This lecture is about misperceived social norms and how long-standing social norms can quickly change.
Readings:
Bursztyn, Leonardo, Georgy Egorov, and Stefano Fiorin. 2020. "From Extreme to Mainstream: The Erosion of Social Norms," American Economic Review.
Bursztyn, Leonardo, Alessandra González, and David Yanagizawa-Drott. 2020. “Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia,” American Economic Review.
Bursztyn, Leonardo and Robert Jensen. 2017. "Social Image and Economic Behavior in the Field: Identifying, Understanding, and Shaping Social Pressure," Annual Review of Economics.
Bursztyn, Leonardo and David Y. Yang. 2022. "Misperceptions about Others," Annual Review of Economics
This lecture will discuss the following topics:
Attitudes to inequality
Fairness preferences
Beliefs about sources of inequality
A Global Outlook
Methodological issues going from the lab to general populations
Readings (* indicates additional reading):
Stantcheva, Stefanie. Perceptions and preferences for redistribution. No. w29370. National Bureau of Economic Research, 2021.
Almås, Ingvild, et al. "Global evidence on the selfish rich inequality hypothesis." Proceedings of the National Academy of Sciences 119.3 (2022): e2109690119.
Almas, I., et al. "Attitudes to Inequality: Preferences and Beliefs." IFS Deaton Review of Inequalities (2022).
*Konow, James. "Fair shares: Accountability and cognitive dissonance in allocation decisions." American economic review 90.4 (2000): 1072-1092.
*Almås, Ingvild, Alexander W. Cappelen, and Bertil Tungodden. "Cutthroat capitalism versus cuddly socialism: Are Americans more meritocratic and efficiency-seeking than Scandinavians?." Journal of Political Economy 128.5 (2020): 1753-1788.
This lecture will discuss the following topics:
Motivation & elicitation of subjective expectations
Identification problem
Measurement
Binary vs. continuous
Using expectations in Structural models
Theoretical model
Empirical specification & Estimation
Policy simulations
Readings:
Bruine de Bruin W, A Chin, J Dominitz, W van der Klaauw, “Chapter 1 - Household surveys and probabilistic questions”, in Handbook of Economic Expectations, edited by Rüdiger Bachmann, Giorgio Topa, Wilbert van der Klaauw, 2023.
Delavande A. 2008. “Pill, Patch or Shot? Subjective Expectations and Birth Control Choice”, International Economic Review, 49(3): 999-1042.
Koşar G and C O'Dea, “Chapter 21 - Expectations data in structural microeconomic models” , in Handbook of Economic Expectations, edited by Rüdiger Bachmann, Giorgio Topa, Wilbert van der Klaauw, 2023
Manski C. 2004: “Measuring Expectations”, Econo
The ultimate goal of scientific research is to accumulate knowledge. Researchers generate hypotheses and collect data to investigate whether or not empirical observations are consistent with these hypotheses. However, even though science aspires toward accuracy in this process, errors are inevitable. A fundamental characteristic that sets empirical science apart from other sources of knowledge is the ability to self-correct; any empirical observation is subject to validation and may be shown to be wrong. The reproducibility of empirical results constitutes a cornerstone of the scientific method. However, due to accumulative evidence emphasizing low levels of replicability, there is increasing concern that a considerable fraction—or even a majority—of published research claims might be false. The drivers of this “credibility crisis” are just as manifold as the institutions involved in the academic enterprise. The course aims to provide a critical view on the “rules of a game named science” and an introduction to remedies to the manifold issues jeopardizing the credibility of scientific results: power calculations, confirmatory research (pre-registration), and open and transparent research practices.
Readings:
Ioannidis, J.P.A. (2005). Why Most Published Research Findings Are False. PLoS Medicine, 2(8): e124.
Munafo, M.R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Wagenmakers, E.-J., Ware, J.J., & Ioannidis, J.P.A. (2017). A Manifesto for Reproducible Science. Nature Human Behaviour, 1: 0021.
Bishop, D. (2019). Rein in the Four Horseman of Irreproducibility. Nature, 568: 435.
Details to be announced.
This lecture will discuss the following topics:
Overview of register data and how to use it to learn about preferences, beliefs and misperceptions, and to inform survey design
Discuss how to leverage register data to study the role of emotions for decision making and beliefs
Discuss how to combine in practice natural occurring variation from registers with survey experiments
Readings:
Taubinsky, D., Butera, L., Saccarola, M., & Lian, C. (2024). Beliefs About the Economy are Excessively Sensitive to Household-Level Shocks: Evidence from Linked Survey and Administrative Data (No. w32664). National Bureau of Economic Research.
Epper, T., Fehr, E., Hvidberg, K. B., Kreiner, C. T., Leth-Petersen, S., & Nytoft Rasmussen, G. (2022). Preferences predict who commits crime among young men. Proceedings of the National Academy of Sciences, 119(6), e2112645119.
This lecture covers the collection of qualitative survey data in economics. We'll begin by exploring techniques for measuring and analyzing top-of-mind responses, along with key applications. Then, we'll discuss a new method for collecting richer qualitative data on a large scale: AI-conducted interviews.
Readings:
Chopra, Felix, and Ingar Haaland. "Conducting qualitative interviews with AI." (2023).
Ingar K Haaland, Christopher Roth, Stefanie Stantcheva, and Johannes Wohlfart. Measuring what is top of mind. Working Paper 32421, National Bureau of Economic Research, May 2024. URL http://www.nber.org/papers/w32421.
Experimenter demand effects arise when participants in a study a) form a belief about what the researcher wishes them to do, and b) distort their behavior accordingly. This lecture will discuss theory and evidence for the empirical relevance of demand effects, give an overview of design practices that are commonly used to mitigate them, and introduce experimental tools that can be used to measure and bound their influence.
Readings (* indicates additional reading):
Experimenter demand effects (de Quidt, Vesterlund, Wilson. Handbook of Research Methods and Applications in Experimental Economics, 2019) Ungated here: https://jondequidt.com/pdfs/EDE_021618.pdf
Measuring and Bounding Experimenter Demand (de Quidt, Haushofer, Roth. AER, 2018). Ungated here: https://jondequidt.com/pdfs/deQuidt_Haushofer_Roth_demand_final.pdf
*Experimenter demand effects in economic experiments (Zizzo. Experimental economics, 2010) https://link.springer.com/article/10.1007/s10683-009-9230-
This lecture will discuss the following topics:
Discuss how to use existing infrastructures (experiments, surveys, registry data) to tackle complex research questions beyond lab experiments or administrative data.
Cover the creation of experiments and their connection to admin data, as well as using natural experiments linked to survey data.
Outline the logistics and stages involved in these research methods.
As examples, we will talk about an RCT on vaccination incentives and a survey examining the effects of wealth on well-being using a natural experiment.
Readings:
Campos-Mercade, P., Meier, A. N., Schneider, F. H., Meier, S., Pope, D., & Wengström, E. (2021). Monetary incentives increase COVID-19 vaccinations. Science, 374(6569), 879-882.
Lindqvist, E., Östling, R., & Cesarini, D. (2020). Long-run effects of lottery wealth on psychological well-being. The Review of Economic Studies, 87(6), 2703-2726.
Details were provided by email
Details were provided by email
This lecture will discuss the following topics:
Pros and cons of collaborating with firms
Outline the logistics and stages involved in these collaborations
Now you have a field partner: so what?
Designing natural field experiments
Combining evidence from other samples/surveys to get into mechanisms
Readings (* indicates additional reading):
Abeler, J., Huffmann, D. B., & Raymond, C. (2024). Incentive Complexity, Bounded Rationality and Effort Provision. Working Paper.
Delfino, A. (2024). Breaking Gender Barriers: Experimental Evidence on Men in Pink-Collar Jobs. American Economic Review.
* Deserranno, E. (2019). Financial incentives as signals: experimental evidence from the recruitment of village promoters in Uganda. American Economic Journal: Applied Economics.
Details to be announced.