Monthly Wages and Salaries Survey, 2000-2023: Secure Access
Bibliographic record
Abstract
<p>The <i>Monthly Wages and Salaries Survey</i> (MWSS) is the main source of information for three key indicators of Short-Term Earnings generated by the Office for National Statistics: the Average Earnings Index, the Average Weekly Earnings and the Index of Labour Costs per Hour.<br> <br>The MWSS is distributed monthly to approximately 8,800 businesses and covers around 12.8 million employees. Companies are required to respond under the Statistics of Trade Act 1947. Businesses are selected from the Inter-Departmental Business Register. Every company with more than 1,000 employees is surveyed. Sampling is random for businesses with fewer than 1,000 employees. The MWSS does not cover businesses with fewer than 20 employees, and so the very smallest businesses in the economy are not represented. The self-employed and government-supported trainees are also not surveyed.<br> <br>The major strength of the MWSS is that it provides comprehensive information on earnings, by industry. In terms of industrial coverage, information on all industries is collected, as defined by the Standard Industrial Classifications (1992). Information on both the public and private sectors is available.<br> <br> <i>Linking to other business studies</i><br>These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.<br><br><span style="font-style: italic;">Latest edition information<br></span>For the thirty-sixth edition (October 2023),&nbsp; two monthly data files for May and June 2023 and one new Excel variable catalogue file have been added to the study.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".