A Snapshot of Policy-Oriented Projects Based on Administrative Benefits Data in the UK

ObjectivesWe present research projects conducted linking administrative benefits data held by local authorities with other datasets. Leveraging the richness of this individual-level monthly data, we demonstrate its potential to support local authorities’ statutory duties and reveal the role played by non-take-up in vulnerable households’ trajectories. MethodsDrawing on our experience working with over 40 local authorities, we showcase projects linking administrative benefits data with local datasets, such as adult and children’s social care records, and external sources, including legal services, water companies, and healthcare providers. We assess the overlap between cohorts in administrative benefits data and low-income populations in external datasets. We outline the key variables included, the legal gateways enabling data sharing, and the challenges of designing (quasi-) experimental setups to evaluate interventions. Administrative benefit data sources include: the Council Tax Reduction Scheme, the Single Housing Benefits Extract and the Universal Credit Data Share. ResultsAdministrative benefits data offer a dynamic snapshot of a household’s financial situation. Local authorities can proactively use and share this data with external processors to fulfil their statutory duties if a legal gateway allows. Identifying households at risk of cash shortfalls before crisis point enables targeted interventions for local welfare schemes and homelessness prevention. By comparing benefit eligibility and uptake, local authorities can run data-driven outreach campaigns and evaluate their impacts. These research projects reveal how interventions – such as legal advice, uptake campaign letters, autoenrollment in social tariffs, or referrals to social care – affect individual financials situations in a timely manner. We analyse when impacts manifest and their duration, offering insights into how administrative benefits data can support the design and evaluation of policy interventions. ConclusionsLocal authorities routinely process longitudinal benefits data, providing real-time insights into low-income households’ socioeconomic situations. Despite its potential for policy-oriented research, this resource remains underutilized. Unlocking its value through responsible data sharing and linkage can enhance evidence-based policymaking, improving support for vulnerable populations.

Authors

Institutions

Publication Details

Journal
International Journal for Population Data Science
Published
2025-08-28
DOI
https://doi.org/10.23889/ijpds.v10i4.3311
Primary Topic
Community Development and Social Impact
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Snapshot of Policy-Oriented Projects Based on Administrative Benefits Data in the UK

Francisco Javier Quintero Cortes, Deven Ghelani, Juliet-Nil Uraz
International Journal for Population Data Science
Community Development and Social Impact
article

A Snapshot of Policy-Oriented Projects Based on Administrative Benefits Data in the UK

Francisco Javier Quintero Cortes, Deven Ghelani, Juliet-Nil Uraz
article en

Abstract

ObjectivesWe present research projects conducted linking administrative benefits data held by local authorities with other datasets. Leveraging the richness of this individual-level monthly data, we demonstrate its potential to support local authorities’ statutory duties and reveal the role played by non-take-up in vulnerable households’ trajectories. MethodsDrawing on our experience working with over 40 local authorities, we showcase projects linking administrative benefits data with local datasets, such as adult and children’s social care records, and external sources, including legal services, water companies, and healthcare providers. We assess the overlap between cohorts in administrative benefits data and low-income populations in external datasets. We outline the key variables included, the legal gateways enabling data sharing, and the challenges of designing (quasi-) experimental setups to evaluate interventions. Administrative benefit data sources include: the Council Tax Reduction Scheme, the Single Housing Benefits Extract and the Universal Credit Data Share. ResultsAdministrative benefits data offer a dynamic snapshot of a household’s financial situation. Local authorities can proactively use and share this data with external processors to fulfil their statutory duties if a legal gateway allows. Identifying households at risk of cash shortfalls before crisis point enables targeted interventions for local welfare schemes and homelessness prevention. By comparing benefit eligibility and uptake, local authorities can run data-driven outreach campaigns and evaluate their impacts. These research projects reveal how interventions – such as legal advice, uptake campaign letters, autoenrollment in social tariffs, or referrals to social care – affect individual financials situations in a timely manner. We analyse when impacts manifest and their duration, offering insights into how administrative benefits data can support the design and evaluation of policy interventions. ConclusionsLocal authorities routinely process longitudinal benefits data, providing real-time insights into low-income households’ socioeconomic situations. Despite its potential for policy-oriented research, this resource remains underutilized. Unlocking its value through responsible data sharing and linkage can enhance evidence-based policymaking, improving support for vulnerable populations.

International Journal for Population Data ScienceVol. 10(4)
London School of Economics and Political Science (GB)
Openalex Percentile: Top 70%
Community Development and Social Impact
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.