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Rhyddhawyd ar: 29 March 2017
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Erthygl
Outlines the impact of improvements to the Average Weekly Earnings (AWE) estimates of small businesses.
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Rhyddhawyd ar: 10 March 2026
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Setiau data
Membership of workplace pension arrangements for UK employees by pension type, weekly pay and Standard Occupational Classification.
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Rhyddhawyd ar: 11 June 2025
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Setiau data
Estimates of changes in monthly employee earnings and employment status attributable to an adverse pregnancy event compared with one year prior to the event.
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Rhyddhawyd ar: 10 March 2026
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Setiau data
Membership of workplace pension arrangements for UK employees by pension type, weekly pay and size of company.
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Rhyddhawyd ar: 11 September 2018
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Erthygl
Analyses of the Average Weekly Earnings (AWE) figures, adjusted for inflation, and of wages and employment contributions underlying single-month movements in the nominal AWE.
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Rhyddhawyd ar: 14 August 2018
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Erthygl
Analyses of the Average Weekly Earnings (AWE) figures, adjusted for inflation, and of wages and employment contributions underlying single month movements in the nominal AWE.
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Rhyddhawyd ar: 21 March 2018
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Erthygl
Analyses of the average weekly earnings (AWE) figures, adjusted for inflation, and of wages and employment contributions underlying single month movements in the nominal AWE, which are published in the UK labour market statistical bulletin.
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Rhyddhawyd ar: 26 October 2016
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Erthygl
This article provides an analysis of the distribution of earnings using ASHE 2016 data and previous ASHE datasets over time. It also considers differences in earnings by gender, region, skill level and working pattern. Earnings growth for those in employment between two consecutive years is discussed.
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Rhyddhawyd ar: 17 April 2018
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Erthygl
Analyses of the average weekly earnings (AWE) figures, adjusted for inflation, and of wages and employment contributions underlying single month movements in the nominal AWE, which are published in the UK labour market statistical bulletin.
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Rhyddhawyd ar: 26 October 2016
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Erthygl
The results of 2 statistical models which explore the relationship between mean hourly earnings excluding overtime and a range of independent variables, based on Annual Survey of Hours and Earnings 2016 provisional results data. There is a particular focus on earnings differences between the public and private sectors.
