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Chapter 17. The Consumer 

Price Index (Updated 2-14-2018)

The Consumer Price Index (CPI) is a measure of the 

average change over time in the prices of consumer 

items—goods and services that people buy for day-today living. The CPI is a complex measure that combines economic theory with sampling and other statistical techniques 

and uses data from several surveys to produce a timely and 

precise measure of average price change for the consumption 

sector of the American economy. Production of the CPI requires the skills of many professionals, including economists, 

statisticians, computer scientists, data collectors and others. 

The CPI surveys rely on the voluntary cooperation of many 

people and establishments throughout the country who, without compulsion or compensation, supply data to the government’s data collection staff. 

Part I. Overview of the CPI

Three CPI series. The Bureau of Labor Statistics (BLS; 

the Bureau) publishes CPI data every month. The three main 

CPI series are

• CPI for All Urban Consumers (CPI-U)

• CPI for Urban Wage Earners and Clerical Workers

(CPI-W)

• Chained CPI for All Urban Consumers (C-CPI-U)

The CPI for All Urban Consumers, or CPI-U, which BLS 

began publishing in January 1978, represents the buying 

habits of the residents of urban or metropolitan areas in the 

United States. The CPI for Urban Wage Earners and Clerical 

Workers, or CPI-W, the oldest of the series, covers a subset of 

the urban population.1

 The prices used for producing these 

two series are the same. The CPI-U and CPI-W differ only in 

1 Specifically, the CPI-U (all-urban) population consists of all urban households in Metropolitan Statistical Areas (MSAs) and in urban places of 2,500 

inhabitants or more. Nonfarm consumers living in rural areas within MSAs 

are included, but the index excludes rural nonmetropolitan consumers and 

the military and the institutional population. The urban wage earner and 

clerical worker (CPI-W) population consists of consumer units with clerical 

workers, sales workers, protective and other service workers, laborers, or 

construction workers. More than one-half of the consumer unit’s income 

has to be earned from these occupations, and at least one of the members 

must be employed for 37 weeks or more in an eligible occupation.

IN THIS CHAPTER

Part I: Overview of the CPI................................................ 1

CPI concepts and scope.................................................. 2

CPI structure and publication......................................... 3

Calculation of price indexes........................................... 3

CPI publication ............................................................... 3

How to interpret the CPI ................................................ 5

Uses of the CPI ............................................................... 5

Limitations of the index ................................................. 6

Experimental indexes..................................................... 6

History of the CPI, 1919 to 2013 .................................... 7

Part II: Construction of the CPI ......................................... 11

Sampling: areas, items, and outlets................................ 11

Area sample .................................................................... 11

Item and outlet samples.................................................. 12

Commodities and services other than shelter................ 12

Shelter ............................................................................ 16

Estimation of price change in the CPI ........................... 18

Estimation of price change for commodities

 and services other than shelter.................................. 18

Item replacement and quality adjustment..................... 19

Estimation of price change for shelter .......................... 20

Special pricing and estimation procedures

 or medical care .......................................................... 22

Special pricing for other items....................................... 25

Special pricing for seasonal items.................................. 26

Other price adjustments and procedures........................ 27

Index calculation ............................................................ 28

Estimation of upper-level price change ......................... 28

Calculation of seasonally adjusted indexes.................... 34

Calculation of annual and semiannual average 

 indexes........................................................................ 35

Average prices ................................................................ 35

Part III: Precision of CPI estimates.................................... 37

Technical references........................................................... 41

Appendix 1. List of published indexes............................... 43

Appendix 2. List of average prices .................................... 55

Appendix 3. Characteristics of CPI changes ..................... 59

Appendix 4. Sample areas and weights ............................. 64

Appendix 5. Expenditure classes, strata, and ELIs........... 68

Appendix 6. Sample allocation methodology for 

commodities and services.............................................. 82

Appendix 7. POPS categories............................................. 88

Appendix 8. Non-POPS sample designs............................ 101

Appendix 9. CPI Relative importances.............................. 103

Note: To reflect new sample areas and pricing cycles

effective with the geographic revision with January 2018 

data, appendix 1 has been updated and appendix 4 has 

been replaced. Changes have been made to several areas; 

please consult appendix 4 for the current list. The entire

CPI chapter of the Handbook of Methods is being updated and is expected to be published in 2020.
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the consumer spending weights used to combine, or average 

together, basic indexes.2

The Chained CPI for All Urban Consumers (or C-CPI-U), 

also represents the urban population as a whole. BLS began 

publishing this series in August 2002 with data beginning 

in January 2000. The prices used in the C-CPI-U are the 

same as those used to produce the CPI-U and CPI-W, but the 

C-CPI-U uses a different formula and different weights to 

combine basic indexes. The formula used in the C-CPI-U accounts for consumers’ ability to achieve the same standard of 

living from alternative sets of consumer goods and services. 

This formula requires consumer spending data that are not 

immediately available. Consequently, the C-CPI-U, unlike 

the other two series, is published first in preliminary form 

and is subject to scheduled revisions. 

CPI populations. A consumer price index measures the 

price-change experience of a particular group called its target population. The CPI uses two target populations for its 

main series:

• All Urban Consumers (the “CPI-U” population)

• Urban Wage Earners and Clerical Workers (the “CPIW” population)

Both the CPI-U and the C-CPI-U target the CPI-U population. The CPI-U population, which covers about 88 percent 

of the U.S. population, covers households in all areas of the 

United States except people living in rural nonmetropolitan areas, in farm households, on military installations, in 

religious communities, and in institutions such as prisons and 

mental hospitals. 

The CPI-W population, the target of the CPI-W, is a subset of the CPI-U population. The CPI-W population consists 

of all CPI-U population households for whom 50 percent or 

more of household income comes from wages and clerical 

workers’ earnings. The CPI-W’s share of the total U.S. population has diminished over the years; the CPI-W population is 

now about 28 percent of the total U.S. population. The CPI-W 

population excludes households of professional and salaried 

workers, part-time workers, the self-employed, and the unemployed, along with households with no one in the labor 

force, such as those of retirees. 

CPI concepts and scope

The CPI provides an estimate of the price change between 

any two periods. The CPI follows the prices of a sample of 

2 Until 1982, BLS maintained separate (but overlapping) samples of outlets and specific items for the CPI-U and CPI-W populations. Given little 

variance in the movements between the CPI-U and CPI-W, BLS dropped the 

separate samples for the CPI-W population. The CPI-U converted to rental 

equivalence effective with the indexes for January 1983; the CPI-W moved 

to rental equivalence 2 years later. Since January 1985, the movements of all 

CPI-W basic indexes have been identical to those of the CPI-U.

items in various categories of consumer spending—such as 

food, clothing, shelter, and medical services—that people 

buy for day-to-day living. The monthly movement in the CPI 

derives from weighted averages of the price changes of the 

items in its sample. A sample item’s price change is the ratio 

of its price at the current time to its price in a previous time. 

A sample item’s weight in this average is the share of total 

consumer spending that it represents. The algebraic formulas 

used for this averaging are called index number formulas.3

A unifying framework for dealing with practical questions that arise in the construction of the CPI is provided by 

the concept of the cost-of-living index (COLI)4. As it pertains 

to the CPI, the COLI for the current month is based on the 

answer to the following question: “What is the cost, at this 

month’s market prices, of achieving the standard of living 

actually attained in the base period?” This cost is a hypothetical expenditure—the lowest expenditure level necessary 

at this month’s prices to achieve the base-period’s living standard. The ratio of this hypothetical cost to the actual cost of 

the base-period consumption basket in the base period is the 

COLI. Unfortunately, because the cost of achieving a living 

standard cannot be observed directly, in operational terms a 

COLI can only be approximated. Although the CPI cannot be 

said to equal a cost-of-living index, the concept of the COLI 

provides the CPI’s measurement objective and is the standard 

by which we define any bias in the CPI. BLS long has said 

that it operates within a cost-of-living framework in producing the CPI.5

 That framework has guided, and will continue 

to guide, operational decisions about the construction of the 

index. 

Because the COLI is not directly observable, the CPI employs index number formulas that offer approximations to 

the measurement objective. The CPI-U and the CPI-W use 

a Laspeyres formula to average the price changes across 

categories of items. It is sometimes said that the Laspeyres 

formula provides an “upper bound” on the COLI index. The 

C-CPI-U uses a Törnqvist formula to average price changes 

across item categories. This formula belongs to a class of formulas called superlative because, under certain assumptions, 

they can provide close approximations to a COLI. Since 1999, 

the CPI program has used the geometric mean formula to average price change within most item categories. Under certain 

assumptions that are likely to be true within most categories, 

an index based on the geometric mean formula will be closer 

to a COLI than will a Laspeyres index. 

3 For a review of index number formulas, their properties, and their relationship to economic theory, see W. E. Diewert, “Index numbers,” in J. 

Eatwell, M. Malgate, and P. Newman eds., The new Palgrave: a dictionary 

of economics, vol. 2 (London: The MacMillan Press, 1987), pp. 767–780. 

4 For more information on the cost-of-living index concept, see the technical references at the end of this chapter.

5 On the use of a cost-of-living index as a conceptual framework for practical decision making in putting together a price index, see Robert Gillingham, “A conceptual framework for the revised Consumer Price Index.” 

Proceedings of the American Statistical Association, Business and Economic Statistics Section (Alexandria: VA, American Statistical Association, 1974), pp. 46–52. 
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Scope 

The cost of maintaining a standard of living is affected by 

phenomena that go beyond the traditional domain of a consumer price index—changes in the cost of consumer goods 

and services. The broadest form of a COLI, which is called 

an unconditional COLI, would reflect changes in non-price 

factors such as crime rates, weather conditions, and health 

status. The objective of the CPI, by contrast, is to provide 

an approximation to a conditional COLI that includes only 

the prices of market goods and services or governmentprovided goods for which explicit user charges are assessed. 

Free goods, characteristics of the environment (such as air 

and water quality), the value of leisure time, and items that 

governments provide at no cost are not in scope, although 

they undeniably can have an impact on the cost of living as 

broadly defined. 

Excluded goods and services. The CPI covers the consumption sector of the U.S. economy. Consequently, it excludes 

investment items, such as stocks, bonds, real estate, and business expenses. Life insurance also is excluded for this reason, although health, household, and vehicle insurance are in 

scope. Employer provided in-kind benefits are viewed as part 

of income. Purchases of houses, antiques, and collectibles are 

viewed as investment expenditures and therefore excluded. 

Gambling losses, fines, cash gifts to individuals or charities, and child support and alimony payments also are out 

of scope. Changes in interest costs or interest rates are now 

excluded from the CPI scope, although some were in the CPI 

for many years. And, for practical reasons, the CPI excludes 

illegal goods and services and the value of home-produced 

items other than owners’ equivalent rent. 

Taxes. Both the CPI and the conditional COLI measure 

changes in expenditures—including the effect of changes in 

sales taxes and similar taxes that are part of the final price of 

consumer products—needed to achieve the base-period standard of living. Neither the CPI nor the COLI, however, measures the change in before-tax income required to maintain 

the base-period living standard. For this reason, neither the 

COLI nor the CPI is affected by changes in income and other 

direct taxes. For certain purposes, one might want to define 

price indexes that include, rather than exclude, income taxes.6

The CPI does include the effects of changes in sales taxes and 

other indirect taxes. As previously noted, however, these are 

included as part of the price of consumer products. No attempt is made to reflect changes in the quantity or quality of 

government services paid for through taxes. 

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One could develop an index along these lines. Such an index (sometimes 

called a tax-and-price index) would provide an answer to a different question (along the lines of “At current prices, what is the least before-tax income needed to buy...”) from the one that is relevant to the CPI. It would 

be appropriate for different uses. For a research measure of a consumption index inclusive of income taxes and Social Security contributions, see 

Robert Gillingham and John Greenlees, “The impact of direct taxes on the 

cost of living.” Journal of Political Economy, August 1987, pp. 775–796.

Government-provided and government-subsidized items. 

The CPI treats as price changes any changes to fees that the 

government charges for items, such as admission to a national park. The CPI also counts the price of subsidized items 

that are available to the general public. For example, governments may subsidize local transit operation. If the subsidy is 

cut and the fare is raised, the CPI will reflect this price increase. On the other hand, the CPI does not reflect changes to 

means-tested (dependent on the recipient’s income) subsidies, 

such as the Supplemental Nutrition Assistance Program or 

Section 8 housing allowances. Changes in such subsidies are 

treated as changes to the recipient’s income and, therefore, 

out of scope. 

CPI structure and publication

Calculation of price indexes

In the CPI, the urban portion of the United States is divided into 38 geographic areas called index areas, and the set 

of all goods and services purchased by consumers is divided 

into 211 categories called item strata. This results in 8,018 (38 

× 211) item–area combinations.

The CPI is calculated in two stages. The first stage is the 

calculation of basic indexes, which show the average price 

change of the items within each of the 8,018 CPI item–area 

combinations. For example, the electricity index for the Boston CPI area is a basic index. The weights for the first stage 

come from the sampling frame for the category in the area. 

At the second stage, aggregate indexes are produced by averaging across subsets of the 8,018 CPI item–area combinations. The aggregate indexes are the higher level indexes; for 

example, the all-items index for Boston is an average of all of 

the area’s 211 basic indexes. Similarly, the aggregate index 

for electricity is an average of the basic indexes for electricity 

in each of the 38 index areas. The U.S. city average All-items 

CPI is an average of all basic indexes. The weights for the 

second stage are derived from reported expenditures from 

the Consumer Expenditure Survey (CE). 

CPI publication

Indexes. Each month’s index value displays the average 

change in the prices of consumer goods and services since 

a base period, which currently is 1982–84 for most indexes. 

For example, the CPI-U for July 2013 was 233.596. One interpretation of this is that a representative set of consumer 

items that cost $100 in 1982–84 would have cost $233.60 in 

July 2013. 

Percent change. Rather than emphasizing the level of the 

index in comparison to the base period, the monthly CPI 

release stresses the CPI’s percent change from the previous 

month and from the previous year. The most commonly reported monthly percent changes are the one-month seasonally adjusted percent change, and the 12-month not seasonally 
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adjusted percent change. For example, the July 2013 CPI was 

233.596 and the July 2012 CPI was 229.104, so the CPI increased 2.0 percent (not seasonally adjusted) from July 2012 

to July 2013. 

CPI area indexes and CPI item indexes. BLS publishes a 

large number of additional CPI index series. (See appendix 

1.) For the CPI-U population areas—the broadest geographic coverage—detailed item indexes for most categories of 

consumer spending are published every month. Also every 

month, BLS publishes all-items indexes, along with a limited 

set of detailed indexes, for the three largest metropolitan areas and for the major geographic areas. In addition, detailed 

food, energy, and shelter indexes are published monthly for 

all CPI publication areas. Bimonthly or semiannually, allitems indexes for selected metropolitan areas are published 

along with the limited set of detailed indexes. 

The primary reason for publishing CPI area-item detail 

indexes is to aid in analysis of movements in the national allitems CPI. Decisions on which detailed indexes to publish 

depend, in part, on the reliability of their estimates7. CPI area 

indexes and CPI item detail indexes use only a portion of the 

CPI sample; this makes them subject to substantially greater 

sampling error than the national CPI. For this reason, BLS 

strongly urges users to consider the U.S. city average allitems CPI for use in escalator clauses.

CPI area indexes. BLS calculates and publishes separate 

area indexes for 

• Four geographic regions (sometimes called census 

regions): Northeast, Midwest, South, and West

• Three population-size classes: large metropolitan areas, small metropolitan areas,8

 and nonmetropolitan 

urban places

• Selected region-size classes—regions cross-classified 

by population size (for example, large metropolitan 

areas in the Northeast)

• Selected metropolitan areas

Comparing the CPI for an area with the U.S. CPI or with 

the CPI for another area gives an indication of differences 

among the areas’ rates of price change. In other words, such a 

comparison indicates whether, over time, prices of items that 

consumers in one area tend to buy have risen more or less 

rapidly than the prices of items that consumers in another 

area tend to buy. It does not indicate whether the average 

level of prices in an area is higher or lower than the average 

level in another area. 

7 Steven Grandits, “Publication strategy for the 1998 revised Consumer 

Price Index,” Monthly Labor Review, December 1996, pp. 26–30, http://

stats.bls.gov/opub/mlr/1996/12/art4full.pdf.

8 Prior to January 1998, the CPI published data for medium and small metropolitan areas, which have been combined to form a single class.

CPI item indexes. BLS classifies the CPI market basket of 

consumer goods and services into a hierarchy of categories. 

The top levels of the item category hierarchy consist of 

• The eight major groups 

• Other groups

• Expenditure classes 

• Item strata

For the U.S. CPI, BLS publishes all levels down to item 

strata. BLS publishes less item detail for the CPI area indexes.

Special aggregations. BLS also calculates and publishes 

indexes for special aggregations, such as energy items, that 

cut across the preceding classification scheme. Some users 

consider the series All items less food and energy to measure 

the ‘core’ rate of inflation. Food and energy are two of the 

most volatile components of the CPI. For this reason, many 

analysts regard the measure of core inflation as more useful 

for their purposes. 

The C-CPI-U. The Chained CPI-U uses a superlative index 

formula which reflects consumers’ behavior in response to 

changes in relative prices. Unfortunately, this requires current expenditure data, and expenditure data become available 

only after a significant lag. Consequently, C-CPI-U index 

values, unlike the values of the CPI-U and CPI-W, are not 

final when first published. Before 2015, BLS issued two annual preliminary estimates before issuing final C-CPI-U data.9

 Starting in 2015, BLS intends to issue four preliminary 

estimates of the C-CPI-U. The “initial” values will come out 

every month concurrent with the CPI-U and CPI-W. In each 

of the following four quarters, “interim” values will replace 

the initial values. One year later, the interim values will be 

replaced with the final C-CPI-U. For example, in February 

2016, the BLS is scheduled to release the January 2016 CPIU, the CPI-W, and the initial C-CPI-U. For the next three 

quarters (i.e., April, July, and October of 2016), BLS will 

publish updated interim C-CPI-U indexes. With the fourth 

revision in January 2017, the January 2016 C-CPI-U will be 

issued as final. 

Seasonally adjusted indexes and percent changes. In addition to the originally computed indexes and percent changes, 

which are called unadjusted indexes and unadjusted percent 

changes, BLS calculates and publishes seasonally adjusted 

series. The unadjusted numbers reflect the change in price 

resulting from all causes, including normal seasonal price 

movement due to regular changes—resulting, for example, 

from weather, harvests, the school year, production cycles, 

model changeovers, holidays, or sales—that recur every year. 

For economic analysis and for other purposes, it is useful to 

9 The first release of C-CPI-U data took place on Aug. 16, 2002. At that 

time, final data for the 12 months of 2000, interim data for the 12 months of 

2001, and initial data for the first 7 months of 2002 were issued.
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remove the estimated seasonal effects from the original indexes and percent changes. To produce the seasonally adjusted 

indexes and percent changes, BLS uses seasonal adjustment 

techniques that remove these effects. BLS seasonally adjusts 

only those CPI series that pass certain statistical criteria and 

for which there is an economic rationale for observed seasonality. For example, while the unadjusted CPI for All items 

was unchanged from June 2013 to July 2013, the seasonally 

adjusted 1-month percent change in the CPI was 0.2 percent. 

Seasonally adjusted indexes are subject to annual revision and 

therefore are not recommended for use in escalation contracts. 

Seasonal adjustment is done only at the national level for the 

U.S. city average CPI-U and CPI-W. Presently, the C-CPI-U 

does not have sufficient historical data to permit calculation of 

stable seasonal factors. 

Average prices. For some food, beverage, and energy items, 

the CPI samples contain enough observations of unique items 

to make possible the computation and publication of meaningful average retail prices. A list of what is covered in the 

published average price series is shown in appendix 2. 

Correction policy. The CPI, unlike many other statistical 

series, does not rely on respondents to transmit data to the 

national office. CPI data collectors collect almost all data 

needed for the CPI-U and CPI-W, so that routine revisions to 

account for late-arriving data are not necessary. Virtually all 

data are received in time for the calculation of indexes for the 

appropriate month. In rare cases, however, when we discover 

that we made an error collecting or compiling information, 

BLS issues corrections to the CPI series in accordance with 

BLS policy and CPI practices.

Corrections to the CPI-U and CPI-W. These series are final 

when issued. The CPI-U and CPI-W are commonly used in 

escalation agreements and to adjust pensions and tax brackets; consequently, revisions can be costly for the users of 

these indexes. For this reason, there is a presumption in BLS 

policy and practice against revisions to the CPI that extend 

back over lengthy periods. When a mistake is discovered, 

CPI staff evaluates the error in the context of BLS guidelines 

for issuing corrections to previously published CPI data.

Corrections to the C-CPI-U. As previously noted, C-CPIU indexes are not final when first issued. They are routinely 

revised, and are not final until the publication of data for the 

second January after initial publication. If the CPI-U and 

CPI-W series are corrected, the C-CPI-U series will be corrected as well. Corrected C-CPI-U indexes will be issued for 

all series affected by the error, as far back as the previous 5 

years. 

How to interpret the CPI

Movements of the indexes from one month to another usually are expressed as percent changes rather than changes in 

index points. The level of the index (relative to its base period) 

affects index point changes, but it does not affect percent 

changes. The following tabulation shows how to compute 

percent changes:

 Index point change

CPI ...........................................................222.742

Less CPI for previous period ................ 221.317

Equals index point change ...........................1.425

 Percent change

Index point difference.....................1.425

Divided by the previous index ....221.317

Equals .............................................0.006

Results multiplied by.............0.006 × 100

Equals percent change ...................... 0.6

Percent changes for periods other than 1 year often are expressed

as annualized percentages. Annualized percent changes indicate 

what the change would be if the CPI continued to change at the 

same rate each month over a 12-month period. These are calculated using the standard formula for compound growth:

Index point difference 1.425

Divided by the previous index 222.742

Equals 0.06

Results multiplied by 100.0 0.06 × 100

Equals percent change 0.6

Percent changes for periods other than 1 year often are expressed as annualized percentages. Annualize

percent changes indicate what the change would be if the CPI continued to change at the same rate eac

month over a 12-month period. These are calculated using the standard formula for compound growth:

  IX / IX –1 100 , annual PC 12/

   m

tmt

where 

IXt is the index in month t,

IXt+m is the index m months after month t, and

PCannual is the annualized percent change.

Uses of the CPI

The CPI affects virtually all Americans because of the many ways in which it is used. Its major uses ar

as follows:

 As an economic indicator. As the most widely used measure of retail inflation, the CPI is a 

major indicator of the effectiveness of Government economic policy. The President, the 

Congress, and the Federal Reserve Board use the movement of the CPI to help formulate an

monitor the effect of fiscal and monetary policies. Business executives, labor leaders, and

other private citizens also use the index as a guide in making economic decisions.

 As a means of adjusting income payments. The index directly affects the income of almos

80 million people. Social Security10 benefits and military and Federal Civil Service pension

payments are all indexed by the CPI. In the private sector, many collective bargaining

agreements tie automatic wage increases to the CPI. Some private firms and individuals use 

the index to keep rents, alimony, and child support payments in line with changing prices.

 As a means of preventing inflation-induced tax changes. Federal (and some state) income 

tax brackets and other parameters are adjusted by the CPI. This prevents inflation from

automatically increasing taxes, a phenomenon called bracket creep. 

 As a deflator of other economic series. Other statistical programs use the CPI or its

components to adjust for price changes and produce inflation-free versions of their series. 

Examples of CPI-adjusted series include components of the U.S. Department of Commerce

National Income and Product Accounts (such as gross domestic product and personal 

where 

IXtis the index in month t,

IXt + m is the index m months after month t, and

PCannual is the annualized percent change.

Uses of the CPI

The CPI affects virtually all Americans because of the many 

ways in which it is used. Its major uses are as follows:

• As an economic indicator. As the most widely used

measure of retail inflation, the CPI is a major indicator

of the effectiveness of Government economic policy. 

The President, the Congress, and the Federal Reserve 

Board use the movement of the CPI to help formulate 

and monitor the effect of fiscal and monetary policies. 

Business executives, labor leaders, and other private 

citizens also use the index as a guide in making economic decisions.

• As a means of adjusting income payments. The index 

directly affects the income of almost 80 million people. Social Security10 benefits and military and Federal 

10 Specific information on the Social Security use of the CPI can be 

found on the Social Security Administration website, http://www.

socialsecurity."gov/cola/."
6

Civil Service pension payments are all indexed by the 

CPI. In the private sector, many collective bargaining 

agreements tie automatic wage increases to the CPI. 

Some private firms and individuals use the index to 

keep rents, alimony, and child support payments in 

line with changing prices.

• As a means of preventing inflation-induced tax 

changes. Federal (and some state) income tax brackets and other parameters are adjusted by the CPI. This 

prevents inflation from automatically increasing taxes, a phenomenon called bracket creep. 

• As a deflator of other economic series. Other statistical programs use the CPI or its components to adjust 

for price changes and produce inflation-free versions 

of their series. Examples of CPI-adjusted series include components of the U.S. Department of Commerce National Income and Product Accounts (such 

as gross domestic product and personal consumption 

expenditures) and retail sales measures and the BLS 

hourly and weekly earnings series.

Limitations of the index

The CPI covers a wide variety of items that all urban consumers purchase, but—because most individuals concentrate 

spending on a relatively small fraction of the total number of 

items available in the market—it contains items that a given 

individual does not purchase. The CPI must represent a composite consumer, and it does not necessarily represent the 

price-change experience of any one individual, household, or 

family. Similarly, the CPI may not be applicable to all questions about price movements for all population groups. 

As previously noted, CPI indexes cannot be used to determine relative living costs. The CPIs for various geographic 

areas of the United States do not indicate the differences in 

price level among them. The change in the CPI for an individual area measures the degree to which prices have changed 

over time within that particular area. It does not show whether 

prices or living costs are higher or lower in that area relative 

to another area or to the United States as a whole. Comparing 

indexes between one area and another indicates which area 

has experienced more rapid price change—not which area has 

a higher price level or higher living costs.

Sampling and non-sampling error. The CPI is estimated 

from a sample of consumer purchases; it is not a complete 

measure of price change. Consequently, the index results may 

deviate slightly from those that would be obtained if all consumer transactions were covered. This is called sampling error. These estimating or sampling errors are statistical limitations of the index. 

A different kind of error in the CPI can occur when, for example, a respondent provides BLS economic assistants with 

inaccurate or incomplete information. This is called nonsampling error. BLS attempts to minimize these errors by 

obtaining prices through personal observation whenever possible, and by correcting errors immediately upon discovery. 

The economic assistants, technicians, and commodity specialists who collect, process, and analyze the data are trained 

to watch for deviations in reported prices that might be due 

to errors. 

A full discussion of the varieties and sources of possible 

error in the index is presented in part III of this chapter, “Precision of CPI Estimates.”

Experimental indexes

Population subgroups. The CPI also calculates and publishes 

some indexes on an experimental basis only. For example, the 

program provides experimental indexes for the elderly. Comparing indexes for such subgroups does not indicate whether 

the prices they pay are higher or lower than the prices other 

groups pay; this comparison indicates only whether prices of 

their items have risen faster or slower than those for other 

groups. Indexes for subgroups of the population are more difficult to construct than indexes for the whole. In particular, 

making sure that samples refer to only part of the population 

may be difficult or impractical. Moreover, making subgroup 

indexes as precise as the national CPI would require that the 

sample sizes be as large. 

The experimental CPI for Americans 62 Years of age and 

older (CPI-E). BLS occasionally issues a report on its experimental index for the elderly. This index, sometimes referred 

to as the CPI for the elderly or CPI-E, is calculated monthly 

and is available on request. It should be emphasized that the 

CPI-E is merely a reweighting of the CPI basic indexes using 

expenditure weights from households headed by someone 62 

years of age or older. There is no attempt to recalculate the 

basic indexes themselves so that they represent the retail outlets and consumption items of older consumers.11

CPI research series. Over the years, BLS has made many 

improvements to the CPI. When BLS changes its methods, it 

always announces them in advance and, if possible, estimates 

the impact the change would have had in recent periods. BLS 

does not, however, revise previously published CPI data to 

reflect the new methods. This practice means that the movement of the CPI reflects not only price change over time but 

also changes to CPI methods. To assist users who wish to use 

the CPI over long periods, BLS publishes the CPI-U Research 

Series Using Current Methods (CPI-U-RS). It provides estimates, for the period since 1977, of what the CPI would have 

been had the most current methods been in effect. Each time 

there are new methods introduced into the CPI, the CPI-U-RS 

is revised from 1978 forward.12

11 For more information, see Consumer Price Index Detailed Report (U.S. 

Bureau of Labor Statistics, February 2000), pp. 5–7..

12 Kenneth J. Stewart and Stephen B. Reed, "CPI research series using 

current methods, 1978–98," Monthly Labor Review, June 1999, pp. 29–38.
7

History of the CPI, 1919 to 2002

The CPI was initiated during World War I, when rapid increases in prices, particularly in shipbuilding centers, made 

such an index essential for calculating cost-of-living adjustments in wages. To provide appropriate weighting patterns 

for the index, so that it would reflect the relative importance 

of goods and services purchased by consumers, studies of 

family expenditures were conducted in 92 industrial centers 

in 1917–1919. Periodic collection of prices was started and, in 

1919, BLS began publication of separate indexes for 32 cities. 

Regular publication of a national index, the U.S. city average, 

began in 1921, and indexes were estimated back to 1913.13

Since its inception, the CPI has been comprehensively 

revised on several occasions to implement updated samples 

and weights, expanded coverage, and enhanced methodologies. For example, the 1998 revision introduced more timely 

consumer spending weights; updated geographic and housing samples; a revised item classification structure; a new 

housing index estimation system; computer-assisted price 

collection; and a new Telephone Point-of-Purchase Survey 

(TPOPS). BLS also has made important improvements to 

the CPI beyond the major revision processes, an example 

being the introduction of the geometric mean formula in

January 1999. Exhibit 1 provides a chronology of revisions 

and improvements to the CPI, and appendix 3 displays

13 Collection of food prices back to 1890 had been initiated in 1903. During the course of the 1917–1919 expenditure survey, retail prices for other 

items were collected in 19 cities for December of each year back to 1914, 

and in 13 other cities back to December 1917 only. Retail prices of food and 

wholesale prices of other items were used to estimate price change from 

1914 back to 1913.

historical changes in base period, population coverage, and 

other index characteristics.

The improvements introduced over the years have reflected not only the Bureau’s own experience and research, but 

also the criticisms and investigations of outsiders. For example, in undertaking the 1940 comprehensive revision of the 

CPI, BLS acted on recommendations made by an Advisory 

Committee appointed by the American Statistical Association. Major studies were conducted during World War II by 

the President’s Committee on the Cost of Living14 and in 1951 

by the House Committee on Education and Labor.15

The 1961 report of the Price Statistics Review Committee (sometimes called the “Stigler Committee”) provided 

impetus for subsequent changes in many aspects of the CPI, 

including the sampling of outlets and items, the treatment of 

quality changes in consumer durables, and the role of costof-living theory.16 Recent studies include the 1996 report 

of the Advisory Commission to Study the Consumer Price 

Index (the “Boskin Commission”)17 and the 2002 report, At 

what price? Conceptualizing and measuring cost-of-living 

and price indexes, by a National Research Council panel of 

the National Academy of Sciences.18 A continuing flow of 

articles in professional journals and books also has contributed to the assessment of the CPI’s quality and of the ways in 

which it might be improved. For a list of published papers, see 

the Technical References at the end of this chapter.

14 Report of the president's committee on the cost of living (Washington, 

Office of Economic Stabilization, 1945). 

15 Consumer Price Index, report of a special subcommittee of the committee on education and labor, Subcommittee Report No. 2 (U.S. Congress, 

House of Representatives, 1951).

16 Government price statistics, hearing before the subcommittee on economic statistics, U.S. Congress, 871. Part 1 (U.S. Congress Joint Economic 

Committee, January 24, 1961). 

17 Final report of the advisory commission to study the Consumer Price 

Index (The Boskin Commission Report) (U.S.Senate Committee on Finance, December 1996).

18 Charles Schultze and Christopher Mackie, eds. At what price? Conceptualizing and measuring cost-of-living and price indexes. (Washington, 

DC: National Academy Press, 2002.
8

Exhibit 1. Chronology of changes in the Consumer Price Index

The Consumer Price Index to 1940

• Began publication of separate indexes for 32 cities (1919): 

 Collected prices in central cities periodically

• "Developed weights from a study that BLS conducted in 1917–1919 of family expenditures in 92 industrial

 centers, reflecting the relative importance of goods and services purchased by consumers"

 Reflected the relative importance of goods and services purchased by consumers

• Collected prices for major groups: Food, clothing, rent, fuels, house furnishings, and miscellaneous

• Limited pricing to items selected in advance to represent their categories

• Began regular publication of a national index, the U.S. city average (1921): 

 Based index on an unweighted average of the city indexes 

 Estimated U.S. city average back to 1913, using food prices only

The 1940 CPI revision: the first comprehensive revision

• Used weights based on 1934–1936 study of consumer expenditures

• Collected prices in the 34 largest cities 

 Implemented a weighted average of cities for the U.S. city average CPI

Improvements made between the 1940 and 1953 revisions

• During World War II: 

Discontinued the pricing of unavailable items, such as new cars and household appliances 

Increased the weight of other items, including automobile repair and public transportation in 1951:

Adjusted weights in seven cities, using a 1947 and 1949 survey of consumer expenditures

 Adjusted weights for the 1950 Census

 Adjusted rent index to remove “new unit bias” caused by rent control

 Added new items to the list of covered items, including frozen foods and televisions

The 1953 CPI revision: the second comprehensive revision

• Used weights from a 1950 expenditure survey conducted in central cities and attached urbanized areas

• Refined the target population to include urban wage earner and clerical worker families

• Added a sample of medium and small cities

• Updated the list of items that the index covered, adding restaurant meals

• Added new sources of price data

• Improved pricing and calculation methods

The 1964 CPI revision: the third comprehensive revision

• Based weights on 1960–1961 expenditure patterns in metropolitan areas

• Added single-person households to target population: urban wage earner and clerical worker households

• Extended pricing to the suburbs of sampled metropolitan areas

• Updated the sample of cities, goods and services, and retail stores and service establishments

Improvements made between the 1964 and 1978 revisions

• Made quality adjustments for new vehicles at model changeover

• Improved treatment of seasonal items

The 1978 CPI revision: the fourth comprehensive revision

• Added a new Consumer Price Index: the CPI for All Urban Consumers, or the CPI-U
9

Exhibit 1. Chronology of changes in the Consumer Price Index—continued

• Renamed the older CPI [as] the CPI for Urban Wage Earners and Clerical Workers, or the CPI-W

• Used weights from a 1972–1973 survey of consumer expenditures and the 1970 census

• Expanded the sample to 85 areas

• Increased minimum pricing frequency from quarterly to bimonthly

• Implemented monthly pricing in five largest areas

• Introduced probability sampling methods at all stages of CPI sampling

• Introduced checklists that define each category of spending

• Developed estimates of the CPI’s sampling error and optimal sample allocation to minimize that error

Improvements made between the 1978 and 1987 revisions

• Began systematic replacement of outlets and their item samples between major revisions (1981):

Implemented new Point-of-Purchase Survey (POPS)

Selected retail outlets with probability proportional to consumer spending therein

Eliminated reliance on outdated secondary-source sampling frames

Began rotating outlet and item samples every 5 years

Began rotating one-fifth of the CPI pricing areas each year

• Introduced rental equivalence concept (January 1983 for the CPI-U; January 1985 for the CPI-W)

The 1987 CPI revision: the fifth comprehensive revision

• Used weights from the 1982–1984 Consumer Expenditure Survey and the 1980 Census

• Updated samples of items, outlets, and areas

• Redesigned the CPI housing survey

• Improved sampling, data collection, data-processing, and statistical estimation methods

• Initiated more efficient sample design and sample allocation

• Introduced techniques to make CPI production and calculation more efficient

Improvements made between the 1987 and 1998 revisions

• Improved housing estimator to account for the aging of the sample housing units

• Improved the handling of new models of vehicles and other goods

• Implemented new sample procedures to prevent overweighting items whose prices are likely to rise

• Improved seasonal adjustment methods

• Initiated a single hospital services item stratum with a treatment-oriented item definition

• Discontinued pricing of the inputs to hospital services

The 1998 CPI revision: the sixth comprehensive revision

• Weights from the 1993–1995 Consumer Expenditure Survey and the 1990 census

• Updated geographic and housing samples

• Extensively revised item classification system

• Implemented new housing index estimation system

• Used computer-assisted data collection

• Added the Telephone Point-of-Purchase Survey (TPOPS):

 Allows rotation of outlet and item samples by item category and geographic area, rather than by area alone

Improvements since the 1998 Revision

• Initiated a new housing survey based on the 1990 census (January 1999):

Estimated price change for owners’ equivalent rent directly from rents

Began using a geometric mean formula for most basic indexes (January 1999):

Mitigates lower level substitution bias
10

Exhibit 1. Chronology of changes in the Consumer Price Index—continued

• Reflects shifts in consumer spending with item categories as relative price change

• Extended the use of hedonic regression to estimate the value of items changing in quality

• Directed replacement of sample items in the personal computer and other categories, to keep samples current

• Implemented 4-year outlet rotation to replace 5-year scheme

• Began within-outlet item rotation for prescription drugs and other item categories

• Implemented biennial weight updates starting January 2002

• Increased sample size of the Consumer Expenditure Survey, so that CPI weights could be based on just 2 years 

of data

Added the Chained Consumer Price Index for All Urban Consumers (C-CPI-U) (August 2002)

• Uses more advanced “superlative” index formula (the Törnqvist formula)

• Corrects upper-level substitution bias

• Expanded collection of price data to all business days of the month (Before 2004, prices were collected the first 

18 business days of the month for the first 10 months of the year and the first 15 business days for November and 

December.)

• Began publishing indexes to three decimal places (January 2007)
11

Part II. Construction of the CPI 

Sampling: areas, items, and outlets

The smallest geographic areas in which pricing is done 

for the CPI are called primary sampling units (PSUs). Within 

these areas, sales outlets are chosen where people shop and 

live. The selected nonshelter outlets are matched to a sample 

of items that these consumers buy. Appendix 4 lists the 87 

PSUs selected for the 1998 revision and the counties contained therein. Prices from these were introduced into CPI 

index calculation with the release of the January 1998 index. 

Area sample

For the purpose of selecting the 1998 CPI PSU sample,19

the entire United States was divided into PSUs. First, BLS 

used the U.S. Office of Management and Budget (OMB) 

definition of Metropolitan Areas (MAs)20 to divide the country into metropolitan and nonmetropolitan areas. The PSUs 

within the metropolitan area are, with five exceptions, always 

OMB-defined MAs.21 In the nonmetropolitan areas, BLS defined the PSU boundaries. In general, a PSU is delineated by 

county borders (with some exceptions in New England) and 

can comprise several counties.

Each PSU was first classified by its size. All PSUs with 

populations larger than 1.5 million were declared to be selfrepresenting and given the size type of A.22 The remaining 

non-self-representing PSUs, metropolitan and nonmetropolitan, are called B and C PSUs, respectively.23 (To avoid confusion, it is important to recognize the distinction between the 

naming conventions for PSUs and those for CPI size-class 

indexes. In general, prices collected in B PSUs are used to 

19 A more detailed description of the 1998 revision area sample selection 

is contained in Janet Williams, Eugene F. Brown, and Gary R. Zion, “The 

challenge of redesigning the Consumer Price Index sample,” Proceedings 

of the Survey Research Methods Section, American Statistical Association, Business and Economic Statistics Section (Alexandria, VA: American 

Statistical Association, 1993), pp. 200–205; and Janet L. Williams, “The 

redesign of the CPI geographic sample.” Monthly Labor Review, December 

1996, pp. 10–17, http://www.bls.gov/opub/mlr/1996/12/art2full.pdf.

20 MAs are Metropolitan Statistical Areas (MSAs), Primary Metropolitan 

Statistical Areas (PMSAs), or Consolidated Metropolitan Statistical Areas 

(CMSAs). For more information, see the Statistical Policy Office of the U.S. 

Office of Management and Budget, Attachments to OMB Bulletin No. 93-

05, Metropolitan areas 1992, Lists I–IV..

21 The five PSU exceptions are the Los Angeles suburbs PSU, the three 

PSUs that together form the New York–Northern New Jersey–Long Island 

CMSA, and the Washington, DC, PSU.

22 Anchorage and Honolulu are A PSUs with smaller populations.

23 When planning began for the 1998 revision of the CPI, one potential 

change envisioned was the publication of a Consumer Price Index for the total U.S.population (called the CPI-T). To accommodate this expanded CPI, 

a larger number of PSUs was selected to cover the population living in rural 

areas outside the metropolitan area.

compute the B/C CPI indexes, and prices in C PSUs are used 

in the computation of the D CPI indexes. The exceptions are 

the Anchorage and Honolulu metropolitan areas, which are A 

PSUs but included in the B/C size class indexes.) 

The second classification variable for PSUs is census region. 

The next phase of the area selection was to stratify (group) 

PSUs in each region-size class; for example, South B into strata 

(groups) of similar PSUs based on their scores on several factors (called stratifying variables). Each A PSU is in a stratum 

by itself, thus, the name “self-representing.” Selection of the 

stratifying variables was based on linear regression modeling 

of price change (sequentially finding sums of a constant and a 

constant times each of a subset of 1990 census and geographic 

PSU variables that best explain CPI price change over different periods). The variables (called geographic variables) used 

in all stratifications except that for the South B PSUs were 

percent urban, the normalized latitude and longitude of the 

PSU’s geographic center, and normalized longitude squared. 

In the stratification of the South B PSUs, percent urban and 

variables used in the 1987 Revision—namely, mean interest 

and dividend income per consumer unit (CU), mean CU wage 

and salary income, percent housing units (HUs) heated by 

electricity, percent HUs heated by fuel oil, percent owner-occupied HUs; percent black CUs, and percent CUs with retired 

person—were used. The program employed to do the stratifications was a modified version of the Friedman-Rubin clustering algorithm, which puts PSUs in the same stratum based on 

their similarities on the stratification variables, while keeping 

the population sizes of the strata approximately equal.

A program was used to select one PSU per stratum so that 

the selected PSUs were well-distributed over the States and 

there were many 1988-sample PSUs among the newly selected ones.24 Prices from the 36 newly-selected non-1988-sample 

PSUs were introduced into CPI index calculations in 1998.

Since 1998, indexes have been published monthly for the 

New York, Los Angeles and Chicago Consolidated Metropolitan Statistical Areas (CMSAs). Indexes for the Washington-Baltimore CMSA, along with the next 10 largest A 

24 In case the CPI-T was judged too costly, a selection was made from 

nonmetropolitan PSUs that would have their urban parts included in the 

CPI-U. Candidate PSUs had to contain some urban population. From these 

candidates, a probability (proportional to the urban population of their stratum) sample for the CPI-U was selected in each region except the Northeast. 

Long after the selection of the PSUs, a decision was made not to publish a 

CPI-T because of its increased cost. At that time, 18 of the previously selected PSUs were dropped from the CPI sample and its increased cost. At that 

time, 18 of the previously selected PSUs were dropped from the CPI sample 

and designated as “Consumer Expenditure Survey only” PSUs.
12

PSUs (not including those contained in the aforementioned 

CMSAs), are published bimonthly. Beginning in January and 

July, semiannual indexes are published for the 12 smallest A 

PSUs. Indexes are also published for the U.S. total as well as 

for region and size class totals, with the exception of the D 

indexes in the Northeast and West. Beginning in 2002, semiannual indexes have been published for Phoenix.

Replicates, which are used in variance calculation, are assigned to each A PSU based on population, with each A PSU 

having either two or four replicates. B and C PSUs are paired 

in each region, with each pair containing a PSU on the even 

and odd monthly pricing cycles, except for single PSU pairs 

in the region-size classes in which the number of PSUs is not 

a multiple of 4. Publication of a region-size class requires at 

least four PSUs (two replicates). The actual allocation of replicates is provided in the next section, along with the allocation of replicate panels.25

Item and outlet samples

Commodities and services other than shelter

Item structure and sampling. The CPI item structure has four 

levels of classification. The eight major groups are made up of 

70 expenditure classes (ECs), which in turn are divided into 

211 item strata. Major groups and ECs do not figure directly 

in CPI sample selection, although ECs are used in smoothing 

item stratum expenditure estimates during composite estimation. Within each item stratum, one or more substrata, called 

entry-level items (ELIs), are defined. ELIs are the ultimate 

sampling units for items as selected by the BLS national office. They represent the level of item definition from which 

data collectors begin item sampling within each sample outlet. (See appendix 5 for a complete list of consumer ECs, item 

strata, and ELIs.) 

To enable the CPI to reflect changes in the marketplace, 

new item and outlet samples are selected each year, on a rotating basis, for approximately 25 percent of the item strata in 

each PSU. Each year, four regional item universes are tabulated from the two most recent years of CE data. Independent 

samples of ELIs are selected from the corresponding regional 

item universe for each item stratum PSU-replicate scheduled 

for rotation that year. Within each sample PSU-replicate, each 

item sample is based on a systematic probability-proportional-to-size (PPS) sampling procedure, in which each ELI has a 

probability of selection proportional to the CPI-U population 

expenditures for the region for the ELI within its stratum. 

Item and outlet sample design. The CPI uses two separate 

sample designs, one for rent and owners’ equivalent rent, and 

one for all other commodities and services. The methodology used to determine the commodities and services item and 

outlet sample design is presented here in brief. The design for 

the rent and owners’ equivalent rent indexes is described later.

25 For the A PSUs, the number of replicate panels (or groups) is the same 

as the number of replicates. Each non-self-representing PSU is allocated 

one replicate panel.

The primary objective of the Commodities and Services 

(C&S) sample design is to determine an allocation of individual item and outlet selections, by item stratum and by PSU, 

replicate, and POPS category (see list that follows), that minimizes the sampling variance of price change measured by the 

all-cities C&S CPI, subject to certain budgetary and sample 

size constraints. Models are used to project the sampling variance and data collection costs in terms of the decision variables 

for the sample design. For these models, all commodities and 

services item strata are grouped into 13 major groups:

• Food at home—non meat staples

• Food at home—meat, poultry, fish

• Food at home—fruits and vegetables

• Other food at home, plus beverages 

 (alcoholic and nonalcoholic)

• Food away from home

• Fuels and utilities 

• Household furnishings and operations

• Apparel 

• Transportation less motor fuel

• Motor fuel

• Medical care 

• Education and communication

• Recreation and other commodities and services 

In brief, the C&S sample allocation methodology is as follows: First, a variance function that projects the variance of 

price change as a function of the preceding variables for the 

commodity and service components is modeled. Second, a 

cost function that predicts the total annual cost of the commodity and service components of the CPI is modeled. Third, 

values for all coefficients of the two functions, including estimates of outlet sample overlap, are estimated. Fourth, nonlinear programming techniques are used to determine approximately optimal sizes for the item and outlet samples needed 

to minimize the CPI variance under varying assumptions of 

annual price change subject to cost constraints. 

The variance and cost functions for the CPI are modeled 

for 15 PSU groups:

PSU group name

• New York City

• New York City suburbs

• Los Angeles City

• Los Angeles suburbs

• Chicago

• Philadelphia and San Francisco

• Detroit and Boston

• Other large self-representing PSUs

• Small self-representing PSUs

• Medium-sized PSUs, Census Region 1

• Medium-sized PSUs, Census Region 2

• Medium-sized PSUs, Census Region 3

• Medium-sized PSUs, Census Region 4

• Small PSUs, Census Regions 1–4

• Anchorage and Honolulu
13

A detailed discussion of the sample allocation methodology is provided in appendix 6. The allocation is resolved with each C&S sample rotation, which occurs twice 

each year, and allocations change as sample frames are 

refreshed and rotated. For ongoing pricing, about 27,000 

outlets are visited each month, with prices collected for 

about 83,400 commodities and services. 

Outlet and price surveys. BLS economic assistants collect prices monthly for food at home, energy, and selected 

other commodity and service item strata in all PSUs. Items 

that are priced monthly typically are those with more 

volatile and variable price movement. Commodities and 

services priced monthly in all PSUs is given in exhibit 2. 

Prices also are collected monthly for all commodity and 

service item strata in the three largest publication areas: 

New York, Los Angeles, and Chicago. Prices are collected 

bimonthly in the remaining PSUs for the C&S item strata 

not cited in the list. Those are assigned to either even- or 

odd-numbered months for pricing. 

Exhibit 2. Consumer Price Index (CPI) items 

priced monthly everywhere

All food at home items

Housing at school, excluding board

Other lodging away from home, including hotels 

and motels

Tenants’ and household insurance

Fuel oil

Propane, kerosene, and firewood

Electricity

Utility (piped) gas service

Used cars and trucks

Gasoline (all types)

Other motor fuels

Tires

Vehicle accessories other than tires

State and local registration, license, and motor vehicle 

property tax

Parking and tolls

Newspapers and magazines

Recreational books

Postage

Delivery services

Land line telephone services

Wireless telephone services

Cigarettes

Tobacco products other than cigarettes

Telephone Point-of-Purchase Survey (TPOPS). The U.S. 

Census Bureau conducts the TPOPS for BLS. The survey 

furnishes data on retail outlets from which metropolitan 

and urban nonmetropolitan households purchased defined 

groups of commodities and services to be priced in the 

CPI. Commodities and services are grouped into sampling categories, called POPS categories. (See appendix 

7.) These categories are based on ELIs as defined in the 

CPI classification structure. Some POPS categories consist of only one ELI, while others consist of multiple ELIs. 

ELIs are combined into a single POPS category when the 

commodities or services generally are sold in the same 

outlets. 

TPOPS uses random-digit dialing to select households 

for participation in the survey. Within each PSU, banks 

of landline telephone and cell phone numbers containing 

at least some residential phone numbers are identified. 

Among these identified banks, numbers are then randomly dialed. Inevitably, some of the dialed numbers such as 

nonworking, ring-no-answer, business, and FAX machine 

telephone numbers are ineligible for TPOPS interviewing. 

Some numbers belong to ineligible households, such as 

military households. 

Eligible respondents include all civilian, noninstitutional persons residing in regular residences, boarding 

houses, student or worker housing, mobile home parks, 

and permanent-type living quarters in hotels and motels, 

as well as staff residing in institutions. Not all of the eligible telephone numbers are productive, however, as respondent refusals are unavoidable. 

BLS specifies a target number of completed TPOPS 

interviews for each PSU. In the small and medium-sized 

PSUs, that target number is 110 completed interviews. 

For most of the self-representing PSUs, the target number 

of completed interviews ranges from 200 to 400. In New 

York City and Chicago, the target number of completed 

interviews is 460. In Los Angeles, the specified goal is 

500.

Upon first contact and after determining the eligibility 

and willingness of the household, a Census Bureau interviewer asks a variety of administrative and demographic 

questions. This information allows BLS to monitor how 

well the selected households represent the overall population, as well as to analyze the shopping patterns of various 

segments of the population.

Any given responsive household is called once a quarter for four successive quarters. Each time, the interviewer administers the survey to the original respondent, if 

possible. During each interview, the respondent is asked 

whether the household had expenditures for a set of POPS 

categories over a duration of time called a “recall period.” Recall periods are POPS-category-specific and vary 

from 1 week to 5 years. The recall period for a specific 

POPS category is defined to produce a sufficient, but not 

excessive, number of outlets for sampling purposes. For 

instance, because households tend to purchase gasoline 

frequently, a 1-week recall period is used. In contrast, 

people tend to purchase cars and funeral services infrequently; therefore a 5-year recall period is assigned. If 

the respondent reports expenditures for a particular POPS 

category, the interviewer prompts the respondent for the 

outlet name, location, and amount spent. At the end of 

each quarter of interviewing, the Census Bureau sends 

the TPOPS outlet frame data to BLS for processing. BLS 
14

processes TPOPS data two quarters at a time. The primary objective of BLS processing of TPOPS data is to select a sample of outlets at which specific items ultimately 

will be priced for inclusion in the CPI. The expenditure 

amounts reported in TPOPS are used as outlet selection 

probabilities.

TPOPS employs a quarterly rotating-panel sample design. On a quarterly basis, every PSU is assigned 1 of 

16 TPOPS questionnaires. Each questionnaire consists 

of up to 16 POPS categories. In a particular quarter and 

for a particular PSU, the selected TPOPS respondents 

are asked about expenditures made for some or all of 

the POPS categories on the assigned questionnaire. During each subsequent quarter of TPOPS interviewing, the 

given PSU is administered a different questionnaire until 

each of the 16 questionnaires has been administered. It 

takes 4 years of quarterly interviewing to rotate through 

all 16 questionnaires. After the 4 years, the cycle for the 

PSU starts over again. In this manner, all TPOPS samples 

are refreshed once every 4 years. This practice is repeated 

for each PSU. The quarterly pattern of assigned POPS categories varies from PSU to PSU in a strategic fashion to 

ensure that every POPS group is assigned to at least a few 

PSUs every quarter.

Outlet sampling procedures. The design for TPOPS provides for the rotation of approximately one-quarter of the 

items in each sample PSU during the course of each year. 

With each rotation, item samples and outlet samples are 

selected for the designated items and corresponding POPS 

categories.

In self-representing PSUs, sample households for 

each TPOPS rotation are divided into two or more independent groups. This process defines two or more 

frames of outlets per category-PSU for outlet selection. The principal purpose of constructing these independent groups, or replicate panels, is for variance 

estimation. A single subset of independently selected ELIs and outlets for all item strata within a PSU is 

called a replicate. The number of replicates per PSU 

group and the number of PSUs in each PSU group are 

given in table 1.

Reported expenditures for each outlet within the frame 

for each POPS category and PSU-replicate are edited 

prior to sample selection. Sometimes, a purchase is reported for an outlet but the amount of expenditures is not 

reported; to ensure a chance of selection for the outlet in 

those cases, the mean expenditure for outlets for the POPS 

category-PSU-replicate is assigned to the outlet. Large 

expenditure totals for an outlet are edited, in some cases, 

to be no greater than 25 percent of the total expenditure 

reported for the POPS category-PSU-replicate. In cases in 

which there are more than 20 outlets reported for a POPS 

category-PSU-replicate, the largest reported expenditures 

are trimmed to be no greater than 10 percent of the total 

reported for that POPS category-PSU-replicate.

Table 1. Construction of replicate panels

PSU group

Number

of

PSUs

Number of 

replicate 

panels

1. New York City 1 4

2. New York City suburbs 2 4

3. Los Angeles City 1 4

4. Los Angeles suburbs 1 2

5. Chicago 1 4

6. Philadelphia and San Francisco 2 4 (2 each)

7. Detroit and Boston 2 4 (2 each)

8. Other large self-representing PSUs 7 14 (2 each)

9. Small self-representing PSUs 12 24 (2 each)

10. Medium-sized PSUs, Census Region 1 8 8

11. Medium-sized PSUs, Census Region 2 10 10

12. Medium-sized PSUs, Census Region 3 22 22

13. Medium-sized PSUs, Census Region 4 6 6

14. Small PSUs, Census Regions 1–4 10 10

15. Anchorage and Honolulu 2 4 (2 each)

Source: U.S. Bureau of Labor Statistics.

Outlet samples are selected independently for each PSU, 

replicate, and POPS category using a systematic PPS sampling procedure. Each outlet in a frame has a probability of 

selection proportional to the total expenditures reported for 

the outlet in the POPS category in the TPOPS survey. In each 

PSU-replicate, all ELIs selected in the item sampling process 

are assigned for pricing to each sample outlet selected from 

the frame for the corresponding POPS categories. When multiple selections of a sample outlet occur, a commensurate increase is made in the number of quotes priced for the outlet. 

Outlet sampling procedures for commodities and services 

not included in the TPOPS. Some commodity and service 

items are excluded from the TPOPS, either because existing 

sampling frames are adequate or because it became apparent 

that the TPOPS would not yield an adequate sampling frame. 

(See appendix 8.) For each of these items (non-POPS), BLS 

either constructs the sampling frame or acquires it from another source. Each non-POPS item has its own sample design. 

The frames consist of all outlets providing the commodity or 

service in each sample area. A measure of size is associated 

with each outlet on the sampling frame. Ideally, this measure 

is the amount of revenue generated by the outlet from the item 

for the CPI-U population in the sample area. Whenever revenue is not available, an alternative measure of size, such as 

employment, number of customers, or sales volume, is substituted. All samples are selected using systematic sampling techniques with probability proportional to the measure of size. 
15

The source of the sampling frame, the definition of the 

sampling unit, the measure of size employed, and the final 

pricing unit for each non-POPS item are presented in appendix 8. 

Merging item and outlet samples. Item and outlet samples, 

which are selected independently, must be merged before data 

collection. A concordance that maps ELIs to POPS categories 

allows each sampled ELI to be assigned for price collection 

to the outlet sample selected for the POPS category that contains it. The number of price quotes for an ELI in each outlet thus equals the number of times the ELI was selected for 

pricing in the PSU- replicate during the item sampling process. The item-outlet sample merge determines the number 

of price quotes assigned for collection in each sample outlet. 

In the outlet sampling process, outlets with large expenditure 

reports may be selected more than once from the frame for 

a given POPS category. An outlet also may be selected from 

the frame for more than one POPS category. If an outlet is 

selected multiple times for a given POPS category, the same 

multiple of price quotes is assigned for collection for each 

sample ELI matching the category. If an outlet is selected for 

more than one POPS category, price quotes are assigned for 

collection for all ELIs selected in each category. 

Selection procedures within outlets. A BLS economic assistant visits each selected outlet. For each ELI assigned to 

the outlet for price collection, the economic assistant uses a 

multistage probability selection technique to select a specific 

item from among all the items the outlet sells that fall within 

the ELI definition. The economic assistant first identifies all 

of the items included in the ELI definition and offered for sale 

by the outlet. When there are a large number of items in the 

ELI, the assistant groups them by common characteristics, 

such as brand, size, or type of packaging. With the assistance 

of the respondent for the outlet, the economic assistant assigns probabilities of selection to each group. 

The probabilities of selection are proportional to the sales 

of the items included in each group. The economic assistant 

may use any of the following four procedures, listed in order 

of preference, for determining the proportion of sales: 

• Obtaining the proportions directly from a respondent 

• Ranking the groups by importance of sales as indicated by the respondent, and then obtaining the proportions directly or using assigned proportions 

• Using shelf space to estimate the proportions, where 

applicable 

• Using equal probability

After assigning probabilities of selection, the economic assistant uses a random-number table to select one group. The 

economic assistant then identifies all items included in the 

selected group, forms groups of those items based on the incommon characteristics, assigns probabilities to each group, 

and uses a random number table to select one. The economic 

assistant repeats this process through successive stages until 

reaching a unique item. The economic assistant describes the 

selected unique item on a checklist for the ELI. Checklists 

contain the descriptive characteristics necessary to identify 

the item among all items defined within the ELI. 

These selection procedures ensure that there is an objective and efficient probability sampling of CPI items other 

than shelter. They also allow broad definitions of ELIs, so 

that the same unique item need not be priced everywhere. 

The wide variety of specific items greatly reduces the withinitem component of variance, reduces the correlation of price 

movement between areas, and allows a substantial reduction 

in the number of quotes required to achieve a given variance. 

Another important benefit from the broader ELIs is a significantly higher likelihood of finding a priceable item within the 

definition of the ELI in the sample outlet. 

This selection process is completed during the visit to the 

outlet to obtain the price for the selected item. Subsequently, 

personal visits or telephone calls are made, either monthly or 

bimonthly, to ascertain that the item is still sold and to obtain 

its current price. 

Computer-assisted data collection for commodities and services. A computer-assisted data collection (CADC) system 

has been used in the C&S survey since September 2002. The 

data collection instrument is composed of two main modules. 

The interactive electronic checklists of item specifications 

allow the data collector to identify the same item upon returning to an outlet, to substitute a similar item, or to initiate 

a new item for pricing. Each ELI is subdivided into clusters, 

with each cluster having its own set of specifications. Checklists contain descriptive information about items, including 

features of the items themselves and components of the item 

that might affect the price. A checklist can be a straightforward list of specifications, or it can be fairly complex, with 

hierarchical dependencies among specifications and complicated mathematical formulas. The interactive electronic 

checklist enforces rules regarding patterns of specifications 

that may be necessary to identify an item. The checklist also 

prevents inconsistencies.

The other module of the C&S CADC collection instrument 

comprises some screens that make up the pricing form and 

various functions pertaining to the task of data collection. For 

instance, some screens enable the economic assistant to organize his or her work at the level of the outlet or the quote, 

while some allow review of collected data or information 

about the outlet and respondent. An economic assistant selects an action, such as substituting a new item for one that is 

unavailable (only options that are appropriate for that action 

are offered in the collection instrument). The pricing screens 

allow the economic assistant to enter the price of the item as 

well as relevant information about it, such as quantity, size, 

unit of size, sales tax, and seasonality; the economic assistant 

can also see the previous price and other data relevant to the 

quote.

Electronic data collection improves data quality in part by 

activating important rules at the moment the data are being 

collected. For instance, a suspiciously large price change can 
16

be noted immediately, rather than after data have been sent 

to Washington, DC and examined by commodity analysts. 

The collection instrument contains multiple edits which either 

warn the economic assistants about some important aspect of 

a quote or prevent them from entering invalid information.

CPI data collection is scheduled in terms of business days 

(that is, weekdays excluding holidays). Before 2004, data collection covered three pricing periods, each comprising 6 business days in most months and 5 days in November and December. Consequently, the last scheduled data collection was 

usually the 18th business day of the month. Beginning with 

data for January 2004, the three pricing periods now are of 

variable length and end on the last business day of the month.

Shelter

The CPI housing unit sample is the source of the data on 

residential rents used to calculate changes in rents for the rent 

of primary residence (rent) index. The housing survey also 

uses these rent data in calculating changes in the rental value 

of owned homes for the owners’ equivalent rent of primary 

residence (OER) index. These two shelter indexes account for 

approximately 30 percent of the total CPI weight.

Weighting during segment sample selection. In the 1999 

Housing Sample, segments were selected with probability 

proportional to size, the size measure being estimated expenditures. In the segment selection process, the segments 

are ordered within each PSU by county and then by segment 

rent level within county. Because the segment selection is 

systematic, this guarantees that not all high-rent or low-rent 

segments are chosen and that the segments will be geographically distributed within the PSU.

Each segment, s, was assigned a probability of selection, P, 

within the PSU, which is the ratio of the cost of housing in the 

segment relative to the cost of housing in the PSU times the 

number of segments selected. Therefore,

where

nPSU = number of segments chosen in the PSU,

and

TCs is as defined in the next paragraph.

Each segment also has a weight Ws , which is the reciprocal of 

the probability of selection. Therefore, 

Ws = 1/Ps.

The total cost of housing in the segment, TC is the cost of 

rented housing in the segment, RCs, plus the cost of owned 

housing in the segment, OCs. RCs is the number of rented 

housing units in the segment Rs times the average rent value 

within the segment (RRs). (OCs) is the number of owned housing units in the segment times an estimated average owner 

equivalent rent value within the segment, IRs. This gives segments with higher valued units (higher rent levels) a higher 

probability of selection and a lower segment weight:

TCs= RCs+ OCs = Rs × RRs+Os × IRs.

The number of owned housing units, the number of rented 

housing units, and the average rent value were taken from 

the decennial census. The estimated average owner equivalent rent value was determined by a linear regression on 

Consumer Expenditure Survey property value, income, and 

number of rooms. The resulting regression coefficients were 

applied to decennial census values for the same independent 

variables to estimate the average owner’s equivalent rent for 

each segment.

The following is the nonlinear regression that was used:

 oerval = bo + (b1 × propval) + (b2 × propval2) + 

 (b3 × income) + (b4 × rooms).

In this equation,

oerval = the value the home would rent for,

propval = the market value of the home,

income = the income of the consumer unit, and

rooms = the number of rooms in the house.

The actual regression coefficients were determined 

uniquely for each index area.

Because rents are not volatile, the housing sample is 

divided into panels; one panel is priced each month and 

each panel is priced twice a year. For example, panel 1 is 

priced in January and July, panel 2 in February and August, and so on through panel 6. The segments within the 

strata are assigned to these panels. These assignments are 

made such that each panel has a representative subsample 

of the PSU. Because each panel is representative of the entire sample, there is never an off-cycle month for the housing survey, a panel of data provides sufficient information 

for monthly publication of the rent and rental equivalence 

(REQ) indexes. Segments were selected within the PSUs 

in multiples of 6, so that each panel had the same sample 

size within a PSU.

About 10,000 segments were selected in the PSUs. The 

housing sample is designed to consist of approximately 

50,000 rental units. Sampling rates were computed for 

each segment so that the sample design would be realized 

after the sampling and screening processes described next 

were completed. 

Sample allocation to PSUs. BLS allocated the sample to 

PSUs based on the estimated total housing expenditure in 

each PSU. The estimated total housing PSU expenditure 

is the sum of the total cost of housing, previously defined, 

across all segments: 

17

ule of the C&S CADC collection instrument comprises some screens that make up the 

d various functions pertaining to the task of data collection. For instance, some screens 

omic assistant to organize his or her work at the level of the outlet or the quote, while 

iew of collected data or information about the outlet and respondent. An economic 

 an action, such as substituting a new item for one that is unavailable (only options that 

for that action are offered in the collection instrument). The pricing screens allow the 

ant to enter the price of the item as well as relevant information about it, such as quantity, 

e, sales tax, and seasonality; the economic assistant can also see the previous price and 

ant to the quote.

collection improves data quality in part by activating important rules at the moment the 

ollected. For instance, a suspiciously large price change can be noted immediately, rather 

have been sent to Washington, DC and examined by commodity analysts. The collection 

ains multiple edits which either warn the economic assistants about some important 

e or prevent them from entering invalid information.

tion is scheduled in terms of business days (that is, weekdays excluding holidays). Before 

ection covered three pricing periods, each comprising 6 business days in most months and 

mber and December. Consequently, the last scheduled data collection was usually the 18th

 the month. Beginning with data for January 2004, the three pricing periods now are of 

and end on the last business day of the month.

g unit sample is the source of the data on residential rents used to calculate changes in 

nt of primary residence (rent) index. The housing survey also uses these rent data in 

nges in the rental value of owned homes for the owners’ equivalent rent of primary 

R) index. These two shelter indexes account for approximately 30 percent of the total CPI 

ng segment sample selection. In the 1999 Housing Sample, segments were selected with 

portional to size, the size measure being estimated expenditures. In the segment selection 

ments are ordered within each PSU by county and then by segment rent level within 

e the segment selection is systematic, this guarantees that not all high-rent or low-rent 

hosen and that the segments will be geographically distributed within the PSU.

s, was assigned a probability of selection, P, within the PSU, which is the ratio of the cost 

e segment TCs relative to the cost of housing in the PSU times the number of segments 

fore,

PSU

s∈PSU

TCs Ps TCs /   n 

 



   ,
17

There are six collection panels. It was desired that the 

segment sample size be equal within each collection panel. 

Thus, the segments were allocated in blocks of 6 segments, 

with a minimum of 72 segments per PSU. For PSUs with multiple replicates, it was desired to have at least 36 segments 

per replicate and an equal sample size in each replicate. It 

was determined that a minimum of 108 segments was needed 

to support publication in areas that were published semiannually and that a minimum of 180 segments was needed for 

areas that are published bimonthly. The one exception was 

Baltimore, which received 108 segments but is published bimonthly as part of the Washington–Baltimore CMSA. As the 

sample size was previously about 10,000 segments and the 

budget for housing data collection was comparable, multiples 

of 6 segments were chosen so that the total would be near 

10,000 segments.

Sampling housing units. After segments have been chosen 

for each PSU, housing units are chosen for collection within 

each segment. Lists of housing units are obtained for each 

segment and an equal probability sample is chosen. In most 

cases, the target number of rental units from each segment 

is five. Prior to sampling, the housing units are ordered by 

address and the sample taken is systematic, ensuring a geographic spread of housing units selected within the segment. 

The sampling rate varies from segment to segment, depending on the expected percentage of rental and owned units 

within the segment. 

Collection. Collection includes the screening of the selected 

housing units to determine if the units are in scope for the 

housing sample. If the unit is in scope, it is initiated. Initiation is the initial collection of rent data, which consists of the 

rent paid and the specific housing services that are associated 

with the unit and the rent paid. These data are the basis for 

all calculations of rent change that occurs during the life of 

the unit in the housing sample. After initiation, the housing 

unit is priced on panel every 6 months. Pricing is very similar to the initiation process, but some previous answers are 

provided. The collection of the housing data, and particularly 

the rent data, is independent. That is, the field staff collects 

the data without giving the respondent the previous answer. 

Previous answers for some non-rent data are provided, so that 

the field staff can confirm certain changes with the respondent. Inherent in all of the structured housing questionnaires 

(screening, initiation, and pricing) are various flow determinations (skip patterns), such that the answer to one question 

determines the next question that must be asked/answered. 

The CADC instrument. The CADC instrument receives the 

screening/initiation schedules electronically. Even though 

the schedules have been assigned to specific panels, the field 

staff has several months to collect the screening/initiation 

schedules. This is referred to as the non-monthly period. The 

field staff obtains answers to various (screening) questions 

(through observation and through direct questioning of eligible respondents) that determine whether an address is in 

scope for the housing sample. The screening criteria consist 

of tenure (whether the unit is renter or owner occupied) and 

other criteria, such as not being in public housing projects, 

being a primary residence, and the tenant not being a relative of the landlord. With the computer, the skip patterns can 

be very efficient. Because the computer has stored all of the 

previously collected data, automated logic checks remove all 

redundant question patterns, thereby reducing the field staff’s 

work and the respondent’s burden. Automated data checking 

ensures that only correct data types are collected, other automated logic checks ensure that collected data are consistent, and the instrument informs the field staff if any required 

data have not been collected. These data checks are being 

performed at the time of collection, so errors and inconsistencies can be corrected while the respondent is present. The 

result is that the data that are sent to Washington are as accurate as possible. (The collection instrument also automatically determines appropriate “scope status”: permanently out 

of scope, temporarily out of scope, incomplete, or complete 

and in scope). 

If the housing unit is found to be out of scope for some 

reason that is not likely to change, the collection instrument 

assigns a scope status of “permanently out of scope” and the 

unit is never visited again. (An example of this would be units 

in public housing projects.) If the housing unit is found to be 

out of scope for some reason that might change, its status is 

“temporarily out of scope,” and another screening/initiation 

attempt is made after a specified waiting period. (An example 

of this might be when the unit is not the primary residence 

for the current tenant, but may become the primary residence 

for some future tenant.) If the screening was incomplete, the 

housing unit is assigned a scope status that results in another 

screening/initiation attempt in 6 months. Selected addresses 

that pass the screening criteria are considered in scope for 

the housing sample and are eligible for the next stage of the 

process, initiation. 

Initiation. The CADC instrument automatically moves the 

interviewer into the initiation portion of the instrument when 

the instrument has determined that the screening is complete 

and the housing unit is in scope. As previously mentioned, 

the collection instrument handles the skip patterns, the automated data and consistency checks, the schedule completion 

checks, and the final initiation status. The screening and initiation data are then electronically transmitted to the housing 

database in Washington, DC.

Pricing. During the non-monthly period, the screening/initiation may have occurred off-panel (or not at all). Therefore, 

the housing units will have to be priced (or perhaps screened/

initiated) on panel. There must be two on-panel prices before 

the unit can be considered usable. The field staff receives, 

electronically, the housing units to price from the Washington, DC, database. The CADC collection instrument automati19

 allocation to PSUs. BLS allocated the sample to PSUs based on the estimated total housing 

ture in each PSU. The estimated total housing PSU expenditure is the sum of the total cost of 

, previously defined, across all segments:

PSU expenditure ∑TC . s∈S

 s

re six collection panels. It was desired that the segment sample size be equal within each 

on panel. Thus, the segments were allocated in blocks of 6 segments, with a minimum of 72 

ts per PSU. For PSUs with multiple replicates, it was desired to have at least 36 segments per 

e and an equal sample size in each replicate. It was determined that a minimum of 108 segments 

ded to support publication in areas that were published semiannually and that a minimum of 180 

ts was needed for areas that are published bimonthly. The one exception was Baltimore, which 

d 108 segments but is published bimonthly as part of the Washington–Baltimore CMSA. As the 

size was previously about 10,000 segments and the budget for housing data collection was 

able, multiples of 6 segments were chosen so that the total would be near 10,000 segments.

ng housing units. After segments have been chosen for each PSU, housing units are chosen for 

on within each segment. Lists of housing units are obtained for each segment and an equal 

lity sample is chosen. In most cases, the target number of rental units from each segment is five. 

 sampling, the housing units are ordered by address and the sample taken is systematic, ensuring 

aphic spread of housing units selected within the segment. The sampling rate varies from segment 

ent, depending on the expected percentage of rental and owned units within the segment. 

on. Collection includes the screening of the selected housing units to determine if the units are in 

or the housing sample. If the unit is in scope, it is initiated. Initiation is the initial collection of 

a, which consists of the rent paid and the specific housing services that are associated with the 

d the rent paid. These data are the basis for all calculations of rent change that occurs during the 

he unit in the housing sample. After initiation, the housing unit is priced on panel every 6months.

is very similar to the initiation process, but some previous answers are provided. The collection 

ousing data, and particularly the rent data, is independent. That is, the field staff collects the data 

 giving the respondent the previous answer. Previous answers for some non-rent data are 

d, so that the field staff can confirm certain changes with the respondent. Inherent in all of the 

ed housing questionnaires (screening, initiation, and pricing) are various flow determinations 

tterns), such that the answer to one question determines the next question that must be 

nswered. 

DC instrument. The CADC instrument receives the screening/initiation schedules electronically. 

ough the schedules have been assigned to specific panels, the field staff has several months to 

he screening/initiation schedules. This is referred to as the non-monthly period. The field staff 

answers to various (screening) questions (through observation and through direct questioning of 

 respondents) that determine whether an address is in scope for the housing sample. The 

ng criteria consist of tenure (whether the unit is renter or owner occupied) and other criteria, such 

eing in public housing projects, being a primary residence, and the tenant not being a relative of 

lord.

e computer, the skip patterns can be very efficient. Because the computer has stored all of the 

sly collected data, automated logic checks remove all redundant question patterns, thereby 

g the field staff’s work and the respondent’s burden. Automated data checking ensures that only 

data types are collected, other automated logic checks ensure that collected data are consistent, 
18

cally moves the interviewer into the pricing portion of the instrument and, as mentioned, the collection instrument handles 

the skip patterns, the automated data and consistency checks, 

the schedule completion checks, and the final schedule status. 

The pricing data are electronically transmitted to Washington, 

where they are reviewed and corrected as necessary. 

These data, along with the initiation or pricing data from 6 

months earlier, are used in the housing pricing relative calculation (PRC) described in the section titled “Estimation of price 

change for shelter.” Occasionally situations occur during pricing that affect the unit’s scope status and, on a scheduled but 

infrequent basis, additional questions are asked to ensure that 

the housing units are still in scope for the housing sample. If 

changes occur, the units are treated as indicated in the section 

titled Initiation, on the basis of their new scope status.

Estimation of price change in the CPI

As stated earlier, the CPI is calculated in two stages. In the 

first stage, basic indexes are calculated for each of the 8,018 

CPI item–area combinations. For example, the electricity index for the Boston CPI area is a basic index. The weights for 

the first stage come from the sampling frame for the category 

in the area. Then, at the second stage, aggregate indexes are 

produced by averaging across subsets of the 8,018 CPI item–

area combinations. The aggregate indexes are the higher-level indexes; for example, the all-items index for Boston is the 

average of all its 211 basic indexes. Similarly, the aggregate 

index for electricity is the average of the basic indexes for 

electricity in each of the 38 index areas. The U.S. city average 

All items CPI is the average of all basic indexes. For the CPIU and CPI-W, the weights for the second stage are the baseperiod expenditures on the item category/areas from the CE. 

Estimation of price change for commodities and 

services other than shelter 

The C&S survey is the CPI’s primary source of price 

change data. Of the 209 C&S item strata, 185 are priced strata. The other 24 C&S strata, all of which have very small 

weights, are, for a variety of reasons, unsampled or truncated 

from pricing. The price movements of unsampled strata are 

imputed from related priced strata. 

For most priced C&S strata, the C&S survey is the primary 

source, meaning that information on price change comes from 

samples that the survey maintains. A few C&S strata, including those for airline fares, intercity train fares, and used vehicles, use secondary sources of data on prices for their samples.

Price relatives. Each month, the processing of the C&S survey data yields a set of price relatives (a price relative is a 

measure of short term price change) for all basic indexes. The 

CPI uses an index number formula to obtain an average price 

change for the items in each basic index’s sample. Prior to 

January 1999, all CPI price relatives used a modified Laspeyres index number formula. This is a ratio of a weighted arithmetic mean of prices in the current period to the same average of the same items’ prices in the previous period, with 

estimated quantities of the items purchased in its sampling 

period serving as weights. In January 1999, most of the item 

strata converted to the geometric mean index formula, which 

is a weighted geometric mean of price ratios (an item’s current price divided by its previous price) with weights equal to 

expenditures on the items in their sampling periods. Calculations for a limited number of strata, including the two shelter 

strata, continue to use the Laspeyres formula, as shown in the 

following list: 26 

1. Selected shelter services (rent of primary residence; 

owners’ equivalent rent of primary residence; and 

housing at school, excluding board)

2. Selected utilities and government charges (electricity; residential water and sewerage maintenance; utility (piped) gas service; State vehicle registration and 

driver’s license)

3. Selected medical care services (physicians’ services; hospital services; dental services; services by 

other medical professionals; and nursing homes and 

adult day care.)

Since January 1999, most item strata have used an expenditure-share-weighted geometric average a,iRG

[t;t-1]

. The other 

strata use the Laspeyres formula average, a,iRL

[t;t-1]

, which all 

strata used prior to 1999. The Laspeyres is a base-period, 

quantity-weighted arithmetic average. Every month, the C&S 

survey system uses the following formulas to compute price 

relatives for each item–area combination (a,i):

In these equations,

a,iRG[t;t-1]

 and a,iRL

[t;t-1]

, are, respectively, the geometric and 

Laspeyres price relatives for area-item combination, a,i, from 

the previous period, t – 1, (either 1 month or 2 months ago), 

to the current month,

Pj,t is the price of the jth observed item in month t for 

area-item combination a, 

Pj,t-1 is the price of the same item in time t – 1,

Pj,POPS is an estimate of the item j’s price in the sampling 

period when its POPS was conducted, 

and

26 See “Planned change in the Consumer Price Index formula,” http://

stats.bls.gov/cpi/cpigm02.htm.

[ ]

(

, ( / )

( / )

=

= ,

∑

∑

∏

,

,POPS ,POPS , 1

,

,POPS ,POPS ,

[ ; 1] ,

∑ )

, , 1

,

; 1 ,

,POPS ,POPS

jε a i

j j j t

jε a i

j j j t

t t

L

a i

W W

jε a i j t

j t

t t

G

a i

W P P

W P P

R

P

P

R

kεai j k
19

Wj,POPS is item j’s weight in the POPS and is defined in 

detail next.

The product in the geometric mean formula and sums in 

the Laspeyres formula are taken over all usable quotes in 

area-item combination a,i. It is important that the price of 

each quote be collected (or estimated) in both months in order to measure price change. 

Quote weights. For each individual quote, the weight Wj,POPS

is computed as

 Wj,POPS = AEfgη/BN,

where

A is the proportion of the total dollar volume of sales for 

the ELI relative to the entire POPS category within 

the outlet (called the outlet’s percent of POPS for the 

ELI),

E is an estimate of the total daily expenditure for the 

POPS category in the PSU by people in the U-population (called the basic weight),

f is a duplication factor that accounts for any special 

subsampling of outlets and quotes,

g is a geographic factor used to account for differences 

in the index area’s coverage when the CPI is changing 

from an area design based on an old decennial census 

to a design based on a more recent census,

η is the number of planned quotes for collection in the 

ELI – PSU, which is also the sum of duplication factors for all quotes in the ELI – PSU; 

y is the sum of duplication factors for uninitiated quotes 

in an ELI – PSU;

N is 1 + (y/(N – y)), which is essentially the ratio of 

planned quotes to quotes with usable prices in both 

periods t – 1 and t for the ELI – PSU; 

and

B is the proportion of the item stratum’s expenditure accounted for by the ELI in the region. 

POPS-period prices. In the Laspeyres formula, the item expenditure weight is divided by an estimate of the item’s price 

in the sampling period to convert the expenditure into an estimated quantity. An item’s POPS period occurred sometime 

before its outlet’s initiation, so that one cannot observe its 

POPS price directly. Instead the price is estimated from the 

item’s price at the time the sample was initiated and the best 

estimates of price change for the period from the POPS period to the initiation period. The formula is

Pj,POPS = Pj,

0

/[IXj,0/IXj,POPS],

where

Pj,0 is the price of the jth item at time 0 (when it was initi 

ated or chosen for the sample), 

IXj,0 is the value of the price index most appropriate for the 

jth item in period 0, the time it was initiated, 

and 

IXj,pops is the value of the same price index in the POPS period

 POPS). 

Item replacement and quality adjustment 

One of the more difficult problems faced in compiling a price 

index is the accurate measurement and treatment of quality 

change due to changing product specifications and consumption patterns. The concept of the CPI requires a measurement through time of the cost of purchasing an unchanging, 

constant-quality set of goods and services. In reality, products 

disappear, products are replaced with new versions, and new 

products emerge. 

When the data collector finds that he or she can no longer 

obtain a price for an item in the CPI sample (most commonly 

because the outlet permanently stops selling it), the data collector uses the CPI replacement procedure to find a new item. 

As explained earlier in the section on CPI item and outlet 

samples, each item stratum consists of one or more ELIs. CPI 

staff economists, called commodity analysts, in Washington, 

DC, have developed checklists that define further subdivisions of each ELI. When seeking a replacement in a retail 

outlet, the data collector first uses the checklist for the ELI to 

find the item the outlet sells that is “closest” to the previously 

priced one. Then the data collector describes the replacement 

item on the checklist, capturing its important specifications. 

The commodity analyst assigned to the ELI reviews all replacements and selects 1 of 3 methods to adjust for quality 

change and to account for the change in item specifications. 

The following example describes the most common type 

of quality adjustment problem. Assume that in period t a data 

collector tries to collect the price for item j in its assigned outlet and is not able to do so because the outlet no longer sells 

the item. A price for item j was collected in period t – 1. Following the procedure, the data collector finds a replacement 

and collects a price for it. The replacement becomes the new 

version (version v + 1) of item j. The decision as to how the 

CPI treats the replacement is made by the commodity analyst 

assigned to the ELI to which item j belongs. The commodity analyst has the descriptions of the two versions of item j. 

In addition, he or she has the t – 1 price, Pv

j,t-1, for the earlier 

version (version v) and the period t price, Pv+1

j,t, of the replacement version v + 1. The following matrix displays the price 

information available to the commodity analyst:

Version Period t-1 price Period t price

Old version

v

Pv

j,t-1 ...

Replacement version

v + 1 ... Pv+1

j,t
20

To use the item in index calculation for period t, we need 

an estimate of Pv+1

j,t-1, the price of the replacement version, v + 1, 

in period t – 1, or we need an estimate of Pv

j,t, the earlier version v, in period t. If there is no accepted way of estimating 

either Pv+1

j,t-1 or Pvj,t, the observation for item j is left out of 

index calculation for period t, meaning that the observation 

is treated as a nonresponse handled by imputation (described 

shortly).

The commodity analyst chooses 1 of 3 methods to handle the 

replacement

• Direct comparison 

• Direct quality adjustment 

• Imputation 

Direct comparison. If the new and old items are essentially 

the same, the commodity analyst deems them directly comparable, and the price comparison between the items is used 

in the index. In this case, it is assumed that no quality difference exists. 

Direct quality adjustment. The most explicit method for 

dealing with a replacement item is to estimate the value of 

the differences. The estimate of this value is called a quality 

adjustment amount QAj,t-1. In this case, 

Pv+1

j,t-1 = Pvj,t-1 + QAj,t-1.

Chief sources of direct quality adjustment information are 

manufacturers’ cost data and hedonic regression. 

Imputation. Imputation is a procedure for handling missing 

information. The CPI uses imputation for a number of cases, 

including refusals, inability to collect data for some other reason (the item may be out of season), and the inability to make 

a satisfactory estimate of the quality change. Substitute items 

that can be neither directly compared nor quality adjusted are 

called noncomparable. For noncomparable substitutions, an 

estimate of constant-quality price change is made by imputation. There are two imputation methods: Cell-relative imputation and class-mean imputation. 

Cell-relative imputation. If there is no reason to believe that 

price change for an item is different from those for the other 

items in its cell or basic index, the cell-relative method is the 

appropriate way to impute. This method is used for missing 

values because in that case we have no knowledge about the 

observation. For noncomparable substitutions, the cell-relative method is prevalent for food and service items. The price 

change between the old item and the noncomparable new 

item is assumed to be the same as the average price change of 

all similar items in 1 month for the same geographic area—

that is, the same as the average price change for the cell for 

that ELI and PSU. 

In this method, which is sometimes referred to as “linking,” the item is effectively left out of the calculation for 1 

month; the cell relative is computed without the observation. 

The price relative (either RL

ai,t,t-1

 or RG

ai,t,t-1

) is computed with 

one less useable quote. 

When there is a new version of the item that is not comparable to the previous version, a price of the new version 

(Pv+1

j,t

) is available. That price is not used in the calculations 

for period t but, in the subsequent period Pv+1

j,t,is used as the 

previous price. If, on the other hand, the reason for the imputation was that the item was temporarily missing (meaning 

that no price was collected), a period-t price must be estimated. For this purpose, the cell relative is used to estimate 

the period-t price: 

Pv

j,t = Rai,t,t-1

 × Pv

j,t-1.

Class-mean imputation. The C&S uses class-mean imputation for many noncomparable replacements, primarily in the 

item strata for vehicles, for other durables including hightech items, and for apparel. The logic behind the class-mean 

procedure is that, for many items, price change is closely associated with the annual or periodic introduction of new lines 

or models. For example, at the introduction of new model 

year vehicles, there are often price increases while, later in 

the model year, price decreases are common. The CPI uses 

the quality adjustment method as frequently as possible to 

handle item replacements that occur when vehicle product 

lines are updated. Class-mean imputation is employed in the 

remaining replacement situations. In those cases the CPI estimates price change from the price changes of other observations that are going through item replacement at the same 

time and were either quality adjusted directly or were judged 

directly comparable. For class-mean imputation, the CPI estimates Pv

j,t, which is an estimate of the current (t) price for 

the old version (v), and uses this estimated current price in 

the calculation of the price relative for period t. The estimated 

current-period price is the previous-period (t – 1) price of the 

old version times a specially constructed price relative for the 

class: 

Pv 

j,t = Pj,t-1

 × cR[t;t-1].

cR[t;t-1] is computed with either the geometric mean or Laspeyres formula over the subset of observations in the ELI to 

which item j belongs. The subset is the class of interest—that 

is, all the comparable and quality-adjusted replacement observations in the same ELI and PSU. 

Review and treatment of outlier price changes. All outlier 

price changes are reviewed by commodity experts. Outlier 

price changes, if accurate, are generally included in the calculation of price relatives. Extreme price changes are bounded, as the geometric mean formula performs poorly for zero 

and near-zero prices.

Estimation of price change for shelter

The rent and OER indexes measure the change in the cost 

of shelter for renters and owners, respectively. Price change 
21

data for these two indexes come from the CPI housing survey. Each month, BLS economic assistants gather information from renter units on the rent for the current month and on 

what services are provided.

Rent. The rent estimates used in the CPI are “contract rents.” 

They are the payment for all services the landlord provides 

in exchange for the rent. For example, if the landlord provides electricity, it is part of the contract rent. The CPI item 

expenditure weights also include the full contract rent payment. The CPI rents are calculated as the amounts the tenants 

pay their landlords plus any rent reductions tenants receive 

for performing services for the landlord (sometimes called 

“rent as pay”) plus any subsidy payment paid to the landlord. 

Reductions for any other reasons are not considered part of 

the rent.

Owners’ equivalent rent (OER). The OER approach to measuring price change for owner-occupied housing started in the 

CPI-U in January 1983 and the CPI-W in January 1985. The 

OER index is designed to measure the change in the rental 

value of owner-occupied housing. In essence, OER measures 

the change in the amount a homeowner would pay to rent, or 

would earn from renting, his or her home in a competitive 

market. It is a measure of the change in the price of the shelter 

service provided by owner-occupied housing. 

PRC for Housing. The housing and the C&S systems do 

not directly calculate indexes. Instead, they produce price 

relatives, and the index estimation system then uses the price 

relatives for basic index calculation. Price relatives are ratios 

of price change from the previous month (t – 1) to the current 

month (t), and basic index calculation updates the last month’s 

indexes (t – 1) to the current month (t). 

Weighting during the PRC. Each calculation begins with a 

segment weight (Ws ) based on the probability of selecting the 

segment. (See earlier section titled “Weighting during segment sample selection.”) To derive the renter weight in the 

segment (RWs ), the segment weight (Ws ) is multiplied by the 

number of renters in the segment, divided by the number of 

renters sampled in the segment:

Similarly, the owners' equivalent rents weight (OWs ) is derived by multiplying the segment weight (Ws ) by the number 

of owners in the segment divided by the number of renters 

sampled in the segment. Because the housing survey collects 

rents and not the implicit rents of owners, the ratio of average 

implicit rent to average rent in the segment is also included in 

the owner’s equivalent rent weight:

The rent and OER estimators. The rent estimator uses the 

change in the “economic rent,” which is basically the “contract rent” adjusted for any changes in the quality of the housing unit. The OER estimator uses the change in the “pure 

rent,” which excludes the cost of any utilities included in the 

rent contract.

The 6-month chained estimator. For the rent index, the current month’s index is derived by applying the sixth root of the 

6-month rent change to the index for the previous month. For 

the OER index, the current month’s index is derived by applying the sixth root of the 6-month OER change to the index 

for the previous month.

The rent estimator uses the change in the “economic rent.” 

Because of the panel structure used in the housing sample, the 

6-month change in rent is based on sampled, renter-occupied 

units that have usable 6-month rent changes. The sum of the 

current (t) economic rents for each usable unit within a segment, weighted by the renter weight for that segment, is divided by the sum of the weighted economic rents six months 

earlier (t – 6). This ratio is used to represent the 6-month 

change in rent for all renter-occupied units in the segment.

In a parallel calculation, the sum of the current (t) pure 

rents for sampled, renter-occupied units within a segment, 

weighted by the owner weights, is divided by the sum of the 

weighted pure rents six months earlier (t – 6). This ratio is 

used to represent the 6-month change in the OER index for 

all owner-occupied units in the segment.

The functions of the PRC have been designed to make use 

of the parallel rent and OER computations. In general, the 

PRC aggregates the weighted rents for the units (i) in the Index Area (a) for the current period (t) and for 6 months previous (t – 6). When the PRC is run for rent, economic rents 

(ERi) and renter weights (RWs ) are used: 

When the PRC is run for OER, pure rents (PRi ) and owner 

weights (OWs) are used. That is,

The index estimation system needs a 1-month price relative, not a 6-month price relative; therefore, the 6th root of the 

RELt 6,t,a is derived:

and then passed to the index estimation system for basic index computation for the rent and OER item strata.

Similarly, the owners equivalent rents weight ( OWs ) is derived by multiplying the segment weight (

by the number of owners in the segment divided by the number of renters sampled in the segment. 

Because the housing survey collects rents and not the implicit rents of owners, the ratio of average 

implicit rent to average rent in the segment is also included in the owner’s equivalent rent weight:

, RR

IR OW

s

s

s

s

s s

n

O  W  

The rent and OER estimators. The rent estimator uses the change in the “economic rent,” which is 

basically the “contract rent” adjusted for any changes in the quality of the housing unit. The OER

estimator uses the change in the “pure rent,” which excludes the cost of any utilities included in the re

contract.

The 6-month chained estimator. For the rent index, the current month’s index is derived by applying 

sixth root of the 6-month rent change to the index for the previous month. For the OER index, the cur

month’s index is derived by applying the sixth root of the 6-month OER change to the index for the 

previous month.

The rent estimator uses the change in the “economic rent.” Because of the panel structure used in the 

housing sample, the 6-month change in rent is based on sampled, renter-occupied units that have usab

6-month rent changes. The sum of the current (t) economic rents for each usable unit within a segmen

weighted by the renter weight for that segment, is divided by the sum of the weighted economic rents 

months earlier (t – 6). This ratio is used to represent the 6-month change in rent for all renter-occupie

units in the segment.

In a parallel calculation, the sum of the current (t) pure rents for sampled, renter-occupied units within 

segment, weighted by the owner weights, is divided by the sum of the weighted pure rents six months 

earlier (t – 6). This ratio is used to represent the 6-month change in the OER index for all owneroccupied units in the segment.

The functions of the PRC have been designed to make use of the parallel rent and OER computations

general, the PRC aggregates the weighted rents for the units (i) in the Index Area (a) for the current 

period (t) and for 6 months previous (t – 6). When the PRC is run for rent, economic rents ( ERi ) and 

renter weights ( RWs ) are used:

. RW ER

RW ER

REL ∑

∑

∈

, -6

∈

,

RENT

-6, ,

i a

s i t

i a

s i t

t t a







When the PRC is run for OER, pure rents ( PRi ) and owner weights ( OWs ) are used. That is,

. OW PR

OW PR

REL ∑

∑

∈

, -6

∈

,

OER

-6, ,

i A

s i t

i A

s i t

t t a







The index estimation system needs a 1-month price relative, not a 6-month price relative; therefore, th

6th root of the RELt 6,t,a is derived: 25

price in the calculation of the price relative for period t. The estimated current-period price is the 

s-period (t – 1) price of the old version times a specially constructed price relative for the class: 

Pv

j,t = Pj,t-1 × cR[t;t-1].

is computed with either the geometric mean or Laspeyres formula over the subset of observations 

LI to which item j belongs. The subset is the class of interest—that is, all the comparable and 

adjusted replacement observations in the same ELI and PSU..

and treatment of outlier price changes. All outlier price changes are reviewed by commodity 

 Outlier price changes, if accurate, are generally included in the calculation of price relatives. 

 price changes are bounded, as the geometric mean formula performs poorly for zero and near

ces.

tion of price change for shelter

t and OER indexes measure the change in the cost of shelter for renters and owners, respectively. 

ange data for these two indexes come from the CPI housing survey. Each month, BLS economic 

ts gather information from renter units on the rent for the current month and on what services are 

d.

he rent estimates used in the CPI are "contract rents." They are the payment for all services the 

 provides in exchange for the rent. For example, if the landlord provides electricity, it is part of 

ract rent. The CPI item expenditure weights also include the full contract rent payment. The CPI 

e calculated as the amounts the tenants pay their landlords plus any rent reductions tenants 

for performing services for the landlord (sometimes called "rent as pay") plus any subsidy 

t paid to the landlord. Reductions for any other reasons are not considered part of the rent.

’ equivalent rent (OER). The OER approach to measuring price change for owner-occupied 

 started in the CPI-U in January 1983 and the CPI-W in January 1985. The OER index is 

d to measure the change in the rental value of owner-occupied housing. In essence, OER

s the change in the amount a homeowner would pay to rent, or would earn from renting, his or 

e in a competitive market. It is a measure of the change in the price of the shelter service 

d by owner-occupied housing.

r Housing. The housing and the C&S systems do not directly calculate indexes. Instead, they 

 price relatives, and the index estimation system then uses the price relatives for basic index 

ion. Price relatives are ratios of price change from the previous month (t – 1) to the current month 

basic index calculation updates the last month’s indexes (t – 1) to the current month (t).

ng during the PRC. Each calculation begins with a segment weight (Ws ) based on the 

ity of selecting the segment. (See earlier section titled “Weighting during segment sample 

n.”) To derive the renter weight in the segment ( RWs ), the segment weight (Ws ) is multiplied by 

ber or renters in the segment, divided by the number of renters sampled in the segment:

RW .

s

s

s s

n

R  W 

. RR

IR

OW = × × s

s

s

s

s s n

O

W

26

Similarly, the owners equivalent rents weight ( OWs ) is derived by multiplying the segment weight (Ws )

by the number of owners in the segment divided by the number of renters sampled in the segment. 

Because the housing survey collects rents and not the implicit rents of owners, the ratio of average 

implicit rent to average rent in the segment is also included in the owner’s equivalent rent weight:

, RR

IR OW

s

s

s

s

s s

n

O  W  

The rent and OER estimators. The rent estimator uses the change in the “economic rent,” which is 

basically the “contract rent” adjusted for any changes in the quality of the housing unit. The OER

estimator uses the change in the “pure rent,” which excludes the cost of any utilities included in the rent 

contract.

The 6-month chained estimator. For the rent index, the current month’s index is derived by applying the 

sixth root of the 6-month rent change to the index for the previous month. For the OER index, the current 

month’s index is derived by applying the sixth root of the 6-month OER change to the index for the 

previous month.

The rent estimator uses the change in the “economic rent.” Because of the panel structure used in the 

housing sample, the 6-month change in rent is based on sampled, renter-occupied units that have usable 

6-month rent changes. The sum of the current (t) economic rents for each usable unit within a segment,

weighted by the renter weight for that segment, is divided by the sum of the weighted economic rents six 

months earlier (t – 6). This ratio is used to represent the 6-month change in rent for all renter-occupied 

units in the segment.

In a parallel calculation, the sum of the current (t) pure rents for sampled, renter-occupied units within a 

segment, weighted by the owner weights, is divided by the sum of the weighted pure rents six months 

earlier (t – 6). This ratio is used to represent the 6-month change in the OER index for all owneroccupied units in the segment.

The functions of the PRC have been designed to make use of the parallel rent and OER computations. In 

general, the PRC aggregates the weighted rents for the units (i) in the Index Area (a) for the current 

period (t) and for 6 months previous (t – 6). When the PRC is run for rent, economic rents ( ERi ) and 

renter weights ( RWs ) are used:

. RW ER

RW ER

REL ∑

∑

∈

, -6

∈

,

RENT

-6, ,

i a

s i t

i a

s i t

t t a







When the PRC is run for OER, pure rents ( PRi ) and owner weights ( OWs ) are used. That is,

. OW PR

OW PR

REL ∑

∑

∈

, -6

∈

,

OER

-6, ,

i A

s i t

i A

s i t

t t a







The index estimation system needs a 1-month price relative, not a 6-month price relative; therefore, the 

6th root of the RELt 6,t,a is derived:

6 RELt-1,t,a  RELt-6,t,a

and then passed to the index estimation system for basic index computation for the rent and OER item 

strata.

Vacancy imputation. Vacant units that were previously occupied by renters are used in the calculation 

Rt,t-1 and Rt,t-6. The vacancy imputation process incorporates several assumptions about the unobserved 

rents of vacant units. It is assumed that rents tend to change at a different rate for units that become 

vacant (and are, therefore, in the process of changing tenants) than for other units. The vacancy 

imputation model assumes that, after an initial lease period, expected rents change at a steady rate unti

thld tt t f thitWhthihit it btth
22

Vacancy imputation. Vacant units that were previously 

occupied by renters are used in the calculation of Rt,t-1 and 

Rt,t-6. The vacancy imputation process incorporates several 

assumptions about the unobserved rents of vacant units. It 

is assumed that rents tend to change at a different rate for 

units that become vacant (and are, therefore, in the process 

of changing tenants) than for other units. The vacancy imputation model assumes that, after an initial lease period, expected rents change at a steady rate until the old tenant moves 

out of the unit. When there is a change in occupant or a unit 

becomes vacant, the rent is assumed to jump at some rate, 

referred to as the “jump rate.” In markets with generally rising rents, this jump rate is usually greater than the average 

rate of change for occupied units. BLS estimates the jump 

rate based on non-vacant sample units in the PSU that have 

had a change in tenant between t – 6 and t. Rent changes for 

nonvacant units without a tenant change are used to calculate 

the average continuous rate of change. These values are used 

to impute rents for vacant units for period t from their rent in 

t – 6.27 The imputed rent, ri,t of the ith vacant rental unit in t is

ri,t = ri,t-1 J if the unit was not vacant in t – 6

or

ri,t = ri,t-1 C6 if the unit was vacant in t – 6,

where J is the 6-month jump rate calculated for the PSU, and 

C is the 1-month steady rate of change. 

The imputation of vacant rents ensures that the unobserved rent change that occurs when a unit becomes vacant 

is reflected in the final rent index. The 6-month rent-change 

estimates capture these changes once the units become occupied.

Non-interview imputations. Units that were previously responding, not currently responding, and are not vacant are 

also imputed and used in the calculation of Rt,t-1 and Rt,t-6. All 

units within a PSU are broken up into high, medium, and low 

rent categories based on their rent level in t – 6. The rents of 

nonresponding, nonvacant units are imputed forward into t 

by using the average rent change of other housing units in 

their respective category.

Aging adjustment. The aging adjustment accounts for the 

small loss in quality as housing units age (or depreciate) 

between interviews. The aging adjustment factors can be 

thought of as 1/(1 – d) where d is the monthly rate of physical depreciation. BLS computes factors for each housing 

unit with regression-based formulas. The formulas account 

for the age of the unit and a number of structural charac27For more information on vacancy imputation, see J.P. Sommers and J.D. 

Rivers, “Vacancy imputation methodology for rents in the CPI,” Proceedings of the American Statistical Association, Business and Economic Statistics Section (Alexandria, VA: American Statistical Association, 1983).

teristics. 28The aging adjustment procedure was introduced 

into the CPI in 1988. 

Special pricing and estimation procedures for 

medical care

Although third parties (mainly government agencies and 

employers) pay much of the cost of medical care on behalf 

of consumers, the medical care component of the CPI covers 

only that part of healthcare commodities, services and health 

insurance premiums that consumers pay for “out of pocket.”29

Medical insurance premiums constitute the largest part of 

consumers’ out-of-pocket spending for medical care. Unlike 

other forms of consumer insurance in the CPI, the data needed from insurers to hold the quality of the insurance policies 

constant are so extensive and so closely held that BLS has not 

been able to construct a constant-quality health insurance index. Consequently, the CPI has employed an indirect method 

for pricing health insurance. In short, the CPI allocates most 

of consumers’ out-of-pocket expenditures on health insurance premiums to the weights for other healthcare services 

and commodities, placing the small remainder, which covers 

the insurance companies’ costs and their profits, into a separate stratum. 

Use of the indirect method for pricing health insurance has 

two important effects on the CPI. First, the relative shares of 

the weights for most of the other CPI medical care item strata 

are increased, because they include their portions of the reallocated consumer expenditure for health insurance premiums.30 Second, the CPI approach to measuring price change 

for medical care items reflects the fact that these items are, 

for the most part, paid for by insurance companies and, therefore, the approach must take account of insurance arrangements such as type of reimbursement method.

Medical care items and their prices. The movement of CPI 

medical care indexes is based on the average change in the 

prices of a sample of items selected to represent them. The 

items are, for example, a prescription for a specific medicine 

or a visit of a specified duration to a doctor or a hospital. 

These are inputs to medical treatments addressing a specific 

28 For further information, see Walter F. Lane, William C. Randolph, and 

Stephen A. Berenson, “Adjusting the CPI shelter index to compensate for 

effect of depreciation,” Monthly Labor Review, October 1988, pp. 34–37.

29 As a consequence, the medical care portion of the CPI is much smaller 

than its portion of the national accounts. 

30 See U.S. Bureau of Labor Statistics, “Direct pricing of health insurance in the Consumer Price Index.” Paper presented at the Sixth Meeting 

of the International Working Group on Price Indices, Canberra, Australia, April 2001, http://www.ottawagroup.org/Ottawa/ottawagroup.nsf/

home/Meeting+6/$file/2001%206th%20Meeting%20-%20U.S.%20

Bureau%20of%20Labor%20Statistics%20-%20Direct%20Pricing%20of%20Health%20Insurance%20in%20the%20Consumer%20

Price%20Index.pdf/. In that study, the share of the CPI weight for health 

insurance under direct health insurance pricing was 2.7 percent, compared 

with 0.3 percent under indirect insurance pricing. At the same time, the 

shares of the other medical care items were commensurately smaller (for 

example, for hospitals, 0.4 percent versus 1.4 percent).
23

medical condition.31 The CPI data collectors, following CPI 

sampling procedures, select the sample items by working 

with respondents in pharmacies, doctors’ offices, hospitals, 

and other outlets that provide medical care. 

The CPI defines the transaction price for medical care 

items as all payments or expected payments received from 

eligible payers, including both the patient and appropriate insurers. In most cases, the field staff is able to collect transaction prices; if the respondent is unable or unwilling to provide 

transaction prices, then cash or self-pay prices are collected, 

except in the case of hospitals where “list prices,” or so called 

charge master prices, are normally not collected unless associated with a self-pay patient. 

CPI medical care indexes. The CPI medical care aggregate 

index covers medical care commodities, which consist of prescription and over-the-counter (OTC) drugs and supplies, and 

medical care services, which include professional services, 

hospital services, and medical insurance. 

The professional medical services expenditure category 

serves as the umbrella for a series of stratum indexes: Physicians’ services, dental care, eye care, and services by other 

medical professionals. The hospital and related services category includes item strata for hospital services, nursing home 

services, and adult day care. Medical insurance, for which 

the weight share is reduced due to indirect pricing, is the remainder of the medical care services category. Details on the 

more difficult pricing issues associated with these item strata 

follow. 

Prescription drugs. In response to technological change and 

the complex marketing of prescription drugs, the CPI program has developed a series of techniques to show the effects 

of such trends. Field staff uses special procedures to handle 

the expiration of a drug’s patent protection and the subsequent introduction of equivalent generic drugs, a prescription 

drug’s conversion to OTC status, and the introduction of new 

pharmaceutical products into the market place. 

Brand vs. generic. Since 1995, a method has been in place 

allowing generic versions of prescription drugs coming off 

patent to have a chance for inclusion in the CPI. Typically, 

6 months after the expiration of the patent for a particular 

prescription drug, the CPI economic assistant (data collector) disaggregates among all the FDA-designated therapeutically equivalent versions of the medicine, including the brand 

name, that are available in each outlet in which the original 

drug is priced. This process allows the newer generics an opportunity to build sales in the individual pharmacy over a 

31 A National Academy of Sciences panel that reviewed CPI practices suggested that BLS should experiment with using selected medical conditions 

as the CPI items. Under that approach, the CPI would follow the movement to new treatments (such as drug therapy replacing surgery) and, to 

the extent possible, show any price changes. (See Charles L. Schultze and 

Christopher Mackie, eds., At what price? Conceptualizing and measuring cost-of-living and price indexes (Washington, DC: National Academy 

Press, 2002), pp. 178–190.

6-month period, and then, through disaggregation, a probability-proportional-to-size statistical technique, the generic 

versions of the drug have a one-time chance for selection in 

proportion to sales volume at the particular outlet. Should 

a generic drug be selected, any price change that occurred 

from brand to generic is reflected in the index.

Prescription vs. over the counter. When a drug in the CPI’s 

prescription sample loses its prescription status and is sold 

as an OTC drug, the CPI retains the item as part of the prescription drug sample while using its OTC price. Thus, the 

prescription drug index series shows any price change that 

occurs as a result of drugs changing status from prescription 

to OTC. The OTC version of the drug remains in the prescription drug sample until it rotates out during the next rotation scheduled for that item. Generally, each sample rotates 

every 4 years. At future sample rotations, the OTC item is 

eligible for initiation in the nonprescription drug stratum and 

ineligible in the prescription drug stratum. Similarly, should 

a drug in the nonprescription sample change its status (that is, 

from OTC to prescription), the CPI would show the resulting 

price change, if any, in the nonprescription drugs and supplies index.

Physicians’ services. This item stratum covers services that 

are performed and billed by private-practice medical doctors. This includes all medical professionals with a Doctor of 

Medicine (M.D.) degree except for ophthalmologists, whose 

services are priced in the eye care stratum. It also includes 

osteopaths (they are not MDs, but often have hospital privileges). House, office, clinic, and hospital visits are included 

as long as the bill comes from the physician.32 At initiation of 

a quote for physicians’ services, the CPI data collector first 

establishes the practitioner’s specialty and then disaggregates 

to an appropriate service. The data collector describes the 

characteristics of the selected visit and any related procedures 

using a CPI checklist specific to the medical specialty. Current Procedural Terminology (CPT) codes are used to help 

describe the item precisely; this description remains fixed for 

the 4 years during which the CPI program follows its price, 

unless either the selected combination of services changes or 

a CPT code definition is modified. Transaction prices in the 

physicians’ services index may include Medicare Part B payments, as well as those fees that the doctor expects to receive 

directly from the patient or from private insurance.

Services by other medical professionals. This stratum covers services performed and billed by medical practitioners 

who are not Medical Doctors (lack an M.D. degree) and are 

not covered in the dental stratum or the eye care stratum. Included here are chiropractic and physical therapy, podiatry, 

32 Ina Kay Ford and Daniel B. Ginsburg, “Medical care in the CPI,” in E. 

Berndt and D. Cutler, eds., Medical care output and productivity, NBER 

Research Studies in Income and Wealth. (Chicago: University of Chicago 

Press, 2001), p. 215.
24

audiology (including hearing aids), acupuncture, nursing, 

nutritional counseling, occupational therapy, and psychology 

and psychotherapy. 

Hospital services. Items in the hospital services stratum cover the hospital portion of a medical treatment, including inpatient and outpatient services. The pricing unit is the hospital 

visit, defined by a date of admission and a date of discharge 

as documented on a hospital bill and usually associated with 

a specific diagnosis or medical condition.33 At initiation, the 

CPI data collector works with the respondent to select a hospital bill based on revenues generated by eligible payers. The 

data collector refers to the bill to describe the item in terms 

of the bundle of goods and services consumed during a time 

frame or visit for the purpose of bringing the patient to the 

physical (or mental) state required for discharge from the hospital. The form that the hospital visit takes as the pricing unit 

is that of its reimbursement method, the method used by the 

insurer to pay the hospital for the services. There are several 

possible types of reimbursement that insurers may write into 

their contracts with providers: Fee-for-service, diagnosisrelated group, per diem, case rate, admission rate, package, 

ambulatory patient group, service units, and capitation. With 

the exceptions of fee-for-service and fee schedule, each type 

of reimbursement reflects either a lump-sum payment based 

on the diagnosis or type of procedure performed or a flat fee 

per unit of service.

Current procedures for selecting hospital services to price in 

the CPI involve the following basic steps:

• Disaggregation by setting to reflect the relative proportions of inpatient services versus outpatient services at the individual hospital outlet level

• Disaggregation by payer (for example, self-pay or insurance company) 

• Selection of hospital bills based on selected payers, 

when the hospital administration will provide them

• Request for the type of reimbursement method and the 

actual or estimated payment for the described hospital 

visit, based on the terms of the contract between the 

provider and the insurer

• Description of the hospital visit, including a bundle 

of procedures, services, equipment use, supplies, and 

materials typically associated with the hospital event 

or episode, as defined by both the bill and the contract 

(the visit).

Monthly pricing and bimonthly pricing consist of updating the reimbursement method and amount based on the 

contract between provider and insurer, and maintaining cur33 At this time, neither the pricing of total treatment paths nor the measurement of patient outcomes is available for the CPI, given the structure of 

the medical care component and the difficulty of evaluating the results of 

treatments.

rent discounts and (for those fee-for-service reimbursements 

based on the hospital charge master with applied discounts) 

list prices.

Health insurance. As previously noted, the CPI employs an 

indirect method to measure price change for health insurance.34 This indirect approach decomposes medical insurance into three parts: 

1. Changes in the prices of medical care items covered 

by health insurance policies 

2. Changes in the cost of administering the policies 

3. Changes in the cost of maintaining reserves and, as 

appropriate, profits

Most of the expenditure for health insurance goes for the 

first item—the part that reflects the insurers’ payments for 

medical treatment. The CPI program allocates this part of 

health insurance spending to the medical care indexes for 

those treatments in proportion to claims paid out for them.35

The remaining weight for the other two parts of insurance 

is for the overhead of the insurers; this is all that remains 

in the CPI health insurance index. Note that it is only consumer-paid insurance that is in scope; out-of-scope or ineligible health insurance receipts include those from employers, 

Medicare Part A (funded through payroll taxes), Medicaid, 

and workers’ compensation.

Price movement over time for the health insurance index in 

the CPI is determined by the movements of the other medical 

care strata, adjusted by changes in the retained earnings ratio. 

(See subsection titled “Retained earnings ratio.”) This process 

yields a measure of price change for insurance of constant 

coverage and utilization. That is, changes in benefit coverage 

and utilization levels generally are offset by compensating 

premium charges and thus do not significantly affect retention 

rates. Implicit in the process is the assumption that the level of 

service from the individual carriers is strictly a function of the 

benefits paid. Other changes in the amount of service provided 

for policyholders, such as more convenient claims handling, 

affect the movement of the index even though, strictly speaking, they should be removed; still, the effects are probably 

small.

Retained earnings ratio. BLS obtains calendar year data for 

premium income, benefit payments, and retained earnings. 

For each year, the ratio of retained earnings to benefit payments is calculated, yielding a retained earnings ratio. The 

latest year’s ratio is divided by the previous year’s ratio to 

obtain the annual relative of change in the ratios. This annual relative of change is converted to a monthly relative (by 

34 See “Measuring price change for medical care in the CPI,” Consumer 

Price Index (U.S. Bureau of Labor Statistics, April 12, 2010), http://www.

bls.gov/cpi/cpifact4.htm. 

35 Ford and Ginsburg, “Medical care in the CPI,” p. 216.
25

taking its 12th root) so the CPI can reflect the change month 

by month over the calendar year. Because it is not feasible 

to obtain the monthly change in price caused by changing 

retention margins, spreading the annual change evenly over 

the year is preferable to reflecting the entire annual change in 

one month.

Special pricing for other items

New vehicles. Prices for new cars and trucks, selected for inclusion in the CPI, pose a special problem because the manufacturer’s suggested retail (sticker) price is not the transaction 

price for most new vehicles. Most automotive dealers offer 

customers concessions on the sticker price or, for models that 

are in high demand, the dealers charge an additional markup 

beyond the sticker price. When collecting the price of new vehicles, BLS economic assistants record all of the components 

of the sticker price separately. This includes the base price 

and the prices for options, dealer preparation, transportation, 

and so forth. In addition, they obtain from the dealer the average rebate, concession, and/or markup during the preceding 

30 days. This enables BLS to estimate the true transaction 

price.

Quality adjustment is also common in calculation of the 

new vehicles index. The most frequently cited example of 

direct quality adjustment in the CPI deals with the annual 

model changeover for new cars and trucks. Each year, price 

adjustments are made to account for the quality differences 

between the old and the new models. In some cases, the adjustments are based on the previous model’s retail price for 

optional equipment. In other cases, the quality adjustments 

must be derived from production cost data supplied by the 

manufacturers. These data are adjusted by estimated manufacturer and retailer markup rates to derive retail values for 

the quality changes.

Adjustments for quality change in the CPI new car index 

include structural and engineering changes that affect safety, 

reliability, performance, durability, fuel economy, carrying 

capacity, maneuverability, comfort , and convenience. Since 

1999, quality adjustments have not been made for changes 

associated with pollution control mandates.36

The derivation of production cost-based quality adjustments for new cars is carried out in association with the BLS 

Producer Price Index and International Price programs. The 

adjustments exclude changes in style or appearance, such as 

chrome trim, unless these features have been offered as options and purchased by customers. Also, new technology 

sometimes results in better quality at the same or reduced 

cost. Usually, no satisfactory value can be developed for such 

a change. In such cases, the quality change is ignored, and 

prices are compared directly.

36 “The treatment of mandated pollution control measures in the CPI,” 

Consumer Price Index (U.S. Bureau of Labor statistics, October 16, 2001), 

http://www.bls.gov/cpi/cpitreat.htm.

In addition to quality adjustments for physical changes to 

cars and trucks, adjustments are made for changes in the warranty coverage provided by auto manufacturers when sufficient data are available to derive estimates of their values. 

Vehicle leasing. The vehicle leasing index was first published 

by BLS in January 2002. The prices used in the index are 

monthly lease payments. As with new vehicles, the agreedupon purchase price of the vehicle must be estimated. BLS 

economic assistants collect the base price and the prices for 

options, dealer preparation, transportation, and so forth. 

Also, any rebates available are included, along with the largest estimated concession or discount the dealer would allow 

for the leased vehicle on the day of pricing. Then, the lease 

terms are applied to obtain the residual value, depreciation 

amount, rent charge, and the total monthly lease payment. 

During the annual model changeover, the quality adjustments 

developed for the CPI new car index are also used in the CPI 

vehicle leasing index. 

Used cars and trucks. Models that are from 2- to 7- years-old 

are priced in the used car and truck index. Data on used vehicle prices are obtained from a secondary source. Once a year, 

each sample vehicle is updated by one model year to maintain 

the same age vehicle. The sample prices are adjusted for quality change by applying the same information used for quality 

adjustment in the new vehicle index. This is done by figuring 

the percentage that the quality adjustments represent of the 

price of the vehicle when it was new. The quality adjustments 

are then assumed to depreciate at the same rate as the car as 

a whole. 

Apparel. The special characteristics of apparel marketing 

have historically caused a number of problems in the maintenance of a constant-quality market basket of apparel in the 

CPI. Many apparel items are seasonal and inventory is constantly fluctuating in reaction to changing fashions. In addition, large price changes are common as marketing practices 

for apparel generally entail introducing such goods at high 

regular prices and discounting to lower sale prices throughout their product lifecycle.

When an outlet discontinues an apparel item, the BLS 

economic assistant follows the CPI substitution procedures to 

find the closest substitute that the outlet offers for sale. These 

procedures are developed by applying the results of hedonic 

regression models and ensure that the economic assistant 

matches many of the price-determining quality characteristics between the substitute and discontinued items. 

Hedonic regression modeling is the technique used to 

determine the importance of the price-determining quality 

characteristics that add or subtract value to a particular good. 

In this approach, an item can be viewed as a collection of 

characteristics that, taken together, provide satisfaction or 

value to the consumer. For example, a woman’s suit can be 

considered an aggregation of its components, such as a jacket 

and skirt or pants, each of which contributes value to the suit 

in the eyes of the consumer. In addition, characteristics of 
26

the suit, such as its fiber content and its construction, add or 

subtract value from the consumer’s standpoint. 

Hedonic regression modeling is a tool that allows commodity analysts to estimate which characteristics are pricedetermining, and how these estimates influence the direction 

and magnitude of a good’s price. This research has resulted 

in improved data collection documents and procedures for 

pricing apparel commodities. By noting the most important 

quality characteristics on data collection documents, economic assistants who collect data for the CPI can hold pricedetermining characteristics constant when pricing seasonal 

and fluctuating inventories. This improvement in data collection documents has enhanced the reliability of apparel price 

estimation, since it increases the number of direct price comparisons or sample observations that can be used for index 

calculations. 

When the economic assistant must substitute to a new 

good or item because the previously priced item is no longer 

available in the retail outlet, the commodity analyst determines whether the items are comparable, noncomparable, or 

can be quality-adjusted by means of applying characteristic 

estimates developed in a hedonic regression model. For comparable items the price of the new item is directly compared 

to the price of the old item and the price change or relative is 

used for index estimation. For noncomparable items the price 

change for the item is imputed via the class-mean imputation 

method. For quality adjustments the price of the discontinued 

item is adjusted based on the difference in characteristics between the discontinued and substitute items using the characteristics value estimates developed in the hedonic regression 

model for the apparel item. For example, if a two-piece men’s 

suit was priced in the apparel price sample and is now no longer available in the outlet, because the retailer will only stock 

three-piece suits instead of two-piece suits, the value of a vest 

included with a three-piece suit can be added to the price of 

the old two-piece suit using the hedonic price estimate for 

the vest and the quality-adjusted price of the old two-piece 

suit can be reliably compared directly with the price of the 

new three-piece suit for a constant quality measure of price 

change. Alternatively, if fiber percentages vary between two 

items, quality adjustment can account for the characteristic 

difference to permit constant quality comparison of the prices of the two goods. 

Other characteristic differences that have been found to be 

statistically significant also have been factored out to permit 

constant-quality price comparisons for apparel items. Hedonic quality adjustments have played a significant role in 

increasing the number of constant quality price changes that 

can be used for index calculations by accounting for differences in quality characteristics when substitutions occur. 

Natural gas. To measure a constant consumption amount for 

the CPI’s utility (piped) gas service index, the data collector initiates a fixed level of energy or heat consumption for 

each observation. The fixed consumption amount is selected 

in Washington, based on household bill expenditure data as 

reported in the CE. Subsequently, when the observation is 

priced each month, field staff collects the cost of that fixed 

amount. This amount is defined as a fixed number of therms 

(a therm is 100,000 British Thermal Units, or BTUs). When 

the surveyed outlet delivers and bills its residential customers 

by the number of therms consumed, the CPI uses the current price per therm to determine the prices of that outlet’s 

observations. But, when piped gas is delivered and billed by 

volume (for example, cubic feet), the CPI program must adjust each quote to account for the fact that the volume of gas 

needed to produce a constant amount of energy or BTUs varies, depending on the quality of gas (BTUs/CF). In this case, 

the amount of gas priced each month is adjusted based on the 

heat value of gas delivered by the outlet as follows: 

Current adjusted consumption = original consumption × (original heat value/current heat value).

This adjustment ensures that a constant amount of energy 

is being priced from month to month for the utility (piped) 

gas service index.

Special pricing for seasonal items. Seasonal items are those 

commodities and services that are available only at certain 

times of the year rather than year round. Down parkas, snow 

skis, and fresh tangelos are examples of seasonal items. 

Special procedures are employed when selecting and pricing items generally available only part of the year to ensure 

that they are appropriately represented in the sample and that 

price changes are correctly included in the calculation of the 

CPI. In particular, the procedures prevent substituting away 

from a seasonal item when it is out of season. 

Although seasonal items can exist in any ELI, some ELIs 

include an especially large percentage of such items and, 

consequently, receive special treatment. These seasonal ELIs 

include most apparel items, fresh fruit, indoor plants and 

cut flowers, fans and air-conditioners, some sports and recreational equipment, and admission to sporting events. The 

designation of an ELI as seasonal or nonseasonal is made at 

the regional level, using the four geographic census regions 

in the CPI design. Some items that exhibit a seasonal selling 

pattern in the Northeast region, for example, may be sold year 

round in the South. In practice, though, nearly all ELIs designated seasonal are seasonal in all four regions.

After the samples for these seasonal ELIs are selected following the normal sample selection procedures, the number 

of quotes is doubled to ensure that, despite the seasonal disappearance of a substantial number of quotes, a large enough 

number of in-season quotes remains to calculate the index. 

The quotes in these ELIs are paired; that is, for each original quote that is selected, a second quote in the same ELI and 

outlet is initiated and priced 6 months later. In the fresh fruit 

ELIs, one quote of each pair is designated January–June, and 

the other quote is designated July-December. In all other seasonal ELIs, one quote of each pair is designated fall/winter, 

and one quote is designated spring/summer. The fall/winter 

and spring/summer designations are used for the nonfood 

quotes because these are the distinctions that are most com-
27

monly used by the retailing industry to categorize seasonal 

merchandise. These seasonal designations are used to help 

establish the specific items eligible for each quote so that 

year-round items and items from each season are initiated in 

their proper proportions. 

Economic assistants attempt to price every item in each 

period during which it is designated for collection, even during those months when the item may be out of its indicated 

season. If the item is available, the price is collected and used 

in the calculation of the CPI. A common practice in marketing seasonal items, particularly seasonal clothing, is to mark 

down prices to clear the merchandise from the stores as the 

end of each season approaches. During the period when a 

seasonal item is unavailable, its price is imputed following 

standard imputation procedures. When an item returns at 

the beginning of its season several months later, the price is 

directly compared with the item’s last price, as it has been 

imputed forward. This completes the circle in a sense: having 

followed the price of the item down to clearance price levels, BLS then follows the price back up to regular (or at least 

higher) prices the following season. Keep in mind that, in this 

context, the “following” season means the same season the 

next year; that is, the following fall/winter season for the fall/

winter sample, and the following spring/summer season for 

the spring/summer sample.

When an item becomes permanently unavailable, the standard procedure is to substitute the most similar item sold in 

the outlet. In the case of a year-round item not in a seasonal 

ELI, this process takes place as soon as the item is permanently unavailable. For items in seasonal ELIs and seasonal 

items in ELIs that are not designated seasonal, however, the 

period during which a substitution can take place is restricted 

to those months when a full selection of appropriate seasonal 

merchandise is available. 

These special initiation, pricing, and substitution procedures are intended to ensure that an adequate sample of items 

is available every month, and that the correct balance of seasonal and year-round items is maintained. As a result, the estimates of price movement for the ELIs that include seasonal 

items correctly reflect price changes not just for items available year round but for the entire universe of items included 

in those ELIs. 

Other price adjustments and procedures 

Bonus merchandise adjustments. Sometimes, products 

are offered with free merchandise included with the purchase of the original item. Such “bonus” items may provide additional satisfaction to consumers, and BLS will, 

therefore, make adjustments to the purchase price to take 

into consideration the value of the bonus merchandise. 

The adjustment made depends on the type of merchandise 

offered and the perceived value of the bonus to the consumer. If the bonus merchandise consists of more of the 

same item, the adjustment is reflected in the price of the 

item. For example, if a manufacturer offers two ounces of 

toothpaste “free with the purchase of the regular 6-ounce 

tube,” the item’s price is adjusted to reflect a decrease in 

the per ounce price. When the bonus is removed, the price 

per ounce returns to its previous level, and a price increase 

is recorded. In this instance, the value to the consumer is 

assumed to be one-third greater during the bonus period. 

If the bonus merchandise consists of an item that has some 

significant value to the consumer, and the item is of a different genre, an adjustment is made to account for the value of the free item when it is feasible to do so. Bonuses that 

are contingent on an additional unrelated purchase, such 

as a free can of soup when purchasing a whole chicken 

from the poultry case, are ignored. 

Cents-off coupons. For a coupon to be used to reduce the 

reported price of an item, the coupon must be either attached to the item, attached to the product’s display shelf, 

dispensed by machines attached to the product’s display 

shelf, located at promotional displays, or distributed to all 

shoppers by product representatives standing in the immediate vicinity of the display shelf. All other coupons 

presented by customers as purchase reductions at the time 

of payment are ineligible. 

Concessions. A concession is a deduction of a specific 

amount from the proposed selling price for the item. The 

usual CPI practice is to subtract from the proposed selling 

price the average concession for the priced item over the 

past 30 days.

Different day pricing. For a subset of items, if the selected 

priced item is not available for sale at the time of collection, prices from up to seven days prior to the actual day 

of collection are eligible. The item must have been offered 

for sale during the previous 7 days and the most recently 

available price is reported. The list of items eligible generally consists of specific items that may not be available 

every day, such as a specific type of fresh fish. 

Discounts. A discount price is a reduced price that is 

available to only certain customers in a specific outlet. If 

the discount is available only during the period of price 

collection, such as that for a grocery-card discounted item, 

the discount is included only if 50 percent or more of sales 

for the affected item are discounted. If the discount is in 

effect for more than one collection period and the discount 

applies to 5 percent or more of the dollar sales of the item 

in the outlet, a probability selection is made to determine 

if the discount should be collected. For example, if the 

regular cash price accounts for 84 percent of sales, senior 

citizens’ discounts account for 10 percent and employee 

discounts account for 6 percent of sales, a one-time probability-based selection is made among the three options to 

determine which price to report. 

Manufacturers’ rebates. When product manufacturers 

offer customers cash rebates at the time of purchase for 

purchases of items priced in the CPI, these rebates are reflected in the index as price reductions. When a rebate is 
28

offered for a priced new vehicle, it is the estimated average 

rebate over the past 30 days that is subtracted from the 

vehicle’s reported price. For vehicle leasing, it is the rebate in effect as of the day the collected price is obtained. 

For mail-in rebate offers, the price of the affected item is 

reported without subtracting the amount of the rebate. An 

attempt is made to determine the proportion of customers 

who take advantage of the rebate, and prior to use in the 

index, the reported price is then adjusted accordingly. 

Membership retail outlets. Outlets that require a membership fee to be paid in order to be able to shop at the outlet 

are eligible for pricing in the CPI. If the actual price paid 

for products varies with the level of membership, a specific 

membership is selected and the reported prices reflect that 

membership level. 

Quantity discounts. Many items in the CPI are sold both individually and in quantity. When consumers are able to purchase an amount greater than a single unit at a discounted 

price, the first multiple-unit price is reported for use in the 

CPI. For example, if the 12-ounce can of corn being priced 

can be purchased at 25 cents for a single can, three cans for 

69 cents, or five cans for $1, the price used in the CPI will be 

the per ounce price of the three cans.

Shoppers’ cards. If a priced outlet issues a card offering a 

“card discount” on selected products purchased by cardholders, such discounts are treated as “temporary discounts” and 

processed as follows. The discount is included only if 50 percent or more of sales for the affected item during the collection period are subject to the card discount.

Special-day prices. If a selected outlet has different prices 

for priced items based on the day of the week when a purchase is made, a selection is made between special-day and 

regular-day purchases, based on revenue. If the “special 

day” is selected, the price collected is for the most recent 

special-day price. 

Utility refunds. Sometimes, public utility commissions require that utilities such as telephone, natural (piped) gas, 

or electricity companies make rebates to their customers. 

These rebates may arise from a number of different causes. 

For example, a utility may be permitted to use a new rate 

schedule temporarily until a final determination is made. 

If the final rates set by the commission are lower than the 

temporary ones, the difference must be refunded for consumption during the period. The CPI does not always view 

such refunds as reflecting current period prices for utility 

services. If all customers, both new and existing, are subject 

to having the refund applied to their bill, then the refund is 

included in the total price calculation. However, if the refund is only applied to those customers who were originally 

subject to the overcharge (i.e., existing customers only) then 

the refund is excluded. This procedure reduces the monthto-month volatility of utility indexes and ensures that they 

reflect current prices and price trends more accurately. Also 

excluded are refunds that are paid directly to consumers in 

a separate check and are not part of the bill. The utility indexes do include current-period credits that are based on 

current consumption, such as purchased gas adjustments 

and fuel adjustments. 

Unit-priced food items. When food items that are sold 

on a unit basis but lack a labeled weight are being priced, 

two items are weighed to permit calculation of an average 

weight for the item. This helps reduce the variability in size 

that occurs among individual, loose items and is not overly 

burdensome for the data collection process. For example, if 

the item being priced is Red Delicious apples, and the price is 

50 cents each, the BLS field staff reports the price of one apple 

and the combined weight of two apples taken from the produce 

rack. In computing the price per ounce, the combined weight is 

divided by 2, and the 50-cent price of the Red Delicious apple is 

divided by this average weight. 

Container deposits. BLS collects information on container deposits for a variety of nonalcoholic and alcoholic beverages to 

reflect the influence of changes in deposit legislation on price 

change. Consumers who purchase throw-away containers are 

considered to be purchasing both the product itself and the convenience of throwing the container away. When a local jurisdiction enacts deposit legislation and no longer allows stores to sell 

throwaway containers, those consumers who were previously 

purchasing throwaway containers may experience a change in 

the price of this convenience. The price of the same-sized container of product plus its deposit establishes an upper bound for 

the price change, because the consumer could retain the former 

convenience by now purchasing returnables and simply throwing them away. In similar fashion, information about deposits 

and the status of legislation can be used to estimate price change 

when a container bill is repealed. Changes due to the enactment 

or repeal of container bills are shown in data for the month in 

which the legislation becomes effective.

Sales taxes. The CPI includes all applicable taxes paid by consumers for services and products purchased. Many prices for 

services and products used to calculate the CPI are collected 

with taxes included because this is the manner in which they 

are sold. Examples are tires and cigarettes. Other prices are collected excluding applicable taxes, with those taxes subsequently 

added in the Washington office. The tax rates for these items are 

determined from secondary sources based on the State, county, 

and local tax structure governing the sale of the service or product at the point of purchase. 

Index calculation

As stated earlier, the CPI is actually calculated in two 

stages. Earlier sections described the first stage of that calculation—how the CPI calculates the basic or elementary 

indexes, which show the average price change of the items in 

each of the 8,018 CPI item–area combinations.
29

The next section describes the second stage of calculation: how the aggregate indexes are produced by averaging 

across the 8,018 CPI item–area combinations.

Estimation of upper level price change 

Aggregation of elementary CPI data into published indexes requires three ingredients: elementary indexes, elementary expenditures to use as aggregation weights, and a price 

index aggregation formula that uses the expenditures to aggregate the sample of elementary indexes into a published 

index. 

Input elementary price indexes 

The CPI-U, CPI-W, and all versions of the C-CPI-U are 

constructed by using the same combination of Laspeyres 

and geometric mean elementary indexes. In other words, the 

prices for each series are combined in the same way to form 

the elementary price indexes.

Input elementary expenditure weights 

To aggregate elementary indexes into published indexes, 

an aggregation weight for each elementary item–area combination is required. The function of the aggregation weight 

is to assign each elementary index a relative importance or 

contribution in the resulting aggregate index. The aggregation weight corresponds to consumer tastes and preferences 

and resulting expenditure choices among the 211 elementary 

items in the 38 elementary areas comprising the CPI sample, 

for a specified period. 

CPI-U and CPI-W. In the CPI-U and CPI-W, aggregation 

weights (AW) are defined as 

37

ed earlier, the CPI is actually calculated in two stages. Earlier sections described the first stage of 

culation—how the CPI calculates the basic or elementary indexes, which show the average price 

of the items in each of the 8,018 CPI item–area combinations.

xt section describes the second stage of calculation: how the aggregate indexes are produced by 

ng across the 8,018 CPI item–area combinations.

ation of upper level price change 

ation of elementary CPI data into published indexes requires three ingredients: elementary 

, elementary expenditures to use as aggregation weights, and a price index aggregation formula 

s the expenditures to aggregate the sample of elementary indexes into a published index.

elementary price indexes

I-U, CPI-W, and all versions of the C-CPI-U are constructed by using the same combination of 

res and geometric mean elementary indexes. In other words, the prices for each series are 

ed in the same way to form the elementary price indexes.

elementary expenditure weights

regate elementary indexes into published indexes, an aggregation weight for each elementary 

ea combination is required. The function of the aggregation weight is to assign each elementary 

 relative importance or contribution in the resulting aggregate index. The aggregation weight 

onds to consumer tastes and preferences and resulting expenditure choices among the 211 

tary items in the 38 elementary areas comprising the CPI sample, for a specified period. 

 and CPI-W. In the CPI-U and CPI-W, aggregation weights (AW) are defined as 

, 100

( ) AW , ,

, ,

 



i a p P Q i a p

 



a p P



, , is the estimated price of item (i) purchased in area (a) by population (p) in period (α), and 

is the estimated quantity of item (i) purchased in area (a) by population (p) in period (β). Period 

he base period of the corresponding elementary item–area index. For example, the “Sports 

ent” (ITEM = RC02) in Seattle (AREA = A423) index has a base period of α = June 1985. CPI 

tary indexes have varying base periods. Most published indexes have an index base period of α =

984. 

antity (β) corresponds to the reference period of the expenditures used to derive the implicit 

y weights needed for Laspeyres aggregation. As of 2014, the CPI-U and CPI-W had an 

iture reference period of β = 2011–2012. Historically, the CPI expenditure reference period was 

d approximately every 10 years. (See table 3.) In 2002, BLS instituted a biennial rotation schedule 

ating the expenditure reference period. Effective with the January 2004 index, the expenditure 

ce period changed from β = 1999–2000 to β = 2001–2002; effective with the January 2006 index,

pdated again to 2003–2004; and so forth. It is worth noting that a change in the expenditure 

ce period results in a change in the implicit quantity (Q) assigned to each elementary index, but 

implicit price component (P) of the aggregation weight (AW) of each elementary index. 

able 2

where i a p Pα

)

, , is the estimated price of item (i) purchased 

in area (a) by population (p) in period (α), and i a pQβ

)

, , is 

the estimated quantity of item (i) purchased in area (a) 

by population (p) in period (β). Period (α) is the base 

period of the corresponding elementary item–area index. 

For example, the “Sports equipment” (ITEM = RC02) in 

Seattle (AREA = A423) index has a base period of α = 

June 1985. CPI elementary indexes have varying base periods. Most published indexes have an index base period 

of α = 1982–1984. 

The quantity (β) corresponds to the reference period 

of the expenditures used to derive the implicit quantity 

weights needed for Laspeyres aggregation. As of 2014, 

the CPI-U and CPI-W had an expenditure reference period of β = 2011–2012. Historically, the CPI expenditure 

reference period was updated approximately every 10 

years. (See table 2.) In 2002, BLS instituted a biennial 

rotation schedule for updating the expenditure reference 

period. Effective with the January 2004 index, the expenditure reference period changed from β = 1999–2000 

to β = 2001–2002; effective with the January 2006 index, 

it was updated again to 2003–2004; and so forth. It is 

worth noting that a change in the expenditure reference 

period results in a change in the implicit quantity (Q) 

assigned to each elementary index, but not the implicit 

price component (P) of the aggregation weight (AW) of 

each elementary index.

Table 2. Expenditure reference periods for the Consumer 

Price Index, All Urban Consumers (CPI-U) and the Urban 

Wage Earners and Clerical Workers (CPI-W), 1917–2015

Expenditure 

reference period

Month

 introduced

Terminal

 month

1917–1919 1919 Dec. 1924

Avg. 1917–1919 and 

1934–1936

Jan. 1925 Dec. 1929

1934–1936 Jan. 1930 Dec. 1949

1947–1949 Jan. 1950 Dec. 1952

1950 Jan. 1953 Dec. 1963

1960–1961 Jan. 1964 Dec. 1977

1972–1973 Jan. 1978 Dec. 1986

1982–1984 Jan. 1987 Dec. 1997

1993–1995 Jan. 1998 Dec. 2001

1999–2000 Jan. 2002 Dec. 2003

2001–2002 Jan. 2004 Dec. 2005

2003–2004 Jan. 2006 Dec. 2007

2005–2006 Jan. 2008 Dec. 2009

2007–2008 Jan. 2010 Dec. 2011

2009–2010 Jan. 2012 Dec. 2013

2011–2012 Jan. 2014 Dec. 2015

Note: Prior to January 1953, previously published indexes often 

were revised retroactively, on the basis of more recent consumer 

expenditure data.

Source: U.S. Bureau of Labor Statistics.

Aggregation weights for the CPI-U and CPI-W are derived from estimates of household expenditures collected 

in the CE. Despite an increase in the CE sample size in 

1999, expenditure estimates at the elementary item–area 

level would be unreliable due to sampling error without 

the use of statistical smoothing procedures. BLS uses two 

basic techniques to minimize the variance associated with 

each elementary item–area base-period expenditure esti-
30

mate. First, data are pooled over an extended period in order to build the expenditure estimates on an adequate sample size. The current reference period (β) uses 24 months 

of data.37 Second, elementary item–area expenditures are 

averaged, or composite-estimated, with item-regional

37 Prior to 2002, the expenditure reference period was based on 36 months 

of data (for example, β = 1993–1995 from 1998 to 2001 and β = 1982–1984 

from 1987 to 1997).

expenditures.38 This has the effect of lowering the variance of 

each elementary item–area expenditure at the cost of biasing 

it toward the expenditure patterns observed in the larger geographical area. This process is summarized in the equations. 

in exhibit 3. 

38 Elementary areas are grouped into city-size classifications by region 

for the purpose of composite estimation. There are four regions (Northeast, 

Midwest, South, and West) and two city-size classifications (A-sized cities 

and non-A-sized cities) for a total of eight regional city-size classifications.

 

Exhibit 3. Estimation of CPI-U elementary aggregation weights 

Expenditure on item (i) in area (a) by population (p) in year (βn) i a p PQ n ( ) , ,

Total expenditures in area (a) by population (p) in year (βn) ∑, ∈ ,

, , ( )

i

i a a p

i a p PQ βn

Share of total expenditures for item (i) in area (a) for population (p) 

 in year (βn) 

Expenditure on item (i) in major area (m) by population (p) in year (βn) 

Total expenditures in major area (m) by population (p) in year (βn) 

Share of total expenditures for item (i) in area (m) for population (p) 

 in year (βn) 

Composite-estimated share of total expenditures for 

 item (i) in area (a) for population (p) in year (βn) 

Estimated expenditure on item (i) in 

 area (a) by population (p) in year (β n) 

Raked expenditure on item (i) in 

 area (a) by population (p) in year (β n) 

Estimated expenditure in expenditure reference period (β) 

∑, ∈ ,

, ,

, ,

, ,

( )

( ) = i

i a m p

i a p β

i a p β

i a p β

n

n

n

PQ

PQ s

∑, ∈ ,

, ,

, ,

, ,

( )

( ) i

i m m p

i m p

i m p

i m p

n

n

n

PQ

PQ s





 

∑

,

, ∈ .

, , , , ( ) ( )

i a

i a m p

i m p PQ n  i a p PQ n

∑, ∈ .

, , ( )

i

i m m p

i m p PQ n

    n n n s s s i,a, p   i,m, p   i,a, p    (1 ) 

n n n PQ PQ s i a p

i

i a a p

i a p  i a p  

 , ,

, ∈ ,

, , ∑, , ) ( ) ~ ~ ( 

































∑1

) ˆ ˆ ( 1 ) ˆ ˆ (

N

n=

i,a,p β i,a,p PQ βn

N

PQ =

∑

∑

∈ ,

∈ ,

, , , ,

) ~ ~ (

( )

) × ~ ~ ) = ( ˆ ˆ ( i,a

i,a e m

i,a,p β

i,a

i,a e m

i,a,p β

i a p β i a p β

n

n

n n

PQ

PQ

PQ PQ
31

 

Exhibit 3. Estimation of CPI-U elementary aggregation weights—continued 

Cost weight in pivot month (v) 















i,a,p a,β

i,a,p a,v

i,a,p PvQβ = i,a,p PQ β × IX

IX

) ˆ ˆ ) ( ˆ ˆ (

Aggregation weight . X

) ˆ ˆ ( ) ˆ ˆ (

, , ,

, ,

, ,

i a p v

i a p v

i a p

I

PQ P Q





  

where 

 p = population (urban or urban wage-earner) 

 a = CPI elementary area 

 i = CPI elementary item 

 e = expenditure class 

 m = One of eight CPI major areas, defined by region and city-size classification. 

 Regions are Northeast, Midwest, South, and West; city-size types are self- 

 representing and non-self-representing 

 P = price 

 Q = quantity 

 N = number of years in the CPI-U expenditure reference period (NOTE: Currently, 

 N = 2.) 

 βn = year belonging to expenditure reference period β (NOTE: n = 1 is 1999 and n = 2 

 is 2000 in the current CPI-U expenditure reference period.) 

 δ = weight assigned to major area (m), where 0 < δ < 1 

 α = lower-level index base period 

 v = year and month, usually December, prior to the month when expenditure weights 

 from reference period β are first used in the CPI 

n

S i,a, p  = estimated expenditures (PQ) for item (i) in area (a) for population (p) as a percent 

 of total CPI expenditures in area (a) in period βn, 

i,a IXα,β = lower-level index of price change from index base period (α) to expenditure 

 reference period (β) for item (i) in area (a) 

i,a IXα,v = lower-level index of price change from index base period (α) to pivot-month (v) 

 for item (i) in area (a)
32

The estimated expenditure i a p PQ βn ) ~ ~ ( , , for item (i) in area 

(a) for population (p) in reference period (β) is derived from 

a weighted average of the item’s relative importance in the 

elementary area (a) and its relative importance in its corresponding region-size classification (m), for each year encompassing reference period (β). The weight (δ) assigned to the 

region-size class (m) and the weight (1 – δ) assigned to the 

elementary area (a) are a function of the variance in each area 

and the covariance of each measure.39 The resulting average 

share (s

) ) is then multiplied by the sum of all expenditures 

in the elementary area in the corresponding year to obtain a 

revised item expenditure. In a process called “raking,” the 

revised item expenditures are adjusted by a factor such that, 

once summed, they equal the unadjusted expenditures at 

the region-size class (m) expenditure class (e) level. Annual 

item–area expenditures (βn) have a lower bound of 1 cent 

($0.01). The raked item expenditures in each year of reference 

period (β) are then averaged to obtain the estimated expenditure in (β). Finally, the estimated expenditure is adjusted by 

the corresponding item–area index to obtain the aggregation 

weight: an expenditure value with an implicit price of period 

(α) and implicit quantity of period (β).

Because the initial version of the C-CPI-U is published 

simultaneously with the CPI-U, it uses expenditure data from 

the same expenditure reference period (β) as the CPI-U as 

aggregation weights. Unlike those in the CPI-U, however, the 

expenditures are not adjusted forward to a December pivot 

month and rebased so that the implicit price corresponds to 

the item–area index base period. Rather, the estimated expenditure weights with implicit prices of period (β) and implicit quantities of period (β) are used as aggregation weights. 

Before 2015, the interim version of each monthly C-CPI-U 

index was published in February of the ensuing year. Hence, 

if the ensuing year was one in which the weight was updated, 

then the interim version of each monthly C-CPI-U was based 

on more contemporaneous expenditures than its initial version. For example, 2012 initial indexes produced in 2012 used 

β = 2009–2010. Interim indexes for 2012 were produced in 

2013 and likewise used β = 2009–2010. Initial indexes for 

2013 also used β = 2009–2010. However, 2013 interim indexes produced in 2014 (a weight update year) were constructed 

using β = 2011–2012.40

Final C-CPI-U. For the final C-CPI-U, which uses the Törnqvist index for upper-level aggregation in a monthly chained 

construct, monthly expenditure estimates for each elementary item–area combination are required as aggregation 

weights. These are derived from the same CE data as the 

39 For more information on composite estimation, see Michael P. Cohen 

and John P. Sommers, “Evaluation of the methods of composite estimation of cost weights for the CPI,” Proceedings of the American Statistical 

Association, Business and Economic Statistics Section (Alexandria, VA: 

American Statistical Association, 1984.) pp. 466–471.

40 Starting in 2015, BLS will begin issuing four preliminary estimates of 

the C-CPI-U, by quarter, with final data issued approximately 1 year after 

the reference month. 

CPI-U aggregation weights. Like the biennial data used for 

CPI-U aggregation, adequacy of the underlying sample size 

from which the expenditure weights are estimated is an issue 

for C-CPI-U aggregation. To minimize the variance of the elementary item–area monthly expenditures, a ratio-allocation 

procedure is adopted to estimate each item–area monthly expenditure from U.S. monthly item expenditures:

Estimation of monthly expenditures at the elementary 

level

Estimated monthly expenditures

 ,

( )

( )

) = ( ) ˆ ˆ (

∑∑

∑

∑

, ∈ ∈

, ,

∈

, ,

, ∈ ,

, , , , a

i a A

T

t T

i a p t

T

t T

a i a p t

i a i A

i a p t i a p t

PQ

PQ

PQ PQ

where

p = population (NOTE: C-CPI-U is produced for the 

urban population only.)

a = CPI elementary area

i = CPI elementary item

A = all CPI elementary areas (“U.S. city average”) 

P = price

Q = quantity

t = month

T = period covering month (t) and 11 months prior to 

month (t)

The monthly expenditure for an item in an elementary area 

is derived in two steps: First, the monthly expenditure for the 

item is summed across all 38 areas to obtain a U.S. monthly 

item expenditure. Second, the U.S. monthly item expenditure 

is allocated among all 38 elementary areas, according to each 

area’s relative expenditure share for the item during the current and preceding 11 months. Note that

i A PQ t i A PQ t ) ˆ ˆ ( ) ( ,  , .

The estimated monthly item–area expenditures have 

a lower bound of 1/12th of a penny ($0.000833), and when 

summed over the calendar year, they have a lower bound 

($.01) equivalent to that of the annual data in the CPI-U expenditure reference period. 

Aggregation formula

A Laspeyres price index is used to aggregate elementary indexes into published CPI-U and CPI-W indexes. The Laspeyres index uses estimated quantities from the predetermined 

expenditure reference period (β) to weight each elementary 

item–area index. These quantity weights remain fixed for a 

2-year period, and then are replaced in January of each even 

year when the aggregation weights are updated. In a Laspey-
33

res aggregation, consumer substitution between items is assumed to be zero. The aggregate index for any given month 

is computed as a quantity-weighted average of the current 

month index divided by the index value in the index base period. Month-to-month price change is then calculated as a ratio of the long-term monthly indexes. The relevant equations 

are as follows:

CPI-U and CPI-W upper-level aggregation formula

Long-term price change

42

mputed as a quantity-weighted average of the current month index 

 index base period. Month-to-month price change is then calculated as a 

ndexes. The relevant equations are as follows:

vel aggregation formula

mentary areas (“U.S. city average”) 

ementary area

ation (the C-CPI-U is calculated for the U-population only.)

ementary item

mentary items (“all-items”)

eriod of the aggregate index (Note: the U.S. city average, all-items CPI

x has a base period of z = 1982–1984.)

eriod of the elementary index (i) in area (a)

nd month, usually December, prior to the month when expenditure 

s from reference period (β) are first used in the CPI

level index of price change from period (α) to month (t) for item (i) in 

a)

level index of price change from period (α) to pivot-month (v) for item 

rea (a)

ation weight from reference period (β) for item (i) in area (a)

ate-level CPI-U index of price change from period (z) to pivot month 

 aggregate item (I) in aggregate area (A) for population (p)

, AW IX

AW IX

IX IX

, ,

, , , , [ ; ]

, ,

, , , , [ ; ]

, , [ ; ] , , [ ; ]













 

i a I A

i a p i a p

L G

v

i a I A

i a p i a p

L G

t

L

I A p z v

L

I A p z t o r

o r

 

 

, IX

IX

IX

, , [ ; -1]

, , [ ; ]

, , [ 1; ] L

I A p z t

L

L I A p z t

I A p t t 

Month-to-month price change

 , IX

IX

IX = , , [ ; -1]

, , [ ; ]

, , [ -1; ] L

I A p z t

L

I A p z t L

I A p t t

where

A = all elementary areas (“U.S. city average”)

a = CPI elementary area

p = population (the C-CPI-U is calculated for the U-

 population only.)

i = CPI elementary item

I = all elementary items (“all-items”)

t = month

z = base period of the aggregate index (Note: the U.S. 

 city average, all-items CPI-U index has a base pe-

 riod of z = 1982–1984.)

α = base period of the elementary index (i) in area (a)

v = year and month, usually December, prior to the 

 month when expenditure weights from reference 

 period (β) are first used in the CPI

 = lower-level index of price change from period (α) to

 month (t) for item (i) in area (a)

 = lower-level index of price change from period (α) to 

 pivot-month (v) for item (i) in area (a)

 

= aggregation weight from reference period (β) for

 item (i) in area (a)

 

= aggregate-level CPI-U index of price change from 

period (z) to pivot month (v) for aggregate item (I) 

in aggregate area (A) for population (p)

In contrast, the C-CPI-U is built by chaining together indexes of 1-month price change. For the final CCPI-U index, 

each monthly index is computed using the Törnqvist formula 

with monthly weights from both the current and the previous month. Consumer substitution behavior is not assumed 

by the Törnqvist formula; rather, it is implicitly accounted 

for by use of current- and base-month expenditures. An index 

of 1-month price change is calculated and then multiplied by 

the index value for the previous month to obtain the currentmonth index value. Following are the relevant equations:

Final C-CPI-U upper-level aggregation formula

Long-term price change

IX IX IX , , , [ ; ] , , [ ; -1] , , [ -1; ]

T

I A p t t

T

I A p z t

T

I A p z t p = ×

Month-to-month price change

, IX

IX

IX ∏

, ∈ ,

2

, , [ ; -1]

, , [ ; ]

, , [ -1; ]

, , -1 , ,

i a I A i a p

L G

t

i a p

L G

T t

I A p t t

i a p t i a p t

or

or

s  s















 





where

p = population (Note: the C-CPI-U is calculat-

 ed for the urban consumer population only.)

a = CPI elementary area

A = aggregate area 

i

 = CPI elementary item

I = aggregate item

z = base period of the aggregate index (NOTE:

 the U.S. city average, all-items C-CPI-U in

 dex has a base-period of z = December 

 1999.)

a = base period of the elementary index (i) in 

 area (a)

t

 = month

i,a IX[α;t] = lower-level index of price change from period (α) to month (t) for item (i) in area (a)

i, a IX[α;t-1] = lower-level index of price change from period (α) to month (t – 1) for item (i) in area 

 (a)

i,a

St

 = expenditure in month (t) for item (i) in

 area (a) as percentage of total expenditures 

 in month (t) for aggregate item (I) in aggre

 gate area (A)

i,a

St-1 = expenditure in month (t-1) for item (i) in 

 area (a) as percent of total expenditures in 

 month (t – 1) for aggregate item (I) in ag-

 gregate area (A)

I,AIXT[z;t] = aggregate-level C-CPI-U Törnqvist index 

 of price change from period (z) to month 

 (t) for aggregate item (I) in aggregate area (A)

i,a IX[α;t]

i,a, p AWβ

L

I ,A, p [z,v] IX

L

I ,A, p [ ,v ] IX 
34

Starting in 2015, BLS began revising the Chained Consumer 

Price Index for All Urban Consumers (C-CPI-U) quarterly, 

and the Constant Elasticity of Substitution (CES) formula 

will replace the adjusted geometric mean formula for the 

calculation of the preliminary versions of that index. 

The initial version of the C-CPI-U will continue to be 

released concurrently with the CPI-U for each calendar month. 

The C.E.S. formula will be used to calculate the C-CPI-U. 

The final version of the index will be released approximately 

10–12 months later, according to the publication schedule 

outlined in the following table: 

C-CPI-U quarterly release schedule 

Index month Quarterly release 

 1 (Feb) 2 (May) 3 (Aug) 4 (Nov) 5 (Feb)

y – 1, 1 Final Final Final Final Final 

y – 1, 2 Final Final Final Final Final 

y – 1, 3 Final Final Final Final Final 

y – 1, 4 Interim3 Final Final Final Final 

y – 1, 5 Interim3 Final Final Final Final 

y – 1, 6 Interim3 Final Final Final Final 

y – 1, 7 Interim2 Interim3 Final Final Final 

y – 1, 8 Interim2 Interim3 Final Final Final 

y – 1, 9 Interim2 Interim3 Final Final Final 

y – 1, 10 Interim1 Interim2 Interim3 Final Final 

y – 1, 11 Interim1 Interim2 Interim3 Final Final 

y – 1, 12 Interim1 Interim2 Interim3 Final Final 

y, 1 Initial Interim1 Interim2 Interim3 Final 

y, 2 Interim1 Interim2 Interim3 Final 

y, 3 Interim1 Interim2 Interim3 Final 

y, 4 Initial Interim1 Interim2 Interim3

y, 5 Interim1 Interim2 Interim3

y, 6 Interim1 Interim2 Interim3

y, 7 Initial Interim1 Interim2

y, 8 Interim1 Interim2

y, 9 Interim1 Interim2

y, 10 Initial Interim1

y, 11 Interim1

y, 12 Interim1

y + 1,1 Initial 

In the table, the lightface italic gray text indicates that the 

final index has been released earlier. Thus, reading down 

the first column shows that, in February of the current 

year (y, 2), the final version will be released for the first 3 

months (January, February, and March) of the previous year 

(y – 1, 1; y – 1, 2; and y – 1, 3) and the initial version will 

be released for January of the current year (y, 1). Column 5 

of the current year corresponds to column 1 of the previous year. The other columns are read similarly. A blank cell 

indicates that it is too early to have an index for that month 

at that time.

The final index will continue to be calculated with the 

Törnqvist formula. In between the initial release and the 

final release, there will be three quarterly updates, noted 

as Interim (1), Interim (2), and Interim (3) in the table. The 

1-month price change for each interim release will be the 

same as the initial version. The interim versions reflect only 

updates to index levels—that is, the value of the index in a 

given month relative to the value in its base period. These 

updates result from the conversion of 1-month price changes 

from initial to final value in preceding months in the monthly 

chained series. 

The CES uses an estimate of consumer substitution that 

lies between the estimates assumed in the geometric mean 

and Laspeyres formulas, and represents a model that is closer 

to actual consumer behavior. This estimate of consumer 

substitution, sigma (σ), is called the elasticity of substitution.

New formulas and quarterly release schedule

The CES month-to-month index relative for a biennial period 

is

��,�,�,��,�

� � ��

�,����,� �

IX�,�,���

IX�,�,����

�

�����

,

where

C = C.E.S index/weight, 

i = elementary item stratum,

I = aggregate item,

a = elementary index area,

A = aggregate index area,

IX = component index,

T = current calendar month in calendar year y (e.g., 

 if IXyymm = IX0308, then t = August and y = 

 2003),

t –1 = calendar month previous to calendar month t,

x = reference period of index (initially, x = 

 December 1999),

 = biennial expenditure reference period,

 = during biennial expenditure reference period 

σ = sigma for component and aggregate index 

 periods and for weight period, and 

V = pivot month index period.

Calculation of seasonally adjusted indexes

Seasonal adjustment. Seasonal adjustment removes the estimated effect of changes that normally occur at the same time 

every year (such as price movements resulting from changing 

climatic conditions, production cycles, model changeovers, 

holidays, and sales). CPI series are selected for seasonal adjustment, if they pass certain statistical criteria and if there is 

an economic rationale for the observed seasonality. Seasonal 

factors used in computing the seasonally adjusted indexes 

are derived, using X-13ARIMA-SEATS seasonal adjustment software. X-13ARIMA-SEATS is an extension of the 

IX�������

� ��� �

�

�

�

�

�

��∑ �� �����������

�

∑ �����������

� ������� � � IX�����

IX�������

�

�����

������� ��

� �

����

�∑ �� �����������

�

∑ �����������

� ������� � �IX�������

IX�������

�

�����

������� ��

� �

����

�

�

�

�

�

�

ܲ௕

௜ǡ௔

ܾܳ

݅ǡܽ
35

X-12 variant of the Census Method II Seasonal Adjustment 

methodology. In some cases, intervention analysis seasonal 

adjustment is carried out using X-13ARIMA-SEATS, to derive more accurate seasonal factors. Consumer price indexes 

may be adjusted directly or aggregatively, depending on the 

level of aggregation of the index and the behavior of the component series.41

Intervention analysis seasonal adjustment. Some index 

series show erratic behavior due to non-seasonal economic 

events (called interventions) or methodology changes. These 

events, which can be one-time occurrences or recurring 

events that happen at infrequent and irregular intervals, adversely affect the estimate of the seasonal component of the 

series. 

Intervention analysis seasonal adjustment allows non-seasonal economic phenomena, such as outliers and level shifts, 

to be factored out of indexes before calculation of seasonal 

adjustment factors. (An outlier is an extreme value for a particular month. A level shift is a change or shift in the price 

level of a CPI series caused by an event, such as an excise tax 

increase or oil embargo, occurring over one or more months.) 

An index series whose underlying trend has experienced a 

sharp and permanent shift will generate distorted results 

when adjusted using the standard X-13ARIMA-SEATS procedure. X-13ARIMA-SEATS regression techniques are used 

to model the distortions and account for them as part of the 

seasonal adjustment process. The result is an adjustment 

based on a representation of the series with the seasonal pattern emphasized. Intervention analysis seasonal adjustment 

also makes it possible to account for seasonal shifts, resulting 

in a better seasonal adjustment in the periods before and after 

the shift occurred. Not all CPI series are adjusted using intervention analysis seasonal adjustment techniques. However, 

for affected series, the resulting seasonal factors better represent the true seasonal pattern than factors calculated without 

these techniques. These seasonal factors are applied to the 

original unadjusted series. Level shifts and outliers, removed 

in calculating the seasonal factors, remain in the resulting 

seasonally adjusted series. 

In recent years, BLS has used intervention analysis seasonal adjustment for various indexes—gasoline, fuel oil, new 

vehicles, women’s and girls’ apparel, educational books and 

supplies, electricity, utility (piped) gas service, water and 

sewerage maintenance, nonalcoholic beverages and beverage materials, and whiskey at home are examples. Series 

are adjusted using intervention analysis techniques when interventions are clearly identified. After a number of years, 

series may revert to adjustment using standard methods. In 

addition, for some series, intervention analysis is used, and 

the resulting series does not show a clear and stable seasonal 

pattern. In these cases, the series is not seasonally adjusted.

41J.A. Buszuwski and S. Scott, "On the use of intervention analysis in 

seasonal adjustment,'' Proceedings of the American Statistical Association, 

Business and Economics Section. (Alexandria, VA: American Statistical 

Association, 1988).

Direct and aggregative adjustment. Each year, BLS seasonally adjusts eligible lower level CPI index series directly with 

the X-13ARIMA-SEATS software using unadjusted indexes 

for the latest 5 to 8 calendar years. CPI index series are adjusted using the multiplicative model.

Most high-level index series are adjusted by the aggregative method, which is more appropriate for broad categories 

whose component indexes show strongly different seasonal 

patterns. Under the aggregative method, direct adjustment is 

first applied to indexes at lower levels of detail, and thereafter 

the adjusted detail is aggregated to yield the higher level seasonally adjusted indexes. If intervention analysis is indicated, 

it will be used in adjusting selected lower level indexes prior 

to aggregation. For those series that have not been selected 

for seasonal adjustment, the original, unadjusted data are 

used in the aggregation process. 

Revision. The seasonal factors are updated annually. Each 

year in February, BLS recalculates and publishes revised seasonally adjusted indexes for the previous 5 years. Seasonally 

adjusted indexes become final in the last and 5th year of revision. Seasonal factors for the past year are used to generate 

seasonally adjusted indexes for the current year starting with 

the release of the January CPI.

Calculation of annual and semiannual average 

indexes

CPI annual average indexes use 12 successive months of 

CPI values as

However, for affected series, the resulting seasonal factors better represent the true seasonal patt

factors calculated without these techniques. These seasonal factors are applied to the original un

series. Level shifts and outliers, removed in calculating the seasonal factors, remain in the result

seasonally adjusted series. 

In recent years, BLS has used intervention analysis seasonal adjustment for various indexes—ga

fuel oil, new vehicles, women's and girls’ apparel, educational books and supplies, electricity, ut

(piped) gas service, water and sewerage maintenance, nonalcoholic beverages and beverage mat

and whiskey at home are examples. Series are adjusted using intervention analysis techniques w

interventions are clearly identified. After a number of years, series may revert to adjustment usin

standard methods. In addition, for some series, intervention analysis is used, and the resulting se

not show a clear and stable seasonal pattern. In these cases, the series is not seasonally adjusted.

Direct and aggregative adjustment. Each year BLS seasonally adjusts eligible lower level CPI 

series directly with the X-13ARIMA-SEATS software using unadjusted indexes for the latest 5 

calendar years. CPI index series are adjusted using the multiplicative model.

Most high-level index series are adjusted by the aggregative method, which is more appropriate 

categories whose component indexes show strongly different seasonal patterns. Under the aggre

method, direct adjustment is first applied to indexes at lower levels of detail, and thereafter the a

detail is aggregated to yield the higher level seasonally adjusted indexes. If intervention analysis 

indicated, it will be used in adjusting selected lower level indexes prior to aggregation. For thos

that have not been selected for seasonal adjustment, the original, unadjusted data are used in the 

aggregation process. 

Revision. The seasonal factors are updated annually. Each year in February, BLS recalculates an

publishes revised seasonally adjusted indexes for the previous 5 years. Seasonally adjusted inde

become final in the last and 5th year of revision. Seasonal factors for the past year are used to ge

seasonally adjusted indexes for the current year starting with the release of the January CPI.

Calculation of annual and semiannual average indexes

CPI annual average indexes use 12 successive months of CPI values as

∑ /12 .

12

1 ,0 12av



t t I I

Semiannual average indexes are computed for the first half of the year (January to June) and for 

second half of the year (July to December) using six successive months of CPI values as 

∑ / 6 ,

6

1 ,0 6av



t t I I

Semiannual average indexes are computed for the first half 

of the year (January to June) and for the second half of the year 

(July to December) using six successive months of CPI values as 

However, for affected series, the resulting seasonal factors better represent the true seasonal patt

factors calculated without these techniques. These seasonal factors are applied to the original un

series. Level shifts and outliers, removed in calculating the seasonal factors, remain in the result

seasonally adjusted series. 

In recent years, BLS has used intervention analysis seasonal adjustment for various indexes—ga

fuel oil, new vehicles, women's and girls’ apparel, educational books and supplies, electricity, ut

(piped) gas service, water and sewerage maintenance, nonalcoholic beverages and beverage mat

and whiskey at home are examples. Series are adjusted using intervention analysis techniques w

interventions are clearly identified. After a number of years, series may revert to adjustment usin

standard methods. In addition, for some series, intervention analysis is used, and the resulting se

not show a clear and stable seasonal pattern. In these cases, the series is not seasonally adjusted.

Direct and aggregative adjustment. Each year BLS seasonally adjusts eligible lower level CPI 

series directly with the X-13ARIMA-SEATS software using unadjusted indexes for the latest 5 

calendar years. CPI index series are adjusted using the multiplicative model.

Most high-level index series are adjusted by the aggregative method, which is more appropriate 

categories whose component indexes show strongly different seasonal patterns. Under the aggre

method, direct adjustment is first applied to indexes at lower levels of detail, and thereafter the a

detail is aggregated to yield the higher level seasonally adjusted indexes. If intervention analysis 

indicated, it will be used in adjusting selected lower level indexes prior to aggregation. For thos

that have not been selected for seasonal adjustment, the original, unadjusted data are used in the 

aggregation process. 

Revision. The seasonal factors are updated annually. Each year in February, BLS recalculates an

publishes revised seasonally adjusted indexes for the previous 5 years. Seasonally adjusted inde

become final in the last and 5th year of revision. Seasonal factors for the past year are used to ge

seasonally adjusted indexes for the current year starting with the release of the January CPI.

Calculation of annual and semiannual average indexes

CPI annual average indexes use 12 successive months of CPI values as

∑ /12 .

12

1 ,0 12av



t t I I

Semiannual average indexes are computed for the first half of the year (January to June) and for 

second half of the year (July to December) using six successive months of CPI values as 

∑ / 6 ,

6

1 ,0 6av



t t I I

where the value of each monthly index is real or interpolated, 

depending on availability.42

For bimonthly indexes, the intermediate indexes are calculated using a geometric mean of the values in the months 

adjacent to the one being estimated. 

Average prices

Average prices are estimated from CPI data for selected 

food and beverage items, utility (piped) gas, electricity, gasoline, automotive diesel fuel, and fuel oil #2 to support the 

research and analytic needs of CPI data users. (See appendix 

2.) Average food prices are published without tax, while the 

other average prices are published with tax included.

42 To be published, a semiannual average must have at least two noninterpolated index values with sufficient samples. An annual average must 

have at least four noninterpolated index values with sufficient samples.
36

For each food and beverage item, the average price for a 

specified unit of size (for instance, pound or gallon) is published monthly for the U.S. city average and for the four regions: Northeast, Midwest, South, and West. Metric-equivalent sizes are shown, as well.

Average prices for utility (piped) gas, electricity, and gasoline are published monthly for the U.S. city average, the four 

regions, the three population size classes, 10 region/size-class 

cross-classifications, and the 14 largest local index areas. For 

utility (piped) gas, average prices per therm are published. 

For electricity, average prices per kilowatt-hour (kWh) are 

published. For gasoline, the average price per gallon is 

published. Average prices for commonly available grades 

of gasoline are published, as well as the average price 

across all grades.

Average prices per gallon for automotive diesel fuel and 

fuel oil #2 are published monthly for the U.S. city average, 

the four regions, the three population size classes, and 10 

of 12 region/size-class cross-classifications. 

All eligible prices are converted to a price per normalized quantity. These prices are then used to estimate a 

price for a defined fixed quantity. For example, prices for 

a variety of package sizes for flour are converted to prices 

per ounce. An average price per ounce of flour is then estimated and multiplied by 16 to yield a price per pound, the 

published quantity.

The average price for collection period t is estimated as 

where Wit is the quote-level expenditure weight of items used 

in the average price estimation for the ELI/PSU/replicate. 

Dividing the expenditure weight by the base price, Pib, 

for a given quote yields an implicit estimate of quantity. 

Thus, the average price is, conceptually, a weighted average of prices, Pit, where the weights are quantity amounts. 

Imputed prices are used in estimating average prices.

= , ∑

∑

i ib it

i it ib it

t

W P

W P P

P
37

Part III. Precision of CPI estimates

An important advantage of probability sampling methods 

is that a measure of the sampling error of survey estimates can 

be computed directly from the sample data. The CPI sample 

design accommodates error estimation by making two or more 

selections (replications) of items and outlets within an index 

area. Therefore, two or more samples of quotes in each selfrepresenting PSU and one in each non-self-representing PSU 

are available. With this structure, which reflects all stages of 

the sample design, variance estimation techniques using replicated samples can be used.

Sources of error

We divide the total error into two sources: sampling error 

and nonsampling error. Sampling error is the uncertainty in 

the CPI caused by the fact that a sample of retail prices is used 

to compute the CPI, instead of using the complete universe of 

retail prices. The sampling variance attributable to the estimation of expenditure weights (see chapter 16 for more detail 

on consumer expenditure weights) is directly incorporated in 

the variance estimates computed for the CPI, due to the fact 

that these expenditures are independently estimated for each 

replicate. Nonsampling error is the rest of the error, and will 

be discussed at the end of this section. Incorrect information 

given by survey respondents and data processing errors are 

examples of nonsampling error.

BLS constantly tries to reduce error in the CPI. Variance and sampling error are reduced by using samples of 

retail prices and samples of consumer expenditures that are 

as large as possible, given resource constraints. The Bureau 

has developed a model that optimizes, on a 2-year basis, the 

allocation of resources. The model indicates the number of 

prices that should be observed in each geographic area and 

each item category to minimize the variance of the U.S. city 

average all-items index. The Bureau reduces nonsampling error through a series of computerized and professional data 

reviews, as well as through continuous survey process improvements and theoretical research. 

Sample design

 Starting in 1978 the CPI’s sample design has accommodated variance estimation by using two or more independent 

samples of items and outlets in each geographic area. This 

allows two or more statistically independent estimates of 

the index to be made. The independent samples are called 

replicates, and the set of all observed prices is called the 

full sample. 

As discussed earlier, BLS calculates CPI indexes for 38 

geographic areas across the United States. The 38 areas consist of 31 self-representing areas and 7 non-self-representing 

areas. Self-representing areas are large metropolitan areas, 

such as the Boston metropolitan area, the St. Louis metropolitan area, and the San Francisco metropolitan area. Nonself-representing areas are collections of smaller metropolitan areas. For example, one non-self-representing area is a 

collection of 32 small metropolitan areas in the Northeast region (Buffalo, Hartford, Syracuse, Burlington, and others) of 

which 8 have been randomly selected to represent the entire 

set. Within each of the 38 areas, price data are collected for 

211 item categories called item strata. Together, the 211 item 

strata cover all consumer purchases. 

Multiplying the number of areas by the number of item 

strata gives 8,018 (= 38 × 211) different area-item combinations for which price indexes need to be calculated. Separate 

price indexes are calculated for each one of these 8,018 areaitem combinations. After calculating all 8,018 of these basiclevel indexes, the indexes are then aggregated to form higher 

level indexes, using expenditure estimates from the CE as 

their weights. 

CPI variances are primarily computed with a stratified 

random groups method, for 1-, 2-, 6- and 12-month percent 

changes. From 1978 to 1998, the BLS computed CPI variances by using a first-order Taylor approximation of the ratio 

of cost weights. This methodology was replaced, beginning 

in January 1998, by the stratified random groups method, in 

which variances are computed separately for certain subsets 

of areas and items, and then those individual variances are 

combined to produce the variance of the entire item–area 

combination. Subsets of items are formed by the intersection 

of the item category with each 1 of the 8 major groups.

Variance estimation using replicates

Let IX(A,I,f,t) denote the index value for area = A, item 

category = I, in month = t, where f indicates that it is the fullsample value, and let IX(A,I,f,t – k) denote the value of the 

same index in month = t – k. The uppercase letter A denotes 

a set of areas, such as the Northeast or Midwest region of the 

country, and the uppercase letter I denotes a set of item strata, 

such as all items or all items less food and energy, or even a 

single item stratum. Also, let IX(A,I,r,t) and IX(A,I,r,t – k) be the 

corresponding index values for replicate = r. Most areas have 

two replicates, but some have more. Then the full-sample kmonth percent change between months t – k and t is computed 

by dividing IX(A,I,f,t) by IX(A,I,f,t – k), subtracting 1, and 

multiplying by 100:

CPI variances are primarily computed with a stratified random groups method, for 1-, 2-, 6- and 

percent changes. From 1978 to 1998, the BLS computed CPI variances by using a first-orde

approximation of the ratio of cost weights. This methodology was replaced, beginning in Janua

by the stratified random groups method, in which variances are computed separately for certain s

areas and items, and then those individual variances are combined to produce the variance of t

item–area combination. Subsets of items are formed by the intersection of the item category wi

of the 8 major groups.

Variance estimation using replicates

Let IX(A,I,f,t) denote the index value for area = A, item category = I, in month = t, where f indica

is the full-sample value, and let IX(A,I,f,t – k) denote the value of the same index in month = t 

uppercase letter A denotes a set of areas, such as the Northeast or Midwest region of the country

uppercase letter I denotes a set of item strata, such as all items or all items less food and energy, 

single item stratum. Also, let IX(A,I,r,t) and IX(A,I,r,t – k) be the corresponding index values for 

= r. Most areas have two replicates, but some have more. Then the full-sample k-month percen

between months t – k and t is computed by dividing IX(A,I,f,t) by IX(A,I,f,t – k), subtractin

multiplying by 100:

– 1 100 . IX( , , , – )

IX( , , , ) ( , , , , – ) 













  A I f t k

A I f t PC A I f t t k

Every index has an aggregation weight AGGWT(A I, f) or AGGWT(A, I, r) associated with it, 

used to combine the index with other indexes to produce indexes for larger geographic areas a

item categories. For example, the aggregation weights are used to combine all 8,018 basic-leve

into higher level indexes such as the U.S. city average-all-items index. The product of an inde

weight is called a cost weight:
38

Every index has an aggregation weight AGGWT(A I, f) or 

AGGWT(A, I, r) associated with it, which is used to combine 

the index with other indexes to produce indexes for larger 

geographic areas and larger item categories. For example, the 

aggregation weights are used to combine all 8,018 basic-level 

indexes into higher level indexes such as the U.S. city average-all-items index. The product of an index and its weight is 

called a cost weight:

CW(A, I, f, t) = IX(A, I, f, t) × AGGWT(A,I, f, t).

A cost weight is an estimate of the total cost in area = A for 

consumption of item category I in month t. A replicate cost 

weight would be indexed with r instead of f. Because the aggregation weights are not indexed by time (except across pivot months; see section titled “Bridging across pivot months”), 

the preceding percent change formula is equivalent to

49

IX(A,I,f,t) denote the index value for area = A, item category = I, in month = t, where f indicates that it 

e full-sample value, and let IX(A,I,f,t – k) denote the value of the same index in month = t – k. The 

ercase letter A denotes a set of areas, such as the Northeast or Midwest region of the country, and the 

ercase letter I denotes a set of item strata, such as all items or all items less food and energy, or even a 

le item stratum. Also, let IX(A,I,r,t) and IX(A,I,r,t – k) be the corresponding index values for replicate

Most areas have two replicates, but some have more. Then the full-sample k-month percent change 

ween months t – k and t is computed by dividing IX(A,I,f,t) by IX(A,I,f,t – k), subtracting 1, and 

tiplying by 100:

– 1 100 . IX( , , , – )

IX( , , , ) ( , , , , – ) 













  A I f t k

A I f t PC A I f t t k

ry index has an aggregation weight AGGWT(A I, f) or AGGWT(A, I, r) associated with it, which is 

d to combine the index with other indexes to produce indexes for larger geographic areas and larger 

m categories. For example, the aggregation weights are used to combine all 8,018 basic-level indexes 

 higher level indexes such as the U.S. city average-all-items index. The product of an index and its 

ght is called a cost weight:

CW(A, I, f, t) = IX(A, I, f, t) × AGGWT(A,I, f, t)

ost weight is an estimate of the total cost in area = A for consumption of item category I in month t. A 

icate cost weight would be indexed with r instead of f. Because the aggregation weights are not indexed 

ime (except across pivot months; see section titled “Bridging across pivot months”), the preceding 

ent change formula is equivalent to

– 1 100 , CW( , , , – )

CW( , , , ) PC( , , , , – ) 













  A I f t k

A I f t A I f t t k

ch is equivalent to 

– 1 100 , CW( , , , – )

CW( , , , )

PC( , , , , – )

∑∑

∑∑

∈ ∈

∈ ∈ 























a A i I

a A i I

a i f t k

a i f t

A I f t t k

ause cost weights are additive from the lowest area-item level up to the highest U.S. city average-all

ms level. The lowercase letter a denotes one of the 38 basic-level areas included in area = A, and the 

ercase letter i denotes one of the 211 item categories. (Note: Item aggregation I can be as small as one 

m stratum or may comprise one or more major groups.) 

which is equivalent to 

– 1 100 , CW( , , , – )

CW( , , , )

PC( , , , , – ) ∑∑

∑∑

∈ ∈

∈ ∈ 























a I Ai

a I Ai

a i f t k

a i f t

A I f t t k

because cost weights are additive from the lowest area-item 

level up to the highest U.S. city average-all-items level. The 

lowercase letter a denotes one of the 38 basic-level areas included in area = A, and the lowercase letter i denotes one of 

the 211 item categories. (Note: Item aggregation I can be as 

small as one item stratum or may comprise one or more major 

groups.) 

For the stratified random groups method, a replicate percent change is defined as follows: At each item–area replicate 

level, the individual full sample cost weight, CW(a, i, f, •) is 

subtracted from the full sample cost weight CW(A, I, f, •), and 

a replicate cost weight, CW(a, i, r, •), is added back in. The 

replicate percent change for area = a, item subset = i, replicate 

= r between months t – k and t is then computed as follows:

50

For the stratified random groups method, a replicate percent change is defined as follows: At each item–

area replicate level, the individual full sample cost weight, CW(a, i, f, ), is subtracted from the full sample 

cost weight CW(A, I, f, ), and a replicate cost weight, CW(a, i, r, ), is added back in. The replicate percent 

change for area = a, item subset = i, replicate = r between months t – k and t is then computed as follows:

–1 100

CW( , , , – ) – CW( , , , – ) CW( , , , – )

CW( , , , ) – CW( , , , ) CW( , , , ) PC ( , , , , – ) 

















  A I f t k a i f t k a i r t kA I f t a i f t a i r t a i r t t k S

for self-representing areas. For non-self-representing areas, another replicate percent change for area = a, 

item category = I, replicate = r between months t – k and t is computed as

– 1 100 , CW( , , , – ) – CW( , , , – ) CW( , , , – )

CW( , , , ) – CW( , , , ) CW( , , , ) PC ( , , , , – )  

















  A I f t k a I f t k a I r t kA I f t a I f t a I r t a I r t t k N

where

CW( , , , ) ∑∑CW( , , , ). a∈A i∈IA I    a i  

The symbol α  A means that the sum is over all basic-level areas within area = A, and the symbol I  I

means that the sum is over major groups within item category = I. 

The variance is computed with the following stratified random groups variance estimation formula:

∑∑ ∑  ⊂ ⊂ ∩ 1

2

PC ( , , , , – ) – PC( , , , , – ) ( –1)

1 [PC( , , , , – )] i I a A S

R

r

S

a a

a

a i r t t k A I f t t k R R V A I f t t k 

PC ( , , , , – ) – PC ( , , , , – ) , ( –1) ∑ 1 ∑ ⊂ ∩ 1

2

a A N

R

r

N

a a

a

a I r t t k A I f t t k

R R 



where S and N are the sets of all self-representing and non-self-representing areas in the CPI’s geographic 

sample, respectively; and A  S and A  N are the sets of all self-representing and nonself-representing 

areas within area = A. The number Ra is the number of replicates in area = a.

When the item category I no longer spans more than one major group, the preceding formula reduces to

PC( , , , , – ) – PC( , , , , – ) . ( – 1)

1 [PC( , , , , – )] ∑ ∑1

2

⊂

Ra

a A a a r

a I r t t k A I f t t k R R V A I f t t k 

Variance estimation without replicates

BLS computes index series for 85 special (SRC) item categories, which are below the item stratum level 

and thus do not have accompanying replicate index values. (CE weights are produced only down to the 

item-stratum level in each index area.) The stratified random groups methodology requires a replicate 

structure. So, for these SRC items (such as butter or pork or new cars), an alternative variance estimation 

method is needed. Given the availability (at the regional and higher area levels) of independent estimates 

for these SRC items, the jackknife variance estimation methodology can be employed. Each area full 

for self-representing areas. For non-self-representing areas, another replicate percent change for area = a, item category = I, 

replicate = r between months t – k and t is computed as

50

For the stratified random groups method, a replicate percent change is defined as follows: At each item–

area replicate level, the individual full sample cost weight, CW(a, i, f, ), is subtracted from the full sample 

cost weight CW(A, I, f, ), and a replicate cost weight, CW(a, i, r, ), is added back in. The replicate percent 

change for area = a, item subset = i, replicate = r between months t – k and t is then computed as follows:

–1 100 CW( , , , – ) – CW( , , , – ) CW( , , , – )

CW( , , , ) – CW( , , , ) CW( , , , ) PC ( , , , , – ) 

















  A I f t k a i f t k a i r t kA I f t a i f t a i r t a i r t t k S

for self-representing areas. For non-self-representing areas, another replicate percent change for area = a, 

item category = I, replicate = r between months t – k and t is computed as

– 1 100 , CW( , , , – ) – CW( , , , – ) CW( , , , – )

CW( , , , ) – CW( , , , ) CW( , , , ) PC ( , , , , – )  

















  A I f t k a I f t k a I r t kA I f t a I f t a I r t a I r t t k N

where

CW( , , , ) ∑∑CW( , , , ). a∈A i∈IA I    a i  

The symbol α  A means that the sum is over all basic-level areas within area = A, and the symbol I  I

means that the sum is over major groups within item category = I. 

The variance is computed with the following stratified random groups variance estimation formula:

∑∑ ∑  ⊂ ⊂ ∩ 1

2

PC ( , , , , – ) – PC( , , , , – ) ( –1)

1 [PC( , , , , – )] i I a A S

R

r

S

a a

a

a i r t t k A I f t t k R R V A I f t t k 

PC ( , , , , – ) – PC ( , , , , – ) , ( –1) ∑ 1 ∑ ⊂ ∩ 1

2

a A N

R

r

N

a a

a

a I r t t k A I f t t k

R R 



where S and N are the sets of all self-representing and non-self-representing areas in the CPI’s geographic 

sample, respectively; and A  S and A  N are the sets of all self-representing and nonself-representing 

areas within area = A. The number Ra is the number of replicates in area = a.

When the item category I no longer spans more than one major group, the preceding formula reduces to

PC( , , , , – ) – PC( , , , , – ) . ( – 1)

1 [PC( , , , , – )] ∑ ∑1

2

⊂

Ra

a A a a r

a I r t t k A I f t t k R R V A I f t t k 

Variance estimation without replicates

BLS computes index series for 85 special (SRC) item categories, which are below the item stratum level 

and thus do not have accompanying replicate index values. (CE weights are produced only down to the 

item-stratum level in each index area.) The stratified random groups methodology requires a replicate 

structure. So, for these SRC items (such as butter or pork or new cars), an alternative variance estimation 

method is needed. Given the availability (at the regional and higher area levels) of independent estimates 

for these SRC items, the jackknife variance estimation methodology can be employed. Each area full 

where

The symbol means that the sum is over all basiclevel areas within area A, and the symbol means that the 

sum is over major groups within item category I. 

The variance is computed with the following stratified 

random groups variance estimation formula:

Table 3. Response rates for commodities and services for the Consumer Price Index, All Urban Consumers 

(CPI-U), U.S. city average, by major group, 2014

Commodities and 

services Eligible Collected Percent  collected

Used in 

estimation

Percent in 

estimation

Outlets 305,184 284,730 93.3 271,931 89.1

Total quotes 1,180,932 970,546 82.2 944,414 80.0

Food and beverages 465,398 418,060 89.8 410,615 88.2

Housing (less shelter) 146,466 125,300 85.5 121,502 83.0

Apparel 135,950 75,102 55.2 70,700 52.0

Transportation 149,721 134,956 90.1 131,796 88.0

Medical care 76,631 40,847 53.3 39,534 51.6

Recreation 84,227 68,427 81.2 65,064 77.2

Education and 

communication 78,164 68,872 88.1 67,039 85.8

Other goods and services 44,375 38,982 87.8 38,164 86.0

Source: U.S. Bureau of Labor Statistics.

α  A

50

For the stratified random groups method, a replicate percent change is defined as follows: At each item–

area replicate level, the individual full sample cost weight, CW(a, i, f, ), is subtracted from the full sample 

cost weight CW(A, I, f, ), and a replicate cost weight, CW(a, i, r, ), is added back in. The replicate percent 

change for area = a, item subset = i, replicate = r between months t – k and t is then computed as follows:

–1 100

CW( , , , – ) – CW( , , , – ) CW( , , , – )

CW( , , , ) – CW( , , , ) CW( , , , ) PC ( , , , , – ) 

















  A I f t k a i f t k a i r t kA I f t a i f t a i r t a i r t t k S

for self-representing areas. For non-self-representing areas, another replicate percent change for area = a, 

item category = I, replicate = r between months t – k and t is computed as

– 1 100 , CW( , , , – ) – CW( , , , – ) CW( , , , – )

CW( , , , ) – CW( , , , ) CW( , , , ) PC ( , , , , – )  

















  A I f t k a I f t k a I r t kA I f t a I f t a I r t a I r t t k N

where

CW( , , , ) ∑∑CW( , , , ). a∈A i∈IA I    a i  

The symbol α  A means that the sum is over all basic-level areas within area = A, and the symbol I  I

means that the sum is over major groups within item category = I. 

The variance is computed with the following stratified random groups variance estimation formula:

∑∑ ∑  ⊂ ⊂ ∩ 1

2

PC ( , , , , – ) – PC( , , , , – ) ( –1)

1 [PC( , , , , – )] i I a A S

R

r

S

a a

a

a i r t t k A I f t t k

R R

V A I f t t k 

PC ( , , , , – ) – PC ( , , , , – ) , ( –1) ∑ 1 ∑ ⊂ ∩ 1

2

a A N

R

r

N

a a

a

a I r t t k A I f t t k

R R 



where S and N are the sets of all self-representing and non-self-representing areas in the CPI’s geographic 

sample, respectively; and A  S and A  N are the sets of all self-representing and nonself-representing 

areas within area = A. The number Ra is the number of replicates in area = a.

When the item category I no longer spans more than one major group, the preceding formula reduces to

PC( , , , , – ) – PC( , , , , – ) . ( – 1)

1 [PC( , , , , – )] ∑ ∑1

2

⊂

Ra

a A a a r

a I r t t k A I f t t k

R R

V A I f t t k 

Variance estimation without replicates

BLS computes index series for 85 special (SRC) item categories, which are below the item stratum level 

and thus do not have accompanying replicate index values. (CE weights are produced only down to the 

item-stratum level in each index area.) The stratified random groups methodology requires a replicate 

structure. So, for these SRC items (such as butter or pork or new cars), an alternative variance estimation 

method is needed. Given the availability (at the regional and higher area levels) of independent estimates 

for these SRC items, the jackknife variance estimation methodology can be employed. Each area full 

i  I
39

where S and N are the sets of all self-representing and nonself-representing areas in the CPI’s geographic sample, respectively; and A ∩ S and A ∩ N are the sets of all self-representing and nonself-representing areas within area = A. The 

number Ra is the number of replicates in area = a.

When the item category I no longer spans more than one 

major group, the preceding formula reduces to

( ) PC ( , , , , – ) – PC ( , , , , – ) . ( – 1)

1

[PC( , , , , – )] =

∑ ∑=1

2

∈

Ra

a A a a r

a I r t t k A I f t t k

R R

V A I f t t k

Variance estimation without replicates

BLS computes index series for 85 special (SRC) item categories, which are below the item stratum level and thus do not 

have accompanying replicate index values. (CE weights are 

produced only down to the item-stratum level in each index 

area.) The stratified random groups methodology requires a 

replicate structure. So, for these SRC items (such as butter or 

pork or new cars), an alternative variance estimation method 

is needed. Given the availability (at the regional and higher 

area levels) of independent estimates for these SRC items, 

the jackknife variance estimation methodology can be employed. Each area full sample cost weight can be subtracted 

from the all-area full sample cost weight to provide a jackknife replicate estimate. By taking the ratio of these replicate 

cost weight estimates at times t and t – k, subtracting 1, and 

multiplying by 100, one obtains the required jackknife replicate percent change value. (For the U.S. city average special 

item estimates, there are 38 independent index areas, and so 

38 jackknife replicate estimates to work with.) 

The full-sample percent change is computed as before (except that item category = I here is smaller even than an item 

stratum):

– 1 100 . CW( , , , - )

CW( , , , ) PC ( , , , , – ) 













  A I f t k

A I f t A I f t t k

The jackknife replicate percent change is computed as follows:

–1 100 . CW( , , , – ) – CW( , , , – )

CW( , , , ) – CW( , , , ) PC ( – , , , , – )  













  A I f t k a I f t k

A I f t a I f t A a I r t t k

Then the variance for the k-month percent change is computed in the usual jackknife form:

( ) PC ( – , , , , – ) – PC ( , , , , – ) . –1

[PC ( , , , , – )] = ∑∈

2

A a A

A

A a I r t t k A I f t t k

N

N

V A I f t t k

Bridging across pivot months

Every 2 years, BLS updates its set of aggregation index 

weights based on CE data collected from the t – 2 and t – 3 

years. In January 2012, BLS replaced its old set of aggregation weights with a new 2-year set of weights from expenditure data collected in 2009–2010. In January 2014, this set 

of weights was replaced by an updated set of weights from 

expenditure data collected in 2011–2012, and so on.

Whenever the variance estimates cross the pivot month (as 

they did in December 2011 and December 2013), a bridging 

factor has to be introduced into any variance calculation that 

crosses the pivot month anywhere between t and t – k months 

(including month t – k, but not including month t). The bridg-

∑∑ ∑  ⊂ ∩ ⊂ 1

2

PC ( , , , , – ) – PC( , , , , – ) ( –1)

1

[PC( , , , , – )]

i S I a A

R

r

S

a a

a

a i r t t k A I f t t k

R R

V A I f t t k





  PC ( , , , , – ) – PC ( , , , , – ) , ( –1) ∑ ∑ 1

⊂ ∩ 1

2

a A N

R

r

N

a a

a

a I r t t k A I f t t k

R R 


40

ing factor is then applied directly to the individual ratio of 

cost weights, for both full-sample and replicate values, inside 

each percent change calculation. Thus, in its most general 

form,

1 100

CW( , , ,new)

CW( , , ,old)

CW( , , , )

CW( , , , ) PC ( , , , , )  













    

               t kt

t t k

for every combination of area and item, and for full-sample 

and replicate values, with the bridging factor defaulting to 1 

whenever not applicable.

The bridging factor, CW(•,•,•, old)/CW((•,•,•, new), essentially allows the old aggregation weight in the bridge’s numerator to cancel out the old aggregation weight in the t – k

cost weight, while the new aggregation weight in the bridge’s 

denominator cancels out the new aggregation weight in the 

t cost weight, leaving IX(•,•,•,t)/X(•,•,•,t – k) free to move 

this level’s percent change without disruption. Note that 

IX(•,•,•,old)/IX(•,•,•,new) = 1 at all times.

Nonsampling error

CPI estimates are subject to nonsampling error as well 

as sampling error. Surveys involve many operations, all of 

which are potential sources of nonsampling error. The errors 

arise from the survey process, regardless of whether the data 

are collected from the entire universe or from a sample of 

the population. The most general categories of nonsampling 

error are coverage error, nonresponse error, response error, 

processing error, and estimation error. 

Coverage error in an estimate results from the omission of 

part of the target population (undercoverage) or the inclusion 

of units from outside of the target population (overcoverage). 

Coverage errors result from the omission of cities, households, outlets, and items that are part of the target populations from the relevant sampling frames or from their double-counting or improper inclusion in the frames. A potential 

source of coverage error is the time lag between the TPOPS 

and the initiation of price collection for commodities and services at sampled outlets. Because of the time lag, the products offered by the outlet at the time pricing is initiated may 

not coincide with the set from which the TPOPS respondents 

were purchasing. 

Nonresponse error results when data are not collected for 

some sampled units because of the failure to interview households or outlets. This can occur when selected households 

and outlets cannot be contacted or refuse to participate in the 

survey. Nonresponse rates during monthly pricing for the CPI 

C&S and housing surveys are shown in tables 3 (page 38) and 

4 (page 40).

Table 4. Response rates for shelter for the Consumer 

Price Index, All Urban Consumers (CPI-U), U.S. city 

average, 2014

Shelter Eligible

Collected, 

data 

reported

No data at 

collection 

or other

Number of total 

units 99,383 72,966 26,417

Percentage of 

eligible units 100 73.4 26.6

Source: U.S. Bureau of Labor Statistics.

Response error results from the collection and use in estimation of incorrect, inconsistent, or incomplete data. Response 

error may arise because of the collection of data from inappropriate respondents, respondent memory or recall errors, 

deliberate distortion of responses, interviewer effects, misrecording of responses, pricing of wrong items, misunderstanding or misapplication of data collection procedures, or 

misunderstanding of the survey needs and/or lack of cooperation from respondents. The pricing methodology in the 

commodities and services component of the CPI allows the 

previous period’s price to be available at the time of collection. This dependent pricing methodology is believed to reduce response variance for measuring change, but may cause 

response bias and lag. The housing component of the CPI 

employs an independent pricing methodology specifically to 

avoid potential response bias. 

Processing error arises from incorrect editing, coding, and 

data transfer. Price data are collected by CADC. Automated 

data checking ensures that only correct data types are collected; other automated logic checks remove all redundant 

question patterns, and the instrument informs the field staff 

when not all required data have been collected. In both systems, errors also can result from software problems in the 

computer processing that cause correctly entered data to be 

lost. Computer screening and professional review of the data 

provide checks on processing accuracy. Occasional studies 

of these processing errors in the CPI have shown them to be 

extremely small. 

Estimation error results when the survey process does not 

accurately measure what is intended. Such errors may be 

conceptual or procedural in nature, arising from a misunderstanding of the underlying survey measurement concepts or a 

misapplication of rules and procedures. 

Substitutions and adjustments for quality change in the items 

priced for the CPI are possible sources of estimation error due 

to procedural difficulties. Ideally, CPI data collection forms 
41

and procedures would yield all information necessary to determine or explain price and quality differences for all items 

defined within an ELI. Because such perfect information is 

not available, BLS economists supplement directly collected data with secondary data. Estimation error will result, if 

the BLS adjustment process—which may require significant 

judgment or lack key data—is misapplied, or if it consistently 

overestimates or underestimates quality change for particular 

kinds of items. 

The effect of the aging of housing units is an example of 

potential estimation error, which is similar to the issue of 

quality change in commodities and services. Until 1988, BLS 

did not adjust for the slow depreciation of houses and apartments over time. BLS research indicates that annual changes 

for the residential rent and owners’ equivalent rent indexes 

would have been 0.1 to 0.2 percent larger if some type of aging adjustment had been included. 

The total nonsampling error of the CPI results from errors 

in the type of data collected, the methods of collection, the 

data processing routines, and the estimation processes. The 

cumulative nonsampling error can be much greater than the 

sampling error. 

Response rates

Response rates are calculated for the CPI at the data collection phase and at the index estimation phase for ongoing pricing. The response rate at the data collection phase is the number 

of responding sample units divided by the sum of (1) the number of eligible sample units and (2) the number of sample units 

with eligibility not determined. A sample unit is eligible if it 

belongs to the defined target population and responses should 

be collected from the unit for one or more items. The response 

rate at estimation is defined as the number of sample units used 

in estimation divided by the sum of (1) the number of eligible 

sample units and (2) the number of sample units with eligibility 

not determined. 

Commodities and services items (any except rent and owner’s equivalent rent) are further broken down into outlets and 

quotes. An “outlet” is a generic term used to describe places 

where prices are collected. A “quote” is a specific item to be 

priced in a specific outlet. There may be from 1 to more than 

50 quotes priced in an outlet. Table 3 shows the relatively low 

percentages of quotes reported collected and used in estimation 

for apparel. Low rates for these items largely can be attributed 

to the design of the apparel sample. Because apparel items are 

commonly in stores only at certain times of the year, most of the 

apparel sample is doubled, with each half of the sample designated for pricing during part of the year. Thus, at any particular time of the year many apparel quotes, although eligible, are 

designated “out of season,” and prices are not collected. For additional information, see the earlier section on seasonal items. 

The response rates for housing (shelter) shown in table 4 include categories for renters only. Owners are out of scope for 

the CPI housing sample. A unit qualifies as renter if its tenure 

status is known either by previous knowledge or is collected in 

the current interview period. The response rates at the data collection phase for housing (shelter) are separated into three categories. If usable information is obtained, the unit is designated 

eligible and data reported. If the assigned unit is located but is 

unoccupied, the unit is designated “eligible, found vacant.” In 

instances where the unit is eligible but no data are available (for 

example refusals), the unit is designated “eligible, other.” The 

response rates at the estimation phase are units that are used in 

either rent or REQ.

Aizcorbe, Ana M., and Patrick C. Jackman. “The commodity 

substitution effect in CPI data, 1982–91.” Monthly Labor Review, December 1993, pp. 25–33. http://www.

bls.gov/opub/mlr/1993/12/art3full.pdf.

Cage, Robert. “New methodology for selecting outlet samples.” Monthly Labor Review, December 1996, pp. 

49–61. http://www.bls.gov/opub/mlr/1996/12/art7full.pdf. 

Cardenas, Elaine M. “Revision of the CPI hospital services 

component.” Monthly Labor Review, December 1996, 

pp, 40–48. http://www.bls.gov/opub/mlr/1996/12/

art6full.pdf. The Conference Board. Measuring 

Prices in a Dynamic Economy: Re-examining the CPI. 

New York: The Conference Board, 1999. 

Dalton, Kenneth V., John S. Greenlees, and Kenneth J. Stewart. “Incorporating a geometric mean formula into the 

CPI.” Monthly Labor Review, October 1998, pp. 3–7. 

http://www.bls.gov/opub/mlr/1998/10/art1full.pdf. 

Diewert, W. E., and A. O. Nakamura, eds. Essays in index 

number theory, vol. 1. Amsterdam: North-Holland 

Publishing Co., 1993. 

Technical references
42

Final report of the advisory commission to study the Consumer Price Index. U.S. Senate, Committee on 

Finance, 104th Cong., 2d sess., 1996. 

Fixler, Dennis, Charles Fortuna, John Greenlees, and Walter Lane. “The use of hedonic regressions to handle 

quality change: the experience of the U.S. CPI.” Paper presented at the Fifth Meeting of the International 

Working Group on Price Indices, Reykjavik, Iceland, 

August. 26, 1999. 

Gillingham, Robert. “A conceptual framework for the Consumer Price Index.” Proceedings of the American 

Statistical Association, Business and Economics 

Section. Alexandria, VA: American Statistical Association, 1974, pp. 46–52.

Gillingham, Robert F. “Measuring the cost of shelter for 

homeowners: theoretical and empirical considerations.” The Review of Economics and Statistics, vol. 

65, no. 2, May 1983, pp. 254–265. 

Grandits, Steven. “Publication strategy for the 1998 revised 

Consumer Price Index.” Monthly Labor Review, December 1996, pp. 26–30. http://www.bls.gov/opub/

mlr/1996/12/art4full.pdf. 

Greenlees, John S., and Charles C. Mason. “Overview of the 

1998 revision of the Consumer Price Index.” Monthly 

Labor Review, December 1996, pp. 3–9. http://www.

bls.gov/opub/mlr/1996/12/art1full.pdf. 

Greenlees, John S., and Robert B. McClelland. “Addressing 

misconceptions about the Consumer Price Index.” 

Monthly Labor Review, August 2008, pp. 3–17. http://

www.bls.gov/opub/mlr/2008/08/art1full.pdf. 

Lane, Walter F. “Changing the item structure of the Consumer Price Index.” Monthly Labor Review, December 1996, pp. 18–25. http://www.bls.gov/opub/

mlr/1996/12/art3full.pdf. 

Leaver, S. G., and others. “Sample redesign for the introduction of the telephone point-of-purchase survey frames 

in 

the commodities and services component of the U.S. Consumer Price Index.” Proceedings of the American Statistical Association, Government Statistics and Social 

Statistics sections. Alexandria, VA: American Statistical Association, 1999, pp. 292–297. 

Leaver, Sylvia, and Richard Valliant. “Statistical problems in 

estimating the U.S. Consumer Price Index.” In Brenda G. Cox et al., eds. Business Survey Methods. New 

York: John Wiley & Sons, Inc., 1995. 

Liegey, Paul. “Hedonic quality adjustments in the U.S. CPI: 

a statistical agency perspective.” Paper presented 

at Center for European Economic Research conference, “Price indices and the measurement of quality 

change,” University of Mannheim, Germany, April 

25–26, 2002. 

Moulton, Brent R. “Basic components of the CPI: estimation of price changes.” Monthly Labor Review, December 1993, pp. 13–24. http://www.bls.gov/opub/

mlr/1993/12/art2full.pdf.

Moulton, Brent R., and Karin E. Moses. “Addressing the 

quality exchange issue in the Consumer Price Index.” 

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The Brookings Institution, 1997.

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Ptacek, Frank, and Robert M. Baskin. “Revision of the CPI 

housing sample and estimators.” Monthly Labor Review, December 1996, pp. 31–39. http://www.bls.gov/

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Price? Conceptualizing and measuring cost-of-living 

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pp. 200–205. 
43

Appendix 1. List of published indexes

CPI-U and CPI-W Indexes Published at the U.S. City Average (National) Level

All items

Food and beverages

 Food

 Food at home

 Cereals and bakery products

 Cereals and cereal products

 Flour and prepared flour mixes

 Breakfast cereal

 Rice, pasta, cornmeal

 Rice*

 Bakery products

 Bread

 White bread*

 Bread other than white*

 Fresh biscuits, rolls, muffins

 Cakes, cupcakes, and cookies

 Fresh cakes and cupcakes*

 Cookies*

 Other bakery products

 Fresh sweetrolls, coffeecakes, doughnuts*

 Crackers, bread, and cracker products*

 Frozen and refrigerated bakery products, pies, tarts, turnovers*

 Meats, poultry, fish, and eggs

 Meats, poultry, and fish

 Meats

 Beef and veal

 Uncooked ground beef

 Uncooked beef roasts

 Uncooked beef steaks

 Uncooked other beef and veal

 Pork

 Bacon, breakfast sausage, and related products

 Bacon and related products*

 Breakfast sausage and related products*

 Ham

 Ham, excluding canned*

 Pork chops

 Other pork, including roasts and picnics

 Other meats

 Frankfurters*

 Lunchmeats*

 Lamb and organ meats*

 Lamb and mutton*

 Poultry

 Chicken

 Fresh whole chicken*

 Fresh and frozen chicken parts*

 Other poultry, including turkey

 Fish and seafood

 Fresh fish and seafood

 Processed fish and seafood

 Shelf stable fish and seafood*

 Frozen fish and seafood*
44

Appendix 1. List of published indexes—continued

 Eggs

 Dairy and related products

 Milk

 Fresh whole milk*

 Fresh milk other than whole*

 Cheese and related products

 Ice cream and related products

 Other dairy and related products

 Fruits and vegetables

 Fresh fruits and vegetables

 Fresh fruits

 Apples

 Bananas

 Citrus fruits

 Oranges, including tangerines*

 Other fresh fruits

 Fresh vegetables

 Potatoes

 Lettuce

 Tomatoes

 Other fresh vegetables

 Processed fruits and vegetables

 Canned fruits and vegetables

 Canned fruits*

 Canned vegetables*

 Frozen fruits and vegetables

 Frozen vegetables*

 Other processed fruits and vegetables, including dried

 Dried beans, peas, and lentils*

 Nonalcoholic beverages and beverage materials

 Juices and nonalcoholic drinks

 Carbonated drinks

 Frozen noncarbonated juices and drinks

 Nonfrozen noncarbonated juices and drinks

 Beverage materials, including coffee and tea

 Coffee

 Roasted coffee*

 Instant and freeze-dried coffee*

 Other beverage materials, including tea

 Other food at home

 Sugar and sweets

 Sugar and artificial sweeteners

 Candy and chewing gum

 Other sweets

 Fats and oils

 Butter and margarine

 Butter*

 Margarine*

 Salad dressing

 Other fats and oils, including peanut butter

 Peanut butter*

 Other foods

 Soups

 Frozen and freeze dried prepared foods

 Snacks
45

Appendix 1. List of published indexes—continued

 

 Spices, seasonings, condiments, sauces

 Salt and other seasonings and spices*

 Olives, pickles, relishes*

 Other condiments

 Baby food

 Other miscellaneous foods

 Prepared salads

 Food away from home

 Full service meals and snacks

 Limited service meals and snacks

 Food at employee sites and schools

 Food at elementary and secondary schools

 Food from vending machines and mobile vendors

 Other food away from home

 Alcoholic beverages

 Alcoholic beverages at home

 Beer, ale, and other malt beverages at home

 Distilled spirits at home

 Whiskey at home*

 Distilled spirits, excluding whiskey, at home*

 Wine at home

 Alcoholic beverages away from home

 Beer, ale, and other malt beverages away from home*

 Wine away from home*

 Distilled spirits away from home*

Housing

 Shelter

 Rent of primary residence

 Lodging away from home

 Housing at school, excluding board

 Other lodging away from home, including hotels and motels

 Owners’ equivalent rent of residences

 Owner’s equivalent rent of primary residence

 Tenants’ and household insurance

 Fuels and utilities

 Household energy

 Fuel oil and other household fuels

 Fuel oil

 Propane, kerosene, firewood

 Energy services

 Electricity

 Utility (piped) gas service

 Water and sewer and trash collection services

 Water and sewerage maintenance

 Garbage and trash collection

 Household furnishings and operations

 Window and floor coverings and other linens

 Floor coverings

 Window coverings

 Other linens

 Furniture and bedding

 Bedroom furniture

 Living room, kitchen, and dining room furniture

 Other furniture

 Infants’ furniture
46

Appendix 1. List of published indexes—continued

 Appliances

 Major appliances

 Laundry equipment*

 Other appliances

 Other household equipment and furnishings

 Clocks, lamps, and decorator items

 Indoor plants and flowers

 Dishes and flatware

 Nonelectric cookware and tableware

 Tools, hardware, outdoor equipment and supplies

 Tools, hardware and supplies

 Outdoor equipment and supplies

 Housekeeping supplies

 Household cleaning products

 Household paper products

 Miscellaneous household products

 Household operations

 Domestic services

 Gardening and lawn care services

 Moving, storage, freight expense

 Repair of household items

Apparel

 Men’s and boys’ apparel

 Men’s apparel

 Men’s suits, sport coats, and outerwear

 Men’s furnishings

 Men’s shirts and sweaters

 Men’s pants and shorts

 Boys’ apparel

 Women’s and girls’ apparel

 Women’s apparel

 Women’s outerwear

 Women’s dresses

 Women’s suits and separates

 Women’s underwear, nightwear, sportswear and accessories

 Girls’ apparel

 Footwear

 Men’s footwear

 Boys’ and girls’ footwear

 Women’s footwear

 Infants’ and toddlers’ apparel

 Jewelry and watches

 Watches

 Jewelry

Transportation

 Private transportation

 New and used motor vehicles

 New vehicles

 New cars and trucks*

 New cars*

 New trucks*

 Used cars and trucks

 Leased cars and trucks

 Car and truck rental
47

Appendix 1. List of published indexes—continued

 

 Motor fuel

 Gasoline (all types)

 Gasoline, unleaded regular*

 Gasoline, unleaded midgrade*

 Gasoline, unleaded premium*

 Other motor fuels

 Motor vehicle parts and equipment

 Tires

 Vehicle accessories other than tires

 Vehicle parts and equipment other than tires*

 Motor oil, coolant, and fluids*

 Motor vehicle maintenance and repair

 Motor vehicle body work

 Motor vehicle maintenance and servicing

 Motor vehicle repair

 Motor vehicle insurance

 Motor vehicle fees

 State and local registration and license fees

 Parking and other fees

 Parking fees and tolls*

 Automobile service clubs*

 Public transportation

 Airline fare

 Other intercity transportation

 Intercity bus fare*

 Intercity train fare*

 Ship fare*

 Intracity transportation

 Intracity mass transit*

Medical care

 Medical care commodities

 Medicinal drugs

 Prescription drugs

 Nonprescription drugs

 Medical equipment and supplies

 Medical care services

 Professional services

 Physicians’ services

 Dental services

 Eyeglasses and eye care

 Services by other medical professionals

 Hospital and related services

 Hospital services

 Inpatient hospital services*

 Outpatient hospital services*

 Nursing homes and adult day services

 Care of invalids and elderly at home

 Health insurance

Recreation

 Video and audio

 Televisions

 Cable and satellite television and radio service

 Other video equipment
48

Appendix 1. List of published indexes—continued

 

 Video discs and other media, including rental of video and audio

 Video discs and other media

 Rental of video or audio discs and other media

 Audio equipment

 Audio discs, tapes, and other media

 Pets, pet products and services

 Pets and pet products

 Pet food*

 Purchase of pets, pet supplies, accessories*

 Pet services, including veterinary

 Pet services*

 Veterinarian services*

 Sporting goods

 Sports vehicles, including bicycles

 Sports equipment

 Photography

 Photographic equipment and supplies

 Film and photographic supplies*

 Photographic equipment*

 Photographers and film processing

 Photographer fees*

 Film processing*

 Other recreational goods

 Toys

 Toys, games, hobbies and playground equipment*

 Sewing machines, fabric and supplies

 Music instruments and accessories

 Other recreation services

 Club dues and fees for participant sports and group exercises

 Admissions

 Admission to movies, theaters, and concerts*

 Admission to sporting events*

 Fees for lessons or instructions

 Recreational reading materials

 Newspapers and magazines

 Recreational books

Education and communication

 Education

 Educational books and supplies

 College textbooks*

 Tuition, other school fees, and childcare

 College tuition and fees

 Elementary and high school tuition and fees

 Child care and nursery school

 Technical and business school tuition and fees

 Communication

 Postage and delivery services

 Postage

 Delivery services

 Information and information processing

 Telephone services

 Wireless telephone services

 Land-line telephone services
49

Appendix 1. List of published indexes—continued

 Information technology, hardware, and services

 Personal computers and peripheral equipment

 Computer software and accessories

 Internet services and electronic information providers

 Telephone hardware, calculators, and other consumer information items

Other goods and services

 Tobacco and smoking products

 Cigarettes

 Tobacco products other than cigarettes

 Personal care

 Personal care products

 Hair, dental, shaving, and miscellaneous personal care products

 Cosmetics, perfume, bath, nail preparations and implements

 Personal care services

 Haircuts and other personal care services

 Miscellaneous personal services

 Legal services

 Funeral expenses

 Laundry and dry cleaning services

 Apparel services other than laundry and dry cleaning

 Financial services

 Checking account and other bank services*

 Tax return preparation and other accounting fees*

 Miscellaneous personal goods

 Stationery, stationery supplies, gift wrap*

 Infants’ equipment*

Special aggregate indexes

All items—old base

All items less energy

All items less food

All items less food and energy

All items less medical care

All items less shelter

All items less food and shelter

All items less food, shelter, and energy

All items less food, shelter, energy, and used cars and trucks

Apparel less footwear

Commodities

Commodities less food

Commodities less food and beverages

Commodities less food and energy commodities

Commodities less food, energy, and used cars and trucks

Domestically produced farm food

Durables

Education and communication commodities

Education and communication services

Energy

Energy commodities

Household furnishings and supplies

Information technology commodities

Nondurables

Nondurables less food

Nondurables less food and apparel

Nondurables less food and beverages
50

Appendix 1. List of published indexes—continued 

Nondurables less food, beverages, and apparel

Other goods

Other personal services

Other services

Recreation commodities

Recreation services

Rent of shelter

Services

Services less energy services

Services less medical care services

Services less rent of shelter

Transportation commodities less motor fuel

Transportation services

Utilities and public transportation

Video and audio products

Video and audio services

Purchasing power of the consumer dollar

Purchasing power of the consumer dollar–old base

* Special index based on a substantially smaller sample.
51

Appendix 1. List of published indexes—continued

CPI-U and CPI-W Indexes Published at the Regional City-Size Class and Local Area Levels

All items

Food and beverages

 Food

 Food at home

 Cereal and bakery products

 Meats, poultry, fish, and eggs

 Dairy and related products

 Fruits and vegetables

 Nonalcoholic beverages and beverage materials

 Other food at home

 Food away from home

 Alcoholic beverages

Housing

 Shelter

 Rent of primary residence

 Owners’ equivalent rent of residences

 Owner’s equivalent rent of primary residence

 Fuels and utilities

 Household energy

 Energy services

 Electricity

 Utility (piped) gas service

 Household furnishings and operations

Apparel

Transportation

 Private transportation

 New and used motor vehicles

 New vehicles

 Used cars and trucks

 Motor fuel

 Gasoline (all types)

 Gasoline, unleaded regular*

 Gasoline, unleaded midgrade*

 Gasoline, unleaded premium*

 Motor vehicle insurance

Medical care

Recreation

Education and communication

 Tuition, other school fees, and childcare

Other goods and services

Special Aggregate indexes

All items—old base

All items less energy

All items less food and energy

All items less medical care

All items less shelter

Commodities

Commodities less food

Commodities less food and beverages

Durables

Education and communication commodities

Education and communication services

Energy

Household furnishings and supplies
52

Appendix 1. List of published indexes—continued 

Nondurables

Nondurables less food

Nondurables less food and beverages

Other goods

Other personal services

Services

Services less medical care services

Services less rent of shelter

Transportation commodities less motor fuel

* Special index based on a substantially smaller sample.

C-CPI-U Indexes Published at the U.S. City Average (National) Level

(The C-CPI-U is issued for national averages only, and employs a December 1999 = 100 reference base.)

All items

Food and beverages

 Food

 Food at home

 Food away from home

 Alcoholic beverages

Housing

 Shelter

 Fuels and utilities

 Household furnishings and operations

Apparel

Transportation

 Private transportation

 Public transportation

Medical care

 Medical care commodities

 Medical care services

Recreation

Education and communication

 Education

 Communication

Other goods and services

Special aggregate indexes

All items less food and energy

Commodities

Durables

Energy

Nondurables

Services
53

Appendix 1. List of published indexes—continued

CPI-U and CPI-W Indexes Published at the National City-Size Class

All items

Food and beverages

 Food

 Food at home

 Food away from home

 Alcoholic beverages

Housing

 Shelter

 Rent of primary residence

 Owners’ equivalent rent of residences

 Owner’s equivalent rent of primary residence

 Fuels and utilities

 Household energy

 Energy services

 Electricity

 Utility (piped) gas service

 Household furnishings and operations

Apparel

Transportation

 Private transportation

 New and used motor vehicles

 New vehicles

 New cars and trucks*

 New cars*

 Used cars and trucks

 Motor fuel

 Gasoline (all types)

 

 Gasoline, unleaded regular*

 Gasoline, unleaded midgrade*

 Gasoline, unleaded premium*

Medical care

 Medical care commodities

 Medical care services

 Professional services

Recreation

Education and communication

Other goods and services

Special aggregate indexes

All items—old base

All items less energy

All items less food

All items less food and energy
54

Appendix 1. List of published indexes—continued

All items less medical care

All items less shelter

Commodities

Commodities less food

Commodities less food and beverages

Commodities less food and energy commodities

Durables

Education and communication commodities

Education and communication services

Energy

Energy commodities

Household furnishings and supplies

Nondurables

Nondurables less food

Nondurables less food and apparel

Nondurables less food and beverages

Nondurables less food, beverages, and apparel

Other goods

Other personal services

Other services

Recreation commodities

Recreation services

Rent of shelter

Services

Services less energy services

Services less medical care services

Services less rent of shelter

Transportation commodities less motor fuel

Transportation services

* Special index based on a substantially smaller sample.
55

Appendix 2. List of average retail price series published by the Bureau of Labor Statistics 

(“X” indicates published)

Title United 

States Region Population size class Area

Energy 

Utility natural gas per therm X X X X

Fuel oil #2 per gallon (3.785 liters) X X X

Electricity per kWh X X X X

Gasoline, all types, per gallon/3.785 liters X X X X

Gasoline, unleaded regular, per gallon/3.785 liters X X X X

Gasoline, unleaded midgrade, per gallon/3.785 liters X X X X

Gasoline, unleaded premium, per gallon/3.785 liters X X X X

Automotive diesel fuel, per gallon/3.785 liters X X X

Cereals and bakery products

Flour, white, all purpose, per lb (453.6 gm) X X

Rice, white, long grain, uncooked, per lb (453.6 gm) X X

Spaghetti and macaroni, per lb (453.6 gm) X X

Bread, white, pan, per lb (453.6 gm) X X

Bread, French, per lb (453.6 gm) X X

Bread, whole wheat, pan, per lb (453.6 gm) X X

Cookies, chocolate chip, per lb (453.6 gm) X X

Crackers, soda, salted, per lb (453.6 gm) X X

Beef and veal 

Ground chuck, 100% beef, per lb (453.6 gm) X X

Ground beef, 100% beef, per lb (453.6 gm) X X

Ground beef, lean and extra lean, per lb (453.6 gm) X X

All uncooked ground beef, per lb (453.6 gm) X X

Chuck roast, USDA Choice, bone-in, per lb (453.6 gm) X X

Chuck roast, graded and ungraded, excluding USDA Prime and 

Choice, per lb (453.6 gm)

X X

Chuck roast, USDA Choice, boneless, per lb (453.6 gm) X X

Round roast, USDA Choice, boneless, per lb (453.6 gm) X X

Round roast, graded and ungraded, excluding USDA Prime and 

Choice, per lb (453.6 gm)

X X

Rib roast, USDA Choice, bone-in, per lb (453.6 gm)1 X X

All uncooked beef roasts, per lb (453.6 gm) X X

Steak, T-bone, USDA Choice, bone-in, per lb (453.6 gm)2 X X

Steak, rib eye, USDA Choice, boneless, per lb (453.6 gm)3 X X

Steak, round, USDA Choice, boneless, per lb (453.6 gm) X X

Steak, round, graded and ungraded, excluding USDA Prime and 

Choice, per lb (453.6 gm)

X X

Steak, sirloin, USDA Choice, bone-in, per lb (453.6 gm)4 X X
56

Title United 

States Region Population size class Area

Steak, sirloin, graded and ungraded, excluding USDA Prime and 

Choice, per lb (453.6 gm)

X X

Steak, sirloin, USDA Choice, boneless, per lb (453.6 gm) X X

Short ribs, any primal source, bone-in, per lb (453.6 gm) X X

Beef for stew, boneless, per lb (453.6 gm) X X

All uncooked beefsteaks, per lb (453.6 gm) X X

All uncooked other beef (excluding veal), per lb (453.6 gm) X X

Pork and other meats

Bacon, sliced, per lb (453.6 gm) X X

Chops, center cut, bone-in, per lb (453.6 gm) X X

Chops, boneless, per lb (453.6 gm) X X

All pork chops, per lb (453.6 gm) X X

Ham, rump, or shank half, bone-in, smoked, per lb (453.6 gm) X X

Ham, boneless, excluding canned, per lb (453.6 gm) X X

All ham (excluding canned ham and luncheon slices), per lb (453.6 

gm)

X X

Ham, canned, 3 or 5 lbs, per lb (453.6 gm) X X

Shoulder picnic, bone-in, smoked, per lb (453.6 gm) X X

All other pork (excluding canned ham and luncheon slices), per lb 

(453.6 gm)

X X

Sausage, fresh, loose, per lb (453.6 gm) X X

Frankfurters, all meat or all beef, per lb (453.6 gm) X X

Bologna, all beef or mixed, per lb (453.6 gm) X X

Lamb and mutton, bone-in, per lb (453.6 gm) X X

Poultry, fish, and eggs 

Chicken, fresh, whole, per lb (453.6 gm) X X

Chicken breast, bone-in, per lb (453.6 gm) X X

Chicken breast, boneless, per lb (453.6 gm) X X

Chicken legs, bone-in, per lb (453.6 gm) X X

Turkey, frozen, whole, per lb (453.6 gm) X X

Tuna, light, chunk, per lb (453.6 gm) X X

Eggs, grade A, large, per doz X X

Eggs, grade AA, large, per doz X X

Dairy products 

Milk, fresh, whole, fortified, per 1⁄2 gal (1.9 lit) X X

Milk, fresh, whole, fortified, per gal (3.8 lit) X X

Milk, fresh, low fat, per 1⁄2 gal (1.9 lit) X X

Appendix 2. List of average retail price series published by the Bureau of Labor Statistics 

(“X” indicates published)—continued 
57

Appendix 2. List of average retail price series published by the Bureau of Labor Statistics 

(“X” indicates published)—continued 

Title United 

States Region Population size class Area

Milk, fresh, low fat, per gal (3.8 lit) X X

Butter, salted, grade AA, stick, per lb (453.6 gm) X X

American processed cheese, per lb (453.6 gm) X X

Cheddar cheese, natural, per lb (453.6 gm) X X

Ice cream, prepackaged, bulk, regular, per 1⁄2 gal (1.9 lit) X X

Yogurt, natural, fruit flavored, per 8 oz (226.8 gm) X X

Fresh fruits and vegetables 

Apples, Red Delicious, per lb (453.6 gm) X X

Bananas, per lb (453.6 gm) X X

Oranges, Navel, per lb (453.6 gm) X X

Oranges, Valencia, per lb (453.6 gm) X X

Cherries, per lb (453.6 gm) X X

Grapefruit, per lb (453.6 gm) X X

Grapes, Thompson, Seedless, per lb (453.6 gm) X X

Lemons, per lb (453.6 gm) X X

Peaches, per lb (453.6 gm) X X

Pears, Anjou, per lb (453.6 gm) X X

Strawberries, dry pint, per 12 oz (340.2 gm) X X

Potatoes, white, per lb (453.6 gm) X X

Lettuce, iceberg, per lb (453.6 gm) X X

Lettuce, romaine, per lb (453.6 gm) X X

Tomatoes, field grown, per lb (453.6 gm) X X

Broccoli, per lb (453.6 gm) X X

Cabbage, per lb (453.6 gm) X X

Carrots, short trimmed and topped, per lb (453.6 gm) X X

Celery, per lb (453.6 gm) X X

Corn on the cob, per lb (453.6 gm) X X

Cucumbers, per lb (453.6 gm) X X

Onions, dry yellow, per lb (453.6 gm) X X

Peppers, sweet, per lb (453.6 gm) X X

Processed fruits and vegetables 

Applesauce, any variety, all sizes, per lb (453.6 gm) X X

Orange juice, frozen concentrate, 12 oz can, per 16 oz (473.2 mL) X X

Beans, dried, any type, all sizes, per lb (453.6 gm) X X

Corn, canned, any style, all sizes, per lb (453.6 gm) X X

Potatoes, frozen, French fried, per lb (453.6 gm) X X

Peaches, any variety, all sizes, per lb X X
58

Title United 

States Region Population size class Area

Other food items

Sugar, white, all sizes, per lb (453.6 gm) X X

Sugar, white, 33–80 oz pkg, per lb (453.6 gm) X X

Margarine, stick, per lb (453.6 gm) X X

Margarine, soft, tubs, per lb (453.6 gm) X X

Shortening, vegetable oil blends, per lb (453.6 gm) X X

Peanut butter, creamy, all sizes, per lb (453.6 gm) X X

Cola, nondiet, cans, 72-oz 6 pk., per 16 oz (473.2 mL) {deposit may 

be included in price.}

X X

Cola, nondiet, per 2 liters (67.6 oz) {deposit may be included in 

price.}

X X

Coffee, 100 percent, ground roast, all sizes, per lb (453.6 gm) X X

Coffee, 100 percent, ground roast, 13.1–20 oz can, per lb (453.6 gm) X X

Coffee, instant, plain, regular, all sizes, per lb (453.6 gm) X X

Potato chips, per 16 oz (453.6 gm) X X

Alcoholic Beverages

Malt beverages, all types, all sizes, any origin, per 16 oz (473.2 mL) X X

Vodka, all types, all sizes, any origin, per 1 liter (33.8 oz) X X

Wine, red and white table, all sizes, any origin, per 1 liter (33.8 oz) X X

1 Last in published 1997.

2 Last published in April 2002.

3 Last published in October 2001.

4 Last published April 1994.

Appendix 2. List of average retail price series published by the Bureau of Labor Statistics

(“X” indicates published)—continued 
59

Group

weights Item weights

18901 None 1901 Varied None Varied Two or more 

persons.

Salaried worker 

earning $1,200 

or less during 

year. No 

limitation on 

wage earners.

No limitation. No limitation. No limitation.

1919 1917–1919 1917–1919 1913

Feb. 1921 2

32

Minimum of 

husband, wife, 

and one child 

who was not a 

boarder or lodger. 

No boarders nor 

more than three 

lodgers present.

Salaried worker 

earning $2,000 

or less. No 

limitation on 

wage earners.

At least 75 percent 

from principal 

earner or others 

who contributed 

all earnings to 

family fund.

No slum or 

charity families; 

White only; in 

area entire year 

and in United 

States 5 years or 

more; no nonEnglishspeaking

families.

Cost of 

living.

Sept. 1935

3

Average

1920–1930

Dec. 19354 1923–1925

Aug. 19405 1934–1936 6

1934–1936 1935–1939 1930 733

Two or more 

persons. Not 

more than two 

boarders or 

lodgers, or guests 

for more than 26 

guest-weeks.

At least $300. 

Salaried worker 

earning less 

than $2,000 

during year or 

less than $200 

during any 

month. No 

upper limitation 

on wage 

earners.

At least $500. Less 

than one-fourth 

from interest, 

dividends,

royalties,

speculative gains, 

rents, gifts, or 

income in kind. 

No rent in 

payment of 

services. Less than 

3 months’ free 

rent. No subsidiary 

clerical worker 

earning $2,000 or 

more.

At least 1,008 

hours spread 

over 36 weeks.

No relief 

families, either 

on direct or 

work relief; 

White only, 

except where 

Black

population was 

significant part 

of total; in area 

9 months or 

more.

Indexes of 

the cost of 

living of 

wage earners 

and lower 

salaried

workers in 

large cities.

May 19418 34

 July 1943 9

1940

Sept. 1945

Consumer

Price Index 

for ModerateIncome

Families in 

Large Cities.

Earnings of 

chief earner

CPI Appendix 3. Characteristics of the Consumer Price Index, 1890 to date

Source and 

amount of family 

income

Length of 

employment

Economic level, 

length of 

residence,

nativity, and 

race

Date Title(s)

Survey providing 

expenditure weight

Base period

Census

providing

population

weights

Number of 

areas

included

Family

composition
60

Group

weights Item weights

Jan. 195110 1947–1949 111934–1936 1950 Two or more 

persons.

No limitation. 

(Family income 

not in excess of 

$10,000.)

Family income 

under $10,000 

after taxes in the 

survey year. No 

minimum income 

limit, except that 

families with no 

income from 

wages or salaries 

were excluded.

Family head 

must have been 

employed at 

least 26 weeks.

No exclusion for 

receipt of relief 

as such, but 

only families 

with wage or 

salary earnings 

included. No 

length of 

residence,

nativity, or 

racial

limitations.

Jan.195312 131950 131950 141947–1949 46

No specific 

requirement,

but major 

portion of 

income of 

family head 

must be from 

employment as 

wage earner or 

salaried clerical 

worker.

Short title:

Consumer

Price Index. 

Complete

name: Index 

of Change in 

Prices of 

Goods and 

Services

Purchased by 

City WageEarner and 

ClericalWorker

Families to 

Maintain

Their Level 

of Living.

Jan. 1962 151957–1959

Jan. 196416 171960–1961 171960–1961 1960 50

Families of two 

or more persons 

and single 

workers; at least 

one full-time 

wage earner.

No limitation.

More than half of 

combined family 

income from wageearner or clericalworker occupation.

A minimum of 

37 weeks for at 

least 1 family 

member.

No restrictions 

other than the 

wage-earner and 

clerical-worker

definition.

Consumer

Price Index 

for Urban 

Wage

Earners and 

Clerical

Workers.

Jan. 196618 56

Jan. 197119 1967

CPI Appendix 3. Characteristics of the Consumer Price Index, 1890 to date—Continued

Economic level, 

length of 

residence,

nativity, and 

race

Date Title(s)

Survey providing 

expenditure weight

Base period

Census

providing

population

weights

Number of 

areas

included

Family

composition

Earnings of 

chief earner

Source and 

amount of family 

income

Length of 

employment
61

Group

weights Item weights

CPI Appendix 3. Characteristics of the Consumer Price Index, 1890 to date—Continued

Economic level, 

length of 

residence,

nativity, and 

race

Date Title(s)

Survey providing 

expenditure weight

Base period

Census

providing

population

weights

Number of 

areas

included

Family

composition

Earnings of 

chief earner

Source and 

amount of family 

income

Length of 

employment

Jan. 197820 211972–1973 221974 1970 85

For earner and 

clerical-worker

indexes, families 

of two or more 

persons and 

single workers; at 

least one fulltime wage 

earner.. No 

limitation for 

urban consumer 

index.

For wage-earner 

and clericalworker indexes, 

more than half of 

combined family 

income from wageearner or clericalworker occupation. 

No limitation for 

urban consumer 

index.

For wage-earner 

and clericalworker indexes, 

a minimum of 

37 weeks for at 

least one family 

member. No 

employment

required for 

urban consumer 

index.

For wage-earner 

and clericalworker indexes, 

no restrictions 

other than the 

wage-earner and 

clerical-worker

definition. No 

limitation for

urban consumer 

index.23

1. Consumer 

Price Index 

for Urban 

Wage

Earners and 

Clerical

Workers

(CPI-W).

2. Consumer 

Price Index 

for All Urban 

Consumers

(CPI-U).

Jan.198324 CPI-U

Jan. 198525 CPI-W

Jan. 198726 271982–1984 281985–1989 1980 91

Families of two 

or more persons 

and single 

workers; at least 

one full-time 

wage earner. 

Students residing 

in collegeregulated housing 

are treated as 

separate family 

units.

Jan. 1988 291982–1984

Jan. 199830 311993–1995 321992–1996 1990

Jan. 200233 341999–2000 351997–2001 1990

July 200236 1999–2000 Dec.1999 Same as CPI-U 

population.

Chained

Consumer

Price Index 

for All Urban 

Consumers

(C-CPI-U).

Jan. 200437 382001–2002 392000–2003

Jan. 200640 412003–2004 422002–2005

Jan. 200843 442005–2006 452004–2007

Jan. 201046 472007–2008 482006–2009

Jan. 201249 502009–2010 512008–2011

Jan. 201452 532011–2012 542010–2013
62

15 Index published in February for January 1962. Indexes also were calculated on bases of 1947–1949 = 100 and 1939 = 100.

16 Index published March 3 for January 1964. Linked to old series as of December 1963. Old series also published during a 6-month overlap period.

29 Index published in February for January 1988. Indexes also calculated on the 1967 = 100 base.

24 Changed homeowners’ costs from asset approach to flow-of-service approach (rental equivalence).

25 Changed homeowners’ costs from asset approach to flow-of-service approach (rental equivalence).

26 Index published in February for January 1987. Linked to old series as of December 1986. Old series also published during a 6-month overlap period.

27 Data were adjusted to December 1986 for weight derivation.

28 Item weights based on Continuing Point of Purchase Survey (CPOPS) from 1985–1989; first “rolling revision.”

9 Census data for 1940 were supplemented by ration book registration data.

10 Index published in March 1951 for January 1951.

5 Indexes between 1925 and 1929 were recomputed retroactively, with group weights based on the average of 1917–1919 and 1934–1936. Indexes between

March 15, 1930, and March 15, 1940, were recomputed retroactively with 1934–1936 group weights.

23 Coverage was expanded to include wage earners and clerical workers in the entire nonfarm parts of metropolitan areas, in addition to those living within

the urbanized areas of metropolitan areas and urban places of 2,500 or more inhabitants.

17 Data were adjusted to December 1963 for weight derivation.

18 Index published in February for January 1966. Linked to old series as of December 1965.

19 Index published in February for January 1971. Indexes were also calculated on the 1957–1959 = 100 base.

20 Index published in February for January 1978. Linked to old series as of December 1977. Old series also published during a 6-month overlap period.

21 Data were adjusted to December 1977 for weight derivation.

22 Item weights based on Points of Purchase Survey in 1974.

11 All-item and group indexes bteween January 1950 and January 1951 were revised retroactively. Rent and all-item indexes were corrected for new-unit

bias from 1940. Old series also published through 1952. 

12 Item weights were revised for only the seven cities for which 1947–1949 expenditure data were available. Index was published in February and January

1953. Linked to old series as of December 1952. Old series also published during a 6-month overlap period.

13 Data were adjusted to 1952 for weight derivation.

14 Indexes also were calculated on the base of 1935–1939 = 100 through December 1957.

30 Index published in February for January 1989. Linked to old series as of December 1997. Old series also published during a 6-month overlap period.

31 Data adjusted to December 1997 for weight derivation.

36 New index. Elementary indexes aggregated by using an adjusted geometric mean for the preliminary versions and a Törnqvist formula for the final

version. Until 2014, first issued in preliminary form (initial); subject to revision in February (interim) and again the following February (final). Starting in 

CPI Appendix 3. Characteristics of the Consumer Price Index, 1890 to date—Continued

40 Index published in February for January 2006. Linked to old series as of December 2005. 

41 Data were adjusted to December 2005 for weight deviation.

7 There were 51 to 56 cities included in the food index.

6 During World War II, weights were adjusted to account for rationing and shortages.

8 Index published in May 1941 for March 14, 1941. Food indexes were based on 51 cities.

1 Food price indexes only.

2 For 19 cities, data were available back to December 1914, and for 13 cities, back to 1917. For the United States, data were available back to the 1913

annual average.

3 Indexes between 1918 and 1929 were recomputed retroactively with population weights based on the average of the 1920 and 1930 censuses.

4 Index published in December 1935 for July 15, 1935; Indexes were also calculated on the 1913 = 100 base.

37 Index published in February for January 2004. Linked to old series as of December 2003. 

38 Data were adjusted to December 2003 for weight deviation.

39 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 1999 forward.

32 At time of revision, the CPOPS covered 1992–1996. The revised TPOPS was introduced in 1998.

33 Index published in February for January 2002. Linked to old series as of December 2001. Old series also published during a 6-month overlap period.

34 Data were adjusted to December 2001 for weight deviation.

35 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 1997 forward.
63

CPI Appendix 3. Characteristics of the Consumer Price Index, 1890 to date—Continued

54 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 2009 forward.

46 Index published in February for January 2010. Linked to old series as of December 2009. 

47 Data were adjusted to December 2009 for weight deviation.

48 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 2005 forward.

49 Index published in February for January 2012. Linked to old series as of December 2011. 

50 Data were adjusted to December 2011 for weight deviation.

52 Index published in February for January 2014. Linked to old series as of December 2013. 

51 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 2007 forward.

53 Data were adjusted to December 2013 for weight deviation.

45 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 2003 forward.

42 TPOPS on 4-year rotation; at the time of expenditure weight update, largely reflected data from 2001 forward.

43 Index published in February for January 2008. Linked to old series as of December 2007. 

44 Data were adjusted to December 2007 for weight deviation.
64

PSU 

code(1) PSU name Pricing 

cycle (2) 

PSU definition (state, and county or 

parish (Louisiana))

Stratum 

population

Percentage 

of index 

population

MA: Essex, Middlesex, Norfolk, 

Plymouth, Suffolk

NH: Rockingham, Strafford

N11B Hartford–West Hartford–East 

Hartford, CT E CT: Hartford, Middlesex, Tolland 5,005,793 1.73

N11C Springfield, MA O MA: Hampden, Hampshire 4,233,926 1.46

NJ: Bergen, Essex, Hudson, Hunterdon, 

Middlesex, Monmouth, Morris, Ocean, 

Passaic, Somerset, Sussex, Union

NY: Bronx, Dutchess, Kings, Nassau, 

New York, Orange, Putnam, Queens, 

Richmond, Rockland, Suffolk, 

Westchester

PA: Pike

DE: New Castle

MD: Cecil

NJ: Burlington, Camden, Gloucester, 

Salem

PA: Bucks, Chester, Delaware, 

Montgomery, Philadelphia

N12C Pittsburgh, PA E

PA: Allegheny, Armstrong, Beaver, 

Butler, Fayette, Washington, 

Westmoreland

4,065,877 1.4

N12D Buffalo–Cheektowaga–Niagara 

Falls, NY E NY: Erie, Niagara 3,483,174 1.2

N12E W1Rochester, NY O NY: Livingston, Monroe, Ontario, 

Orleans, Wayne, Yates 3,925,318 1.36

N12F Reading, PA O PA: Berks 3,562,332 1.23

IL: Cook, De Kalb, Du Page, Grundy, 

Kane, Kendall, Lake, McHenry, Will

M IN: Jasper, Lake, Newton, Porter

WI: Kenosha

S23B Detroit–Warren–Dearborn, MI E MI: Lapeer, Livingston, Macomb, 

Oakland, St. Clair, Wayne 4,296,250 1.48

IN: Dearborn, Ohio, Union

E KY: Boone, Bracken, Campbell, Gallatin, 

Grant, Kenton, Pendleton

OH: Brown, Butler, Clermont, Hamilton, 

Warren

N23D Cleveland–Elyria, OH O OH: Cuyahoga, Geauga, Lake, Lorain, 

Medina 3,257,953 1.12

N23E Columbus, OH O

OH: Delaware, Fairfield, Franklin, 

Hocking, Licking, Madison, Morrow, 

Perry, Pickaway, Union

3,758,510 1.3

N23F Milwaukee–Waukesha–West Allis, 

WI O WI: Milwaukee, Ozaukee, Washington, Waukesha 3,256,494 1.12

N23G Dayton, OH O OH: Greene, Miami, Montgomery 3,924,320 1.36

Philadelphia–Camden–Wilmington, 

PA–NJ–DE–MD

Region 2—Midwest, Division 3—East North Central

S23A Chicago–Naperville–Elgin, 

IL–IN–WI 9,461,105 3.27

N23C Cincinnati, OH–KY–IN 3,395,853 1.17

5,965,343 2.06

Region 1—Northeast, Division 1—New England

S11A Boston–Cambridge–Newton, 

MA–NH 4,552,402 1.57

Region 1—Northeast, Division 2—Middle Atlantic

S12A New York–Newark–Jersey City, 

NY–NJ–PA M 19,567,410 6.76

O

S12B E

Appendix 4. 2018 CPI geographic sample
65

PSU 

code(1) PSU name Pricing 

cycle (2) 

PSU definition (state, and county or 

parish (Louisiana))

Stratum 

population

Percentage 

of index 

population

N23H W1Flint, MI O MI: Genesee 3,911,189 1.35

N23I W2Janesville–Beloit, WI E WI: Rock 3,745,126 1.29

N23J W3Frankfort, IN E IN: Clinton 3,427,365 1.18

O

MN: Anoka, Carver, Chisago, Dakota, 

Hennepin, Isanti, Le Sueur, Mille Lacs, 

Ramsey, Scott, Sherburne, Sibley, 

Washington, Wright

WI: Pierce, St. Croix

E IL: Bond, Calhoun, Clinton, Jersey, 

Macoupin, Madison, Monroe, St. Clair

MO: Franklin, Jefferson, Lincoln, St. 

Charles, St. Louis, St. Louis City, 

Warren

E IA: Harrison, Mills, Pottawattamie

NE: Cass, Douglas, Sarpy, Saunders, 

Washington

N24D W2Wichita, KS O KS: Butler, Harvey, Kingman, Sedgwick, 

Sumner 2,842,770 0.98

N24E Lincoln, NE O NE: Lancaster, Seward 3,288,318 1.14

E MN: Wilkin

ND: Richland

DC: District of Columbia

O MD: Calvert, Charles, Frederick, 

Montgomery, Prince George’s

VA: Alexandria City, Arlington, Clarke, 

Culpeper, Fairfax, Fairfax City, Falls 

Church City, Fauquier, Fredericksburg 

City, Loudoun, Manassas City, 

Manassas Park City, Prince William, 

Rappahannock, Spotsylvania, Stafford, 

Warren

WV: Jefferson

S35B Miami–Fort Lauderdale–West Palm 

Beach, FL E FL: Broward, Miami–Dade, Palm Beach 5,564,635 1.92

S35C Atlanta–Sandy Springs–Roswell, 

GA E

GA: Barrow, Bartow, Butts, Carroll, 

Cherokee, Clayton, Cobb, Coweta, 

Dawson, DeKalb, Douglas, Fayette, 

Forsyth, Fulton, Gwinnett, Haralson, 

Heard, Henry, Jasper, Lamar, 

Meriwether, Morgan, Newton, Paulding, 

Pickens, Pike, Rockdale, Spalding, 

Walton

5,286,728 1.83

S35D Tampa–St. Petersburg–Clearwater, 

FL O FL: Hernando, Hillsborough, Pasco, Pinellas 2,783,243 0.96

S35E Baltimore–Columbia–Towson, MD E

MD: Anne Arundel, Baltimore, Baltimore 

City, Carroll, Harford, Howard, Queen 

Anne’s

2,710,489 0.94

NC: Cabarrus, Gaston, Iredell, Lincoln, 

Mecklenburg, Rowan, Union

SC: Chester, Lancaster, York

Washington–Arlington–Alexandria, 

DC–VA–MD–WV

S24B St. Louis, MO–IL

N24C W2Omaha–Council Bluffs, NE–IA

Region 3—South, Division 5—South Atlantic

S35A 5,636,232 1.95

N35F O

W3Charlotte–Concord–Gastonia, 

NC–SC 3,035,149 1.05

2,974,017 1.03

N24F W3Wahpeton, ND–MN 2,947,903 1.02

2,787,701 0.96

Region 2—Midwest, Division 4—West North Central

S24A Minneapolis–St. Paul–Bloomington, 

MN–WI 3,348,859 1.16

Appendix 4. 2018 CPI geographic sample—continued
66

PSU 

code(1) PSU name Pricing 

cycle (2) 

PSU definition (state, and county or 

parish (Louisiana))

Stratum 

population

Percentage 

of index 

population

N35G W1Orlando–Kissimmee–Sanford, FL E FL: Lake, Orange, Osceola, Seminole 2,642,941 0.91

N35H Richmond, VA E

VA: Amelia, Caroline, Charles City, 

Chesterfield, Colonial Heights City, 

Dinwiddie, Goochland, Hanover, 

Henrico, Hopewell City, King William, 

New Kent, Petersburg City, Powhatan, 

Prince George, Richmond City, Sussex

3,027,856 1.05

N35I Raleigh, NC E NC: Franklin, Johnston, Wake 2,549,176 0.88

N35J Greenville–Anderson–Mauldin, SC O SC: Anderson, Greenville, Laurens, 

Pickens 3,094,518 1.07

N35K W3Winston–Salem, NC E NC: Davidson, Davie, Forsyth, Stokes, 

Yadkin 2,637,083 0.91

N35L Cape Coral–Fort Myers, FL O FL: Lee 3,091,153 1.07

N35M Ocala, FL O FL: Marion 2,568,744 0.89

N35N Gainesville, FL E FL: Alachua, Gilchrist 2,913,140 1.01

N35O W2Wilmington, NC O NC: New Hanover, Pender 2,736,321 0.94

N35P W2Jacksonville, NC E NC: Onslow 3,100,604 1.07

N35Q W1Clarksburg, WV O WV: Doddridge, Harrison, Taylor 2,563,098 0.89

IN: Clark, Floyd, Harrison, Scott, 

Washington

KY: Bullitt, Henry, Jefferson, Oldham, 

Shelby, Spencer, Trimble

N36B Birmingham–Hoover, AL O AL: Bibb, Blount, Chilton, Jefferson, 

Shelby, St. Clair, Walker 2,483,606 0.86

GA: Catoosa, Dade, Walker 2,620,595 0.9

TN: Hamilton, Marion, Sequatchie

N36D W4Huntsville, AL E AL: Limestone, Madison 2,801,399 0.97

N36E Florence–Muscle Shoals, AL O AL: Colbert, Lauderdale 2,550,408 0.88

N36F W1Meridian, MS E MS: Clarke, Kemper, Lauderdale 2,397,313 0.83

S37A Dallas–Fort Worth–Arlington, TX O

TX: Collin, Dallas, Denton, Ellis, Hood, 

Hunt, Johnson, Kaufman, Parker, 

Rockwall, Somervell, Tarrant, Wise

6,426,214 2.22

S37B Houston–The Woodlands–Sugar 

Land, TX E

TX: Austin, Brazoria, Chambers, Fort 

Bend, Galveston, Harris, Liberty, 

Montgomery, Waller

5,920,416 2.04

N37C San Antonio–New Braunfels, TX O TX: Atascosa, Bandera, Bexar, Comal, 

Guadalupe, Kendall, Medina, Wilson 2,436,095 0.84

N37D Oklahoma City, OK E OK: Canadian, Cleveland, Grady, 

Lincoln, Logan, McClain, Oklahoma 2,812,948 0.97

N37E Baton Rouge, LA E

LA: Ascension, East Baton Rouge, East 

Feliciana, Iberville, Livingston, Pointe 

Coupee, St. Helena, West Baton Rouge, 

West Feliciana

2,543,610 0.88

N37F Lafayette, LA O LA: Acadia, Iberia, Lafayette, St. Martin, 

Vermilion 2,444,837 0.84

N37G Brownsville–Harlingen, TX O TX: Cameron 2,581,037 0.89

N37H Amarillo, TX E TX: Armstrong, Carson, Oldham, Potter, 

Randall 2,756,117 0.95

N36C Chattanooga, TN–GA E

N36A

W4Louisville/Jefferson County, 

KY–IN

Region 3—South, Division 7—West South Central

O 2,529,624 0.87

Region 3—South, Division 6—East South Central

Appendix 4. 2018 CPI geographic sample—continued
67

PSU 

code(1) PSU name Pricing 

cycle (2) 

PSU definition (state, and county or 

parish (Louisiana))

Stratum 

population

Percentage 

of index 

population

N37I W2Russellville, AR O AR: Pope, Yell 2,620,998 0.91

N37J W3Paris, TX E TX: Lamar 2,851,943 0.98

S48A Phoenix–Mesa–Scottsdale, AZ E AZ: Maricopa, Pinal 4,192,887 1.45

S48B Denver–Aurora–Lakewood, CO O

CO: Adams, Arapahoe, Broomfield, 

Clear Creek, Denver, Douglas, Elbert, 

Gilpin, Jefferson, Park

2,543,482 0.88

N48C Las Vegas–Henderson–Paradise, 

NV E NV: Clark 3,227,960 1.11

N48D Provo–Orem, UT E UT: Juab, Utah 3,724,271 1.29

N48E Yuma, AZ O AZ: Yuma 3,840,701 1.33

N48F W3St. George, UT O UT: Washington 3,206,759 1.11

S49A Los Angeles–Long 

Beach–Anaheim, CA M CA: Los Angeles, Orange 12,828,837 4.43

S49B San Francisco–Oakland–Hayward, 

CA E CA: Alameda, Contra Costa, Marin, San Francisco, San Mateo 4,335,391 1.5

S49C Riverside–San Bernardino–Ontario, 

CA O CA: Riverside, San Bernardino 4,224,851 1.46

S49D Seattle–Tacoma–Bellevue, WA E WA: King, Pierce, Snohomish 3,439,809 1.19

S49E San Diego–Carlsbad, CA O CA: San Diego 3,095,313 1.07

S49F Honolulu, HI O HI: Honolulu 1,360,301 0.47

S49G Anchorage, AK E AK: Anchorage, Matanuska–Susitna 523,154 0.18

O OR: Clackamas, Columbia, Multnomah, 

Washington, Yamhill

WA: Clark, Skamania

N49I W1Santa Rosa, CA E CA: Sonoma 5,163,670 1.78

N49J Chico, CA E CA: Butte 4,623,339 1.6

N49K W4Moses Lake, WA O WA: Grant 4,363,676 1.51

Region 4—West, Division 8—Mountain

Region 4—West, Division 9—Pacific

(1) PSU code (1st character: S—self-representing or N—non-self-representing; 2nd character: region number; 3rd character: 

division number; 4th character: A–Q, depending on number of PSUs within a Census division).

(2) E = Even months, O = Odd months, M = Monthly.

Note: The superscripts W1–W4 designate the respective wave during which each new PSU will enter the index; no 

designation indicates a continuing PSU.

Source: U.S. Bureau of Labor Statistics.

N49H Portland–Vancouver–Hillsboro, 

OR–WA 5,208,366 1.8

Appendix 4. 2018 CPI geographic sample—continued
68

MAJOR GROUP: FOOD AND BEVERAGES

 CEREALS AND BAKERY PRODUCTS

FA Cereals and cereal products

FA01 Flour and prepared flour mixes

FA011 FLOUR AND PREPARED FLOUR MIXES

FA02 Breakfast cereal

FA021 BREAKFAST CEREAL 

FA03 Rice, pasta, cornmeal

FA031 RICE, PASTA, CORNMEAL

FB Bakery products

FB01 Bread

FB011 BREAD

FB02 Fresh biscuits, rolls, muffins

FB021 FRESH BISCUITS, ROLLS, AND MUFFINS

FB03 Cakes, cupcakes, and cookies

FB031 CAKES AND CUPCAKES (EXCLUDING FROZEN)

FB032 COOKIES

FB04 Other bakery products

FB041 CRACKERS AND BREAD, AND CRACKER PRODUCTS

FB042 SWEETROLLS, COFFEE CAKE, AND DOUGHNUTS (EXCLUDING FROZEN)

FB043 FROZEN BAKERY PRODUCTS, AND FROZEN/REFRIGERATED DOUGHS AND BATTERS

FB044 PIES, TARTS, TURNOVERS (EXCLUDING FROZEN)

MEATS, POULTRY, FISH, AND EGGS

FC Beef and veal

FC01 Uncooked ground beef

FC011 UNCOOKED GROUND BEEF

FC02 Uncooked beef roasts

FC021 UNCOOKED BEEF ROASTS

FC03 Uncooked beef steaks

FC031 UNCOOKED BEEF STEAKS

FC04 Uncooked other beef and veal

FC041 UNCOOKED OTHER BEEF AND VEAL

FD Pork

FD01 Bacon, breakfast sausage, and related products

FD011 BACON, BREAKFAST SAUSAGE, AND RELATED PRODUCTS

FD02 Ham

FD021 HAM

FD03 Pork chops

FD031 PORK CHOPS

FD04 Other pork, including roasts and picnics

FD041 OTHER PORK INCLUDING ROASTS AND PICNICS

FE Other meats

FE01 Other meats

FE011 FRANKFURTERS

FE012 LUNCHMEATS

FE013 LAMB, ORGAN MEATS, AND GAME

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items
69

FF Poultry

FF01 Chicken

FF011 CHICKEN

FF02 Other poultry including turkey

FF021 OTHER POULTRY INCLUDING TURKEY

FG Fish and seafood

FG01 Fresh fish and seafood

FG011 FRESH FISH AND SEAFOOD

FG02 Processed fish and seafood

FG021 PROCESSED FISH AND SEAFOOD

FH Eggs

FH01 Eggs

FH011 EGGS

DAIRY AND RELATED PRODUCTS

FJ Dairy and related products

FJ01 Milk

FJ011 MILK

FJ02 Cheese and related products

FJ021 CHEESE AND RELATED PRODUCTS

FJ03 Ice cream and related products

FJ031 ICE CREAM AND RELATED PRODUCTS

FJ04 Other dairy and related products

FJ041 OTHER DAIRY AND RELATED PRODUCTS

FRUITS AND VEGETABLES

FK Fresh fruits

FK01 Apples

FK011 APPLES

FK02 Bananas

FK021 BANANAS

FK03 Citrus fruits

FK031 CITRUS FRUITS

FK04 Other fresh fruits

FK041 OTHER FRESH FRUITS

FL Fresh vegetables

FL01 Potatoes

FL011 POTATOES

FL02 Lettuce

FL021 LETTUCE

FL03 Tomatoes

FL031 TOMATOES

FL04 Other fresh vegetables

FL041 OTHER FRESH VEGETABLES, INCLUDING FRESH HERBS

FM Processed fruits and vegetables

FM01 Canned fruits and vegetables and other shelf-stable fruits and vegetables

FM011 CANNED FRUITS AND VEGETABLES AND OTHER SHELF-STABLE FRUITS AND VEGETABLES

FM02 Frozen fruits and vegetables

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued
70

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

FM021 FROZEN FRUITS AND VEGETABLES

FM03 Other processed fruits and vegetables, including dried

FM031 OTHER PROCESSED FRUITS AND VEGETABLES, INCLUDING DRIED

NONALCOHOLIC BEVERAGES AND BEVERAGE MATERIALS

FN Juices and nonalcoholic drinks

FN01 Carbonated drinks

FN011 CARBONATED DRINKS

FN02 Frozen noncarbonated juices and drinks

FN021 FROZEN NONCARBONATED JUICES AND DRINKS

FN03 Nonfrozen noncarbonated juices and drinks

FN031 NONFROZEN NONCARBONATED JUICES AND DRINKS

FP Beverage materials, including coffee and tea

FP01 Coffee

FP011 COFFEE

FP02 Other beverage materials, including tea

FP021 TEA

FP022 OTHER BEVERAGE MATERIALS

OTHER FOOD AT HOME

FR Sugar and sweets

FR01 Sugar and artificial sweeteners

FR011 SUGAR AND ARTIFICIAL SWEETENERS

FR02 Candy and chewing gum

FR021 CANDY AND CHEWING GUM

FR03 Other sweets

FR031 OTHER SWEETS

FS Fats and oils

FS01 Butter and margarine

FS011 BUTTER AND MARGARINE

FS02 Salad dressing

FS021 MAYONNAISE, SALAD DRESSING, AND SANDWICH SPREADS

FS03 Other fats and oils, including peanut butter

FS031 PEANUT BUTTER AND OTHER NUT BUTTERS

FS032 OTHER FATS AND OILS

FT Other foods

FT01 Soups

FT011 SOUPS

FT02 Frozen and freeze-dried prepared foods

FT021 FROZEN AND FREEZE-DRIED PREPARED FOODS

FT03 Snacks

FT031 SNACKS

FT04 Spices, seasonings, condiments, sauces

FT041 SALT AND OTHER SEASONINGS AND SPICES

FT042 OLIVES, PICKLES, RELISHES

FT043 SAUCES AND GRAVIES

FT044 OTHER CONDIMENTS (EXCLUDING OLIVES, PICKLES, AND RELISHES)

FT05 Baby food
71

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

FT051 BABY FOOD

FT06 Other miscellaneous foods

FT061 PREPARED SALADS

FT062 OTHER MISCELLANEOUS FOODS

FOOD AWAY FROM HOME

FV Food away from home

FV01 Full-service meals and snacks

FV011 FULL-SERVICE MEALS AND SNACKS

FV02 Limited-service meals and snacks

FV021 LIMITED-SERVICE MEALS AND SNACKS

FV03 Food at employee sites and schools

FV031 FOOD AT EMPLOYEE SITES AND SCHOOLS

FV04 Food from vending machines and mobile vendors

FV041 FOOD FROM VENDING MACHINES AND MOBILE VENDORS

FV05 Other food away from home

FV051 BOARD, CATERED EVENTS, AND OTHER FOOD AWAY FROM HOME

ALCOHOLIC BEVERAGES

FW Alcoholic beverages at home

FW01 Beer, ale, and other malt beverages at home

FW011 BEER, ALE, AND OTHER MALT BEVERAGES AT HOME

FW02 Distilled spirits at home

FW021 DISTILLED SPIRITS AT HOME

FW03 Wine at home

FW031 WINE AT HOME

FX Alcoholic beverages away from home

FX01 Alcoholic beverages away from home

FX011 ALCOHOLIC BEVERAGES AWAY FROM HOME

H MAJOR GROUP: HOUSING

SHELTER

HA Rent of primary residence

HA01 Rent of primary residence

HA011 RENT OF PRIMARY RESIDENCE

HB Lodging away from home

HB01 Housing at school, excluding board

HB011 HOUSING AT SCHOOL, EXCLUDING BOARD

HB02 Other lodging away from home, including hotels and motels

HB021 RENTAL OF LODGING AWAY FROM HOME

HC Owners' equivalent rent of primary residence

HC01 Owners' equivalent rent of primary residence

HC011 OWNERS' EQUIVALENT RENT OF PRIMARY RESIDENCE

HC09 Unsampled owners' equivalent rent of secondary residence
72

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

HD Tenants' and household insurance

HD01 Tenants' and household insurance

HD011 TENANTS' AND HOUSEHOLD INSURANCE

FUELS AND UTILITIES

HE Fuel oil and other fuels

HE01 Fuel oil

HE011 FUEL OIL

HE02 Propane, kerosene, and firewood

HE021 OTHER HOUSEHOLD FUELS

HF Gas (piped) and electricity

HF01 Electricity

HF011 ELECTRICITY

HF02 Utility (piped) gas service

HF021 UTILITY (PIPED) GAS SERVICE

HG Water and sewer and trash collection services

HG01 Water and sewerage maintenance

HG011 RESIDENTIAL WATER AND SEWERAGE SERVICE

HG02 Garbage and trash collection

HG021 GARBAGE AND TRASH COLLECTION

HOUSEHOLD FURNISHINGS AND OPERATIONS

HH Window and floor coverings and other linens

HH01 Floor coverings 

HH011 FLOOR COVERINGS

HH02 Window coverings

HH021 CURTAINS AND DRAPES

HH022 WINDOW COVERINGS

HH03 Other linens

HH031 BATHROOM LINENS

HH032 BEDROOM LINENS

HH033 KITCHEN AND DINING ROOM LINENS

HJ Furniture and bedding

HJ01 Bedroom furniture

HJ011 MATTRESS AND FOUNDATIONS

HJ012 BEDROOM FURNITURE OTHER THAN MATTRESS AND SPRINGS

HJ02 Living room, kitchen, and dining room furniture

HJ021 SOFAS, FURNITURE COVERS, AND DECORATIVE PILLOWS

HJ022 LIVING ROOM CHAIRS

HJ023 LIVING ROOM TABLES

HJ024 KITCHEN AND DINING ROOM FURNITURE

HJ03 Other furniture

HJ031 INFANTS' FURNITURE

HJ032 OUTDOOR FURNITURE

HJ033 OCCASIONAL FURNITURE

HJ09 Unsampled furniture

HJ090 RENTAL OF FURNITURE
73

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

HK Appliances

HK01 Major appliances

HK011 REFRIGERATORS AND HOME FREEZERS

HK012 LAUNDRY EQUIPMENT

HK013 RANGES AND COOKTOPS

HK014 MICROWAVE OVENS

HK02 Other appliances

HK021 FLOOR CLEANING EQUIPMENT

HK022 SMALL ELECTRIC KITCHEN APPLIANCES

HK023 OTHER ELECTRIC APPLIANCES

HK09 Unsampled appliances

HK090 PORTABLE DISHWASHERS

HL Other household equipment and furnishings

HL01 Clocks, lamps, and decorator items

HL011 LAMPS AND LIGHTING FIXTURES

HL012 HOUSEHOLD DECORATIVE ITEMS AND CLOCKS

HL02 Indoor plants and flowers

HL021 INDOOR PLANTS AND FRESH CUT FLOWERS

HL03 Dishes and flatware

HL031 DISHES

HL032 FLATWARE

HL04 Nonelectric cookware and tableware

HL041 NONELECTRIC COOKINGWARE

HL042 TABLEWARE AND NONELECTRIC KITCHENWARE

HM Tools, hardware, outdoor equipment, and supplies

HM01 Tools, hardware, and supplies

HM011 PAINT, WALLPAPER TOOLS, AND SUPPLIES

HM012 POWER TOOLS

HM013 MISCELLANEOUS HARDWARE, SUPPLIES, AND EQUIPMENT

HM014 NONPOWERED HANDTOOLS

HM02 Outdoor equipment and supplies

HM021 POWERED LAWN AND GARDEN EQUIPMENT, AND OTHER OUTDOOR ITEMS

HM022 LAWN AND GARDEN MATERIALS, OTHER DECORATIVE ITEMS, AND PESTICIDES

HM09 Unsampled tools, hardware, outdoor equipment, and supplies

HM090 UNSAMPLED ITEMS

HN Housekeeping supplies

HN01 Household cleaning products

HN011 LAUNDRY AND CLEANING PRODUCTS

HN012 LAUNDRY AND CLEANING EQUIPMENT

HN02 Household paper products

HN021 HOUSEHOLD PAPER PRODUCTS

HN03 Miscellaneous household products

HN031 MISCELLANEOUS HOUSEHOLD PRODUCTS

HP Household operations

HP01 Domestic services

HP011 DOMESTIC SERVICES

HP02 Gardening and lawn care services

HP021 GARDENING AND LAWN CARE SERVICES

HP03 Moving, storage, freight expense

HP031 MOVING, STORAGE, FREIGHT EXPENSE
74

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

HP04 Repair of household items

HP041 APPLIANCE REPAIR

HP042 REUPHOLSTERY OF FURNITURE

HP043 INSIDE HOME MAINTENANCE AND REPAIR SERVICES

HP09 Unsampled household operations

HP090 UNSAMPLED ITEMS

A MAJOR GROUP: APPAREL

APPAREL COMMODITIES

MEN'S AND BOYS' APPAREL

AA Men's apparel

AA01 Men's suits, sport coats, and outerwear

AA011 MEN'S SUITS

AA012 MEN'S SPORT COATS AND TAILORED JACKETS

AA013 MEN'S OUTERWEAR

AA02 Men's furnishings

AA021 MEN'S UNDERWEAR, HOSIERY, AND NIGHTWEAR

AA022 MEN'S ACCESSORIES

AA023 MEN'S ACTIVE SPORTSWEAR

AA03 Men's shirts and sweaters

AA033 MEN'S SHIRTS, SWEATERS, AND VESTS 

AA04 Men's pants and shorts

AA041 MEN'S PANTS AND SHORTS

AA09 Unsampled men's apparel

AA090 UNSAMPLED ITEMS

AB Boy's apparel

AB01 Boy's apparel

AB011 BOYS' OUTERWEAR

AB012 BOY'S SHIRTS AND SWEATERS

AB013 BOYS' UNDERWEAR, NIGHTWEAR, HOSIERY, AND ACCESSORIES

AB014 BOYS' SUITS, SPORT COATS, AND PANTS

AB015 BOYS' ACTIVE SPORTSWEAR

AB09 Unsampled boy's apparel

AB090 UNSAMPLED ITEMS

WOMEN'S AND GIRLS' APPAREL

AC Women's apparel 

AC01 Women's outerwear

AC011 WOMEN'S OUTERWEAR

AC02 Women's dresses

AC021 WOMEN'S DRESSES

AC03 Women's suits and separates

AC031 WOMEN'S TOPS

AC032 WOMEN'S SKIRTS, PANTS, AND SHORTS

AC033 WOMEN'S SUITS AND COORDINATES

AC04 Women's underwear, nightwear, sportswear, and accessories

AC041 WOMEN'S UNDERWEAR AND OTHER UNDERGARMENTS

AC042 WOMEN'S HOSIERY AND ACCESSORIES
75

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

AC043 WOMEN'S ACTIVE SPORTSWEAR

AC09 Unsampled women's apparel

AC090 UNSAMPLED ITEMS

AD Girls' apparel 

AD01 Girls' apparel

AD011 GIRLS' OUTERWEAR

AD012 GIRLS' DRESSES 

AD013 GIRLS' TOPS

AD014 GIRLS' SKIRTS, PANTS, AND SHORTS

AD015 GIRLS' ACTIVE SPORTSWEAR

AD016 GIRLS' UNDERWEAR, SLEEPWEAR, HOSIERY, AND ACCESSORIES

AD09 Unsampled girls' apparel

AD090 UNSAMPLED ITEMS

AE Footwear 

AE01 Men's footwear

AE011 MEN'S FOOTWEAR

AE02 Boys' and girls' footwear

AE021 BOYS' FOOTWEAR

AE022 GIRLS' FOOTWEAR

AE03 Women's footwear

AE031 WOMEN'S FOOTWEAR

AF Infants' and toddlers' apparel 

AF01 Infants' and toddlers' apparel

AF011 INFANTS' AND TODDLERS' OUTER PLAY, DRESS, SLEEPWEAR, AND ACCESSORIES

AF012 INFANTS' AND TODDLERS' UNDERWEAR AND DIAPERS

AG Jewelry and watches

AG01 Watches

AG011 WATCHES

AG02 Jewelry

AG021 JEWELRY

T MAJOR GROUP: TRANSPORTATION

PRIVATE TRANSPORTATION

TA New and used motor vehicles

TA01 New vehicles

TA011 NEW CARS AND TRUCKS

TA012 NEW MOTORCYCLES

TA02 Used cars and trucks

TA021 USED CARS AND TRUCKS

TA03 Leased cars and trucks

TA031 VEHICLE LEASING

TA04 Car and truck rental

TA041 AUTOMOBILE AND TRUCK RENTAL

TA09 Unsampled new and used motor vehicles

TA090 UNSAMPLED ITEMS
76

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

TB Motor fuel

TB01 Gasoline (all types)

TB011 REGULAR UNLEADED GASOLINE

TB012 MID-GRADE UNLEADED GASOLINE

TB013 PREMIUM UNLEADED GASOLINE

TB02 Other motor fuels

TB021 AUTOMOTIVE DIESEL FUEL

TB022 ALTERNATIVE MOTOR FUELS

TC Motor vehicle parts and equipment

TC01 Tires

TC011 TIRES

TC02 Vehicle accessories other than tires

TC021 VEHICLE PARTS AND EQUIPMENT OTHER THAN TIRES

TC022 MOTOR OIL, COOLANT, AND FLUIDS

TD Motor vehicle maintenance and repair

TD01 Motor vehicle body work

TD011 MOTOR VEHICLE BODY WORK

TD02 Motor vehicle maintenance and servicing

TD021 MOTOR VEHICLE MAINTENANCE AND SERVICING

TD03 Motor vehicle repair

TD031 MOTOR VEHICLE REPAIR

TD09 Unsampled vehicle maintenance and repair

TD090 UNSAMPLED SERVICE POLICIES

TE Motor vehicle insurance

TE01 Motor vehicle insurance

TE011 MOTOR VEHICLE INSURANCE

TF Motor vehicle fees

TF01 State motor vehicle registration and license fees 

TF011 STATE MOTOR VEHICLE REGISTRATION AND LICENSE FEES 

TF03 Parking and other fees

TF031 PARKING FEES AND TOLLS

TF032 AUTOMOBILE SERVICE CLUBS

TF09 Unsampled motor vehicle fees

TF090 UNSAMPLED ITEMS

PUBLIC TRANSPORTATION

TG Public transportation

TG01 Airline fare

TG011 AIRLINE FARES

TG02 Other intercity transportation

TG021 INTERCITY BUS FARES

TG022 INTERCITY TRAIN FARES

TG023 SHIP FARES

TG03 Intracity transportation

TG031 INTRACITY MASS TRANSIT

TG032 TAXI FARE

TG033 CAR AND VANPOOLS

TG09 Unsampled public transportation

TG090 UNSAMPLED ITEMS
77

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

M MAJOR GROUP: MEDICAL CARE

MEDICAL CARE COMMODITIES

 MF Medicinal drugs

MF01 Prescription drugs

MF011 PRESCRIPTION DRUGS

MF02 Nonprescription drugs

MF021 NONPRESCRIPTION DRUGS 

 MG Medical equipment and supplies

MG01 Medical equipment and supplies

MG011 DRESSINGS AND FIRST-AID KITS

MG012 MEDICAL EQUIPMENT FOR GENERAL USE

MG013 SUPPORTIVE AND CONVALESCENT MEDICAL EQUIPMENT

MG09 UNSAMPLED RENT OR REPAIR OF MEDICAL EQUIPMENT

MEDICAL CARE SERVICES

MC Professional services

MC01 Physicians' services

MC011 PHYSICIANS' SERVICES

MC02 Dental services

MC021 DENTAL SERVICES

MC03 Eyeglasses and eye care

MC031 EYEGLASSES AND EYE CARE

MC04 Services by other medical professionals

MC041 SERVICES BY OTHER MEDICAL PROFESSIONALS

MD Hospital and related services

MD01 Hospital services

MD011 HOSPITAL SERVICES

MD02 Nursing homes and adult daycare

MD021 NURSING AND CONVALESCENT HOME CARE

MD022 ADULT DAYCARE

MD03 Care of invalids and elderly at home

MD031 CARE OF INVALIDS, ELDERLY AND CONVALESCENTS IN THE HOME

ME01 Commercial health insurance

ME011 COMMERCIAL HEALTH INSURANCE, RETAINED EARNINGS

ME02 Blue Cross/Blue Shield

ME021 BLUE CROSS/BLUE SHIELD HEALTH INSURANCE, RETAINED EARNINGS

ME03 Health maintenance plans

ME031 HEALTH MAINTENANCE PLANS, RETAINED EARNINGS

ME04 Medicare and other health insurance

ME041 MEDICARE AND COMMERCIAL MEDICARE SUPPLEMENTS, RETAINED EARNINGS

R MAJOR GROUP: RECREATION

RA Video and audio 

RA01 Televisions 

RA011 TELEVISIONS

RA02 Cable and satellite television and radio service

RA021 CABLE AND SATELLITE TELEVISION AND RADIO SERVICE
78

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

RA03 Other video equipment

RA031 OTHER VIDEO EQUIPMENT

RA04 Video discs, and other media, including rental of video and audio 

RA041 PRERECORDED VIDEO DISCS/DIGITAL FILES/DOWNLOADS AND OTHER MEDIA

RA042 RENTAL OF VIDEO OR AUDIO DICS AND OTHER MEDIA

RA05 Audio equipment

RA051 AUDIO COMPONENTS, RADIOS, TAPE RECORDERS/PLAYERS, AND OTHER EQUIPMENT

RA06 Audio discs, tapes, and other media 

RA061 AUDIO DISC TAPES, DIGITAL FILES, AND DOWNLOADS

RA09 Unsampled video and audio

RA090 UNSAMPLED ITEMS

RB Pets, pet products, and services

RB01 Pets and pet products

RB011 PET FOOD

RB012 PURCHASE OF PETS, PET SUPPLIES, ACCESSORIES

RB02 Pet services, including veterinary

RB021 PET SERVICES

RB022 VETERINARIAN SERVICES

RC Sporting goods

RC01 Sports vehicles, including bicycles

RC011 OUTBOARD MOTORS AND POWERED SPORTS VEHICLES

RC012 UNPOWERED BOATS AND TRAILERS

RC013 BICYCLES AND ACCESSORIES

RC02 Sports equipment

RC021 GENERAL SPORTS EQUIPMENT, EXCLUDING WATER

RC022 WATER SPORTS EQUIPMENT

RC023 HUNTING, FISHING, AND CAMPING EQUIPMENT

RC09 Unsampled sporting goods

RC090 UNSAMPLED ITEMS

RD Photography

RD01 Photographic equipment and supplies

RD011 FILM AND PHOTOGRAPHIC SUPPLIES

RD012 PHOTOGRAPHIC EQUIPMENT

RD02 Photographers and film processing

RD021 PHOTOGRAPHER'S FEES

RD022 FILM PROCESSING

RD09 Unsampled photography

RD090 UNSAMPLED RENT AND REPAIR OF PHOTOGRAPHIC EQUIPMENT 

RE Other recreational goods

RE01 Toys and games

RE011 TOYS, GAMES, HOBBIES, AND PLAYGROUND EQUIPMENT

RE012 VIDEO GAME HARDWARE, SOFTWARE, AND ACCESSORIES

RE02 Sewing machines, fabric, and supplies

RE021 SEWING ITEMS

RE03 Music instruments and accessories

RE031 MUSIC INSTRUMENTS AND ACCESSORIES

RE09 Unsampled recreation services

RE090 UNSAMPLED ITEMS
79

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

RF Recreation services

RF01 Club membership dues and fees for participant sport and group exercises

RF011 CLUB DUES AND FEES FOR PARTICIPANT SPORTS AND GROUP EXERCISES

RF02 Admissions

RF021 ADMISSION TO MOVIES, THEATERS, CONCERTS, AND OTHER REOCCURING EVENTS

RF022 ADMISSION TO SPORTING EVENTS

RF03 Fees for lessons or instructions

RF031 FEES FOR LESSONS OR INSTRUCTIONS

RF09 Unsampled recreation services

RF090 UNSAMPLED ITEMS

RG Recreational reading materials

RG01 Newspapers and magazines

RG011 SINGLE-COPY NEWSPAPERS AND MAGAZINES

RG012 NEWSPAPER AND MAGAZINE SUBSCRIPTIONS

RG02 Recreational books

RG021 BOOKS PURCHASED THROUGH BOOK CLUBS

RG022 BOOKS PURCHASED AT RETAIL OUTLETS OTHER THAN BOOK CLUBS

RG09 Unsampled recreational reading materials

RG090 UNSAMPLED ITEMS

E MAJOR GROUP: EDUCATION AND COMMUNICATION

EDUCATION

EA Educational books and supplies

EA01 Educational books and supplies

EA011 COLLEGE TEXTBOOKS

EA012 ELEMENTARY AND HIGH SCHOOL BOOKS AND SUPPLIES

EA013 ENCYCLOPEDIAS AND OTHER SETS OF REFERENCE BOOKS

EA09 Unsampled educational books and supplies

EA090 UNSAMPLED ITEMS

EB Tuition, other school fees, and childcare

EB01 College tuition and fees

EB011 COLLEGE TUITION AND FIXED FEES

EB02 Elementary and high school tuition and fees

EB021 ELEMENTARY AND HIGH SCHOOL TUITION AND FIXED FEES

EB03 Childcare and preschool

EB031 DAYCARE AND PRESCHOOL

EB04 Technical and business school tuition and fees

EB041 TECHNICAL AND BUSINESS SCHOOL TUITION AND FIXED FEES

EB09 Unsampled tuition, other school fees, and childcare

EB090 UNSAMPLED ITEMS

COMMUNICATION

EC Postage and delivery services

EC01 Postage

EC011 POSTAGE

EC02 Delivery services

EC021 DELIVERY SERVICES
80

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

Information and information processing

ED Telephone services

ED03 Wireless Telephone services

ED031 WIRELESS TELEPHONE SERVICES

ED04 Telephone services, land-line

ED041 TELEPHONE SERVICES, LANDLINE

EE Information and information processing other than telephone services

EE01 Personal computers and peripheral equipment

EE011 PERSONAL COMPUTERS AND PERIPHERAL EQUIPMENT

EE02 Computer software and accessories

EE021 COMPUTER SOFTWARE AND ACCESSORIES

EE03 Computer information processing services

EE031 INTERNET ACCESS AND OTHER INFORMATION SERVICES

EE04 Other information processing equipment

EE041 TELEPHONE, PERIPHERAL EQUIPMENT, AND ACCESSORIES

EE042 CALCULATORS, TYPEWRITERS, AND OTHER INFORMATION PROCESSING EQUIPMENT

EE09 Unsampled information and information processing

EE090 UNSAMPLED ITEMS

G MAJOR GROUP: OTHER GOODS AND SERVICES

GA Tobacco and smoking products

GA01 Cigarettes

GA011 CIGARETTES

GA02 Tobacco products other than cigarettes

GA021 TOBACCO PRODUCTS OTHER THAN CIGARETTES

GA09 Unsampled tobacco and smoking products

GA090 UNSAMPLED ITEMS

GB Personal care products

GB01 Hair, dental, shaving, and miscellaneous personal care products

GB011 PRODUCTS AND NONELECTRIC ARTICLES FOR THE HAIR

GB012 DENTAL AND SHAVING PRODUCTS, INCLUDING NONELECTRIC ARTICLES

GB013 DEODORANT/SUNTAN PREPARATIONS, SANITARY/FOOTCARE PRODUCTS

GB014 ELECTRIC PERSONAL CARE APPLIANCES

GB02 Cosmetics, perfume, bath, nail preparations, and implements

GB021 COSMETICS, PERFUME, BATH, NAIL PREPARATIONS, and IMPLEMENTS

GB09 Unsampled personal care products

GB090 WOMEN'S HAIRPIECES/WIGS AND RENT/REPAIR PERS. CARE APPLS

GC Personal care services

GC01 Haircuts and other personal care services

GC011 HAIRCUTS AND OTHER PERSONAL CARE SERVICES

GD Miscellaneous personal services

GD01 Legal services

GD011 LEGAL SERVICES

GD02 Funeral expenses

GD021 FUNERAL EXPENSES

GD03 Laundry and dry cleaning services

GD031 LAUNDRY AND DRY CLEANING SERVICES

GD04 Apparel services other than laundry and dry cleaning
81

Appendix 5. Major groups, expenditure classes, item stratum, and entry-level items—continued

GD041 SHOE REPAIR AND OTHER SHOE SERVICES

GD042 CLOTHING ALTERATIONS, RENTALS, AND REPAIRS

GD043 WATCH AND JEWELRY REPAIR

GD05 Financial services

GD051 CHECKING ACCOUNTS AND OTHER BANK SERVICES

GD052 TAX RETURN PREPARATION AND OTHER ACCOUNTING FEES

GD09 Unsampled items

GD090 UNSAMPLED ITEMS

GE Miscellaneous personal goods

GE01 Miscellaneous personal goods

GE011 STATIONERY, STATIONERY SUPPLIES, AND GIFT WRAP

GE012 LUGGAGE

GE013 INFANTS' EQUIPMENT
82

CPI Appendix 6: Sample Allocation Methodology for Commodities and 

Services

The primary objective of the commodities and services sample design is to determine values for all 

sample design variables that minimize the sampling variance of 6-month price change for the 

commodities and services portion of the CPI. The sample design variables are the number of entrylevel items (ELIs) to select in each item stratum and the number of outlets to select per Telephone 

Point of Purchase Survey (TPOPS) category-replicate panel in each Primary Sampling Unit (PSU). To 

that end, the variance of price change for the commodities and services portion of the CPI and the total 

annual cost of data collection and processing are modeled as functions of the design variables. These 

models allow the sample design problem to be expressed as that of minimizing the total variance of 

price change, subject to various cost and sample allocation constraints. Within this framework, 

nonlinear programming methods are used to solve the problem for optimal values of the sample design 

variables. 

Certain simplifying assumptions are made to render the problem tractable and operationally more 

manageable. The number of PSUs, the number of replicate panels per PSU, and the classification of 

ELIs into item strata have been determined in previous work (Williams et al., 1993; Lane, 1996). Item 

strata are divided into 13 item groups for the design: four food-at-home groups (nonmeat staples; meat, 

poultry, and fish; fruits and vegetables; and other food at home and alcoholic and nonalcoholic 

beverages); food away from home; household furnishings and operations; fuels and utilities; apparel; 

transportation less motor fuel; motor fuel; medical care; education and communications; and recreation 

and other commodities and services. The 87 PSUs are divided into 15 groups according to size and 

number of replicate panels. (See the table that follows.) It is assumed that the same item and outlet 

sample sizes will apply to all PSUs within the same PSU group. This assumption reduces the allocation 

problem to that of determining the number of ELI selections per replicate panel by PSU group and item 

group [{Kij, i = 1, ..., 15, j = 1, ..., 13}, i = PSU group, j = item group], and the number of outlet 

selections per TPOPS category per replicate by PSU group and item group {Mij, i = 1, ..., 15, j = 1, ..., 

13}. These are the design variables.

PSU Groups for Commodities and Services Design 

PSU

group 

Name PSU 

group 

Name 

1. New York City 8. Smaller self-representing PSUs 

2. New York City 

 suburbs 

9. Non-self-representing PSUs, 

 Census Region 1 

 10. Non-self-representing PSUs, 

 Census Region 2 

3. Los Angeles suburbs 11. Non-self-representing PSUs, 

 Census Region 3 

4. Chicago 12. Non-self-representing PSUs, 

 Census Region 4 

5. Philadelphia and 

 San Francisco 

13. Smaller non-self-representing 

 PSUs, Census Regions 2–4 

 (there isn’t one for Region 1) 

6. Detroit and Boston 14. 

.

 Los Angeles City 

7. Other large self- 

 representing 

 PSUs 

15 Anchorage, AK, and Honolulu, HI 
83

Let 2

Total  be the total price change variance for the commodities and services portion of the CPI, and 

let CTotal be the total annual cost of data collection. Then the sample design problem can be 

expressed as that of minimizing 2

Total  subject to the following cost and sample allocation 

constraints:

 

CTotal ≤ Total data collection budget for commodities and services,

Mij ≥2 , i = 1,..., 15, j = 1 ,..., 13,

Kij ≥ Number of item strata in PSU group i, item group j, i = 1,..., 15, j = 1,..., 13,

Kij ≤Maximum number of item hits in PSU group i, item group j, i = 1,..., 15, 

j = 1,..., 13, and 

Average number of item hits per stratum–index area in PSU group i, item group  9, 

i = 1, ..., 15, j = 1, ..., 13.

A detailed description of BLS sample allocation methods follows. 

The sampling variance function 

Variance component models attempt to allocate parts of the total sampling variance to different sources 

of variation. For the commodities and services item–outlet sample, the following four sources of 

variation are modeled: PSU selection, item selection, outlet selection, and a residual component that 

includes other sources, such as sampling within the outlet. 

The variance function for the commodities and services sample design is modeled for index areas. Each 

self-representing PSU is a single index area. Non-self-representing PSUs represent seven index areas, 

with the sample for each area represented by 2 to 22 PSUs. In the equations that follow, both k,

representing what we shall call a super-index area, and k', representing an index area, are used. The 

only difference between the two is that the three index areas for smaller non-self-representing PSUs by 

Census region are combined into one super-index area for all small non-self-representing PSUs. As 

mentioned earlier, the variance model assumes that the total variance of price change for item group j

within super-index area k can be expressed as a sum of four components: 

. 2

error, , 2outlet, , 2tem, , 2psu, , 2  j,k   j k   i j k   j k   j k

In this equation, 

2  psu, j,k is the component of variance due to sampling of PSUs in non-self-representing areas, 

2  item, j,k is the component of variance due to sampling of ELIs within item strata, 

2  outlet, j,k is the component of variance due to sampling of outlets, and 

2  error, j,k is a residual component of variance that includes disaggregation, the final stage of within-outlet 

item selection . 

Similarly, it is assumed that the variance of price change of an individual sampled unit or quote has the 

same structure: 

. 2

unit,error, , 2unit,outlet, , 2unit,item, , 2unit,psu, , 2  unit, j,k   j k   j k   j k   j k
84

In this equation, 

2  unit, j,k is the total variance of price change of an individual sampled unit or quote for item j in 

super-index area k,

2  unit,psu, j,k is the component of unit variance due to sampling of PSUs in non-self-representing areas, 

2

unit,item, j,k σ is the component of unit variance due to sampling of ELIs within item strata, 

2 σ unit,outlet, j,k

is the component of unit variance due to sampling of outlets, and 

2

unit,error,j,k σ is the corresponding residual component of unit variance. 

Thus, the projected sampling variance for a given index area k in PSU group i is 

( ) ( ) ( ) ( ) + , , , + , , + , RI

' 4

2

unit,psu, , ′

3

2

unit,error, , ′

2

2

unit,outlet, ,

1

2

unit,item, ,

13

=1 '∈

2 ∑∑ ,

k

j k

ij ij k

j k

ij ij k

j k

ij k

j k

j k k

j k

K N M K N M K N f N

σ

f

σ

f

σ

f

σ

where 

in which 

PCk is the price change in super-index area k,

Hk is the number of replicate panels per PSU in the super-index area k,

NROj is the outlet initiation response rate for major group j,

NRQVj is the quote quote-level response rate for major group j for variance projection, 

NPVj is the weighted sum of non-POPS categories in major group ji, with each category 

weighted by its probability of selection, for variance projection, 

' Mij is the number of unique in-scope outlets selected per PSU replicate, modeled as the 

quadratic function ( ) ' 2 Mij  AVijMij  BVijMij of the outlet sample size, where AVij

and BVij are coordinates that are determined by the unique outlet cost predictor 

function program, and 

Nk is the number of PSUs in super-index area k.

  PCk  2 

( ) ( )

( ) [ ] ( )

( ) [ ] ( () )

( ) = the number of non-self-representing PSUsin index area ,

, , = NRQV , and

, , = ' + NPV NRO ,

, = , , ,

' 4

3

, , 2

1

N k'

M K N N H M K

M K N N H M N H M

K N N H K

k

ij ij k k k ij ij j

ij ij k k k i j k k i j j j

ij k k k ij

f

f

f

f
85

The sampling variance of price change for the All U.S. City Average commodities and services index 

is

RI , 2

, 2 2 , 2Total k j k

j k

  j kw 

where 

RI j,k is the relative importance of item group j in super-index area k, scaled to sum to 1.0 over all 

commodities and services item groups, and 

wk is the 1990 Census population weight of super-index area k.

Relative importances of item groups are obtained from the most recent 2 years of the Consumer 

Expenditure Survey and are the proportion of total expenditures in super-index area k that come from 

item group j.

The cost function 

The modeled costs of the commodities and services portion of the CPI are the costs of initiation data 

collection and travel and of pricing data collection (personal visit and telephone) and travel. Each of 

these models is developed in terms of outlet- and quote-related costs and as a function of the design 

decision variables. 

Initiation costs 

Outlet related initiation costs. For PSU group i and major group j, outlet-related costs for initiation are 

given by 

CI ( , )= 0.25 (CO + COT ) ( + + NPC ), where 2

j ij ij ij ij j ij j O Mij Kij Ni Hi AC M BC M M

( ) O Mij Kij CI , is the outlet-related initiation cost for major group j in PSU group i,

Ni is the number of PSUs in group i,

Hi is the number of replicates per PSU in PSU group i,

COj is the compensation initiation cost per outlet for major group j,

NPCj is the weighted sum of non-POPS categories in major group j, with each category 

weighted by its probability of selection, 

COTj is the per diem and mileage cost per outlet for major group j,

and   2 ACij Mij  BCij Mij is a quadratic overlap function used to predict the number of unique sample 

outlets and to account for the overlap of elements in the outlet sample within and between major 

groups for a replicate panel. Like AVij and BVij, ACij and BCij are coordinates determined by the unique 

outlet cost predictor function program. The number 0.25 accounts for the rotation or reinitiation of the 

outlet sample in one-fourth of the sample TPOPS categories’ PSUs each year. 

Quote related-initiation costs. Quote related initiation costs are given by 

CI ( , ) = 0.25 WOD CQ NRO , where Q Mij Kij NiHi j j Mij Kij j

CI ( , ) Q Mij Kij is the quote-related cost of initiation for major group j in PSU group i,

WODj is a seasonal items initiation factor for major group j, 

CQj is the initiation cost per quote for major group j, and

NROj is as before. 
86

Repricing costs 

The costs of ongoing price data collection and processing are also developed as both outlet- and quoterelated costs. 

Outlet-related repricing costs.For PSU group i and major group j, outlet-related costs for ongoing 

pricing are given by 

CP ( , ) = MBO NRO ( + + NPC ) (CPVO +CPO ) (1 RTO )+CTO RTO ], where 2

O Mij Kij ij Ni Hi j ACijMij BCijMij j

Mij j j j j j

CPO(Mij,Kij) is the total outlet-related cost for ongoing pricing for major group j in PSU group I,

CPVOj is the compensation cost (time spent in travel) for a personal visit for pricing per outlet 

for major group j,

CPOj is the travel cost (per diem and mileage) for a personal visit for pricing per outlet for 

major group j (equal to 3.33 for every j), 

RTOj is the proportion of outlets priced by telephone for major group j,

CTOj is the per-outlet cost for telephone collection, (equal to 3.43 for every j initially), 

NPOj is the weighted sum of non-POPS categories in major group j, with each category 

weighted by its probability of selection for cost projections, 

MBOij is a factor used to adjust for the monthly–bimonthly mix of outlets by PSU and major 

group, and NROj and NPCj are as before. 

Quote-related repricing costs. Quote-related costs for ongoing pricing are given by 

CP ( , ) = MBQ NRQC [CPVQ (1 RTQ ) + CTQ RTQ ], where Q M ij Kij ij Ni H i M ij Kij j j j j j

CPQ(Mij,Kij) is the total quote-related cost for ongoing pricing, 

MBQij is a factor to adjust for the monthly–bimonthly mix of quotes by PSU and major 

product group. 

CPVQj is the per-quote cost (compensation not spent in travel) for a personal visit for 

pricing, 

RTQ,j is the proportion of telephone collected quotes for major group j,

CTQ,j is the per quote cost for telephone collection for major group j, and

NRQCj is the quote-level response rate for projecting costs for major group j.

Total cost function

The total cost function associated with data collection for commodities and services, summed over all 

item groups and PSU groups, is then given by 

=∑[CI ( , ) +CI ( , ) +CP ( , ) +CP ( , )], ,

Total

i j

C O Mij Kij Q Mij Kij O Mij Kij Q Mij Kij

and the sample design problem can be expressed as that of minimizing the total variance, 2  Total ,

subject to the following constraints: 

CTotal ≤Total expenditure limit, 

Mij ≥2 , i = 1, ..., 15, j = 1, ...,13,

Kij  Number of item strata in PSU group i, item group j,i = 1, ..., 15, j = 1, ..., 13, 
87

Kij ≤Maximum number of item hits in PSU group i, item group j, i = 1 ,..., 15, j = 1, ..., 13, 

and

Average number of item hits per stratum-index area in PSU group i, item group  9, 

i = 1, ..., 15, j = 1, ..., 13. 

We note here that the last set of constraints is added to address concerns regarding small-sample bias at 

the elementary index level by ensuring a minimum average sample allocation of nine expected quotes 

in total per index area–item stratum combination. 

Model coefficients 

The parameters of the cost function are estimated from agency administrative records dating from 

fiscal year 1996 forward and a Time and Travel Study conducted by the BLS Office of Field 

Operations (OFO). Distinctions between personal visits and telephone collection of data are made on 

the basis of information from OFO and from an analysis of commodities and services microdata 

conducted within the BLS Prices Statistical Methods Division. Response rates for each item group are 

derived from field initiation records and ongoing pricing experience. 

Because outlet samples are selected independently for each TPOPS category and outlets may be listed 

in the sample frames for more than one TPOPS category, an individual outlet may be selected more 

than once. For example, a grocery store could be selected for both bakery products and dairy products. 

Thus, the number of unique outlets realized by the sampling process is needed to project outlet-related 

costs. Quadratic regressions are used to predict the number of unique outlets realized in sample 

selection as a function of designated sample size. The regressions are developed and reevaluated with 

each rotation by using the most current sampling frames available for each item to model the number 

of unique outlets obtained in simulations of sampling procedures for each PSU and item group as a 

function of designated sample sizes. 

Components of price change variance are computed with the use of restricted maximum-likelihood 

estimation methods with commodities and services price microdata, the most recent estimates being 

based on price data collected in 2009–2011. Component estimates are developed for 6-month price 

changes for the 13 item groups for each index area and month. Mean unit components of variance 

estimates are then computed by averaging the unit components of variance across months. 

Solutions 

Solutions are found with the SAS procedure PROC OPTMODEL. For each item group, the number of 

item selections is bounded below by the number of strata in the item group. 

ELI selections are then distributed among item strata within each item group, with consideration given 

to differences in relative importance, estimates of variances for production stratum-level price change 

and response rates among the item strata within each item group, as well as special problems identified 

by commodity analysts and field staff. Similarly, designated outlet sample sizes are distributed among 

the various TPOPS categories in item groups in order to manage variations in expected response rates 

and respondent burden. 

In general, recent sample designs have shifted resources in many item groups from sampling many 

outlets to sampling fewer outlets, with more item selections per outlet. This shift is due primarily to the 

large residual component of price change sampling variance estimated for most item groups, coupled 

with a trend of an increasing number of unique outlets realized in TPOPS sampling. 
88

1

Appendix 7. Point-of-Purchase Survey (POPS) categories1

The following tabulation lists retail entry-level items (ELIs) and numbers, by category, in the BLS point-ofPurchase Survey (POPS): 

POPS Category and title, ELI number and title 

F14 Fresh fish or fresh seafood 

FG011 Fresh fish and seafood 

F15 Processed fish or processed seafood, including frozen, canned, or cooked 

FG021 Processed fish and seafood 

F20 Miscellaneous dairy products, including yogurt, powdered milk, or coffee creamers 

FJ041 Other dairy and related products 

F29 Canned fruits or vegetables 

 FM011 Canned and other shelf-stable fruits and vegetables 

F30 Frozen fruits or vegetables 

 FM021 Frozen fruits and vegetables 

F31 Dried or other processed fruits and vegetables 

 FM031 Other processed fruits and vegetables, including dried 

F32 Carbonated Drinks 

 FN011 Carbonated Drinks 

F33 Noncarbonated juices or drinks, frozen and non frozen 

FN021 Frozen noncarbonated juices and drinks 

FN031 Non frozen noncarbonated juices and drinks 

F34 Roasted, instant, or freeze-dried coffee 

 FP011 Coffee 

F35 Powdered drinks, tea, cocktail mixes, or ice 

FP021 Tea 

 FP022 Other beverage materials 

F36 Sugar or artificial sweeteners 

FR011 Sugar and artificial sweeteners 

F37 Candy or chewing gum 

FR021 Candy and chewing gum 
89

2

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

F38 Jellies, syrups, honey, molasses, marshmallows, icings, or fudge mixes 

 FR031 Other sweets 

F39 Butter or margarine 

FS011 Butter and margarine 

F40 Salad dressing or mayonnaise 

FS021 Mayonnaise, salad dressing, and sandwich spreads 

F41 Peanut butter, or cooking fats and oils 

FS031 Peanut butter and other nut butters 

FS032 Other fats and oils 

F47 Baby food 

 FT051 Baby food 

F48 Prepared salads or salad bars, excluding restaurants 

FT061 Prepared salads 

F49 Easy-to-prepare canned or packaged foods, excluding fruits, vegetables, and soups 

FT062 Other miscellaneous foods 

F50 Full-service meals or snacks, such as meals at sit-down restaurants 

FV011 Full-service meals and snacks 

F51 Limited-service meals or snacks, such as meals from fast food restaurants or delivered meals 

FV021 Limited-service meals and snacks 

F52 Meals or snacks at schools or employer-provided cafeterias, dining rooms, or snack bars 

FV031 Food at employee sites and schools 

F53 Food or beverages from vending machines or mobile vendors 

FV041 Food from vending machines and mobile vendors 

F54 Catered events or board 

FV051 Board, catered events, and other food away from home 

F55 Beer, ale, sake, or other malt beverages for home use 

FW011 Beer, ale, and other malt beverages at home 

F56 Hard liquor for home use 

FW021 Distilled spirits at home 

F57 Wine for home use 

FW031 Wine at home 

F58 Alcoholic beverages served in bars, restaurants, clubs, or similar places 

FX011 Alcoholic beverages away from home 

F59 Cereal, rice, pasta, cornmeal or flour 

FA011 Flour and prepared flour mixes 

FA021 Breakfast cereal 

FA031 Rice, pasta, and cornmeal 
90

3

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

F60 Bread, cookies, or other baked goods, including frozen 

FB011 Bread 

FB021 Fresh biscuits, rolls, and muffins 

FB031 Cakes and cupcakes (excluding frozen) 

FB032 Cookies 

FB041 Crackers, and bread and cracker products 

FB042 Sweet rolls, coffeecakes, and doughnuts (excluding frozen) 

FB043 Frozen bakery products and frozen/refrigerated doughs and batters 

FB044 Pies, Tarts, and turnovers (excluding frozen) 

F61 Meat, including beef, poultry, pork, or lunch meats 

FC011 Uncooked ground beef 

FC021 Uncooked beef roasts 

FC031 Uncooked beef steaks 

FC041 Other uncooked beef and veal 

FD011 Bacon, breakfast sausage, and related products 

FD021 Ham 

FD031 Pork chops 

FD041 Other pork, including roasts and picnics 

FE011 Frankfurters 

FE012 Lunch meats 

FE013 Lamb, organ meats, and game 

FF011 Chicken 

 FF021 Other poultry, including turkey 

F62 Milk, eggs, cheese, ice cream, or frozen yogurt 

FH011 Eggs 

FJ011 Milk 

FJ021 Cheese and cheese products 

FJ031 Ice cream and related products 

F63 Fresh Fruits or Vegetables 

FK011 Apples 

 FK021 Bananas 

 FK031 Citrus fruits 

 FK041 Other fresh fruits 

 FL011 Potatoes 

 FL021 Lettuce 

 FL031 Tomatoes 

 FL041 Other fresh vegetables, including fresh herbs 

F64 Frozen prepared foods, chips, nuts, or other snacks or soups 

FT011 Soups and soup bases 

FT021 Frozen and freeze-dried prepared foods 

FT031 Snacks 

F65 Condiments, spices, sauces, or gravies 

FT041 Salt and other seasonings and spices 

FT042 Olives, pickles, and relishes 

FT043 Sauces and gravies 

 FT044 Other condiments (excluding olives, pickles, and relishes) 
91

4

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

H02 Lodging away from home, such as hotels, motels, or vacation rentals 

 HB021 rental of lodging away from home 

H03 Fuel oil for household heating

 HE011 Fuel oil 

H04 Propane, firewood, coal, or charcoal used for household heating or cooking 

HE021 Other household fuels 

H05 Residential water or sewer service 

HG011 Residential water and sewerage service 

H06 Garbage or trash collection service 

HG021 Garbage and trash collection 

H07 Floor coverings, such as hard surface tiling, carpets, or scatter rugs 

HH011 Floor coverings 

H08 Window coverings, such as curtains, drapes, or blinds 

 HH021 Curtains and drapes 

 HH022 Window coverings 

H09 Household linens, such as kitchen or bathroom towels, bedding, or tablecloths 

HH031 Bathroom linens 

HH032 Bedroom linens 

 HH033 Kitchen and dining room linens 

H10 Bedroom furniture, including mattresses or springs 

HJ011 Mattresses and foundations 

 HJ012 Bedroom furniture other than mattresses and springs 

H14 Infants’ furniture 

HJ031 infants’ furniture 

H15 Outdoor furniture 

HJ032 outdoor furniture 

H16 Other furniture, including entertainment centers, bookcases, or desks 

HJ033 Occasional furniture 

H17 Refrigerators or home freezers 

HK011 Refrigerators and home freezers 

H18 Washers or dryers 

HK012 Washers and dryers 

H19 Stoves, Ovens, or microwave ovens 

HK013 Ranges and cooktops 

 HK014 Microwave ovens 

H20 Vacuums or other electric floor-cleaning equipment 

HK021 Floor-cleaning equipment 
92

5

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

H21 Small electric kitchen appliances or clothing irons 

HK022 Small electric kitchen appliances 

H22 Heating or cooling equipment or home safety devices 

HK023 Other electric appliances 

H23 Household decorative items, including clocks or lamps 

 HL011 Lamps and lighting fixtures 

 HL012 Household decorative items and clocks 

H24 Dishes, glassware, or flatware 

HL031 Dishes 

HL032 Flatware 

H25 Nonelectric kitchen utensils, cookware, or bake ware 

HL041 Non electric cooking ware 

HL042 Table ware and nonelectric kitchenware 

H26 Indoor plants or fresh-cut flowers 

 HL021 Indoor plants and fresh-cut flowers 

H27 Paint, Wallpaper tools, or related supplies 

HM011 Paint, wallpaper tools, and supplies 

H28 Power Tools 

HM012 Power tools 

H29 Non powered tools or miscellaneous hardware 

HM013 Miscellaneous hardware, supplies, and equipment 

 HM014 Non powered tools 

H30 Barbeque grills, powered lawn and garden equipment, or other outdoor items 

 HM021 Lawn and garden equipment and outdoor equipment and grills 

H31 Lawn and garden supplies or insecticides 

 HM022 Lawn and garden materials, other decorative items, and pesticides 

H32 Household laundry and cleaning products or supplie

 HN011 Laundry and cleaning products 

 HN012 Laundry and cleaning equipment 

H33 Paper napkins, paper towels, facial tissue, or toilet paper 

 HN021 Household paper products 

H34 Other disposable products, such as plastic or foil wraps, garbage bags, paper plates, 

batteries, or light bulbs

HN031Miscellaneous household products 

H35 Housekeeping services 

 HP011 Domestic Services 

H36 Gardening or Lawn Care Services 

 HP021 Gardening and lawn care services 
93

6

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

H37 Moving, storage, or freight services 

 HP031 Moving, storage, and freight expense 

H38 Major appliance repair 

 HP041 Appliance repair 

H39 Reupholstery of household furniture 

 HP042 Reupholstery of furniture 

H40 Inside home maintenance or repair 

 HP043 Inside home maintenance and repair services 

H41 Living room, dining room, or kitchen furniture 

HJ021 Sofas, furniture covers, and decorative pillows 

HJ022 Living room chairs 

 HJ023 Living room tables 

HJ024 Kitchen and dining room furniture 

A01 Men’s suits or blazers

AA011 Men’s suits 

 AA012 Men’s sport coats and tailored jackets 

A02 Men’s coats or jackets

 AA013 Men’s outerwear 

A03 Men’s socks, underwear, sleepwear, or bathrobes 

 AA021 Men’s underwear, hosiery, nightwear, and loungewear 

A04 Men’s accessories, such as ties, belts, or wallets 

 AA022 Men’s accessories 

A05 Men’s active sportswear, such as exercise apparel or bathing suits 

AA023 Men’s active sportswear 

A06 Men’s shirts, sweaters, or vests 

AA033 Men’s shirts, sweaters, and vests

A07 Men’s pants, jeans, or shorts 

AA041 Men’s pants and shorts 

A08 Boys’ clothing or accessories 

AB011 Boys’ outerwear 

AB012 Boys’ shirts and sweaters 

AB013 Boys’ underwear, sleepwear, hosiery, and accessories 

AB014 Boys’ suits, sport coats, and pants 

 AB015 Boys’ active sportswear 

A09 Women’s Outerwear 

AC011 Women’s outerwear 

A10 Women’s dresses 

AC021 Women’s dresses 
94

7

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

A11 Women’s tops, skirts, pants, or shorts 

AC031 Women’s tops 

 AC032 Women’s skirts, pants, and shorts 

A12 Women’s suits or suit components 

AC033 Women’s suits and coordinates 

A13 Women’s underwear or nightwear 

AC041 Women’s underwear, nightwear, and other undergarments 

A14 Women’s hosiery or accessories 

AC042 Women’s hosiery and accessories 

A15 Women’s active sportswear, such as exercise apparel or bathing suits 

AC043 Women’s active sportswear 

A16 Girls’ clothing or accessories

AD011 Girls’ outerwear 

AD012 Girls’ dresses 

AD013 Girls’ tops 

AD014 Girls’ skirts, pants, and shorts 

AD015 Girls’ active sportswear 

 AD016 Girls’ underwear, sleepwear, hosiery, and accessories 

A17 Men’s footwear

 AE011 Men’s footwear 

A18 Boys’ or girls’ footwear 

AE021 Boys’ footwear 

AE022 Girls’ footwear 

A19 Women’s footwear 

AE031 Women’s footwear 

A20 Infants’ and toddlers’ clothing or accessories, excluding underwear and diapers 

 AF011 Infants’ and toddlers’ outer, play, dress, and sleepwear and accessories 

A21 Infants’ and toddlers’ underwear or diapers 

AF012 Infants’ and toddlers’ underwear and diapers 

A22 Watches 

AG011 Watches 

A23 Jewelry 

AG021 Jewelry 

T01 New cars, trucks, or vans 

TA011 New car and truck purchase 

T02 New motorcycles 

TA012 New motorcycles 
95

8

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

T03 Leased cars, trucks, or vans 

TA031 Vehicle leasing 

T04 Car, truck, or van rental 

TA041 Automobile and truck rental 

T05 Gasoline, diesel, or alternative fuels 

TB011 Regular unleaded gasoline 

TB012 Midgrade unleaded gasoline 

TB013 Premium unleaded gasoline 

TB021 Automotive diesel fuel 

 TB022 Alternative motor fuels 

T07 Tires 

TC011 Tires 

T08 Vehicle parts or accessories 

TC021 Vehicle parts and equipment other than tires 

T09 Motor oil, coolants, or fluids 

TC022 Motor oil, coolants, and fluids 

T10 Motor vehicle bodywork 

TD011 Motor vehicle bodywork 

T11 Motor vehicle maintenance, inspections, or towing 

TD021 Motor vehicle maintenance and servicing 

T12 Motor vehicle repair

TD031 Motor vehicle repair 

T15 Parking fees or tolls 

 TF031 Parking fees and tolls 

T16 Automobile service clubs 

TF032 Automobile service clubs 

T17 Ship travel or passenger cruises 

TG023 Ship fares 

T18 Intracity mass transit 

TG031 Intracity mass transit 

T19 Taxi or cab fare 

TG032 Taxi fare 

T20 Car or van pools 

TG033 Car and van pools 

M01 Prescription drugs 

MF011 Prescription drugs 

M02 Nonprescription drugs 

 MF021 Nonprescription drug
96

9

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

M03 Medical equipment, supplies, or dressings 

 MG011 Dressings and first-aid kits 

MG012 Medical equipment for general use 

 MG013 Supportive and convalescent medical equipment 

M04 Physician’s services 

 MC011 Physician’s services 

M05 Dental services 

MC021 Dental services 

M06 Eyeglasses or eye care 

 MC031 Eyeglasses and eye care 

M07 Services by other medical professionals 

 MC041 Services by other medical professionals 

M08 Hospital services 

 MD011 Hospital services 

M09 Adult daycare 

 MD022 Adult daycare 

R01 Televisions 

RA011 Televisions 

R02 Cable or satellite television or radio service 

RA021 Cable and satellite television and radio service 

R03 DVD players, camcorders, or other video equipment 

RA031 Other video equipment 

R04 Prerecorded video, such as dvds, digital files, downloads, and other media 

 RA041 Prerecorded video discs, digital files, downloads, and other media 

R05 Rental of DVDs or video games 

RA042 Rental of video or audio discs, tapes, digital files, or downloads 

R06 Audio equipment for automobiles or home 

RA051 Audio components, radios, tape recorders/players, and other equipment 

R07 Prerecorded or blank audiotapes, CDs, or records 

RA061 Audio discs and tapes, prerecorded and blank 

R08 Pet food 

RB011 Pet food 

R09 Pets, pet supplies, or accessories 

RB012 Purchase of pets, pet supplies, and accessories 

R10 Pet services, such as grooming, boarding, or training 

RB021 Pet services 
97

10

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

R11 Veterinarian services 

RB022 Veterinarian services 

R12 Outboard motors or powered sports vehicles

 RC011 Outboard motors and powered sports vehicles 

R13 Unpowered boats or trailers 

RC012 Unpowered boats and trailers 

R14 Bicycles or bicycling accessories 

RC013 Bicycles and accessories 

R15 General sports equipment 

RC021 General sports equipment, excluding water 

 RC022 Water sports equipment 

R16 Hunting, fishing, or camping equipment 

RC023 Hunting, fishing, and camping equipment 

R17 Film or film development supplies or disposable cameras 

RD011 Film and photographic supplies 

R18 Cameras or other photographic equipment, excluding film 

RD012 Photographic equipment 

R19 Photographer’s fees 

RD021 Photographer’s fees 

R20 Digital photo prints or film development 

RD022 Film processing 

R21 Toys, games, hobby supplies, or playground equipment 

RE011 Toys, games, hobbies, and playground equipment 

R22 Video game hardware, games, or accessories 

RE012 Video game hardware, software, and accessories 

R23 Sewing machines, fabric, or sewing supplies 

RE021 Sewing items 

R24 Musical instruments or musical accessories 

RE031 Musical instruments and accessories 

R25 Club membership dues for fraternal or civic organizations or fees for participant sports 

 RF011 Club dues and fees for participant sports and group exercises 

R26 Admissions, such as to movies, concerts, or theme parks 

RF021 Admission to movies, theaters, concerts, and other reoccurring events 

R27 Admissions to sporting events 

RF022 Admissions to sporting events 

R28 Recreational lessons or instruction 

RF031 Fees for lessons or instruction 
98

11

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

R29 Single-copy newspapers or magazines 

RG011 Single-copy newspapers and magazines 

R30 Newspaper or magazine subscriptions 

RG012 Newspaper and magazine subscriptions 

R31 Books purchased through book clubs 

 RG021 Books purchased through book clubs 

R32 Other books, audiobooks, or e books 

RG022 Books purchased at other than book clubs 

E01 College or university-level textbooks 

EA011 College textbooks 

E02 Elementary or high school textbooks 

EA012 Elementary and high school books and supplies 

E03 Encyclopedias or other sets of reference books 

EA013 Encyclopedias and other sets of reference books 

E04 Tuition or fixed fees for a college or university 

EB011 College tuition and fixed fees 

E05 Tuition or fixed fees for private elementary or high schools 

EB021 Elementary and high school tuition and fixed fees 

E06 Daycare providers, including nursery schools 

EB031 Daycare and preschool 

E07 Tuition or fixed fees for technical or vocational schools

 EB041 Technical and business school tuition and fixed fees 

E08 Delivery services

 EC021 Delivery services 

E11 Personal computers or peripheral equipment 

EE011 Personal computers and peripheral equipment 

E12 Computer software, computer accessories, and blank media, including memory cards, 

 recordable discs and other forms 

 EE021 Computer software and accessories 

E13 Accessing the Internet at home and away, including separate or bundled charges 

 EE031 Internet access and other information services 

E14 Home or cellular telephones, answering machines, or other phone accessories 

EE041Telephones, peripheral equipment, and accessories 

E15 Calculators, typewriters, or other information-processing equipment 

EE042 Calculators, typewriters, and other information-processing equipment 
99

12

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

E16 Cellular telephone service 

 ED031 Wireless phone service 

E17 Local or long-distance landline telephone service, including 

Prepaid phone cards 

ED041 Telephone service landline 

G01 Cigarettes 

 GA011 Cigarettes 

G02 Cigars, smoking tobacco, or chewing tobacco 

GA021 Tobacco products other than cigarettes 

G03 Hair Products, such as shampoo, sprays, pins, or combs 

 GB011 Products and nonelectric articles for the hair 

G04 Dental or shaving products 

 GB012 Dental and Shaving Products, Including Nonelectric Articles 

G05 Deodorant, feminine hygiene products, suntan lotions, or foot care products 

 GB013 Deodorant/suntan preparations and sanitary/foot care products 

G06 Electric personal care appliances, such as shavers or hair dryers 

GB014 Electric personal care appliances 

G07 Cosmetics, perfumes, or bath and nail preparations 

 GB021 Cosmetics/perfume/bath/nail preparations and implements 

G08 Personal care services, such as haircuts, nail services, or tanning 

GC011 Haircuts and other personal care services 

G09 Legal services 

 GD011 Legal fees 

G10 Funeral services 

 GD021 Funeral expenses 

G11 Laundry or dry cleaning services 

 GD031 Laundry and dry cleaning services 

G12 Shoe repair or other shoe services 

 GD041 Shoe repair and other shoe services 

G13 Clothing rental, alterations, or repairs 

 GD042 Clothing alterations, rentals, and repairs 

G14 Watch or jewelry repair 

GD043 Watch and jewelry repair 

G15 Checking account fees, credit card fees, or other bank services 

GD051 Checking accounts and other bank services 
100

13

Appendix 7. Point-of-Purchase Survey (POPS) categories—continued 

G16 Tax return preparation or other accounting services 

GD052 Tax return preparation and other accounting services 

G18 Stationary, school supplies, or gift wrap 

GE011 Stationery, stationery supplies, and gift wrap 

G19 Luggage, briefcases, or other carrying cases 

GE012 Luggage 

G20 Infants’ equipment, such as strollers, car seats, bottles, or dishes 

 GE013 Infants’ equipment 

 

1

 Some POPS category numbers have been changed to reflect changes in item composition. 
101

1

Appendix 8. Non-Point-of-Purchase Survey (Non-POPS) sample designs 

For each non-POPS entry-level item (e.g., electricity), the following information is given: 

1. Source of the universe data 

2. Sampling unit for outlets 

3. Measure of size 

4. Desired final pricing unit 

EC011 Postage 

1. The distribution of household mail by type of postal service and postal zone, as determined by the 

postal service in the Household Mailstream Study, Final Report, prepared for the U.S. Postal Service. 

2. U.S. Postal Service 

3. Postal revenue for each type of postal service and postal zone 

4. Specific postal service and zones traveled 

MD031 Care of invalids, elderly, and convalescents in the home 

1. Center for Medicare and Medicaid Services (CMS) Home Healthcare Care system 

2. Facilities providing adult home care in each CPI sample area 

3. Each facility within a given CPI sample area has an equal chance of being selected. 

4. Specific services provided 

HD011/TE011 Insurance—tenants/motor vehicle 

1. National Association of Insurance Commissioners 

2. Insurance companies serving the states in which CPI sample areas are located 

3. Total revenue for noncommercial policies, by type of insurance 

4. Specific policy serving within each CPI sample area 

HF011 Electricity 

1. a. Consumer Expenditure (CE) survey 

b. Direct information from the regional field offices 

2. Electric utility companies reported in the CE survey or electric utility companies serving each of the 

CPI sample areas 

3. Expenditures for electricity, as reported in the CE survey, or an estimate of the number of residential 

customers provided by the field 

4. Specific type of service for a specific number of kilowatt hours 

HF021 Utility natural gas 

1. a. Consumer Expenditure (CE) survey 

b. Direct information supplied by the regional field offices 

2. Gas utility companies reported in the CE survey or gas companies serving each of the CPI sample areas 

3. Expenditures for natural gas, as reported in the CE survey or an estimate of the number of residential 

customers provided by the field 

4. Specific type of service and specific number of cubic feet or therms of gas 
102

2

Appendix 8. Non-Point-of-Purchase Survey(Non-POPS) sample designs—continued 

MD021 Nursing and convalescent home care 

1. Center for Medicare and Medicaid Services (CMS) Nursing Home Care system 

2. Facilities providing nursing home care in each CPI sample area 

3. Number of beds 

4. Specific accommodations and services provided 

TA021 Used vehicles 

1. J. D. Power data 

2. Selection of used vehicles, based on vehicle sales data from J. D. Powers 

3. Expenditures reported for used vehicles in the CE survey 

4. Specific used vehicles with specific options; prices collected from the National Automobile Dealers 

Association (NADA) Official Used Car Guide 

TF011 State vehicle registration and driver’s license 

1. Each state’s Department of Motor Vehicles 

2. State motor vehicle department in each CPI sample area 

3. Revenue generated by each type of fee 

4. Specific class/vehicle registration, type of license, or vehicle property tax 

TG011 Airline fares 

1. Department of Transportation data file consisting of a 10-percent sample of all passenger itineraries 

originating in the United States 

2. All airlines providing service from any CPI sample area 

3. Total number of nonbusiness passengers per airline, per trip itinerary, per fare class 

4. Specific trip itinerary and fare class for the selected airline 

TG021 Intercity bus fares 

1. Scheduled intercity bus trips from each CPI sample area 

2. Bus companies serving each CPI sample area 

3. Number of buses that leave for a given destination 

4. Specific trip itinerary and fare class 

TG022 Intercity train service 

1. Data file of intercity train trips provided by Amtrak and the Alaskan Railroad 

2. Amtrak and the Alaskan Railroad 

3. Number of tickets sold 

4. Specific trip and class 
103

Table 1 (2011-2012 Weights). Relative importance of components in the Consumer Price Indexes: U.S.

city average, December 2014

(Percent of all items)

Item and group

U.S. city average 

CPI-U CPI-W

Expenditure category

All items .......................................................................................................... 100.000 100.000

Food and beverages ..................................................................................... 15.272 16.011 

Food ........................................................................................................... 14.257 15.052

Food at home ........................................................................................... 8.427 9.259 

Cereals and bakery products .................................................................. 1.138 1.255 

Cereals and cereal products ................................................................. .370 .420 

Flour and prepared flour mixes ........................................................... .048 .052 

Breakfast cereal .................................................................................. .197 .221 

Rice, pasta, cornmeal ......................................................................... .126 .147 

Bakery products .................................................................................... .767 .835 

Bread .................................................................................................. .230 .247 

Fresh biscuits, rolls, muffins ............................................................... .116 .121 

Cakes, cupcakes, and cookies ........................................................... .189 .208 

Other bakery products ........................................................................ .233 .259 

Meats, poultry, fish, and eggs ................................................................. 2.014 2.376 

Meats, poultry, and fish ........................................................................ 1.880 2.221 

Meats .................................................................................................. 1.229 1.478 

Beef and veal .................................................................................... .582 .714 

Uncooked ground beef ................................................................... .238 .292 

Uncooked beef roasts ..................................................................... .085 .098 

Uncooked beef steaks .................................................................... .207 .253 

Uncooked other beef and veal ........................................................ .053 .071 

Pork .................................................................................................. .372 .438 

Bacon, breakfast sausage, and related products ........................... .141 .161 

Ham ................................................................................................ .078 .091 

Pork chops ...................................................................................... .064 .083 

Other pork including roasts and picnics .......................................... .089 .103 

Other meats ...................................................................................... .275 .326 

Poultry ................................................................................................ .360 .439 

Chicken ............................................................................................. .294 .369 

Other poultry including turkey ........................................................... .066 .069 

Fish and seafood ................................................................................ .291 .305 

Fresh fish and seafood ..................................................................... .148 .149 

Processed fish and seafood ............................................................. .142 .156 

Eggs ..................................................................................................... .134 .155 

Dairy and related products ..................................................................... .898 .950 

Milk ....................................................................................................... .283 .320 

Cheese and related products ............................................................... .286 .287 

Ice cream and related products ............................................................ .126 .132 

Other dairy and related products .......................................................... .204 .211 

Fruits and vegetables ............................................................................. 1.379 1.419 

Fresh fruits and vegetables .................................................................. 1.076 1.088 

Fresh fruits .......................................................................................... .575 .580 

Apples ............................................................................................... .083 .086 

Bananas ........................................................................................... .087 .102 

Citrus fruits ....................................................................................... .146 .159 

Other fresh fruits ............................................................................... .259 .233 

Fresh vegetables ................................................................................ .500 .509 

Potatoes ........................................................................................... .075 .080 

Lettuce .............................................................................................. .072 .077 

Tomatoes .......................................................................................... .102 .116 

Other fresh vegetables ..................................................................... .251 .236 

Processed fruits and vegetables .......................................................... .303 .331 

Canned fruits and vegetables ............................................................. .157 .177 

Frozen fruits and vegetables .............................................................. .088 .091 

Other processed fruits and vegetables including dried ....................... .057 .063 

Nonalcoholic beverages and beverage materials ................................... .955 1.092 

Juices and nonalcoholic drinks ............................................................. .699 .831 

Carbonated drinks .............................................................................. .285 .348 

Frozen noncarbonated juices and drinks ............................................ .014 .018 

Nonfrozen noncarbonated juices and drinks ...................................... .400 .465 

Beverage materials including coffee and tea ........................................ .256 .261 

Coffee ................................................................................................. .158 .150 

Other beverage materials including tea .............................................. .099 .110 

Appendix 9. Relative importance of components in the Consumer Price Indexes, 

2011−2012 weights: U.S. city average, December 2014
104

Table 1 (2011-2012 Weights). Relative importance of components in the Consumer Price Indexes: U.S.

city average, December 2014-Continued

(Percent of all items)

Item and group

U.S. city average 

CPI-U CPI-W

Expenditure category

Other food at home ................................................................................. 2.043 2.167 

Sugar and sweets ................................................................................. .299 .302 

Sugar and artificial sweeteners .......................................................... .054 .066 

Candy and chewing gum .................................................................... .185 .178 

Other sweets ...................................................................................... .060 .058 

Fats and oils ......................................................................................... .245 .270 

Butter and margarine .......................................................................... .077 .075 

Salad dressing .................................................................................... .062 .067 

Other fats and oils including peanut butter ......................................... .107 .128 

Other foods ........................................................................................... 1.499 1.595 

Soups ................................................................................................. .093 .093 

Frozen and freeze dried prepared foods ............................................ .285 .315 

Snacks ................................................................................................ .330 .345 

Spices, seasonings, condiments, sauces ........................................... .292 .302 

Baby food ........................................................................................... .055 .084 

Other miscellaneous foods ................................................................. .444 .456 

Food away from home .............................................................................. 5.830 5.793 

Full service meals and snacks ................................................................ 2.823 2.361 

Limited service meals and snacks .......................................................... 2.413 2.830 

Food at employee sites and schools ...................................................... .212 .233 

Food from vending machines and mobile vendors ................................. .064 .092 

Other food away from home ................................................................... .319 .276 

Alcoholic beverages ................................................................................. 1.015 .959 

Alcoholic beverages at home ................................................................. .597 .564 

Beer, ale, and other malt beverages at home ...................................... .274 .364 

Distilled spirits at home ......................................................................... .073 .055 

Wine at home ....................................................................................... .250 .145 

Alcoholic beverages away from home .................................................... .418 .395 

Housing ........................................................................................................ 42.173 40.464

Shelter ........................................................................................................ 32.711 31.105

Rent of primary residence ........................................................................ 7.159 9.800 

Lodging away from home ......................................................................... .839 .461 

Housing at school, excluding board ........................................................ .172 .080 

Other lodging away from home including hotels and motels .................. .666 .380 

Owners’ equivalent rent of residences ..................................................... 24.339 20.511 

Owners’ equivalent rent of primary residence ........................................ 22.918 19.967 

Unsampled owners’ equivalent rent of secondary residences ............... 1.421 .544 

Tenants’ and household insurance ........................................................... .375 .333 

Fuels and utilities ........................................................................................ 5.273 5.903 

Household energy .................................................................................... 4.051 4.578 

Fuel oil and other fuels ........................................................................... .236 .208 

Fuel oil .................................................................................................. .139 .118 

Propane, kerosene, and firewood ......................................................... .097 .090 

Energy services ...................................................................................... 3.815 4.369 

Electricity .............................................................................................. 2.940 3.436 

Utility (piped) gas service ..................................................................... .875 .933 

Water and sewer and trash collection services ........................................ 1.222 1.326 

Water and sewerage maintenance ......................................................... .945 1.037 

Garbage and trash collection .................................................................. .277 .288 

Household furnishings and operations ....................................................... 4.189 3.455 

Window and floor coverings and other linens ........................................... .266 .208 

Floor coverings ....................................................................................... .047 .028 

Window coverings .................................................................................. .053 .040 

Other linens ............................................................................................ .166 .140 

Furniture and bedding .............................................................................. .769 .666 

Bedroom furniture ................................................................................... .268 .233 

Living room, kitchen, and dining room furniture ...................................... .363 .318 

Other furniture ........................................................................................ .128 .096 

Unsampled furniture ............................................................................... .009 .019 

Appliances ................................................................................................ .271 .276 

Major appliances .................................................................................... .147 .152 

Other appliances .................................................................................... .120 .118 

Unsampled appliances ........................................................................... .004 .006 

Other household equipment and furnishings ............................................ .479 .443 

Clocks, lamps, and decorator items ....................................................... .257 .267 

Indoor plants and flowers ....................................................................... .107 .083 

Appendix 9. Relative importance of components in the Consumer Price Indexes,

2011−2012 weights: U.S. city average, December 2014─continued
105

Table 1 (2011-2012 Weights). Relative importance of components in the Consumer Price Indexes: U.S.

city average, December 2014-Continued

(Percent of all items)

Item and group

U.S. city average 

CPI-U CPI-W

Expenditure category

Dishes and flatware ................................................................................ .041 .030 

Nonelectric cookware and tableware ...................................................... .074 .063 

Tools, hardware, outdoor equipment and supplies ................................... .710 .574 

Tools, hardware and supplies ................................................................. .189 .198 

Outdoor equipment and supplies ............................................................ .367 .247 

Unsampled tools, hardware, outdoor equipment and supplies ............... .154 .129 

Housekeeping supplies ............................................................................ .847 .910 

Household cleaning products ................................................................. .337 .391 

Household paper products ..................................................................... .247 .273 

Miscellaneous household products ........................................................ .263 .246 

Household operations ............................................................................... .848 .378 

Domestic services .................................................................................. .279 .073 

Gardening and lawncare services .......................................................... .279 .117 

Moving, storage, freight expense ........................................................... .116 .081 

Repair of household items ...................................................................... .066 .055 

Unsampled household operations .......................................................... .107 .052 

Apparel ......................................................................................................... 3.343 3.595

Men’s and boys’ apparel ............................................................................. .834 .973 

Men’s apparel ........................................................................................... .653 .737 

Men’s suits, sport coats, and outerwear ................................................. .104 .095 

Men’s furnishings .................................................................................... .185 .198 

Men’s shirts and sweaters ...................................................................... .196 .223 

Men’s pants and shorts .......................................................................... .160 .207 

Unsampled men’s apparel ...................................................................... .007 .014 

Boys’ apparel ............................................................................................ .181 .236 

Women’s and girls’ apparel ........................................................................ 1.439 1.427 

Women’s apparel ...................................................................................... 1.210 1.151 

Women’s outerwear ................................................................................ .118 .121 

Women’s dresses ................................................................................... .155 .146 

Women’s suits and separates ................................................................ .550 .544 

Women’s underwear, nightwear, sportswear and accessories .............. .378 .328 

Unsampled women’s apparel ................................................................. .010 .012 

Girls’ apparel ............................................................................................ .229 .276 

Footwear ..................................................................................................... .725 .821 

Men’s footwear ......................................................................................... .218 .297 

Boys’ and girls’ footwear ........................................................................... .178 .220 

Women’s footwear .................................................................................... .329 .304 

Infants’ and toddlers’ apparel ..................................................................... .135 .193 

Jewelry and watches .................................................................................. .211 .181 

Watches .................................................................................................... .046 .075 

Jewelry ..................................................................................................... .164 .106 

Transportation .............................................................................................. 15.289 18.015 

Private transportation ................................................................................. 14.167 17.211 

New and used motor vehicles .................................................................. 5.720 6.886 

New vehicles .......................................................................................... 3.551 3.527 

Used cars and trucks .............................................................................. 1.591 2.827 

Leased cars and trucks .......................................................................... .397 .353 

Car and truck rental ................................................................................ .073 .048 

Unsampled new and used motor vehicles .............................................. .109 .130 

Motor fuel .................................................................................................. 3.979 5.214 

Gasoline (all types) ................................................................................. 3.904 5.100 

Other motor fuels .................................................................................... .075 .114 

Motor vehicle parts and equipment .......................................................... .435 .532 

Tires ........................................................................................................ .285 .320 

Vehicle accessories other than tires ....................................................... .150 .213 

Motor vehicle maintenance and repair ..................................................... 1.168 1.206 

Motor vehicle body work ......................................................................... .057 .066 

Motor vehicle maintenance and servicing .............................................. .492 .494 

Motor vehicle repair ................................................................................ .587 .607 

Unsampled service policies .................................................................... .032 .040 

Motor vehicle insurance ............................................................................ 2.300 2.837 

Motor vehicle fees .................................................................................... .565 .535 

State motor vehicle registration and license fees ................................... .312 .336 

Parking and other fees ........................................................................... .235 .186 

Unsampled motor vehicle fees ............................................................... .018 .013 

Public transportation ................................................................................... 1.122 .805 

Airline fare ................................................................................................ .702 .442 

Other intercity transportation .................................................................... .157 .090 

Appendix 9. Relative importance of components in the Consumer Price Indexes, 

2011−2012 weights: U.S. city average, December 2014─continued
106

Table 1 (2011-2012 Weights). Relative importance of components in the Consumer Price Indexes: U.S.

city average, December 2014-Continued

(Percent of all items)

Item and group

U.S. city average 

CPI-U CPI-W

Expenditure category

Intracity transportation .............................................................................. .260 .267 

Unsampled public transportation .............................................................. .004 .006 

Medical care ................................................................................................. 7.716 6.308 

Medical care commodities .......................................................................... 1.772 1.423 

Medicinal drugs ........................................................................................ 1.696 1.378 

Prescription drugs ................................................................................... 1.345 1.108 

Nonprescription drugs ............................................................................ .351 .270 

Medical equipment and supplies .............................................................. .076 .045 

Medical care services ................................................................................. 5.944 4.885 

Professional services ................................................................................ 3.032 2.474 

Physicians’ services ............................................................................... 1.590 1.303 

Dental services ....................................................................................... .804 .699 

Eyeglasses and eye care ....................................................................... .284 .237 

Services by other medical professionals ................................................ .354 .235 

Hospital and related services ................................................................... 2.159 1.738 

Hospital services .................................................................................... 1.853 1.634 

Nursing homes and adult day services ................................................... .174 .080 

Care of invalids and elderly at home ...................................................... .132 .024 

Health insurance ....................................................................................... .753 .673 

Recreation .................................................................................................... 5.750 5.131 

Video and audio .......................................................................................... 1.847 2.054 

Televisions ................................................................................................ .133 .148 

Cable and satellite television and radio service ........................................ 1.468 1.668 

Other video equipment ............................................................................. .029 .031 

Video discs and other media, including rental of video and audio ............ .090 .099 

Audio equipment ....................................................................................... .066 .054 

Audio discs, tapes and other media ......................................................... .044 .040 

Unsampled video and audio ..................................................................... .016 .015 

Pets, pet products and services ................................................................. 1.058 .959 

Pets and pet products ............................................................................... .659 .680 

Pet services including veterinary .............................................................. .399 .279 

Sporting goods ........................................................................................... .400 .430 

Sports vehicles including bicycles ............................................................ .181 .214 

Sports equipment ..................................................................................... .214 .211 

Unsampled sporting goods ....................................................................... .005 .006 

Photography ............................................................................................... .120 .095 

Photographic equipment and supplies ..................................................... .058 .034 

Photographers and film processing .......................................................... .062 .059 

Unsampled photography .......................................................................... .001 .002 

Other recreational goods ............................................................................ .381 .387 

Toys .......................................................................................................... .277 .291 

Sewing machines, fabric and supplies ..................................................... .050 .048 

Music instruments and accessories .......................................................... .042 .031 

Unsampled recreation commodities ......................................................... .011 .017 

Other recreation services ........................................................................... 1.724 1.063 

Club dues and fees for participant sports and group exercises ................ .602 .325 

Admissions ............................................................................................... .640 .501 

Fees for lessons or instructions ................................................................ .211 .128 

Unsampled recreation services ................................................................ .271 .109 

Recreational reading materials ................................................................... .220 .144 

Newspapers and magazines .................................................................... .123 .081 

Recreational books ................................................................................... .094 .062 

Unsampled recreational reading materials ............................................... .002 .000 

Education and communication ..................................................................... 7.062 6.875 

Education .................................................................................................... 3.325 2.544 

Educational books and supplies ............................................................... .203 .202 

Tuition, other school fees, and childcare .................................................. 3.122 2.342 

College tuition and fees .......................................................................... 1.853 1.183 

Elementary and high school tuition and fees .......................................... .377 .253 

Child care and nursery school ................................................................ .725 .772 

Technical and business school tuition and fees ..................................... .039 .042 

Unsampled tuition, other school fees, and childcare .............................. .128 .092 

Communication ........................................................................................... 3.737 4.332 

Appendix 9. Relative importance of components in the Consumer Price Indexes,

2011−2012 weights: U.S. city average, December 2014─continued
107

Table 1 (2011-2012 Weights). Relative importance of components in the Consumer Price Indexes: U.S.

city average, December 2014-Continued

(Percent of all items)

Item and group

U.S. city average 

CPI-U CPI-W

Expenditure category

Postage and delivery services .................................................................. .144 .104 

Postage .................................................................................................. .130 .095 

Delivery services .................................................................................... .014 .009 

Information and information processing ................................................... 3.593 4.228 

Telephone services ................................................................................ 2.462 3.030 

Wireless telephone services ................................................................. 1.624 2.199 

Land-line telephone services ................................................................ .837 .831 

Information technology, hardware and services ....................................... 1.132 1.198 

Personal computers and peripheral equipment ...................................... .272 .249 

Computer software and accessories ...................................................... .068 .054 

Internet services and electronic information providers ........................... .711 .819 

Telephone hardware, calculators, and other consumer information

items ............................................................................................... .068 .062 

Unsampled information and information processing .............................. .012 .014 

Other goods and services ............................................................................. 3.394 3.600 

Tobacco and smoking products .................................................................. .718 1.187 

Cigarettes ................................................................................................. .661 1.111 

Tobacco products other than cigarettes ................................................... .050 .066 

Unsampled tobacco and smoking products .............................................. .006 .009 

Personal care ............................................................................................. 2.676 2.413 

Personal care products ............................................................................. .724 .709 

Hair, dental, shaving, and miscellaneous personal care products ......... .369 .375 

Cosmetics, perfume, bath, nail preparations and implements ................ .348 .325 

Unsampled personal care products ........................................................ .007 .008 

Personal care services ............................................................................. .638 .576 

Haircuts and other personal care services ............................................. .638 .576 

Miscellaneous personal services .............................................................. 1.122 .953 

Legal services ......................................................................................... .316 .250 

Funeral expenses ................................................................................... .173 .123 

Laundry and dry cleaning services ......................................................... .276 .288 

Apparel services other than laundry and dry cleaning ............................ .034 .022 

Financial services ................................................................................... .228 .189 

Unsampled items .................................................................................... .095 .080 

Miscellaneous personal goods ................................................................. .192 .176 

Special aggregate indexes

All items .......................................................................................................... 100.000 100.000

Commodities ................................................................................................. 37.880 41.147 

Commodities less food and beverages ...................................................... 22.608 25.136 

Nondurables less food and beverages ..................................................... 13.658 15.131 

Nondurables less food, beverages, and apparel .................................... 10.315 11.536 

Durables ................................................................................................... 8.950 10.005 

Services ........................................................................................................ 62.120 58.853

Rent of shelter .............................................................................................. 32.336 30.772 

Transportation services ................................................................................ 5.625 5.784 

Other services .............................................................................................. 11.955 11.005 

All items less food ......................................................................................... 85.743 84.948 

All items less shelter ..................................................................................... 67.289 68.895 

All items less medical care ........................................................................... 92.284 93.692 

Commodities less food ................................................................................. 23.623 26.095 

Nondurables less food .................................................................................. 14.673 16.090 

Nondurables less food and apparel .............................................................. 11.330 12.495 

Nondurables ................................................................................................. 28.930 31.142 

Apparel less footwear ................................................................................... 2.619 2.774 

Services less rent of shelter ......................................................................... 29.784 28.081 

Services less medical care services ............................................................. 56.176 53.968 

Energy .......................................................................................................... 8.030 9.792

All items less energy ..................................................................................... 91.970 90.208 

All items less food and energy .................................................................... 77.713 75.156 

Commodities less food and energy commodities ..................................... 19.408 20.672 

Energy commodities ............................................................................... 4.215 5.423 

Services less energy services .................................................................. 58.305 54.484 

Domestically produced farm food ................................................................. 7.094 7.761 

Utilities and public transportation .................................................................. 10.089 11.197 

Appendix 9. Relative importance of components in the Consumer Price Indexes,

2011−2012 weights: U.S. city average, December 2014─continued