  The finding of small price changes in many retail price datasets is often
viewed as a puzzle. We show that a possible explanation for the presence of
small price changes is related to sales volume, an observation that has been
overlooked in the existing literature. Analyzing a large retail scanner price
dataset that contains information on both prices and sales volume, we find that
small price changes are more frequent when products sales volume is high. This
finding holds across product categories, within product categories, and for
individual products. It is also robust to various sensitivity analyses such as
measurement errors, the definition of small price changes, the inclusion of
measures of price synchronization, the size of producers, the time horizon used
to compute the average sales volume, the revenues, the competition, shoppers
characteristics, etc.
