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Search with learning in the retail gasoline market

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  • Xiaosong Wu
  • Matthew S. Lewis
  • Frank A. Wolak

Abstract

This article estimates a model of optimal search where consumers learn the distribution of gasoline prices during their driving trips. Our model incorporates traffic information and leverages this ordered search environment to recover parameters of the search and learning process using only station‐level price and market share data. We find that learning is a crucial component of search in this market. Consumers' prior beliefs regularly deviate from the true price distribution but are updated quickly following each new price observation. Counterfactuals reveal how these learning dynamics generate asymmetric search patterns commonly associated with asymmetric cost pass‐through.

Suggested Citation

  • Xiaosong Wu & Matthew S. Lewis & Frank A. Wolak, 2024. "Search with learning in the retail gasoline market," RAND Journal of Economics, RAND Corporation, vol. 55(2), pages 292-323, June.
    • Handle: RePEc:bla:randje:v:55:y:2024:i:2:p:292-323
    DOI: 10.1111/1756-2171.12466
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