Programme
About the programme
M.Sc. Health Economics is a full-time, four-semester, English-language master’s programme at the University of Duisburg-Essen. This research-oriented programme addresses some of the most significant challenges facing modern societies across the globe: the causes of population ageing and its consequences for different sectors of the economy; socioeconomic inequality in health and the interplay between health, education, and labour-market outcomes; the funding of healthcare and how the reimbursement of healthcare providers affects their incentives to provide care on equitable grounds.
Programme Structure
Your study programme will be divided into 4 type of modules:
- Compulsory modules
- Elective courses
- Seminar
- Master's thesis
While core modules and most electives are taught in English, students have the opportunity to attend selected courses held in German.
Please note that only the (German) documents published at the UDE homepage of the Master's programme are legally binding. The information displayed here is for orientation only. This also applies to the module overview below.
Find the most recent course overview here.
Compulsory Modules
5 Compulsory Courses | 30 ECTS
These include:
- Health Economics I
- Health Economics II
- Micro-econometrics
Knowledge in applied econometrics and relevant economics theory
Elective Modules
9 Elective Courses | 54 ECTS
Modules from fields of:
- Economics (minimum of 4)
- Business Administration
- Medical Management
Specializations in econometrics, other economics fields (behavioural economics), or business
Seminars
1 seminar | 6 ECTS
Topics of Health Economics and other disciplines such as:
- Health & Development
- Econometrics
- Labour Economics
Exercising work with research papers
Master's thesis
Compulsory Module | 30 ECTS
Eligibility criteria:
- Min. 60 ECTS
- Min. 24 ECTS from compulsory courses
- Min. 6 ECTS from seminar
Develop and investigate your own research question
Chose supervision from relevant chairs
Semester plan (start winter semester)
Semester plan (start summer semester)
Compulsory Modules
In what follows, we present the six compulsory modules. Scroll down to find descriptions of elective courses!
Find the full overview of courses here: module handbook (German, note that some of the most current changes in the course structure are not yet implemented in the module handbook).
Find the interactive course catalogue.
Health Economics I
Health Economics I introduces students to the fundamental principles and challenges of economics within the context of healthcare systems. The primary objectives of the course are threefold.
First, students explore methodologies for measuring health outcomes and assessing the quality of healthcare services. This involves understanding metrics such as life expectancy, morbidity rates, and patient satisfaction scores, crucial for evaluating the effectiveness of healthcare interventions.
Second, the course delves into potential market failures within the healthcare sector. Students examine scenarios where free-market principles may not lead to optimal outcomes, such as asymmetric information between patients and providers, supplier-induced demand, and issues surrounding the pharmaceutical market's pricing and competition.
A significant component of 'Health Economics I' focuses on the role of individuals as producers of their own health. This includes analysing how lifestyle choices, preventive care, and health behaviours influence overall health outcomes and health care expenditures, providing insights into personal health investments and societal implications.
Health Economics II
In this course you will study the economics of health care systems. The course sets out ambitious aims:
First, to familiarize students with the diverse institutions and key players within healthcare sectors across different countries. This includes understanding the roles and interactions of entities such as hospitals, insurers, pharmaceutical companies, and regulatory bodies.
Secondly, students will explore the microeconomic underpinnings driving the behaviours and decisions of these health care players. This involves analysing motives such as profit maximization, risk management, and patient welfare considerations, crucial for grasping the complexities of health economics.
A significant focus lies on normative analysis, equipping students with the tools to assess when and how governments should intervene in health care markets. This includes evaluating the effectiveness of regulatory frameworks, subsidy schemes, and public health initiatives in achieving equitable and efficient health outcomes.
Public Economics I:
Theory of Taxation
This course provides students with a comprehensive introduction to the economic theory of taxation.
It covers fundamental concepts in public economics and economic modelling, focusing on both normative and positive tax analysis.
You will learn how to theoretically and empirically examine the behavioural responses of individuals and firms to various forms of taxation, including commodity, income, and corporate taxation: Is there an optimal tax on personal income? Should we introduce a wealth tax to combat growing inequality?
In addition to theoretical learning, students will engage in practical applications through projects that analyse and discuss current topics in current tax policy.
Public Economics II:
Social Policy & Empirics
This course focuses on the allocative and distributive justifications for state-led redistribution, along with the government's role in regulating private insurance sectors.
The curriculum is designed to provide students with a nuanced understanding of various topics in public economics.
During the course, we will cover a broad range of topics (pension systems, education, health poverty traps), analysing the role of the government when markets fail.
A significant emphasis is placed on equipping students with modern tools used in empirical public economics, making it ideal for those interested in understanding the broader impacts of government spending on society and the economy.
Microeconometrics
This course offers a comprehensive introduction to various microeconometric models and estimation techniques, with a particular focus on nonlinear statistical models.
Topics include models for panel data, discrete dependent variables, censoring, and duration analysis. The course combines theoretical lectures with practical tutorials, enabling students to apply the statistical methods to real-world data. Through critical evaluation of empirical studies and hands-on exercises, students will develop the skills needed to interpret research findings and formulate their own research proposals, providing a solid foundation for advanced empirical economic analysis.
Seminar
In the seminar module, you can choose from a wide range of seminars offered by various faculty members. These seminars provide the opportunity to specialise in a specific field and learn about the latest contributions to the academic literature. The seminar is also designed to help you develop your writing and presentation skills. You will author a term paper on a topic of your choice and present it to the group. Additionally, you will provide feedback on other students’ term papers. If you find your topic particularly engaging, you may develop it further into your final master’s thesis.
Master's Thesis
The Master’s thesis is a crucial part of your MSc program in Health Economics. It serves as the final examination of your scientific training in this program. Your thesis will demonstrate your ability to independently solve a specific problem from your field using scientific methods within a set timeframe.
The thesis counts for 30 credits, and you have 26 weeks to complete it.
Eligibility Criteria
To be eligible to start your Master’s thesis, you must have:
- Successfully completed compulsory modules worth 36 credits
- Passed the seminar worth 6 credits
- Accumulated a total of 60 credits
Supervision & Topic Selection
Your thesis topic will be supervised by a faculty member from the Economics Faculty who is involved in the Health Economics programme.
You have the right to propose your own thesis topic, but there are also opportunities to write a thesis on topics related to ongoing research projects.
In the Health Economics programme, a Master’s thesis typically requires an independent scientific inquiry analysing real data with state-of-the-art methods. We offer comprehensive and hands-on structured supervision, including regular workshops with senior faculty and continual one-on-one advisory sessions with junior faculty.
Evaluation Process
Your thesis will be evaluated by two examiners, typically including your supervisor.
Their assessments will be documented in writing. If there is a significant discrepancy between their grades, a third examiner will be appointed.
Your final grade will be the average of the two best grades, provided at least two grades are “sufficient” (4.0) or better.
Elective Modules & Seminars
You will find a short description of selected courses below.
Find the full overview of courses here: module handbook (German, note that some of the most current changes in the course structure are not yet implemented in the module handbook).
Find the interactive course catalogue.
Causality & Programme Evaluation
A course designed to delve deep into the state-of-the-art of modern econometrics, this course illuminates the pivotal role of causal analysis in economics, presenting research designs suitable to address key policy questions: What are the effects of retirement on health? How does medical spending impact life expectancy?
In a discipline where randomized experiments are often impractical, you will explore cutting-edge methods like Difference-in-Differences, Regression Discontinuity Design, and Marginal Treatment Effects.
You will gain proficiency in identifying, designing, and critically assessing empirical studies.
This course is ideal for those gearing up for advanced research or a thesis, and promises not just knowledge but a mastery of the tools that define contemporary economic inquiry.
Inequality in Health
The course delves into the complex interplay between health and socioeconomic outcomes, focusing on disparities that persist despite striking improvements in public health.
The course is based on recent empirical research and promises a deep dive into the economic analysis of health inequalities from various angles.
You will explore how socioeconomic factors influence health outcomes across different demographics and regions.
The curriculum spans critical topics like measuring and decomposing health inequality, understanding the gradient of health across socioeconomic groups, and examining policy interventions that impact early life health and education.
Whether you're interested in policy-making, research, or healthcare management, 'Inequalities in Health' offers a stimulating and comprehensive exploration of one of society's most significant challenges.
Applied Labour Economics
The aim of the course is to provide students with the skills to competently interpret, evaluate and question labour market studies. By means of given data sets, the students will learn to independently apply different econometric methods to current issues in labor market economics.
The students understand how to use quantitative methods in a differentiated way, to develop hypotheses, and to test these empirically. By independently working on the computer, the students will be enabled to independently develop empirical research designs, econometric analyses, and to prepare the results. Furthermore, students will be encouraged to evaluate empirical work critically.
Current Topics in Empirical Analysis
This is a specialized course designed to equip students with advanced empirical methods applicable to health economics research.
The course begins with an exploration of various sources of health data, emphasizing tools to describe and analyse categorical, duration, panel, and count data. Students will learn to apply advanced modelling techniques such as Bayesian approaches and instrumental variable methods to derive meaningful insights from large health datasets.
Each lecture adopts a 'lab-style' approach, combining theoretical concepts with hands-on exercises in Stata. Students receive lecture notes, working codes, and datasets to facilitate practical learning.
The curriculum is structured around essential topics in applied health economics, ensuring students gain proficiency in modelling health care costs, analysing self-reported health heterogeneity, and understanding the dynamics of health variables.
Health Care Systems
This course is a comprehensive seminar offered in the Summer Semester, which aims to explore various health care systems globally, emphasizing their economic implications, efficiency, equity, and cost control mechanisms.
The seminar integrates lectures and discussions with academic presentations, providing insights into European healthcare institutions and addressing future challenges in the field. The course content covers comparative analyses of health care systems across different countries, leveraging empirical approaches to evaluate economic factors influencing health care policies.
Key aspects of the course include understanding the impact of health on economic productivity and societal well-being, the rising health expenditures relative to GDP, and the strategic importance of reforming existing health care systems.
Students will engage in critical discussions and presentations on topics ranging from moral hazard and market failures to the influence of economic incentives and technological innovations in healthcare.
Current Topics in Health Economics Research
This course offers students a comprehensive exploration into contemporary research and methodologies in the field of health economics.
Led by guest professors, the course combines structured tutorials with active participation in the weekly Essen Health Economics Seminar to provide students with a robust academic experience.
Throughout the semester, students engage deeply with current empirical research presented during the seminar series. The primary objectives are to foster a critical understanding of empirical methodologies in health economics and to equip students with the skills to formulate and articulate their own research proposals. Central to the course are the tutorial sessions and the Essen Health Economics Seminar itself, where students critically evaluate and write referee reports on research presented by visiting scholars and faculty members.
This approach not only enhances their ability to assess empirical findings but also cultivates their capacity to contribute meaningfully to ongoing academic discussions in health economics.
Bayesian Econometrics
Bayesian econometrics provides an alternative view on performing empirical work which allows treating parameters as random variables and quantifying uncertainty about them via Bayes’ theorem.
We first provide a conceptual introduction to the Bayesian paradigm, paying attention to concepts like prior selection, conjugacy, statistical decision theory, model comparison and Bayesian asymptotics.
We also discuss the Bayesian view on classical quantities such as p-values or confidence intervals. We then discuss computational tools to perform Bayesian inference such as Gibbs sampling, Metropolis-Hastings or Hamiltonian Monte Carlo.
The third part of the course applies these concepts and methods to provide Bayesian approaches to workhorse empirical tools such as linear regression, LASSO, VARs, limited dependent variables models, GLS or multiple testing. R labs accompany the course to illustrate how to practically implement the techniques discussed.
Recent Developments in Econometrics
The course discusses econometric theory and methods at an advanced level. We adopt a generalized method-of-moments perspective to introduce and study the properties of a variety of leading empirical techniques such as linear regression, instrumental variables, generalized method of moments, three stage least squares, seemingly unrelated regressions or panel data models.
We put particular emphasis on, typically, asymptotic statistical properties of estimators (e.g., consistency and asymptotic normality) and associated quantities, such as suitable standard errors to conduct asymptotically valid inference. We make an effort to prove all main results. Simulation studies accompany the theoretical discussion. The later part of the course additionally introduces a likelihood-based perspective and applies it to linear as well as nonlinear (panel data) regression models. R labs accompany the course to illustrate how to practically implement the techniques discussed.
Statistical Learning
The course introduces a variety of popular predictive data science tools, also known as machine or statistical learning tools. Examples include (classification and regression) trees and random forests, support vector machines, discriminant analysis, (logistic) regression, splines, generalized additive models, principal component analysis or penalization methods such as ridge and LASSO.
We also discuss how to assess the predictive performance of these methods via tools such as cross-validation, bootstrapping or area under the curve. We aim to give a broad overview rather than delve into technical details. R labs accompany the course to illustrate how to practically implement the techniques discussed.
In addition, participants apply the methods to supplied or self-chosen datasets to supply predictions and assess their quality. Students discuss their results in a term paper and a presentation.
Nonparametric Econometrics
The course provides applicable methods in situations in which linearity assumptions are not suitable.
We start with density estimation in univariate and pay attention to practical issues such as kernel and bandwidth selection. We derive the bias and variance of these estimators. We extend the analysis to multivariate contexts and discuss relevant additional complications such as the curse of dimensionality. We then move on to econometric problems such as Nadaraya-Watson and local linear and quadratic regression and again study the asymptotic properties of these techniques.
Further special topics include inference, handling discrete covariates or nonparametric instrumental variables approaches. R labs accompany the course to illustrate how to practically implement the techniques discussed.
Statistical Modeling of Extremes
The course presents the fundamental statistical methods for univariate and multivariate extremes. First, we introduce the classic result on the convergence of sample maxima (as opposed to the convergence of sample means covered by the central limit theorem). We then extend this result to the kth largest observation, before moving on to modelling exceedances over high thresholds. Some more specialized topics include extreme value statistics for time series and non-stationary sequences, such as sequences involving, e.g., seasonalities. Finally, we cover the theory of multivariate extremes. Throughout the course we provide concrete empirical applications to flood levels and human life expectancy, among others. The accompanying tutorials serve to deepen the theoretical understanding as well as to enable students to work on real life data in R.
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