Abstract

This study explores the causal impact of obesity on employment using data from the UK Biobank, a comprehensive dataset combining genetic, health, and socioeconomic infor- mation. We apply a Mendelian randomisation (MR) approach, using a genetic risk score (GRS) for higher body mass index (BMI) as an instrumental variable. This method lever- ages genetic predispositions determined at conception, enabling us to isolate the exogenous variation in obesity while addressing key endogeneity concerns, such as omitted variable bias and reverse causation. To account for the heterogeneity in obesity’s impact across diverse demographic and socioeconomic factors, we apply a combination of IV techniques and the person-centered treatment effects (PeT) framework. This methodological approach extends beyond traditional average treatment effect estimations, uncovering nuanced, individual- specific impacts that reflect the complex relationship between obesity and employment probabilities. Our findings reveal a significant and negative causal effect of obesity on employment across all estimation methods. The nonlinear approaches, including marginal treatment and PeT effects, yield the same results with differences in the marginal effects. Gender-specific subsample analyses indicate that the employment penalty associated with obesity is more pronounced among females in the linear models, whereas the PeT effects approach reveals the opposite pattern. Overall, these results highlight the wider economic burden of obesity, particularly its adverse impact on employment