they could ease the burden and increase the reliability of household price reporting (see below).

The second problem in constructing subgroup indexes relates to the size and cost of the survey(s) that would be required. The size of a monthly panel survey needed to collect price data from individual households that could be cross-classified simply by income, age, and region would be unprecedented. For example, simply distinguishing 5 income and 5 age groups, with no regional classification, would require that prices be collected for 25 separate groups. To keep the burden of monthly reporting within reason, the number of categories of goods on which a household could be asked to report would have to be limited to only a fraction of the 218 CPI strata, the number depending on the frequency with which items within the category were typically purchased.5 If, on average, each household were limited to reporting on, say, 15 categories, the number of demographic/expenditure category cells would exceed 300. Incorporating a geographic classification would expand this number many-fold. To ensure a continuous supply of price quotes in each stratum for a sufficiently large sample of identical or closely comparable goods, it would be necessary to have a substantial number of households in each cell, since in most strata individual households would not be purchasing an identical item month after month. Without research and testing, the required size of the overall sample can only be guessed, but it would undoubtedly be very large.6

There are ways, however, in which the size of the needed sample might be significantly reduced. For circumstances in which a household does not purchase a good in a particular month, an item price might be imputed from a household with partially matching characteristics from an adjacent cell, with only a small loss in precision. Moreover, when research and experimentation identify strata for which the variation in prices paid by households across adjacent and nearby demographic groups is small, the relevant demographic cells might be combined, thereby further reducing sample size requirements. To the extent that the use of handheld scanners and technological aids can be implemented to reduce reporting burden, households could report prices and expenditure data on a larger number of strata, also leading to a reduction in the overall sample size.7


The TPOPS survey, which does not require price reporting, limits the number of categories assigned to a household to somewhere between 10 and 16. The diary survey of the CEX solicits weekly data from each household on a large number of food and other frequently purchased categories of goods, but only for a 2-week period.


The appropriate sample size would be determined in part by the variance of the prices paid within each cell for the items in particular strata; see Chapter 9.


The number of cells could be modestly reduced if all households within a given demographic group were asked to report on very infrequently purchased items, such as automobiles or major appliances; and for some categories like utilities and public transportation, a common price could be assumed for all demographic groups within a given area (although expenditure data by subgroup would be needed).

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