M. B., Cavusgil, S. T., & Diamantopoulos, A. (2002). Antecedents and Actions of Export Pricing Strategy: A Conceptual Framework and Research Propositions. European Journal of Marketing, 36, 159–188. https://doi.org/10.1108/03090560210412746 DOI: https://doi.org/10.1108/03090560210412746
Indounas, K., & Avlonitis, G. J. (2009). Pricing Objectives and Their Antecedents in the Services Sector. Journal of Service Management, 20, 342–374. https://doi.org/10.1108/09564230910964426 DOI: https://doi.org/10.1108/09564230910964426
Tan, Q., & Sousa, C. M. P. (2015). Leveraging Marketing Capabilities into Competitive Advantage and Export Performance. International Marketing Review, 32(1), 78-102. https://doi.org/10.1108/IMR-12-2013-0279 DOI: https://doi.org/10.1108/IMR-12-2013-0279
Huang, K. Y., Li, Z. F., Pan, N. P., & Ni, J. W. (2023). Enterprise Digital Transformation and Share of Labor Income. Economic Review, 2, 15–30. https://doi.org/10.19361/j.er.2023.02.02
Freund, C. L., & Weinhold, D. (2004). The Effect of the Internet on International Trade. Journal of International Economics, 62(1), 171–189. https://doi.org/10.1016/S0022-1996(03)00059-X DOI: https://doi.org/10.1016/S0022-1996(03)00059-X
Acemoglu, D., & Restrepo, P. (2020). The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand. Cambridge Journal of Regions, Economy and Society, 13(1), 25–35. https://doi.org/10.1093/cjres/rsz022 DOI: https://doi.org/10.1093/cjres/rsz022
Goldfarb, A., & Trefler, D. (2018). AI and International Trade. NBER, 24254. https://doi.org/10.3386/w24254 DOI: https://doi.org/10.3386/w24254
Maity, S., & Majumder, A. (2025). A Comparative Study on the Financial Inclusion Status of G20 Countries. Journal of Decision Analytics and Intelligent Computing, 5(1), 14–24. https://doi.org/10.31181/jdaic10015022025m DOI: https://doi.org/10.31181/jdaic10015022025m
Alguacil, M., Turco, A. L., & Martínez-Zarzoso, I. (2022). Robot Adoption and Export Performance: Firm-level Evidence from Spain. Economic Modelling, 114, 105912. https://doi.org/10.1016/j.econmod.2022.105912 DOI: https://doi.org/10.1016/j.econmod.2022.105912
Brynjolfsson, E., Hui, X., & Liu, M. (2019). Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform. Management Science, 65(12), 5449–5460. https://doi.org/10.1287/mnsc.2019.3388 DOI: https://doi.org/10.1287/mnsc.2019.3388
DeStefano, T., & Timmis, J. (2024). Robots and Export Quality. Journal of Development Economics, 168, 103248. https://doi.org/10.1016/j.jdeveco.2023.103248 DOI: https://doi.org/10.1016/j.jdeveco.2023.103248
Lyu, Y., Zhang, H. T., & Gao, K. L. (2024). On Extension of China’s Industrial Chain in the Era of Artificial Intelligence: Evidence from Firms’ Imports of Industrial Intelligent Equipment. China Industrial Economics, 1, 56-74. https://doi.org/10.19581/j.cnki.ciejournal.2024.01.004
Czarnitzki, D., Fernández, G. P., & Rammer, C. (2023). Artificial Intelligence and Firm-level Productivity. Journal of Economic Behavior & Organization, 211, 188-205. DOI: https://doi.org/10.1016/j.jebo.2023.05.008
Zhao, C. M., & Chu, T. T. (2024). Study on the Influence of Artificial Intelligence Application on the Technical Complexity of Export——Based on Chinese Industry Panel Data. 6, 58-66. https://doi.org/10.13580/j.cnki.fstc.2024.06.003
Melitz, M. J., & Ottaviano, G. I. P. (2008). Market Size, Trade, and Productivity. Review of Economic Studies, 75, 295–316. https://doi.org/10.1111/j.1467-937X.2007.00463.x DOI: https://doi.org/10.1111/j.1467-937X.2007.00463.x
Gaglio, C., Kraemer-Mbula, E., & Lorenz, E. (2022). The effects of digital transformation on innovation and productivity: Firm-level evidence of South African manufacturing micro and small enterprises. Technological Forecasting and Social Change, 182, 121785. https://doi.org/10.1016/j.techfore.2022.121785 DOI: https://doi.org/10.1016/j.techfore.2022.121785
Cosar, A. K., & Guner, N., & Tybout, J. (2016). Firm Dynamics, Job Turnover, and Wage Distributions in an Open Economy. American Economic Review, 106(3), 625–663. https://doi.org/10.1257/aer.20110457 DOI: https://doi.org/10.1257/aer.20110457
Niu, Z. H., Jin, H., & Li, X. Z. (2025). The Impact of Smart Manufacturing on Firm Markup. Journal of Guangdong University of Finance & Economics, 9, 1–13. https://doi.org/10.20209/j.gcxb.441711.20250926.001
Xia, T., Zhou, J. H., & Sun, J. W. (2024). Development of Digital Economy, Government Intervention, and Urban Economic Resilience. China Soft Science, 5, 111–121.
Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37-50. https://doi.org/10.1016/j.bushor.2019.09.003 DOI: https://doi.org/10.1016/j.bushor.2019.09.003
Parteka, A. & Kordalska, A. (2023). Artificial Intelligence and Productivity: Global Evidence from AI Patent and Bibliometric Data. Technovation, 125, 102764. https://doi.org/10.1016/j.technovation.2023.102764 DOI: https://doi.org/10.1016/j.technovation.2023.102764
Autor, D., Dorn, D., Katz, L. F., & Patterson, C. (2020). The Fall of the Labor Share and the Rise of Superstar Firms. Quarterly Journal of Economics, 135(2), 645–709. https://doi.org/10.1093/qje/qjaa004 DOI: https://doi.org/10.1093/qje/qjaa004
Grashof, N. & Kopka, A. (2023). Artificial Intelligence and Radical Innovation: an Opportunity for All Companies?. Small Business Economics, 61(2), 771-797. https://doi.org/10.1007/s11187-022-00698-3 DOI: https://doi.org/10.1007/s11187-022-00698-3
Li, C. M., Xu, Y., & Zheng, H., et al. (2023). Artificial Intelligence, Resource Reallocation, and Corporate Innovation Efficiency: Evidence from China's Listed Companies. Resources Policy, 81, 103324. https://doi.org/10.1016/j.resourpol.2023.103324 DOI: https://doi.org/10.1016/j.resourpol.2023.103324
Healy, P. M., & Palepu, K. G. (2001). Information Asymmetry, Corporate Disclosure, and the Capital Markets: A Review of the Empirical Disclosure literature. Journal of Accounting and Economics, 31(1), 405–440. https://doi.org/10.1016/S0165-4101(01)00018-0 DOI: https://doi.org/10.1016/S0165-4101(01)00018-0
Ma, S. Z., Pu, F. Q., & Xiao, Z. H. (2024). The Flow and Value Creation of Agricultural Big Data: A Perspective Based on Supply—Demand Matching. Issues in Agricultural Economy, 8, 13–24. https://doi.org/10.13246/j.cnki.iae.2024.08.007
Bartik, T. J. (1991). Who Benefits from State and Local Economic Development Policies?. W.E. Upjohn Institute. https://www.jstor.org/stable/j.ctvh4zh1q DOI: https://doi.org/10.17848/9780585223940
Goldsmith-Pinkham, P., Sorkin, I., & Swift, H. (2020). Bartik Instruments: What, When, Why, and How. American Economic Review, 110(8), 2586–2624. https://doi.org/10.1257/aer.20181047 DOI: https://doi.org/10.1257/aer.20181047
Yao, J. Q., Zhang, K. P., & Guo, L. P, et al. (2024). How Does Artificial Intelligence Improve Firm Productivity? Based on the Perspective of Labor Skill Structure Adjustment. Management World, 40(2), 101-116. https://doi.org/10.19744/j.cnki.11-1235/f.2024.0018
Crinò, R., & Ogliari, L. (2017). Financial Imperfections, Product Quality, and International Trade. Journal of International Economics, 104, 63–84. https://doi.org/10.1016/j.jinteco.2016.10.005 DOI: https://doi.org/10.1016/j.jinteco.2016.10.005
Beck, T. H. L., Levine, R., & Levkov, A. (2010). Big Bad Banks? The Winners and Losers from Bank Deregulation in the United States. Journal of Finance, 65(5), 1637–1667. https://doi.org/10.1111/j.1540-6261.2010.01589.x DOI: https://doi.org/10.1111/j.1540-6261.2010.01589.x
Chetty, R. (2009). Is the Taxable Income Elasticity Sufficient to Calculate Deadweight Loss? The Implications of Evasion and Avoidance. American Economic Journal: Economic Policy, 1(2), 31–52. https://doi.org/10.1257/pol.1.2.31 DOI: https://doi.org/10.1257/pol.1.2.31
Eckel, C., Iacovone, L., Javorcik, B., & Neary, J. P. (2015). Multi–Product Firms at Home and Away: Cost–versus Quality–Based Competence. Journal of International Economics, 95(2), 216–232. https://doi.org/10.1016/j.jinteco.2014.12.012 DOI: https://doi.org/10.1016/j.jinteco.2014.12.012
Jiang, T. (2022). Mediating Effects and Moderating Effects in Causal Inference. China Industrial Economics, 5, 100–120. https://doi.org/10.19581/j.cnki.ciejournal.2022.05.005
James, L., & Amil, P. (2003). Estimating Production Functions Using Inputs to Control for Unobservables. The Review of Economic Studies, 70(2), 317–341. https://doi.org/10.1111/1467-937X.00246 DOI: https://doi.org/10.1111/1467-937X.00246
Anderson, R. C., Duru, A., & Reeb, D. M. (2009). Founders, Heirs, and Corporate Opacity in the United States. Journal of Financial Economics, 92(2), 205–222. https://doi.org/10.1016/j.jfineco.2008.04.006 DOI: https://doi.org/10.1016/j.jfineco.2008.04.006
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