Data linkage for early intervention in the UK: Parental social license and social divisions

• Published in: Data & Policy 2021-01, Vol.3, Article e34
• Main Authors: Edwards, Rosalind, Gillies, Val, Gorin, Sarah
• Format: Article
• Language: English
• Subjects: Children; Coronaviruses; COVID-19; Data analysis; Disadvantaged; Disengagement; Early intervention; Families & family life; Family policy; Households; Inequality; Information sharing; Informed consent; Intervention; Legitimacy; Licenses; Minority & ethnic groups; Minority groups; operational data linkage; Pandemics; Panel data; Parents; Parents & parenting; Predictions; Predictive analytics; Prevention; Public records; Public services; social divisions; social license; Social policy
• ISSN: 2632-3249; 2632-3249
• DOI: 10.1017/dap.2021.34

Electronic linking of public records and predictive analytics to identify families for preventive early intervention increasingly is promoted by governments. We use the concept of social license to address questions of social legitimacy, agreement, and trust in data linkage and analytics for parents of dependent children, who are the focus of early intervention initiatives in the UK. We review data-steered family policy and early intervention operational service practices. We draw on a consensus baseline analysis of data from a probability-based panel survey of parents, to show that informed consent to data linkage and use is important to all parents, but there are social divisions of knowledge, agreement, and trust. There is more social license for data linkage by services among parents in higher occupation, qualification, and income groups, than among Black parents, lone parents, younger parents, and parents in larger households. These marginalized groups of parents, collectively, are more likely to be the focus of identification for early intervention. We argue that government awareness-raising exercises about the merits of data linkage are likely to bolster existing social license among advantaged parents while running the risk of further disengagement among disadvantaged groups. This is especially where inequalities and forecasting inaccuracies are encoded into early intervention data gathering, linking, and predictive practices, with consequences for a cohesive and equal society.