My PhD project is an ESRC-funded project through the Advanced Quantitative Methods stream and based in the ESRC’s Administrative Data Research Centre for Scotland (ADRC-S). I’m based in the dept. of Urban Studies in the University of Glasgow and I’m supervised by Prof. Nick Bailey and Dr. Richard Papworth. The aim of this project is to provide a richer understanding of how poor mental health, namely Severe Mental Illness (SMI), such as psychosis, can shape the employment prospects for individuals in Scotland throughout their lifespan: how poor health may lead to employment loss or hinder the return to employment for those out of work. It will be based primarily on the quantitative analysis of longitudinal data on individual health and employment histories, constructed from administrative datasets from around Scotland.

The low levels of employment for people with long-term or complex mental health conditions are a major public health issue since they impact not only on individual welfare but also on the national economy and public expenditure. Many efforts to raise employment rates for this group have been made, through reforms of employment services as well as health interventions, with varying success and failure. This project will study these issues within Scotland for two reasons. First, there is scope for new policy interventions with the devolution of welfare and employment programme powers to Scotland where they sit alongside responsibilities for health and education. The Scottish Government and others are exploring a range of new interventions for both health and employment support. As such, better information to support their decisions is clearly needed. Secondly, there are particular opportunities to study these issues through the exploitation of linked administrative datasets in Scotland. These can provide a longitudinal picture of individual health and employment histories. These data come from: the benefits system for periods of unemployment or inactivity through ill-health; the tax system for employment or earnings history; and the health services for health events, providing a unique way to inform future interventions for psychosis and employment support.


  • Public Health (Global Mental Health)
  • Psychiatric epidemiology
  • Poverty, deprivation, and social exclusion
  • Health inequalities in marginalised groups (Roma/Gypsy/Traveller)
  • Welfare systems and conditionality
  • ‘Invisible’ disabilities (Traumatic Brain Injury, Chronic Pain)
  • ‘Complex’ mental health difficulties (Psychosis, OCD, Anxiety)
  • Mixed methods (inc. causal inference and advanced statistics), lived experience, and survivor co-production

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