tude of the quality change or new goods problem has not revealed broadly applicable techniques for correcting these biases.

In contrast, a set of generally accepted methods has emerged for addressing other perceived index problem areas, most notably substitution bias. Shapiro and Wilcox (1996) describe solutions to the substitution component of the bias problem as harvesting the “low-hanging fruit” of the CPI bias problem. Sticking with the harvesting metaphor, solutions to quality change and new goods bias problems must be the fruit at the top of the tree, the kind that requires expensive tools to reach or that may not be reachable at all.

The theoretical COLI perspective provides a rationale for tracking the value to consumers of new models and commodities and suggests why, for certain purposes, an index should be adjusted to reflect these changes. However, the COLI theory is less illuminating when it comes to directing research toward appropriate corrective techniques. Indeed, finding approaches for accurately dealing with changing goods and new goods is the most difficult obstacle to fulfilling the Boskin commission’s prescriptions for BLS to establish a cost-of-living index as its objective in measuring consumer prices. Reflecting the difficulty of the issue, the Boskin commission report did not advance any formal recommendations about how BLS could improve its measurement of quality change.6 The Boskin commission suggested perhaps that BLS should be doing more of the things it already does to correct for quality change bias, but seemed to concede that it did not have new ideas for approaching the problem. Summarizing the commission’s report, Gordon and Griliches (1997:84) write:

The difficult questions posed by quality change and the continual arrival of new products . . . have been called the “house-to-house combat of price measurement.” Because the magnitude of quality-change bias differs so much across product categories, any overall evaluation must be conducted “down in the trenches,” taking individual categories of consumer expenditure, assessing quality-change bias for each category, and then aggregating using appropriate weights.”

The Conference Board (1999:21) study group concurred: “In an advanced, dynamic economy like ours, there is no alternative to thorough, detailed analyses that slog through the data category by category, item by item. This is difficult, costly work, but no shortcuts are available.” Such conclusions reinforce the premise that general solutions, equivalent to the use of superlative indexes or geomeans to address substitution bias, do not exist to correct for quality change

6  

In contrast, 5 of the commission’s 17 recommendations deal directly with a form of substitution bias—for which concrete options (superlative and superlative approximation indexes) exist. Individual commission members have elsewhere advocated expanding the use of hedonic regression methods to control for quality change for specific product types (see, for example, Boskin et al., 1998:14).



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