The impact of AI pilot zones on market competition: Causal insights from policy implementation

Abstract

Research background: Artificial intelligence (AI) is a major catalyst for innovation and economic transformation, reshaping competition across industries. In 2019, China launched AI pilot zones to stimulate AI innovation while managing its economic and societal impacts. These zones aim to foster AI ecosystems through targeted investments, infrastructure, and favorable policies. However, concentration of resources and market power within these zones raises concerns about their effects on competition. This study examines how AI pilot zones influence competitive dynamics, focusing on the mechanisms driving market concentration and firm behavior.

Purpose of the article: The causal impact of AI pilot zones on market competition in China is examined. A difference-in-differences (DID) methodology is used to explore the impact of these zones on firm entry, market concentration, and innovation. This study seeks to understand if pilot zones enhance competition by lowering entry barriers and supporting innovation or they exacerbate market concentration by favoring large firms.

Methods: The DID methodology uses firm-level data from AI pilot and non-pilot regions (2010–2023). Market competition is assessed through concentration ratios based on revenue and income from top firms. Robustness tests, including placebo tests, and instrumental variable approaches are applied to ensure the reliability of results. Agency theory and resource dependence theory are used to explain the mechanisms through which AI policies impact competition, specifically access to resources, information asymmetry, technological diffusion and government support.

Findings & value added: The study findings show that AI pilot zones positively influence competition by reducing market concentration and encouraging new firm entry. While larger firms benefit initially from policy support, the long-term effects include a more competitive market with greater firm diversification and innovation. Mechanistically, this study identifies that policy-induced enhanced information transparency, mitigated financial constraints, targeted government subsidies, and accelerated technological diffusion enable smaller firms to compete more effectively. This support helps to overcome entry barriers by providing critical resources, thereby encouraging the entry of new players and facilitating innovation. Furthermore, the policy creates a competitive environment where even incumbents must innovate to maintain their market position. The study highlights that AI pilot zones, while initially benefiting larger firms, contribute to a more competitive market by diffusing technological advancements and resources equitably across firms. This research contributes empirical evidence on the role of AI policies in shaping competition and offers valuable insights for policymakers aiming to balance innovation and competition in AI-driven economies.

Keywords

artificial intelligence, market competition, AI policy, market concentration, difference-in-differences

References

1. Abadie, A., Drukker, D., Herr, J. L., & Imbens, G. W. (2004). Implementing matching estimators for average treatment effects in Stata. Stata Journal, 4(3), 290–311. DOI: https://doi.org/10.1177/1536867X0400400307
   View in Google Scholar
2. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. DOI: https://doi.org/10.1086/705716
   View in Google Scholar
3. Aghion, P., Bergeaud, A., & Van Reenen, J. (2023). The impact of regulation on innovation. American Economic Review, 113(4), 2894–2936. DOI: https://doi.org/10.1257/aer.20210107
   View in Google Scholar
4. Aghion, P., Bloom, N., Blundell, R., Griffith, R., & Howitt, P. (2005). Competition and innovation: An inverted-U relationship. Quarterly Journal of Economics, 120(2), 701–728. DOI: https://doi.org/10.1162/0033553053970214
   View in Google Scholar
5. Alder, S., Lin, Y., & Zilibotti, F. (2016). Economic reforms and industrial policy in a panel of Chinese cities. Journal of Economic Growth, 21(4), 305–349. DOI: https://doi.org/10.1007/s10887-016-9131-x
   View in Google Scholar
6. Ali, M., Khan, T. I., Khattak, M. N., & ŞENER, İ. (2024). Synergizing AI and business: Maximizing innovation, creativity, decision precision, and operational efficiency in high-tech enterprises. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100352. DOI: https://doi.org/10.1016/j.joitmc.2024.100352
   View in Google Scholar
7. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press. DOI: https://doi.org/10.1515/9781400829828
   View in Google Scholar
8. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. DOI: https://doi.org/10.1257/jep.29.3.3
   View in Google Scholar
9. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. DOI: https://doi.org/10.1177/014920639101700108
   View in Google Scholar
10. Breuer, M., Leuz, C., & Vanhaverbeke, S. (2025). Reporting regulation and corporate innovation. Journal of Accounting and Economics, 101769. DOI: https://doi.org/10.1016/j.jacceco.2025.101769
    View in Google Scholar
11. 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. DOI: https://doi.org/10.7208/chicago/9780226613475.003.0001
    View in Google Scholar
12. Cantoni, D., Chen, Y., Yang, D. Y., Yuchtman, N., & Zhang, Y. J. (2017). Curriculum and ideology. Journal of Political Economy, 125(2), 338–392. DOI: https://doi.org/10.1086/690951
    View in Google Scholar
13. Central People's Government of the People's Republic of China [Government of PRC]. (2021) (December 6). 17 AI pilot zones built in China. Government of the People's Republic of China. Retrieved from https://english.www.gov.cn/statecouncil/ministries/202112/06/content_WS61ae0e58c6d09c94e48a1c59.html.
    View in Google Scholar
14. Chen, L., Tu, R., Huang, B., Zhou, H., & Wu, Y. (2024). Digital transformation’s impact on innovation in private enterprises: Evidence from China. Journal of Innovation and Knowledge, 9(2), 100491. DOI: https://doi.org/10.1016/j.jik.2024.100491
    View in Google Scholar
15. Comunale, M., & Manera, A. (2024). The economic impacts and the regulation of AI: A review of the academic literature and policy actions. IMF Working Papers, 065. DOI: https://doi.org/10.5089/9798400268588.001
    View in Google Scholar
16. Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024). Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small- and medium-sized enterprises. International Journal of Production Research, 62(15), 5417–5456. DOI: https://doi.org/10.1080/00207543.2023.2179859
    View in Google Scholar
17. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. DOI: https://doi.org/10.2307/2095101
    View in Google Scholar
18. Gans, J. S. (2024). Market power in artificial intelligence. NBER Working Paper Series, w32270. DOI: https://doi.org/10.3386/w32270
    View in Google Scholar
19. Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: A systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816–837. DOI: https://doi.org/10.1108/IJEBR-05-2021-0426
    View in Google Scholar
20. Goldfarb, A., & Tucker, C. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57–71. DOI: https://doi.org/10.1287/mnsc.1100.1246
    View in Google Scholar
21. Hadlock, C. J., & Pierce, J. R. (2010). New evidence on measuring financial constraints: Moving beyond the KZ index. Review of Financial Studies, 23(5), 1909–1940. DOI: https://doi.org/10.1093/rfs/hhq009
    View in Google Scholar
22. Hagiu, A., & Wright, J. (2025). Artificial intelligence and competition policy. International Journal of Industrial Organization, 103134. DOI: https://doi.org/10.1016/j.ijindorg.2025.103134
    View in Google Scholar
23. 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(1-3), 405–440. DOI: https://doi.org/10.1016/S0165-4101(01)00018-0
    View in Google Scholar
24. Hering, L., & Poncet, S. (2014). Environmental policy and exports: Evidence from Chinese cities. Journal of Environmental Economics and Management, 68(2), 296–318. DOI: https://doi.org/10.1016/j.jeem.2014.06.005
    View in Google Scholar
25. Huang, Y., Liu, S., Gan, J., Liu, B., & Wu, Y. (2024). How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China. Energy Economics, 140, 108011. DOI: https://doi.org/10.1016/j.eneco.2024.108011
    View in Google Scholar
26. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. DOI: https://doi.org/10.1016/0304-405X(76)90026-X
    View in Google Scholar
27. Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. American Economic Review, 75(3), 424–440.
    View in Google Scholar
28. Lam, B. M., Lui, G. M., & Shum, C. (2020). Social trust, market competition, and tax avoidance: Evidence from contemporary China. Journal of Forensic Accounting Research, 5(1), 94–122. DOI: https://doi.org/10.2308/JFAR-19-010
    View in Google Scholar
29. Li, Z., Xie, B., Chen, X., & Fu, Q. (2024). Corporate digital transformation, governance shifts and executive pay-performance sensitivity. International Review of Financial Analysis, 92, 103060. DOI: https://doi.org/10.1016/j.irfa.2023.103060
    View in Google Scholar
30. Lin, B., & Yang, Y. (2025). Building efficiency: How the national AI innovation pilot zones enhance green energy utilization? Evidence from China. Journal of Environmental Management, 387, 125945. DOI: https://doi.org/10.1016/j.jenvman.2025.125945
    View in Google Scholar
31. Liu, L., Wang, Y., & Xu, Y. (2023). A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data. American Journal of Political Science. DOI: https://doi.org/10.1111/ajps.12723
    View in Google Scholar
32. Lundvall, B. Å., & Rikap, C. (2022). China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems. Research Policy, 51(1), 104395. DOI: https://doi.org/10.1016/j.respol.2021.104395
    View in Google Scholar
33. Madanchian, M. (2024). The impact of artificial intelligence marketing on e-commerce sales. Systems, 12(10), 10. DOI: https://doi.org/10.3390/systems12100429
    View in Google Scholar
34. Mijit, R., Hu, Q., Xu, J., & Ma, G. (2025). Greening through AI? The impact of artificial intelligence innovation and development pilot zones on green innovation in China. Energy Economics, 146, 108507. DOI: https://doi.org/10.1016/j.eneco.2025.108507
    View in Google Scholar
35. Nunn, N., & Qian, N. (2011). The potato’s contribution to population and urbanization: Evidence from a historical experiment. Quarterly Journal of Economics, 126(2), 593–650. DOI: https://doi.org/10.1093/qje/qjr009
    View in Google Scholar
36. OECD. (2024). Artificial intelligence, data and competition. OECD Digital Economy Papers, 315.
    View in Google Scholar
37. Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row.
    View in Google Scholar
38. Porter, M. E., & van der Linde, C. (1995). Toward a new conception of the environment-competitiveness relationship. Journal of Economic Perspectives, 9(4), 97–118. DOI: https://doi.org/10.1257/jep.9.4.97
    View in Google Scholar
39. Rogers, E. M. (1995). Diffusion of innovations. Free Press.
    View in Google Scholar
40. Schumpeter, J. A. (1942). Capitalism, socialism and democracy. Harper & Brothers.
    View in Google Scholar
41. Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297–1331. DOI: https://doi.org/10.1080/00472778.2024.2379999
    View in Google Scholar
42. Tan, W., Tang, Q., Sun, W., & Du, X. (2025). Unintended consequences: Examining the effects of government digital regulation on corporate fintech innovation in China. Emerging Markets Review, 64, 101221. DOI: https://doi.org/10.1016/j.ememar.2024.101221
    View in Google Scholar
43. Wang, Q., Zhang, F., & Li, R. (2025). Artificial intelligence and sustainable development during urbanization: Perspectives on AI R&D innovation, AI infrastructure, and AI market advantage. Sustainable Development, 33(1), 1136–1156. DOI: https://doi.org/10.1002/sd.3150
    View in Google Scholar
44. Wu, M., Liu, C., & Huang, J. (2021). The special economic zones and innovation: Evidence from China. China Economic Quarterly International, 1(4), 319–330. DOI: https://doi.org/10.1016/j.ceqi.2021.11.004
    View in Google Scholar
45. Yi, J., Hong, J., Chung Hsu, W., & Wang, C. (2017). The role of state ownership and institutions in the innovation performance of emerging market enterprises: Evidence from China. Technovation, 62, 4–13. DOI: https://doi.org/10.1016/j.technovation.2017.04.002
    View in Google Scholar
46. Zhang, Y., Wang, Z., & Zhang, X. (2021). The special economic zones and innovation: Evidence from China. Research in Globalization, 3, 100050.
    View in Google Scholar
47. Zhen, X., & Zhou, Y. (2025). Digital transformation and corporate creditworthiness. Finance Research Letters, 74, 106742. DOI: https://doi.org/10.1016/j.frl.2025.106742
    View in Google Scholar
48. Zhou, K. Z., Gao, G. Y., & Zhao, H. (2017). State ownership and firm innovation in China: An integrated view of institutional and efficiency logics. Administrative Science Quarterly, 62(2), 375–404. DOI: https://doi.org/10.1177/0001839216674457
    View in Google Scholar