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We produce relative importance of components in the Consumer Price Index for All Urban Consumers (CPI-U) and the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W). These data are to be used in conjunction with the CPI-U and CPI-W released in that same year. BLS publishes this data once a year, using December data. Relative importance data is also published monthly at the U.S. level in the news release tables.
Table 1 contains data for the U.S. city average for all categories. In years where the weights used in the construction of the CPI are updated, table 1 is produced using both the old and new weights to allow users to compare them. Tables 2 through 6 contain data for selected categories for the regions, metropolitan areas, population size classes, and cross-classifications of area and population size class. Table 7 contains the relative importance of the all items index for the U.S. city average and size class, region and region size class, and metropolitan areas.
The relative importance of a component is its expenditure or value weight expressed as a percentage of all items within an area or an area within the U.S. When the value weights are collected they represent average annual expenditures, and their relative importance ratios show approximately how the index population distributes expenditures among the components. Relative importance ratios represent an estimate of how consumers would distribute their expenditures as prices change over time.
Relative importance ratios cannot be used as estimates of current spending patterns or as indicators of changing consumer expenditures in the intervals between weight revisions because consumption patterns are influenced by factors other than price change. These factors include income, variations in climate, family size, and availability of new and different kinds of goods and services.
Relative importance ratios of components in the national or local area Consumer Price Indexes can be used in the construction of indexes for special combinations of items. In such instances, relative importance ratios are used as weights to combine relative changes in prices of the selected components over specified periods.Additional information on the procedure for deriving index weights from consumer expenditure data is available in the Consumer Expenditure Survey and Consumer Price Index sections of the BLS Handbook of Methods.
To estimate a relative importance for a component for a month other than December, one can use its previous published relative importance and update it by published price changes. For example, suppose you want to estimate the relative importance of energy for the CPI-U in September 2017.
You need the published relative importance for energy for December 2016 and the December 2016 and Sept.ember 2017 indexes for energy and for all items. Enter the weights and indexes for these two item categories (see table A). The updated weight column is the December published weight times the relative change between December 2016 and September 2017. In this example, the updated weight for energy is 7.039 * (215.711/193.306) = 7.8549. For all items, the updated weight is 100.000 * (246.819/241.432) = 102.2313. To calculate the updated relative importance for energy where the weight for all items is normalized to 100, divide the updated weight for energy by the updated weight for all items, times 100. In this example, the estimated relative importance for energy in September 2017 is (7.8549 / 102.2313) X 100 = 7.683.
Item | Published relative importance Dec. 2016 | Index Dec. 2016 | Index Sep. 2017 | Updated weight Sep. 2017 | Updated weight Sep. 2017 (normalized so that all items=100) |
---|---|---|---|---|---|
Energy |
7.039 | 193.306 | 215.711 | 7.8549 | 7.683 |
All items |
100.000 | 241.432 | 246.819 | 102.2313 | Normalized to 100.000 |
Continuing the above example, energy prices increased 6.4 percent over the 12 months ending October 2017, while the all items index increased 2.0 percent. How does one figure out the "contribution" of the energy component to the all items change? Asked another way, what proportion of the all items increase can be attributed to the energy component?
First, estimate the updated relative importance for energy for September 2017 (see the last column in Table A). Second, multiply the updated expenditure weight of energy times its relative price change in October (7.683 X 1.064 = 8.175). Similarly, the updated expenditure weight for all items is 100 X 1.02 = 102.000 (see table B).
The change in the expenditure weight for energy in October is 8.175-7.683=0.492 and the change in the expenditure weight for all items in October is 102.000-100.00=2.000.
The contribution of energy to the all items change equals the change in the expenditure weight for energy divided by the change in the expenditure weight for all items. Specifically, the contribution of energy to all items in this example is 0.492 / 2.000 = 0.246 or 24.6 percent. Said another way, nearly one fourth of the increase in the October index was due to the increase in energy prices.
Item | Normalized/updated weight for Sep. 2017 (from Table A) | Price change from Oct. 2016 to Oct. 2017 (expressed as a relative) | Updated weight Oct. 2017 | Differences in weights |
---|---|---|---|---|
Energy |
7.683 | +6.4 percent (1.064) | 8.175 | 10.456-10.352=0.492 |
All items |
100.000 | +2.0 percent (1.020) | 102.000 | 102.000-100.000=2.000 |
Contribution of energy to all items |
- | - | - | 0.492/2.000=0.246 (24.6 percent) |
Last Modified Date: April 24, 2019