Big Data Tax Enforcement and Corporate Earnings Management — Evidence from the Golden Tax Project Phase IV
DOI:
https://doi.org/10.63808/ftd.v2i3.487Keywords:
Fully digitalized e-invoice, Gig data tax enforcement, Earnings management, Staggered difference-in-differences, Information transparencyAbstract
An issue under examination here is whether data-driven tax administration leads to governance externalities in financial reporting. In this paper, I use the staggered introduction of China’s fully digitalized e-invoice issuance pilot, the dated element of the fourth phase of the Golden Tax project. Using 23,256 firm-year observations of China’s listed A-share firms from 2018 to 2024, I find that a firm-year’s participation in the pilot leads to a reduction in absolute performance-matched discretionary accruals by 0.0050, around 8.2 per cent of the sample mean, and a reduction in real earnings management with an insignificant effect size. The equality of the standardized effects cannot be rejected at the five percent significance level against the signed measure of real manipulation, but it can be marginally rejected against its absolute counterpart, and therefore the asymmetric effect is qualified. Reductions in analyst forecast error and discretionary book-tax difference are consistent with information and tax-avoidance channels, respectively, and the effect occurs mainly for non-state-owned firms, firms with low-quality audits, and input-intensive industries. Inference at the treatment assignment level results in a wild cluster bootstrap p-value of 0.058.
References
[1] Desai, M. A., Dyck, A., & Zingales, L. (2007). Theft and taxes. Journal of Financial Economics, 84(3), 591–623. https://doi.org/10.1016/j.jfineco.2006.05.005
[2] Desai, M. A., & Dharmapala, D. (2006). Corporate tax avoidance and high-powered incentives. Journal of Financial Economics, 79(1), 145–179. https://doi.org/10.1016/j.jfineco.2005.02.002
[3] Hanlon, M., Hoopes, J. L., & Shroff, N. (2014). The effect of tax authority monitoring and enforcement on financial reporting quality. The Journal of the American Taxation Association, 36(2), 137–170. https://doi.org/10.2308/atax-50820
[4] Bellon, M., Dabla-Norris, E., Khalid, S., & Lima, F. (2022). Digitalization to improve tax compliance: Evidence from VAT e-invoicing in Peru. Journal of Public Economics, 210, 104661. https://doi.org/10.1016/j.jpubeco.2022.104661
[5] Li, J., Wang, X., & Wu, Y. (2020). Can government improve tax compliance by adopting advanced information technology? Evidence from the Golden Tax Project III in China. Economic Modelling, 93, 384–397. https://doi.org/10.1016/j.econmod.2020.08.009
[6] Su, L. (2025). Tax administration digitization and corporate tax avoidance: A quasi-natural experiment based on the Golden Tax III project in China. Baltic Journal of Economics, 25(1), 175–199. https://doi.org/10.1080/1406099X.2025.2488723
[7] Wu, X., Ying, S. X., You, J., Wu, X., & Wu, H. (2026). The effect of big data-driven tax enforcement on audit pricing: Evidence from China. The British Accounting Review, 58(1), 101531. https://doi.org/10.1016/j.bar.2024.101531
[8] Chen, W., & Xu, J. (2024). Can digital tax enforcement reduce the risk of corporate debt default? Economic Analysis and Policy, 83, 1041–1060. https://doi.org/10.1016/j.eap.2024.08.010
[9] Huang, Z. (2026). Crowding-out effect of digitalized tax administration on corporate green innovation: Evidence from China’s Golden Tax Phase IV. Finance Research Letters, 87, 109067. https://doi.org/10.1016/j.frl.2025.109067
[10] State Taxation Administration of the People’s Republic of China. (2024). Announcement on the promotion and application of fully digitalized electronic invoices (Announcement No. 11 of 2024). https://fgk.chinatax.gov.cn/zcfgk/c100012/c5236067/content.html
[11] Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163–197. https://doi.org/10.1016/j.jacceco.2004.11.002
[12] Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42(3), 335–370. https://doi.org/10.1016/j.jacceco.2006.01.002
[13] Cohen, D. A., & Zarowin, P. (2010). Accrual-based and real earnings management activities around seasoned equity offerings. Journal of Accounting and Economics, 50(1), 2–19. https://doi.org/10.1016/j.jacceco.2010.01.002
[14] Shenzhen CSMAR Data Technology. (2025). China Stock Market and Accounting Research database [Data set]. https://data.csmar.com
[15] Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254–277. https://doi.org/10.1016/j.jeconom.2021.03.014
[16] Callaway, B., & Sant’Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001
[17] Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199. https://doi.org/10.1016/j.jeconom.2020.09.006
[18] Borusyak, K., Jaravel, X., & Spiess, J. (2024). Revisiting event-study designs: Robust and efficient estimation. The Review of Economic Studies, 91(6), 3253–3285. https://doi.org/10.1093/restud/rdae007
[19] Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90(3), 414–427. https://doi.org/10.1162/rest.90.3.414
[20] Roth, J. (2022). Pretest with caution: Event-study estimates after testing for parallel trends. American Economic Review: Insights, 4(3), 305–322. https://doi.org/10.1257/aeri.20210236
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Guangwei Hao
This work is licensed under a Creative Commons Attribution 4.0 International License.