Dr Matteo Galizzi

This course is compulsory on the Executive MSc in Behavioural Science. This course is not available as an outside option.

The course aims to introduce students to the main methodological concepts and tools in behavioural science. To achieve this objective, the course combines rigorous conceptual discussion with hands-on practical applications. The course covers: The beauty of experiments: how randomization solves the sample selection bias; Randomized controlled experiments from the lab to the field: taxonomy, principles, best practices; Statistical tools: distributions and their moments, the inference problem; Experimental design: between-subjects design, block/stratified randomization, matched-pair design, within-subjects design, cluster randomization, the mechanics of randomization; Introduction to econometrics: simple and multiple linear regression models, econometric analysis of experimental data; Tests of hypothesis: principles and practices, parametric and non-parametric tests in practice; Sampling: optimal sample size, sample size in practice, useful rules of thumbs; Experimental best practices and challenges: ethics, recruitment, informed consent form, attrition, non-compliance, external validity, data-linking; When randomization is not possible: before and after, matching, natural experiments, difference-in-difference, regression discontinuity design; Outcomes and preferences: surveys, measuring risk and time preferences; Game theory and behavioural game theory: games and strategic decision-making, measuring social preferences.

17 hours and 30 minutes of lectures and 5 hours of seminars in the LT.

Students will be expected to produce 1 piece of coursework in the LT.

• Angrist, J.D., Pischke J-S. (2009). Mostly Harmless Econometrics: an Empiricist’s Companion. Princeton: Princeton University Press.

• Burtless, G. (1995). The case for randomized field trials in economic and policy research. Journal of Economic Perspectives, 9(2), 63-84.

• Camerer, C.F. (2003). Behavioral Game Theory: Experiments in Strategic Interaction. Princeton: Princeton University Press.

• Cameron, A.C., Trivedi, P.K. (2009). Microeconometrics Using Stata. College Station, TX: Stata Press.

• Dolan, P., Galizzi, M.M. (2014). Getting policy-makers to listen to field experiments. Oxford Review of Economic Policy, 30(4), 725-752.

• Dolan, P., Galizzi, M.M. (2015). Like ripples on a pond: behavioural spillovers and their consequences for research and policy. Journal of Economic Psychology, 47, 1-16.

• Gerber, A.S., Green, D.P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: Norton & Company.

• Glennerster, R., Takavarasha, K. (2013). Running Randomized Evaluations: a Practical Guide. Princeton: Princeton University Press.

• Harrison, G.W., List, J.A. (2004). Field experiments. Journal of Economic Literature, XLII, 1009-1055.

• Kohler, U., Kreuter, F. (2012). Data Analysis Using Stata. College Station, TX: Stata Press.

• List, J.A. (2006). Field experiments: a bridge between the lab and naturally occurring data. Advances in Economic Analysis and Policy, 6, 8.

• Mitchell, M.N. (2015). Stata for the Behavioural Sciences. College Station, TX: Stata Press.

• Rowntree, D. (1981). Statistics Without Tears: an Introduction for Non-Mathematicians. London: Penguin Books.

• Wheelan, C. (2013). Naked Statistics: Stripping the Dread from the Data. New York: W.W. Norton & Company.

Take home exam (100%) in the LT.