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Using data from three leading e-commerce platforms in China, we study the impact of the dispersion of sellers’ ranking positions on the price dispersion of homogeneous commodities. The major findings are as follows: The dispersion of the ranking positions of sellers weakens market competition and intensifies price dispersion. The dispersion of ranking positions has a heterogeneous effect on sellers’ pricing strategies with different ranking positions. In addition, under the combined action of search costs and long-tail effects in e-commerce markets, there is an invisible market boundary in online markets.

The identical agent, identical good Bertrand game is associated with prices at marginal cost — the Bertrand Paradox. If consumers make occasional mistakes I show that the standard Bertrand game gives rise to positive profits and prices above marginal cost. Some firms charge low prices to capture the bulk of the sales while others charge high prices selling to mistaken consumers. Furthermore, with free entry the Diamond Paradox arises; a full measure of the firms choose the monopoly price. As a result, the Diamond Paradox arises in an environment with zero search costs by replacing searching costs with searching errors.

How can we detect real estate bubbles? In this paper, we propose making use of information on the cross-sectional dispersion of real estate prices. During bubble periods, prices tend to go up considerably for some properties, but less so for others, so that price inequality across properties increases. In other words, a key characteristic of real estate bubbles is not the rapid price hike itself but a rise in price dispersion. Given this, the purpose of this paper is to examine whether developments in the dispersion in real estate prices can be used to detect bubbles in property markets as they arise, using data from Japan and the U.S. First, we show that the land price distribution in Tokyo had a power-law tail during the bubble period in the late 1980s, while it was very close to a lognormal before and after the bubble period. Second, in the U.S. data we find that the tail of the house price distribution tends to be heavier in those states which experienced a housing bubble. We also provide evidence suggesting that the power-law tail observed during bubble periods arises due to the lack of price arbitrage across regions.

We offer insights on how distance-related trade costs may best be inferred from price-dispersion measures. Using a simple spatial model of price dispersion, we argue that measures of price dispersion that are not spatially informed can mislead researchers into concluding that distance-related costs are small even when such costs are the major determinant of price dispersion. With intra-United States data on eleven goods, we find that distance-related costs are large and are indeed underestimated when inferred from standard, non-spatial, price dispersion measures. Our empirical findings have implications for studies of market integration policies (such as trade liberalization and currency unions) and the significance of economic geography.