Price asymmetry and retailers heterogeneity in Brazilian gas stations

In a competitive market situation, a symmetric price transmission is expected, and the speed of adjustment of the market should be equal, no matter in which direction input prices are going (up or down). When inputs' prices increase, firms need to pass on costs to avoid negative profit situation. When they go down, firms' reaction is in a direction to avoid market share losses. Therefore, if firms react faster when inputs' prices increase than when they decrease (positive asymmetry), it means a capture of consumers' surplus by the firms. When firms' reaction is slowly when inputs' prices decrease than when they decreases (negative asymmetry), the surplus transfer is from firms to consumers. So far, studies regarding price asymmetry in Brazil used only aggregated database, which likely suffers by summation bias. In a hypothetical city with just two gas stations, one with positive asymmetric behavior and other with negative one, there is high chance that this city accepts the null of a symmetric behavior. The present study will try to overcome this problem with a gas station level dataset. The National Agency for Petroleum, Natural Gas and Biofuels (ANP) has a detailed database with weekly information for gas stations in an unbalanced database, where more than 40% of population is covered every week. This firm-level database has information as purchase and selling price for gasoline, name of gas stations, brand and complete address. This information allows answering if there is price asymmetry in Brazil at firm level. Because database has more than 2 million of observations for more than 17.000 different gas stations, it is also possible to obtain results of price asymmetry against fixed effects to check which of these effects matter to change the likelihood of firms to have price asymmetry. Results indicate that there is heterogeneity regarding price transmission among firms: 71% of gas stations had no asymmetry, 23% had a positive asymmetry pattern and 6% of them had negative asymmetry. Regarding which fixed effects could explain the probability to have a positive asymmetry, higher margins, and a minor number of rivals nearby and be a non-white flag increase the probability of having positive asymmetry. These results strength relations between market power and positive asymmetry and inaugurate a link between spatial competition and price asymmetry transmission.