Rice, H.P. orcid.org/0000-0002-6895-8325, Höhn, A., Meier, P. orcid.org/0000-0001-5354-1933 et al. (2 more authors) (2025) An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources. Scientific Data, 12. 1230. ISSN: 2052-4463
Abstract
To address the scarcity of small-area datasets focused on economic inclusion, we created a harmonised dataset describing the extent and enablers of economic inclusion in Great Britain. The result, the SIPHER (Systems Science in Public Health and Health Economics Research) Inclusive Economy (Ward Level) dataset, consists of 13 indicators describing economic inclusion at electoral ward level (N = 7,973 of 8,020 wards, 2022 boundaries), for 2019–2021. The dataset was curated based on administrative statistics (mostly open-source) and the SIPHER Synthetic Population, a validated, survey-based, full-scale synthetic population dataset derived from the UK Household Longitudinal Study (UKHLS): Understanding Society, and aggregate-level population statistics. The dataset also includes summary measures of population health – age-standardised Short Form Health Survey (SF-12) mental and physical health component scores – and supplementary demographic indicators describing the population structure. For validation, a range of comparisons against deprivation indices and other data provide strong evidence of the dataset’s added value and utility for applications in research and policy requiring high-quality estimates at a granular spatial resolution.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2025. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jul 2025 09:59 |
Last Modified: | 25 Jul 2025 09:59 |
Status: | Published online |
Publisher: | Nature Research |
Identification Number: | 10.1038/s41597-025-05502-x |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229585 |