Data and Code for: Expectations-Based Loss Aversion May Help Explain Seemingly Dominated Choices in Strategy-Proof Mechanisms
Principal Investigator(s): View help for Principal Investigator(s) Bnaya Dreyfuss, Harvard University; Ori Heffetz, Cornell University, Hebrew University, NBER; Matthew Rabin, Harvard University
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
|---|---|---|---|
| code_submission | 07/09/2021 05:28:AM |
Project Citation:
Dreyfuss, Bnaya, Heffetz, Ori, and Rabin, Matthew. Data and Code for: Expectations-Based Loss Aversion May Help Explain Seemingly Dominated Choices in Strategy-Proof Mechanisms. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-10-21. https://doi.org/10.3886/E141561V1
Project Description
Summary:
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Deferred Acceptance (DA), a widely implemented algorithm, is meant to improve allocations: under classical preferences, it induces preference-concordant rankings. However, recent evidence shows that—in both real, large-stakes applications and experiments—participants frequently play seemingly dominated, significantly costly, strategies that avoid small chances of good outcomes. We show theoretically why, with
expectations-based loss aversion, this behavior may be partly intentional. Reanalyzing existing experimental data on random serial dictatorship (a restriction of DA), we
show that such reference-dependent preferences, with a degree and distribution of loss
aversion that explain common levels of risk aversion elsewhere, fit the data better than
no-loss-aversion preferences.
expectations-based loss aversion, this behavior may be partly intentional. Reanalyzing existing experimental data on random serial dictatorship (a restriction of DA), we
show that such reference-dependent preferences, with a degree and distribution of loss
aversion that explain common levels of risk aversion elsewhere, fit the data better than
no-loss-aversion preferences.
Scope of Project
Subject Terms:
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lab experiment;
discrete choice;
maximum likelihodd
JEL Classification:
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B49 Economic Methodology: Other
D47 Market Design
D82 Asymmetric and Private Information; Mechanism Design
D84 Expectations; Speculations
D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
B49 Economic Methodology: Other
D47 Market Design
D82 Asymmetric and Private Information; Mechanism Design
D84 Expectations; Speculations
D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
Data Type(s):
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experimental data
Methodology
Data Source:
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Li, S. (2017). Obviously Strategy-Proof Mechanisms. American Economic Review,
107(11):3257–87.
107(11):3257–87.
Unit(s) of Observation:
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Decisions (in a lab experiment)
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