Data and Code for: Discounts and Deadlines in Consumer Search
Principal Investigator(s): View help for Principal Investigator(s) Dominic Coey, Facebook; Bradley Larsen, Stanford University; Brennan Platt, Brigham Young University
Version: View help for Version V2
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Project Citation:
Coey, Dominic, Larsen, Bradley, and Platt, Brennan. Data and Code for: Discounts and Deadlines in Consumer Search. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. https://doi.org/10.3886/E119387V2
Project Description
Summary:
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We present a new equilibrium search model where consumers initially search among discount opportunities, but are willing to pay more as a deadline approaches, eventually turning to full-price sellers. The model predicts equilibrium price dispersion and rationalizes discount and full-price sellers coexisting without relying on ex-ante heterogeneity. We apply the model to online retail sales via auctions and posted prices, where failed attempts to purchase reveal consumers' reservation prices. We find robust evidence supporting the theory. We quantify dynamic search frictions arising from deadlines and show how, with deadline-constrained buyers, seemingly neutral platform fee increases can cause large market shifts.
Data and code are available on the authors' websites and the AER data repository. Some of the data is proprietary and can only be accessed through following the instructions provided in the included README file.
Data and code are available on the authors' websites and the AER data repository. Some of the data is proprietary and can only be accessed through following the instructions provided in the included README file.
Scope of Project
Subject Terms:
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Equilibrium search;
deadlines;
discount channels;
mechanism choice;
auctions;
online markets
JEL Classification:
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C73 Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
D44 Auctions
D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
C73 Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
D44 Auctions
D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Data Type(s):
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observational data;
other;
survey data
Methodology
Data Source:
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Survey data comes from an original nationwide survey we administered to consumers about their search behavior via Qualtrics. Data on eBay platform fees is data we collected from archived pages the Wayback Machine. Simulated data is simulated from our estimated model. Raw eBay transaction-level data comes directly from eBay's internal database, and can be accessed by following the instructions in the README file.
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