increased by 50 percent. The recently requested increase in the CEX sample size from an effective annual sample size of 5,870 to about 7,500 per year approximately does this.
The decision to update CPI-U and CPI-W expenditure weights every 2 years beginning in 2002 was based on a tradeoff between timeliness and concern about “chain drift,” which can occur when the price indexes of non-identical items must be linked.3 BLS agreed with critics (such as Boskin et al., 1996) that the weights should be updated more often than every decade or so as in the past, but little theory or empirical evidence existed to provide guidance on the optimal frequency of updates. BLS chose to move to the more frequent end of the spectrum, every 2 years. There were some operational issues that argued for not updating every year. For instance, BLS reports that there is an advantage to having “off years” in which changes in CEX forms can be implemented without the time pressure of employing the data in the CPI. The main reason, however, was simply that the approach of updating weights every year, which would require overlapping 2-year CE weights, was untested and its statistical properties were uninvestigated. BLS noted that, in its experience, changing index formulas can produce unexpected and undesirable results, so it decided to err on the side of caution by not going to annual updating.
The CEX targeted sample sizes are 6,160 per quarter for the Quarterly Interview Panel Survey and 5,870 per year for the Diary Survey. Because an increased sample size will produce an increase in the precision of an unbiased estimate, recommendations to increase the CEX sample size (primarily directed at the Diary Survey) have tended to be of the “more is better” variety. However if, as we have pointed out, the weights from the CEX are not unbiased, a decrease in sampling variability might actually increase mean squared error, which is what we ultimately care about.
A recent report from the Conference Board recommended increasing the annual sample size of the CEX “perhaps initially to 30,000 households.” The
Index (chain) drift refers to the possible bias that can arise when separate price indexes are linked. For example, suppose there are three periods, 0, 1, and 2. A price index could be computed for period 2 relative to period 0 in one step using fixed weights, or a “chain index” could be computed by multiplying the price index from 0 to 1 by the price index from 1 to 2. If each price is stochastic but stationary around a fixed level, or all prices are stationary around the same trend, so that relative prices vary in the short run but not in the long run, chain indexes are likely to be biased in comparison with fixed-base indexes. If relative prices return to their period 0 value in period 2, the chained index will generally differ from unity; this difference is the chain drift.