Using administrative data to understand community wellbeing

This research project is from the UCL Institute for Global Prosperity.

Project lead: Professor Lasana Harris, Senior Lecturer at the UCL Department of Experimental Psychology
Co-lead first stage of research: Dr Saffron Woodcraft, Executive Lead at PROCOL UK at the UCL Institute for Global Prosperity
Funders: The Nuffield Foundation and the Understanding Communities Fund
 

The challenge

Knowledge about place-based wellbeing is currently derived from a combination of self-reported data from surveys and qualitative studies, economic data, and space and service provision data.

Local governments hold substantial behavioural data on their servers, including council tax records, closed-circuit television feeds, noise complaints, and library membership. However, this information is not currently used to analyse and develop new policies focused on wellbeing.

This information is more objective than self-reported data, but it also carries a variety of ethical concerns.

Our approach

This project explores whether government agencies can use behavioural data, administrative and other data that records the activities and acts of community members, to gain additional insight into place-based community wellbeing.

The first stage of the project consisted of conducting a review of the current landscape of community wellbeing measurement through a behavioural lens. We identified different definitions of community wellbeing, indices and frameworks that are currently used in the UK.

We also identified common conceptual domains, for example community connections and belonging, healthy environments, and employment and opportunities, that can be measured using behavioural data sources routinely collected by local authorities and government agencies.

The project also aimed to identify where there is no need to collect behavioural data as it already exists, as well as new data sources and ways of measurement that complement existing frameworks and contribute to creating new knowledge about community wellbeing.

The project is structured across five work packages:

• WP1: Review of landscape
• WP2: Ethical approach to using behavioural data
• WP3: Behavioural data demonstrators with local authority partners
• WP4: Impact assessment of demonstrators
• WP5: Behavioural Data for Community Insight Network

Findings

According to the findings, a widely accepted definition of community wellbeing does not yet exist. A useful starting point is:

“Community wellbeing is the combination of social, economic, environmental, cultural and political conditions identified by individuals and their communities as essential for them to flourish and fulfil their potential.”
– Wiseman and Brasher, 2008

We found that wellbeing indicators can be objective or subjective, and focus on individuals or on the community. But this understanding can also be somewhat flexible, and it is useful to think in terms of a spectrum from subjective to objective; and from individual to community, as shown in the diagram below:

This diagram shows how community wellbeing can be understood across a spectrum, from subjective experiences like happiness to objective factors such as health, education, and living conditions, at both individual and community levels.

As per our review, the wellbeing and the UK community wellbeing measurement landscape can be very succinctly summarised in a timeline:

This timeline shows how ideas and measures of wellbeing have developed over time, bringing together key concepts such as happiness and quality of life with major frameworks and indices used to assess wellbeing at individual and community levels.

Barriers to using administrative data and AI

The project developed a proof-of-concept Community Well-being Index Dashboard based on the ‘Good Life Euston Model’ as a starting point for understanding community well-being. By running administrative data through an AI model, the researchers were able to generate well-being scores for every local authority in England.

While the study brings to light the huge potential that administrative data and AI could bring to understanding the needs and challenges of communities, it also highlights the barriers:

1.  Local authorities do not currently have the capacity or infrastructure to enable the widespread use of administrative data to measure community wellbeing.
2. Along with data infrastructure reform, we recommend legal and policy reform around data and data governance including debate on public and community data ownership and use.
3. Experts should work alongside community members to outline potential for misuse of data, and discussions around data privacy infrastructure and institutional trust.
4. Community well-being is complex and context specific. Further research is needed to investigate the multiple dimensions of community well-being and to better understand the dynamics between community well-being and other socio-economic variables.

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