graphic characteristics, would help justify further work and provide clues as to where subsequent effort ought to be concentrated.

  1. Several private marketing firms have established panels of consumer households who use scanners to report prices and expenditures on certain classes of goods, generally those purchased from supermarkets, drugstores, and other mass merchandisers. BLS could work with these firms to investigate the potentialities and limitations of these kind of data for meeting its needs. For example, is the product identification sufficiently precise to track identical items over time and to make and monitor substitution decisions? What is the attrition rate among the panels? How comprehensively can purchases be reported? Cooperative arrangements with these private firms might be helpful in proceeding with the study of price-level differences suggested above.10

  2. In what, if any, categories of goods could “unit value pricing” be used as a way of tracking the prices through time? Experiments could be conducted that compare time series of various strata or entry-level item (ELI) indexes already calculated by the BLS with those that would result from unit value type calculations. To the extent that unit value indexes do closely and consistently track previously estimated BLS indexes, the net effect of explicit and implicit quality adjustments has presumably been negligible. If a number of categories do lend themselves to unit value calculation, the sample size and respondent burden of the household survey outlined above could be significantly reduced.

  3. BLS might select a limited number of categories of goods that could not be identified through handheld scanners and construct its own identification dictionary and product codes. Either as a separate survey or as part of the regular diary survey within the CEX, several panels of households drawn from different income groups could be furnished with handheld computers and asked to record, over a period of some months, the prices, quantities, and product codes of items they purchased. This experiment could shed light on several important questions, such as how reliably product identification can be reported and, for particular strata, what sample size would be needed to generate a sufficiently large set of matched price quotes each month. This experiment might be conducted from groups selected within the existing CEX survey.11

The results from one or more of these investigations would provide information that would help in deciding whether to proceed further in the direction of a more ambitious pilot project to collect price and expenditure data for one or

10  

A recent NBER/CRIW conference considered many of these questions (see Richardson, 2000; Feenstra and Shapiro, 2001; Hawkes and Piotrowski, 2000). For an overview of papers presented at the conference, go to http://www.nber.org/reporter/fall00/conferences/CRIW.html.

11  

Independent information about the extent of the longer-term variation in the trend of housing costs among subgroups of households, cross-classified in various ways, could be obtained from the Census Bureau’s biennial American Housing Survey.



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