Labour Force Survey Two-Quarter Longitudinal Dataset, April 2001 - March 2023: Secure Access
Bibliographic record
Abstract
<p><b>Background</b><br> The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.<br><br><b>Longitudinal data</b><br> The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.<br> <br> <b>Secure Access data</b><br> Secure Access longitudinal datasets for the LFS are available for two-quarters (SN 7908) and five-quarters (SN 7909). The two-quarter datasets are available from April 2001 and the five-quarter datasets are available from June 2010. The Secure Access versions include additional, detailed variables not included in the standard 'End User Licence' (EUL) longitudinal datasets (see under GNs 33315 and 33316).<br> <br> Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to:</p><ul><li>day, month and year of birth</li><li>standard occupational classification (SOC) relating to second job, job made redundant from, last job, apprenticeships and occupation one year ago</li><li>five digit industry subclass relating to main job, last job, second job and job one year ago</li></ul>These extra variables are not available for every quarter or dataset. Users are advised to consult the 'LFS Variable Catalogue' file available in the <i>Documentation</i> section below for further information.<br> <br><p><span style="font-weight: bold;">Occupation data for 2021 and 2022 data files</span><br></p><p>The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023:&nbsp;<a title="Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022" href="https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022</a>.</p><p><span style="font-weight: bold;">2022 Weighting</span></p><p>The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.</p><p></p> <p><span style="font-style: italic;">Documentation</span><span style="font-weight: 700;"><br></span>The study documentation presented in the Documentation section includes data dictionaries for all years, and the most recent LFS documentation only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request.<br><br><span style="font-style: italic;">Latest edition information</span><span style="font-weight: 700;"><br></span>For the eighteenth edition (July 2023), a data file covering October 2022 - March 2023 has been added to the study.</p>
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.024 | 0.030 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.178 |
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".