The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
At What Price?: Conceptualizing and Measuring Cost-of-Living and Price Indexes
Scanner indexes can be produced on the current CPI schedule. Regarding quote timing, CPI and scanner data cover similar periods within the month; scanner data have the advantage of covering weekends and holidays, which CPI data do not.
For many cases, scanner data cover the entire domain of products within any given item strata and area cell, which is important for methodological consistency.
The scanner indexes can be produced in a manner generally consistent with BLS sampling procedures.
The sample is rotated and can be refreshed at least as often as under current CPI practices.
Indexes work with both standard geomean and superlative formulas.
The cost implications of introducing scanner data and reducing field price observations have yet to be fully evaluated by BLS.
Limitations of Store-Based Scanner Data
Despite the numerous potential advantages described above, issues remain to be sorted out before BLS can proceed toward systematic integration of point-of-sale scanner data into the CPI; these issues relate to pricing, coverage (both geographic and item-specific), cost, integration of scanner data with other data sources, and reliance on private-sector data.
In addition to unit valuation (already discussed), pricing issues include treatment of taxes and comparability between private-sector scanner data and Census Bureau/BLS data. The CPI collects prices without sales taxes; then a calculated tax is applied separately using secondary data. Scanner data also do not include taxes. However, since ACNielsen does not disclose the exact location of outlets, it is not always clear what tax rate should be added to item prices. For the cereal experiment, it was not a problem since New York has no tax on most food items. However, in general, a solution to this problem needs to be found by vendors or BLS. One possibility would be to calculate a population-weighted average sales tax each month for each item based on the outlet usage patterns of consumers in each geostrata (Richardson, 2000).
Coverage issues include geographic definitions and saturation of scanner equipment. Geographic-area definitions for the CPI and for currently produced scanner datasets do not match. Scanner data markets are generally smaller than the census-defined metropolitan areas on which the CPI is based. ACNielsen is currently working to map most of the United States into CPI geographic areas, though when the project is complete, there will still be some gaps (e.g., ACNielsen does not cover Anchorage). Even for the covered areas, scanner price data are not available for all outlets at which items from any given CPI strata are sold. In the