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Search with Learning in the Retail Gasoline Market

This article estimates a model of optimal search where consumers learn the distribution of gasoline prices during their driving trips. Our estimation incorporates traffic information and leverages the ordered search environment to recover parameters of the search and learning process using only station-level price and market share data. We find learning to be 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 that these learning dynamics can help explain commonly observed patterns of asymmetric cost pass-through.