indexes for breakfast cereal in the New York City area from data provided by Nielsen. To date, Laspeyres, Tornqvist, and geomean indexes have been produced; Paasche and Fisher indexes are under consideration. The BLS team is moving to construct the index for broader geographic areas as well. As additional areas are added, they will use current CPI aggregation weights and Laspeyres formula (Richardson, 2000:11).

Scanner data offer several potential advantages. First, such data could streamline item pricing procedures. Using computer-captured scanner data could reduce the number of manual steps in the C&S survey required to produce subaggregate indexes. Scanner price data may replace or reduce the need to visit stores to price items.

Second, scanner data could generate a more representative selection of items for pricing. Scanner data include the universe of products sold (at outlets that have scanner technology), whereas the current quote sampling method only records prices for a small fraction of items on store shelves. CPI price quotes are drawn from items at outlets made eligible by selection in the most recent POPS sample. Scanner quotes are available if the item has been sold during the pricing period. For the CPI, BLS collects prices for selected items whether or not they have been sold at the POPS-identified outlet. In contrast, transactions scanner data pick up volume of sales. Some stores also maintain files that drive the price identification system and indicate the shelf price of all items for some period, such as a week, whether or not they were sold. CPI outlet and item samples are rotated periodically, every 4 years under current practice. In contrast, since scanner data can include the universe of transacted prices at covered outlets, samples are refreshed continuously and new items appear in the data much more frequently. For the BLS’s ScanData geomean and Laspeyres test indexes, weights and item samples are updated each year on the basis of the previous year’s expenditure patterns (Richardson, 2000).

Third, scanner data could improve sampling accuracy. Scanner data have introduced new capacity to calculate highly accurate average prices for specific commodities. The large number of outlets and item price points associated with scanner data offer the potential to greatly decrease sample variance and improve data precision. As pointed out by both the Boskin commission (Boskin et al., 1996) and the Conference Board (1999), the high volume of scanner data would allow for production of indexes at finer levels of product detail. Additionally, scanners record actual transaction prices, not shelf prices at which transactions may or may not have taken place for the relevant period. These features may help certain data users, particularly those that perform industry studies or types of analyses where average price movement over fairly short periods is more relevant than shelf price at a given point in time. The tentative result of BLS’s ScanData New York experiment—which provides some evidence as to how far these scanner data may improve underlying data quality—have been quite promising. The indexes produced from scanner data have displayed less variability than the CPI

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