Houston, Donald and Kollydas, Konstantinos and Blake-Carr, Reen and Sissons, Paul
(2026).
Labour Market, Economic, Health, Demographic, Migrant and Covid-19 Infection Data Collated for NUTS3 Regions in Great Britain, 2018-2021.
[Data Collection]. Colchester, Essex:
UK Data Service.
10.5255/UKDA-SN-858630
The Coronavirus pandemic has led to increases in retirement and long-term sickness, and Brexit and the pandemic together have led to a reduction in the number of EU workers in the UK. Together, these changes amount to a large reduction in the size of the workforce, which is the primary reason for difficulties faced by employers in most sectors recruiting staff since the ending of 'lockdown', as well as issue of pay and conditions and their geographical and social inequalities.
Little is known about the uneven geography across the UK in these sources of reductions in the workforce in driving the sharpest rises in job vacancies in rural areas and some London boroughs, precisely the areas most dependent on foreign labour. This information is important in designing policies to effectively address "Levelling Up" the economic fortunes of different parts of the UK, with some places short of workers, at least in the short term; and others short of jobs, in particular well-paid jobs.
The UK Government has promised a transformation to a high-wage economy following Brexit, predicated on the view that reduced labour supply will stimulate investment and innovation to raise productivity, and that the UK has become locked-in to a low-cost economic model dependent on cheap international labour. The research will produce new datasets as the latest evidence becomes available, including the 2021 Census of Population, analysis and insights to assess this claim and its geography, by examining links between local changes to local labour demand, supply, wages, productivity and unemployment.
More generally, the research will better understand the impact of Brexit and the pandemic on local labour markets and local economies in different parts of the UK, to inform planning for future economic resilience to 'shocks', and to assess the effectiveness of the UK new immigration policy in meeting labour demand and skills shortages in all parts of the UK.
Data description (abstract)
This dataset provides a range of labour market, economic, health, demographic and migrant data for NUTS3 regions of Great Britain for 2018 and 2021. The dataset is novel because it provides a geographical disaggregation for 168 NUTS3 regions across GB. The dataset allows the geographically uneven impacts of Brexit and the COVID-19 pandemic on local labour markets to be investigated.
| Data creators: |
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| Sponsors: |
ESRC
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| Grant reference: |
ES/X005828/2
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| Topic classification: |
Health Economics Demography (population, vital statistics and censuses) Labour and employment
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| Keywords: |
ECONOMIC ACTIVITY, ILL HEALTH, LABOUR MIGRATION, AGEING POPULATION, COVID-19
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| Project title: |
LEVELLING UP LABOUR SUPPLY
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| Grant holders: |
Donald Houston
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| Project dates: |
| From | To |
|---|
| 31 March 2023 | 30 May 2025 |
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| Date published: |
28 Jul 2026 13:01
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| Last modified: |
28 Jul 2026 13:01
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| Temporal coverage: |
| From | To |
|---|
| 1 January 2017 | 31 December 2022 |
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| Geographical area: |
NUTS3 regions |
| Country: |
Great Britain |
| Spatial unit: |
European Union Geographies > NUTS-III Areas |
| Data collection method: |
Data from several secondary datasets and published sources have been collated and linked into a new unique dataset of 168 NUTS3 regions covering the whole of Great Britain. Datasets were linked using NUTS3 area codes. An overview of data sources, methods of aggregation to NUTS3 from smaller geographies and assessment of data quality are provided in the section "Data sourcing, processing and preparation". A full list of data sources by variable is provided as in the "Data sources by variable" worksheet within the Excel file containing the data. Units of measurement for each variable are defined in column headings in the "Data" worksheet within the downloadable Excel file. |
| Observation unit: |
Geographic unit |
| Kind of data: |
Numeric |
| Type of data: |
Geospatial data
, UK survey data |
| Resource language: |
English |
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| Data sourcing, processing and preparation: |
Employment and population variables (including EU/non-EU migrants identified by country of birth) have been calculated based on counts of persons aged 16-64 years from the Office for National Statistics' Annual Population Survey (APS) Three-Year Pooled Datasets for January 2017 - December 2019 and for January 2020 - December 2022, using a NUTS3 identifier in these underlying micro datasets. ONS person weights for the 3-year pooled datasets were applied to produce population totals and correct for non-response bias.
Job vacancy data were obtained under special research license from the data provider Adzuna via ESRC's Urban Big Data Centre. Counts of job vacancies were produced using Local Authority District codes provided by Adzuna, aggregated to NUTS3 regions using ONS's LAD (2018) to NUTS Lookup Table available on the ONS Geoportal (https://geoportal.statistics.gov.uk/datasets/0de287b886954f54b4c2fffcfd514079_0/explore). In a small number of NUTS3 regions in parts of western and northern Scotland which are smaller than their corresponding local authority, vacancies were manually allocated to the appropriate NUTS3 area from detailed place names (mainly settlements) available in the Adzuna data.
Covid-19 infection data were downloaded for Local Authority Districts from the UK Government's Coronavirus data dashboard (https://coronavirus.data.gov.uk). Cumulative Covid-19 cases as at 31-12-2021 for Local Authority Districts were aggregated to NUTS3 regions using ONS's LAD (2018) to NUTS Lookup Table available on the ONS Geoportal (https://geoportal.statistics.gov.uk/datasets/0de287b886954f54b4c2fffcfd514079_0/explore). In a small number of NUTS3 regions in parts of western and northern Scotland which are smaller than their corresponding local authority, Covid-19 cases in the parent local authority were allocated to each constituent NUTS3 area in proportion to total population.
The "Leave" vote in the 2016 EU Referendum was calculated from a download of the referendum results by Local Authority District from the Electoral Commission website (https://www.electoralcommission.org.uk/research-reports-and-data/our-reports-and-data-past-elections-and-referendums/results-and-turnout-eu-referendum). Votes in Local Authority Districts were aggregated to NUTS3 regions using ONS's LAD (2018) to NUTS Lookup Table available on the ONS Geoportal (https://geoportal.statistics.gov.uk/datasets/0de287b886954f54b4c2fffcfd514079_0/explore). In a small number of NUTS3 regions in parts of western and northern Scotland which are smaller than their corresponding local authority, the percentage voting "Leave" in the parent Local Authority was imputed for each constituent NUTS3 area.
All other variables (chiefly labour productivity, workplace employment and earnings) are available for NUTS3 areas in existing datasets. These variables were collated from existing public-access economic and labour market data published by the Office for National Statistics, either via downloads from the ONS website or extraction using the Nomis web database (https://www.nomisweb.co.uk/). The urban/rural classification of NUTS3 areas was taken from Eurostat.
The percent of employment in sectors affected by Covid-19 'lockdown' was calculated from four digit SIC codes in the Business Register & Employment Survey, which were extracted for NUTS3 areas from Nomis. Following Joyce & Xu (2020), sectors classed as being directly affected by the Covid-19 lockdown were (four digit SIC codes in brackets): Non-food, non-pharmaceutical retail (4719, 4730–4772, 4776, 4799); passenger transport (4910, 4931–4939, 5010, 5030, 5110); accommodation and food (5510–5630); travel (7911–7990); childcare (8510, 8891); arts and leisure (9001–9329 except ‘artistic creation’ 9003); personal care (9601–9609 except ‘funeral and related activities’ 9603); domestic services (9700). Ref: Joyce, R and Xu, X. (2020). Sector shutdowns during the coronavirus crisis: which workers are most exposed?. London: IFS. Available at: https://ifs.org.uk/publications/sector-shutdowns-during-coronavirus-crisis-which-workers-are-most-exposed (accessed 4th July 2020).
The statistical distribution of each variable was assessed and checked for outliers. All values appear plausible and distributions either normally distributed or skewed as expected (e.g. population and employment concentrations in cities). Given concerns over disruption to response rates and non-response bias in the Annual Population Survey during the Coronavirus pandemic, weighted population totals for subgroups under investigation from the three-year pooled APS 2020-21 were compared against 2021 Census of Population results, which revealed a very close correspondence for the vast majority of NUTS3 areas.
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| Rights owners: |
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| Contact: |
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| Notes on access: |
The Data Collection is available for download to users registered with the UK Data Service.
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| Publisher: |
UK Data Service
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| Last modified: |
28 Jul 2026 13:01
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Available Files
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