Official Statistics

Technical report: DCMS National Economic Estimates: 2011 to 2020

Updated 22 November 2024

© Crown copyright 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: psi@nationalarchives.gov.uk.

Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.

This publication is available at https://www.gov.uk/government/statistics/dcms-sector-national-economic-estimates-2011-to-2020/technical-report-dcms-national-economic-estimates-2011-to-2020

1. 1. Overview of release

The “DCMS Sector National Economic Estimates: 2011 to 2020” publication reports measures of Gross Value Added (GVA), productivity, employment and earnings for the DCMS sectors. The estimates in the publication are consistent with national (UK) estimates, published by the Office for National Statistics (ONS).

1.1 1.1 Code of Practice for Statistics

In June 2019, the DCMS Sector Economic Estimates: Employment, business demographics, and annual GVA were badged as National Statistics. This affirms that the statistics have met the requirements of the Code of Practice for Statistics. The estimates of earnings, productivity and monthly GVA are experimental official statistics, as they are newly developed and undergoing evaluation. They are published in order to involve users and stakeholders in the assessment of their suitability and quality at an early stage.

1.2 1.2 Users

The users of these statistics fall into five broad categories:

  • Ministers and other political figures
  • Policy and other professionals in DCMS 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 DCMS sectors, helping to understand how current and future policy interventions can be most effective.

2. 2. Sector definitions

2.1 2.1 Overview of DCMS Sectors

Main sector definitions

The Economic Estimates measure the economic contribution to the UK of each sector for which DCMS has responsibility:

  • Civil Society
  • Creative Industries
  • Cultural Sector
  • Digital Sector
  • Gambling
  • Sport
  • Telecoms
  • Tourism

The majority of DCMS sector definitions are implemented using the Standard Industrial Classification 2007 (SIC) codes. The SIC is a means of classifying businesses according to their main economic activity. This means nationally consistent sources of data can be used and enables international comparisons.

The 4-digit SICs that make up each DCMS sector and sub-sector are shown in the tables published alongside this report. For example, in the Creative Industries sector, the ‘Advertising and Marketing’ subsector is made up of these 4-digit industry codes:

7021 Public relations and communication activities
7311 Advertising agencies
7312 Media representation

The development of individual sector definitions as new sectors have fallen within the department’s remit, and the interdisciplinary nature of DCMS policy areas, has resulted in overlap between DCMS sectors. For example, the Cultural Sector is defined using SIC codes that are nearly all within the Creative Industries, whilst the Telecoms Sector is completely within the Digital Sector.

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 DCMS Sectors, and therefore best fit SIC codes have been used to produce these estimates.

For more detail on the definition of each individual sector, and the limitations in each case, please see the respective sections below.

Figure 1: The overlap between DCMS Sectors in terms of SIC codes. Users should note that this does not give an indication of the magnitude of the value of overlap.

Civil Society

Civil Society is unlike traditional industries as it is defined by the type of organisation or employment rather than the sector of activity. For example, a healthcare charity would be classified in the healthcare sector. Where possible, data are provided from official sources, however these are unlikely to capture the full scope of the sector, some of which lies outside standard national accounting frameworks. Estimates of civil society given here are therefore likely to underestimate the sector.

Note that volunteering, a key element of Civil Society, has not been included in the figures due to being part of the informal economy. The ONS Household Satellite Account, estimated that volunteering contributed £24bn to the UK economy in 2016 (this includes only formal volunteering activities). The 2018 estimate for volunteering has been delayed due to COVID-19.

For GVA in current prices, the published Civil Society figure covers non-market charities in the NPISH (non-profit institutions serving households) sector. It does not include market provider charities who have passed the market test and therefore sit in the corporate sector (these data are not currently measured by ONS on a National Accounts basis), mutuals, social enterprises or community interest companies. An estimate for chained volume measures has not been included in this release due to lack of underpinning data for the DCMS sectors as a whole in 2020, but may be developed in the future if there is sufficient user demand.

Estimates of employment in Civil Society are defined as those who work in a “charity, voluntary organisation or trust”. This classification is only available for first, or main, jobs, in contrast to employment estimates for other sectors that include second jobs. This also applies to earnings estimates derived from the same source (the Annual Population Survey, APS).

For earnings estimates derived from Annual Survey of Hours and Earnings (ASHE), jobs are considered to be part of the civil society sector if the organisation they work for is classed as not-for-profit in the ASHE dataset.

Creative Industries

The Creative Industries consists of all industries that match the definition in the government’s 2001 Creative Industries Mapping Document. Specifically, “those industries which have their origin in individual creativity, skill and talent and which have a potential for wealth and job creation through the generation and exploitation of intellectual property”. A more detailed definition is available in the methodology document.

According to this definition, the following sub sectors are included: ‘Crafts’, ‘Film, TV, video and photography’, ‘IT, software and computer services’, ‘publishing’, ‘Museums, galleries and libraries and Music’, ‘performing and visual arts’.

This includes some overlap with different sectors, for example, Crafts is also included in the cultural sector. The definition used for the Creative Industries in this release does not allow consideration of the value added of “creative” to the wider economy, such as Creative Occupations outside the Creative Industries. DCMS policy responsibility is for creative industries across the economy and therefore this is a significant weakness in the current approach.

Cultural Sector

DCMS defines the Cultural Sector as those industries with a cultural object at the centre of the industry. These are partitioned into the following sub-sectors: ‘Arts’, ‘Film, TV and Music’, ‘Radio’, ‘Photography, Crafts, Museums and Galleries’, ‘Library and archives’, ‘Cultural education’, ‘Operation of historical sites and similar visitor attractions’.

This definition overlaps with those for different sectors, for example, Crafts is also included in the Creative Industries.

There are significant limitations to the DCMS measurement of the cultural sector arising from the lack of detailed disaggregation possible using the standard industrial classifications. There are many cases where culture forms a small part of an industry classification and therefore cannot be separately identified and assigned as culture using standard data sources. DCMS consulted on the definition of Culture and published a response in April 2017.

It is recognised that, due to the limitations associated with SIC codes, the SIC code used in past publications as a proxy for the Heritage sector (91.03 - Operation of historical sites and building and similar visitor attractions) is likely to be an underestimate of this sector’s value. We have changed the name of the Heritage sector to ‘Operation for historical sites and similar visitor attractions’ to reflect this.

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 media sector”. An outline of the SIC (Standard Industrial classification) codes included 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).

According to this definition, the following sub sectors are included: ‘Manufacturing of electronics and computers’, ‘Wholesale of computers and electronics’, ‘Publishing (excluding translation and interpretation activities)’, ‘Software publishing’, ‘Film, TV, video, radio and music’, ‘Telecommunications’, ‘Computer programming, consultancy and related activities’, ‘Information service activities’ and ‘Repair of computers and communication equipment’.

This includes some overlap with different sectors, for example, the ‘Film, TV, video, radio and music’ industries are also included in the Creative Industries.

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

Gambling

The definition of gambling used in DCMS Sectors Economic Estimates is consistent with the internationally agreed definition, SIC 92 (‘Gambling and betting activities’).

Sport

For the purposes of these publications the EU agreed core/statistical Vilnius definition of sport has been used, this incorporates only those 4-digit SIC codes which are predominately sport (see tables published alongside this technical report).

Telecoms

The definition of telecoms used in the DCMS Sectors Economic Estimates 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.

Tourism

Tourism is defined by the characteristics of the consumer in terms of whether they are a tourist or resident. It differs from “traditional” industries, such as gambling or telecoms, which are defined by the goods and services produced themselves, meaning that a different approach to defining the industry must be used.

The UK estimates are based on the methodology and definition set out in the UN International Recommendations for Tourism Statistics 2008 (IRTS 2008), using a satellite account approach, where the element of each industry that is directly supported by tourism is identified by calculating the proportion of consumers that are tourists for each industry.

3. 3. Methodology

3.1 Employment estimates methodology

Employment is estimated as the number of filled jobs, based on two datasets produced by the ONS; the Annual Population Survey (APS), and the Labour Force Survey (LFS).

Data sources

Details of both surveys are available on the ONS website. In brief; the APS dataset consists of four combined waves of the LFS, as well as an additional boost sample. Our main employment estimates are calculated using APS data as the larger sample size means it can be used to generate estimates at a greater level of industry detail. However, as the APS combines multiple waves of the LFS it does not include data from non-core questionnaire modules. For topics that are covered by the rotating modules, such as socio-economic background, we therefore use the relevant wave of the LFS.

Method

The ONS assign weights to respondent microdata to ensure the aggregated results are representative for the general population. These are summed by SIC code to generate employment estimates for each DCMS sector. For Tourism, ratios taken from the Tourism Satellite Account are used to apportion the number of jobs in each sector that are directly attributable to Tourism.

Employment estimates cover all first and second occupations, with the exception of civil society, as information on second jobs is not available for this sector.

In order to count the number of filled jobs, the data are restricted to those who are employees or self-employed in both first (main) jobs and second jobs.

With the exception of Tourism and Civil Society, each sector is made up of full 4-digit SICs. To calculate the overall DCMS total, we aggregate the values for all of the unique SIC codes from the sectors, without duplication, to avoid double counting.

In accordance with APS guidance, figures are suppressed when the number of respondents is below a certain threshold in order to prevent any disclosure of personal data in the statistics. In some instances, secondary and tertiary suppression has been applied where suppressed data could be inferred from other figures.

Limitations

As with all data from surveys, there will be an associated error margin surrounding these estimates[footnote 1]. 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 DCMS sectors.

The Annual Population Survey (APS) is considered to be the best source of information for headline estimates of UK jobs, including employed and self-employed jobs, although the industry breakdowns are based on self-reporting of individuals which can be inconsistent with how businesses are allocated in National Accounts. However, as sources based on business survey data (e.g. the Business Register and Employment Survey, Annual Survey of Hours and Earnings do not include the demographic information that is available in the APS, the APS has been used in this analysis for its demographic information and information on occupations.

This approach is not consistent with national estimates by industry, but is comparable with national totals for all industries, and demographic information at a national level.

3.2 Earnings estimates methodology

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.

Data sources

The Annual Survey of Hours and Earnings (ASHE) is a sample taken from the PAYE system and provides the most reliable data on earnings for UK employees, however it has limited demographic information.

The APS provides self-reported earnings data and estimates are therefore less robust than those from the ASHE. The APS includes more demographic information and can be used to estimate earnings for different demographic groups.

Method

The data tables derived from the ASHE use three different measures of earnings. The filters used are consistent with ONS analysis:

  • Weekly pay is used for most of the analyses in the report. The weekly filter is employees on adult rates whose earnings for the pay period were not affected by absence. Additionally, employees who do not have a valid work region and who are less than 16 years old are filtered out because the age and region variables are required for weighting.
  • Hourly pay excluding overtime is used to calculate the gender pay gap, and uses the same filters as weekly pay.
  • Annual gross pay is provided as data tables. The annual filter is employees on adult rates who have been in the same job for more than one year. Additionally, employees who do not have a valid work region and who are less than 16 years old are filtered out. Employees with missing or zero annual gross salaries are also filtered out.

The estimates derived from the APS dataset use hourly gross pay. This is only available for first (main) jobs.

The headline statistics for both ASHE and APS are based on the median rather than the mean. The median is the value below which 50% of employees fall. It is ONS’s preferred measure of average earnings as it is less affected by a relatively small number of very high earners and the skewed distribution of earnings, so provides a better indication of typical pay than the mean.

Limitations

The ASHE data used for this analysis are robust and have a number of strengths:

  • Size and coverage - the ASHE dataset contains information on approximately 180,000 jobs in all industries, occupations and regions, making it the most comprehensive source of earnings information in the UK and enabling a vast range of analyses.
  • Quality - alternative sources of earnings information such as the APS rely on self-report or proxy data, which are known to be less reliable than information from employers’ administrative systems.
  • Uniqueness - for many uses, ASHE is the main data source and for some uses it is the only data source.

but there are some limitations of which users should be aware:

  • Due to data collection difficulties during the 2020 COVID-19 pandemic, the sample achieved in the 2020 ASHE was about 25% smaller than usual, at 136,000 jobs.
  • Analyses presented here have been calculated on a consistent basis in DCMS. Due to minimal differences in the methodology and analysis used to calculate the median, results in this report may not match the ONS published results, in particular when looking at further breakdowns to some data e.g. by region or age. These differences are small but should be treated with caution.
  • Lack of personal demographic information - characteristics such as ethnicity, religion, education, disability and pregnancy are not recorded in the ASHE dataset.
  • The quality of estimates at low levels of disaggregation can be poor.
  • The dataset does not cover those who are self-employed.

Definitions of DCMS sectors have their own limitations that are explained more fully in the ‘DCMS sector definitions’ section of this report.

A fuller description of the strengths and limitations of the Annual Survey of Hours and Earnings (ASHE) can be found in the Quality and Methodology Information report and the Guide to sources of data of earnings and income.

Data on earnings and SIC codes from the APS are self-reported, and therefore less reliable, but the dataset enables estimation of socio-demographic breakdowns that are not available from the ASHE. A detailed description of the APS and its limitations is available on the ONS website.

3.3 GVA estimates methodology

This section summarises the methodology used to produce monthly GVA estimates and the annual GVA estimate derived from the monthly series. These estimates of GVA are not as robust as our regular annual estimates, but allow us to produce more timely estimates based on other sources of data. The methodology for our annual estimates of GVA is available as a separate methodology note.

GVA measures the additional value added to the economy by each individual producer, industry or sector in the UK. It can be defined as the total output for a particular area, less any inputs. The monthly GVA is expressed as chained volume measures (‘real terms GVA’), where the effect of inflation is removed.

Data sources

The following sources are used as input to the monthly GVA calculation:

Method

The general principle is to first extract approximate GVA (aGVA) for the latest year that we have annual data for in the ABS, then extrapolate that GVA to recent months by using the monthly service and production index numbers published by the ONS.

Index numbers are a useful way to express economic data time series. They express a price/quantity compared with a reference value (e.g. the GVA in 2019). The reference value always has an index number of 100. In subsequent years, percentage increases push the index number above 100, and percentage decreases push the figure below 100 (e.g. an index number of 102 means a 2% rise from the base year, i.e. a 2% rise in GVA since 2018).

We take the annual GVA for the base year, and we know what the percentage increase/decrease is for each month in the Index of Services/Production. Therefore, we can estimate the actual figures for GVA for each month of interest.

For all DCMS sectors excluding Tourism (and Civil Society):

  • We calculate GVA for the reference year, by using aGVA from the ABS to apportion GVA in the National Accounts to a more granular industry level (e.g. SIC 32.12)
  • Extrapolate from the base year value using the Index of Services and Index of Production, which are at 2 digit SIC level. We assume that the trend at the 3 or 4 digit SIC level will be the same as at the 2 digit SIC level
  • Aggregate the GVA for each SIC into DCMS sectors (or clusters)

The experimental estimates of annual GVA are obtained by summing monthly GVA estimates over the year.

Limitations

These timely estimates should only be used to illustrate general trends, rather than be taken as definitive figures. These figures will not be as accurate as our annual National Statistics release of gross value added for DCMS sectors.

You can use the monthly estimates, and the derived annual totals, to:

  • Look at relative indicative changes in GVA over time for DCMS sectors and subsectors

You should not use the monthly estimates, or the derived annual totals, to:

  • Quantify GVA for a specific month or year
  • Measure absolute change in GVA over time
  • Determine findings for DCMS sectors that are defined using more detailed industrial classes (due to the data sources only being available at broader industry levels)

Civil Society is not currently included in the monthly GVA estimates as it is not defined in the same way as other sectors, using industrial codes.

Limitations in the data sources can be found in individual quality reports for the ABS, the IoP and the IoS on the ONS website.

Testing of the annual estimate produced from the monthly estimates has shown that the figure is usually within 4 percent of the regular annual GVA.

3.4 Productivity estimates methodology

This chapter summarises the methodology used to produce labour productivity estimates as GVA per filled job. Labour Productivity for DCMS Sectors (excluding Tourism and Civil Society). Labour Productivity is defined as output per unit of labour input and effectively shows changes in output over time for the same amount of labour input.

GVA / Employment = Labour Productivity

This release reports Labour productivity expressed in GVA per Job. This was calculated by output (Gross Value Added) divided by the number of productivity jobs. The number of jobs used for the calculation of productivity is different to our employment estimates. Our employment estimates are based on a household survey and use self-reported industry classification. 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.

Data Sources

The following data sources were used in the production of GVA per Job for DCMS sectors (excluding Tourism and Civil Society):

Method

The basis for the estimates of input for the productivity calculation are the Productivity Jobs estimates produced annually by the ONS. These are based on data from the ONS’s Short Term Employment Survey (with the Business Register Employment Survey used to benchmark annually), the Labour Force Survey, and administrative data from the Ministry of Defence, to give a more accurate estimate of employment than would be possible from a household survey alone. These data are formatted to be consistent with the industrial classifications applied to economic output (GVA) in the National Accounts.

Productivity Jobs reports Employment at the division level (2 digit SIC codes), but DCMS sectors are defined at industry class level (4 digit SIC codes); the jobs were assigned to each 4 digit industry class based on the number of employees (Annual Business Survey, ABS) and the number of self-employed (Annual Population Survey, APS), by:

  • extracting the number of filled employee jobs from the ABS at industry level (e.g. SIC 32.12)
  • calculating the number of filled employee jobs from the ABS at division level (e.g. SIC 32, by aggregating industries in the division)
  • calculating the number of self-employed filled jobs from the APS as industry level (e.g. SIC 32.12)
  • calculating the number of self-employed filled jobs from the APS at division level (e.g. SIC 32, by aggregating industries in the division)
  • Summing together employed and self-employed filled jobs at the industry class level
  • Summing together employed and self-employed filled jobs at the division level
  • calculating the proportion of the division filled jobs accounted for by each industry class (e.g. Employment for SIC 32.12 as a proportion of SIC 32)
  • applying the proportion for each industry class to the division Productivity Jobs, to get an estimate of productivity jobs for each industry class that is consistent with national (UK) estimates

This method, consistent with national estimates using both the ABS and APS, is preferable to using only the ABS or the APS. There are differences between the two methods of measurements of employment in terms of coverage. For example, the method using the ABS and APS only covers only the UK Non-Financial Business Economy, a subset of the whole economy that excludes large parts of agriculture, all of public administration and defence, publicly provided health care and education, and the financial sector.

The industry class level Productivity Jobs estimates are summed to the sub-sector and sector level. These summed values are then used to divide GVA estimates taken from DCMS Economic Estimates for each DCMS sector and sub-sector (excluding Tourism and Civil Society) to generate estimates of productivity, given as output per filled job.

Limitations

Limitations affecting the Annual Population Survey are as outlined in the section on employment estimates.

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 DCMS sectors. Further information on the quality of the ABS data is published by the Office for National Statistics.

There have also been two survey design changes in recent years (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 Supply and Use Tables, these changes should have a minimal impact on the estimates of DCMS sector GVA.

Exclusion of Tourism and Civil Society

As these estimates are experimental we have focussed on the more methodologically straightforward sectors. Tourism may be considered for future releases.

Civil Society has not been included in this release as the input and output data are not consistent. Civil Society GVA is calculated using SIC codes in the Non-Profit Institutions Serving Households (NPISH) data, whereas Civil Society filled job estimates are based on whether the employing organisation is a charity, voluntary organisation or trust, and not on a SIC code basis.

3.5 Summary of data sources

In summary, the data presented in this report:

  • are based on official statistics data sources
  • are based on internationally-harmonised codes
  • are based on survey data (Annual Business Survey, Annual Population Survey, and Productivity Jobs) 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.
  • comparable 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. 4. Quality assurance processes

This chapter summarises the quality assurance processes applied during the production of the DCMS Sector National Economic Estimates: 2011 to 2020 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 validation and accuracy of the source data, as well as the various processes in place to ensure quality for the data sources used in this publication are outlined in the relevant links below. Information presented here on the data sources are taken from various ONS technical reports and should be credited to colleagues at the ONS.

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).

Annual Business Survey (ABS)

For more information on quality assurance processes used during the production and analysis of ABS, see Annual Business Survey technical report: August 2018.

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.

Annual Survey of Hours and Earnings (ASHE)

The ASHE is the preferred source of earnings data for the UK. Details of quality assurance for the ASHE can be found in the quality report.

The following data sources have also been used and information on ONS quality assurance can be found at the links.

,###Quality Assurance Processes at DCMS

The majority of quality assurance of the data underpinning the DCMS Sector National Economic Estimates: 2011 to 2020 release takes place at ONS, through the processes described above. However, further quality assurance checks are carried out within DCMS.

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 and data delivery

Where possible, published data is used. For survey data, where case-level data is required, we receive microdata from the ONS:

LFS, APS, ASHE

For these datasets produced by ONS, DCMS discussed our data requirements with ONS and these are formalised as Data Access Agreements (DAAs). The DAA in each case 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 have 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

Data Analysis quality assurance checks

At the analysis stage, data are aggregated to produce information about DCMS sectors and sub-sectors. The release will be checked as to whether:

  • values are consistent with national estimates
  • there is any missing data
  • the correct SIC codes have been aggregated together to form DCMS sector and sub-sector estimates comparisons of data across time periods and sectors make sense, and any unusual figures are fully understood

Publication quality assurance checks

Finalised figures are disseminated as ODS tables and a written report (which includes written text, graphs, tables and infographics) published on GOV.UK. 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 and DCMS Chief Economist
  • checking the published GOV.UK page again after publishing

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
  • what feedback have we received from engaging with users

5. 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
  • 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. DCMS is aware of other estimates of DCMS 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.

Table 1 shows different sources of analysis measuring the economic contribution of different DCMS policy areas from our arm’s-length bodies. It is recognised that there will be many other sources of evidence from industry bodies, for example, which have not been included in this table. This will be developed over time to capture a wider spectrum of stakeholder’s releases. We encourage statistics producers within DCMS sectors who are not represented in the table to contact the economic estimates team at evidence@dcms.gov.uk.

Table 1. Alternative data sources measuring economic contribution of DCMS sectors

Sector Sub-sector Organisation Summary of use
Civil Society Civil Society ONS ONS publishes a household satellite account which includes an estimate for volunteering for 2015 and 2016. This is based on the DCMS Community Life Survey and multiplying participation by the median earnings. However these figures should not be included in the GVA figure for the economy due to volunteering being part of the informal economy, and therefore not captured in the ONS’s methodology for calculating GVA. The latest year for which data is available is currently 2016.
Creative Industries and Cultural Sector Arts Arts Council England (ACE) ACE provides a value of GVA and employment accountable by the Arts and Culture industry. They use similar SIC codes to DCMS’ Economic Estimates, but rather than using the supply and use tables and then the Annual Business Survey to inform the proportions to use, ACE use only the Annual Business Survey and therefore an approximate measure of GVA.
Creative Industries and Cultural Sector Film, TV, video, radio and photography; IT, software and computer services British Film Institute (BFI) BFI provides a value of GVA and FTE employment accountable by the Screen sector. The analysis uses a bespoke economic impact model developed for this study, reflecting current best practice in economic impact modelling, aligning the study with current government evaluation methodology (HM Treasury Green Book 2018).
Creative Industries and Cultural Sector Museums, Galleries and Libraries; Museums and Galleries Arts Council England (ACE) ACE commissioned a report on the economic impact of museums in England in 2013. This methodology is very different to that of the DCMS Sector Economic Estimates, in particular the definition of museums was much wider. ACE have identified the limitations with using SIC codes for museums, namely that to be included in the official statistical surveys, the museum needs to be registered for PAYE or VAT, which means some of the small museums would not be included in these official sources. The same applies to local authority delivered museum services which would be coded under the Public Administration SIC code. As a result ACE have used a bottom-up approach of developing a database of museums in England then using various sources to identify the economic measures for each museum. This is for England and was produced in 2013.
Cultural Sector Heritage Historic England Historic England provides a value of GVA and employment accountable by the Heritage sector. Historic England use a satellite account approach to measure the heritage sector. Satellite accounts measure a sector by aggregating shares of other SICs, estimated using Standard Occupational Classification (SOC) codes primarily and additional information. They can serve several purposes, e.g. monitoring progress under specific policy theme. While potentially useful, the quality of the data depends on that of the evidence used to estimate the appropriate share of existing SICs. These figures are useful in building the sectoral narrative, and in advocacy work (e.g. in speeches, alongside our sector estimates). However the scope of the industries included is much wider than for DCMS’ estimates.
Gambling Gambling Gambling Commission The Gambling Commission produces industry statistics twice a year on gross gambling yield, employment and number of businesses. The methods are different to DCMS’ Economic Estimates to reflect the different data sources available to the Gambling Commission and their policy needs. The Gambling Commission derive their estimates from the operators. As it is a license requirement for operators to submit returns the data collection is essentially a census. This has benefits compared to using a sample survey. DCMS define the gambling sector as SIC 92; however it is likely that there will be companies outside of SIC 92 included in the Gambling Commission statistics. For example, some working men’s clubs may hold a license but would not be classified under SIC 92 by virtue of their other primary activities. Finally, Gambling Commission do not produce an estimate of GVA; instead they provide Gross Gambling Yield (GGY), which is the amount retained by gambling operators after the payment of winnings but before the deduction of operation costs, excluding the national lottery. This is because this measure is understood by the sector as a whole and is internationally comparable. This means the Gambling Commission can compare historically and internationally, but it does mean it is not comparable against other sectors.
Sport Sport Sport England Sport England produces an estimate of the GVA and number of FTE jobs generated by sport and sport-related activity. This was updated in 2017/18 and covers England only. GVA is split by participation and consumption. The definition is wider than the statistical definition used by DCMS, but is similar to the sport satellite account approach based on the Vilnius definition. This means elements such as sport broadcasting are included. While potentially useful, the quality of the data depends on that of the evidence used to estimate the appropriate share of existing SICs.
Sport Sport UK Sport UK Sport has produced estimates of the contribution of the Olympic and Paralympic sports. Whilst this is not fully comparable with DCMS’ estimates due to its much narrower scope, it uses a similar methodology to the DCMS Sport satellite account. Please note that this Sport satellite account is not currently part of the DCMS Sector Economic Estimates so there will be further differences in methodology and scope of industries. UK Sport use a satellite account approach for a portfolio of sports. They produce a GVA and employment estimates, using a range of sources: ABS/ASHE, 2014 Input-Output tables, Participation data and company accounts. Whilst these are not the exact same data sources as DCMS uses, or the most up to date, they do enable a comparison to DCMS statistics. They are therefore a robust estimate if the user is looking for specific Olympic and Paralympic sports. However, as with all satellite accounts, the quality of the data depends on that of the evidence used to estimate the appropriate share of existing SICs.
Tourism Tourism VisitBritain VisitBritain have commissioned a report to value the number of jobs and economic contribution in the Tourism industry. This is based on a bespoke model, but the direct tourism industry figures have consistency with the Tourism Satellite Account methodology, which DCMS uses for its Tourism estimates. It is based on 2008 to 2011, so is more outdated than DCMS estimates.
Computer Games Computer Games UKIE and NESTA UKIE has a website dedicated to statistics and other useful information about the UK games industry. This includes statistics on GVA (national and regional), employment, exports and imports, number of businesses, and investment, which are based on their latest official publications. In partnership with UKIE, NESTA has produced national and regional estimates of the economic contribution of the computer games industry, including number of businesses and GVA. This is based on a ‘big data’ modelling approach where researchers identified games companies through their digital footprint, rather than using official industrial (SIC) codes or surveys. The latest estimate is for 2014, so is more outdated than DCMS estimates.

  1. 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. Sampling error is affected by a number of factors including sample size. Despite these issues, sample surveys are still used as a means of data collection because they have lower associated costs than censuses in terms of both time and money. In addition to sampling errors, there is also the potential for non-sampling error, which cannot be easily quantified. For example, undetected deficiencies may occur in the survey register and errors may be made by the contributors when completing the survey questionnaires. ↩

Back to top