Measuring the health impact of Universal Basic Income as an upstream intervention: holistic trial design that captures stress reduction is essential

Abstract

Background: In the context of the UK Government’s ‘prevention agenda’, Laura Webber and colleagues have called for a ‘health in all policies’ approach. Universal Basic Income (UBI) is a system of cash transfers to citizens. Recent research suggests it could significantly benefit population health, including via reducing stress. However, a Finnish trial of a policy with similarities to UBI has influenced debate. This was reported as a failure due to a policy objective of reducing unemployment, despite demonstrating significant benefits to well-being.Aims and objectives: In this piece, we seek to advance the debate about the cost-benefit of UBI by identifying knowledge gaps and proposing a means of designing effective trials.Methods: We review UBI trial design and findings in comparison with social gradient in health literature and biopsychosocial theory to identify knowledge gaps.Findings: We highlight a need to refocus UBI trials on improved health, including via reduced stress, to provide policy makers the means of producing accurate cost-benefit analysis. Previous trials have either not reflected likely UBI policy or failed to measure impacts that enable accurate analysis. We contend that interdisciplinary work is required to establish trials that observe factors known to drive the social health gradient. Finally, we argue that statistical modelling is needed to extrapolate shorter-term findings to long-term population-level outcomes.Discussion and conclusions: Resource allocation by Government and/or major funders is required to produce evidence that enables accurate analysis of UBI. Such trials would provide a platform for interdisciplinary work resulting in joined-up evidence and policy.Key messagesExisting Universal Basic Income trial designs have not enabled accurate assessment of the policyInterdisciplinarity is needed in trials to observe key factors driving the social health gradientStatistical modelling is essential to produce population-level evidence for policy developmentFinancial resource must be directed to establishing more thorough and evidence-based trials