Official Statistics

Making Tax Digital for Income Tax business population statistics: background quality report

Published 13 August 2025

1. Contact

• Organisation unit – Knowledge, Analysis and Intelligence (KAI)
• Name – B Skittrall and C Breeze
• Function – Statistician, KAI Operations, Strategy and Transformation
• Mail address – 1st floor, 7&8 Wellington Place, Leeds, LS1 4AP
• Email – personaltax.statistics@hmrc.gov.uk

2. Statistical presentation

2.1 Data description

This publication provides information about the Income Tax Self Assessment (ITSA) population that will be impacted by Making Tax Digital (MTD). It is based on Self Assessment returns for the 2023 to 2024 tax year.

This publication provides official statistics on the number of businesses by MTD qualifying income. It also includes the splits of customers by the type of qualifying income they have (landlord income, self-employment income or self-employment and landlord income), agent representation and if software was used to submit their Self Assessment tax return.

Data in the tables included is rounded to the nearest thousand and therefore cell data may not add up to totals.

2.2 Classification system

A unique taxpayer reference (UTR), assigned to each registered tax entity, is used to aggregate the data.

2.3 Sector Coverage

These statistics cover individuals who have Making Tax Digital qualifying income in tax year 2023 to 2024.

2.4 Statistical concepts and definitions

Income Tax

Income Tax is a tax on an individual’s income over the course of a tax year.

Income Tax Self Assessment (ITSA)

Income Tax Self Assessment is a system HM Revenue and Customs (HMRC) uses to collect Income Tax.

Self-employment Income

Self-employment income is defined as individuals with gross income (also known as turnover) from self-employment income sources.

Landlord Income

Landlords are defined as individuals with income from a property. This can include both UK and foreign property.

Qualifying Income

MTD qualifying income is the combined gross income from self-employment and landlord activities, as described on this guidance page on gov.uk.

Agent representation

The individual has completed a 64-8 form authorising someone else to deal with HMRC on their behalf.

Tax year

The statistics are aggregated into the 2023 to 2024 tax year. This means it includes the individual’s activities between 6 April 2023 and 5 April 2024, as reported on their 2023 to 2024 Self Assessment return.

2.5 Statistical unit

The statistical units are individuals who have submitted a Self Assessment return.

2.6 Statistical population

All individuals who have filed a Self Assessment return including business (self- employment or property) income.

3. Statistical processing

3.1 Source Data

The data for Self Assessment income comes from the Income Tax Self Assessment tax return pages. Self Assessment returns (online returns, paper returns, and amendment returns) submitted by customers are captured directly to Computerised Environment for Self Assessment (CESA). At the end of each day, the latest version of the return (and all other data) held is passed from CESA to another HMRC system, the Corporate Data Warehouse (CDW). Monthly extracts are then taken from CDW for analysis. The analytical extract covers all customers excluding Special Customer Records (SCRs) cases, which are cases that are removed to provide enhanced protection for certain customers because of their employment or personal circumstances.

3.2 Frequency of data collection

ITSA tax returns are filed by taxpayers annually. The data for this publication is extracted after 31 March after the filing deadline 31 January for the most recent tax year.

Each year some returns are submitted after the filing deadline. The returns and amendments submitted after 31 March will not be included in this publication.

3.3 Data collection

Data on ITSA is sourced from the HMRC tax administrative system.

3.4 Data validation

Checks carried out on the data include:

• automated checks take place when loading data into the analysis database. Inconsistencies are automatically repaired if possible; otherwise, the record is flagged as invalid
• analysts check that the number of records loaded into the analysis database is as expected
• analysts check any outliers in the data which are then examined on a case-by-case basis. Outlier checks sometimes result in adjustments to the dataset as required to improve accuracy or prevent skewing

3.5 Data compilation

Aggregating data

Data are aggregated using a unique taxpayer reference number assigned to each tax entity. This unique number does not change across financial years.

4. Quality Management

Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. You are welcome to contact us directly with any comments about how we meet these standards by emailing personaltax.statistics@hmrc.gov.uk . Alternatively, you can contact OSR by emailing or via the OSR website.

4.1 Data Assurance

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.

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.

Every piece of analysis is unique, and as a result there is no single quality assurance (QA) checklist that contains all the QA tasks needed for every project. Nonetheless, analysts in KAI use a checklist that summarises the key QA tasks, and is used as a starting point for teams when they are considering what QA actions to undertake.

Teams amend and adapt it as they see fit, to take account of the level of risk associated with their analysis, and the different QA tasks that are relevant to the work.

At the start of a project, during the planning stage, analysts and managers make a risk-based decision on what level of QA is required.

Analysts and managers construct a plan for all the QA tasks that will need to be completed, along with documentation on how each of those tasks are to be carried out and turn this list into a QA checklist specific to the project.

Analysts carry out the QA tasks, update the checklist, and pass onto the Senior Responsible Officer for review and eventual sign off.

4.2 Quality assessment

The QA for this project adhered to the framework described in section 4.1, Quality assurance, and the specific procedures undertaken were as follows:

Stage 1: Specifying the question

Up to date documentation was agreed with stakeholders setting out outputs needed and by when; how the outputs will be used; and all the parameters required for the analysis.

Stage 2: Developing the methodology

Methodology was agreed and developed in collaboration with stakeholders and others with relevant expertise, ensuring it was fit for purpose and would deliver the required outputs.

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

• analysis was produced using the most appropriate software and in line with good practice guidance
• data inputs were checked to ensure they were fit-for-purpose by reviewing available documentation and, where possible, through direct contact with data suppliers.
• QA of the input data was carried out
• the analysis was audited by someone other than the lead analyst checking code and methodology

Stage 4: Running and testing the model/code

• results were determined to be explainable and in line with expectations

Stage 5: Drafting the final output

• checks were completed to ensure internal consistency (for example, totals equal the sum of the components)
• the final outputs were independently proofread and checked

5. Relevance

This section covers the degree to which statistical information meets user needs. See the GSS Quality Statistics in Government guidance for additional information on the relevance dimension of quality.

5.1 User needs

This analysis is likey to be of interest to:

• national government, policy makers and MPs
• regional and local governments
• academia and research bodies
• media
• business community
• software developer community
• general public

5.2 User satisfaction

Formal investigations into user satisfaction have not been undertaken, however KAI are always open to ideas for new analysis to meet changing user requirements.

5.3 Completeness

It is a legal requirement that all qualifying tax entities submit an ITSA tax return, at the required time. Penalties exist for non-compliance. The statistics contained in this report can therefore be considered as complete as they can be.

It is possible that there will be a small number of Self Assessment cases yet to file a return after the data are drawn from transactional systems.

6. Accuracy and reliability

6.1 Overall Accuracy

This analysis is based on administrative data, and accuracy is addressed by eliminating non-sampling errors as much as possible through adherence to the quality assurance framework.

The potential sources of error include:

• tax entities entering incorrect information onto the ITSA tax return form
• human or software error when entering the returns data into the ITSA tax system
• tax entities not completing their ITSA return by the required date
• mistakes in the programming code used to analyse the data and produce the statistics

6.2 Sampling error

Samples are not used to compile the analysis, instead it is based on administrative data from HMRCs ITSA tax system. Sampling error is therefore not relevant.

6.3 Non-sampling error

Coverage error

All qualifying tax entities must register with HMRC for ITSA Income Tax by a specific date. In some scenarios tax entities will not comply with this.

It is likely some tax entities file late and therefore will be considered out of scope for these statistics.

These statistics do not include SCRs, and no adjustment is made to account for these.

Measurement error

The main sources of measurement error could be categorised as respondent errors and include the following:

• tax entities may make errors entering their information onto the ITSA tax return form, whether this is done on paper or electronically
• ITSA return data is subsequently entered onto the system either manually or by electronic transmission, which is another point at which data may be altered due to human or software error

Non response error

When analysing the population for the latest available year, figures are not necessarily available for all tax entities, as some may not have completed their ITSA return by the required date.

Processing error

It is possible that errors exist in the programming code used to analyse the data and produce the statistics. This risk is reduced through developing a good understanding of the complexities of ITSA data, and thoroughly reviewing and testing the programs that are used.

6.4 Data revision

The statistics in this publication will not be revised.

6.5 Seasonal adjustment

Seasonal adjustment is not applicable for this analysis.

7. Timeliness and punctuality

7.1 Timeliness

This analysis is published around 18 months after the end of the latest tax year.

The reason it is published at this time is due to a number of factors.

This includes:

• the SA filing deadline taking place in the January after the tax year the returns relate to
• one of the timeframes used in this analysis is the 31 March of the latest calendar year
• it takes around 20 days for analysts at HMRC to compile and QA the statistics from the raw data

7.2 Punctuality

In accordance with the Code of Practice for official statistics, the exact date of publication will be given no less than one calendar month before publication on both the Schedule of updates for HMRC’s statistics and the Research and statistics calendar of GOV.UK.

Any delays to the publication date will be announced on the HMRC National Statistics website.

The full publication calendar can be found on both the Schedule of updates for HMRC’s statistics and the Research and statistics calendar of GOV.UK.

8. Coherence and comparability

8.1 Comparability over time

The statistics in this publication are compared at a single time frame: 31 March, one year following the end of the tax year, 31 March 2025 for the 2023 to 2024 tax year.

8.2 Coherence – cross domain

There are no instances where different sources are used to provide data for the same variables.

8.3 Coherence - sub-annual and annual statistics

These statistics are based on ITSA returns received from individuals up to 31 March of the latest calendar year. These statistics are not directly comparable to other statistical releases due to methodological and timing differences:

• Statistics about personal incomes are assessed using the annual survey of Personal Incomes (SPI) which is a sample dataset representing the UK Income Tax paying population
• Property rental income statistics covers taxpayers that receive income from renting UK property that is declared via ITSA. Whereas these tables include all gross income from UK and foreign property.

8.4 Coherence – internal

Rounding of numbers may cause some minor internal coherence issues as the figures within a table may not sum to the total displayed. Effort has been made to ensure totals between tables remain constant where appropriate.

9. Accessibility and clarity

9.1 News release

There have not been any press releases linked to this data over the past year.

9.2 Publication

The tables and associated commentary are published and available on gov.uk.

Tables are published in the OpenDocument format, and the associated commentary as an accessible HTML webpage.

The documents comply with the accessibility regulations set out in the Public Sector Bodies (Websites and Mobile Applications) (No. 2) Accessibility Regulations 2018.

Further information can be found in HMRC’s accessible documents policy.

9.3 Online databases

This analysis is not used in any online databases.

9.4 Micro-data access

Access to this data is not possible in micro-data form, due to HMRC’s responsibilities around maintaining confidentiality of taxpayer information.

9.5 Other

There are no other dissemination formats available for this analysis.

9.6 Documentation on methodology

All methodology can be found in the Statistical processing section of this report.

9.7 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 section 4 of this document.

10. Cost and burden

Because all necessary data for this publication is obtained from an administrative data source, ITSA tax administration data, there is no additional burden on tax entities or HMRC tax inspectors to provide information.

It is estimated to take about 20 days FTE to produce the annual analysis and publication.

More information on reporting cost and burden (PDF, 4.54MB)

11. Confidentiality

This section covers confidentiality policy and practice.

11.1 Confidentiality – policy

HMRC has a legal duty to maintain the confidentiality of taxpayer information.

Section 18(1) of the Commissioners for Revenue and Customs Act 2005 (CRCA) sets out our duty of confidentiality.

This analysis complies with this requirement.

11.2 Confidentiality – data treatment

The statistics in these tables are presented at an aggregate level so identification of individual tax entities is not 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 be disclosive, 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.