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At What Price?: Conceptualizing and Measuring Cost-of-Living and Price Indexes
monthly price changes for all the individual items in that stratum. For example, within the “new passenger cars” stratum, the monthly price changes for Mercedes, Buicks, Chevys, and Hondas are all averaged together, as are those for soccer balls, hockey helmets, golf clubs, and other items in the “sporting goods” stratum. The price changes for men’s clothing purchased at Brooks Brothers and at Walmart are similarly combined in the “men’s suits” stratum index. In this process, all within-stratum heterogeneity is lost.2 And since the price changes are collected from retail stores, there is no way to assemble the data so as to make a direct link between the particular price, quality, and brand of items purchased and the economic or demographic characteristics of those who purchased them.3 At the second or upper-level stage of estimation an overall CPI is calculated as an average of the 218 stratum indexes, with each index assigned a weight equal to the proportion of total consumer expenditures devoted to purchases of the goods in that stratum, estimated from the Consumer Expenditure Survey (CEX).
BLS and individual researchers have, on occasion, produced indexes for subgroups in the population—for the elderly, the poor, or, in a recent BLS report, the individual quintiles of the income distribution—by reweighting the stratum indexes with expenditure weights that represent the budget allocations of the particular demographic subgroup as determined from the CEX. Although the across-stratum weights are different in each subgroup index, the individual stratum price indexes are the same in all of them. Generally, the subgroup indexes that have been produced have not risen at a substantially different rate than the overall CPI, although at times there have been exceptions (see the second technical note to this chapter for a summary of such comparisons). But such indexes distinguish one subgroup of households from another solely by the differences in the way each one allocates its expenditures among expenditure categories. Only the across-stratum heterogeneity is accounted for. The individual stratum price indexes are averages and so do not capture within-stratum heterogeneity—the fact that those at the upper end of the scale typically buy the higher-quality items within any given category of goods are the first to acquire many types of new products, shop in high-end grocery and apparel stores, live in areas with high rents, are far more likely to be covered by medical insurance and to fly business
This is a highly truncated description of a more complicated process, but it accurately depicts the essence of the procedure.
From a TPOPS-type survey augmented with additional data on household characteristics, it would be possible to determine the average economic and demographic profile of the people who patronized each retail outlet in the BLS sample. One could then observe any differences in the extent to which price inflation varied by category of goods among stores whose patrons had different characteristics. This would provide useful information about the consequences of heterogeneity of prices paid, but in the absence of a direct link between prices paid and individual purchasers, the association between economic and demographic characteristics and prices paid would be quite indirect and partial (see below for a brief discussion of this approach).