Looking ahead – developments in public sector finance statistics: 2026

The monthly public sector finance (PSF) statistics are published jointly by the Office for National Statistics (ONS) and HM Treasury. They provide users with information about the UK government's fiscal position.

Fiscal statistics are compiled in accordance with the international statistical guidance. Both the international guidance and the ONS methodology are periodically updated to keep pace with the evolving nature of the economy. These methodological changes are aimed at better capturing the economic substance of government actions. However, they may cause retrospective revisions to the historical estimates.

This article lists the methodology areas under review or that we expect to review. This gives users the means to assess the potential impact of such updates on the fiscal statistics. We do not try to pre-empt new government policies or events in the wider UK economy. Methodological issues that arise after this publication will be discussed in the next update of this article or in our more frequent Economic statistics classifications and developments in public sector finances articles

Overview of improvements

In late 2024, we initiated a programme of work to improve our measurement of the balance sheet data. This work formed part of the joint programme with HM Treasury (HMT) and the Office for Budget Responsibility (OBR).

In the Autumn Budget 2024, the UK government introduced fiscal rules that referenced different statistics to those used in the previous rules. These included public sector net financial liabilities (PSNFL), an aggregate that captures both the debt that the public sector owes and the financial assets it holds.

The fiscal aggregates are measured in accordance with the statistical standards, notably the European System of National and Regional Accounts 2010 (ESA 2010). A different measure of net financial liabilities can also be constructed using accounting data such as HMT's Whole of Government Accounts. The 2025 edition of this article summarised the main differences between accounting and statistical estimates. It also provided further information about our approach to recording government loan assets in public sector finance (PSF) statistics. The valuation of loan assets was particularly prominent owing to the inclusion of the National Wealth Fund and the British Business Bank in the PSF statistics in September 2025.

This year, we will focus on other cases where the statistical and accounting standards are not aligned, leading to different recognition or measurement. Specifically, we expect to refine the estimates relating to the following economic activities or entities in the PSF bulletin to be published on 22 September 2026:

• funded public sector pensions

• equity in multilateral development banks

• student loans

• central government leases

• insurance assets and liabilities

Alongside these improvements, we will continue the work on including the UK and devolved public financial institutions in the PSF statistics. The PSF statistics to be published in September 2026 are expected to include the full balance sheet of the Scottish National Investment Bank (SNIB). Finally, we are improving the presentation of the balance sheet data to meet the changing analytical needs.

Understanding changes in public sector net financial liabilities

In the PSF bulletin published on 19 June 2026, we introduced a new set of tables breaking down the movements in PSNFL into transactions and other economic flows.

Fiscal statistics identify flows resulting from direct interactions – transactions – separately from other value and volume changes in the balance of assets and liabilities. In this respect, fiscal statistics differ from accounting.

Financial instruments recorded at market value are affected by price fluctuations. Those that are not may still be sensitive to changes in economic assumptions and modelling methods. For example, a change in labour market assumptions would likely result in a change in the value of student loan assets held by government, as explained in Student loans in the public sector finances: a methodological guide. Similarly, a change in life expectancy or the discount rate would result in a change to the valuation of pension liabilities, as described in Pensions in the public sector finances: a methodological guide.

Since 2019, we have been producing analytical tables showing a breakdown of flows into transactions and other changes. However, these statistics were only available for the estimates based on the International Monetary Fund's Government Finance Statistics Manual 2014 (GFSM 2014) standard. In the PSF bulletin published on 19 June 2026, we introduced additional tables for our headline fiscal statistics compiled in accordance with the European System of National and Regional Accounts 2010 (ESA 2010) standard.

More work will be done in the future to improve the accuracy of these estimates. New statistical methods will need to be developed for when the source data are not granular enough to fully support statistical needs. This work will improve the accuracy of the breakdown of financial flows into transactions and other flows. We expect to make gradual improvements to the underlying data as each type of financial instrument is reviewed. Many of the improvements we expect to make in 2026 and 2027 are described in this article.

Updates to the estimates of public sector pension funds' liabilities

In our Looking ahead – developments in public sector finance statistics: 2025 article, we explained how the transition to the new generation of international statistical standards may affect how we measure government pension liabilities in PSF. The work on the implementation of the new statistical standards in PSF statistics is expected to take place over the next several years.

In the PSF bulletin to be published on 22 September 2026, we expect to update our pension estimates with the results of the two latest actuarial valuations conducted by the Government Actuary's Department (GAD). GAD's Local Government Pension Scheme: review of the actuarial valuations of funds as at 31 March 2019 report and LGPS England and Wales (E&W): review of the actuarial valuations of funds as at 31 March 2022 report contain further information about the valuations exercise.

Actuarial valuation is a complex process. Detailed valuation reports are typically available more than two years after the reference period. Most public sector schemes, including the Local Government Pension Scheme (LGPS), conduct the valuation every three to four years.

Furthermore, the statistical methodology is not aligned with either the scheme-wide valuation reports prepared in accordance with the LGPS Scheme Advisory Board (SAB) guidance or the local valuation reports based on the Chartered Institute of Public Finance and Accountancy (CIPFA) guidance. The adaptation of the valuation estimates to the statistical basis requires us to conduct further work with input from GAD. This creates a considerable time lag between the valuation reference year and the integration of the estimates in PSF statistics. As a result, we will be revising the estimates from financial year ending (FYE) March 2020.

For the periods after FYE March 2025, we will be using the independent Office for Budget Responsibility's (OBR) forecast. Although the same process had been followed previously, we expect the forecast to be revised to reflect the updated baseline estimates incorporating the GAD valuation for 2022 and subsequent annual reports.

The update to the pension estimates is expected to lead to an upward revision to public sector net borrowing (PSNB) of about £2 billion in the FYE March 2020, and smaller revisions in subsequent years. The broad balance sheet aggregate, PSNFL, is expected to be reduced by around £25 billion in FYE March 2020. The impact on the narrower public sector net debt (PSND) measure is expected to stay within £1 billion of the present estimates. We expect to provide a full impact assessment, including the time series, in the PSF bulletin to be published on 21 August 2026.

These revisions are predominantly explained by the more recent valuation data being used to measure the value of the public sector's funded pension liabilities. We have taken the opportunity to make other improvements, such as benchmarking of the pension funds' asset values to those reported by the GAD. This enables a consistent source to be used for both the pension funds' assets and liabilities. However, the differences between the new and our earlier asset estimates based on the ONS Financial Survey of Pension Schemes are relatively minor.

Replacing modelled estimates with the outturn data may cause material retrospective revisions. We cannot fully eliminate further revisions in the future. However, we have worked with the GAD and the OBR over 2025 and 2026 to strengthen our nowcasting methodology.

Recognition and valuation of equity in multilateral development banks

In the PSF bulletin to be published on 22 September 2026, we expect to improve the recording of government investments in multilateral development banks (MDBs). The MDBs comprise supranational organisations such as the World Bank's International Bank for Reconstruction and Development (IBRD) and International Development Association (IDA), which provide financial and technical assistance to developing countries. This change will affect the time series from FYE 1997.

MDB funds providing grants and highly concessional loans mostly to low-income countries are differentiated from funds that extend conventional loans to the more creditworthy, often middle-income developing countries. The former category of funds requires periodic replenishment as the resources are spent on development grants and highly subsidised loans. These replenishments are recorded as expenditure in the form of capital transfers. Conversely, the UK's contributions to the funds providing conventional loans give rise to a financial asset for government. They are recorded as acquisitions of equity in MDBs.

The practical application of this approach can be more nuanced, particularly as the MDBs continue to develop their financing models. The relatively novel MDB hybrid capital has the features of both debt and equity and has no direct parallels in the statistical taxonomy. Furthermore, MDBs may issue hybrid capital with slightly different characteristics. There are further differences in the way countries invest in MDB's hybrid capital. Some, including the UK, may waive coupon payments to ensure the investment qualifies as official development assistance (ODA).

Noting the characteristics of the existing UK investments in hybrid capital and the lack of strong debt features, we treat them as equity assets in fiscal statistics. This approach aligns with the treatment of such investments in the annual financial statements. However, we will review the treatment of any future hybrid capital investments separately, to ensure the recording as equity assets remains appropriate and reflects the economic characteristics of the instrument. As well as distinguishing between investments with stronger debt and equity features, we will consider whether potential future investments may need to be recorded as expenditure in the form of capital transfers, based on the characteristics of a specific MDB.

In addition to reviewing the treatment of novel investment modalities, we also expect to improve the broader measurement of the balance sheet asset values. As the market value does not exist, we will align the estimates with equivalent values reported in the statutory accounts of the Foreign, Commonwealth and Development Office (FCDO) and its predecessor departments, and HM Treasury.

These changes in recording are expected to result in an upward revision to the central government equity asset value of £1.1 billion in FYE March 2025, reducing PSNFL by the same amount.

Updates to student loan modelling

We expect to integrate an improved student loan forecast model into the statistical dataset alongside the next routine update of the student loan estimates. These updates normally take place in the quarter after the fiscal events.

In 2019, we changed the treatment of student loans in fiscal statistics. The approach ensured that the PSNFL only captured the proportion of the nominal loan balance that was expected to be repaid. Our Student loans in the public sector finances article explains the conceptual approach in detail.

Since the development of the original statistical methodology in 2019, the Department for Education (DfE) has improved and extended the economic models that underpin the production of the statistical estimates. More information about these models can be found in the DfE's Student Loans Forecast Modelling Pipeline article. However, not all innovations were transmitted into the statistical estimates.

As part of the efforts to improve the measurement of PSNFL, the DfE refined the method of producing statistical estimates from its student loans forecast. The new reverse cashflow simulation improves the accuracy of the statistical estimates of the outstanding loan balance, capital transfer, and interest accruals, without changing the overall conceptual approach. The refinements also enable the statistical estimates to better account for complex scenarios, such as the relative timing between loan drawdown, expected repayments and interest accruals during the study period.

These improvements to the forecasting methodology will not affect the historical data. However, a balance sheet estimate of the loans expected to be repaid will be re-estimated under the improved methodology. Based on the present set of economic assumptions, we expect the outstanding loan balance to be revalued up by £2.6 billion at the point of implementation. This will lead to a reduction in PSNFL of the same value. No other fiscal aggregates will be affected at the time of implementation.

Updates to modelling of central government lease liabilities

We expect to update the central government lease estimates from FYE 2021 in the PSF bulletin to be published on 22 September 2026.

In 2022, we revised the historical recording of central government property leases in statistics. Our Recent and upcoming changes to public sector finance statistics: August 2022 article provides further information about this change.

The new method largely relied on statistical modelling based on the HM Treasury's Whole of Government Accounts (WGA) data. These data are available with a lag of up to two years. Furthermore, the WGA have since adopted the International Financial Reporting Standard 16: Leases (IFRS 16). In principle, this should not have affected the statistical estimates, which are compiled in accordance with a different set of standards. In practice, the change in the conceptual basis of the source data has required us to review and adapt the statistical methodology. This ensures that methodological changes in the source data do not result in a substantial break in the statistical time series.

However, it is important to note that only two years of source data on the new basis are available at the time of publication. Such a short interval is not sufficient to fully evaluate the modelling approach. Furthermore, the international statistical community continues to consider ways of bridging the gap between the accounting and statistical reporting requirements for leases. This work is led by the International Monetary Fund and forms part of the update of the global government finance statistics standards. We expect further international guidance to become available over the next two to three years. In the meantime, we will keep our methodology under review and will make further adjustments as necessary.

We are also reviewing the way we estimate lease liabilities in the periods for which the WGA data are not yet available. In the next two to three years, we expect to transition to using the data on central government expenditure on leases reported through HM Treasury's Online System for Central Accounting and Reporting (OSCAR) database. Using the OSCAR data should enable us to better model the trajectory of the lease liability. However, the comprehensiveness and granularity of the OSCAR data on leases remains under review. In the meantime, we will continue to model the leases estimates in the years for which the WGA dataset is not yet available.

Insurance assets and liabilities

Insurance presents another area of divergence between accounting and statistical standards. The implementation of the International Financial Reporting Standard 17: Insurance Contracts (IFRS 17), which superseded the International Financial Reporting Standard 4: Insurance Contracts (IFRS 4), changed both the measurement and the presentation of insurance assets and liabilities in financial reporting, complicating our data collection. In response to this change, we will update the methodology for compiling the data for Pool Reinsurance Company Limited (Pool Re) in the bulletin to be published on 22 September 2026.

The statistical recording of insurance remains more aligned with the accounting treatment used by reporting entities under IFRS 4. Still, further modelling was required to quantify the output of the insurance providers – a concept necessary for valuing their contribution to nominal gross value added (GDP).

The transition to the source data prepared in accordance with the IFRS 17 accounting standard required an adaptation of our methodology. We have developed a new mapping to link the new accounting presentation to the equivalent statistical concepts. The new data will replace estimates based in part on the extrapolation of the last set of IFRS 4-based source data.

As part of this work, we have made use of the newly available granular data on the holdings of UK government bonds (gilts) by public sector insurance providers. Government debt securities including gilts held by other public sector entities are subject to consolidation for PSNFL purposes. This means that both the gilt liability and the corresponding insurance provider's financial asset are eliminated from the central government balance sheet. In practice, better identification of intra-public sector gilt holdings improves our measurement of the insurance providers' other financial assets, affecting the overall PSNFL estimate.

Overview of developments

There is an ongoing need to implement classifications decisions to reflect movements of institutional units across the public sector boundary. Some of these classifications reflect:

• new government policies and changes to the machinery of government
• substantial changes in the way organisations operate
• the evolution of international statistical standards 

For more information on statistical classifications, see our Public sector classification guide and forward work plan. This section only covers the reviews we are undertaking or expecting to undertake that may result in large changes to our fiscal and debt statistics.

For practical reasons, our statistics can usually include a comprehensive balance sheet of newly established organisations from the point they become operationally autonomous from their parent organisation. As such, we will continue to monitor the activities of Great British Energy and the National Housing Bank, aiming to formally review their classification and implement it in public sector finance (PSF) statistics when the entities become fully established.

Assessment of funding streams to UK universities

In 2026, we intend to conclude the review of the statistical classification of the transactions in which UK universities engage. This work aims to ensure we are recording the various funding streams in a way that is consistent with their economic nature, for example, as subsidies, current transfers, or payments for services.

The current review does not include an assessment of universities' institutional sector classification. However, classification decisions relating to the recording of the transactions UK universities engage in may inform any future assessments of the institutional sector classification of universities.

There have been many changes in the higher education sector since the early 2000s. This includes changes to legislation and public sector funding arrangements, and updates to the statistical standards. Several reforms have altered the mechanisms of public sector funding for the higher education sector over this period, including:

• changes in tuition fees
• changes to the student loans system
• the replacement of the Higher Education Funding Councils in England with UK Research and Innovation and the Office for Students

We intend to publish the outcome of the classification assessment of the universities' funding streams in our Economic statistics classifications and developments in public sector finances article later this year.

Assessment of the utilities sector

We routinely review new legislation to ensure that any indicators of potential government control are identified and considered in our classification process. For the water sector, this includes the Water (Special Measures) Act 2025, and any action taken in response to the Independent Water Commission's review, as indicated in the February 2026 government white paper, A new vision for Water (PDF, 3.2MB).

We will also monitor developments in the energy sector. The National Energy System Operator Limited (NESO), which has responsibility for planning Britain's electricity and gas networks, and operating the electricity system, has not yet been classified. An assessment to determine its subsector classification will be conducted in 2026. Additionally, there are plans to classify the companies and assets associated with the construction of the Sizewell C nuclear power plant and the nuclear Regulated Asset Base levy in 2026.

Most aspects of utility industries in the UK, such as energy and water, have been privatised since the 1990s. As new markets were created, new frameworks and bodies were established to regulate them. Industries such as energy and water require large amounts of capital investment and are often monopolistic in nature. Regulation in these industries typically includes price controls and setting service standards, but can sometimes extend to mandating or imposing restrictions on certain actions.

Most utility suppliers are private non-financial corporations, with the exception of Scottish Water, which is a public non-financial corporation owned by the Scottish Government, and all Northern Ireland (NI) utility suppliers, which have different governance arrangements and are all classified to the central government subsector. The regulators, such as the Office of Water Services (OFWAT) and the Office of Gas and Electricity Markets (OFGEM), have historically been classified to the central government subsector.

General regulation does not constitute public sector control for the purposes of defining the statistical boundary of the public sector. However, excessive regulation or contractual arrangements implemented by government may influence the actions of organisations to the extent that government has the ability to determine their general corporate policy. For example, regulation or contractual arrangements may:

• include controls over governance or remuneration
• prevent a company from exiting the market or diversifying its activities
• mandate a company to implement a specific government policy

We will review the economic substance of policies introduced by government, which may be regulatory or non-regulatory in nature, to ensure they are accurately represented in our statistics. We have classified several energy schemes as imputed tax and subsidy schemes since the early 2000s. This includes Contracts for Difference and the Warm Home Discount scheme. However, we have not yet formally classified some initiatives for statistical purposes, such as the Energy Companies Obligation and Energy Intensive Industry Levy and Support Payments scheme.

We have classified similar energy schemes where transactions between private sector entities were mandated by government, for example, through legislation or regulation. In most cases, such transactions are "rearranged" through government accounts in fiscal statistics and are recorded as imputed taxes and subsidies.

The international guidance on rearranged transactions continues to evolve. We will consider the emerging guidance in the United Nations' System of National Accounts 2025 (2025 SNA) and the proposed updates to the International Monetary Fund's Government Finance Statistics Manual (GFSM) as we conduct classifications assessments.

We will continue monitoring developments in the utilities sector, and will undertake classification reviews if we identify substantial changes in the operation of utilities sector bodies.

Assessment of the railway industry

We will monitor the progression of the Railways Bill, which establishes Great British Railways. We expect to conduct a wider assessment of the railway industry once this bill has received Royal Assent. We expect to review the classification of rolling stock leases as part of this wider assessment.

Train operating companies (TOCs) that had entered into emergency measures agreements (EMAs) with the UK and Scottish Governments during the coronavirus (COVID-19) pandemic were classified in July 2020 to the public sector for statistical purposes. Under the EMAs, normal franchise mechanisms were amended. This means that almost all revenue and cost risk was transferred to the governments.

TOCs also had restrictions placed on their ability to borrow money, and they could not make substantial changes to fares or staffing levels without government's agreement. The EMAs were later replaced by Emergency Recovery Measures Agreements (ERMAs), followed by National Rail Contracts, which did not affect the subsector classification of TOCs.

Some TOC contracts have ended since July 2020. Their operations have been brought into public ownership and are managed by DfT Operator (DFTO, formerly known as DfT OLR Holdings Limited), a subsidiary company of the Department for Transport. We have undertaken classification assessments on several contracts taken under the operator of last resort function when their national rail contracts ended. These are South Western Railway on 25 May 2025, c2c in July 2025, and Greater Anglia in October 2025. As the National Rail Contracts with the remaining TOCs come to an end, they will all become subsidiary companies of the DFTO. This process is planned to be completed by the end of 2027.

A statistically important question relates to the rolling stock leases. The statistical framework maintains a distinction between operating and finance leases. Rolling stock leases have been historically considered operating leases, for statistical purposes. This means that the rolling stock assets and the associated imputed loan liability were not reported on the public sector balance sheet in fiscal statistics. We expect to review the treatment of rolling stock leases as part of the wider review of the rail sector.

Review of accrual methodology

In 2026, we will commence a review of the accrual methodology applied to social benefits and a selection of revenue transactions such as payments for emission permits.

An overarching principle in fiscal statistics is the recording of effects of economic events in the period in which they occur, irrespective of whether cash was received or paid. This is known as the accrual basis of recording.

For some transactions, the application of the accrual principle is complicated by the presence of a long time interval between the initiation of an action and its completion. There may also exist multiple points to which the event could be attributed. For example, a benefit payment could be recorded when an eligible claimant applies for it; or when the officials have processed the application; or when the benefit becomes payable; or when the payment is marked as cleared. While the international statistical standards provide some guidance on the appropriate time of recording for each type of economic transactions, complex scenarios require further consideration to determine which point in time is the most appropriate one.

One such scenario relates to erroneous payments of Universal Credit. Information about the causes and magnitude of the erroneous payments is available in the Department for Work and Pensions' (DWP) Fraud and error in the benefit system, Financial Year Ending (FYE) 2025 publication. The issue for the fiscal statistics is how to record the overpayments.

As a general principle, amounts paid in error must be deducted from (netted off) the expenditure on benefits. However, this could mean:

• deducting total overpayments as identified, and recording an asset equal to the total value of overpayments, less the amounts that have been recovered

• deducting the amounts that are expected to be recovered, and recording an asset equal to the amounts expected to be recovered, less the amounts that have been recovered

• deducting the amounts that have been recovered, without recording on the balance sheet the DWP claim on individuals

Another example of complex accrual methodology is used for recording emission trading permits. Permits, such as those issued under the UK Emissions Trading Scheme (UK ETS), allow businesses in aviation, power and industrial sectors to emit specific quantities of greenhouse gas in return for a payment to government.

In statistics, tax revenue for schemes such as the UK ETS should be recorded when the permits are surrendered, rather than when they are auctioned by the government. In practice, the time interval between the auction and the permit surrender complicates the determination of the correct time profile of government revenue.

Starting in 2026, we will review the instances of complex accrual methodology to ensure that they best reflect the economic reality. We expect to implement any potential changes incrementally as we identify the potential improvements from 2027.

Updates to the international statistical frameworks

The United Nations Statistical Commission (UNSC) endorsed the 2025 System of National Accounts (2025 SNA) and the seventh edition of the Balance of Payments Manual (BPM7) as the new international standards for compiling national accounts and balance of payments statistics in March 2025. The International Monetary Fund's (IMF) Statistics Department continues to work on an update for the Government Finance Statistics Manual 2014 (GFSM 2014), with an expected completion date of December 2027. In addition, the United Nations Statistics Division is working on an update to the Classification of the Functions of Government.

The list of fiscal statistics topics that are being reviewed as part of the global update is available in the IMF's Update of the Government Finance Statistics Manual 2014 statement.

We continue to assess how the new generation of the statistical standards should be implemented across our suite of macroeconomic statistics. In our Looking ahead – developments in public sector finance statistics: 2025 article, we explained how the transition to the new generation of international statistical standards may affect the way in which we measure government pension liabilities and data assets. We expect to explain the impact of other changes on UK's fiscal statistics in the future editions of this article.