Why are product prices in online markets not converging?
Bottom Line:
Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close.This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price.Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers' preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking.
View Article:
PubMed Central - PubMed
Affiliation: National Institute of Informatics, Tokyo, Japan ; Graduate School of Economics, University of Tokyo, Tokyo, Japan ; The Canon Institute for Global Studies, Tokyo, Japan.
ABSTRACT
Show MeSH
Why are product prices in online markets dispersed in spite of very small search costs? To address this question, we construct a unique dataset from a Japanese price comparison site, which records price quotes offered by e-retailers as well as customers' clicks on products, which occur when they proceed to purchase the product. The novelty of our approach is that we seek to extract useful information on the source of price dispersion from the shape of price distributions rather than focusing merely on the standard deviation or the coefficient of variation of prices, as previous studies have done. We find that the distribution of prices retailers quote for a particular product at a particular point in time (divided by the lowest price) follows an exponential distribution, showing the presence of substantial price dispersion. For example, 20 percent of all retailers quote prices that are more than 50 percent higher than the lowest price. Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close. This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price. Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers' preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking. |
Related In:
Results -
Collection
getmorefigures.php?uid=PMC3756077&req=5
Mentions: In this section, we look at statistical regularities regarding the price rank at which customers click on the “Go to retailer’s check-out page,” as well as the price rank at which retailers post their prices when they enter the market. Figure 3 shows the relationship between the price rank of a retailer and the probability that customers click on that retailer for a specific product, namely the Sony Blu-ray disc recorder with the model number “BDZ-AT700.” The figure indicates that although the retailer offering the lowest price attracts the largest number of clicks, this falls far short of an overwhelming majority, and that the click probability of the retailer offering the tenth lowest price is not zero. The click probability for the first-ranked retailer (offering the lowest price) is about 14 percent, that for the second-ranked retailer (offering the second lowest price) is about 11 percent, and that for the tenth-ranked retailer is about 3.3 percent. This probability distribution is well approximated by the exponential function(2)where is the probability of being clicked at rank , and is a coefficient, which is estimated to be 0.122. |
View Article: PubMed Central - PubMed
Affiliation: National Institute of Informatics, Tokyo, Japan ; Graduate School of Economics, University of Tokyo, Tokyo, Japan ; The Canon Institute for Global Studies, Tokyo, Japan.