|Survey name||Monthly Wages and Salaries Survey (MWSS)|
|How compiled||Sample survey based|
|Geographic coverage||Great Britain|
|Last revised||25 October 2017|
The average weekly earnings (AWE) measure is Office for National Statistics’s (ONS’s) lead indicator of short-term changes in earnings. It was introduced in response to the recommendations made by the Turnbull-King Review of the Average Earnings Index. AWE was first published as an experimental statistic in 2005. After a programme of methodological development and review AWE was accredited as a National Statistic in late 2009 and replaced the Average Earnings Index in January 2010 as our lead measure of changes in earnings.
AWE is an important economic indicator. It is used by the Bank of England and HM Treasury to measure the inflationary pressure emanating from the labour market. More broadly, AWE is widely used as a measure of wages growth, sometimes in pay negotiations but more often in contract escalation clauses.
AWE is published monthly and is designed to capture changes in average earnings of employees in Great Britain. Average weekly earnings for any given month is the ratio of estimated total pay for the whole economy, divided by the total number of employees. As a result, AWE is not a measure of rates of pay and can be affected by changes in the composition of an enterprise’s workforce. AWE estimates are usually expressed as a level, in pounds per employee per week, which represents the month as a whole.
AWE is calculated from returns to the Monthly Wages and Salaries Survey (MWSS) and is weighted to be representative of the Great Britain economy as a whole. The self-employed, HM armed forces and government-supported trainees are excluded from the statistics.
AWE is published both including and excluding bonus payments. The “headline rates” of AWE are the changes in seasonally adjusted average weekly earnings, including and excluding bonuses, comparing the latest 3 months with the same 3 months in the previous year. Most lower- level series are published both as levels and as single month annual growth rates.
The headline AWE measures are published through the Labour market statistical bulletin. This is also where information about upcoming changes in methodology is published. Supplementary analyses by sector and industry are also published.
There have been two major changes to the short-term earnings statistics published by ONS since 2010. The first was the replacement of the Average Earnings Index (AEI) with AWE in January 2010 and the subsequent withdrawal of AEI after the publication of July 2010 data. The second was the move in October 2010 to the use of the Standard Industrial Classification 2007: SIC 2007 from the previous 2003 classification; this is equivalent to the European Union classification system NACE revision 2 down to and including the 4-digit class level. We publish a table to help users of the series determine the nearest currently published equivalent to series they previously used.
This report contains the following sections:
About the output
How the output is created
Validation and quality assurance
Concepts and definitions
Other information, relating to quality trade-offs and user needs
Sources for further information or advice
This report provides a range of information that describes the quality of the data and details any points that should be noted when using the output.
We have developed Guidelines for Measuring Statistical Quality; these are based upon the five European Statistical System (ESS) Quality Dimensions. This report addresses these quality dimensions and other important quality characteristics, which are:
timeliness and punctuality
coherence and comparability
output quality trade-offs
assessment of user needs and perceptions
accessibility and clarity
More information is provided about these quality dimensions in the following sections.Back to table of contents
(The degree to which statistical outputs meet users’ needs.)
Average weekly earnings (AWE) is designed primarily to measure short-term changes in earnings. It was developed in consultation with the Bank of England and HM Treasury in order to measure the inflationary pressure emanating from earnings growth. AWE is not a measure of rates of pay or settlements, though these are factors that influence AWE growth.
A major source for AWE is the Monthly Wages and Salaries Survey (MWSS). The sample is drawn from the Inter-Departmental Business Register (IDBR), which is also used to weight the data. The Annual Survey of Hours and Earnings (ASHE) is also an input, providing estimates of pay for employees of small businesses.
AWE is designed to produce robust estimates at whole economy level. The major strength of the MWSS is that it provides comprehensive information on earnings by industry. We publish series for eight higher-level sectors and 24 lower-level industries.
AWE reflects changes to the composition of the workforce. In AWE, all other things being equal, an increase in the relative number of employees in a high-paying industry will cause average earnings to rise. This is because the mix of jobs would have changed so that there are more high-paying jobs. Conversely, an increase in the relative number of employees in low-paying industries would cause average earnings to fall. The previous lead measure of earnings, the AEI did not reflect changes in the composition of the workforce in this way.
This effect is sometimes called the employment contribution to earnings growth, as opposed to the wages contribution, which reflects changes in earnings at individual companies, such as pay rises, promotions and changes in the composition of individual company workforces. In addition to AWE growth, we publish separate estimates of the wage and employment contributions to AWE growth in supplementary tables called the AWE decomposition.
The fitness for purpose of the AWE statistic was considered in the 2008 Weale Review. This noted the development work that had taken place so far and made recommendations for further work. The main development work identified by this report was completed in September 2009. Following the completion of this work, AWE was accredited as a National Statistic by the UK Statistics Authority.
The range of AWE outputs produced has expanded since AWE first became the lead measure of earnings to meet user needs. In response to user requests, we now produce series for private sector services, a time series of the proportion of whole economy employment accounted for by each published aggregate and seasonally adjusted index numbers.
AWE is only currently available from January 2000 and many users would like us to publish a longer historic time series. An experimental whole economy historic series including bonuses has been developed. There is also interest in producing AWE by size of company, although this is a longer-term aim.
Timeliness and punctuality
(Timeliness refers to the lapse of time between publication and the period to which the data refer. Punctuality refers to the gap between planned and actual publication dates.)
The timing of the publication of AWE is a trade-off between timeliness and accuracy. Because they are short-term indicators, the main users want the figures to be available as soon as possible after the end of the period to which they refer, even if this means that they are subject to some revision the following month.
AWE is published monthly. It is generally published on a provisional basis around 6 to 7 weeks after the end of the month in question, although sometimes a week later in the months following Christmas and Easter. The unadjusted estimates are finalised the following month; that is, 10 to 11 weeks after the end of the reference period. Seasonally adjusted estimates are subject to further revisions at later dates (see Revisions Policy).
There have been no recent instances of publication of the AWE being delayed.
For more details on related releases, the GOV.UK release calendar provides 12 months’ advanced notice of release dates. If there are any changes to the pre-announced release schedule, public attention will be drawn to the change and the reasons for the change will be explained fully at the same time, as set out in the Code of Practice for Official Statistics.Back to table of contents
The main data source for average weekly earnings (AWE) is the Monthly Wages and Salaries Survey (MWSS). This is issued to approximately 9,000 businesses each month, covering around 13.8 million employees. The sample is drawn at the individual business level from the Inter-Departmental Business Register (IDBR).
The sample is stratified by business size, industry and legal status. In other words, businesses in Great Britain are divided up into groups, based on the number of people they employ, the industry they operate in and whether they are in the public or private sector. Within each group, or stratum, every business has an equal chance of being selected for MWSS.
Businesses with 1,000 or more employees are always selected. Businesses with between 20 and 1,000 employees are sampled, although in strata where there are a very small number of businesses, an individual business may always be selected. Businesses with fewer than 20 employees are excluded from MWSS, to control Office for National Statistics (ONS) costs and respondent burden at small businesses. Employment at these businesses is taken from the IDBR, and pay is estimated using a factor derived from the Annual Survey of Hours and Earnings (ASHE), which does cover small businesses.
The survey collects the total amount paid and the number of people on the payroll at the business level, separately for weekly- and monthly-paid employees. For monthly-paid employees, businesses are asked to give total pay for the month. This can be a calendar month or a month of 4 or 5 weeks (which is recorded on the questionnaire). Employees paid on a “4, 4, 5” week cycle may occasionally be paid twice in a calendar month, in which case only one month of pay should be included.
For weekly-paid employees, businesses are asked to give total pay for the last week in the month, or another week if the last week of the month is unrepresentative for some reason. If pay is fortnightly, the respondent should divide the figure by two to derive an estimate of weekly pay.
The following list indicates what is and isn’t covered in MWSS returns.
Included in MWSS:
regular pay, gross of any deductions (for example, Income Tax, employee National Insurance contributions) is part of total pay
one-off bonus or commission payments are collected separately on the questionnaire; this includes both cash awards and stock options if these are paid through the payroll
pay award arrears are collected separately on the questionnaire; this specifically covers earnings arising from a backdated pay increase, not late payment of overtime, bonuses
Excluded from MWSS:
employer National Insurance contributions and contributions to pension schemes
benefits in kind
stock options not paid through the payroll
In a given month, data returns are accepted for the current and previous month. The current month will be published on a provisional basis and finalised the following month. MWSS has a target response rate of 83%, of which 98% should have been cleared through ONS’s editing and validation procedures. This ensures that sufficient responses have been received to make a robust estimate of average earnings growth. Typically, this target is met or exceeded. Both of these refer to the current (provisionally published) month. Response is monitored by size of business and industrial sector.
Validation of the data takes place at an individual business level. Individual responses are chased and suspect data returns investigated by dedicated teams at ONS. Businesses due to report a bonus are targeted for additional response-chasing. Unit level validation looks chiefly for large changes in pay and employment, as well as any comments the respondent may have made when returning the survey.
During processing, the pay is “weeklyised” - that is, it is placed on a consistent weekly basis. Monthly pay is divided by the number of weeks in the month to make it comparable with weekly pay. For calendar months, the average number of weeks in a month, approximately 4.348, is used.
Data are imputed for businesses that do not respond to MWSS. Regular pay and employment are carried forward from the latest valid response from that business, up to 5 months before the response in question. Bonus pay is imputed using the bonus per employee in the same month in the previous year and the latest valid employment figure. If no valid response is available, or the latest valid response is longer ago than this, the remaining observations are re-weighted to compensate. Partial non-response, if not resolved during the validation process, is treated as invalid.
As discussed, data are validated and questionable responses that have a significant effect on results are queried with the respondent. At the end of this process, responses judged by ONS still to be invalid are deleted, which forces the data to be imputed as described previously. Where some information about the data return is available (for example, if only some of the data have been returned), an appropriate data return can be constructed. Deletions and constructions are only implemented at the end of each round, to allow respondents the maximum time to provide correct data. Deletions and constructions made in the provisional results are re-assessed the following month. Typically, around 50 responses a month are deemed invalid and are imputed or constructed in some way, although this number does vary.
Outlier detection is automatic. The regular pay and bonus per employee of a candidate business are compared with the average of similar businesses (in terms of employment, industry and whether they are in the public or private sector). Cases that are particularly extreme are deemed to be outliers. Outliers are given a weight of “1” in the AWE calculation – that is, they only represent themselves.
Each business represents a number of similar businesses, based on public or private status, business size and industry. The number it represents is updated monthly according to the IDBR. These weights are then adjusted for outliers and non-responders that cannot be imputed.
The team responsible for AWE results examines and queries individual responses based on their actual or potential impact on the main published AWE aggregates. The impact of responses is re-assessed daily during the round, with outlier detection, imputation and response weighting updated each time.
AWE itself is an estimate of average earnings per employee per week. It is calculated by dividing total (weighted) pay by total (weighted) employment. Lower level aggregates are calculated in the same way with the appropriate subset of responses. Separate estimates of total pay (including bonuses), bonus pay and arrears pay are made.
The higher-level series – the whole economy and eight higher-level sector aggregates – are seasonally adjusted for publication in the Labour market statistical bulletin. Total pay, bonus pay and regular pay (excluding bonuses) for each sector (a total of 27 series) are seasonally adjusted using X13-ARIMA. Percentage changes are then derived from the seasonally adjusted average pay series.
Each of the 27 series is seasonally adjusted separately, to ensure the optimum seasonal adjustment of each series. The result of this is that relationships that hold in the unadjusted series do not necessarily hold for the seasonally adjusted series. For example, regular pay plus bonus pay gives total pay before seasonal adjustment, but not necessarily after seasonal adjustment. Note that pay award arrears are excluded from the seasonally adjusted estimates, as they disrupt the seasonal adjustment of the data. The seasonal adjustment parameters are reviewed annually to ensure they remain appropriate.
AWE is published as a level of earnings, in pounds per employee per week. To meet user demand, main AWE series are also published as indices, based to 2000 equals 100. These indices are calculated by dividing AWE for the current month by the average of the months in 2000 and multiplying by 100.
The level of AWE is then used to calculate annual rates of growth. For non-seasonally adjusted series, ONS calculates the single month annual growth rate; that is, the percentage change between a given month and the same month a year earlier. For seasonally adjusted series, ONS additionally calculates the 3-monthly growth rate; that is, the percentage change between the 3 months ending in the given month and the corresponding 3 months in the previous year. ONS commentary on the estimates focuses on the 3-monthly growth rate, which is less volatile than the single month growth rate.Back to table of contents
(The degree of closeness between an estimate and the true value.)
The main threats to the accuracy of average weekly earnings (AWE) are sampling error, unit response error and changes in the classification of individual or groups of businesses.
Like all statistics derived from sample-based surveys, AWE is subject to sampling error. The only way to avoid sampling error entirely would be to obtain data for every business in Great Britain. As this would be costly and impractical, we have to estimate AWE growth for the whole economy by taking a random sample. This estimate can be different from the “actual” rate of AWE growth, depending on which businesses are selected.
Appropriate sample design can help minimise sampling error. As previously described, AWE is calculated using responses from the Monthly Wages and Salaries Survey (MWSS). Businesses with 1,000 or more employees are always selected, while those with fewer than 20 employees are not covered by the survey but are estimated separately.
Businesses with between 20 and 1,000 employees are sampled, with the design optimised to minimise the variability of total pay for the whole economy. Some sparsely-populated sections of the sample are fully enumerated to ensure sufficient coverage, despite having fewer than 1,000 employees.
When a business is selected for MWSS, it remains in the sample for up to 60 months, although updates to the sample frame (the Inter-Departmental Business Register (IDBR)) and sample re-designs can cause this to be shorter. The long duration of selection helps to reduce the variability of year-on-year growth rates.
Sampling variability estimates are published for single month growth rates (including and excluding bonuses) in the Labour market statistical bulletin and supplementary tables. These take the form of 95% confidence intervals of the growth rate. This means that if a series of independent samples were taken to estimate the AWE growth rate and a confidence interval calculated for each sample, then 95% of the confidence intervals would contain the “actual” rate of AWE growth (the one that would be obtained if Office for National Statistics (ONS) could survey every business in the country).
Typical 95% confidence intervals for AWE whole economy single month annual growth are:
plus or minus 0.4 percentage points excluding bonuses
plus or minus 0.7 percentage points including bonuses (January to April)
plus or minus 0.5 percentage points including bonuses (May to December)
Similar values are published for each sector and industry alongside the data.
Unit response error
Much of the quality assurance effort is directed towards ensuring data returns are accurate. Suspect data returns are queried and treated in the data. Where a unit (business) does not respond, or gives an invalid response, data are imputed as described in the How the output is calculated section. The imputation process (which carries forward previous data from the respondent) assumes no change in pay or employment since the last return and no change in bonus per employee since the previous year.
Occasionally unit response errors are only detected months after they originally occur and the AWE Revisions Policy does not generally allow for the error to be corrected beyond the current and previous month. This is felt to be an acceptable compromise in making AWE a timely, stable and cost-effective indicator of earnings.
Updates to the sample frame (the IDBR) cause individual businesses to change their contribution to AWE. Sometimes, businesses change classification as the nature of their business changes. More commonly, ONS receives new information about a business, leading to a reassessment of their classification. Major changes are made to IDBR and take effect in AWE immediately. Smaller changes are saved up and made in January each year. As with unit response error, such changes are not backdated in AWE.
On occasion, reclassifications can affect a group of businesses and lead to significant changes in AWE. This issue is discussed in more detail under the Comparability and coherence section.
The Revisions Policy for non-seasonally adjusted AWE series is:
a given month is first published as provisional, 6 to 7 weeks after the data month end
the same month of data is revised the following month (around 10 to 11 weeks after the end of the data month), to allow for late and amended data returns from respondents
as described elsewhere, data revisions are not generally made after this time; however, an exceptional revision may be made if the impact is sufficiently significant, as assessed by comparing the impact of making the revision on any single month whole economy AWE growth rate against the sampling variability of that series
a change in methodology can lead to revisions to the entire historic time series; notice of such a change would be published in advance in the Labour market statistical bulletin
The Revisions Policy for the seasonally adjusted series is the same, with two additional considerations:
when seasonally adjusted data for a given month t is published, as well as t–1 being revised, t–2 is also revised, as well as the same 3 months in the previous year (that is, t–12, t–13 and t–14); these are the months that have been found to be most affected by the updated estimate of the seasonal pattern of the series
the seasonal adjustment parameters are updated annually, in line with our policy on seasonal adjustment; this update can lead to revisions to the historic time series extending back at least 3 years and possibly throughout the entire time series
(Coherence is the degree to which data that are derived from different sources or methods, but refer to the same topic, are similar.)
There are three other sources of earnings data produced by ONS.
The Annual Survey of Hours and Earnings (ASHE) ASHE is a structural survey, designed to provide detailed information about the levels, distribution and make-up of earnings and paid hours worked for employees by geographic location, industry, occupation and so on.
ASHE is used to look at distributions of employee earnings and so ASHE outputs lead with median rather than the mean as its measure of average earnings. AWE on the other hand is a measure of mean pay. The distribution of pay is such that median earnings tend to be lower than mean earnings. This is because the presence of a few very well-paid employees in the distribution increases the mean but has little effect on the median.
However, it is also possible to produce mean earnings using ASHE. These figures tend to be higher than the equivalent AWE figures. The main differences are:
AWE is carried out at an aggregate level, rather than an employee level; in particular, when a lot of people are joining and/or leaving an organisation, monthly payroll employment (the number of people paid for work done that month) can be quite different to the number of employees at any given time
ASHE and AWE are processed differently with respect to weighting, non-response and extreme values (outliers)
the timing of the ASHE survey (referenced to April each year) has some impacts on the capture of bonus payments; as a result, AWE tends to capture bonus payments better
In combination, these factors tend to result in ASHE mean earnings being a more accurate estimate of gross pay (though not necessarily bonus pay) than AWE. However, AWE is a timelier estimate of earnings and thought to be fit for the purpose of accurately estimating changes in the level of earnings.
The Labour Force Survey (LFS) LFS, an ONS social survey, also collects data on the earnings of individuals. As such, data can be published separately for full-time and part-time workers, for men and women, and by region of employment. The data are published quarterly, which means these data are almost as timely as the AWE.
The LFS data differ from the AWE in that they are weighted based on individuals rather than businesses. They rely on individuals’ own assessments of which industry they work in, which ONS believes are a less accurate way of representing the workforce than using the IDBR. For example, contract cleaners working at a government department might consider themselves as public sector, while ONS would define them as being employees of the cleaning contractor and hence private sector.
LFS earnings data are usually presented for full-time employees and so are significantly higher than AWE figures. It is possible to analyse weekly earnings for all staff and the figures that result are broadly similar in level to AWE. The most noticeable difference is that LFS earnings data are much less seasonal than AWE, with no significant peak in Quarter 1 (January to March), when the majority of bonuses are paid. Bonuses are theoretically within the scope of the LFS data. A possible reason for bonuses not making a significant impact in the LFS data is that many LFS responses are given by proxy and they may fail to report bonus payments.
It is possible to use national accounts wages and salaries and employee jobs to derive a wages and salaries per job measure, which is in theory comparable with AWE.
Total wages and salaries is a component of the household sector accounts. In the most recent periods (approximately the last 2 years), it is calculated by multiplying AWE and employee jobs. The figure is then subject to adjustments to make it compliant with national accounts definitions. Historic data (more than 2 years old) are subsequently benchmarked using personal taxation data from HM Revenue and Customs, at which point AWE provides information on the quarterly path of wages and salaries, but not the overall level. Finally, the data are subject to balancing adjustments so they reconcile with the other elements of the national accounts.
Employee jobs are a subset of the workforce jobs dataset. The definition of employee jobs is comparable with AWE, but the adjustments to bring them into line with national accounts definitions include sectors not covered by AWE or employee jobs (such as HM armed forces and Northern Ireland), so wages and salaries per job is not directly comparable with AWE. Finally, employee jobs are a point in time measure and will generally be slightly lower than total payroll employment for the month.
For these reasons, AWE tends to be lower than the wages and salaries per job measure.
(Comparability is the degree to which data can be compared over time and domain, for example, geographic level.)
Comparable AWE time series are available going back to the year 2000. A project to publish AWE estimates for the whole economy, including bonuses, between 1963 and 2000 was completed in September 2013.
Major reclassifications of businesses, driven by changes in the sample frame, can lead to apparent discontinuities in AWE. These are discontinuities in the sense that the classification change causes a sudden, sustained change in AWE. However, these are caused by genuine changes in coverage, rather than a change in methodology. There have been three particular cases recently, each of which is footnoted on the tables that appear in the Labour market statistical bulletin.
Firstly, in July 2009, the banks that received financial support from the government were reclassified from the private to the public sector in the AWE sample frame. It resulted in higher average public sector pay (due to an influx of employees to the public sector on higher than average wages) and a fall in average private sector pay (due to the loss of high-earning workers).
Lloyds Bank then reverted back to the private sector in April 2014 following the sale of some government-owned shares to private sector investors. This caused a fall in average public sector pay and an increase in average private sector pay. We estimate that, if the reclassification had not occurred, the public sector single month growth rate from April 2014 would have been around 0.3 percentage points higher and the corresponding private sector growth rate would have been around 0.1 percentage points lower.
Secondly, sixth form and further education colleges were reclassified from the private to the public sector in June 2010. This caused a fall in public sector pay (due to an influx of employees on lower than average wages) but had relatively little impact on the private sector. In June 2012, due to changes in legislation, sixth form and further education colleges were reclassified to the private sector.
This affected the public and private sector single month growth rates from June 2012 to May 2013, and the 3-month average growth rates between April to June 2012 and May to July 2013. We estimate that, if the reclassification had not occurred, the public sector single month growth rates between June 2012 and May 2013 would have been between 0.6 and 0.8 percentage points lower and the corresponding private sector growth rates would have been between 0.1 and 0.2 percentage points higher.
Thirdly, Royal Mail was privatised in October 2013. This change from public to private sector resulted in a relatively small change in the results.
From August 2010 onwards, the AWE sample was designed, processed and weighted using the Standard Industrial Classification 2007: SIC 2007, having used SIC 2003 previously. All AWE historic time series were re-estimated on a SIC 2007 basis (see Crane, October 2010). The transition to SIC 2007 led to a substantial re-design of the MWSS sample from August 2010 onwards, which leads to increased sample error of the growth rates between August 2010 and July 2011. We publish a table to help users of the series determine the nearest currently published equivalent to series they previously used.Back to table of contents
(Concepts and definitions describe the legislation governing the output and a description of the classifications used in the output.)
The main source for average weekly earnings (AWE), the Monthly Wages and Salaries Survey, is a statutory survey carried out in accordance with the 1947 Statistics of Trade Act. The production of AWE is not specifically required by any legislation, but is driven primarily by the requirements of HM Treasury and the Bank of England.
The survey is stratified by and weighted with respect to legal status (public or private sector). This classification follows the UK National Accounts Sector Classifications conventions. The survey is also stratified by and weighted with respect to industry. From October 2010, AWE has been produced and published using Standard Industrial Classification 2007: SIC 2007. Prior to this, SIC 2003 was used for the same purpose.Back to table of contents
Output quality trade-offs
(Trade-offs are the extent to which different dimensions of quality are balanced against each other.)
As described under Accuracy, there is an inherent trade-off between accuracy and timeliness. The provisionally published data are timely (within 6 to 7 weeks of the end of the month in question) but subject to change. The final revised estimates are more reliable and available 10 to 11 weeks after the end of the month in question. Data revisions are only accepted after this in exceptional circumstances (as described in the Revisions Policy), which ensures the stability of AWE over time.
Assessment of user needs and perceptions
(The processes for finding out about user and users, and their views on the statistical products.)
The assessment of user needs and perceptions involves the processes for finding out about users and uses, and their views on the statistical products.
The main users of average weekly earnings (AWE) are the Bank of England and HM Treasury. This is partly about understanding the state of the labour market (alongside employment rates, hours worked) and partly about measuring the inflationary pressures emanating from wage growth. Regular meetings are held with these main users to understand their needs. There is a great deal of wider media and analytical interest in AWE for the same reasons.
More broadly, AWE is used widely as a measure of wages growth, sometimes in pay negotiations but more often in contract escalation clauses. This is where the charge for a good or service delivered over many years increases to compensate the supplier for increasing costs. We are responsive to the needs of these users of the AWE. For example, the range of AWE index number series has been widened and a private sector services series introduced following requests from users.
These users have had to negotiate two major changes to the Office for National Statistics’s (ONS’s) earnings statistics in the last 4 years.
Firstly, contracts set up before 2010 would be based around the Average Earnings Index (AEI). Now that AEI has been withdrawn, users have had to move to Average Weekly Earnings (AWE).
Secondly, the switch to Standard Industrial Classification 2007: SIC 2007 has meant that some industrial aggregates have changed and users have had to decide on a nearest equivalent. To assist in this process, we have published a table of nearest equivalents, so that users can see which series is closest in definition to the AEI (or AWE SIC 2003) they set their contract up to use.Back to table of contents
Accessibility and clarity
(Accessibility is the ease with which users are able to access the data, also reflecting the format in which the data are available and the availability of supporting information. Clarity refers to the quality and sufficiency of the release details, illustrations and accompanying advice.)
Our recommended format for accessible content is a combination of HTML webpages for narrative, charts and graphs, with data being provided in usable formats such as CSV and Excel. Our website also offers users the option to download the narrative in PDF format. In some instances other software may be used, or may be available on request. Available formats for content published on our website but not produced by us, or referenced on our website but stored elsewhere, may vary. For further information please refer to the contact details at the beginning of this report.
For information regarding conditions of access to data, please refer to the following links:
Terms and conditions (for data on the website)
Access to microdata via the Virtual Microdata Laboratory
In addition to this Quality and Methodology Information, basic quality information relevant to each release is available in the quality and methodology section of the relevant statistical bulletin.