Without the benefit of extensive research on each of the areas raised in this chapter, the panel cannot make many definitive recommendations with respect to the data inputs to the CPI. We recognize that the BLS has undertaken research projects in these areas, and so BLS’s inclusion in our discussion should not be taken as an indication that it has been negligent in its research efforts. It merely means that the panel recognizes the importance of these areas of research and hopes that they will continue systematically and thoroughly.
Research into the accuracy and sample size of the CEX should be a high priority among topic areas relating to the data collection process for the CPI. The panel concluded that it is likely that CEX estimates of consumer expenditure shares are biased, perhaps seriously. There is no obvious benefit to increasing the survey sample size if nonsampling error dominates sampling error—one would simply be achieving more precise estimates of the wrong thing.
Recommendation 9-1: Before additional resources are directed toward increasing its sample size (beyond the current plan), the accuracy of the CEX should be carefully evaluated. Assessing the net advantages of using the BEA’s PCE to produce the upper-level weights for the national CPI should be part of this evaluation.
At the very least, research by BLS (and BEA) into the sources of divergence between PCE- and CEX-derived expenditure weights needs to be extended so that these differences can be more fully understood. Even if the current system is ultimately maintained, the effort will produce additional guidance about how the CEX might be improved.
Recommendation 9-2: If categories can be reasonably well matched between the CPI and PCE, so that comparable item strata indexes can be created, a program should be set up to produce an experimental CPI that uses PCE-generated weights at the upper (218 item) level but that is otherwise no different from the CPI.
If full item-by-item mapping turns out to be too problematic, it might still be possible to use PCE estimates for major item categories where the PCE and CEX have comparable coverage. For such categories, estimated totals from the CEX could be forced to equal the PCE estimates, which might allow the PCE to correct for undercoverage in the CEX in much the way that demographic projections are used to correct for undercoverage in household surveys such as the Current Population Survey. The distribution among lower-level aggregations would be determined by the CEX distribution. Investigating how well such experimental indexes perform seems especially sensible given the high cost of revamping the CEX survey or increasing its sample size. We would very much like to see a