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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 V1

Name File Type Size Last Modified
AppendixG.do text/x-stata-syntax 22.8 KB 07/24/2020 12:24:PM
DeadlinesNumericEvaluation.nb text/plain 241.2 KB 07/24/2020 11:54:AM
FeeData.csv text/csv 1.8 KB 07/23/2020 11:35:AM
FeeData.dta application/x-stata 7 KB 07/23/2020 11:44:AM
ModelSimulator.do text/x-stata-syntax 6 KB 07/23/2020 09:17:PM
ParameterEstimation.do text/x-stata-syntax 3.4 KB 11/18/2019 09:43:AM
README.pdf application/pdf 68.5 KB 11/05/2019 12:43:PM
SimulatedData.csv text/csv 104.1 MB 07/23/2020 12:20:PM
SimulatedData.dta application/x-stata 92.9 MB 07/23/2020 12:20:PM
SingleParameterEstimationAll.do text/x-stata-syntax 10.6 KB 11/08/2019 05:29:AM

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], 2020-07-24. https://doi.org/10.3886/E119387V1

Project Description

Summary:  View help for Summary 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. 

Scope of Project

Subject Terms:  View help for Subject Terms Equilibrium search; deadlines; discount channels; mechanism choice; auctions; online markets
JEL Classification:  View help for JEL Classification
      C73 Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
      D44 Auctions
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Data Type(s):  View help for Data Type(s) observational data; other; survey data

Methodology

Data Source:  View help for Data Source 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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Published Versions

V2 [2020-11-23]

V1 [2020-07-24]

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