Price Search and Periodic Price Discounts

Xing Zhang (), Tat Y. Chan () and Ying Xie ()
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Xing Zhang: School of Management, Fudan University, 200433 Shanghai, China
Tat Y. Chan: Olin Business School, Washington University in St. Louis, St. Louis, Missouri, 63130
Ying Xie: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Management Science, 2018, vol. 64, issue 2, 495-510

Abstract: This paper empirically investigates the interplay between buyer search behavior and firm pricing strategy in a commodity market where many firms compete in price to sell homogeneous products. We use the heterogeneity in buyer search cost to rationalize why a firm offers periodic price discounts and enjoys a high profit in such a market. An estimation method is proposed to recover the nonparametric distribution of buyer search cost from the price and transaction volume data. With both data, we show that the search cost distribution can be estimated nonparametrically in a sequential search model, and estimation results from a nonsequential search model are robust to the assumption on the maximum number of searches. We also show that the commonly observed high-low pricing strategy can be an optimal strategy for firms when buyers search for price information sequentially. As an empirical application, we use the proposed method to estimate the two search models using data on a commodity product sold by a firm in a business-to-business market. The models predict a high profit margin for the firm, which is consistent with the data, but the sequential search model fits with the observed price distributions and supply costs better than the nonsequential search model. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2641 . This paper was accepted by Matthew Shum, marketing.

Keywords: price variation; price competition; search cost; mixed pricing strategy; nonparametric empirical likelihood method (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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