inflation for item categories that are weighted very differently across subindexes, there may be no clear pattern in which specific price changes would tend to cancel one another out. It is also possible that the available household data are simply inadequate to tease out a significant income-inflation rate relationship. Of course, the bulk of the research identified here is empirical; there is no obvious theoretical basis to assume that the relationship between inflation rates and income group will diverge more or less in the future. Future research may be productively directed toward examining the extent to which suspected index biases correlate to household income. For instance, economists have long argued that quality change and, hence, quality change bias may be more prominent among luxury goods (which would presumably give CPI-type indexes an upward bias for high-income groups). Boskin et al. (1998) challenge the notion that benefits from quality improvements and new products accrue disproportionately to the wealthy; however, there is little empirical documentation to forcefully support either assertion.
Social security is by far the largest government outlay directly adjusted using the CPI. This, along with the perception that the elderly are more vulnerable to adverse affects associated with price inflation, has stimulated research emphasizing this group. Also, the CPI for medical care, a comparatively important component of elderly expenditures, has increased more rapidly that has the overall CPI in recent years; however, measuring medical care costs is extremely complicated and it is hard to assess the accuracy of this CPI component.
The most systematic evidence on inflation faced by the elderly has evolved from a 1987 congressional directive to BLS to develop an experimental index for the population over age 62. In testimony to Congress, Mason (1988) reported the first set of results calculated under the program. For the period 1982-1987, the CPI-U, which captures spending habits of approximately four-fifths of the U.S. population, rose 18.2 percent; the CPI-W, which captures a subset of about one-third of the population rose 16.5 percent; the experimental index for the elderly (CPI-E) rose a slightly higher 19.5% (Amble and Stewart, 1994).
Amble and Stewart updated the results for the ongoing indexing program. For the period 1987-1993, the CPI-U rose 26.3 percent, the CPI-W rose 25.5 percent, and the experimental index for the elderly rose slightly more, 28.7 percent. Stewart and Pavalone (1996) completed the series through 1995, producing similar results. For the period 1990-1995, the CPI-U rose 14.7 percent, the CPI-W rose 14.1 percent, and the CPI-E rose 15.9 percent.
BLS’s experimental index consistently produced slightly higher inflation rates for the elderly during the 1980s and 1990s. However, this does not necessarily mean that the elderly have truly faced more rapid increases in living costs. To understand potential inaccuracies of the CPI-E as a true cost-of-living index for