Number of sellers, average prices, and price dispersion

Abstract

Abstract:A variety of models provide differing predictions regarding the effect of an increase in the number of competitors in a market (seller density) on prices and price dispersion. We review different approaches to generating equilibrium price dispersion and then empirically estimate the relationship between seller density, average product price, and price dispersion in the retail gasoline industry using four unique gasoline price data sets. Controlling for station-level characteristics, we find that an increase in station density consistently decreases both price levels and price dispersion across four geographical areas. JEL Codes: L13, D43, D83

Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets

“…Walsh and Whelan (1999), among others, have adopted the notion of heterogeneous demand elasticity as a key source of price dispersion. Barron et al (2004) show that an increase in the own price elasticity of demand will result in a decrease in the average markups. This will lead to a reduction in price dispersion, as the increase in own price elasticity lowers prices of all sellers toward their respective marginal costs.…”

Understanding gasoline price dispersion

“…Van Meerbeeck has observed that prices vary with stations located along a highway always charge the maximum price determined by an agreement between the oil industry and the Belgian government, and prices are below the maximum price in markets with su¢ cient competition and that the number of local competitors does not have a large impact on retail gasoline prices. Barron et al (2004) have used cross-sectional gasoline price data covering 3,197 gas stations in four metropolitan areas (Phoenix, AZ, Tuscon, AZ San Diego, CA, and San Francisco, CA) for only one day in 1997 (where that one day di¤ers across metropolitan areas). They have found that an increase in station density consistently decreases both price levels and price dispersion across four geographical areas.…”

“…To capture the part of the local agglomeration externalities through f G g in Equation 3.14 (that have negative e¤ects on gas-station-level gasoline prices), since we have a spatial analysis of gasoline prices, we need corresponding spatial data for the size and spatial distribution of economic activity around gas station g. Accordingly, we use the standard deviation of the nighttime lights around gas station g as a proxy for f G g :…”

“…To capture the part of the local agglomeration externalities through f N g in Equation 3.14 (that have negative e¤ects on gas-station-level gasoline prices), we have calculated the number of gas stations f N g around gas station g (in Equation 3.14) as the number of gas stations for which we have price data at least once during our sample period of 144 days. The number of gas stations for each state/district (for the pooled sample through time) is given in the …rst column of Table 1 To be consistent with our spatial measures introduced above, we calculated the number of gas stations f N g in our sample (by using their latitudes and longitudes from Google Map) depending on our di¤erent spatial measures, above.…”

“…2 Therefore, gasoline price di¤erences not only exist across states but also within states; accordingly, a detailed spatial analysis (rather than a state-level analysis) is required to understand the details behind gasoline price dispersion. consumption, production, and distribution) with a comprehensive gas-station level gasoline retail price data (with about half a million price observations) obtained from all around the U.S. 3 In the model, consumer-producers get utility out of their consumption of goods as well as gasoline which is required for transportation purposes. The necessity for transportation also depends on the distribution of economic activity around the individual who is assigned a speci…c spatial location.…”