Linking administrative benefits and child protection data to uncover poverty's hidden impact

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Natalia Valdebenito Contreras
Tamara Godoy Jalil
Rick Hood

Abstract

This study examines the relationship between household financial circumstances and children’s social care (CSC) involvement using newly linked local administrative data. Household benefits data (SHBE and UCDS) were securely linked and anonymised with Children in Need (CIN) records from six local authorities in London and South England, covering 2019–2021. Financial precarity is defined as households living below the relative poverty line or experiencing a cash shortfall. Children living in financial precarity were not more likely to be initially referred to CSC. However, once referred, they were significantly more likely to experience higher-intensity statutory interventions, including having a Child Protection Plan (12% vs 9%) and being re-referred (32% vs 29%). We estimate that an additional 270 Child Protection Plans were made over the study period among children referred from households below the poverty line. The 2020–21 Universal Credit (UC) uplift provides a natural experiment to examine the role of income support. Households receiving the uplift were substantially less likely to be in financial precarity, with a 17.5 percentage-point relative improvement. Children in uplift-eligible households were more likely to be referred to CSC but less likely to receive subsequent statutory protective interventions, suggesting that improved financial stability reduces the need for high-intensity CSC involvement. These findings demonstrate the value of ethically governed administrative data linkage for evaluating social policy. They highlight the importance of policies that strengthen families’ financial circumstances as part of a preventative approach to improving child wellbeing and reducing statutory demand.

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How to Cite
Contreras, N. V., Jalil, T. G. and Hood, R. (2026) “Linking administrative benefits and child protection data to uncover poverty’s hidden impact”, International Journal of Population Data Science, 11(5). doi: 10.23889/ijpds.v11i5.3590.