Official Statistics

Background quality report: Measuring tax gaps 2022

Updated 23 June 2022

Contact

Contact organisation unit: Knowledge, Analysis and Intelligence (KAI) – Customer Compliance Strategy

Contact name: A Johal

Contact person function: Head of Tax Gaps Team

Contact email address: taxgap@hmrc.gov.uk

Statistical presentation

Data description

The publication provides an estimate of the tax gap, which is the difference between the amount of tax that should, in theory, be paid to HMRC, and what is actually paid.

HMRC has published ‘Measuring tax gaps’ on an annual basis since 2009.

Classification system

‘Measuring tax gaps’ includes the total tax gap, as a monetary value and as a percentage of theoretical tax liabilities. It includes:  

• estimates for the breakdown of the tax gap by type of tax (such as Value Added Tax or Corporation Tax, for example)  
• estimates for the breakdown of the tax gap by customer group (such as large businesses, individuals or criminals, for example)  
• illustrative estimates of the tax gap by taxpayer behaviour (such as avoidance, error or evasion, for example)  

All estimates are included as a value and as a percentage of theoretical tax liabilities. Both customer group and behaviour estimates are subject to a higher level of uncertainty than the total tax gap and the estimates by type of tax.

Sector coverage

The tax gap estimates cover the taxes administered by HMRC from tax year 2005 to 2006 up to 2020 to 2021, but don’t include taxes and duties administered elsewhere (for example, Council Tax, business rates, and Vehicle Excise Duty) or charges, such as the congestion charge. These estimates also exclude error and fraud in tax credits, which are published separately in Child and Working Tax Credits error and fraud statistics 2020 to 2021.

Estimates of error and fraud associated with the coronavirus (COVID-19) support schemes are not covered in this publication. Provisional estimates of error and fraud in the COVID-19 support schemes for the year ending 31 March 2021 were published in the HMRC Annual Report and Accounts for 2020 to 2021. Read further detail on the estimates in Measuring error and fraud in the COVID-19 support schemes: methodology and approach. Updated estimates of error and fraud in the COVID-19 support schemes will be published in the HMRC Annual Report and Accounts for 2021 to 2022.

Statistical concepts and definitions

Definitions for all terms listed in the publication can be accessed in ‘Measuring tax gaps 2022 edition’.

Statistical unit

The units that are investigated in this publication are the values of tax gap by type of tax for individuals, businesses, public bodies and charities that are required to pay taxes directly to HMRC.

Statistical population

The statistical population are all individuals, businesses, public bodies, and charities that are required to pay taxes directly to HMRC. This includes criminal gangs who engage in organised crime.

Businesses are defined as small, mid-sized or large. These can include United Kingdom (UK) limited companies; any foreign company with a UK branch or office; and clubs, sole proprietors, charities, public bodies, co-operatives, partnerships or other unincorporated associations, such as community groups or sports clubs, for example.

‘Individuals’ refers to both wealthy and non-wealthy individuals. Individuals who only have to pay taxes via the PAYE scheme are not a part of the population, since their employer is required to participate in the PAYE scheme and make direct payments to HMRC. Therefore, the population for the PAYE scheme is the employer not the employees.

Further information about the definitions of HMRC’s customer groups are available in the annual report and accounts.

Reference area

The geographic region covered by the data is the UK. This includes all activity that results in a requirement to pay taxes to HMRC, whether that activity takes place in the UK or in another part of the world. Fully devolved taxes, like the Landfill Disposals Tax and Land Transaction Tax, have been excluded from tax gap calculations because they are not administered by HMRC.

Time coverage

The tax gap measures the tax gap by tax year, from 2005 to 2006 up to 2020 to 2021. In ‘Measuring tax gaps 2022’ the latest estimates are for the tax year running from the beginning of April 2020 until the end of March 2021 (tax year 2020 to 2021).

Statistical processing

The ‘Measuring tax gaps 2022 edition’ includes statistics on many different types of taxes, with each tax gap estimate using the most relevant and fit for purpose data sources and methodology.

Detailed information about the statistical processing methodology of each type of tax, including the source data, data collection and data compilation, can be found in the Methodological annex.

Data collection

The data used to estimate the tax gap comes from a variety of sources, with three of the main types being random enquiry programme data, survey data and administrative data.

The random enquiry programme data is recorded by HMRC compliance officers. Further information about random enquiry data can be found in the Methodological annex.

We use survey data collected by third parties, as well as HMRC. Most of it comes from the Office for National Statistics (ONS), although the Hidden Economy Survey was collected by NatCen social research. You can read more about the ONS data collection methods is on the data strategy section of the ONS website. More information about the data collection methods of the Hidden Economy Survey can be found in the research report Hidden Economy in Great Britain on GOV.UK.

The administrative data is recorded at the point of payment for receipts, on settlement of enquiries for compliance yield data, and at the point of reporting for non-payment and avoidance data. In ‘Measuring tax gaps 2022 edition’, adjustments are made for compliance yield and non-payment where they were materially impacted by COVID-19, due to HMRC prioritising the delivery of government support to protect people’s livelihoods and helping businesses. For more information please see COVID-19: how HMRC will continue to support customers and the economy. More information about HMRC administrative data can be found in the HMRC statement of administrative sources.

Data validation

There are many models that are brought together to create the total tax gap estimates. These models are combined by a reproducible analytical pipeline (RAP) process using R software to aggregate estimates and provide summary numbers. These are used in the publication to provide the size of the total tax gap, as well as different breakdowns of the tax gap.

The RAP process checks the R files that are input, to assure that the data are in the correct file type and have the same dimensions and column names as in the template. It checks that the columns have either a character or numerical type, and that the data frame has been updated.

The process has been reviewed by multiple tax gap analysts and an independent review of it has also taken place. Individual estimates have been checked by the corresponding analyst who produced the estimates, to ensure that they carry through to summary totals as expected. The results produced by the RAP process have also been compared with ‘Measuring tax gaps 2021 edition’ to understand changes in trend.

Quality management

Quality assurance

All official statistics produced by Knowledge, Analysis and Intelligence (KAI) at HMRC must meet the standards in the Code of Practice for Statisticsproduced by the UK Statistics Authority and all analysts adhere to best practice, as set out in the ‘Quality’ pillar.

Analytical Quality Assurance describes the arrangements and procedures put in place to ensure analytical outputs are error free and fit–for-purpose. It is an essential part of KAI’s way of working, as the complexity of our work and the speed at which we are asked to provide advice means there is a high risk of error, which can have serious consequences on KAI’s and HMRC’s reputation, decisions, and in turn, on peoples’ lives.

There are different approaches to measuring tax gaps. VAT and Excise tax gaps are mainly estimated using a ‘top-down’ approach, by comparing the estimated tax due from consumer expenditure data, with tax receipts. Most other components are estimated using a ‘bottom-up’ approach, based on HMRC’s operational data and management information. The way we estimate each tax gap component and the data we use is set out in the relevant chapters, with additional information in the ‘Methodological annex’.

Each tax gap estimate methodology is unique, so there is no single quality assurance (QA) checklist that contains all the QA tasks needed for every estimate. Analysts in KAI, however, use a checklist that summarises the key QA tasks, and this is used as a starting point for teams who are considering what QA actions to undertake. Teams amend and adapt it as appropriate, to take account of the level of risk associated with the methodology, and the different QA tasks that are relevant.

Analysts and managers plan the QA tasks that need to be completed, documenting how each one is going to be carried out, then turn it into a QA checklist, which is specific to the estimate. Analysts and peers carry out the QA tasks, update the checklist, and pass it onto the Senior Responsible Officer for review and sign off.

Tax gap estimates go through multiple reviews, where tax gap analysts, policy, compliance and risk stakeholders sense-check the data, methodologies, and outputs and apply wider understanding and context to them. Any inconsistencies between the output data and the related datasets are identified and corrected during this process.

Just under three-fifths (58%) of our methodologies, measuring 79% of the tax gap, are produced using established methods. Where we are not able to use established methods, we produce illustrative estimates using experimental methods.

We publish a Methodological annex, alongside the main publication, which contains methodological details for each estimate.

A trade-off on quality is made, to include individual estimates of the tax gap that are more illustrative (and based on experimental methodologies), to give us a more complete picture of the overall tax gap.

Another trade-off is the use of up-rating techniques to produce indicative estimates for later tax periods, to enable comparison across all taxes. These indicative estimates may often be subject to later revisions.

Quality assessment

All models that are used to calculate the tax gap go through a quality assurance process, to ensure that they meet the standards in the Code of Practice for Statistics.

The QA process ensures that the models meets the standards set out in the code of practice. Every piece of analysis is unique, and as a result there is no single QA checklist that contains all the QA tasks needed for every project. In general, the models go through the following procedures:

Stage 1 – Specifying the question

Checks that up-to-date documentation is agreed with stakeholders, setting out what outputs are needed and by when, how they will be used and all the parameters required for the analysis.

Stage 2 – Developing the methodology

Checks that any methodology is agreed and developed in collaboration with stakeholders and others with relevant expertise, ensuring it is fit for purpose and will deliver the required outputs.

Stage 3 – Building and populating a model/piece of code

Checks that analysis is produced using the most appropriate methodology, data and software, and is in line with good practice guidance.

Data inputs are checked to ensure they are fit–for–purpose, by reviewing available documentation and, where possible, through direct contact with data suppliers.

QA of the input data is carried out.

The analysis is audited by someone other than the lead analyst – checking the code and the methodology.

Stage 4 – Running and testing the model/code

Results are compared with those produced in previous years and differences understood and determined to be genuine.

Checks that results are comparable with independent estimates, that the differences between them are understood, that the results can be explained and are in line with expectations.

Stage 5 – Drafting the final output

Checks that the outputs are internally consistent with the outputs of other models.

The final results are independently proofread and checked.

Relevance

User needs

This analysis is likely to be of interest to the following: 

• national government – policy makers and MPs 
• academics and research bodies  
• media 
• business community 
• general public 
• international revenue authorities and organisations

User satisfaction

HMRC is always looking for ways to improve the quality of our tax gap estimates. In the ‘Measuring tax gaps 2022 edition’, there are contact details for anyone who wants to give us feedback.

As part of our continuous effort to improve the statistics, we are also inviting feedback on the publication via a survey. This will help us to understand who uses it and how we can best meet their needs. A link to the survey is in ‘Measuring tax gaps 2022 edition’.

HMRC consults with people via stakeholder forums and conferences, including the Chartered Institute of Taxation, the Association of Accounting Technicians, the Association of Taxation Technicians, the Association of Chartered Certified Accountants, the Office for Tax Simplification, the Institute for Fiscal Studies, Public Economics UK, the Commonwealth Association of Tax Administrators, and the Tax Administration Research Centre.

HMRC continuously engages with academics, international institutions and foreign fiscal authorities, as well as the UK’s National Audit Office (NAO), the Office for Statistics Regulation (OSR) and the ONS, to share methodologies and best practices and help us estimate the tax gap as accurately as possible.

In May 2022, HMRC presented at the Intra-European Organisation of Tax Administrations (IOTA) digital workshop on tax gap estimation and use. The event allowed delegates to identify practical solutions for enhancing tax administrations’ capacity in measuring tax gaps, with the final goal of improving their performance.

The OSR, the regulatory arm of the UK Statistics Authority, completed a compliance check in 2019 of the extent to which HMRC’s tax gap statistics meet the standards for the Code of Practice for Statistics. The OSR praised the HMRC team highly, saying that it had ‘proven to be highly committed and engaged when working to enhance the trustworthiness, quality and value of these statistics’. They also stated that ‘HMRC is world-leading in measuring tax gaps and is setting the bar for others to follow’.

In 2018, HMRC hosted the International Tax Gap conference on ‘Overcoming obstacles to tax gap measurement’ attended by around 50 delegates from HMRC, tax authorities and finance ministries worldwide, and the International Monetary Fund (IMF). The event enabled delegates to share a range of methodological approaches used to estimate the tax gap and explore ways of improving them.

The IMF assessed the way in which the UK calculates its tax gap, and concluded that HMRC produced one of the most comprehensive studies of the tax gap available internationally. They concluded that the models and methodologies used by HMRC to estimate the tax gap across taxes were sound and consistent with the general approaches used by other countries.

Completeness

The individual tax gaps for the latest year are all published in Table 1.1 of the ‘Measuring tax gaps 2022 edition’, with a more detailed breakdown shown in the relevant section. If it has not been possible to estimate a tax gap for a given year, then the reasons are explained in the relevant section.

The publication meets all requirements based on relevant legislation, regulation and guidelines.

Accuracy and reliability

Overall accuracy

Our tax gap estimates are official statistics produced with the highest levels of quality assurance and adhere to the framework for the Code of Practice for Statistics.

This code assures objectivity and integrity – providing the framework to ensure that statistics are trustworthy, good quality, valuable and give producers of official statistics the detailed practices they must commit to when producing and releasing official statistics.

The figures presented in the ‘Measuring tax gaps 2022 edition’ are our best estimates based on the information available, but there are sources of uncertainty and potential error. This means that the estimate might differ from the ‘true value’, so it is best to focus on the trend in the results rather than the absolute numbers when interpreting the findings.

We have had to adjust some of our methodologies due to the impacts of COVID-19, which has reduced volatility in our estimates but may have introduced additional uncertainties. Future ‘Measuring tax gaps’ publications’ revisions may be more varied compared to previous years as a result.

Some estimates are more robust than others, as illustrated by the uncertainty ratings that we have applied in Table 1.1. An uncertainty rating is given to every model in our 2020 to 2021 estimates. Uncertainty relates to a range of possible factors that can affect the accuracy of a statistic, including the impact of measurement or sampling error (related to sample surveys) and all other sources of bias and variance that exist in a data source. More information on uncertainty ratings can be found in the Methodological annex in the section labelled ‘Uncertainty’.

HMRC has data that covers virtually all tax sources, and tax gap calculations have been reviewed and endorsed internally by our analysts and externally by bodies such as the OSR. Our methodologies and data sources are regularly reviewed and improved.

HMRC is confident that the calculations are as accurate as they can be, and that the estimates are made on the same basis each year.

Sampling error

Where possible, HMRC has estimated the likely impact of sampling errors by calculating statistical confidence intervals. These give margins of error within which we would expect the true value to lie 95% of the time, if there were no systematic errors. They indicate the extent to which changes in the estimates between years can be confidently interpreted as true changes. They do not take account of systematic errors that might cause the central estimate to be too low or too high over the whole series.

Further information about sampling error can be found in the Methodological annex.

Non-sampling error

Non-sampling errors are systematic and less straightforward to deal with, as they are not defined by statistical assessments that allow for easy interpretation. To highlight the effect of these biases, HMRC presents some of the tax gaps as ranges. For example, estimates based on the random enquiry programme apply a non-detection multiplier to the 95% confidence interval. A similar approach is used for estimates based on risk-based enquiry programmes.

Further information about non-sampling error can be found in the Methodological annex.

Data revision

Estimates for some of the tax gap components are often subject to revision, due to improvements in the data available, the methodologies used, and projections based on more recent years. Following good practice, revisions made to previous years’ estimates and the reasons for the changes are clearly shown within the publication.  A detailed description of the revisions since last year’s publication to the estimates due to data and methodology changes are set out at the end of ‘Annual tax gap findings’ and are numerically presented in table 1.7 in the online tables for ‘Measuring tax gaps 2022 edition’. Further details of revisions are also presented in online tables 1.8 and 2.2.

Timeliness and punctuality

Timeliness

The publication is released as soon as possible after the estimates are produced and quality assurance has taken place.

There is typically a lag of around fourteen months from the end of the latest accounting period to the date of publication. This delay is due to the availability and complexity of data. The design of the UK tax system allows individuals and businesses to report their income up to a year after the end of the accounting period, and it can take some time after this to report robust results. Estimates can also rely on external survey data, which is not available sooner.

Punctuality

These are official statistics, so the publication date for each edition of ‘Measuring tax gaps’ is announced at least one month in advance, in line with the Code of Practice for Statistics. It has always been published on time, with the exception of: 

‘Measuring tax gaps 2020 edition’

While we normally publish in June, this release was delayed to July due to the disruption and re-prioritisation of resources caused by the COVID-19 pandemic. The additional time enabled HMRC to produce these statistics to the expected standard of quality.

‘Measuring tax gaps 2021 edition’

This edition was published in September 2021, instead of the usual June release date. The delay allowed more Mandatory Random Enquiry Programme (MREP) cases to reach settlement; because MREP cases were completed at a slower rate due to COVID-19 related disruptions to compliance activity. The additional time enabled HMRC to produce these statistics to the expected standard of quality.

Coherence and comparability

Geographical comparability

This analysis is presented for a single region – the United Kingdom, except for the diesel tax gap, which presents Great Britain and Northern Ireland separately.

Comparability over time

A time series of the tax gap is published, covering 16 years, from 2005 to 2006 up to 2020 to 2021. Any changes that have been made to the methodology are reflected in all years of the time series and are therefore fully comparable and consistent across the time series.

The percentage tax gap provides a better measure of compliance over time. It accounts for some of the effects of inflation, economic growth and changes to tax rates, whereas the cash figure does not. For instance, in a growing economy where the tax base is increasing, even if the percentage tax gap remained level, the cash figure would grow.

Cross-domain coherence

There are no cross-domain coherence issues, since all terminology is standardised and is defined in ‘Measuring tax gaps 2022 edition’, and models, such as the VAT gap, and the national accounts framework.

Internal coherence

The HMRC data used within each of the models are coherent as they align to HMRC-wide definitions and terminology.

Accessibility and clarity

News release

A press release is published every year on the GOV.UK website about the tax gap, on the same day as the ‘Measuring tax gaps’ publication.

Publication

Measuring tax gaps is published in an HTML format, and can be found on ‘Measuring tax gaps 2022 edition’.

The publication contains the tax gap estimates by type of tax. Where appropriate, charts have been published alongside the figures to show the trend in tax gaps. Technical terms used and list of abbreviations can be accessed by hovering over them with the mouse. GOV.UK writing style is followed to deliver the information clearly to the users.

Online databases

Separate excel tables are published alongside the main tax gap report, containing additional tables that are not included in the main publication, as well as additional years in the series.

Micro-data access

In May 2011, the HMRC Datalab was launched to allow approved researchers access to de-identified taxpayer and customs data, for research and analysis purposes that benefit HMRC’s functions. By making the taxpayer-level data available in a safe and secure way, the Datalab supports knowledge sharing and policy making and facilitates new areas of research using improved methodologies whose findings should benefit society.

Researchers can submit a proposal on GOV.UK, requesting the use of other datasets if the information needed is currently not available in the Datalab.

Other

There are no other dissemination formats available for this analysis.

Documentation on methodology

The Methodological annex is published in HTML alongside the main tax gap report.

Quality documentation

All official statistics produced by KAI must meet the standards in the Code of Practice for Statistics produced by the UK Statistics Authority and all analysts adhere to best practice as set out in the ‘Quality’ pillar.

Information about quality procedures for this analysis can be found in the Quality Management section of this document.

Cost and respondent burden

The main cost is staff time for HMRC’s tax gap team and for compliance officers to undertake compliance checks on cases included as part of the Mandatory Random Enquiry Programmes. Around 20 professional analysts work on improving, producing, assuring and explaining the tax gap estimates.

The main customers are internal, since estimates are produced to inform strategy and decisions about resource allocation within HMRC. The additional process required for the annual publication takes about two to three months, focusing on clearances and communication.

Most of the data is derived from existing sources, such as internal tax case management systems and ONS administered surveys, so there is minimal additional cost.

There are operational and opportunity costs associated with HMRC’s MREPs, which are used to estimate some of the direct tax gaps.

The direct HMRC staff cost is about £5 million per year, but these enquiries also generate tax revenue and help HMRC to evaluate and improve the effectiveness of risk assessment processes.

Confidentiality

Confidentiality policy

The statistics contained within the publication are presented at aggregate levels. As a result, issues of confidentiality relating to individuals’ or tax entities’ details are not applicable.

Production and dissemination of ‘Measuring tax gaps’ follows the Code of Practice for Statistics and HMRC’s published policy on Confidentiality and Access.

A list of individuals who have pre-release access to tax gap statistics is published on the HMRC pages on GOV.UK. The list is updated each year, maintaining the principles and rules set out in the pre-release access to official statistics.

Confidential data treatment

The statistics in these tables are presented at a highly aggregate level, to minimise the possibility of any individual person or organisation being identified.

If a potential risk did exist, statistical disclosure control (SDC) would be applied to cells within the tables. SDC is the application of methods to ensure confidential data is not disclosed to parties who don’t have authority to access it.

SDC modifies data, so that the risk of data subjects being identified is within acceptable limits, while making the data as useful as possible.

Disclosure in this analysis is avoided by applying rules that prevent categories of data containing small numbers of contributors, and small numbers of contributors that are very dominant.

If a cell within a table is determined to disclose confidential data, its contents would be suppressed either by removing the data or combining categories.

Further information on anonymisation and data confidentiality best practice can be found on the Government Statistical Service’s website.