Accredited official statistics

Economic Estimates: Employment and Earnings in the Digital Sector, January 2022 to December 2022 - Technical and quality assurance report

Updated 22 November 2024

1. Overview of release

The statistics release ‘Economic Estimates: Employment in DCMS sectors and Digital sector, January 2022 to December 2022.’ provides estimates of the number of filled jobs and median hourly earnings in the Digital sector for the 12-month period between January 2022 and December 2022.

These estimates are derived from a single data source (Annual Population Survey, or APS) and contain breakdowns including, but not limited to, employment type (i.e. employed or self-employed), region of work, nationality, sex and ethnicity.

Employment and earnings estimates based on APS data are an average over the 12-month period from January 2022 to December 2022. Users should be aware the APS is not the preferred source for earnings estimates at the aggregate level, with estimates based on the ASHE (Annual Survey for Hours and Earnings) providing a more robust measure. However, the APS does enable us to provide demographic breakdowns of earnings that the ASHE data does not. More detail on the APS is available in section 3.

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 (DSIT). Although previously included in the DCMS Sector employment estimates, estimates for the Digital and Telecoms sectors are now presented separately.

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

1.1 Code of Practice for Statistics

DCMS Sector Employment Estimates series is a National Statistic and has been produced to the standards set out in the Code of Practice for Statistics. In June 2019, a suite of DCMS Sector Economic Estimates, including employment estimates, were badged as National Statistics. This affirms that these statistics have met the requirements of the Code of Practice for Statistics. The earnings estimates are a newer series of Official Statistics that are produced to the standards of the Code of Practice but have not yet been badged.

This followed a report by the Office for Statistics Regulation in December 2018, which stated that the series could be designated as National Statistics subject to meeting certain requirements. Since the report, we have striven to improve our 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. We will continue to improve the series in the future, in line with the recommendations of the report. We encourage our users to engage with us so that we can improve our statistics and 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 DCMS/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 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. 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.

2.1 Details and limitations of sector definitions

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

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. The SIC codes used to produce these estimates are a ‘best fit’, subject to the limitations described in the following section.

Digital Sector

The definition of the Digital sector is based on the 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. Policy responsibility is for digital across the economy and therefore this is a significant weakness in the current approach.

Telecoms

The definition of the Telecoms Sector is consistent with the internationally agreed definition, SIC 61, Telecommunications. Please note that as well as appearing as a sector on its own, Telecoms is also entirely included within the Digital Sector as one of the sub-sectors.

3. Methodology

3.1 Data Sources

In this release, both employment and earnings statistics are calculated using the Office for National Statistics (ONS) Annual Population survey (APS).

Annual Population Survey

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

3.2 Method

The majority of the data processing is done by ONS, with DCMS receiving cleaned and weighted respondent level data. We then process the data to give estimates for employment and earnings.

Employment estimates

To produce our employment estimates we remove any respondents who are not in work from the dataset for analysis. We define ‘in work’ as those with a first job who are an employee or self-employed and those who have a second job who are employees, self-employed or have otherwise not stated.

As we estimate employment as the number of filled jobs, we restructure the data to be on a per job basis, rather than a respondent basis. We then select entries that are relevant for a particular measure (e.g. all entries with an SIC code of 26.11 for total employment in Manufacture of electronic components) and aggregate over the associated population weights to generate an estimate of the total filled jobs.

Earnings estimates

The ONS definition of earnings is the payment received by employees in return for employment. Most analyses of earnings consider only gross earnings, which is earnings before any deductions are made for taxes (including National Insurance contribution), pensions contributions, student loan deductions, and before payment of benefits. Further information is available from the ONS publication: A guide to sources of data on income and earnings.

The APS provides a self-reported value of gross hourly pay for a respondent’s main (first) job. To produce our earnings estimates we filter for all employees who have a main job, and select entries that are relevant for a particular measure (e.g. all entries with a Digital Sector SIC code). In contrast to the employment estimates, we then aggregate over the respective income weights before producing a median (middle, or 50th percentile) value for each grouping (e.g. median hourly pay in the Digital Sector).

Users should be aware, estimates based on the APS are not used as DCMS’s headline measure of earnings. DCMS also publishes estimates based on the Annual Survey for Hours and Earnings (ASHE) for the Digital Sector, which are more robust at the aggregated level. This is because estimates derived from the APS are based on self reported values and often have low sample sizes due to respondents not always being asked the earnings question. However, DCMS publishes experimental earnings estimates using the APS, in addition to those using ASHE, because it gives access to more detailed demographic information.

Disclosure control

As part of the production process we also apply disclosure control measures to prevent the identification of any respondents. We suppress values where the number of respondents for a cell is below a set threshold. Where appropriate, we also apply secondary suppression to prevent disclosure via differencing.

3.3 Changes in this release

Typically, Digital Sector estimates based on the APS include a variety of demographic breakdowns, including age, sex, region of work, nationality, ethnicity, disability, highest qualification and occupation grouping. However, for this release the standard APS employment and earnings data will not contain information on occupation grouping. This is because ONS have identified an issue with the way their underlying survey data has been assigned to the refreshed SOC2020 codes that were used to calculate these estimates. ONS have now resolved the issue and we will look to publish updated estimates in due course.

Estimates for the number of filled jobs by highest level of education for the period January 2022 to December 2022 are based on a new variable in the underlying Annual Population Survey (APS) dataset. Because of this we advise caution when making comparisons to equivalent estimates covering time periods prior to January 2022.

From January 2022 to March 2022 new qualifications have been added to the survey after a review identifying gaps in the Labour Force Survey (LFS) questionnaire at the ONS.

4. Quality assurance processes

This document 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 DCMS.

Quality assurance processes at ONS

Quality assurance at ONS takes place at a number of stages. The various stages and the processes in place to ensure quality for the data sources are outlined below. It is worth noting that information presented here on data sources are taken from the Annual Population Survey (QMI). This work should be credited to colleagues at the ONS.

ONS Annual Population Survey

The purpose of the APS is to provide information on important social and socio-economic variables at local levels. The APS is not a stand-alone survey, but uses data from the Labour Force Survey (LFS) and a local sample boost.

Sample design

The APS survey year is divided into quarters of 13 weeks. From January 2006, it has been conducted on the basis of calendar quarters: January to March (Quarter 1), April to June (Quarter 2), July to September (Quarter 3) and October to December (Quarter 4). The APS design is not stratified.

The APS data set is created by taking waves 1 and 5 from four successive quarters, with rolling-year data from the English, Welsh and Scottish Local Labour Force Survey, to obtain an annually representative sample of around 80,000 households. Over the period of the 4 quarters, waves 1 and 5 will never contain the same households to avoid the inclusion of responses from any household more than once in the dataset.

Sampling frame

The sampling frame for the survey in Great Britain is the Royal Mail Postcode Address File (PAF) and the National Health Service (NHS) communal accommodation list. Due to the very low population density in the far north of Scotland (north of the Caledonian Canal), telephone directories are used as sampling frames. A systematic sample is drawn each quarter from these three sampling bases, and as the PAF is broken down geographically, the systematic sampling ensures that the sample is representative at regional level. In Northern Ireland, the Rating and Valuation Lists (which serve for the administration of land taxes) are used.

Data collection

Interviews in all waves are carried out either on a face-to-face basis with the help of laptops, known as Computer Assisted Personal Interviews (CAPI) or on the telephone, known as Computer Assisted Telephone Interviews (CATI). Information is collected using a software package called Blaise.

Validation and quality assurance

• Accuracy is the degree of closeness between an estimate and the true value. As both surveys are sample surveys, they provide estimates of population characteristics rather than exact measures. At ONS, confidence intervals are used to present the sampling variability of the survey. For example, with a 95% confidence interval, it is expected that in 95% of survey samples, the resulting confidence interval will contain the true value that would be obtained by surveying the whole population.
• Comparability is the degree to which data can be compared over time and domain, coherence is the degree to which data are derived from different sources or methods but refer to the same topic and are similar. Some sources provide data that overlap with APS/LFS data on employment, unemployment and earnings. More information on these sources are available in the Annual population survey (APS) QMI.
• Statistical disclosure control methodology is also applied to the datasets before release. This ensures that information attributable to an individual is not disclosed.
• On each quarterly LFS dataset, the variable frequencies are compared with the previous period. This identifies any significant discontinuities at an early stage. All discontinuities judged significant are investigated to determine the reason for the discontinuity. Is it the product of questionnaire revision or processing error, derived variable revision or error or real-world change? This process also ensures that the metadata associated with each variable is correct.
• Specific main derived variables are checked in detail by extracting the underlying variables and recalculating in another application, then comparing the results with the values in the dataset. This ensures that the program used to calculate the derived variables is working correctly.

Quality assurance processes at DCMS

The majority of quality assurance of the data underpinning this DCMS Sectors Economic Estimates release takes place at ONS, through the processes described above. However, further quality assurance checks are carried out within DCMS at various stages.

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.

Data requirements

For APS data, DCMS discusses its 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 improve the understanding of the data at DCMS, helping us to ensure we receive the correct data and use it appropriately.

Production and data analysis

At the production stage, data are aggregated up to produce information about the Digital Sector and sub-sectors before inputting the data into the formal data tables ready for analysis. Disclosure control is also applied as part of this process.

The statistical lead ensures a number of quality assurance checks are undertaken during this process. Where relevant these checks typically include:

• whether disaggregations sum to the overall total. E.g:
  • Do sub-sectors within the Digital Sector sum to the Digital Sector total?
  • Do the individual regional breakdowns sum up to the total for that sector?
• “Sense checks” of the data. E.g.:
  • Are the estimates similar from one year to the next? How do the figures compare with ONS published totals?
  • Looking at any large differences between the data and possible causes to these.
• Checking that the correct SIC codes have been aggregated together to form Digital Sector (and sub-sector) estimates. Are all SIC codes we require included? Are there any non-Digital SIC codes that have been included by accident?
• Checking it is not possible to derive disclosive data from the figures that will be published.
• Making sure the correct data has been pasted to the final tables for publication, are accessible, formatted correctly, and have appropriate documentation.

Having checked the quality of the data, analysis is then conducted to outline the key trends and patterns. This is then checked to ensure all statements, figures and charts are correct.

Dissemination

Finalised figures are published as OpenDocument spreadsheets on GOV.UK, with summary text on the webpage. These are produced by the workforce statistics lead who, beforehand, checks with the ONS on details of how to interpret the statistics. Before publishing, a quality assurer checks the figures match between the tables and the GOV.UK page summary. The quality assurer also makes sure any statements made about the figures (e.g. regarding trends) are correct according to the analysis and checks spelling or grammar errors.

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 constructing sector classifications from Standard Industrial Classification (SIC) codes. However, we accept 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.

We are aware of other estimates relevant to the Digital sector. 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 SIC’s.

The ONS use the quarterly Labour Force Survey for their 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 the ONS published figures will differ slightly from ours.

For employment statistics more broadly, the main alternative is the Business Register and Employment Survey (BRES). This has the advantage of asking businesses directly about their employees and hence is likely to capture the sector of employees more accurately than a household survey. However, it does not contain the range of demographic breakdowns that the APS does, which enables us to build a fuller picture of employment in our sectors, using a still-robust data source, and does not include the self-employed.

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 evidence@dcms.gov.uk.

6. Further information

For enquiries on this release, please email evidence@dcms.gov.uk.

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 Twitter via @DCMSInsight.

This release is a National Statistics publication and has been produced to the standards set out in the Code of Practice for Statistics.