experimental economics summer school : Program content

by Nicolas Jacquemet

The course provides an overview of the use of controlled experiments to investigate research questions in economics and social sciences. This 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 is 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)?

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

Selected key reference

• Jacquemet N. & L’Haridon O., 2018, Experimental Economics: Method and Applications, Cambridge University Press.

by 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.

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. & Holt C. A., 1993, Experimental Economics, Princeton University Press.
• Friedman D. & Sunder S., 1994, Experimental Methods: A Primer for Economists, Cambridge University Press.
• Guala F., 2005, The Methodology of Experimental Economics, New York: Cambridge University Press.

by 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. & Sprenger C., 2012, “Estimating time preference from convex budgets”, American Economic Review, 102(7), pp 3333-3356.
• Cohen J. et al., 2020, “Measuring Time Preferences”, Journal of Economic Literature, 58(2), pp 299-347.
• Epper T. et al., 2020, “Time Discounting and Wealth Inequality”, American Economic Review, 110(4), pp 1177-1205.
• Halevy Y., 2015, “Time consistency: Stationarity and time invariance”, Econometrica, 83(1), pp 335-352.

by 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 psychometric 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

• Crosetto P. & Filippin A., 2016, “A theoretical and experimental appraisal of four risk elicitation methods”, Experimental Economics, 19(3), pp 613-641.
• Crosetto P., Metaret meta-analysis website.
• Holzmeister F. et al., 2020, “What Drives Risk Perception? A Global Survey with Financial Professionals and Laypeople”, Management Science, 66(9), pp 3977-4002.
• Pedroni A. et al., 2017, “The risk elicitation puzzle”, Nature Human Behaviour volume 1, pp 803-809.
• Starmer C., 2000, “Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk”, Journal of economic literature, 38(2), pp 332-382.

by 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 numerical simulations, including recovery analyses. Applications to decision under risk, intertemporal choices, reinforcement learning and well-being will be provided.

Selected key reference

• Wilson R. & Collins A, 2019, “Ten simple rules for the computational modeling of behavioral data”, ELife.

by 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 and nonparametric regressions will be discussed together with examples and practical suggestions on how to implement them.

Selected key references

• Hollander M. et al., 2014, Nonparametric Statistical Methods, Wiley.
• Racine J.S., 2008, “Nonparametric Econometrics: A Primer, Foundations and Trends”, Econometrics, 3(1), pp 1-88.
• Silverman B.W., 1986, Density Estimation for Statistics and Data Analysis, Chapman & Hall.

by Maël 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), pp 555-579.
• Glimcher P. W. & Fehr E., 2014, Neuroeconomics: Decision Making and the Brain, Academic Press.
• Gul F. & Pesendorfer W., 2008, “The Case for Mindless Economics”, in Caplin A. & Schotter A., The Foundations of Positive and Normative Economics, Oxford University Press.