Preparing for the Phased-Out Full Expensing Provision: Implications for Corporate Capital Investment Decisions
Keywords:
Full expensing, capital investmentAbstract
The phase-out of the full expensing provision marks a significant turning point for businesses, requiring a strategic reassessment of capital investment decisions. Full expensing, which allowed companies to deduct the full cost of qualifying assets in the year of purchase, provided a strong incentive for businesses to invest in equipment, machinery, and technology, driving growth and innovation. With this benefit now phasing out, companies must adapt to a depreciation model that spreads deductions over several years, challenging the financial flexibility many companies previously relied on. This shift will have far-reaching implications, especially for manufacturing, construction, and technology industries, which depend heavily on capital-intensive assets. Organizations may need to reconsider the timing & scale of planned investments to avoid cash flow constraints while balancing short-term needs with long-term growth objectives. Additionally, companies must re-evaluate their tax planning strategies to optimize the remaining benefits of the phased-out provision and ensure compliance with evolving tax regulations. Navigating this transition requires a forward-looking approach to financial strategy. Businesses can benefit from proactive planning, including detailed analysis of future capital needs, prioritizing investments aligned with strategic goals, and identifying opportunities to leverage other tax incentives or financing options. Cash flow management has become more critical than ever, as spreading deductions over time could create short-term liquidity challenges. Companies should also explore ways to boost operational efficiency & reduce costs, ensuring resilience amid the financial adjustments necessitated by the new tax landscape. Collaboration with financial advisors, tax professionals, and internal stakeholders will be vital to crafting customized strategies that mitigate the impact of these changes while maintaining a competitive edge. The phase-out also requires greater alignment between financial planning and broader business objectives. Companies can preserve innovation and growth potential by integrating capital investment decisions with strategic priorities, even in a less favourable tax environment.
Downloads
References
Mintz, J. (2018). Global implications of US tax reform. ifo Schnelldienst, 71(07), 22-35.
Chalk, M. N. A., Keen, M. M., & Perry, M. V. J. (2018). the tax cuts and jobs act: An appraisal. International Monetary Fund.
Gravelle, J. G. (2005). The 2004 corporate tax revisions as a spaghetti western: Good, bad, and ugly. National Tax Journal, 58(3), 347-365.
Barro, R. J., & Furman, J. (2018). Macroeconomic effects of the 2017 tax reform. Brookings papers on economic activity, 2018(1), 257-345.
Wiser, R. H., & Pickle, S. J. (1998). Financing investments in renewable energy: the impacts of policy design. Renewable and Sustainable Energy Reviews, 2(4), 361-386.
Chittenden, F., & Derregia, M. (2010). The role of tax incentives in capital investment and R&D decisions. Environment and Planning C: Government and Policy, 28(2), 241-256.
Bruckner, C. (2020). Doubling Down on a Billion Dollar Blind Spot: Women Business Owners and Tax Reform. Am. U. Bus. L. Rev., 9, 1.
Auerbach, A. J., Devereux, M. P., & Simpson, H. (2008). Taxing corporate income (No. w14494). National Bureau of Economic Research.
Enis, C. R., & Christ, L. F. (1999). Implications of Phase‐Outs on Individual Marginal Tax Rates. Journal of the American Taxation Association, 21(1), 45-72.
Bradford, D. F. (2005). A tax system for the twenty-first century. Toward Fundamental Tax Reform, 11-33.
Slemrod, J., & Bakija, J. (2017). Taxing ourselves: a citizen's guide to the debate over taxes. MIT press.
Angelo, B., Ayres, D., & Stanfield, J. (2018). Power from the ground up: Using data analytics in capital budgeting. Journal of Accounting Education, 42, 27-39.
Gale, W. G., Gelfond, H., Krupkin, A., Mazur, M. J., & Toder, E. J. (2019). Effects of the tax cuts and jobs act: A preliminary analysis. National Tax Journal, 71(4), 589-612.
Shaviro, D. N. (2003). Rethinking tax expenditures and fiscal language. Tax L. Rev., 57, 187.
Peterson, P. P., & Fabozzi, F. J. (1999). Analysis of financial statements (Vol. 54). John Wiley & Sons.
Thumburu, S. K. R. (2022). Real-Time Data Transformation in EDI Architectures. Innovative Engineering Sciences Journal, 2(1).
Thumburu, S. K. R. (2022). Post-Migration Analysis: Ensuring EDI System Performance. Journal of Innovative Technologies, 5(1).
Gade, K. R. (2022). Data Analytics: Data Fabric Architecture and Its Benefits for Data Management. MZ Computing Journal, 3(2).
Gade, K. R. (2022). Data Modeling for the Modern Enterprise: Navigating Complexity and Uncertainty. Innovative Engineering Sciences Journal, 2(1).
Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
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, 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
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
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
Muneer Ahmed Salamkar. Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, Mar. 2023, pp. 668-94
Naresh Dulam, et al. “Data Mesh Best Practices: Governance, Domains, and Data Products”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, May 2022, pp. 524-47
Naresh Dulam, et al. “Apache Iceberg 1.0: The Future of Table Formats in Data Lakes”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Feb. 2022, pp. 519-42
Naresh Dulam, et al. “Kubernetes at the Edge: Enabling AI and Big Data Workloads in Remote Locations”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Oct. 2022, pp. 251-77
Naresh Dulam, et al. “Data Mesh and Data Governance: Finding the Balance”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Dec. 2022, pp. 226-50
Sarbaree Mishra, et al. “Leveraging in-Memory Computing for Speeding up Apache Spark and Hadoop Distributed Data Processing”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Sept. 2022, pp. 304-28
Sarbaree Mishra. “Comparing Apache Iceberg and Databricks in Building Data Lakes and Mesh Architectures”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 278-03
Sarbaree Mishra. “Reducing Points of Failure - a Hybrid and Multi-Cloud Deployment Strategy With Snowflake”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Jan. 2022, pp. 568-95
Sarbaree Mishra, et al. “A Domain Driven Data Architecture for Data Governance Strategies in the Enterprise”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Apr. 2022, pp. 543-67
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
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
