Posted by D. Daniel Sokol
Babur De los Santos (Department of Business Economics and Public Policy, Indiana University Kelley School of Business), Ali Hortacsu (University of Chicago and NBER) and Matthijs R. Wildenbeest (Department of Business Economics and Public Policy, Indiana University Kelley School of Business) discuss Search with Learning.
ABSTRACT: This paper provides a method to estimate search costs in an environment in which consumers are uncertain about the price distribution. Consumers learn about the price distribution by Bayesian updating their prior beliefs. The model provides bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for electronics sold online we show how to use these bounds to estimate search costs. Estimated search costs are sizable and are found to relate to consumer characteristics in intuitive ways. The model outperforms a standard sequential search model in which the price distribution is known to consumers.
Featured News
Uruguayan Antitrust Scrutiny Puts Major Meatpacking Deal Between Marfrig and Minerva on Hold
May 19, 2024 by
CPI
Alaska Airlines Seeks Dismissal of Consumer Lawsuit Over $1.9 Billion Hawaiian Airlines Buy
May 19, 2024 by
CPI
Idaho Attorney General Orders Split of Kootenai Health and Syringa Hospital
May 19, 2024 by
CPI
Court Rejects T-Mobile’s Appeal Bid in Antitrust Case Over Sprint Merger
May 19, 2024 by
CPI
Google Requests Judge, Not Jury, to Decide on Antitrust Case
May 19, 2024 by
CPI
Antitrust Mix by CPI
Antitrust Chronicle® – Ecosystems
May 9, 2024 by
CPI
Mapping Antitrust onto Digital Ecosystems
May 9, 2024 by
CPI
Ecosystems and Competition Law: A Law and Political Economy Approach
May 9, 2024 by
CPI
Ecosystem Theories of Harm: What is Beyond the Buzzword?
May 9, 2024 by
CPI
Open Ecosystems: Benefits, Challenges, and Implications for Antitrust
May 9, 2024 by
CPI