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Tariffs, Trade Wars, and Earnings Strategies: A Comparative Analysis of Real Earnings Management in the United States and China

Article DOI: https://doi.org/10.57017/ajelg.v1.i2(2).07
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Author(s):
Abstract:

This study examines the impact of tariff risk exposure (TRE) on real earnings management (REM) in publicly listed firms in the US and China, and investigates how institutional characteristics, such as political connections and firm size, moderate this relationship. Using a longitudinal panel dataset from 2018 to 2024, the study employs industry and year fixed-effects regressions, alongside robustness checks with a Weighted Tariff Exposure Index and multiple REM proxies (abnormal operating cash flow, discretionary expenses, production costs, selling general and administrative). The results indicate that higher TRE significantly increases REM, with Chinese firms exhibiting stronger responses than US firms. institutional characteristics captured through a composite institutional context measure moderate this relationship. Managers, investors, and policymakers can use these findings to anticipate and mitigate tariff-induced operational and financial risks. The study integrates institutional and agency theories, providing a cross-country perspective on REM under trade policy shocks.


© The Author(s) 2025. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.


Article’s history: Received 7th of November, 2025; Revised 19th of December, 2025; Accepted for publication 27th December, 2025; Available online: 30th of December, 2025. Published as article in Volume I, Issue 2(2), 2025.

How to cite:

Bangash, I. (2025). Tariffs, Trade Wars, and Earnings Strategies: A Comparative Analysis of Real Earnings Management in the United States and China. Applied Journal of Economics, Law and Governance, Volume I, Issue 2(2), 217-235. https://doi.org/10.57017/ajelg.v1.i2(2).07


CRediT Authorship Contribution Statement: Israfil Bangash: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft.


Acknowledgments: This research did not receive any funding or research grant.


Conflict of Interest Statement: The author declares that there is no conflict of interest.


Declaration on the Use of Generative AI and AI-Assisted Technologies: The author has not used AI-assisted technologies or generative AI for analysing or writing the manuscript.


Data Availability Statement: The data that support the findings of this study are available from the author upon reasonable request.


Ethical Approval Statement: Ethical approval and informed consent were not applicable because this study was based on a review of published literature and policy documents and did not involve human participants, animals, or the collection of personal data.


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