Replication data for: Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors
Principal Investigator(s): View help for Principal Investigator(s) Gregory Lewis
Version: View help for Version V1
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Empirics.do | text/plain | 19.4 KB | 10/11/2019 11:52:AM |
LICENSE.txt | text/plain | 14.6 KB | 10/11/2019 11:52:AM |
ebaydatafinal.dta | application/octet-stream | 275.4 MB | 10/11/2019 11:52:AM |
readme.txt | text/plain | 3.2 KB | 10/11/2019 11:52:AM |
Project Citation:
Lewis, Gregory. Replication data for: Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors. Nashville, TN: American Economic Association [publisher], 2011. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-11. https://doi.org/10.3886/E112439V1
Project Description
Summary:
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Since Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods markets. The remarkable growth in online used goods auctions thus poses a puzzle. Part of the solution is that sellers voluntarily disclose their private information on the auction web page. This defines a precise contract -- to deliver the car shown for the closing price -- which helps protect the buyer from adverse selection. I test this theory using data from eBay Motors, finding that online disclosures are important price determinants, and that disclosure costs impact both the level of disclosure and prices. (JEL D44, D82, L81)
Scope of Project
JEL Classification:
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D44 Auctions
D82 Asymmetric and Private Information; Mechanism Design
L81 Retail and Wholesale Trade; e-Commerce
D44 Auctions
D82 Asymmetric and Private Information; Mechanism Design
L81 Retail and Wholesale Trade; e-Commerce
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