dimension of quality). They interpreted housing survey data as indicating that apartments increased in size by 20 percent between 1976 and 1993. Moulton and Moses (1997) countered, arguing that (1) rents generally do not increase proportionately with apartment size and (2) more importantly, that careful examination of data from the American Housing Survey and elsewhere suggests that the Boskin commission overstated historical increases in apartment sizes by perhaps a factor of three.
Appliances and Electronics The commission’s bias estimates for this category are the largest—3.6 percent per year for the period 1973-1994 and 5.6 percent per year for 1994-1996. Due to the identifiable and quantifiable nature of appliance characteristics, and probably also to a priori notions about advances in the sector, research into this category of consumer spending is more extensive than for any other. Thus, the commission was able to access direct evidence, and the overall category estimate was extrapolated from items for which studies have been produced. The body of evidence included research by commission member Gordon (1990, cited in Boskin et al., 1996) of model-by-model comparisons from Consumer Reports. Moulton and Moses acknowledge that bias estimates for this category were probably the best documented by the Boskin commission: the report cites a number of academic and government studies that “develop hedonic adjustment models and find upward bias for personal computers, television, video equipment, and other items in this category” (Moulton and Moses, 1997:317).
Apparel The Boskin commission used a “conservative reestimation” of figures from Gordon’s Sears catalog index, which rose less rapidly than the CPI subindex, to arrive at a 1 percent annual bias for the category. The main shortcoming of the experiment, according to Moulton and Moses (1997), is that Gordon measured year-to-year price changes only for the subset of apparel items that remained identical. The methodology links out—or deletes—the price increases associated with new product lines; the entire observed price change is assumed to reflect quality change. This approach produces misleading estimates if manufacturers are most likely to hike prices when new lines and varieties are introduced, as suggested by BLS studies. Also, apparel prices are known to be affected by lower-level substitution bias because of cross-outlet and seasonal volatility that allows consumers to find similar items at very different prices, depending on the store and on shopping times. Because methods to minimize substitution bias have been applied by BLS to apparel items, Moulton and Moses (1997:318) note that “it is unclear whether the Advisory Commission avoided double counting when sorting through these various sources of bias to produce its estimate of quality bias.”
Transportation (New and Used Vehicles/Motor Fuel) On the basis of studies showing increased quality and increased service lifetime, the Boskin commission estimated an annual bias of 0.59 percent for automobiles. The esti-