We illustrate the impact of detailed data in empirical economic research by considering how the increased data availability has changed the scope and focus of studies on retail gasoline pricing. We show how high-volume, high-frequency price data help to identify and explain long-term trends using original data for the Dutch retail gasoline market. We find that 22% of the observed increase in the highway/off-highway price gap can be explained by the trend towards more unmanned stations; another 13% can be explained by major-to-non-major re-brandings. In one of the first applications of event study analysis to non-financial price data, we show that the adjustment to the new, lower price level is almost immediate in case of manned-to-unmanned conversions but takes one to two months in case of major-to-non-major re-brandings. The impact of both events is asymmetric with no measurable price impact of changes in the opposite direction.
retail gasoline pricing big data competitive spillovers event study analysis