Program content
For long, experiments have been seen as a specific domain within economics (“experimental economics”). The experimental method is now widely considered as a tool among others in the typical economist’s toolbox. Its use has shed light on many areas, on the theoretical side (decision theory, game theory, markets) as on the more applied and descriptive ones (policy, development, labor econ., IO, health econ., etc.). It has led to the blossoming of “behavioral economics”, but its general relevance goes much deeper than that. The purpose of this program is to provide young researchers with the methodological skills required to pursue experiments in their own research.
Main lecture : Experimental methodology : objectives, methods and uses of experiments in economics, by Nicolas Jacquemet
The course provides an overview of the use of controlled experiments to investigate research questions in economics and social sciences. The focus is methodological, and illustrated by examples and applications taken from the literature. The course goes through the main methodological questions related to the use of controlled experiments to produce empirical knowledge: why are controlled experiments needed (i.e., what kind of question ios best answered thans to controlled experiments)? How to design a controlled experiment (i.e., internal validity issues)? And what are experimental results useful for (i.e., external validity)?
Selected key reference
Jacquemet N., L’Haridon O, (2018), “Experimental Economics: Method and Applications”. Cambridge University Press.
This course takes place every day
Structure
- Why? The need for experimental methods in economics
- How? Laboratory experiments in practice
- What for? What laboratory experiments tell us
Workshop: present your paper
Participants will have the opportunity to submit a paper to be presented within this program. Selected papers will be presented in front of participants and faculty in slots reserved for such presentations.
TOPIC 1: Methods and tricks of the trade – Béatrice Boulu-Reshef
The first session is an “experimental lecture”, in which by putting participants in the shoe of an experimental subject aims at pointing to classical problems and solutions to the practical implementation of a lab experiment. The purpose is to show them in vivo how experimental protocols can be implemented and illustrate the relevance or shortcomings of some practices and methods. Moreover, being in the shoes of a subject in the lab makes salient what typical mistakes can be avoided when researchers design an experiment and what effects some features can have on observed behavior. Either classic studies that have set standards for the field or counter-examples to good practice will be run. The second session is dedicated to the writing of instructions, a topic often neglected although of critical importance. The goal of instructions is to make sure that the experimental subjects fully understand the rules of an experiment. If subjects do not understand the rules, control is lost, and the experiment is invalidated and unpublishable. Thus, learning how to write experimental instructions is crucial. This lecture focuses on teaching how to write experimental instructions. We explain what experimental instructions need to achieve, and hence, list the rules that are commonly used by experimental economists. We also review the features of experimental instructions that are debated in the literature. The lecture reviews typical instructions for the main types of experiments. Students will work on writing instructions and will discuss their work in class. The third session deals with how to extend lab experiment to the field.
Selected key references Davis, D. and C. A. Holt, (1993), “Experimental Economics”, Princeton University Press.
D. Friedman and S. Sunder, (1994), “Experimental Methods: A Primer for Economists”, Cambridge University Press.
Guala F. (2005), “The Methodology of Experimental Economics”. New York: Cambridge University Press.
TOPIC 2: Time Preferences – Thomas Epper
Time preferences are critical drivers of economic behavior in many contexts. Various experimental methods have been developed to measure them. These methods serve to elicit time preferences as control variables in more general experiments, but also to test decision-theoretic models. These methods are presented in this lecture with a specific focus on their relative merits. A special emphasis will be put on robust methods to elicit patience (longer-run discount rates), present bias (hyperbolic discounting) and intertemporal utility.
Selected key references Andreoni, J. and Sprenger, C. (2012). “Estimating time preference from convex budgets”. American Economic Review, 102(7), 3333-3356.
Cohen, J., Ericson, K., Laibson, D. and White, J. (2020). “Measuring Time Preferences”. Journal of Economic Literature, 58(2). 299-347.
Epper, T., Fehr, E., Fehr-Duda, H., Kreiner, C., Lassen, D., Leth-Petersen, S. and Rasmussen, G. (2020). “Time Discounting and Wealth Inequality”. American Economic Review, 110(4), 1177-1205.
Halevy, Y. (2015). “Time consistency: Stationarity and time invariance”. Econometrica, 83(1), 335-352.
TOPIC 3: Preferences under uncertainty – Paolo Crosetto
This class will briefly introduce the concept of risk attitudes, to then move to assess how different methods of eliciting and measuring them have been developed over the years and their psychometrics properties. By means of a large meta-analysis and of recent literature, we will show what are the main problems with the existing measures, and how we can improve their stability, internal and external validity.
Selected key references Andreas Pedroni, Renato Frey, Adrian Bruhin, Gilles Dutilh, Ralph Hertwig & Jörg Rieskamp. “The risk elicitation puzzle”. Nature Human Behaviour volume 1, pages 803–809 (2017)
Crosetto, Paolo, and Antonio Filippin. “A theoretical and experimental appraisal of four risk elicitation methods.” Experimental Economics 19 (2016): 613-641
Crosetto, P (ongoing). Metaret meta-analysis website, https://paolocrosetto.shinyapps.io/METARET_APP
Felix Holzmeister, Jürgen Huber, Michael Kirchler, Florian Lindner, Utz Weitzel, Stefan Zeisberger (2020) “What Drives Risk Perception? A Global Survey with Financial Professionals and Laypeople”. Management Science 66(9):3977-4002.
Starmer, Chris. “Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk.” Journal of economic literature 38.2 (2000): 332-382.
TOPIC 4: Behavioral Data Science – Bastien Blain
This class will introduce methods related to experimental data and design. In particular, one lecture will be dedicated to design optimization and the second one on model fit through numeric simulations, including recovery analyses. Applications to decision under risk, intertemporal choices, reinforcement learning and well-being will be provided.
Selected key reference Robert C Wilson, Anne GE Collins (2019) “Ten simple rules for the computational modeling of behavioral data” eLife 8:e49547
TOPIC 5: Econometrics for experimental data – Angelo Secchi
This module provides an overview on econometrics for experimental data with a strong emphasis on non-parametric techniques. Estimating probability densities, distribution free tests et nonparametric regressions will be discussed together with examples and practical suggestions on how to implement them.
Selected key references Silverman, B.W, (1986), Density Estimation for Statistics and Data Analysis, Chapman & Hall.
Racine, J.S, (2008), “Nonparametric Econometrics: A Primer, Foundations and Trends” in Econometrics: Vol. 3: No 1, pp 1-88.
Hollander, M., Dougla A. Wolfe and E. Chicken, (2014), Nonparametric Statistical Methods, third edition, Wiley.
TOPIC 6: Introduction to Neuroeconomics – Mael Lebreton
Neo-classical approaches to individual decision-making historically put a strong emphasis on choices, through revealed preference approaches, and have remained oblivious about the actual cognitive processes and mechanisms underlying decision (Gul & Pesendorfer, 2008). This class will first discuss the history, goals and methods of the neuroeconomics research agenda, which explicitly aimed at filling this gap, by investigating the neural and cognitive bases of human choices (Camerer et al., 2004; Glimcher & Fehr, 2014). It will then illustrate how important concepts and methods from cognitive neuroscience such as evidence accumulation and attention, neural range adaptation and cognitive noise can contribute to inform theories of human economic decision making.
Selected key references Camerer, C. F., Loewenstein, G., & Prelec, D, (2004), “Neuroeconomics: Why economics needs brains”. The Scandinavian Journal of Economics, 106(3), 555–579.
Glimcher, P. W., & Fehr, E, (2014), “Neuroeconomics: Decision Making and the Brain”. Academic Press. New York.
Gul, F., & Pesendorfer, W, (2008), “The Case for Mindless Economics. In A. Caplin & A. Schotter, The Foundations of Positive and Normative Economics”. Oxford University Press.
Contents – Experimental Economics