Official Statistics

Economic Estimates: Employment in the Digital Sector, January 2024 to December 2024 – Technical and quality assurance report

Published 10 July 2025

1. Overview of release

This technical report covers the ‘Economic Estimates: Employment in the Digital Sector, January 2024 to December 2024’ release. The release provides estimates of employment in the Digital Sector and the United Kingdom (UK) overall based on the latest 2024 data from the Annual Population Survey (APS) run by the Office for National Statistics (ONS).

This series has been reclassified from Accredited Official Statistics to Official Statistics in development following declining sample sizes in underlying data. See Section 2 for further information.

In February 2023, Machinery of Government changes moved responsibility for the Digital and Telecommunications Sectors from the Department for Culture, Media and Sport (DCMS) to the Department for Science, Innovation and Technology (DSIT). DSIT has been responsible for publishing estimates for the Digital Sector since April 2024. The previous release in this series can be found on the DSIT Digital Sector Economic Estimates: Employment webpage. Releases prior to this can be found on the DCMS webpage.

This ‘Economic Estimates: Employment in the Digital Sector, January 2024 to December 2024’ release provides estimates of the number of filled jobs (including both employed and self-employed, and both full-time and part-time jobs) in the Digital Sector measuring the calendar year 12-month period between January 2024 and December 2024. A list of the subsectors included in the Digital Sector is included in Section 4: Sector definitions.

These estimates are derived from the ONS Annual Population Survey (APS) and contain demographic breakdowns including, but not limited to, employment type (i.e. employed or self-employed), International Territorial Level 1 (ITL1) region of work, nationality, sex, and ethnicity. Employment estimates are based on APS data collected over the 12-month period from January 2024 to December 2024. 

The ONS is the provider of the underlying APS data used for the analysis presented within this release. As such, the same data sources are used for the Digital Sector as those used for national estimates, enabling comparisons to be made on a consistent basis.

2. Code of Practice for Statistics

The statistics in this series (including this release) will be classified as Official Statistics in Development until further review. Previous releases in the Digital Sector Economic Estimates: Employment series have been classed as Accredited Official Statistics– meaning they comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics. The Office for Statistics Regulation (OSR) has now removed accreditation at our request, following ONS reporting concerns with the quality of estimates for smaller segments of the APS population, which the Digital Sector Economic Estimates: Employment series depends upon.

Reduced APS coverage of Digital Sector SIC codes reduces the reliability of our employment estimates. Survey responses relating to the Digital Sector form a small proportion of the APS. Decreases in APS response rates means that employment estimates are based on a smaller number of responses which have been weighted to represent a larger proportion of the UK population. Relying on a small number of responses to make estimates of a large proportion of the population results in high levels of volatility as small changes in the number of responses have larger effects on employment estimates. This increased volatility affects the certainty about any observed changes in Digital Sector employment estimates, as these observed changes are potentially attributable to random fluctuations.

Our approach in moving the Digital Sector Economic Estimates: Employment series to Official Statistics in Development is in line with ONS’s decision to consider their APS based labour market statistics as Official Statistics in Development until further review.

In addition to reclassifying Economic Estimates: Employment in the Digital Sector estimates as Official Statistics in Development, we have included data on the coefficient of variation (CV) for each estimate in this release to provide an indication of the statistical robustness of each estimate. In future we will also review the statistical robustness of employment estimates in previous releases of the series, so that we can provide a full picture of how the APS issues have affected our Digital Sector estimates over time. Users may find additional data sources covered in Section 8 helpful to support employment estimates reported here.

We will continue to monitor the reliability of underlying data, review the designation and provide caveats for the Digital Sector Economic Estimates: Employment series, where appropriate, in line with the Code of Practice for Statistics. You are welcome to contact us directly with any queries about how we meet these standards by emailing economicestimates@dsit.gov.uk.

Alternatively, you can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website.

The Economic Estimates produced by DSIT follow the same methodology as those produced by DCMS. DSIT will continuously review this methodology and will develop and make improvements to the series where relevant. We will clearly state our methodology in our documentation so that users can see any divergence from the methodology used in the DCMS produced statistics. We encourage our users to engage with us so that we can improve our statistics and identify gaps in the statistics that we produce.

3. Users

The users of these statistics fall into five broad categories:

• Ministers and other political figures.
• Policy and other professionals in DSIT and other government departments.
• Industries and their representative bodies.
• Charitable organisations.
• Academics.

The primary use of these statistics is to monitor the performance of the industries in the Digital Sector, helping to understand how current and future policy interventions can be most effective.

4. Sector definitions

In order to produce these Economic Estimates, it is necessary to define the make-up of the economy and the sectors comprising it. The Digital Sector and Telecommunications Sector definitions are based on the Standard Industrial Classification 2007 (SIC) codes.  This allows data sources to be nationally consistent and enables international comparisons.

4.1 Digital Sector

The definition of the Digital Sector is based on the Organisation for Economic Cooperation and Development (OECD) definition of the ‘information society’. This is a combination of the OECD definition for the ‘ICT Sector’ and ‘Content and Media Sector’. An overview of the SIC codes included in each of these sectors is available in the OECD Guide to Measuring the Information Society (see Box 7.A1.2 on page 159 and Box 7.A1.3 on page 164).

Table 1: SIC codes included in the Digital Sector by Digital Subsector (adapted from OECD, 2011)

Digital subsector	SIC codes included
Manufacturing of electronics and computers	26.11, 26.12, 26.2, 26.3, 26.4, 26.8
Wholesale of computers and electronics	46.51, 46.52
Publishing (excluding translation and interpretation activities)	58.11, 58.12, 58.13, 58.14, 58.19
Software publishing	58.21, 58.29
Film, TV, video, radio and music	59.11, 59.12, 59.13, 59.14, 59.2, 60.1, 60.2
Telecommunications	61.1, 61.2, 61.3, 61.9
Computer programming, consultancy and related activities	62.01, 62.02, 62.03, 62.09
Information service activities	63.11, 63.12, 63.91, 63.99
Repair of computers and communication equipment	95.11, 95.12

4.2 Details and limitations of sector definitions

DSIT holds policy responsibility for the digital industry and services across the economy and within sectors. The definition we use in this release for the Digital Sector, using SIC codes, does not consider the value added from ‘digital’ services to the wider economy e.g. digital work that takes place in diversified businesses in other industries such as health care or construction. It therefore does not include the value added to the economy from businesses which carry out digital services as part of their output in other areas of the economy.

There are also substantial limitations to the underlying SIC classifications. As the SIC codes were finalised in 2007, their relevance for important elements of the UK economy related to the Digital Sector is less robust as the balance and make-up of the economy changes. This is particularly relevant for the Digital Sector, in which, there are likely to be several emerging sectors that are not accurately identified by SIC codes, such as cyber-security and artificial intelligence. The SIC codes used to produce these estimates are a ‘best fit’, subject to these limitations.

The Office for National Statistics (ONS) recently ran a consultation on the UK’s adoption of industrial classification of economic activity and have developed a set of guiding principles for UK Revision to address some of these issues. This revision process is ongoing with the design of the UK framework planned to be concluded by March 2026.

5. Methodology

5.1 Data sources

In this release employment estimates are calculated using the Office for National Statistics (ONS) Annual Population Survey (APS). The majority of the data processing is done by the ONS, with DSIT receiving cleaned and weighted respondent-level data. We then process and aggregate the data to give employment estimates.

5.2 Annual Population Survey

The APS is a household survey that combines two waves of the Labour Force Survey (LFS) with an additional sample boost. Information collected includes the details of employment (e.g. location, industry, seniority, occupation, and income), circumstances (e.g. housing tenure and health) and demography (e.g. nationality, age, and ethnicity). Responses are weighted to population totals.

As covered in Section 2, the APS has experienced a decline in response rate which reduces the reliability of our employment estimates. In response to ONS reporting concerns about underlying APS data, we have requested that the Accredited Official Statistics status be removed from this series and have included coefficient of variation data to provide an indication of the statistical robustness of each employment estimate.

5.3 Employment estimates

To produce our Employment estimates we only include respondents that are ‘in work’ from the APS dataset for analysis. The APS provides data on an individual level for both a respondent’s first job, and if applicable, a respondent’s second job as separate variables. Therefore, in the dataset across these two variables, we define ‘in work’ as those with a first or second job who are categorised as an employee or self-employed. The data presented in this report, and the accompanying Employment data tables, therefore includes both employed and self-employed workers.  

As ‘employment’ in this release is estimated as the number of filled jobs, we restructure the data to be on a per job basis, rather than a per respondent basis. We then select entries that are relevant for a particular grouping (e.g. all entries with a SIC code of 26.11 for total employment in the ‘Manufacture of electronic components’ subsector) and aggregate over the associated population weights to generate an estimate of the total number of filled jobs. This means that some respondents may be included in the Employment data tables twice if they have both a first and second job.

Data tables relating to the Employment estimates provide demographic breakdowns across the Digital Sector, UK overall, and the Digital subsectors for employment status (employed/self-employed), ITL1 region of work, nationality, sex, ethnicity, age, highest level of education, working pattern (full time/part time), managerial status, socio-economic group (National Statistics Socio-economic Classification), and Equality Act disability status. There are additional breakdowns combining these selected specified variables with employment status. It is important to note that employment estimates for many of these demographic breakdowns are based on small sample sizes and so are considered unreliable. Users can review associated coefficient of variation data in the data tables of this release as an indication of the level of statistical robustness of employment estimates for each demographic breakdown. We have also removed breakdowns to the individual SIC code level due to the low sample sizes for these groups.

5.4 Disclosure control

As part of the production process, we apply disclosure control and quality assurance measures to prevent the identification of any respondents. We suppress values where the number of respondents for a particular demographic breakdown is below a set threshold (below or equal to 3 responses). Where appropriate, we also apply secondary suppression to prevent disclosure via differencing (i.e. being able to calculate the disclosed value from the other values presented). These values are instead replaced with a ‘c’. Additionally, any demographic breakdowns for which there are no respondents or there is missing data are replaced with a ‘w’. Further information is available in the ‘Respondent sample sizes’ sheet in the data release, which also highlights where the number of respondents comprising a value is deemed to be of a small sample size (below 30 responses).

5.5 Measures of variability

In order to provide an indication of the statistical robustness of employment estimates, we have carried out variability analysis. This analysis is presented as coefficient of variation (CV) data alongside the standard data tables and as confidence intervals in time series graphs in the main report. CV is the standard deviation divided by the estimate value and expressed as a percentage. Similar to the standard error, the closer the coefficient of variation is to zero, the more precise the estimate is. In this context, confidence intervals represent a range within which there is a 95% chance of the population value falling based on sample data collected. This variability analysis was performed according to ONS guidance and assumptions on statistical robustness based on CV levels which were taken from ONS Annual Survey of Hours and Earnings publications.

CV and confidence interval calculations were performed whilst accounting for APS survey design using the R “survey” package. During these calculations the “strata variable” was set to unitary authority/local authority (UALA), the “cluster variable” was set to a household variable derived from the unique person identifier (CASENO) and the “weight variable” was set to APS person weight (PWTA22). The “domain variable” and “variable of interest” were set depending upon the data being analysed.

6. Changes in this release

We have carried through the changes made to the ‘Economic Estimates: Employment in the Digital Sector’ series reported in the previous release when DSIT became responsible for publishing estimates for the Digital sector.

As covered in Section 2: Code of Practice for Statistics, these statistics have been reclassified as Official Statistics in Development from Accredited Official Statistics. We have removed breakdowns to the individual SIC code level due to low sample sizes. Additionally, we have included coefficient of variation and confidence interval measures of variability to provide further information on the statistical robustness of employment estimates.

7. Quality assurance processes

This section summarises the quality assurance processes applied during the production of these statistics by our data providers, the Office for National Statistics (ONS), as well as those applied by DSIT.

7.1 Quality assurance processes at ONS

Quality assurance at ONS is carried out during multiple data production stages. Methodological and quality assurance information in regard to the APS can be found in the Annual Population Survey QMI.

7.2 Validation and quality assurance at DSIT

Disclosure control is also applied as part of this process. Published data tables are thoroughly checked to ensure disclosive values are not included (breakdowns including 3 or fewer responses), and that it is not possible to derive these disclosive values via differencing from the data published. In the respondent sample sizes sheets of each release, breakdowns with no responses (0 responses), those with disclosive values (3 or fewer responses) and those with small sample sizes (fewer than 30 responses) have been highlighted.

8. External data sources 

It is recognised that there are always different ways to define sectors, but their relevance depends on what they are needed for. Government generally favours classification systems which are:

• Rigorously measured.
• Internationally comparable.
• Nationally consistent.
• Ideally applicable to specific policy interventions.

These are the main reasons for constructing sector classifications in this series from SIC codes. However, we acknowledge that there are limitations with this approach and alternative definitions and methodologies can be useful where a policy-relevant grouping of businesses crosses the existing SIC codes.

The ONS uses the quarterly Labour Force Survey (LFS) for its estimates of UK-wide employment rates. Our APS Employment estimates of the number of filled jobs in the Digital Sector takes a similar approach. However, as the APS uses two waves of the LFS, the datasets are not directly comparable and result in the ONS published figures for employment in the UK overall differing from our estimates of employment for the UK overall.

For Employment estimates more broadly, the main alternative data source is the Business Register and Employment Survey (BRES). This has the advantage of asking businesses directly about their employees and is, therefore, more likely to capture employment more accurately than a household survey. However, the BRES does not contain the range of demographic breakdowns and the self-employed data which the APS provides. Use of the APS, therefore, enables us to build a fuller picture of employment in the Digital Sector, using a relatively robust data source.

It should be noted that trends in employment for the Information and Communication sector (an ONS defined sector of the economy that is a rough proxy for the Digital Sector) reported in BRES differs substantially from changes in employment reported in this series since the pandemic. BRES data for 2024 will be released in October and may show a different trend to these results - both APS and BRES data contribute to the evidence base around Digital Sector employment. APS is used in this series as it provides demographic data not available in BRES.

It is recognised that there will be other sources of evidence from industry bodies, for example, which have not been included above. We encourage statistics producers within the Digital Sector who have not been referenced to contact the Economic Estimates team at economicestimates@dsit.gov.uk.

9. Further information

For further details about the estimates or for enquiries on this release, please email: economicestimates@dsit.gov.uk.

For general queries relating to DSIT Official Statistics, please contact: statistics@dsit.gov.uk.

The ‘Economic Estimates: Employment in the Digital Sector’ release is now classified as Official Statistics in Development.

For more information on the Code of Practice for Statistics see https://code.statisticsauthority.gov.uk/.