Official Statistics

DCMS Economic Estimates: Monthly GVA – Technical and quality assurance report

Published 17 February 2022

This document covers the following topics:

• an overview of the content covered in the regular statistical release ‘DCMS Sectors Economic Estimates: Monthly GVA’
• an overview of DCMS Sectors, how they are defined, and limitations of these definitions
• the methodology underlying the statistical release, including data sources
• the processes used to check that the estimates have been produced correctly
• other sources of information for the DCMS sectors
• further information, including contact details for DCMS statisticians.

1. Overview of release

1.1 What this release covers

The statistics release ‘DCMS Economic Estimates: Monthly GVA’ provides a monthly estimate of the contribution of DCMS Sectors 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, though less accurate, estimates than our annual figures. They are based on incomplete information and should only be used to illustrate general trends, rather than be taken as definitive measures.

The method set out in this chapter can be used to produce monthly GVA estimates expressed in chained volume measures (i.e. taking into account inflation) for the following DCMS sectors:

• Creative Industries
• Cultural Sector
• Digital Sector
• Gambling
• Sport
• Telecoms

This note does not include a procedure for generating monthly GVA estimates for Civil Society.

Other organisations, including some of DCMS’ arm’s length bodies, produce alternative GVA measures for DCMS sectors. These alternative measures can be found in Section 6.

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

We publish these figures every quarter. As always, we welcome feedback from users on the current statistics portfolio and suggestions for any changes they would like to see in the future. Please email evidence@dcms.gov.uk.

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. Sector definitions

2.1 Overview of DCMS Sectors

The sectors for which DCMS has responsibility are:

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

However, DCMS Sector Economic Estimates: Monthly GVA does not include Civil Society or Tourism, as monthly GVA estimates are not available for these sectors at present.

In order to measure the size of the economy it is important to be able to define it. DCMS uses a range of definitions based on internal or UK agreed definitions. All 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.

Each sector definition has been designed to be the best possible measure of that sector, rather than being considered as part of a greater whole. This approach has led to 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.

Figure 1 below visually shows 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. A list of SIC codes appearing in each sector and subsector can be found in the tables accompanying the release.

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. 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. The following section describes limitations for specific DCMS sector definitions.

Creative Industries

The Creative Industries were defined in the Government’s 2001 Creative Industries Mapping Document as “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”.

To allow the Creative Industries to be measured, DCMS worked with others to develop a statistical definition of the Creative Industries which reflects this definition. DCMS uses a “Creative Intensity” to determine which industries (at 4 digit SIC) are Creative. The Creative Intensity is the proportion of occupations in an industry that are creative and, if the intensity is above a set threshold, that industry is typically defined as Creative. More information can be found in the 2016 methodology document.

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

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 used by DCMS 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. DCMS policy responsibility is for digital across the economy and therefore this is a significant weakness in the current approach.

Sport

The definition of sport used in the release is based on the EU agreed core/statistical Vilnius definition, which includes only SIC codes which are predominately sport. In our release of national estimates of GVA, we also include estimates of Sport GVA based on the Sport Satellite Account. These estimates are based on the broad Vilnius definition, which is a more wide-ranging measure of sport that considers the contribution of sport across a range of industries, for example sport advertising, and sport-related construction. Caution should be made when using the Sport Satellite Account, as proportions have not been updated since 2016 and results are not comparable to those of other sectors. Sport GVA in the Sport Satellite Account is calculated by estimating the proportion of the SIC code that applies to sport, and then applying this proportion to the total GVA for each industry, finally summing to produce total sport GVA for each year.

3. Methodology

3.1 Data sources

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

• Annual low-level aggregates of UK output gross value added (GVA) (published 12 November 2020) - 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) - 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 DCMS 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 DCMS 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, and products in the Tourism sector.

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’. DCMS 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 DCMS sector estimates. The following is one possible method to illustrate general trends in monthly GVA. This method can also be used to produce bespoke sector estimates based on user needs.

Calculate current price GVA for 2019

The first step in this method is to obtain current price GVA for 2019 for each industry in DCMS sectors. This is because the monthly indexes are referenced to 2019 = 100, so we need to use 2019 current price data 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 SIC code digits).

As DCMS 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 32.12)
• extracting aGVA from the ABS at division level (e.g. SIC 32)
• calculating the proportion of the division aGVA that each class accounts for (e.g. aGVA for SIC 32.12 as a proportion of SIC 32)
• applying the proportion for each class to the division GVA in the ONS current price tables

We can use this approach for all cases where DCMS sectors are defined by a 3 or 4 digit SIC. The list of SIC codes in each sector and subsector are available from the Economic Estimates Methodology page.

We then divide the current price GVA for each SIC code by 12 to get the average monthly GVA, not annual GVA, for 2019.

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 DCMS 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. Further information 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 DCMS industries. These industries (SIC codes) can then be aggregated for each month to give estimates for DCMS sectors and subsectors.

Method for SIC 91: Libraries, archives, museums and other cultural activities

A different approach is used to apportion GVA to 4 digit level for SIC 91. In the ABS data, the aGVA for aspects of SIC 91 can be negative (this is due to under coverage in the ABS and high levels of funding from local authorities). For this reason, turnover data from the ABS is used instead of aGVA to allocate SIC 91 at the 4-digit level. This is the only SIC where this approach has been used.

Accounting for sector overlap in the DCMS total

There is an overlap between DCMS sector definitions, which means we cannot simply add the different sectors together to form a DCMS total figure. This overlap must be estimated in order to avoid double counting when calculating a DCMS monthly total.

The DCMS monthly total (excluding Tourism and Civil Society) is estimated by looking at which SIC codes appear in multiple sectors. All unique SICs can then be aggregated to give a total monthly figure with no overlap.

3.3 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) and, as with all data from surveys, 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.
• 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. Validation and accuracy

The timeliness of monthly GVA data is crucial to understand changes in GVA as a result of Covid-19. However, there is a trade off with accuracy as these estimates are based on incomplete data.

4.1 Comparability with annual estimates

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 (which will be published in 2022 when data becomes available).

This is partly because this method uses indexes for 2 digit SIC codes (the lowest level of detail available) to estimate monthly GVA for more detailed 4 digit SIC codes (how DCMS sectors are defined). The trend in GVA for the overall 2 digit SICs may not be an accurate reflection of the change in GVA at the more granular level. For example, this method estimates GVA from retail of sports goods based on the trend for all retail activities (not just sports goods), and estimates sports education based on the trend for all education. It’s likely that sectors such as sports education have been affected differently over lockdown compared to education in general. The use of 2 digit SIC indexes also means that 4 digit SICs within the division will follow exactly the same trend. For example, SICs 93.11, 93.12, 93.13 and 93.19 all use the same division index for SIC 93 to estimate monthly GVA.

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 DCMS sectors). One possible check can be to aggregate monthly GVA estimates over each year and compare this with the annual published GVA figure. For each sector or subsector, there was approximately a 0 to 4% difference between the aggregated monthly GVA estimate for 2019 and the GVA figure for 2019 in the latest DCMS Economic Estimates: Gross Value Added release. However, users should note that published estimates are provisional and subject to planned revisions.

Estimates in this release have been revised to use 2019 data from the ABS, and rebased to 2019. 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

Monthly indexes

The monthly GVA for 2019, 2020 and 2021 has been estimated using 2019 data as a baseline. Monthly GVA for 2020 and 2021, in particular the latest months, will be extremely 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. We have estimated that monthly GVA estimates for the Digital Sector in 2019 using the first monthly indexes published were approximately 1-4% higher than the “true” figure. This drops to 1-3% above the “true” figure once the monthly indexes were 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).

Other ONS data sources

Information on the validation and accuracy for the current price GVA tables and the Annual Business Survey are available in the technical report published alongside the latest annual DCMS Economic Estimates: Gross Value Added release.

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 DCMS Economic Estimates: Gross Value Added release.

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 DCMS

The majority of quality assurance of the data underpinning the release takes place at ONS. 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

For the ABS 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.

Data analysis quality assurance checks

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

• the GVA proportions are similar to last year, and if not, whether this is because of changes to the methodology or changes in sample size
• there is any missing data
• the percentage changes each year look similar
• the updated data has been used, including the revised back series data.
• the correct SIC codes have been aggregated together to form DCMS sector and sub-sector estimates

Publication quality assurance checks

Finalised figures are disseminated within OpenDocument Format tables, published on GOV.UK. These are produced by the GVA statistics lead. Before publishing, a quality assurer checks the data tables to ensure any errors are noticed and corrected. 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
• contacting press office to ensure they are aware of the release date
• 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
• engaging with users of the publication to get feedback

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 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 2 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 2. 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.

7. Further information

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

For general enquiries contact:

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

Telephone: 020 7211 6000

DCMS statisticians can be followed on Twitter via @DCMSInsight.

The Economic Estimates of DCMS Sectors 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://www.statisticsauthority.gov.uk/code-of-practice/.

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 perhaps be 18% or 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. ↩