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Project Summary

The aim is to create macroeconomic models that quantify the impact of prevention, ageing, and medical innovation on the economy—including implications for fiscal sustainability, labour supply, productivity, and even monetary policy—by developing and calibrating general-equilibrium, heterogeneous-agent frameworks in which both life expectancy and healthy-life expectancy are endogenous outcomes of preventive investment, health R&D, and labour-market dynamics.
The research will:

• Quantify macroeconomic feedbacks of chronic‑disease burdens on growth, capital accumulation and fiscal sustainability.
• Analyse the distributional consequences of ageing, health investment, and medical innovation—including how chronic disease, prevention, and access to new technologies shape inequality in health, income, and lifetime welfare.
• Identify optimal retirement and labour‑force‑participation policies when older workers face rising morbidity risk but possess valuable human capital.
• Compute the distribution of willingness‑to‑pay for prevention across the wealth‑ and income‑space, showing how policy design (e.g. subsidies vs mandates) can maximise welfare and affect income and/or wealth inequality.
• Embed directed medical R&D—where firms allocate research effort across disease areas—in order to derive endogenous paths of medical‑progress‑based growth and to study how prevention policies reshape innovation incentives.

Why it matters
Governments confront intertwined challenges: exploding chronic‑disease costs, shrinking working‑age populations and uneven access to preventive care. Existing macro models typically treat health as exogenous or focus only on mortality. A framework that links healthy ageing, productive ageing and directed innovation is essential for:

• Making the general‑equilibrium case for preventive spending
• Guiding the design of retirement ages and flexible‑work schemes
• Prioritising public R&D subsidies toward high‑impact disease areas
• Informing health technology appraisals with state‑contingent (e.g. across income or wealth distribution) welfare metrics rather than average cost‑per‑QALY figures.

Why EIT is the place
EIT uniquely combines secure access to linked electronic health records, earnings, and pension data—critical for empirical calibration—with GPU/CPU clusters for solving high‑dimensional equilibrium systems. Interaction with EIT’s prevention, clinical and AI/data‑science teams ensures biologically realistic health processes and cutting‑edge numerical methods, while Oxford’s economics and policy faculties provide a rich intellectual environment for macro‑public‑finance questions.

Potential Supervisors  

• Supervisors are to be confirmed

Skills Recommended

• Graduate micro & macro theory (dynamic optimisation, GE)
• Solid quantitative background in maths/stats (stochastic calculus, numerical methods)
• Programming experience (Python, Julia or MATLAB)
• Motivation to work across economics, data science and health domains

Skills to be Developed

• Continuous‑time and discrete‑time heterogeneous‑agent modelling
• Sparse‑matrix & GPU‑accelerated solvers for HJB–KFE systems
• Calibration & validation with linked administrative micro‑data
• Welfare‑analysis techniques for health‑economics and public‑finance audiences
• Policy‑simulation and translation skills for decision‑makers

University DPhil Courses 

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