BLS now does in the item replacement process, would not be likely to have a big effect on the CPI. Hedonic adjustments tend to wash out relative to those produced by the implicit adjustments that they replace (the computer index is the exception). Confining hedonic adjustment to cases of noncomparable substitutions for anything other than very high turnover products is unlikely to significantly affect CPI component indexes. Also, current BLS rules for replacing disappearing products further minimize quality differences between outgoing and incoming products which, in turn, lessens the importance of which type of quality adjustment is ultimately selected. However, as the Moulton et al. (1998) TV study suggests, the application of hedonic adjustments in a different way and on a larger scale might produce more significant downward adjustments. The panel believes that the BLS should proceed cautiously in its efforts to integrate hedonics into the CPI. Further research, testing, and evaluation of hedonic methodology and specific applications should precede expansion of its use, such as to sample rotation—something that the panel is not in principle opposed to—where the impact on index growth would likely be more significant.
Hedonic methods are not a cure-all for indexing problems related to quality change. Regression techniques do not deal with increases in product variety (e.g., of fruits and vegetables during the winter); nor do they help much with the problem of truly new goods (e.g., cellular phones). The main thing to be said for hedonic methods is that there is nothing better for dealing with certain aspects of the quality change problem. This is not an elegant defense, but it is a powerful one. To a large extent, this reality shapes our recommendations in this area.
BLS should systematically investigate quality change across CPI components.
Recommendation 4-1: In addition to its targeted intuitive approach (in which BLS selects for adjustment items thought a priori to have undergone quality change), BLS should pursue experiments to analyze quality change in randomly selected items in order to increase the probability that within-sample quality change biases—both upward and downward—will be identified. Currently, hedonic regression analysis is the leading candidate to serve as the main analytical tool in such experiments.
One issue that will have to be addressed in such a program is the level of detail that is used in the item selection process. Selection could be randomized across the broad 211 item strata, at more detailed ELI levels, or somewhere in between. Whatever level of disaggregation is chosen, it is logical that selection probability should be proportional to expenditure (perhaps adjusted to account