Search with learning: understanding asymmetric price adjustments

In many retail markets, prices rise faster than they fall. We develop a model of search with learning to explain this phenomenon of asymmetric price adjustments. By extending our static game analysis to the dynamic setting, we demonstrate that asymmetric price adjustments arise naturally. When a positive cost shock occurs, all the searchers immediately learn the true state; the search intensity, and hence the prices, fully adjust in the next period. When a negative cost shock occurs, it takes longer for nonsearchers to learn the true state, and the search intensity increases gradually, leading to slow falling of prices.