Looking ahead – developments in public sector finance statistics: 2022

This article provides information about the areas of fiscal statistics that we aim to review and potentially improve over the coming years. It forms part of the wider transparency strategy for the public sector finance (PSF) statistics.

This year's article reinstates the publication of our long-term work programme. During the coronavirus (COVID-19) pandemic, we temporarily suspended the publication of long-term plans and focused on explaining imminent updates to methodology through a separate, monthly set of publications.

While our plans cover a long-term horizon, we do not attempt to pre-empt new government policies or events in the wider UK economy. Any material methodological issues that arise after publication will be discussed in the next annual update of this article or, should they be affecting fiscal statistics in the short-term, in our monthly article.

Each monthly Public sector finances bulletin can contain some degree of methodological change to reflect movements across the public sector boundary (for example, following the sale of a public corporation) or new transactions (such as new types of support grants). We provide information about such "routine" changes through the monthly Recent and upcoming changes to public sector finances articles.

In contrast, this article focusses on larger changes that require extensive research and development programmes before they can be incorporated into official statistics. This can happen, for example, with transactions and stocks, where the value is estimated through economic modelling. Even if some provisional estimates are available from non-statistical sources, new methods or data sources need to be assessed. This is to ensure they meet the quality requirements and are generally compliant with the Code of Practice for Statistics.

We also discuss major statistical classifications and methodologies we expect to review in the future, without prejudicing the conclusion of the reviews. For example, we will review the anticipated reforms in the rail industry when they are implemented; however, the implications of such government policies on our measurement of public finances are not, at this stage, clear.

To provide an indication of when we plan to introduce changes to the official statistics, we have chosen to categorise developments into short-term or long-term projects.

Short-term changes are those that we aim to implement in public sector finance (PSF) statistics within 18 months of this article's publication date. This generally means implementation in the PSF bulletin that will be published either in September 2022 or September 2023.

Short-term improvements include:

• the publication of public sector net worth on a European System of Accounts 2010 basis
• improvements to public corporations' data
• revised recording of central government leases
• improved treatment of national non-domestic rates
• detailed information on gilt liabilities interest

The section on short-term developments also provides an update on our plans for implementing some of the classification decisions associated with complex methodology.

Long-term developments are those where work is expected to extend beyond the 18-month horizon. Some of the improvements listed in this section may require substantial changes to data collection, methods or processes, often across multiple organisations. In other cases, our reviews (including those related to statistical classification) may entail no change to the statistics.

In general, projects that are categorised as long term include:

• developing local government statistics
• reviewing non-financial assets and depreciation
• data on contingent liabilities and other potential obligations
• treatment of the railway industry
• treatment of higher education funding

We expect to review the list of long-term developments on a regular basis as events take place that affect the UK public sector. Where appropriate, we will change our plans to reflect policy developments (including future government action, legislative changes and developments) to ensure that our statistics show the underlying economic reality as completely and as quickly as possible. We will also consider the implications of any future changes to the international statistical manuals we use to compile our statistics, as well as changes to our capacity to take forward these developments.

Where there are significant changes to our planned development schedule, we will inform users in line with our broader transparency arrangements.

Public sector net worth

In June 2021, we announced our intention to extend our balance sheet metrics to include a new measure of public sector net worth (PSNW). PSNW is an aggregate summarising the total value of the public sector's assets (both financial and non-financial) and its liabilities. It is more comprehensive than the existing balance sheet aggregates, such as public sector net debt (PSND) or public sector net financial liabilities (PSNFL).

PSNW is part of a set of internationally recognised statistical aggregates that can be used to analyse the public sector finances. However, as there are different statistical frameworks and different analytical presentations within those frameworks, there are also several different versions of PSNW. The Office for National Statistics (ONS) currently produces two measures of PSNW, and we are working on a further measure that would be fully consistent with the headline fiscal aggregates published in the public sector finances (PSF) bulletin.

There are substantial conceptual differences between existing measures of PSNW and the fiscal aggregates such as PSND, which complicate comparisons between them. These differences stem from the application of public sector boundary, valuation of debt instruments, and practical factors such as timing of revisions.

We therefore plan to introduce a measure of PSNW that would be a direct extension of public sector net financial liabilities (PSNFL) and public sector net debt (PSND). This would complete our suite of public finance statistics based on the European System of Accounts 2010 (ESA 2010).

Wider balance sheet aggregates such as PSNW require robust data on non-financial assets and financial instruments such as equity, derivatives and pension liabilities, which were not used to derive the fiscal measures historically. In recent years, we have made considerable progress in recording public sector financial assets and liabilities beyond the scope of our main balance sheet aggregate, public sector net debt (PSND).

The most significant improvements to date include the revised recording of student loan assets and estimates of government liabilities in relation to funded public sector pension schemes and the coronavirus loan guarantee schemes. While none of these assets and liabilities affect PSND, they are all essential for the accurate measurement of the wider balance sheet aggregates, including PSNW.

Improvement to the underlying balance sheet data will remain our priority. Sections of this article devoted to public corporations' data, the development of local government statistics, and the review of non-financial assets, provide more information about the improvements we still expect to make.

We will try to make some of these improvements in 2022. This should include better data relating to public corporations' balance sheet. Other improvements, such as strengthened public sector non-financial asset data, may take longer to implement. Given this, it would not be reasonable to wait until the work on all areas of the balance sheet is complete. We therefore expect to start publishing the new measure of PSNW in 2022. We will, however, continue improving the underlying data beyond the date of the first publication.

Improvements to public corporations' data

In September 2022, we expect to implement the outcome of the public corporations' data review, which we announced prior to the coronavirus (COVID-19) pandemic. Although we had to delay the implementation of the review findings during the coronavirus pandemic, we have used the time to improve the flows affecting the public corporations’ sub-sector in addition to the balance sheet.

In statistics, the term "corporation" is distinct from "company". While there are over 3,000 entities in the public sector that could be broadly described as companies because of their legal structure, most of these entities are classified to the central or local government sub-sector in reflection of their economic substance.

Most companies classified to the general government sector pose few complications with regards to their treatment and data collection. This is thanks to the administrative implications of the classification; such companies would generally report their data through their sponsoring department.

The same cannot be said about companies that are deemed to operate on a market basis and classified outside of the general government sector. We have worked with HM Treasury to improve our coverage of such market public corporations by extending the use of the Whole of Government Accounts (WGA) dataset to fill the gaps in statistical data collection.

This work has not been without its challenges. There are important differences between the recognition of financial assets and liabilities under the European System of Accounts 2010 (ESA 2010) and International Financial Reporting Standards (IFRS), which underpin the WGA. We explained in the Wider measures of government debt article. Furthermore, even where both frameworks recognise an asset or a liability in principle, the IFRS taxonomy does not always align to that of the ESA 2010. The same is true for valuation methods.

We have finished the work to adapt the WGA data on public corporations for use in fiscal statistics based on ESA 2010. We expect to publish the indicative impacts of this change on the fiscal aggregates in summer 2022. We then expect to transition to the new data source from the August 2022 bulletin, which will be published in September 2022.

Our improvements to the public corporation dataset will not stop in September 2022. The WGA data are both granular and robust; however, there is a considerable time lag between the reference period and their publication. Beyond the 18-month horizon, we expect to hold a separate review of statistical methods used to estimate the public corporations' impact until the WGA data become available.

Recording of central government leases

In September 2022, we expect to transition to the new treatment of leases for central government organisations.

Historically, it was possible to use business accounting estimates as a source of leasing data. This was because the International Accounting Standard 17 (IAS 17) (the standard describing the recording of leases) and the international statistical framework were closely aligned.

However, in 2016, IAS 17 was replaced by the new International Financial Reporting Standard 16 (IFRS 16). This is substantially different conceptually from both IAS 17 and the statistical framework.

We have now considered how to compile leasing data recorded in line with the new IFRS 16 standard. While most property leases were accounted for as operating leases under the preceding standard (IAS 17), they had substantial risk transfer from the lessor to the lessee. As such, it would have been appropriate to record them as "financial leases" (a statistical term for finance leases).

Having come to this conclusion, we have worked with HM Treasury to source appropriate data for future public sector leasing and, separately, to revise the historic data. Retrospectively revising historical data is difficult, because the granular information that is required to convert operating lease into financial lease recording is not readily available.

For the central government sector alone, there are thousands of individual lease agreements. Therefore, statistical assessment on a lease-by-lease basis is only practical for the largest of these; for the rest, we have applied an en bloc approach to modelling.

The effects of the coronavirus (COVID-19) pandemic delayed the full implementation of IFRS 16 across the public sector organisations. We will monitor the implementation of IFRS 16 in other parts of the public sector and will transition to the new methodology at an appropriate time.

Treatment of national non-domestic rates

In the national accounts and fiscal statistics, tax receipts are generally recorded on an accruals basis rather than a cash receipt basis. In other words, we record government revenue at the point where the tax liability arose for businesses and individuals, rather than when the tax was actually paid.

Accrued revenue estimates for national non-domestic rates (NNDR), also known as business rates, are currently calculated using a mixture of cash information and assessments of likely receipts.

This method is based on:

• the total number of taxable properties across local authorities

• their applicable tax rates and reliefs

• the amount that taxpayers are liable to pay in a financial year

• other relevant adjustments, which vary slightly for different parts of the UK because NNDR is a devolved tax

We are currently reviewing our methodology to establish what improvements we can make to our measurement of accrued NNDR. This is prompted by changes in business rates retention by local authorities and by the introduction of the option to pay the rates in 12 monthly instalments (rather than the default 10). We are working with relevant government departments to ensure that all aspects of NNDR will be reflected in the new accruals process. This includes tax receipts, the recording of reliefs, and the flows between central and local government.

We will use data from the Valuation Office Agency to help establish new apportionments by sector, and data published by the Department for Levelling Up, Housing and Communities (DLUHC) (including data on cash receipts, arrears and refunds from their quarterly revenue collection). Considering that over-payments, arrears and refunds of NNDR occur, we will apply the accruals methodology to the analyses of these data. This will improve the recording of government receipts and the balance sheet aggregates. We expect to implement our improved estimates of NNDR in September 2022.

Detailed information on gilt liabilities and interest

In the next 18 months, we also expect to start publishing more detailed information on UK government gilt liabilities, including a breakdown of interest payable on gilts and some supplementary statistics.

Gilts (short for "gilt-edged securities") are UK central government debt securities. Gilts are issued by the Debt Management Office (DMO) on behalf of HM Treasury (HMT).

There are two broad types of gilts: conventional gilts and index-linked. A conventional gilt attracts a fixed interest payment ("coupon") and guarantees to repay the fixed amount of principal at maturity. The coupon and the principal of an index-linked gilt are uplifted in line with movements in the Retail Prices Index (RPI).

Gilt liabilities are the single largest component of PSND. In fiscal statistics, they are recorded at their redemption price (known as "face value"). For conventional gilts, this is does not change between issuance and maturity, whereas the face value of index-linked gilts is adjusted with the RPI. In accordance with statistical rules, this adjustment is applied continuously rather than at the discrete points the uplifts are calculated by the DMO. To increase the transparency around our estimates of government debt, we expect to start publishing the breakdown of conventional and index-linked gilt liabilities, as recorded on a statistical basis.

Statistical treatment in the recording of interest also deserves increased transparency. In economic statistics, the term "interest" is wider than just periodic cash payments such as coupon payments. Uplifts of the principal of index-linked gilts and, for gilts issued at a discount, the unwinding of that discount over the lifetime of the gilts (gilts sold at a premium decrease total net interest) are also included in interest expenditure.

Such recording stems from the statistical principle that interest accrues continuously between issuance and maturity of a debt instrument, irrespective of when it becomes payable. A contractual uplift to the face value, or a convergence of issuance price with the face value (in other words, with the redemption price) are seen as economically equivalent to an accruing coupon and are recorded in a similar way. We aim to start publishing more information on various forms of gilt interest, beyond the aggregated total.

Finally, we plan to provide further supplementary information, such as the average interest rate on gilt liabilities. We will provide further details about gilt work in the next update to this article. We also plan to fully integrate the supplementary statistics into the PSF dataset towards the end of the 18-month period.

Implementation of classifications decisions

There is also an ongoing need to update statistical classifications. This is to reflect movements across the public sector boundary or decide on the most appropriate recording of new transactions. We will continue to implement such routine classifications in the monthly PSF bulletin on an ongoing basis and, whenever possible, without delay.

However, a small number of routine classification cases require more work to collect and compile the data. Where we judge that a classification decision is associated with a particularly complex change to our methods, or involves a material revision to the historic series (on its own or in combination with other updates), we aim to introduce the change through an annual change package (conventionally in September of each year).

In the last two change packages, we incorporated into fiscal statistics:

• the classification of the organisation engaged in terrorism reinsurance activity by Pool Reinsurance Company Ltd (Pool Re)

• the train operating companies brought under public sector control by the Emergency Measures Agreements (see section on the rail sector for more details

• provisions for calls under the coronavirus loan guarantee schemes (Coronavirus Business Interruption Loan Scheme, Coronavirus Large Business Interruption Loan Scheme and Bounce Back Loans Scheme)

In recent months, we have announced several classifications that may similarly take longer than average to be fully implemented in the PSF statistics. These include the UK Infrastructure Bank, the Development Bank of Wales and a flood reinsurance scheme (Flood Re). This is because the methodology around certain financial activities is particularly complex, owing to significant differences between the statistical guidance and financial reporting requirements.

If we make further classification decisions that involve a substantial lag to full implementation, we will take interim measures to ensure that the fiscal aggregates remain a good reflection of the government's true fiscal position. We will explain these in an update to the monthly article titled Recent and upcoming changes to public sector finance statistics. Under such intermediate treatment, transactions may have asymmetric impacts; for example, cash spending may be recorded, but the balance sheet impacts may be incomplete.

Development of local government statistics

Local authority data provided by the Department for Levelling Up, Housing and Communities (DLUHC) and the devolved administrations are used to report expenditure, revenue, assets, and liabilities data for the local government sub-sector within the public finance statistics.

Like in other sectors, local government data are subject to the implementation of routine classification decisions and are required to align with international reporting standards. There are, however, areas of development that are specific to local government.

To improve our understanding of data flows within the sector, and between local government and other organisations, we require more detailed breakdowns (for example, of loans, equity, and debt liabilities). This will provide us with data flows on both a gross and net basis for local government. Similarly, better information on equity holdings will support the production of new fiscal indicators, such as public sector net worth.

We also need to ensure we capture all organisations that fall within the boundary of the local government sector (for instance, where local authorities are working jointly with other organisations).

Another area for consideration is the timeliness and frequency of data. We hope to explore several avenues for improving data timeliness and data frequency, such as the extended use of administrative data to supplement existing statistics.

The Office for National Statistics (ONS) recently supported DLUHC with a review of their local government finance (LGF) statistics, which concluded towards the end of 2021. The review considered various areas for improving these statistics, including their presentation and accessibility, and the collection of additional, and more granular, breakdowns. DLUHC will shortly publish a summary of the findings of the review, and that will influence the timetable for improvements to the local government data within the public sector finances statistics.

Review of public sector non-financial assets and depreciation

Over the next three years, we expect to make progress in improving our estimates of public sector non-financial assets (NFA). Estimating stocks and flows of NFA is an important aspect of producing national accounts and will form part of our new measure of public sector net worth. These data also feed into flow measures used in the public sector finances, including current budget deficit and net investment.

Non-financial assets consist of produced assets (including fixed assets), such as buildings and transport equipment, and non-produced assets, such as land. Produced fixed assets are estimated using the internationally-recommended perpetual inventory method (PIM), following the guidance in the Organisation for Economic Cooperation and Development's (OECD) Measuring Capital manual 2009. This is also supported by the European System of Accounts 2010 (ESA 2010) and the United Nation's System of National Accounts 2008 (SNA 2008).

The PIM combines flows of gross fixed capital formation data and other data on changes in the volume of assets with assumptions about the service lives, retirement profiles and depreciation patterns of fixed assets by industry. This produces estimates of the gross stock, net stock and consumption of fixed capital (CFC). Consumption of fixed capital represents the reduction in the value of the fixed assets used in the production process during the accounting period resulting from physical deterioration, normal obsolescence or normal accidental damage. The method for estimating consumption of fixed capital in statistics is different from methods used to estimate depreciation for business accounting purposes.

In recent years, a number of improvements have been made to capital stock estimates. In particular, in preparation for the UK National Accounts Blue Book 2019, we undertook a review of the asset service lives and the related flows used in the measurement of capital stock.

The Blue Book 2020 included methodological changes to professional fees, which were incorporated into fiscal statistics in September 2021.

We now expect to further strengthen our NFA estimates by reviewing some of the sources, methods and assumptions with a particular impact on the public sector. This includes a review of activities such as the implementation of reclassification decisions, the transfer of some nuclear power stations to central government, and road detrunking.

More generally, transfers of used assets between public and private sectors present a challenge. This is because the residual value of the assets (and the remaining life length) has to be estimated on a national accounts basis, and correct assumptions have to be used following the sector transition. We will also endeavour to provide a full reconciliation between the opening and closing stocks of the NFA. This would include adjustments for unforeseen obsolescence and other causes of impairment.

In all the examples provided, the statistical approach differs from the accounting treatment, making some conventional data inputs (for example, statutory accounts) less useful. To ensure treatment is in line with statistical guidance, we therefore expect the improvements to the NFA estimates for the public sector finances to take several years to implement. However, we are committed to starting the research phase in the coming months.

While the NFA review will materially strengthen our estimate of public sector net worth (PSNW), it will not affect public sector net debt (PSND) or public sector net financial liabilities (PSNFL). Similarly, the improvements to the public sector capital stock estimates will not directly affect public sector net borrowing (PSNB), thanks to netting. However, public sector CFC features within the calculation of receipts, current expenditure and net investment (or capital expenditure) aggregates of the PSF statistics.

Our focus will be on non-financial assets within the core public finance statistics frameworks. Other types of asset (such as some environmental assets) currently lie outside these core frameworks. However, they could feature in expanded, experimental, or supplementary measures of public sector assets in the future.

Data on contingent liabilities and other potential obligations

We aim to improve the coverage and presentation of data on contingent liabilities, which will be reported in Government Finance Statistics Manual (GFSM) tables.

Contingent liabilities are possible obligations that only arise when specific conditions prevail in the future. As such, there is uncertainty over whether a payment will be required or not, and its potential size.

Contingent liabilities can be explicit or implicit; explicit contingent liabilities are defined as legal or contractual agreements that stipulate the conditions under which payment would be required. These include one-off guarantees, which may involve the government guaranteeing existing debt of another body or providing contingent credit facilities (for example, extending a loan under specific circumstances). Explicit contingent liabilities other than one-off guarantees can include potential legal claims, indemnities or uncalled share capital.

Implicit contingent liabilities do not arise from a legal or contractual source, but are recognised after a condition or event is realised. This includes:

• possible future spending arising from unguaranteed obligations of other public sector units (if they fail to meet them)

• government ensuring solvency of the banking sector

• net obligations of future social security benefits

National accounts manuals, IMF's government finance statistics manual 2014 (GFSM 2014), and business accounting are all broadly consistent in their definitions of contingent liabilities. They also report contingent liabilities as supplementary information rather than in the main accounts.

The national accounts manuals and GFSM 2014 differ to commercial accounting standards in the case of provisions. A body may set aside funds to cover unexpected events or other potential future obligations, known as provisions. Unlike contingent liabilities, provisions have a probable likelihood of payment where uncertainty lies around timing and value of the final payment. Provisions affect statements of financial position and income or expenditure of a body in their commercial accounts. National accounts and GFSM frameworks do not treat provisions as liabilities because the related event has yet to materialise.

While not conceptually different, the presentation and reporting of contingent liabilities and other potential obligations is different between the ESA 2010 and GFSM 2014. The GFSM presentation of contingent liabilities makes a greater distinction between explicit and implicit contingent liabilities. It also encourages reporting of potential legal claims and indemnities.

We aim to improve our coverage of contingent liabilities and other potential obligations using HM Treasury's administrative datasets. Improvements will be reflected in the GFSM tables, which may be further adapted. As these improvements relate to memorandum items to the accounts, they will have no impact on any of the fiscal aggregates.

Analysis of the rail sector

In 2018, the government commissioned a review led by Keith Williams to consider the structure of the UK rail industry. The review findings formed the basis of the Williams-Shapps plan for rail. We expect to consider the impact of any changes to the structure of the rail industry on statistical classifications and, consequently, on the fiscal aggregates.

Since the privatisation of the British Railways in the 1990s, the ONS had considered most units operating in the rail industry as belonging to the private sector. This changed when Railtrack Plc was placed in "railway administration" in 2001, following a number of accidents and financial difficulties in preceding years. Railtrack Plc owned the majority of the UK railway infrastructure; this is why the reclassification moved the infrastructure component of railways back into the public sector. The company was subsequently replaced by a new infrastructure manager, Network Rail Ltd (also a public sector body under the international statistical rules).

At the same time, train operating companies largely remained in the private sector (as defined for statistical purposes) until the coronavirus (COVID-19) pandemic began. Franchises operated by the government-controlled operator of last resort were an exception to this general principle (sometimes for a short period between the termination of one franchise agreement and the beginning of another). Examples of train operating companies we considered to be part of the public sector included East Coast Main Line Company, London North Eastern Railway (both operated the InterCity East Coast franchise in different periods) and Northern Trains.

In response to the coronavirus pandemic and restrictions on movement, train operating companies with government franchise agreements transitioned onto emergency measures agreements (EMAs). Under EMAs (subsequently replaced by the similar emergency recovery measures agreements (ERMAs) and national rail contracts (NRCs)), the government became responsible for covering the qualifying costs of train operators. There was also potential for operators to earn a small management and performance fee.

Using internationally agreed statistical rules, we judged that EMAs (and subsequent agreements) amounted to effective public sector control over all franchise train operating companies. This is because most of the revenue and cost risk associated with rail travel had transferred to the public sector. Furthermore, the agreements placed restrictions on train operating companies' ability to borrow and make important decisions relating to their corporate policy.

The Williams-Shapps plan for rail would mark an end to the present type of contracts between government and train operating companies. The new passenger service contracts (PSCs) are expected to replace franchises. They will see a transition to a model where the government is procuring a service from privately owned operators.

While the outline of the PSCs is broadly known, the exact terms and conditions are not. We will therefore conduct a classification assessment of the companies operating under the new contracts when more details become available.

The reform will also lead to a restructure of units that had been under public sector control before the coronavirus pandemic began. Network Rail and parts of other public organisations will be integrated into a new body called Great British Railways. We will consider the sub-sector classification of Great British Railways and the statistical treatment of transactions in the rail sector as part of our review.

Treatment of higher education funding

We will review the public funding of higher education to ensure we are recording the various streams of financing consistently with their economic nature (for example, as subsidies, current transfers, or payments for services).

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). In 2016, we announced a classification review of universities. However, this was paused while awaiting the UK government's decision on future higher education funding arrangements.

Several reforms have altered the mechanisms of public sector funding for the higher education sector, including:

• a rise in tuition fees

• changes to the student loans system

• the replacement of the Higher Education Funding Council for England with new bodies

We recently reviewed the treatment of student loans. This review deals with an element of implicit public sector expenditure related to cancellation of the student loans that will not be repaid by the graduates. Following the review of student loans, we want to examine the broader funding arrangements that the universities have. This will help us to learn whether funding streams originating in the public sector are recorded consistently and correctly. An important part of the review is to assess whether UK universities should be considered market producers, because they are simultaneously involved in the provision of education and research activities.

While some institutions cover much of their costs through tuition fees alone, more research-intensive universities have a higher proportion of income from other sources. For these institutions, it is important to examine the income generated through research activities. Where public sector organisations are the ultimate sources of research funding, we will make sure that such transactions are transparently recorded in the fiscal statistics. This element of university income includes amounts paid, for example, by the health authorities or government units, for specific research projects.

This assessment is particularly challenging. Public research funding can reach universities through a variety of streams, which vary from one institution to another. We aim to review various types of public funding beyond the student loan system. This is to ensure they are reflected in fiscal statistics appropriately, for example, as current transfers or payments for services.

The expected final government response to the review of post-18 education and funding has the potential to further reshape this relationship. In the wake of these changes, we have committed to reviewing all transactions between the public sector and universities beyond student loans.

This analysis will be conducted alongside the sector classification review of universities against the latest ESA 2010 guidance. However, there are clear linkages between the two dimensions of our analysis. For instance, if a specific university is found to be a market rather than a non-market producer from a classification point of view, certain grants may have to be recorded as subsidies instead of transfers in accordance with the statistical rules. Similarly, if we find that the conditions attached to a particular stream of public funding can significantly influence a university's corporate policy, we will take this into account when assessing the statistical classification of universities receiving that stream of funding.

We want to increase transparency around methodology and classification reviews that are likely to affect the fiscal statistics. Therefore, we will continue to share our plans through the annual updates to this article. In the case of smaller or imminent changes, we will share our plans through the monthly article titled Recent and upcoming changes to public sector statistics.

Detailing the likely numerical impact of the ongoing or planned methodology improvements would be helpful to users. They could assess the potential impact on the fiscal aggregates and possible future government policies. We remain committed to publishing provisional estimates of impacts on the fiscal aggregates ahead of integrating new methods into public sector finance (PSF) statistics. In summer 2022, we aim to supplement the PSF bulletin with a set of tables showing the expected impact of the changes that will be implemented in September 2022.

Finally, we will update our methodological guide to PSF statistics. If we judge that any individual change is complex enough to require a further detailed explanation, we will publish an in-depth methodological guide to that specific area. Our articles titled Student loans in the public sector finances: a methodological guide and Pensions in the public sector finances: a methodological guide are the two most recent examples.