The per-household cost of the current CEX survey is about five times greater than that of the monthly Current Population Survey (CPS). So even with a cost-saving sample design, such a collection system would be very expensive. Yet there would be cost offsets. The new system could supplant most or all of the current CEX and TPOPS surveys, and it would provide valuable information useful for other statistical purposes so that not all of the extra costs would have to be charged to the construction of the CPI.

An alternative approach exists for associating the prices paid for specific items with the demographic characteristics of the purchasers.8 Specifically, a household survey could be periodically used (say every 2 to 3 years) to collect a baseline sample of specific items that were purchased in each ELI or POPS category, with an identification of the outlets from which they were purchased; scanners might be used to get detailed product specifications. The survey itself would secure income and demographic data from each household. BLS field agents would then proceed to collect monthly prices on these items from the identified outlets. The item prices could be assigned back to the appropriate demographic subgroup with the appropriate weights. (Implicit in this scheme is the idea that any given item priced at each outlet might end up being attributed to several or many demographic subgroups but presumably with different relative weights within each.)

The sample would still have to be very large to possess the appropriate number of cells. A rotating sample would have to cover purchases in every month of the year to avoid seasonal bias. And using scanners to enter the product specifications without prices would be just about as demanding as entering them together with prices. But a continuous reporting of monthly prices by households would not be necessary.

While the resulting subgroup indexes of strata prices would reflect the specific kinds and qualities of items purchased and the specific outlets patronized by each subgroup, this data collection system would be unable to take into account differences which might exist among subgroups if they differ in the extent to which they concentrate their purchases at times and in outlets where sales occur. The presence or absence of this kind of shopping behavior may or may not turn out to be an important factor affecting the average prices paid by one group relative to another.

If it turns out to be feasible, the collection of data that tie individual prices to household characteristics would make it possible to determine whether or not the cost of living faced by particular subgroups tends to change at different rates,

8  

The core of this approach was suggested by one of the reviewers of an earlier draft of the panel’s report.



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