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Health Economics for Research - ECO00081M

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  • Department: Economics and Related Studies
  • Module co-ordinator: Prof. Luigi Siciliani
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25

Module summary

This module aims to provide research students with the applied microeconomic and microeconometric skills necessary to understand and undertake research in health economics.

Module will run

Occurrence Teaching period
A Semester 2 2024-25

Module aims

This module aims to provide research students with the applied microeconomic and microeconometric skills necessary to understand and undertake research in health economics in preparation for writing their thesis.The module covers a selection of topics in microeconomics and microeconometrics applied to research developments in health economics with a strong emphasis on methods (either mathematical derivation of a microeconomic model or econometric methods with a focus on causality).

Module learning outcomes

Upon completing this module successfully, a student should be able to have a working knowledge of microeconomic models and of microeconometric methods and understand how to apply them in the health economics context and within their research (for example by developing a theoretical model on a new topic, and identifying the relevant data and econometric methods to test a specific hypothesis).

Assessment

Task Length % of module mark
Essay/coursework
Essay		 N/A 100

Special assessment rules

None

Reassessment

None

Module feedback

Feedback will be in line with University policy

Indicative reading

Empirical methods:

An overview of some key ideas for effective data visualization and how they can be applied to econometric analysis of health care costs and outcomes is given by:

Jones, A.M., “Data visualization and health econometrics”, Foundations and Trends in Econometrics, 9, 1-78, 2017. DOI: 10.1561/0800000033.

Conventional approaches to econometric modelling for health care costs are reviewed in:

Jones, A.M., “Models for health care”, Oxford Handbook of Economic Forecasting, Hendry, D. and Clements, M. (eds.), Oxford: Oxford University Press, 625-654, 2011.

An overview of machine learning for economists is provided by:

Athey, S. and Imbens, G.W., Machine learning methods that economists should know, Annual Review of Economics, 11: 685-725, 2019.

https://www.annualreviews.org/doi/pdf/10.1146/annurev-economics-080217-053433

An overview of using machine learning for policy evaluation is provided by:

Kreif, N. and DiazOrdaz, K., Machine Learning in Policy Evaluation: New Tools for Causal Inference, Oxford Online Research Encyclopedia, OUP, 2020.

https://doi.org/10.1093/acrefore/9780190625979.013.256

An introductory overview of the econometrics of policy evaluation in health economics can be found in (see link to pdf above):

Jones, A.M. and Rice, N. "Econometric evaluation of health policies" in Oxford Handbook of Health Economics, Glied, S. and Smith, P.C. (eds), Oxford: Oxford University Press, 2011.

Background reading that covers more established methods for panel data analysis in health economics is provided by:

Jones, A.M., “Panel data methods and applications to health economics”, Palgrave Handbook of Econometrics. Volume 2, Mills, T.C. and Patterson, K. (eds.), London: Palgrave MacMillan, 557-631, 2009.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.