CEX) to ensure an adequate sample of prices for each ELI area cell for the CPI. Yet the real advantage of a survey that links prices paid for specific items to the purchasing households is that, in principle, from such data one could calculate average prices paid for specific items by different household types. The big question is what size household sample would be required to support such an index or, more realistically, how big a sample would be needed to make an experimental pilot project work. This question is discussed in Chapter 8.
In this section we outline how scanner data work and identify some potential operational and measurement benefits that may be gained by increasing their use; we also point out limitations. However, reflecting the panel’s charge, the primary emphasis is on how the use of scanner data (and electronic data in general) might allow greater conceptual flexibility when constructing price or cost-of-living indexes. The discussion comments on the extent to which current BLS research and experimental programs may affect CPI pricing procedures. The panel also assesses the value of incorporating scanner-based pricing methods within the context of its more general recommendations concerning the feasibility and advisability of pursuing a COLI approach. We first look at the potential of point-of-sale scanner data and how it could be used to improve data accuracy and price collection procedures. We then look at the more futuristic idea of household-based scanner data.
The most obvious way in which scanner data could be used to support the CPI would be as a replacement for or supplement to the C&S survey of outlets. Scanners in retail outlet checkout counters record Universal Product Codes that identify specific products and their manufacturers. These data are collected, collated, and sold by two major producers of scanner data: ACNielsen and Information Resources, Inc. (IRI).
A growing literature on the topic is beginning to provide an indication of the feasibility, as well as the benefits and drawbacks, of using scanner data in the production of price indexes. While academic researchers in both the United States and Europe have begun exploring how scanner data could be used to improve the statistical properties of price indexes, BLS has moved to the forefront on work in the area.8 Reinsdorf (1996) successfully constructed a basic item-level index for coffee using scanner data. Currently, the BLS’s ScanData initiative is producing