reflects the preferences of the shopper who searches out the lowest prices each week, and also the consumer who stockpiles during a particularly good special, but then purchases nothing until the next special” (Richardson, 2000:12). In some instances, few consumers purchase at the shelf price that the BLS agent happens to observe. How many people buy Chicken-of-the-Sea tuna fish when the Bumblebee next to it is on sale for half price? Feenstra and Shapiro (2001) cite marketing literature indicating that there is substantial consumer substitution across weeks in response to price changes and advertising. Also, their own data on canned tuna show a high degree of price variation and substantial response of consumer demand to that variation (Feenstra and Shapiro, 2001). Using shelf prices assumes rigidity in consumer shopping behavior, since items in each week of pricing are treated independently and that elasticity of substitution among them is zero (Richardson, 2000). Proponents of unit value pricing argue that is it better to consider purchases in different weeks of a month as purchases of the same good in the context of consumers’ utility. It is certainly worth noting as well that, at some level, price averaging must take place to construct any price index.
Whatever the outcome of these specific questions, it is clear that scanner data allow researchers to look at all sorts of interesting things. They facilitate comparisons of series that combine price data in different ways, including alternative index formulas, such as short time-lag superlatives. The ScanData team, for instance, was able to compute several indexes contemporaneously (using a Paasche construction as the lower bound with which to test other indexes). Additionally, the sheer volume and detail of scanner data also facilitate hedonic analyses of quality change (such as Ioannidis and Silver, 1999). Even when scanner data are ultimately not used to construct an index, availability of the data can only advance the pace of research that leads to improvements in the index generally.
Early results for the ScanData cereal test indicate that introduction of scanner data may have a significant effect on index performance. For the February 1998 through June 2000 period, cereal inflation for the New York metropolitan statistical area, as measured by the CPI, rose from (a re-based) 100 to 101.1. The geomean scanner index completed the series at 104.9. This 3.8 percent difference may have been attributable to several factors. First, the universe of outlets for the two indexes was not identical; ScanData was missing data from a wholesale club. There was also a sharp decrease in the regular CPI for cereal in October 1999 that did not appear in the scanner data and is difficult to explain. Also, the Tornqvist index rose more rapidly than did the geomean, indicating that, at least for cereal in New York, elasticity of substitution is less than 1.0, as assumed under the geomean method (Richardson, 2000).
It is also important to assess the extent of practical advantages of scanner data that might add to the viability of its regular use. The ScanData experiment has produced favorable results in a number of areas showing, specifically, that: