Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers

Abstract

Online prices are increasingly being used for a variety of inflation measurement and research applications, yet little is know about their relation to prices collected offline, where most retail transactions take place. This paper presents the results of the first large-scale comparison of online and offline prices simultaneously collected from the websites and physical stores of 56 large multi-channel retailers in 10 countries. I find that price levels are identical about 72% of the time for the products sold in both locations, with significant heterogeneity across countries, sectors, and retailers. The similarity is highest in electronics and clothing and lowest in drugstores and office-supply retailers. There is no evidence of online prices varying with the location of the ip address or persistent browsing habits. Price changes are not synchronized but have similar frequencies and average sizes. These results have implications for National Statistical Offices and researchers using online data, as well as those interested in the effect of the Internet on retail prices.

Consumer heterogeneity and online vs. offline retail spatial competition

“…This variation among consumers leads to different consumer behaviors and permits us to fully capture various competition scenarios. In the ensuing analysis, we assume m < t for three reasons: (a) Convenience is always a central reason for the patronage of direct online purchase (Eastlick and Feinberg 1999); (b) The online transportation cost is being reduced under the irreversible trend that scattered and small distribution centers allow for more efficient delivery (Wu 2010); and (c) Some online retailers offer free shipping fees, although this could potentially mean that they also adjust their online prices to compensate (Cavallo 2017).…”

Subspace Clustering of Very Sparse High-Dimensional Data

“…In this section, we compare the performance of our proposed method against state-of-the-art subspace, and standard clustering algorithms on the task of clustering Amazon product names dataset [9]. This dataset contains five broad product categories: Electronics, Home and appliances, Mix, Office products, and Pharmacy and Health.…”

“…Then an appropriate dissimilarity measure is used to merge these subspaces into meaningful clusters. We apply the algorithm on a dataset of product names obtained from Amazon website and made available by the The Billion Prices Project [9], and show that its performance is competitive with state-of-the-art (subspace) clustering algorithms.…”

Law of One Price, Distance, and Borders

“…For the list of goods in the database, we select those markets were at least one good is sold in Argentina, the neighboring country of Uruguay. To check which goods are sold in Argentina, we search if each good in our database is in any of the supermarkets in Table 1 of Cavallo (2017), that list a series of retailers that publish their price information on line.…”