Suchergebnisse

The Thanksgiving–Christmas holiday period is a major sales period for US retailers. Due to higher store traffic, tasks, such as restocking shelves, handling customers' questions and inquiries, running cash registers, cleaning and bagging, become more urgent during holidays. As a result, the holiday‐period opportunity cost of price adjustment may increase dramatically for retail stores, which should lead to greater price rigidity during holidays. We test this prediction using weekly retail scanner price data from a major Midwestern supermarket chain. We find that, indeed, prices are more rigid during holiday periods than non‐holiday periods. For example, the econometric model we estimate suggests that the probability of a price change is lower during holiday periods, even after accounting for cost changes. Moreover, we find that the probability of a price change increases with the size of the cost change, during both the holiday as well as non‐holiday periods. We argue that these findings are best explained by higher price adjustment costs (menu cost) the retailers face during the holiday periods. Our data provides anatural experimentfor studying variation in price rigidity because most aspects of market environment such as market structure, industry concentration, the nature of long‐term relationships, contractual arrangements, etc. do not vary between holiday and non‐holiday periods. We, therefore, are able to rule out these commonly used alternative explanations for the price rigidity, and conclude that the menu cost theory offers the best explanation for the holiday period price rigidity.

Every time you buy a can of tuna or a new television, its bar code is scanned to record its price and other information. These "scanner data" offer a number of attractive features for economists and statisticians, because they are collected continuously, are available quickly, and record prices for all items sold, not just a statistical sample. But scanner data also present a number of difficulties for current statistical systems. Scanner Data and Price Indexes assesses both the promise and the challenges of using scanner data to produce economic statistics. Three papers present the results of work in progress at statistical agencies in the U.S., United Kingdom, and Canada, including a project at the U.S. Bureau of Labor Statistics to investigate the feasibility of incorporating scanner data into the monthly Consumer Price Index. Other papers demonstrate the enormous potential of using scanner data to test economic theories and estimate the parameters of economic models, and provide solutions for some of the problems that arise when using scanner data, such as dealing with missing data