The Implementation of Pillar Two: Global Minimum Tax and Its Impact on Multinational Financial Reporting
Keywords:
Global Minimum Tax, OECDAbstract
The implementation of Pillar Two, a cornerstone of the OECD/G20 Inclusive Framework on Base Erosion and Profit Shifting (BEPS), marks a transformative step in global tax reform by introducing a global minimum tax aimed at curbing profit shifting and ensuring that multinational enterprises (MNEs) pay a fair share of taxes. This initiative addresses longstanding concerns about tax competition and revenue erosion by establishing a standardized approach to taxing profits in jurisdictions with low or no corporate tax rates. For multinational financial reporting, Pillar Two represents both a challenge and an opportunity. On the one hand, MNEs face increased complexity in compliance as they must navigate the intricacies of new tax calculations, reporting standards, and inter-jurisdictional coordination. On the other hand, this framework provides a more transparent landscape for tax accountability, fostering greater consistency and transparency in financial disclosures. The global minimum tax is set to influence transfer pricing strategies, deferred tax asset valuation, and effective tax rate management, compelling organizations to rethink their financial planning and reporting processes. Moreover, the uniformity introduced by Pillar Two could reduce the competitive disadvantages previously faced by businesses operating in high-tax jurisdictions. This article explores the principles underpinning Pillar Two, its technical implementation, and its implications for financial reporting, focusing on how MNEs can adapt to maintain compliance while minimizing disruptions. It highlights the need for proactive measures, such as enhanced internal controls, robust data systems, and strategic tax planning, to navigate the evolving global tax landscape. By analyzing the intersection of tax policy and corporate reporting, this discussion sheds light on the significance of Pillar Two as a step towards global tax equity and its enduring impact on multinational financial strategies.
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References
Christians, A. (2016). BEPS and the new international tax order. BYU L. Rev., 1603.
Laffitte, S., Martin, J., Parenti, M., Souillard, B., & Toubal, F. (2020). International corporate taxation after COVID-19: Minimum taxation as the new normal. CEPII Policy Briefs, 30, 1-6.
Botzem, S. (2012). The politics of accounting regulation: Organizing transnational standard setting in financial reporting. Edward Elgar Publishing.
Novotny-Farkas, Z. (2016). The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe, 13(2), 197-227.
Umoren, A. O., & Enang, E. R. (2015). IFRS adoption and value relevance of financial statements of Nigerian listed banks. International journal of finance and accounting, 4(1), 1-7.
Burton, G. F., & Jermakowicz, E. K. (2015). International financial reporting standards: a framework-based perspective. Routledge.
Rahman, A., Yammeesri, J., & Perera, H. (2010). Financial reporting quality in international settings: A comparative study of the USA, Japan, Thailand, France and Germany. The International Journal of Accounting, 45(1), 1-34.
Gaston, E., & Song, M. I. (2014). Supervisory roles in loan loss provisioning in countries implementing IFRS. International Monetary Fund.
Pasiouras, F. (2008). International evidence on the impact of regulations and supervision on banks’ technical efficiency: an application of two-stage data envelopment analysis. Review of Quantitative Finance and Accounting, 30, 187-223.
Alexander, D., & Archer, S. (2000). On the myth of “Anglo-Saxon” financial accounting. The international journal of accounting, 35(4), 539-557.
Bradbury, D., Hanappi, T., & Moore, A. (2018). Estimating the fiscal effects of base erosion and profit shifting: data availability and analytical issues. Transnational Corporations Journal, 25(2).
AlMotairy, O. S., & Stainbank, L. J. (2014). Compliance with international education standards in Saudi Arabia: Policy and educational implications. Journal of Business Studies Quarterly, 5(4), 5.
KsiężaK, P., & Fischbach, B. (2017). Triple bottom line: The pillars of CSR. Journal of corporate responsibility and leadership, 4(3), 95-110.
Nurunnabi, M. (2015). The impact of cultural factors on the implementation of global accounting standards (IFRS) in a developing country. Advances in accounting, 31(1), 136-149.
Davies, H., & Green, D. (2013). Global financial regulation: The essential guide (Now with a Revised Introduction). John Wiley & Sons.
Thumburu, S. K. R. (2020). Enhancing Data Compliance in EDI Transactions. Innovative Computer Sciences Journal, 6(1).
Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
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
Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Naresh Dulam, and Venkataramana Gosukonda. “AI in Healthcare: Big Data and Machine Learning Applications ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Aug. 2019
Naresh Dulam. “Real-Time Machine Learning: How Streaming Platforms Power AI Models ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
Naresh Dulam, et al. “Data As a Product: How Data Mesh Is Decentralizing Data Architectures”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
Naresh Dulam, et al. “Data Mesh in Practice: How Organizations Are Decentralizing Data Ownership ”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
Sarbaree Mishra. “Moving Data Warehousing and Analytics to the Cloud to Improve Scalability, Performance and Cost-Efficiency”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
