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ECON42650

Academic Year 2025/2026

Experiments in Economics (ECON42650)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Professor Orla Doyle
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

“It is possible to make significant progress against poverty in the world by focusing on well-defined questions and being as rigorous as possible in answering those questions in the real world” Prof. Esther Duflo, Nobel Prize Banquet Speech 2019.

This module sits firmly in Duflo’s vision for the role of economists in public policy by creating empirically grounded research which uses causal methods to inform policy debates and design. This module provides a detailed overview of the key methods for examining causal relationships and testing theoretical mechanisms in behavioural economics. The module is divided into two sections. First, we will examine the use of randomised controlled field trials in behavioural economics. Second, we examine the use of online and lab experiments in behavioural economics. The module will cover all aspects of conducting an experiment including study design, ethical approval, randomisation, power analysis, measurement, analysis, and validity. For your assignment, you will have the opportunity to design and conduct a lab/survey experiment. The aim of the assignment is to apply the theoretical frameworks covered in the module to the practical application of field and online and lab experiments.

About this Module

Learning Outcomes:

At the end of the module, you will be able to:
• Describe the key experimental and causal methods in behavioural economics
• Articulate key methodological debates on causal methods
• Demonstrable theoretical knowledge on the key processes involved in conducting field, online, and lab experiments
• Evaluate the strengths and limitations of field, online, and lab experiments
• Apply practical experience in designing a field, online, and lab experiment.

Indicative Module Content:

Week Topic Lecturer
1 Module Overview & Introduction to Online experiments Vessela Daskalova
2 Field Experiments: Introduction to Randomised Controlled Trials I Orla Doyle
3 Field Experiments: Introduction to Randomised Controlled Trials II Orla Doyle
4 Lab Experiments: Designing Lab Experiments for Strong Inference Pete Lunn
5 Field Experiments: Methods of Randomisation Orla Doyle
6 Field Experiments: Power Analysis & Sample Size Orla Doyle
7 Field Experiments: Measurement & Analysis Orla Doyle
8 Field Experiments: More Analysis & Threats to Validity Orla Doyle
9 Lab Experiments: Data Analysis Vessela Daskalova
10 Lab Experiments: Laboratory “Exhibits” in Behavioural Economics Lucie Martin
11 Lab Experiments: A Moment of Creation (Kahneman and Tversky, 1979) Pete Lunn

12 Lab Experiments: Replication Vessela Daskalova

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Autonomous Student Learning

200
Total

224

Approaches to Teaching and Learning:
Multiple different teaching and learning approaches will be used in this module including reflective learning, class discussion, group work, lectures and tutorials.

The use of AI is only permitted in this module to improve the quality of your writing in terms of grammer, spelling, flow etc.

Plagiarism risk is high when using AI, and it is not acceptable to submit AI-generated content as it is. If you use AI to content for your group project, this is considered academic misconduct. If you copy and paste entire AI-generated answers, it will be considered plagiarism. Even AI co-created work is considered collusion if you present it all as your own. Note, AI-generated lists of publications and other sources are predictions only; they may not truly exist, therefore you should check all sources of information provided for accuracy. Please note that if academic misconduct is suspected, you may be asked to discuss or explain components of your assignment to determine the authenticity of the work”. Please refer to the UCD Student Academic Misconduct Procedure.


Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: Design, implement, and analyse a lab/online survey experiment of your choice. Week 15 Alternative linear conversion grade scale 40% No
100
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Vessela Daskalova Lecturer / Co-Lecturer
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