Official Statistics

Economic Estimates: Digital Sector Monthly GVA – Technical and quality assurance report

Updated 17 December 2024

1. Overview of release

The statistical release ‘Economic Estimates: Digital Sector Monthly GVA’ provides a monthly estimate of the contribution of the Digital Sector in the UK, measured by GVA (gross value added). GVA measures the contribution to the economy of each individual producer, industry or sector in the UK.

These estimates of monthly GVA are more timely, although less accurate, than annual GVA figures (DCMS and digital sector GVA 2022). The estimates for GVA within this statistical release are based on Office for National Statistics (ONS) datasets (listed in Section 3.1) which are frequently revised and should only be used to illustrate general trends, rather than be taken as definitive measures.

The method set out in section 3 of this report can be used to produce monthly GVA estimates expressed in chained volume measures (i.e. taking into account inflation) for the Digital Sector.

The monthly estimates in the publication are consistent with UK monthly estimates of GVA published by ONS.

These figures are published every quarter. As always, we welcome any feedback from users and are happy to receive any suggestions for changes they would like to see for this release in the future. Please email [email protected].

1.1 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 Digital Sector and its subsectors, helping to understand how current and future policy interventions can be most effective.

2. Sector definitions

2.1 Overview of DSIT Sectors

In February 2023, the machinery of government changes meant that responsibility for the Digital Sector (including the Telecommunications Sector) moved from the Department for Culture, Media and Sport (DCMS) to the newly created Department for Science, Innovation and Technology (DSIT).

In order to measure the size of the economy it is important to be able to define it. The definitions for both the Digital and the Telecommunications Sectors are based on Standard Industrial Classification 2007 (SIC) codes. This means nationally consistent sources of data can be used and enables international comparisons.

Although the Telecommunications Sector is considered as a sector in its own right, the Telecommunications Sector is completely contained within the Digital Sector as defined by SIC codes. It is therefore called a subsector in our data tables.

2.2 Details and limitations of sector definitions

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

There are substantial limitations to the underlying classifications. The SIC was finalised in 2007. Therefore, as the balance and make-up of the economy changes, the SIC is less able to provide the detail for important elements of the UK economy related to the Digital Sector. The SIC codes used to produce these estimates are a ‘best fit’, subject to the limitations described in the following section.

2.3 Digital Sector

The definition of the Digital Sector used by DSIT is based on the Organisation for Economic Co-operation and Development (OECD) definition of the ‘information society’. This is a combination of the OECD definition for the “ICT sector” as well as including the definition of the “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 2011 (see Box 7.A1.2 on page 159 and Box 7.A1.3 on page 164).

The definition used for the Digital Sector does not allow consideration of the value added of “digital” to the wider economy e.g. in health care or construction. DSIT policy responsibility is for digital across the economy and therefore this is a significant weakness in the current approach.

3. Methodology

3.1 Data sources

The following publicly available data sources were used in the production of monthly GVA estimates for the Digital Sector:

• Annual low-level aggregates of UK output gross value added (GVA) (published May 2024) - used to obtain 2019 current price GVA for 2-digit SIC (Standard Industrial Classification) codes.
• Annual Business Survey 2019 (ABS) aGVA estimates (published May 2021) – Approximate GVA (aGVA – ABS derived GVA estimates) used to allocate GVA for more detailed industrial classes (4-digit SIC codes) which are not available in the table above.
• Monthly Index of Production (IoP) (published monthly) - monthly indexes of GVA for broad industrial divisions related to production, used to estimate monthly GVA for DSIT sectors.
• Monthly Index of Services (IoS)  (published monthly) - monthly indexes of GVA for broad industrial divisions related to services, used to estimate monthly GVA for DSIT sectors.
• Monthly gross domestic product (published monthly) - monthly indexes of GVA for industrial sections and for the whole UK economy, which is not available in the IoP or IoS. This data source can be used to estimate monthly GVA for the UK economy as a whole.

3.2 Method

ONS publishes the monthly indexes identified above for broad industrial divisions, otherwise referred to here as 2-digit SIC (Standard Industrial Classification) codes. SIC codes are used to classify business establishments by their main economic activity, and classifications are nested, with more detailed 4-digit ‘classes’ sitting within 3-digit ‘groups’, which sit inside 2-digit ‘divisions’. DSIT sectors are defined at the detailed industrial ‘class’ level.

The general principle of the monthly GVA method is to assign monthly indexes of GVA to more detailed industrial groups and aggregate these into DSIT sector estimates. The following is one possible method to illustrate general trends in monthly GVA.

3.2.1 Calculate current price GVA for 2019

The first step in this method is to obtain current price GVA for 2019 for each industry in the Digital Sector. This is because the monthly indexes are referenced to 2019 = 100, so 2019 current price data is used as a starting point.

Current price GVA for 2019 can be obtained from the ONS tables of low-level aggregates of UK output gross value added (GVA). These tables contain current price data for broad industry divisions (coded to 2-digit SIC codes).

As DSIT sectors are defined at the more detailed ‘class’ level (4-digit SIC codes), which are not available in these GVA tables, we use data from the Annual Business Survey (ABS) to estimate GVA at the more detailed level. We use approximate GVA (aGVA) to apportion output GVA from broad industrial divisions to detailed industrial classes, by:

• Extracting aGVA from the ABS at class level (e.g. SIC 26.11).
• Extracting aGVA from the ABS at division level (e.g. SIC 26).
• Calculating the proportion of the division aGVA that each class accounts for (e.g. aGVA for SIC 26.11 as a proportion of SIC 26).
• Applying the proportion for each class to the division GVA in the ONS current price tables.

aGVA differs from GVA. It is an estimate of GVA taken from the ABS survey data that does not cover all economic sectors. Standard GVA is considered more accurate, covers all of the UK economy, and is derived from other sources.

ABS data from 2019 was used in prior releases by the Department for Culture, Media and Sport (DCMS) due to the effect of the COVID-19 pandemic on aGVA resulting in unreliable data for 2020 and 2021. Use of ABS 2019 data is maintained here to allow easier comparison with previous releases, with the potential of moving to the use of ABS 2022 data in the near future.

This approach can be used for all cases where DSIT sectors are defined by a 3 or 4-digit SIC. The list of SIC codes in each sector and subsector are available in Table 1 of each Economic Estimates: Digital Sector Monthly GVA release. We then divide the current price GVA for each SIC code by 12 to get the average monthly GVA, not annual GVA, for 2019.

3.2.2 Use monthly indexes to extrapolate monthly output from 2019

The next step is to obtain the monthly index for each SIC code, published in the Index of Services and Index of Production. These datasets contain the monthly growth/fall in output for the services and production industries, using 2019 as the reference year. These can also be obtained using the ONS time series explorer to download only the relevant indices. There are a range of variables in these datasets, for example ‘Weights’ and ‘3m on 3m growth’ for each SIC code. We extract seasonally adjusted ^{[footnote 1]} indexes in chained volume measures (adjusted for inflation) for each 2-digit SIC code.

These indexes are only available for broad industrial divisions (2-digit SIC codes), but DSIT sectors are defined using detailed classes (4-digit SIC codes). We have assumed that trends in GVA for 4-digit SIC codes will follow trends for 2-digit SIC codes, and have therefore applied the division index for each 3 or 4-digit SIC code. This is a significant assumption; therefore, further information about its implications is available in Section 4 of this document.

For each relevant SIC code, we multiply the index for each month of interest by the average monthly GVA for 2019 (calculated in the step above). This gives the GVA (adjusted for inflation) for all months of interest for DSIT industries. These industries (SIC codes) can then be aggregated for each month to give estimates for DSIT sectors and subsectors.

Users should note that the calculations used to produce chained volume measures mean that additivity over aggregations is lost. That is to say that component subsectors do not necessarily sum to Digital Sector totals. For further information on chained volume measure methods and limitations, see the ONS National Accounts.

If calculating percentage contribution of digital subsectors to the Digital Sector, we recommend that users use the sum of digital subsector GVA as a denominator to ensure that the total percentage contribution of digital subsectors sums to 100%. However, users should note that the non-additivity problem means that percentages calculated this way will have reduced accuracy as distance increases from the reference year (2022) and that absolute GVA values should not be derived using this method.

3.2.3 Method to calculate estimates in 2022 prices

From November 2023, DCMS published monthly and Summed Monthly GVA estimates in 2022 prices. This change does not affect growth rates but means that GVA estimates are closer to 2022 current prices.

To calculate estimates in current prices, the GVA estimates for DSIT sectors and subsectors are divided by the 2022 monthly average GVA to obtain indexes with 2022 = 100. Each index is then multiplied by the 2022 current prices estimate, based on the industry level data published by ONS in the quarterly national accounts.

3.3 Summary of data sources

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

• official statistics data sources
• internationally-harmonised codes
• survey data (Annual Business Survey) and hence there will be an associated error margin surrounding these estimates ^{[footnote 2]}

This means the estimates are comparable:

• at both 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, are therefore hard to capture and may be excluded or mis-coded.

4. Validation and accuracy

4.1 Comparability with annual estimates

Monthly estimates should only be used to illustrate general trends, rather than be taken as definitive figures. These figures will not be as accurate as the annual Accredited Official Statistics release of gross value added for the Digital Sector (which will be published when the relevant national accounts data becomes available).

This is partly because this method uses indexes for 2-digit SIC codes (the industrial ‘division’ level and the lowest level of detail available) to estimate monthly GVA for more detailed 4-digit SIC codes (the industrial ‘class’ level and how DSIT sectors are defined). Therefore, variation across 4-digit SIC codes class within a 2-digit SIC code division cannot be captured within these monthly GVA estimates. This presents an issue when creating our statistical series as classes within a division are likely to have changed by differing amounts over a time period. Additionally, we may only be interested in some of the classes within a division; rather than the division as a whole.

For example, both SIC code 46.51 (the industrial class: ‘Wholesale of computers, computer peripheral equipment and software’) and SIC code 46.65 (the industrial class: ‘Wholesale of office furniture’) are components of SIC code 46 (the industrial division: ‘Wholesale trade, except of motor vehicles and motorcycles’) across different industrial groups. SIC code 46.51 is included in our definition of the Digital Sector, whereas SIC code 46.65 is not. The trend in GVA for SIC codes 46.51 and 46.65 are estimated based on the trend for SIC code 46.

From 2019 to 2020, an annual comparison that would feature an impact from the COVID-19 pandemic, GVA of SIC code 46 featured a decrease of 0.7%. SIC code 46.51 and SIC code 46.65 may have experienced different impacts of the COVID-19 pandemic. This is because people spending more time at home may have increased consumer purchase of computer equipment and software and therefore increased GVA for SIC code 46.51 (‘Wholesale of computers, computer peripheral equipment and software’). Whereas more people working from home may have decreased GVA for SIC code 46.65 (‘Wholesale of office furniture’).  

There is no simple way of measuring the accuracy of the monthly GVA estimates (the extent to which the estimate measures the underlying “true” value of monthly GVA in DSIT sectors). One possible check can be to aggregate monthly GVA estimates over each year and compare this with the annual published GVA figure. The difference between our latest “Summed Monthly” GVA estimate for 2022 and the annual GVA figure for 2022 in the latest DCMS Economic Estimates: Gross Value Added annual release is 0.4% for the Digital Sector and 0.0% to 1.3% across digital subsectors. Users should note that published estimates are provisional and subject to planned revisions, and these numbers may fluctuate as a result.

Estimates in this release are based to 2019, the last year ONS national accounts were balanced. The base year refers to the year against which other years’ economic data are compared to measure real growth, removing the effects of inflation or deflation. The accuracy of monthly GVA figures is likely to be lower for 2020 and 2021, due to changes in the economy during COVID-19 leading to greater divergence of class GVA within the division.

4.2 Accuracy of data sources

4.2.1 Monthly indexes

The monthly GVA, starting from January 2019, has been estimated using 2019 data as a baseline. Monthly GVA for subsequent years will be more uncertain.

Index figures for the most recent months are provisional and subject to revision.

ONS publishes revision triangles for the Index of Services, Index of Production and monthly GDP time series, which can be used to assess uncertainty. Using the first monthly indexes published, DCMS estimated that monthly GVA estimates for the Digital Sector in 2019 were approximately 1% to 4% higher than the “true” figure. This dropped to 1% to 3% above the “true” figure once the monthly indexes were next revised ^{[footnote 3]}.

The published monthly indexes should be viewed as a proxy for GVA. Whilst GVA is defined as “total outputs minus total inputs”, in practice, more information is available on outputs than on inputs so changes in output are frequently used as an approximate indicator of changes in GVA.

Monthly time series are volatile and should be interpreted alongside quarterly or yearly estimates to give a better indication of actual growth trends.

Further information on quality and methodology is available for the Index of Services, Index of Production, and Monthly Gross Domestic Product time series. Users should note these documents were last updated in January 2017 and so some information is out of date, such as the reference year used (currently 2019).

5. Quality assurance processes

5.1 Quality assurance processes at ONS

Information on the quality assurance processes for the current price GVA tables and the Annual Business Survey are available in the technical report published alongside the latest annual Economic Estimates: Digital Sector GVA release (published by DCMS).

Information on the quality assurance processes for the Index of Services can be found in the Index of Services QMI. Information on the quality assurance processes for the Index of Production can be found in the Index of Production QMI.

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
• the proportion of the Digital Sector accounted for by each subsector are comparable to previous publications

6. 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 DSIT constructing sector classifications from Standard Industrial Classification (SIC) codes. However, DSIT accepts that there are limitations with this approach and alternative definitions can be useful where a policy-relevant grouping of businesses crosses existing Standard Industrial Classification (SIC) codes. DSIT is aware of other estimates of DSIT sectors. These estimates use various methods and data sources, and can be useful for serving several purposes, e.g. monitoring progress under specific policy themes such as community health or the environment, or measuring activities subsumed across a range of SICs.

In future releases we intend to develop a record of different sources of analysis measuring the economic contribution of different DSIT policy areas from our arm’s-length bodies and other external organisations. We encourage statistics producers for DSIT policy areas who produce alternative data sources to contact the economic estimates team at [email protected]. 

7. Further information

For enquiries on this release, please email [email protected].

For general queries relating to official statistics, please contact: [email protected]

The Economic Estimates: Digital Sector Monthly GVA release is an Official Statistics publication and has been produced to the standards set out in the Code of Practice for Statistics. For more information, see https://code.statisticsauthority.gov.uk/.

1. Seasonal adjustment removes regular variation from a time series. Regular variation includes effects caused by differing month lengths, different activity near particular events such as shopping activity before Christmas and regular holidays such as the May bank holiday. ↩

2. Sampling error is the error caused by observing a sample (as in a survey) instead of the whole population (as in a census). While each sample is designed to produce the “best” estimate of the true population value, a number of equal-sized samples covering the population would generally produce varying population estimates. This means we cannot say an estimate of, for example, 20% is very accurate for the whole population. Our best estimates, from the survey sample, suggest that the figure is 20%, but due to the degree of error, the true population figure could be between two values, perhaps 18% and 22%. This is not an issue with the quality of the data or analysis; rather it is an inherent principle when using survey data to inform estimates. ↩

3. These figures were calculated using 2016 as the reference year instead of 2019, as the first monthly estimates published for 2019 were based on using 2016 as the reference year. ↩