DOI: 10.5255/ukda-sn-9064-1
title: Quarterly Labour Force Survey Household Dataset, October - December, 2022
title: Quarterly Labour Force Survey Household Datasets, 1990-2023

description: &lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt;&lt;br&gt;The &lt;em&gt;Labour Force Survey&lt;/em&gt; (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 &lt;em&gt;Quarterly Labour Force Survey&lt;/em&gt; (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.&lt;br&gt;&lt;br&gt;&lt;strong&gt;Household datasets&lt;/strong&gt;&lt;br&gt;Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.&lt;br&gt;&lt;br&gt;&lt;strong&gt;Change to coding of missing values for household series&lt;/strong&gt;&lt;br&gt;From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.&lt;br&gt;&lt;br&gt;&lt;strong&gt;LFS Documentation&lt;/strong&gt;&lt;br&gt;The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS &lt;a title="Labour Force Survey User Guidance" href="https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/methodologies/labourforcesurveyuserguidance"&gt;LFS User Guidance&lt;/a&gt; page before commencing analysis.&lt;br&gt;&lt;br&gt;&lt;strong&gt;Additional data derived from the QLFS&lt;/strong&gt;&lt;br&gt;The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.&lt;br&gt;&lt;br&gt;&lt;strong&gt;End User Licence and Secure Access QLFS Household datasets&lt;/strong&gt;&lt;br&gt;Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.&lt;br&gt;&lt;br&gt;&lt;strong&gt;Changes to variables in QLFS Household EUL datasets&lt;/strong&gt;&lt;br&gt;In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.&lt;br&gt;&lt;br&gt;&lt;strong&gt;Review of imputation methods for LFS Household data - changes to missing values&lt;/strong&gt;&lt;br&gt;A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;span style="font-weight: bold;"&gt;Occupation data for 2021 and 2022 data files&lt;/span&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;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: &lt;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);"&gt;Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022&lt;/a&gt;.&lt;/p&gt; &lt;br&gt;