In 2022, while working on a Chart of Accounts, I became closely involved with the practical application of GFSM 2014. That experience changed somewhat the way I viewed Government Finance Statistics. One may say that it is easy to see GFS primarily as a statistical reporting framework, which is used to classify government transactions and produce fiscal statistics after budget execution has taken place. However, from a PFM perspective, this tends to place GFS toward the end of the process. Yet a well-designed Chart of Accounts shows how closely GFS is connected with what happens much earlier.
The way transactions are classified affects how governments record expenditure and revenue, consolidate fiscal information and move from individual accounting entries to fiscal reports. The quality of GFS statistics therefore depends heavily on the quality and structure of the underlying PFM systems. The relationship works in the other direction as well. GFS provides a common structure for understanding the fiscal position of government. Its distinction between revenue, expense, nonfinancial assets, financial assets and liabilities provides information relevant to fiscal analysis and fiscal decision-making, rather than serving statistical reporting alone.
Policy lending is a useful example. When government provides a loan to a public corporation, it normally acquires a financial asset rather than recording an expense. The statistical classification is relatively straightforward. The PFM implications are less so. Understanding the transaction requires looking beyond its classification. Why is government providing financing? What is the financial position of the corporation and the likelihood of repayment? What fiscal risks arise from the transaction? How does the loan affect government's financing requirements and balance sheet? These questions link an individual transaction to policy objectives, fiscal risk and public financial management.
And this is where I see an important value of GFS for PFM. GFS does not tell policymakers whether providing the loan is the right policy decision. It does provide a structured way to identify the economic nature of the transaction and connect flows with stocks, assets and liabilities.
The ongoing update of GFSM 2014 reflects this broader connection. A discussion note titled “GFS within Fiscal Analysis and Policymaking,” (
https://www.imf.org/-/media/files/data/statistics/gfsm/global-consultations/discussion-note-231-gfs-in-fiscal-analysis-policymaking-consultation-march2026.pdf) explores the role of GFS in fiscal analysis and policymaking, including its links with core PFM functions such as budgeting, accounting, reporting, forecasting and fiscal risk management.
This raises a broader question for PFM practitioners: should GFS remain primarily an international statistical reporting framework in our day-to-day work, or should its concepts play a more direct role in budgeting, fiscal analysis, fiscal risk management and policy decisions?
My experience with the Chart of Accounts points toward the second view. Closer links between budget classification, accounting, financial reporting and fiscal statistics make it easier to follow the economic substance of government decisions, from the original transaction to their effect on the broader fiscal position.
For me, this is where GFS has value for PFM. It provides a consistent fiscal language connecting individual government transactions with the wider fiscal picture.
