cereal experiment, there were CPI quotes that were not included in the scanner universe (in this case, they were from mass merchandisers). Small mom-and-pop stores also frequently do not use scanner technology. Efforts are currently under way at ACNielsen to expand the depth of outlets covered in its datasets. Also, “migrating” quotes come into play when purchases are made across CPI areas. The POPS sample covers purchases in adjacent areas, but these patterns cannot be inferred from scanner data. In other words, the POPS covers purchases of consumers from a certain area while the scanner datasets cover purchases made by any household in a particular area, which is not the CPI objective. It may be possible to construct a scanner index as a weighted index from the areas in which consumers of a given area shop (Richardson, 2000), but this certainly adds complication back into the system.
Scanner data coverage is most broad based for items sold in supermarket outlets, while there is virtually no coverage in service sectors. Hawkes and Piotrowski (2000:1) of ACNielsen report that 43 of the 211 CPI item categories can “in large measure, be represented through scanning data obtained from Supermarkets, Mass Merchandisers, and Drug Stores.” These categories account for about 10 percent of all consumer expenditures and about 24.2 percent of expenditures for goods (excluding services such as rent). Item coverage constraints alone severely limit the impact that use of store-based scanners can have on the overall CPI.
In terms of cost, the budget tradeoff between purchasing data from private vendors and traditional price data collection must be evaluated, as BLS is in the process of doing. Another issue concerns integration of scanner-based subindexes (possibly superlative) with traditional sampling-based item indexes: What are the statistical and index performance ramifications when subindexes are compiled using different types of data?
Finally, BLS currently does not have to rely on private outside sources for fundamental pricing data. The ramifications on CPI production of changing this must be explored. For instance, ACNielsen and IRI buy their data from chains, and at times chains decide to no longer sell these data. This means that, while a given store has a positive probability of being in the traditional CPI sample, its probability of being in the scanner dataset is zero. Thus, problems of continuity with the scanner data universe could arise.
Household scanner technology could be adopted in one of three ways: it could be used to improve the accuracy and coverage of the current household surveys, particularly the CEX; it could also be used in a combined CEX/POPS survey; or, more ambitiously, it could be the technical centerpiece of a house-hold-based panel survey that produces both expenditure share and price information that would be used to produce household or subgroup indexes. Any plan to