Looking ahead – developments in public sector finance statistics: 2025

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 preempt 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 the Autumn Budget 2024, the UK government announced changes to the fiscal framework. It introduced new fiscal rules that referenced different statistics to those used in the previous rules. These include the investment rule to reduce net financial debt, or public sector net financial liabilities (PSNFL), as a proportion of gross domestic product (GDP) in current prices. The PSNFL aggregate captures not just the debt that the public sector owes, but also the financial assets it holds.

The Office for National Statistics (ONS) first produced PSNFL statistics in 2016. Since then, improvements have been made to the comprehensiveness and the valuation of public sector financial assets and liabilities that had historically been out of scope of the narrower measure of public sector net debt (PSND). The most notable improvements have been to the estimates of student loan assets and to estimates of government liabilities in relation to funded public sector pension schemes.

This section describes further work being undertaken by the ONS to improve the measurement and presentation of PSNFL, as part of the joint programme of work with HM Treasury (HMT) and the Office for Budget Responsibility (OBR).

Information about the work on improving the pensions data, which is undertaken as part of the PSNFL work stream and links to the international statistical standards we follow, is outlined in Section 4: Updates to the international statistical frameworks.

Understanding changes in public sector net financial liabilities

In December 2025, we expect to introduce a new set of tables breaking down the movements in PSNFL into transactions, such as acquisitions and disposals of financial assets and liabilities, and other economic flows, such as revaluations.

Fiscal statistics identify flows resulting from direct interactions between economic agents - transactions - separately from other value and volume changes in the balance of assets and liabilities. Fiscal statistics differ from accounting in this respect.

Financial instruments recorded at market value are affected by price fluctuations. Those that are not may 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 our methodology article 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.

Historically, there has been limited interest in understanding the nature of non-transactional changes. Because the two types of flows are not differentiated in accounting in the same way as they are in statistics, the breakdown of balance sheet changes is not always available in existing data sources and we must often model this.

More recently, there has been increased interest in the broad balance sheet measures and their reconciliation with the flow aggregates of public sector current budget deficit (PSCBD) and public sector net borrowing (PSNB). This means movements in value and volume will need to be reported transparently. Their role in changes to PSNFL will need to be distinguished from public sector transactions, such as expenditure on subsidies or receipt of tax revenue.

We produce supplementary fiscal statistics compiled in accordance with the International Monetary Fund's Government Finance Statistics Manual 2014 (GFSM 2014). These statistics already include detailed analytical tables that show a breakdown of flows by type of asset and liability. The equivalent tables for fiscal statistics based on the headline European System of Accounts 2010 (ESA 2010) will be expanded to meet the increased interest in balance sheet analysis.

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 in transactions and other flows. However, it is not expected to materially affect the values of PSNFL and the other fiscal aggregates.

The new detailed tables are expected to be available in our public sector finance (PSF) bulletin, to be published on 19 December 2025. We expect to make gradual improvements to the underlying data as each type of financial instrument is reviewed. We expect to make the first improvements in 2026.

Comprehensiveness and valuation of the central government balance sheet data

We started reviewing instances of central government lending and equity investments in 2025. We did this to ensure that they are captured in fiscal and debt statistics and that they are valued in accordance with the correct statistical methodology. We will work together with HMT to make incremental improvements to the comprehensiveness and valuation of our central government balance sheet data from our PSF bulletin, to be published on 19 September 2025. We expect most improvements during 2026 and 2027. 

The concept of net financial liabilities can be derived from both statistical and accounting data, such as HMT's Whole of Government Accounts. However, both the instrument recognition boundary and the valuation methodology differ between the accounting standards adopted by the UK government and the international statistical frameworks.

Many types of government financial assets and liabilities are not regularly traded on the market, so they lack an observable price. For non-negotiable (untraded) instruments, such as loans, the overarching principle in economic statistics is the symmetric recording of the amount that a debtor must pay to the creditor to extinguish the claim in both the creditor and debtor's balance sheets.

This means that fair value adjustments embedded in the creditor's accounting valuation are largely absent from the statistical balance sheet. Either nominal values or face values are typically used in statistics where observable market prices do not exist. This ensures symmetry in the compilation of the UK national accounts and means an internally consistent picture of the entire economy can be produced.

However, the international statistical standards recognise that public bodies may engage in quasi-fiscal operations that have the legal form of lending or equity injections. Some of these operations fail to provide government with, respectively, an effective financial claim or an expectation of a realistic return on investment. Such transactions may be recorded as capital transfers and no asset would be recognised on the statistical balance sheet.

Where only part of the loan (including interest accrued on the balance) is expected to be repaid, the transaction may be separated into the statistical loan and capital transfer components. This is demonstrated in our treatment of student loans.

In practice, partitioning is not applied to loan schemes where at least one following conditions holds:

• a small minority of borrowers may be expected to default or not repay

• the lender is expecting to recover the losses from the interest accruing on the performing loans or from the collateral

• the probability of non-repayment cannot be reliably estimated, including for accounting purposes 

Such loans are recorded at nominal value, with write-offs (or debt cancellations) recognised at the time they take place. We explain the application of this rule to the UK public development banks in the Implementation of public sector development banks subsection of Section 3: Developments associated with institutional classifications reviews. Further details on partitioning can be found in Chapters 4.5, 4.8 and 4.9 of Eurostat's Manual on Government Deficit and Debt - Implementation of ESA 2010: 2022 edition (PDF, 6MB).

There are other conceptual differences between statistics and accounting balance sheets. These differences come from factors such as the time of recording of economic transactions, which are transmitted into the balance sheet values, and the time profiles of other accounts receivable and payable.

In other cases, statistical estimates must be remodelled. This is the case for lease liabilities, following the adoption of the International Financial Reporting Standard 16: Leases (IFRS 16), as explained in our Looking ahead - developments in public sector finance statistics: 2023 article. The accounting changes following the International Financial Reporting Standard 17: Insurance Contracts (IFRS 17) also move the accounting treatment of insurance assets and liabilities away from the statistical equivalent.

These conceptual discrepancies all make statistical data collection complicated. It is rare that two sets of estimates would be produced by government departments to comply with both statutory accounting and statistical requirements. We expect to work with HMT to address gaps in our coverage or valuation and to establish new data collection mechanisms to ensure the continuous production of quality fiscal and debt statistics.

We expect to complete this piece of work within 3 years of this publication, because of the large volume of data being reviewed.

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.

Implementation of public sector development banks

We expect to fully include public sector development banks within the statistical boundary for the public sector in our Public sector finances (PSF) bulletin, to be published on 19 September 2025. 

We reviewed the statistical classification of the British Business Bank, the UK Infrastructure Bank (renamed National Wealth Fund in 2024), and the Development Bank of Wales between 2021 and 2022. These bodies operate independently from government. However, we judged that they should be deemed part of the central government subsector for statistical purposes. This is because of public sector control over their general corporate policy, combined with constraints on both the asset and the liability sides of their balance sheets. We continue to monitor changes in the scope of the National Wealth Fund's activities and will review its subsector classification if appropriate.

The integration of these units into the PSF statistics has taken time, owing to the conceptual challenges associated with the valuation of their balance sheets. Special rules apply to loans provided by public bodies, which form a substantial part of the development banks' balance sheets, as explained in the Comprehensiveness and valuation of the central government balance sheet data subsection of Section 2: Improvements to the measurement and presentation of public sector net financial liabilities.

Many types of loans provided by the public development banks can be described as concessional. This means they have lower interest rates, more favourable repayment terms, or less strict eligibility requirements than can be offered by commercial lenders. Concessional loans may include an implicit subsidy element, particularly in cases where the loans are provided at an interest rate below the lender's own cost of capital.

Concessional loans are recorded at nominal value in fiscal statistics if they provide public development banks with an effective financial claim on the debtor, meaning they are expected to be repaid fully with contractual interest. Any cost to government arising from the provision of such loans is recorded in the public sector current budget deficit (PSCBD) and public sector net borrowing (PSNB) as the difference between interest receivable on the loans and the associated expenditure.

Some lending schemes administered by the public investment banks take on a material credit risk arising from expected non-repayments. This notably applies to the Start Up Loan scheme and its devolved equivalents. The value of the expected credit loss means there is an expectation that only a proportion of the loans will be repaid. We will partition the provision of such loans into expenditure and statistical lending components. This is in accordance with the international rules outlined in the European System of Accounts 2010 (ESA 2010) and clarified in Eurostat's Manual on Government Deficit and Debt - Implementation of ESA 2010: 2022 edition (PDF, 6MB).

The balance sheet impact of such treatment is broadly consistent with how these loans are valued in the statutory accounts prepared in accordance with the International Financial Reporting Standards (IFRS). We will use the accounting estimates as a basis for partitioning.

We will provide an indicative assessment of the impact on the fiscal aggregates of including the public sector development banks, in accordance with the PSF transparency strategy. We expect to publish this assessment in our Economic statistics classifications and developments in public sector finances article, to be released on 21 August 2025. 

Assessment of funding streams to UK universities

We intend to conclude our review of the transactions in which UK universities engage in 2026. This 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 in England, Northern Ireland, Scotland and Wales.

Most UK universities have been classified to the non-profit institutions serving households sector since the adoption of the European System of Accounts 1995 (ESA 1995) statistical framework. 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 implemented the treatment of student loans in September 2019. This dealt with an element of implicit public sector expenditure related to the cancellation of student loans that will not be repaid by graduates. For more information, see our Student loans in the public sector finances: a methodological guide. We are now examining the broader funding arrangements for universities, to investigate whether public sector funding streams are recorded consistently and correctly.

Some institutions cover most of their costs through tuition fees alone. However, more research-intensive universities have a high proportion of income from other sources. It is important to examine the income generated for these institutions through research activities. Where public sector organisations are the sources of research funding, we will ensure such transactions are accurately recorded in the fiscal statistics. This element of university income includes amounts paid for specific research projects, for example, by health authorities or other government units.

The assessment of public research funding is particularly challenging. This is because the funding can reach universities through a variety of streams, which vary between institutions. We aim to review public funding beyond the student loan system to ensure it is reflected in fiscal statistics appropriately.

Assessment of the utilities sector

We will continue monitoring the developments in the utilities sector on an ongoing basis. We will undertake classification reviews if we identify substantial changes in the operation of utilities sector bodies.

Most aspects of utility industries in the UK, such as energy and water, have been privatised since the mid-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.

General regulation does not constitute public sector control for the purposes of defining the statistical boundary of the public sector. 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 sector. 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.

Heavy regulation or contractual arrangements implemented by government may influence the actions of organisations to the extent that they nearly have control over 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 continuously review new legislation, such as the new Water (Special Measures) Act 2025, to identify instances of public sector control, including in combination with preexisting regulation. We will also monitor developments in the energy sector relating to Sizewell C, as recently announced in the UK government's Spending Review 2025. We are also awaiting the Independent Water Commission's report on reforms to the water sector regulatory system, which is expected to be published this Summer, and the UK government's response to the recommendations.

We will also 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 the 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. 

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 Accounts 2025 (2025 SNA) and the proposed updates to the International Monetary Fund's Government Finance Statistics Manual (GFSM) as we conduct classifications assessments. 

Assessment of the railway industry

We will continue to review the classification of the train operating companies (TOCs) and the wider rail industry as reforms progress.

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 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 a subsidiary company of the UK Government-owned DfT Operator Limited (formerly known as DfT OLR Holdings Limited). The UK government announced in 2025 that three further franchises would be taken under the operator of last resort function when the current national rail contracts end. These are South Western Railway on 25 May 2025, c2c in July 2025, and Greater Anglia in October 2025. These changes do not affect the statistical treatment of most TOCs as public non-financial corporations.

We will continue to assess the wider reforms of the railway industry, summarised in the Department for Transport's update on Great British Railways and the public ownership programme.

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 classification of the rolling stock leases as part of our wider assessment.

The United Nations Statistical Commission (UNSC) endorsed the 2025 System of National Accounts (2025 SNA) and the 7th 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.

A complete 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 are continuing to assess how the new generation of the statistical standards should be implemented across our suite of macroeconomic statistics. This section only introduces the topics that are known, at the time of writing, to have a material impact on the fiscal aggregates.

Recognition of data assets

Reasons for change

The recognition of data as an asset is one of the main methodological innovations of the 2025 SNA. The production of data has become fundamental to many business models. Most traditional businesses now produce some form of data to either increase returns or lower costs. A large amount of this data is used repeatedly in production for more than a year, but no fixed asset category existed under the 2008 SNA to measure it. The 2025 SNA addresses this gap, recognising data as non-financial assets and the production of data as gross fixed capital formation (GFCF), or "net investment in non-financial assets" in fiscal statistics terminology.

Definition of data assets

For national accounting purposes, data are defined by the 2025 SNA as "information content that is produced by accessing and observing phenomena; and recording, organizing and storing information elements from these phenomena in a digital format, which provide an economic benefit when used in productive activities." This means that economic statistics will be measuring data in its most basic form, collecting pieces of information by gaining access to observable events to measure and record observations about features of interest. 

Data must provide an economic benefit for more than one year to be considered within the production and asset boundary. Data in the national accounts only focus on digitised data, for practical reasons to do with its measurement.

Examples of data production activities include:

• data collection - gathering data through surveys, interviews, web scraping, active report creating software, or other methods used to compile information for analysis

• data entry - inputting data into databases or spreadsheets from various sources, such as paper documents, digital files, or online forms 

Measurement and valuation of data

Conceptually, the total output of data generation in an economy should include data produced on an own-account basis and data purchased in market transactions. However, international studies have shown that most data used in the economy are obtained on an own-account basis. The own-account production of data should be valued by a sum of cost methodology, as recommended in the United Nations' Handbook on measuring data in the System of National Accounts (PDF, 1,933KB). This approach is generally used when an observable market transaction is unlikely to be available. 

This guidance also recommends viewing information on market transactions in data as an additional and complementary data source. Given their low contribution to the overall data production activity, market transactions in data can improve a country's estimates of data output, rather than serve as a fundamental part of the compilation process.

Implementation plans and impact on the fiscal aggregates

We are currently working on developing the methodology for measuring the data production activity and value of the existing data assets in the UK. We have a proposed timeline from 2029 to 2030 to introduce this into our core national accounts and fiscal statistics.

The recognition of data assets would not directly affect the estimates of public sector net financial liabilities (PSNFL) and public sector net debt (PSND). Both of these balance sheet aggregates cover various combinations of financial assets and exclude all forms of non-financial assets. Being a type of non-financial asset, data are included in the broader public sector net worth (PSNW) aggregate.

However, the change is likely to cause revisions to the flow aggregates. Public sector net borrowing (PSNB) may be marginally affected, so that the time of recording of gross fixed capital formation on an accrual basis may differ from the time of recording activities that are not capitalised. However, public sector current budget deficit (PSCBD) will be reduced by the value of the data-generating activities less the associated consumption of fixed capital (depreciation). This will instead be recognised within public sector net investment (PSNI) as long as the definitions of both aggregates remain unchanged.

Review of pensions methodology

Reasons for change

HM Treasury (HMT) announced new fiscal rules in October 2024. A broader balance sheet statistic was chosen as the reference. This statistic - PSNFL (sometimes known as net financial debt) - includes the UK government's funded pensions obligations. We initiated work to improve the quality of the public sector pensions data towards the end of 2024. These improvements, such as to our nowcasting methodology, are expected to be implemented incrementally over several years.

We will also consider the impact of the international statistical standards' update on the treatment of public sector pensions. The recording of pensions underwent a major change during the implementation of the current generation of statistical standards: the UNSC System of National Accounts 2008 (2008 SNA) and Eurostat's European System of Accounts 2010 (ESA 2010). The next generation of the standards are not expected to cause material changes. However, the differences that already exist between ESA 2010 and 2008 SNA and GFSM 2014 are expected to remain after the update.

We may review the pensions methodology in our fiscal and wider economic statistics, if we are guided more in the future by the UNSC and the IMF frameworks. Further information about such reviews would be covered in future editions of this article or in separate methodological articles.

Nowcasting methodology

We expect to strengthen the methods we use to estimate the value of pension liabilities following the latest available actuarial valuation.

Pensions differ from most elements of fiscal statistics because they rely heavily on actuarial modelling. Most pensions in the public sector are provided by defined benefit (DB) schemes. DB pension schemes use a formula to determine the benefits payable to each of the scheme's members. The formula often includes factors such as salary (either career average or final), length of service, and age at retirement, among others. This means the size of the pension liability depends on demographic assumptions, such as life expectancy, economic assumptions, such as pre-retirement wage of the scheme members, and discount rates used to value the future liability.

Actuarial valuation is a complex process. Most public sector schemes, such as the Local Government Pension Scheme (LGPS), conduct it every three to four years. This creates a considerable time lag in the availability of the actuarial estimates. Until these valuations become available, we will forecast the pension liability using our best knowledge of the economic climate. This is often based on the independent Office for Budget Responsibility's (OBR) forecast.

Replacing modelled estimates with the outturn data may cause material retrospective revisions. We cannot fully eliminate the revisions. However, we expect to work with the Government Actuary's Department (GAD) and the OBR to strengthen our nowcasting methodology. We will consider whether the nowcasting should be done at a scheme level, to better reflect the specific circumstances they face, or at aggregate level, where this approach would be more suitable.

Institutional coverage

The adoption of the ESA 2010 in 2014 led to a classification review of pension schemes with government involvement. This review identified which schemes should be considered part of the public sector, in accordance with the statistical rules. We included these newly classified funded public sector pension schemes and made general improvements to methods and data sources, in our Public sector finances (PSF), UK: August 2017 bulletin. 

We have worked with GAD since then to identify a further set of smaller pension schemes with government involvement. We are undertaking a classification assessment of these smaller schemes and expect to conclude it by mid-2026. We are also reviewing the classification of the Atomic Weapons Establishment pension scheme in the coming months. We will then review other pension schemes, including the BT Pension Scheme and the Universities Superannuation Scheme.

We do not expect the potential inclusion of additional funded DB pension schemes in PSF statistics to materially affect the fiscal aggregates. Minor revisions to both the balance sheet and the flow aggregates are likely, but their direction and magnitude will depend on the funding position of each scheme.

Discount rate assumptions

There are notable differences between the international standards in the discount rate used to value obligations under public sector DB schemes. We expect to review the discount rate assumptions used for public sector pensions. This will be part of the transition to the next generation of the international standards and a wider review of discount rates across economic statistics. 

To arrive at the present value of pension liabilities, expected future payments are discounted to reflect the time value of money. In accounting, the rate of discounting depends on the advice of each actuary at the time of compiling each scheme's accounts. This means that they vary scheme by scheme and over time. In ESA 2010-based statistics, a stable discount rate is used to value the obligations under government-managed pension schemes. Even though this rate may not reflect the economic climate in the short term, it is believed to be a reasonable approximation of the long-term interest rate faced by pension funds. The discount rate aims to limit volatility of pension estimates that would otherwise be transmitted into the fiscal aggregates. It also improves comparability and consistency of estimates for various schemes and across countries. This then allows for the creation of a coherent measure of household wealth in the national accounts.  

The UK continues to follow the ESA 2010 approach in its fiscal statistics. Under this approach, the discount rates are set in compliance with the methodology of the Ageing Report: Economic and Budgetary Projections for the EU member states. The report and its underlying assumptions is updated periodically. In practice, this means that a nominal rate of 5% (3% real) was used for the ESA 2010-based pension estimates up to and including the 2016 calendar year, and a nominal rate of 4% (2% real) has been used since.

The global UNSC and IMF standards do not specify the discount rate to be used in the valuation of the pension liabilities. If the fiscal statistics followed these standards more closely, it would allow both a transition to scheme-specific discount rates that are consistent with the actuarial valuations, and a continued application of a standardised discount rate across all schemes. If a standardised rate is chosen, the rate itself and the frequency of its reviews should also be determined. More frequent reviews may improve the accuracy of the balance sheet estimates at a point it time, but they will also generate volatility in the fiscal aggregates and the national accounts and complicate the interpretation of trends in the time series.

Over the next few years, we expect to work closely with GAD, the OBR and other experts to determine the best approach to discounting pension obligations in both fiscal and the wider economic statistics.

A change in the discount rate would affect both the balance sheet and the flow fiscal aggregates. The balance sheet data would be directly affected by the change in the current value of pension liabilities. The effect on flows stems from the effect of the passage of time on the present value of the pension liability - in other words, the unwinding of the discount rate - that is a seen as expenditure in fiscal statistics.

Interest rate assumptions

While considering the discount rate, we also expect to review the approach to estimating property income accruing on the assets of funded DB pension schemes, such as the Local Government Pension Scheme.

The current approach aims to maintain conceptual consistency between the revenue and expenditure side of the accounts. The expenditure side is represented by the unwinding of the discount rate, where the rate - 4% nominal (2% real) - is an approximation of the long-term interest rates faced by the pension schemes. This expenditure is treated as property income payable.

For government-managed DB schemes, we apply a consistent approach in valuing income by using the same statistical interest rate of 4% nominal to derive the value of property income receivable. This approach provides a reasonable approximation of the long-term interest rate faced by pension funds. However, it may yield markedly different results to the interest and dividends accruing to the pension funds in the short term.

We will therefore review our approach to estimating property income receivable, so that it remains conceptually consistent with the way property income payable is calculated.

Treatment of unfunded pension schemes

As we consider the implementation of the new international standards, we expect to review the treatment of unfunded pension schemes in fiscal and the wider economic statistics.

The ESA 2010 framework considers obligations under all government-managed unfunded schemes to be similar to contingent liabilities. Such obligations are included in the supplementary statistics, such as our UK National Accounts Table 29: Accrued-to-date pension entitlements in social insurance dataset. However, they are not recognised as a liability on the public sector balance sheet in the core national accounts publications such as the Blue Book or in the ESA 2010-based fiscal statistics.

The UNSC's 2008 SNA and the IMF's GFSM 2014 recognise liabilities under most occupational DB schemes, such as the Principal Civil Service Pension Scheme. The exception are those schemes that operate as part of the wider social security schemes or are close to them in design.

We already produce fiscal statistics under both the ESA 2010 and the GFSM 2014 standards. However, our headline fiscal aggregates, such as PSNB, PSND, and PSNFL, currently follow Eurostat's approach. The difference between the two approaches is explained in further detail in our Pensions in the public sector finances: a methodological guide.

Granularity of the balance sheet data

We aim to improve the quality of the data we publish by around 2027, as part of the UK's adherence to the international initiatives, such as the Data Gaps Initiative (DGI). The outcome of this work will include more detailed breakdowns of the public sector data to support fiscal analysis and decision-making. However, it is not expected to change the headline statistical aggregates.

The DGI was launched in 2009 by the G20 Finance Ministers and Central Bank Governors (FMCBG). Its aim was to develop recommendations on closing the gaps in global data collection and dissemination identified during the 2008 global financial crisis. The initiative's subsequent phases, DGI-2 and DGI-3, focused on both the implementation of the original recommendations and new priority areas, such as digitalisation and climate change.

We also continue to adhere the IMF Special Data Dissemination Standard Plus (SDDS Plus). This is the IMF's highest tier of standards for the dissemination of economic and financial data. This voluntary standard was also influenced by lessons from the 2008 global financial crisis.

The UK remains committed to the DGI and continues the work on implementing its recommendations. In relation to fiscal statistics, this typically takes the form of more granular data provision. For example, we aim to provide a breakdown of government debt by counterparty and a breakdown by residual maturity.

For more information on these initiatives, see the G20 DGI Recommendations and IMF's SDDS Plus overview. For an example of our published data, see the IMF's Government Finance Statistics framework in the public sector finance: Appendix E dataset.