Accredited official statistics

Economic Estimates: Digital Sector Annual Gross Value Added (2019 to 2023) – Technical and quality assurance report

Published 6 March 2025

1. Overview of release

This technical report covers the ‘Economic Estimates: Digital Sector Annual Gross Value Added (2019 to 2023)’ release.

These statistics provide an estimate of the annual contribution of the Digital Sector and its associated subsectors to the UK economy, measured by gross value added (GVA). GVA is the measure of the value of goods and services produced in an area, industry or sector of an economy, defined by the value of output minus the value of intermediate consumption. It is used in the estimation of gross domestic product (GDP):

GVA + Taxes on Products − Subsidies on Products = GDP

Estimates of taxes and subsidies are not available at an industry level. We therefore use GVA as the headline economic measure at an industry level.

The release reports GVA expressed as both:

• Current price GVA (i.e. ‘nominal GVA’), which gives the best ‘instantaneous’ measure of the value to the economy, but is not adjusted for inflation.
• Chained volume measures (CVM) GVA (i.e. ‘real terms GVA’), where the effect of inflation is accounted for.

The estimates in this publication are consistent with national (UK) estimates, published by the Office for National Statistics (ONS).

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. Previous releases of the Economic Estimates in the DCMS and Digital Sectors series can be found on the DCMS webpage.  

1.1 Code of Practice for Statistics

The ‘Economic Estimates: Digital Sector Annual Gross Value Added’ series contains statistics classified as Accredited Official Statistics.

In June 2019, a suite of DCMS Sector Economic Estimates, including Annual GVA, were badged as Accredited Official Statistics (previously called National Statistics). This affirms that these statistics have met the requirements of the Code of Practice for Statistics. DSIT will continue to comply with these standards in the estimates that we produce for the Digital Sector.

This followed a report by the Office for Statistics Regulation (OSR) in December 2018, which stated that the series could be designated as Accredited Official Statistics subject to meeting certain requirements. Since the report, DCMS improved the publications by providing summaries of other notable sources of data, more detail on the nature and extent of the overlap between the sectors, and further information on the quality and limitations of the data. The development of the Digital element of these publications has been continued at DSIT. 

These statistics have not been formally assessed for compliance with the Code of Practice for Statistics since their transfer from DCMS. The OSR is planning to assess the statistics for compliance with the Code of Practice for Statistics in the near future. We commit to producing and publishing the statistics in line with the Code pillars of Trustworthiness, Quality and Value. Accredited Official Statistics are referred to as National Statistics in the Statistics and Registration Service Act 2007. 

Accreditation signifies their compliance with the authority’s Code of Practice for Statistics which broadly means these statistics are: 

• Managed impartially and objectively in the public interest.

• Meeting identified user needs.

• Produced according to sound methods.

• Well explained and readily accessible.

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. 

DSIT is actively considering how to continue to improve the series, in line with the recommendations of the OSR report. We recently consulted on pausing or ceasing some of the statistical releases within the Economic Estimates series, the response to which can be found here, so that we can prioritise improvements to our key statistical releases.

As part of our continuing review process, we have also made improvements to these estimates by incorporating balanced deflators and providing additional detail to our chained volume measure calculations methodology. These are discussed in the methodology section.

As we progress with the improvements, we will clearly state where this results in a divergence of methodology with the DCMS produced statistics. We continue to encourage our users to engage with us as we improve our statistics and look to identify gaps in the statistics that we produce.  

1.2 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.

2. 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.  

The UK SIC is a hierarchical five-digit system. From order of highest to lowest level of aggregation, where each level is divided into the next, the UK SIC hierarchy is defined by:

• Section, denoted by letters, which are collections of divisions.
• Division, denoted by 2-digit SIC codes.
• Group, denoted by 3-digit SIC codes.
• Class, denoted by 4-digit SIC codes.
• Subclass, denoted by 5-digit SIC codes.

There are 21 sections, 88 divisions, 272 groups, 615 classes and 191 subclasses in the UK SIC hierarchy.

As an illustrative example of the SIC hierarchy, in Section J (SIC 58-63), the division “Information service activities” (SIC 63), is comprised of the groups “Data processing, hosting and related activities; web portals” (SIC 63.1) and “Other information service activities” (SIC 63.9). The group defined by SIC 63.1 is then further broken down into the classes “Data processing, hosting and related activities” (SIC 63.11) and “Web portals” (63.12).

2.1 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).  In effect, the Digital Sector definition used in this publication is defined at the four-digit SIC code level:

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, 26.20, 26.30, 26.40, 26.80
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, 59.20, 60.10, 60.20
Telecommunications	61.10, 61.20, 61.30, 61.90
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

2.2 Details and limitations of sector definition

This section looks at sector definitions in more detail and provides an overview of limitations.

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 other industries such as health care or construction. By not including the value added to the economy from digital services, our definition is likely to underestimate the size of the Digital Sector.

There are also limitations to the underlying SIC classifications and its application. As the SIC codes were finalised in 2007, subsequent changes to the balance and make-up of the UK’s economy have decreased the relevance of SIC codes for important elements of the economy related to the Digital Sector; so, making the use of SIC codes less robust. This is particularly relevant for the Digital Sector, within which there are likely to be several emerging sectors that are not accurately identified by SIC codes, such as cyber security and artificial intelligence. In the UK, companies select a SIC code on their incorporation, with limited external verification of the accuracy of this selection. Therefore, SIC codes used to produce these estimates are a ‘best fit’, subject to these limitations.  

3. Revised and provisional estimates

This release contains revised and provisional Gross Value Added (GVA) estimates. The status of GVA estimates are updated as Annual Business Survey (ABS) or current price GVA data becomes available. As discussed in the methodology section, ABS data is used to apportion division level current price GVA data to the class level. Current price GVA data is used in output data tables and calculation of implied deflators.

In this release, revised estimates use the relevant year of ABS data and balanced Supply and Use Table (SUT) data. Revised estimates use the most accurate underlying data that are available and are therefore the most accurate GVA estimates. Revised estimates may be further revised as underlying current price data is updated.

Provisional estimates use the previous year’s ABS data and output GDP low-level aggregate data. Provisional estimates are less accurate than revised estimates as they are produced before relevant ABS data is available and rely on more timely but less accurate output current price data.

It is DSIT practice to align economic statistics with ONS National Accounts where possible and keep the statistics representative of the structure of the UK economy. Figures from 2019 to 2022 have therefore been revised and further revisions can be expected when future updates are made by ONS. 

4. Methodology

4.1 GVA - current prices

This first section presents the methodology for estimates of Annual GVA expressed in current prices, i.e. ‘nominal GVA’, which does not take into account the effect of inflation.

Data sources (current prices)

The following data sources were used in the production of Annual GVA (current prices) for the Digital Sector:

• Blue Book 2024 consistent Supply and Use Tables (SUT) (published 31 October 2024)
• GDP low-level aggregates from the latest Quarterly GDP National accounts (published 23 December 2024)
• Unsuppressed Annual Business Survey (ABS) approximate GVA (aGVA) estimates.

Method (current prices)

The most reliable estimate of GVA comes from the Supply and Use Tables (SUT) produced annually by ONS and made consistent with the latest Blue Book release. Producing this estimate involves balancing data drawn from many different sources, forming one robust estimate for each of the 112 industries in the SUT matrix up to 2022. ONS produce this ‘balanced’ data through their supply and use framework, which involves data confrontation and validation.

The latest GDP low-level aggregates release contains annual output GVA estimates for 2023. Output GVA is more timely but is not balanced to the income and expenditure measurements of GVA, making it less accurate.  To form a 2019 to 2023 current price timeseries, we therefore append unbalanced 2023 output GVA estimates to balanced 2019 to 2022 GVA estimates from SUT. Estimates derived from unbalanced output GVA, i.e. for 2023, are marked as provisional.

The SUT matrix and GDP low-level aggregates report GVA at Division level (2-digit SIC codes), but the Digital Sector and its subsectors are defined at the Class level (4-digit SIC codes). This means a method for apportioning the GVA from the division level to the class level must be applied.

This is achieved by using approximate Gross Value Added (aGVA) data from the UK non-financial business economy (Annual Business Survey), by:

• Extracting aGVA from the ABS at the Class level (e.g. 4-digit, SIC 46.51).
• Calculating aGVA from the ABS at Division level (e.g. 2-digit, SIC 46), by aggregating industries in the division.
• Calculating the proportion of the division aGVA that each Class accounts for (e.g. aGVA for 4-digit SIC 46.51 as a proportion of 2-digit SIC 46).
• Applying the proportion for each class to the division GVA in the SUT, to get the GVA estimate for each Class. These estimates are then consistent with the National Accounts.

For the provisional GVA results, in which unbalanced current price data is used (2023 in this release), the corresponding unsuppressed ABS data for that year was not yet available at the time of release. In this case, we used ABS data from the next available year (2022 in this release) to apportion the current price data.

In the case of there being no ABS data available for specific SIC codes and years of interest, due to lack of coverage or otherwise, we opt to use ABS data for those SIC codes from an alternate year in which ABS data is available. Hence, we would apportion the current price GVA data for a given SIC code and year by the ABS aGVA data from the most suitable year we have available, likely the previous year. In this release, this affects the following:

• SIC code 26.80 has no aGVA estimate reported in the 2021 ABS. As such, we use the 2020 ABS aGVA estimates for apportioning current price GVA for SIC 26 for 2021 into its constituent 4-digit SIC codes.

Following the apportioning process, we then aggregate the produced GVA for each Class (4-digit SIC code) into the Digital Sector and subsectors.

This method, using the National Accounts consistent SUT matrix, is preferable to only using aGVA from the ABS. There are differences in coverage between the two measures of gross value added in the SUT and ABS. For example, GVA covers the whole of the UK economy while aGVA covers only the UK Non-Financial Business Economy, a subset of the whole economy that excludes several key elements including public administration and defence, publicly provided health care and education, the financial sector, and much of agriculture.

There are also conceptual differences between the two measures of GVA. For example, some production activities such as illegal smuggling of goods must be included in the National Accounts but are outside the scope of the ABS. In addition, the National Accounts data have gone through the Supply and Use balancing process, which reconciles all three estimates of GDP. Using balanced GVA makes comparison with the wider UK economy more straightforward and ensures that non-market production is included in the Digital Sector estimates.

More information on the differences between National Accounts GVA and Approximate GVA can be found in the article, ‘A Comparison between Annual Business Survey and National Accounts Measures of Value Added’ from the ONS.

Method limitations (current prices)

Estimates from the Annual Business Survey (ABS) are subject to various sources of error, with sampling errors published at a 4-digit SIC level. While these data provide the best available source of information there is often volatility, especially at the 4-digit SIC level which is used to produce estimates for the Digital Sector. Further information on the quality of the ABS data is published by the ONS in the ABS quality measures and the ABS QMI. Users may also refer to the discussion of uncertainty in surveys published by the ONS.

There have also been two survey design changes (expanding the ABS population in 2015 and re-optimising the sample in 2016), but as the survey outputs are used only to provide a proportion of the SUT, these changes should have a minimal impact on comparisons with historical Digital Sector GVA.

For the 2019 and 2020 collections, the Annual Business Survey achieved smaller than usual sample sizes, and this means that results for those years are less certain. This will increase uncertainty in our Annual GVA estimates in these years, particularly for smaller subsectors such as ‘Repair of computers and communication equipment’ or ‘Software publishing’.

4.2 GVA - chained volume measures

This section presents the methodology for estimates of Annual gross value added (GVA) for the Digital Sector, expressed in Chained Volume Measures (CVMs), i.e. ‘real-terms GVA’, which takes into account the effect of inflation. For further information on CVM background and methodology, visit the ONS website.

CVMs estimates are volume measures that are obtained by chain-linking. Volume measure (also referred to as constant price) series, are the current price data deflated using a price index (deflator) from a single base period, effectively removing the influence of changes in prices over time (i.e. inflation or deflation).

In CVMs, base periods are typically updated each year and CVM series are created by linking together individual series with different base years that overlap in one period, which is considered to reflect more accurately volume changes over time. The methodology for deriving a CVM series in this publication is consistent with the methodology used in the National and Regional Accounts.

Data sources (chained volume measures)

The following data sources are used in the production of GVA (chained volume measures) for the Digital Sector and its subsectors:

• Blue Book 2024 consistent Supply and Use Tables (SUT) (published 31 October 2024)
• GDP low-level aggregates from the latest Quarterly GDP National accounts (published 23 December 2024)
• Unsuppressed Annual Business Survey (ABS) approximate GVA (aGVA) estimates.

Method (chained volume measures)

In order to derive a Chained Volume Measure (CVM) we make use of the relationship:

value = volume x price

Current price estimates, discussed in the section prior, are the ‘value’ component of this equation. The current price data is broken down by industry for each of the aggregated industries included within the Digital Sector remit. The ‘price’ component of this equation in our method comes from implied deflators, calculated from ONS SUT and GDP low-level aggregates.

The implied deflators are calculated by dividing the current price (CP) series by the chained volume measure (CVM) series and multiplying by 100 to produce a percentage value:

implied deflator = CP / CVM

These implied deflators therefore incorporate the balancing process used to produce the National Accounts and are produced using the same methodology as the implied regional deflators published alongside the Regional Accounts. This method is an update on our previous use of unbalanced output industry deflators, see section 4.3 for more details.

For each 4-digit SIC code in the Digital Sector, the ‘volume’ (written here as KP, or constant price) series is obtained by dividing the current price series (written here as CP) by the deflator (price) series.

KP = CP / price

To create a chained volume measure, the value series in previous year’s prices (PYP) and current year’s prices (CYP) is calculated. The definition of the PYP and CYP series changes depending on whether the year in question is before, or after the selected chain-linking base year.  In our methodology, the chain-linking base year is defined to be the last year which has been balanced through input-output supply and use tables (SUT). In this publication, the chain-linking base year is 2022 to remain in line with National Accounts data published by ONS.

For years up to and including the chain-linking base year, the CYP series is the current price (CP) series:

CYP_t = KP_t x price_t= CP_t

where the subscript t denotes time (year).

The Previous Year Price (PYP) series is given by:

PYP_t = KP_t x price_t-1

When constructing a CVM series, the selected chain-linking base year also defines the reference year for the series, and as such the constant price (KP) series equals the current price (CP) series for the chain-linking base year.

For years after the chain-linking base year, both the PYP and CYP series are defined to be constant price (KP) series for that year, divided by the value of the constant price (KP) series for the base year, multiplied by the current price (CP) series of the chain-linking base year.  In effect, this means that for years following the chain-linking base year, the PYP, CYP and KP series are equivalent:

PYP_t = CYP_t = KP_t

The PYP series and CYP series are then summed across relevant SIC codes for each year, to give a PYP and CYP aggregate series for the Digital Sector and its subsectors.

These are used to obtain scaling factors at sector and subsector level. When t ≥ base year, the scaling factor is one. When t < base year, the scaling factor is given by:

SF_t = (CYP_t+1 / PYP_t+1) x SF_t+1

The CVM is then calculated for each sector and subsector. When t ≤ base year, CVM is:

CVM_t = SF_t x CYP_t

When t > base year, CVM is given by:

CVM_t = SF_t x PYP_t

The output is a CVM series from 2019 to 2023 for each sector and subsector.

Users should note that the methodology for chained volume measures means they are not additive prior to the base year. This means the sum of subsector values would not equal Digital Sector values prior to 2022.

CVM reference year

Notionally, a reference year in a chained volume measure (CVM) series means that the CVM values for the reference year (in monetary value) will be equal to the corresponding current price values for the same period. Equivalently, in a CVM index series, the index for the reference year would be equal to 100. When constructing a CVM series, usually the selected chain-linking base year and the reference year are the same. In this current annual GVA publication, the base year and reference year are both 2022.

4.3 Changes in this release

Annual GVA figures for 2019 to 2022 have been revised since the last ‘DCMS Economic Estimates: Annual GVA’ publication containing Digital Sector GVA in February 2024. These revisions take into account the latest balancing of the National Accounts and revisions of the Annual Business Survey 2019 to 2020 data. National Accounts GVA is open to revisions back to 1997 each year. These are planned revisions and an integral part of the balancing process. Our revisions report contains further information on the effect of these revisions on our GVA estimates.

Revisions in this release are likely to be larger than usual, in part due to recent changes in the frequency of base year updates in the National Accounts. In the Blue Book 2022 and 2023, the last base year remained at 2019 due to the effects of the COVID-19 pandemic on the structure of the economy. In the Blue Book 2024, ONS have returned to their pre-pandemic approach to chain-linking by moving the last base year on to 2022. As the base year has been moved forward over a longer time period than usual, the revisions to volume estimates as a result of changing base years may be larger than usual in the Blue Book 2024. Given that we update our base year in line with ONS, larger volume estimate revisions will therefore be carried forward to our annual GVA release as well.

As mentioned previously, in this release we have updated the deflators we use in producing the volume estimates to be implied deflators, calculated from GVA reported in the SUT and GDP low-level aggregates. This replaces the (formerly experimental) domestic output industry deflators used in previous releases. The updates to the deflators used when calculating CVMs will influence annual GVA CVM estimates in this release. ONS were consulted on this methodology update. Our revisions report contains further information on the effect of these methodological updates on our GVA estimates.

Due to the methodological changes described here, estimates in this release should not be compared with estimates provided in previous releases. Pre-2019 figures will be updated in the next release.

4.4 Summary of data sources

In summary, the data presented in this report on annual GVA are based on:

• Official Statistics data sources.
• Internationally harmonised codes.
• Survey data (Annual Business Survey and Regional Accounts) and, as with all data from surveys, there will be an associated error margin surrounding these estimates.

This means the estimates are both comparable:

• At a national and international level.
• Over time, allowing trends to be measured and monitored.

However, this also means the estimates are subject to limitations of the underlying classifications of the make-up of the UK economy. For example, the Standard Industrial Classification (SIC) codes were developed in 2007 and have not been revised since. Emerging sectors, such as artificial intelligence and cyber security, are therefore hard to capture and may be excluded or mis-coded.

5. Quality assurance processes

5.1 Quality assurance processes at ONS

Quality assurance at ONS takes place at a number of stages. The validation and accuracy of the source data, as well as the various processes in place to ensure quality for the data sources used in the regional GVA publication, are outlined in the relevant links below.

National balanced GVA tables

Sections 6 (‘Important quality issues’) and Section 7 (‘The quality of Blue Book estimates’) of the Blue Book 2024 background notes detail some of the quality issues associated with National Accounts, whilst Section 10 (‘Code of Practice’) outlines that quality assurance reviews are performed in line with the Code of Practice for Statistics.

Annual Business Survey (ABS)

For more information on quality assurance processes used during the production and analysis of ABS, as well as validation and accuracy of the estimates, see the Annual Business Survey QMI and the Annual Business Survey technical report.

5.2 Quality assurance processes at DSIT

The majority of quality assurance of the data underpinning the release takes place at ONS. Further quality assurance checks are carried out within DSIT. These include checking:

• Growth rates are comparable to previous publications, and if not, that the differences are justified and explainable.
• The proportion of the Digital Sector accounted for by each subsector are comparable to previous publications.
• Current prices and CVM GVA data matches the National Accounts at a 2-digit level, where comparable, and for UK totals.

6. 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.