The COVID-19 pandemic created an unprecedented economic and social shock, leading governments around the world to implement emergency employment protection measures. In the United Kingdom, the Coronavirus Job Retention Scheme (CJRS), widely known as the furlough scheme, was introduced in March 2020. The scheme aimed to cushion the economic fallout by subsidising wages for workers who were temporarily unable to work. While furlough effectively mitigated income loss and prevented large-scale unemployment, its broader effects on individual wellbeing—particularly mental health—are still underexplored.
This paper examines the causal impact of being furloughed on cognitive aspects of mental health. Cognitive mental health, encompassing concentration, confidence, and decision-making, is crucial not only for individual functioning but also for long-term employability and productivity. Understanding whether temporary detachment from work leads to cognitive strain has important implications for the design of future employment retention policies.
Using panel data from the UK Household Longitudinal Study (UKHLS) COVID-19 waves, we estimate the effect of furlough on cognitive symptoms derived from the General Health Questionnaire (GHQ-12). To address endogeneity concerns, we instrument furlough status with a binary indicator of whether the individual reported being unable to work from home prior to the pandemic. This instrumental variable (IV) approach allows us to isolate the effect of furlough from unobserved factors that may jointly influence mental health and employment status.
Our findings suggest that furlough has a statistically significant negative effect on cognitive mental health. The effect is particularly pronounced among lower-income individuals, although heterogeneity by age, gender, and relationship status appears limited. These results highlight the potential psychological trade-offs embedded in employment protection schemes and underscore the importance of incorporating mental health considerations into labour policy design.