Official Statistics

Digital Sector Economic Estimates: Productivity – technical and quality assurance report

Published 28 March 2024

This document covers the following topics:

1. an overview of the content covered in the statistical release ‘Digital sector Economic Estimates: productivity 2022 (provisional)’ 
2. an overview the digital sector, how it is defined, and limitations of this definition
3. the methodology underlying the statistical release, including data sources 
4. the processes used to check that the estimates have been produced correctly
5. further information, including contact details for DCMS statisticians.  

1. Overview of release

The statistics release ‘Economic Estimates: Productivity in the DCMS and digital sectors 2022 (provisional)’ reports two measures of labour productivity for the digital sector. Labour productivity is defined as output generated by each unit of labour input. These estimates use Gross Value Added (GVA) as the output, with number of hours worked and number of jobs as the inputs.

The estimates in the publication are designed to be consistent with national (UK) estimates, published by the Office for National Statistics (ONS), where possible.

1.1 Official statistics in development

These statistics are labelled as official statistics in development. Official statistics in development are official statistics that are undergoing development and will be tested with users, in line with the standards of trustworthiness, quality and value in the Code of Practice for Statistics. These productivity estimates are designed to complement our other economic estimates and to give a deeper understanding of the economic performance of the digital sector to the UK economy. They are being published as official statistics in development because:

• they include a new measure of productivity (output per hour) and updated methodology for output per job, the measure used for DCMS and Digital sector 2019 productivity estimates, previously published.
• the methodology is still in development
• we will be seeking user feedback on the usefulness of the statistics, the suitability of the methodology used and how clearly the statistics are communicated, including explanations about quality. 

Statistics for the digital sector are presented separately as responsibility for these policy areas now sits with the Department for Science, Innovation and Technology (DSIT). From April 2024, DSIT will be responsible for publishing and developing digital sector statistics, including any productivity estimates. 

Please contact DSIT with any feedback on these statistics. All feedback is welcome. In particular, please share your views on:

• the methodology and data sources used
• the presentation of these measures and explanations about the quality of the data
• suggestions for how these statistics could be further improved
• how you are using the estimates 

Please contact [email protected] by 24th May 2024 with any feedback.

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 digital and telecoms sectors, helping to understand how current and future policy interventions can be most effective.

2. Sector definitions

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

Although telecoms is considered as a sector in its own right, the telecoms sector is completely contained within the digital sector as defined by SIC codes.

In February 2023, Machinery of Government changes meant that responsibility for the digital and telecoms sectors moved from DCMS to the newly created Department for Science, Innovation and Technology. Although previously included in the DCMS sector estimates, estimates for the digital and telecoms sectors are now presented separately. Following this change, many of the industries included in the digital sector still form part of the DCMS industry definition, as they are included in the definition of the creative industries.

2.2 Details and limitations of sector definitions

There are substantial limitations to the underlying classifications. As the balance and make-up of the economy changes, the SIC, finalised in 2007, is less able to provide the detail for important elements of the UK economy related to the digital sector, and therefore best fit SIC codes have been used to produce these estimates.

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. Policy responsibility is for digital across the economy and therefore this is a significant weakness in the current approach.

3. Methodology

This chapter summarises the methodology used to produce productivity estimates.

3.1 Output per hour

This first section presents the methodology for estimates of output per hour. This is the preferred measure of productivity as it accounts for different working patterns.

3.1.1 Data sources

The following data sources were used in the production of output per hour for the digital sector:  

• Digital sector Economic Estimates: Gross Value Added 2022 (provisional) (published 15 February 2024)
• Annual Population Survey microdata (aggregated data published 18 April 2023)

3.1.2 Method (output per hour)

Digital sector provisional estimates of productivity in2022 are used as the output variable for this measure. Current price estimates are used to give a series of level estimates for each year in that year’s prices. 

The input for this calculation is the number of hours worked. This is estimated using data from the Annual Population Survey (APS). The survey records actual hours worked by respondents and self-reported SIC code for both first and second jobs. The data is weighted and aggregated to sector level to produce annual estimates of hours worked.

Output per hour is then calculated as GVA divided by hours worked for each sector and subsector. 

3.2 Output per job

This second section presents the methodology for estimates of output per filled job for the digital sector. The estimated number of jobs used to calculate this productivity measure differs from the digital sector Economic Estimates employment series. Our employment estimates are based on a household survey and use self-reported industry classification, which may be less accurate. Productivity jobs are based on data from various surveys, providing better coverage of those in e.g. short term work or the armed forces, and better industry assignment for employees.

3.2.1 Data Sources

The following data sources were used in the production of output for job for DCMS sectors:  

• Digital sector Economic Estimates: Gross Value Added 2022 (provisional) (published 15 February 2024)
• Annual Population Survey microdata (aggregated data published 18 April 2023)
• Productivity jobs (published 24 October 2023)

3.2.2 Method (output per job)

Digital sector provisional estimates of GVA in2022 are used as the output variable for this measure. Current price estimates are used to give a series of level estimates for each year in that year’s prices. 

The input for this calculation is the number of jobs. This is estimated using industry estimates from the productivity jobs series. The industry level data is published at 2-digit SIC code level, so data from the Annual Population Survey (APS) is used to calculate ratios of jobs at 4-digit SIC level to jobs at 2-digit SIC level, including first and second jobs and both employed and self-employed people. The data is weighted and the ratios are applied to the industry level productivity jobs to produce estimates at the 4-digit SIC level. These are aggregated to sector level.

Output per job is then calculated as GVA divided by the number of jobs for each sector. Estimates of output per job are only available at sector level while we continue to develop the methodology for this measure.

3.3 Summary of data sources

In summary, the data presented in this report on productivity

• are based on Official Statistics data sources
• are based on internationally-harmonised codes
• have been calculated to follow the ONS methodology as closely as possible
• are based on survey data and, as with all data from surveys, there will be an associated error margin surrounding these estimates

This means the estimates are:

• comparable at both a national and international level. 

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.  

3.4 Strengths and limitations

Strengths of these estimates

• These estimates have been calculated to follow the ONS methodology as closely as possible, to aid comparability to UK national estimates.
• The output measure used is the GVA published in the digital sector annual GVA publication, giving consistency across Digital Sector Economic Estimates.
• Annual Population Survey data allows us to estimate actual hours worked, rather than usual or contracted hours.

Limitations of these estimates

• The underlying data for these estimates includes the Annual Population Survey estimates of hours worked, and of proportions of jobs in each 4-digit SIC code. While this enables us to estimate actual hours worked and proportions of jobs at a 4-digit SIC level, responses are self-reported, and SIC codes may therefore be less accurate.

• The ONS productivity jobs series uses the Labour Force Survey, which has experienced falling response rates. ONS have therefore paused publication of industry level estimates of output per job. These will resume after they have analysed the impact of Labour Force Survey (LFS) reweighting. Since this publication uses the industry level estimates, the output per job measure is less robust and should be used with caution.
• Output per job estimates are based on ONS productivity jobs which differ from those used for DCMS employment estimates due to differences in the underlying data sources. However, the key messages about employment in DCMS sectors in 2022 are similar in each of these.
• These estimates will not align exactly with the latest equivalent estimates from ONS, largely due to the use of our own annual GVA estimates, based on data that has since been revised.

3.5 Changes in this release

In this release we have:

• updated the output per job methodology. We have published output per job once before, as earlier official statistics in development. These estimates should not be directly compared to the previously published ones, as the methodology has since changed and the data used to produce the older estimates has since been substantially revised. The methodology for these estimates has also been updated, as employment data is no longer available from the Annual Business Survey.
• Included a new measure of output per hour worked, which has the advantage of accounting for different working patterns.

4. Quality assurance processes

This chapter summarises the quality assurance processes applied during the production of the digital sector Economic Estimates: productivity 2022 statistics. This includes a detailed account of the quality assurance processes and the data checks carried out by our data providers (Office for National Statistics, ONS) as well as by DCMS.

4.1 Quality Assurance Processes at ONS

Quality assurance at ONS takes place at a number of stages. The various processes in place to ensure quality for the data sources used in the productivity publication are outlined below. It is worth noting that information presented here on the data sources are taken from various ONS technical reports and should be credited to colleagues at the ONS.

4.1.1 Workforce Jobs

For more information on quality assurance processes used during the production and analysis of Workforce Jobs (which is the primary data source that is adjusted to a reporting unit basis to make Productivity Jobs), see the Workforce Jobs QMI report.

Workforce jobs estimates are revised annually when employee jobs are benchmarked to estimates from Business Register Employment Survey (BRES).

4.1.2 Labour Force Survey (LFS) and Annual Population Survey (APS)

The Annual Population Survey (APS) is a continuous household survey, covering the UK. The topics covered include employment and unemployment, as well as housing, ethnicity, religion, health and education.

The purpose of the APS is to provide information on important social and socio-economic variables at local levels. The published statistics enable monitoring of estimates between censuses for a range of policy purposes and provide local area information for labour market estimates. The APS is not a stand-alone survey, but uses data combined from two waves of the main Labour Force Survey (LFS) with data collected on a local sample boost.

Labour Force Survey (LFS) estimates are subject to revisions generated by mid-year population estimates and every 10 years they are revised to census totals.

More details can be found in the ONS quality report.

4.1.3 Digital sector Economic Estimates GVA 2022

For details of the methodology, data sources and quality assurance processes in the production of DCMS sector GVA estimates, please refer to the technical document.

4.2 Quality Assurance Processes at DCMS

The majority of quality assurance of the data underpinning the Digital Sector Economic Estimates: Productivity release takes place at ONS, through the processes described above. However, further quality assurance checks are carried out within DCMS. Information about DCMS quality assurance of the digital sector GVA estimates is available in the corresponding technical report.

Production of the report is typically carried out by one member of staff, whilst quality assurance is completed by at least one other, to ensure an independent evaluation of the work.

4.2.1 Data requirements and data delivery

For the APS data, DCMS discussed our data requirements with ONS and these are formalised as a Data Access Agreement (DAA). The DAA covers which data are required, the purpose of the data, and the conditions under which ONS provide the data. Discussions of requirements and purpose with ONS improved the understanding of the data at DCMS, helping us to ensure we receive the correct data and use it appropriately. 

DCMS checks that the data delivered by ONS match what is listed in the Data Access Agreement (DAA). For this particular release we check that: 

• We have received all data at the 4 digit SIC code level, which is required for us to aggregate up to produce estimates for our sectors and sub-sectors. 
• Data at the 4 digit SIC code has not been rounded unexpectedly. This would cause rounding errors when aggregating up to produce estimates for our sectors and subsectors.

4.2.2 Data Analysis quality assurance checks

At the analysis stage, data are aggregated to produce information about DCMS sectors and sub-sectors. The productivity statistics lead checks whether:

• there is any missing data
• the correct SIC codes have been aggregated together to form digital sector and sub-sector estimates

A statistics colleague not involved in the coding and analysis checks whether:

• any new code or changes to code used in the calculations (in this case using the statistical software r) makes sense and produces the expected results
• the correct input data is used (matching published data, incorporating any revisions, latest available data)
• further calculations and analysis are correct.

4.2.3 Publication quality assurance checks 

Finalised figures are disseminated within Excel tables and a written report published on GOV.UK. These are produced by the GVA statistics lead. Before publishing, a quality assurer checks the data tables as well as the report to ensure minimal errors. This is checked against a QA log where comments can be fed back and actioned accordingly. The quality assurer also makes sure any statements made about the figures (e.g. regarding trends) are correct according to the analysis and checks for spelling or grammatical errors.

Proofreading and publication checks are done at the final stage, including: 

• checking the figures in the publication match the published tables
• checking the footnote numbering is correct
• making sure hyperlinks work
• checking chart/table numbers are in the correct order
• ensuring the publication is signed off by DCMS Head of Profession for Statistics 
• contacting press office to ensure they are aware of the release date
• checking the published GOV.UK page again after publishing

4.2.4 Post publication

Once the publication is released, DCMS reviews the processes and procedures followed via a wash up meeting. This occurs usually a week after the publication release date and discusses:

• What went well and what issues were encountered
• What improvements can be made for next time
• Engaging with users of the publication to get feedback

5. 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, and 
• ideally applicable to specific policy interventions. 

These are the main reasons for DCMS constructing sector classifications from Standard Industrial Classification (SIC) codes. However, DCMS 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.

6. Further information

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. Alternatively, you can contact OSR by emailing [email protected] or via the OSR website.

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

For general enquiries contact: 

Department for Culture, Media and sport
100 Parliament Street London
SW1A 2BQ

Telephone: 020 7211 6000 

DCMS statisticians can be followed on X via @DCMSInsight.