This module will introduce students to the practice of experimentation in economics, as developed in the last sixty years. Emphasis will be on the methodology, in particular, statistical techniques necessary for establishing causality and valid inference, non-parametric and other useful non-standard statistical tests for experimental data, formal analysis of how scientific knowledge accumulates, in particular with respect to replication and meta-analysis.
Aims and Objectives
Having successfully completed this module you will be able to:
- Learn about non-parametric techniques.
- A commitment to continued reflection, evaluation and self-improvement.
- A commitment to scholarship in economics to achieve the highest professional standards.
- Reflect on the use of modern econometric techniques for quasi-experimental and observational data.
- Assess whether the design and analysis of existing empirical research promotes sound replicable and incremental knowledge.
- Learn about the design of lab and field experiments.
- A commitment to work with and learn from colleagues.
- Employ the quantitative skills necessary for designing experiments and analysing the evidence, as well as for performing observational studies.
- Describe the basic types of experimental economic research, as well as the main findings of the relevant literature.
- Adopt and develop a sound quantitative approach to economic problems, e.g. through their MSc dissertation.
- Reflect on scientific method, including replication and meta-analysis.
The main topics to be covered are:
Topic 1: Introduction to experimental economics and the main types of economic experiments.
Topic 2: The design of economic experiments: randomization, power analysis and controlling for failures of randomization.
Topic 3: Empirical results from the lab: market and auctions, coordination and public goods, bargaining and social preferences, and individual decision-making.
Topic 4: Other approaches: field experiments and neuroeconomics.
Topic 5: Synthesizing the evidence: replications and meta-analysis.
Learning and Teaching
Teaching and learning methods
This is a module consisting of 20 hours of lectures and classes. Module hand-outs, lectures notes and problem sets will be made available via Blackboard.
|Total study time||100|
Resources & Reading list
Nicolas Jacquemet and Olivier L'Haridon (2019). Experimental Economics: Method and Applications. Cambridge: Cambridge University Press.
Murray Webster and Lane Sell (2007). Laboratory Experiments in the Social Sciences. Elsevier.
Harris Cooper, Larry Hedges, Jeffrey Valentine (2009). The Handbook of Research Synthesis and Meta-Analysis. Russel Sage Foundation.
John H Kagel and Alvin ERoth (1995). The Handbook of Experimental Economics. New Jersey: Princeton University Press.
The module will be assessed through a take-home assignment (worth 70% of the final mark) and two pieces of coursework (each worth 15% of the final mark). This is supported by continuous formative assessment through problem sets. This is the same for internal repeat. Assessment for external repeat and referal is thorugh 100% take-home assignment.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Repeat type: Internal & External