Theses

Formal requirements

The following documents describe the formal requirements your paper should adhere to:

Applying for a master thesis

In general, you are responsible to ensure fulfilling all necessary requirements to be eligible for writing a thesis and also comply with all the rules prescribed by your study program. You need to make sure that by consulting the Prüfungsordnung and/or the Studienbüro/program office.

You need to submit a formal application by e-mail (digital.economics@uni-mainz.de). It should include the following information:

  • The study program you are enrolled in and the number of semesters you have been studying
  • Your most recent transcript
  • A recent CV
  • A short letter of motivation, describing your general areas of interest, one or several potential topics you can imagine working on (if available), and your methodological preferences (e.g. whether you want to work with data in your thesis).

The submission deadlines for applications are:

  • February 1 (for the summer term)
  • August 1 (for the winter term)

Procedures after your application

  • If your application has been submitted on time, we will contact you to arrange an office hour were we will either suggest you a topic or discuss your own suggested topic with you.
  • Following this office hour, you are required to draft an exposé based on the discussed topic. Please hand in a printed version and send an electronic version (PDF) to digital.economics@uni-mainz.de.
  • After having received your exposé you will be notified of the success of your application. Since we receive many applications, we reserve the right to decline your application.
  • If you are accepted, you are required to start your thesis at the beginning of the quarter that you applied for.

Writing your thesis

  • During your thesis you can contact your supervisor to arrange office hours. There is no formal limit on the number and structure of office hours, rather this depends on individual arrangements with your supervisor with regard to your concrete topic and the corresponding supervision requirements.
  • After each office hour (within 1 or 2 days) you need to send your supervisor a short summary documenting your main points of discussion and in particular anything you agreed upon during the office hour.
  • In each quarter there is a colloquium where all students currently writing their thesis with us are required to present their current status and also be present and active to the presentations of their fellow students.
  • Please make sure you acknowledge the Formal Requirements.

Potential Topics

Supervisor Christiane Buschinger

  • Primary fields: behavioral economics, motivated cognition, memory, belief formation
  • Secondary fields: discrimination and stereotypes, social economics (social norms, peer effects)
  • Possible formats: extensive literature review, empirical work (data analysis, experiment)
  • Prerequisites: prior knowledge in behavioral economics, experimental economics, econometrics, microeconomics / game theory
  • Requirements for empirical work: (advanced) knowledge of econometric methods; for data analysis, strong skills in Stata or Python
  • Exemplary directions for empirical work: design and implementation of online or lab experiments, replication and extension of published articles (with available data)
  • Other: Student can bring their own dataset and pitch a related research question

Supervisor Julian Detemple

  • Primary fields: behavioral & experimental economics, in particular measurement of preferences, beliefs, and mental models in online environments
  • Secondary fields: development economics, political economy
  • Possible formats: extensive literature review, empirical work (re-analysis of experimental data, analysis of text data, collection & analysis of new data)
  • Prerequisites: prior knowledge in behavioral economics (ideally measurement of preferences or cognitive foundations), experimental economics, microeconomics / game theory
  • Requirements for empirical work: (advanced) knowledge of econometric methods; for data analysis, strong skills in Stata or Python (in particular data analysis); for analysis of text data, strong skills in Python (in particular NLP and machine learning)
  • Exemplary directions for empirical work: replication and extension of published articles (with available data), design and implementation of online experiments / surveys, theory-guided analysis of text data on reasoning in experimental games
  • Other: Student can bring their own dataset and pitch a related research question

Supervisor Marius Dietsch

  • Primary fields:Behavioural economics/household finance, risk and time preferences, nudging.
  • Secondary fields: social preferences, economic psychology.
  • Possible formats: extensive literature review, empirical work.
  • Prerequisites: Prior knowledge in advanced microeconomics, advanced knowledge in econometrics (especially panel data analysis), behavioural economics, experimental economics, nudging.
  • Prerequirements for empirical work: Advanced knowledge of econometric methods and/or machine learning, strong skills in R or Stata.
  • Exemplary directions for empirical work: Re-estimation of dataset from a lab experiment (research field: (behavioural) household finance. Connecting existing micro-economic (lab) datasets, e.g. on behavioural measurements (i.e. time, risk or social preferences). Student can bring their own dataset for analysis and pitch a related research question.

Supervisor Markus Eyting

  • Primary fields: microeconomic foundations of information processing and belief formation, discrimination and disparities, behavioral frictions due to scarcity
  • Secondary fields: elicitation of subjective beliefs, organ donations, machine learning
  • Possible formats: extensive literature review, empirical work (data analysis, experiments)
  • Prerequisites: Prior knowledge in advanced microeconomics, behavioral economics, experimental economics, game theory, digital economics, microeconometrics, machine learning is beneficial.
  • Prerequisites for empirical work: knowledge of econometric methods and/or machine learning, strong skills in R, python or Stata.
  • Exemplary directions for empirical work: Set-up and implementation of lab experiment (discrimination, organ donation, scarcity), ML prediction task, Replication analysis
  • Other: Student can bring their own dataset and pitch a related research question.

Supervisor Marcel Spieske

  • Primary fields: risk/ skewness preferences, Humain-AI interaction, complexity.
  • Secondary fields: sustainable finance, cognition
  • Possible formats: extensive literature review, empirical work (data analysis, experiments)
  • Prerequisites: Prior knowledge in advanced microeconomics, behavioral economics, experimental economics, game theory, digital economics, microeconometrics, machine learning is beneficial.
  • Prerequirements for empirical work: Advanced knowledge of econometric methods and/or machine learning, strong skills in R, or Stata.
  • Exemplary directions for empirical work: Re-estimation of dataset from a lab experiment (research field: (behavioral) finance). ML prediction task, Set-up and implementation of lab experiment (behavioral measurement). Student can bring their own dataset for analysis and pitch a related research question.