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Why are product prices in online markets not converging?

Mizuno T, Watanabe T - PLoS ONE (2013)

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
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.

Show MeSH
Cumulative distribution of prices divided by the lowest price.The series denoted by ♦ shows the distribution of price quotes available at 0∶00 on December 16, 2011, relative to the lowest price at that time. The dotted line is a reference line representing an exponential function with an exponent of 2.2.
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pone-0072211-g001: Cumulative distribution of prices divided by the lowest price.The series denoted by ♦ shows the distribution of price quotes available at 0∶00 on December 16, 2011, relative to the lowest price at that time. The dotted line is a reference line representing an exponential function with an exponent of 2.2.

Mentions: Let us begin by examining price dispersion on Kakaku.com. The series denoted by ♦ in Figure 1 shows the cumulative distribution of price quotes relative to the lowest price for each product available at 0∶00 on December 16, 2011. The tail of this distribution follows an exponential function of the form(1)where is defined as for each product and the estimate of the coefficient is 0.22. This figure shows that the fraction of retailers whose price quotes are more than 50 percent higher than the lowest price (i.e., ) is about 20 percent, clearly indicating the presence of wide price dispersion. This result can be seen as further evidence against an important law in economics, the law of one price (LOP), which, as discussed extensively by [20], is also violated in a range of other markets.


Why are product prices in online markets not converging?

Mizuno T, Watanabe T - PLoS ONE (2013)

Cumulative distribution of prices divided by the lowest price.The series denoted by ♦ shows the distribution of price quotes available at 0∶00 on December 16, 2011, relative to the lowest price at that time. The dotted line is a reference line representing an exponential function with an exponent of 2.2.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756077&req=5

pone-0072211-g001: Cumulative distribution of prices divided by the lowest price.The series denoted by ♦ shows the distribution of price quotes available at 0∶00 on December 16, 2011, relative to the lowest price at that time. The dotted line is a reference line representing an exponential function with an exponent of 2.2.
Mentions: Let us begin by examining price dispersion on Kakaku.com. The series denoted by ♦ in Figure 1 shows the cumulative distribution of price quotes relative to the lowest price for each product available at 0∶00 on December 16, 2011. The tail of this distribution follows an exponential function of the form(1)where is defined as for each product and the estimate of the coefficient is 0.22. This figure shows that the fraction of retailers whose price quotes are more than 50 percent higher than the lowest price (i.e., ) is about 20 percent, clearly indicating the presence of wide price dispersion. This result can be seen as further evidence against an important law in economics, the law of one price (LOP), which, as discussed extensively by [20], is also violated in a range of other markets.

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
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.

Show MeSH