Exploring the BEAT (Base Erosion and Anti-Abuse Tax) under the TCJA: The Effect on Multinational Tax Strategies
Keywords:
Base Erosion, Anti-Abuse Tax, BEAT, cross-border transactionsAbstract
Established by the Tax Cuts and Jobs Act (TCJA), the Base Erosion and Anti-Abuse Tax (BEAT) has changed the tax climate for multinational corporations by tackling profit-shifting strategies that reduce the U.S. tax base. BEAT is designed to discourage deductible payments to overseas affiliates by adding an additional tax liability on companies exceeding certain base-erading limits. This abstract looks at how BEAT influences tax policies of multinational companies, hence demand a study of supplier chains, intercompany pricing, and careful tax planning. BEAT has generated operational and compliance difficulties, especially for businesses with significant cross-border operations even if it aims to guarantee a minimum tax liability in the United States. Multinationals must manage intricate computations, thresholds, and reporting obligations to reduce potential tax liabilities. The extensive application of the tax and the absence of international tax credit provisions have resulted in instances of double taxation, prompting corporations to reevaluate their worldwide financial frameworks. This analysis emphasizes critical case studies to demonstrate the tangible effects of BEAT on corporate tax planning, elucidating techniques such as restructuring intercompany agreements, reallocating functions and risks, and reassessing the utilization of cost-sharing arrangements. Furthermore, it emphasizes the necessity of proactive planning, strong transfer pricing rules, and strategic collaboration with tax experts to ensure compliance while maximizing tax results. This abstract examines the adaptive tactics of multinational organizations in response to the BEAT's requirements, highlighting their efforts to sustain competitiveness in the global market.
References
1. Waclawik, J. (2018). Understanding International Tax Avoidance and Tax Evasion Post-TCJA. UIC L. Rev., 52, 975.
2. Herzfeld, M. (2021). Designing international tax reform: lessons from TCJA. International Tax and Public Finance, 28(5), 1163-1187.
3. Barker, R., & Eccles, R. G. (2018). Should FASB and IASB be responsible for setting standards for nonfinancial information?. Available at SSRN 3272250.
4. DeNovio, N. J., Fisher, M. L., Shashy, S. N., & McCrain, E. P. (2019). Planning for Tax Controversies Before, During and After the Deal: New Dynamics in Cross-Border M&A Under the TCJA. International Tax Journal, 8.
5. Cort, T., & Esty, D. (2020). ESG standards: Looming challenges and pathways forward. Organization & Environment, 33(4), 491-510.
6. Soled, J. A. (2020). Upstream Tax Planning: A Case Study of Why Congress Should Institute a General Anti-Abuse Rule. NCL Rev., 99, 643.
7. Viswanathan, M. (2019). The Games They Will Play: Tax Games, Roadblocks, and Glitches Under the New Legislation.
8. Yoder, L. D. (2019). Taxation of CFC Income: The Paradigm. Int'l Tax J., 45, 3.
9. Winden, A. W. (2020). Jumpstarting Sustainability Disclosures. Bus. LAw., 76, 1215.
10. Kamin, D., Gamage, D., Glogower, A., Kysar, R., Shanske, D., Avi-Yonah, R., ... & Kane, M. (2018). The games they will play: Tax games, roadblocks, and glitches under the 2017 tax legislation. Minn. L. Rev., 103, 1439.
11. Leatherman, D. (2017). The Treatment of Corporations and Partnerships under the TCJA. Transactions: Tenn. J. Bus. L., 19, 509.
12. Beps, I. F. O. (2018). Tax Challenges Arising from Digitalisation–Interim Report 2018.
13. Vizcarra, H. V. (2020). The reasonable investor and climate-related information: changing expectations for financial disclosures. Envtl. L. Rep., 50, 10106.
14. Williams, C. (2016). Comment Letter to the SEC in response to its Concept Release on Business and Financial Disclosure Required by Regulation SK, 81 FR 23915.
15. Jebe, R. (2019). The convergence of financial and ESG materiality: Taking sustainability mainstream. American Business Law Journal, 56(3), 645-702.
16. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
17. Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
18. Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
19. Gade, K. R. (2021). Data-Driven Decision Making in a Complex World. Journal of Computational Innovation, 1(1).
20. Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
21. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
22. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
23. Thumburu, S. K. R. (2020). Enhancing Data Compliance in EDI Transactions. Innovative Computer Sciences Journal, 6(1).
24. Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
25. Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
26. Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
27. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
28. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
29. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
30. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
31. Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
32. Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
33. Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
34. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
35. Naresh Dulam, et al. “Kubernetes Operators for AI ML: Simplifying Machine Learning Workflows”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, June 2021, pp. 265-8
36. Naresh Dulam, et al. “Data Mesh in Action: Case Studies from Leading Enterprises”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Dec. 2021, pp. 488-09
37. Naresh Dulam, et al. “Real-Time Analytics on Snowflake: Unleashing the Power of Data Streams”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 91-114
38. Naresh Dulam, et al. “Serverless AI: Building Scalable AI Applications Without Infrastructure Overhead ”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, May 2021, pp. 519-42
39. Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
40. Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-37
41. Sarbaree Mishra, and Jeevan Manda. “Incorporating Real-Time Data Pipelines Using Snowflake and Dbt”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Mar. 2021, pp. 205-2
42. Sarbaree Mishra. “Building A Chatbot For The Enterprise Using Transformer Models And Self-Attention Mechanisms”. Australian Journal of Machine Learning Research & Applications, vol. 1, no. 1, May 2021, pp. 318-40
43. Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
44. Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.