Woodcraft, Saffron; Harris, Lasana; Nnamoko, Nonso; Lu, Saite; Procter, Jack; Chaudhary, Mrinal; (2025) Using administrative data and artificial intelligence to understand community well-being. UCL, Institute for Global Prosperity: London, UK.

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Abstract

This report summarises the findings of an interdisciplinary research project exploring the viability and utility to local authorities of using administrative data—data that records residents’ behaviours rather than self-reported perception data collected in surveys—and AI to gain insight into place-based community well-being. Administrative data is currently widely used in the private sector to understand consumer behaviour. Yet it is under-utilized in the public sector, even though data recording library membership, parking charges, noise complaints, and recycling are routinely collected by local authorities. These are rich data sources that can be leveraged to better understand community well-being. Given the financial constraints faced by many local authorities and the high costs of collecting survey data, using administrative data could be a cost-effective way to make the most of an under-used public resource. Similarly, the use of AI by the public sector has been proposed by government to find efficiencies and cost-savings.

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