Attachment IX - The 2018 Revision of the Consumer Price Geographic Sample

October 2016

The 2018 revision of the Consumer Price Index
geographic sample
The Consumer Price Index (CPI) program updates its
sample of geographic areas on the basis of the most recent
decennial census, to ensure that the sample accurately
reflects shifts in the U.S. population. This article describes
CPI’s latest area-sample redesign, which will be used with
the introduction of 2018 price indexes.

UPDATE: SEPTEMBER 14, 2017
The new area design implementation plan to introduce
new primary sampling units (PSUs) in four waves over a
4-year period beginning in January 2018 has been

Steven P. Paben
[email protected]

modified as follows:
• The introduction of new PSUs in waves 2–4 has
been delayed by 1 year.
• Three new PSUs in wave 2 will continue to be
“proxied” by a dropping PSU; these three PSUs
will now be imputed for the first 2 years of the new
area design.
In addition, BLS will continue to publish monthly regionsize class indexes for A- and B/C-sized cities in the four
census regions.

Steven P. Paben is the Chief of the Division of
Price Statistical Methods, Office of Prices and
Living Conditions, U.S. Bureau of Labor
Statistics.
William H. Johnson
[email protected]
William H. Johnson is a mathematical statistician
in the Division of Price Statistical Methods, Office
of Prices and Living Conditions, U.S. Bureau of
Labor Statistics.
John F. Schilp
[email protected]

The CPI sample-design process involves multiple stages. In
the first stage, a sample of geographic areas is selected. In
subsequent stages, a sample of outlets in which area
residents make retail purchases, a sample of specific retail
goods and services, and a sample of residential housing

1

John F. Schilp is a mathematical statistician in the
Division of Price Statistical Methods, Office of
Prices and Living Conditions, U.S. Bureau of
Labor Statistics.

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units are selected. While these latter samples are rotated on a regular basis, the geographic sample has
traditionally been rotated once every 10 years. The 2018 area revision will mark the geographic sample’s first
rotation since 1998.1
Historically, a new area sample had been selected and implemented after each decennial census. This selection
was done jointly with the Consumer Expenditure Survey (CE) program, from which CPI obtains expenditure
weights for price indexes and selection probabilities for goods and services. Because all new geographic areas
were rotated into the CPI all at once, field collection had to continue in old sampling areas while survey operations
were starting up in new areas. This practice typically caused a spike in field collection costs, which had to be
covered through a special funding initiative. Effective with the 2018 geographic redesign, CPI will rotate its sample
to new geographic areas on a continuous basis, over a period of consecutive years, until all new areas have been
brought into the sample.2 This approach is expected to be more cost effective.
In general, the process used to select the 2018 area sample is similar to the process used in the 1998 area
revision.3 The basic steps in both cases include the following:
• Determine sample classification variables
• Construct primary sampling units (PSUs)
• Determine the number of sampled PSUs
• Determine stratification variables
• Allocate sample4 and assign PSUs to strata
• Select a sample of PSUs
Despite these similarities, the new process has introduced some notable methodological changes within each of
the basic steps. First, the sample classification structure has been changed. The 1998 design classified areas into
four Census regions by two size classes for a total of eight groups; the 2018 design classifies these areas into nine
Census divisions.5 Second, the area definitions of PSUs have been updated to reflect the most recent Office of
Management and Budget’s (OMB) area definitions.6 Third, in the new design, the number of sampled PSUs in the
CPI has been reduced from 87 to 75. Finally, changes were made to the stratification variables and the sampling
process for selecting PSUs. The purpose of this article is to provide a more detailed explanation of the
aforementioned methodological changes in the 2018 area revision and to describe the plan for rotating PSUs to
the new area sample over a 4-year transition period.

Determine sample classification variables
In the CPI, geographic sample variables represent one dimension of the overall index classification structure. In
the current area design, the urban portion of the United States is divided into 38 geographic areas, called index
areas. In addition, the set of all goods and services purchased by consumers is divided into 211 categories, called
item strata. Combining these two dimensions results in 8,018 (38 × 211) item–area combinations, or basic cells.
Resource constraints can limit the size of the sample in each of these basic cells and lead to a small sample
measurement bias.7 Previous research performed by the U.S. Bureau of Labor Statistics (BLS) found that,
because of a deficient sample size in some basic item–area cells, the all-items CPI for All Urban Consumers (CPIU) exhibited a finite sample bias of 0.2 to 0.3 percent per year. Since the magnitude of the bias is inversely related
to sample size, an increase in the number of price quotes per item–area cell would proportionally reduce the bias

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in the sample mean for each cell and, in turn, lower the finite sample bias for the overall index.8 For the new area
design, CPI made a conscious effort to partially address this issue by reducing the number of index areas, thereby
increasing the average number of price quotes per basic item–area cell.

In the 1998 sample design, areas were first classified by location, on the basis of one of four Census regions:
Northeast, Midwest, South, and West. Then, each area was classified into one of three population-size classes:
self-representing areas (A-size), medium nonself-representing areas (B-size), and small nonself-representing
areas (C-size).9 In the 2018 sample design, areas were first classified by location, into one of nine Census
divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South
Central, West South Central, Mountain, and Pacific. The Census divisions represent a further breakdown of
Census regions. (See figure 1.) In addition, each area was classified into one of two population-size classes—selfrepresenting or nonself-representing—with the use of the size cutoff described later in the article, in the section on
determining the number of sampled PSUs.
The main impetus for using the nine Census divisions instead of the Census regions and size classes from the
1998 sample design was to create and support indexes that are more locally defined. In order to maintain
approximately the same number of classification groups for nonself-representing areas, CPI combined the B- and
C-size classes. The proportion of medium- and small-size areas within a Census division was determined through
a process called controlled selection (see section on sample selection). A BLS study found that the use of by-

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division indexes had little effect on estimates for the U.S. all-items index, regional CPI indexes, and 12-month
standard errors.10
Given the change in classification for the nonself-representing areas (from Census region and size class to
Census division), the only way to reduce the number of index areas was to decrease the existing number (31) of
self-representing areas.11

Construct primary sampling units
After each decennial census, OMB releases a new set of definitions for statistical areas. The current definitions
assign counties surrounding an urban core area to geographic entities called Core-Based Statistical Areas
(CBSAs). The assignment is based on each county’s degree of economic and social integration (as measured by
commuting patterns) to the urban core. There are two types of CBSAs: metropolitan and micropolitan. A
metropolitan CBSA has an urban core of more than 50,000 people, and a micropolitan CBSA has an urban core of
10,000 to 50,000 people. CBSAs may cross state borders. In addition, OMB defines Combined Statistical Areas
(CSAs), which are combinations of two or more CBSAs.
In the 1998 area sample design, the CPI program distinguished among A-, B-, and C-sized PSUs. The B-sized
PSUs were Metropolitan Statistical Areas (MSAs), defined by OMB in 1993; the C-sized PSUs were urban parts of
non-MSA areas; and the A-sized PSUs were an MSA mixture, in which some MSAs were combined to maintain
continuity with area definitions from the 1987 CPI geographic revision. Because CBSAs are the conceptual
successor of earlier metropolitan area definitions, using the metropolitan and micropolitan CBSA definitions for
nonself-representing areas was a natural choice. However, there was a question whether the CSA definitions
should be used for self-representing PSUs. In some cases, the A-sized PSUs in the 1998 sample more closely
resembled CSA definitions; in other cases, they more closely resembled the new metropolitan CBSA definitions.
The problem with CSA definitions is that they often create a very large geographic area in which CPI has to
conduct field operations and collect prices. For this reason, CPI decided to strictly adhere to the new metropolitan
CBSA definitions for self-representing PSUs.
Currently, BLS publishes the CPI-U, which covers approximately 87 percent of the U.S. population. With the
introduction of the CBSA concept to the CPI, the CPI-U coverage will increase to 94 percent of the U.S. population
reflected in the 2010 census.12 The area sample frame will comprise 381 metropolitan CBSAs, representing
approximately 85 percent of the population, and 536 micropolitan CBSAs, representing approximately 9 percent of
the population.

Determine the number of sampled PSUs
For the area sample, CPI has traditionally selected one PSU per stratum. The number of strata determines the
total number of PSUs in the sample. Specifying the number of strata depends on a variety of factors, including that
number’s expected overall impact on the accuracy of the U.S. all-items CPI and the total budget available for data
collection. With respect to accuracy, special consideration is given to the expected impact on sampling variance
and the expected impact on bias.

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Currently, the CPI has 87 urban PSUs, whereas the CE has 91 PSUs (75 urban and 16 rural).13 With the 2018
area revision, the CPI program will reduce the total number of PSUs in the CPI, to 75. This reduction will
eventually allow CPI to collect prices in the same set of PSUs as that used by the CE program to collect
expenditure information.14 In addition, the reduction will lower the overall cost of implementing the new sample,
because of an expected increase in the percentage of overlapping areas and a decrease in the number of new
areas. Most importantly, the change will increase the average number of price quotes per index area and,
therefore, help address the small sample bias created by basic item–area indexes. Of course, maintaining the
same number of total quotes in the CPI and reducing the number of PSUs would increase the standard error of
CPI estimates, because of the loss of information in the PSU component of variance. However, using variance
models, CPI estimated that the 6-month standard error of the U.S. all-items CPI would see a modest increase of 2
to 5 percent.15 These estimates were based on the assumption that the total number of quotes in the CPI would be
maintained and that the number of self-representing PSUs would be reduced from 31 to 23.
To determine the ideal number of self-representing PSUs in the new area sample with 75 PSUs, the CPI program
again used variance models to simulate 6-month variance estimates in the U.S. all-items CPI for different
population-size cutoffs. The simulation showed that, for any population cutoff between 2.0 and 3.0 million, the
range of the modeled 6-month standard errors was extremely narrow. (The largest simulated difference was
around 1 percent, which is the difference between, say, a standard error of .0657 and .0650.) This narrow range
gave CPI some flexibility in determining the exact population cutoff. The cutoff was ultimately set at 2.5 million,
which resulted in 23 self-representing PSUs. These PSUs include 21 units whose population is greater than 2.5
million and 2 additional units—Anchorage, AK, and Honolulu, HI. Anchorage represents all CBSAs in Alaska, and
Honolulu represents all CBSAs in Hawaii. These CBSAs are unique because the locations of both states make
price change in their markets geographically isolated from that in other markets. For this reason, the CBSAs in
Alaska and Hawaii are treated as separate geographic strata.
With 23 self-representing PSUs and nine Census divisions, the new area design will yield 6,752 basic indexes (32
index areas by 211 item strata) for the U.S. all-items CPI. This reduction (approximately 16 percent) in the number
of basic indexes will help address the small sample bias in index estimates.

Determine stratification variables
The goal of area stratification is to reduce the overall sampling variance in the CPI. This is achieved by grouping
nonself-representing PSUs whose characteristics are similar and highly correlated with price change and
consumption behavior. In the 1998 sample design, four independent variables were used for stratifying the nonselfrepresenting PSUs: normalized (centered and scaled by the range) longitude, the square of normalized longitude,
normalized latitude, and percent urban. Instead of simply repeating the stratification from the previous area
revision, CPI reanalyzed the stratification process from scratch. To determine the best possible area stratification,
the effort involved not only the reassessment of geographic variables, such as latitude and longitude, but also an
analysis of potential demographic variables. The decision to investigate demographic variables (besides percent
urban) was highly influenced by the introduction of the American Community Survey (ACS), which replaced the
decennial census long-form survey.16 The ACS introduced rolling 3-year estimates that would cover every
community with a population greater than 20,000 and 5-year estimates that would cover every community in the
nation.17 Previously, the decennial census provided only a snapshot of the demographic variables.

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The ACS presented 52 main topics of social characteristics in its 3-year estimates for 2005–07. It would have been
difficult to investigate all of these variables when modeling CPI percent-change estimates. Therefore, the first task
was to limit the set of ACS variables to a manageable list, and the second task was to aggregate these statistics to
the CBSA level. This approach produced 23 variables that were used in the first stage of modeling.
The first task involved eliminating variables that were thought to have little explanatory power in the CPI. The
guiding philosophy was to take ACS variables—such as race, educational attainment, and property statistics—that
could affect price change. Housing variables were selected because indexes for shelter make up a large
proportion of the CPI. The second task involved aggregating the county-level ACS data to the CBSA level. If
median statistics for all member counties were available, they were averaged. Examples of such averages include
the average median property value and the average median income for a CBSA. Other CBSA statistics, expressed
as an average or a percentage, were calculated with the use of a weighted average based on the requisite
county’s population. These calculations yielded statistics such as the median household property value and the
percentage of the CBSA population that is Native American.
The final list of housing and demographic variables considered for potential inclusion in the stratification model was
as follows:
• Housing—average median household property value, average number of vehicles per household, percent of
family households, percent of occupied housing units, percent of owner housing units, and housing units per
square mile
• Population density—population per square mile and percent urban
• Age—percent of people in their twenties and percent of people ages 35 to 44
• Race and gender—percent male, percent African American, percent Native American, and percent Asian
• Income—percent in poverty, average median household income, and average total median earnings
• Education18—percent with less than 9th-grade education, percent with 9th- to 12th-grade education, percent
with a high school diploma, percent with some college, percent with an associate’s degree, percent with a
college degree, and percent with a graduate degree
To model these stratification variables, CPI developed a series of nonoverlapping all-items price relatives19 for
each PSU in the current area sample, using the same timeframe as that for the ACS demographics. The ACS
variables investigated spanned the period 2005–07 and were released in December 2008. Unofficial price relatives
for B- and C-sized PSUs were produced to serve as responses in the modeling procedure and used in conjunction
with the existing price relatives for A-sized PSUs. The 12-month (December to December) price relatives used
were for 2004–05, 2005–06, 2006–07, and 2007–08. This approach provided four responses for each PSU, along
with a set of covariates, to be used in a repeated-measures modeling procedure.
A backward-elimination process was used to limit the set of variables to those with small p-values ( p < .05).
Anchorage and Honolulu could be included because the initial model did not contain longitude and latitude. Being
obvious geographic outliers, these two areas were later removed from models that included latitude and longitude.
The regression variables, along with their resulting p-values (Pr > F), were treated as “effects.” It is customary to
first remove the effect with the highest p-value, rerun the model (which would slightly change the p-values), and
then repeat the steps for the effect with the next-highest p-value. This backward-elimination process of removing

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one variable at a time continues until all remaining effects have small p-values. CPI used this process to arrive at
the final model of all significant variables. (See table 1.)
Table 1. Initial demographic model based on 87 current CPI PSUs
Effect
Period
region(1)

Census
Number of households
Household property value

Numerator DF

Denominator DF

F-value

p-value

1

81

173.42

< .0001

3

81

10.27

< .0001

1
1

81
81

12.51
11.51

.0007
.0011

Notes:
(1) Census region was used in lieu of Census division for two reasons. First, the current sample design was not intended to support division estimates.

Second, the determination of the stratification model was completed before it was decided to implement the Census divisions.
Source: U.S. Bureau of Labor Statistics.

To determine the predictive power of a particular model, CPI used linear regression to examine each 1-year allitems price relative in the 2005–08 timespan. The first linear model used the 1-year price relative for December
2004 to December 2005 as the dependent variable, the second used the 1-year price relative for January 2005 to
January 2006, the third for February 2005 to February 2006, and so forth, until every 1-year price relative was
used. These linear models produced a distribution of R-squared statistics. The mean of this distribution was
calculated to describe the predictive power of each model. This process was repeated for lower level 1-year price
indexes for energy, local services, food and beverages, and local shelter. The backward-elimination process,
described earlier, was implemented for each of these subindexes. The R-squared statistic was again calculated to
determine the predictive quality of the final set of variables for each subindex. However, none of these variables
proved to be highly predictive, as they all had R-squared statistics smaller than 0.3. Models also were evaluated
for each of the four Census regions, again with little success. In addition to the variables from the ACS, the
following stratification variables from the previous area revision were considered for each PSU in the current
sample: longitude, latitude, longitude squared, latitude squared, and percent urban. However, these variables also
had very little predictive power and were not always significant in the all-items model.
Once several final models were derived, the PSUs included in these models were stratified with an “equal
population” constraint, under which each stratum would have a population within 10 percent of the mean of all
strata in the index area. Because this constraint conflicted with the variable for number of households, which is
highly correlated with population, that variable was excluded from the models.
In addition, since the area sample design is also intended to support the CE, ACS variables were investigated for
correlation with expenditure estimates. Average median household income and average median property value
were, by far, the best demographic predictors of consumer expenditures (the two-variable model investigated for
the 2005–08 timeframe had an R-squared statistic of 0.65).
Finally, three separate stratification models were investigated: a seven-variable model, a six-variable model, and a
four-variable model. The seven-variable model included percent urban, income, property value, longitude, latitude,
longitude squared, and latitude squared. The six-variable model contained the same variables, except for percent
urban, and the four-variable model excluded longitude squared, latitude squared, and percent urban.

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Ultimately, the four-variable model (longitude, latitude, median property value, and median household income) was
selected, because longitude squared and latitude squared added little predictive value. None of the four variables
in the model turned out to be an influential predictor of CPI price change over time and across regions. Table 2
shows the model’s predictive accuracy (R-squared) over time for each Census region and subcategory
investigated.
Table 2. R-squared for Census regions and various subcategories, final model
Census region
Index

Total
Northeast

All items
Energy
Food and beverages
Housing
Local services

.158
.216
.164
.276
.076

Midwest
.311
.529
.338
.469
.201

South

.224
.162
.174
.206
.098

West

.160
.261
.153
.236
.207

.330
.483
.392
.407
.173

Source: U.S. Bureau of Labor Statistics.

The final stratification model of longitude, latitude, median property value, and median household income is a
compromise between having significant predictors for the CE and retaining geographic variables used in the
previous area design. The addition of the income and property-value variables will greatly enhance the area
stratification for the CE and substantially reduce the between-PSU variance. It will not, however, significantly
increase the explanatory power of the model for price change. Despite this, producing more reliable consumer
expenditure estimates will help in the calculation of the CPI. Because the new stratification model is very different
from its predecessor, a sample-overlap procedure will be used to retain as many nonself-representing PSUs from
the 1998 area sample as possible.

Allocate sample and assign PSUs to strata
To allocate sample to the nonself-representing PSUs, CPI excluded the population for the self-representing PSUs
for each Census division. Table 3 presents the proportional-to-population-size sample allocation, by Census
division, for the 2018 geographic area design. There are 23 self-representing PSUs, which account for
approximately 39 percent of the total U.S. population and about 42 percent of the CPI-U population. There are 52
nonself-representing PSUs, which represent the remaining 58 percent of the CPI-U population and include both
metropolitan and micropolitan areas.
Table 3. Distribution of selected sample units, by Census division, 2018 revision
Census division

Nonself-representing PSUs

Total
1—Northeast
2—Middle Atlantic
3—East North Central
4—West North Central

Self-representing PSUs
52
2
4
8
4

See footnotes at end of table.

8

Total
23
1
2
2
2

75
3
6
10
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Table 3. Distribution of selected sample units, by Census division, 2018 revision
Census division

Nonself-representing PSUs

5—South Atlantic
6—East South Central
7—West South Central
8—Mountain
9—Pacific

Self-representing PSUs
12
6
8
4
4

Total
5
0
2
6
7

17
6
10
10
11

Source: U.S. Bureau of Labor Statistics.

The next phase of the selection process was to assign the nonself-representing PSUs within each Census division
to strata based on a model of the four stratification variables (latitude, longitude, median household income, and
median property value).20 The primary objective of the PSU stratification was to minimize the between-PSU
component of variance by making the PSUs within each stratum as homogeneous as possible with respect to the
four stratification variables. In addition, to further minimize the variance, strata within each Census division had to
be kept with approximately the same population. In the 1998 design, this type of constrained clustering problem
was solved with a Friedman-Rubin hill-climbing algorithm.21 For the 2018 design, CPI developed a new heuristic
stratification algorithm based on k-means clustering and zero–one integer linear programming.22

Select a sample of PSUs
The final step of the selection process was to select one PSU per stratum. However, before making that final
selection, CPI had to employ two special selection procedures: a sample-overlap procedure and a controlledselection procedure.

Sample-overlap methodology
In the 1998 design, a sample-overlap procedure was used to select the nonself-representing areas for the CPI and
CE surveys.23 Sample-overlap procedures increase the expected number of nonself-representing geographic
areas that would be reselected in the new design. Because the use of an overlap procedure results in fewer new
areas that need to be rotated in the sample and in fewer existing areas that need to be rotated out of the sample, it
lowers the expected costs of operational changes (e.g., hiring and training of new field staff) associated with the
new area design.
Two different sample-overlap procedures were considered: one proposed by Walter Perkins and one by Lawrence
Ernst.24 The Perkins procedure is a heuristic method that was used in previous redesigns. The Ernst procedure
uses linear programming. Because linear programming is employed in optimization, using the Ernst procedure
would result in a higher expected number of overlapping PSUs and, consequently, lower overall cost of switching
to the new area design for both the CPI and the CE. Only nonself-representing metropolitan PSUs were deemed
eligible for the procedure. Micropolitan areas were deemed ineligible because they did not have enough renters for
the CPI Housing Survey. Each micropolitan area must have enough renters for two samples (of six panels each)
during the decade between area redesigns. In the past, the CPI program sometimes had to extend the area
definition for the CPI Housing Survey, to include outlying rural counties and, thus, ensure that the survey had

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enough renters. Table 4 allows a comparison between the expected number of overlapping PSUs calculated with
the two procedures and the expected number of nonself-representing areas selected independently.
Table 4. Expected sample overlap of PSUs, by census division(1)
Sample-overlap procedure
Census division

PSU design

Independent selection
Perkins

Total
1—Northeast
2—Middle Atlantic
3—East North Central
4—West North Central
5—South Atlantic
6—East South Central
7—West South Central
8—Mountain
9—Pacific

58
2
4
8
4
14
6
8
6
6

13.2
.5
.7
2.9
.8
3.2
.8
2.1
1.4
.9

Ernst

19.3
.7
1.2
3.6
1.4
4.5
1.1
2.6
2.6
1.6

28.6
1.0
1.7
4.8
2.1
7.0
2.2
4.4
3.2
2.2

Notes:
(1) This analysis was done for a total of 87 PSUs (58 nonself-representing) before it was decided to move to the final design of 75 PSUs (52 nonself-

representing).
Source: U.S. Bureau of Labor Statistics.

Ultimately, the Ernst procedure was selected and used, because it increased the overlap between the PSUs in the
new area sample and those in the 1998 sample. The outcome of the procedure gave a new set of selection
probabilities, which were used to select the sample.

Controlled selection
Controlled selection is a process of selecting a random sample of PSUs such that the probability of selecting
certain preferred combinations of PSUs increases and the probability of selecting nonpreferred combinations of
PSUs decreases. This is accomplished by controlling the interaction among the PSU selections in different strata.
Given that only one sample is ultimately selected, there may be important reasons for preferring some possible
sample outcomes over others. It is usually judged that balancing the sample with respect to one or more additional
variables (besides those from the strata) would increase the degree of confidence in the inferences made about
the population. If information on such additional variables is available, then the population may be crossclassified
and an implicit stratification achieved with reference to each of these additional variables. However, multiple
crossclassifications can be overly restrictive, making it impossible to select a sample that meets all of the
constraints.
Controlled selection can be used to control for a variety of variables. In the 1998 design, the number of PSUs per
state and the number of PSU overlaps were controlled. For example, if Florida, given its population share in the
South, was expecting 2.3 PSUs from that region, then controlled selection gave a 30-percent chance of the state
getting 3 PSUs and a 70-percent chance of it getting 2 PSUs. In this case, controlled selection eliminated the
possibility of selecting samples with fewer than two PSUs or more than three PSUs.

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For the 2018 redesign, the greatest concern was about controlling the number of micropolitan areas selected in the
sample; controlling by state was deemed a secondary concern. The change in classification for the nonselfrepresenting index areas (from Census region and size class to Census division) meant that controlling the
number of PSUs per state would be of less value.
Controlled selection is a computationally intensive process. The solution time for a controlled-selection problem
increases exponentially with the size of the problem (e.g., the number of strata). Using the software package
SOCSLP, CPI was unable to solve a two-variable controlled-selection problem for the South region.25 Therefore,
only micropolitan status was used as a control variable, and this was done at the Census-region level.

Sample outcome
After adjusting the sample selection probabilities with the use of the Ernst sample-overlap procedure and
employing controlled selection for the micropolitan areas, CPI randomly selected one PSU per stratum. The
resulting (final) area sample for the 2018 revision is shown in the appendix. Thirty-three of the 87 PSUs in the
1998 design will be dropped from the CPI. Two of these exclusions are due to treating the New York, NY, CBSA as
one PSU; previously, that CBSA was treated as three PSUs. Meanwhile, only 21 of the 75 PSUs in the 2018
design will be considered new areas. Of the 21 new areas, 14 are metropolitan CBSAs and 7 are micropolitan
CBSAs.
In January 2018, CPI will begin publishing indexes for the nine Census divisions, in addition to releasing the
current four regional estimates. However, given the new area design, CPI will no longer publish “region by city
size” index estimates. Because of the reduction in the number of self-representing areas, the program will be
unable to support separate index estimates for Cincinnati, OH; Cleveland, OH; Milwaukee, WI; Pittsburgh, PA; and
Portland, OR.26 One other formerly published area, Kansas City, MO, was not reselected as part of the 2018 area
revision. The remaining 23 self-representing areas listed in the appendix (denoted by an “S” in the PSU code) will
continue to have area indexes published under the new area design.

New area design implementation plan
After selecting the final area design, BLS determined the process for implementing the new geographic sample
into the four surveys used to construct the CPI. The four surveys are the CE, the Telephone Point-of-Purchase
Survey (TPOPS), the Commodity and Services (C&S) survey, and the Housing Survey. In all previous CPI
geographic revisions, the conversion process occurred all at once: that is, the administration of each survey
switched from the old area sample to the new area sample in its entirety, albeit at different points in time. For
example, for the 1998 revision, the CE was switched to the new sample design in 1996; TPOPS was used to
identify outlet frames in new PSUs during the 1995–96 period; and the initial round of data collection for the
Housing and C&S surveys was completed by the fall of 1997, so that the CPI could be computed by January 1998,
on the basis of the new area design.
For the 2018 area revision, the CE fully converted to the new sample in 2015. However, for the other three surveys
(which are directly managed by the CPI program), the 21 new PSUs have been divided into groups whereby the
new PSUs will be introduced over a 4-year span. This rotation process will distribute the cost of introducing new

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PSUs into the Housing and C&S surveys, avoiding a spike in data collection costs before the full CPI conversion to
the new area design.
The calculation of price indexes under the new area design will begin in January 2018, with the introduction of the
first set of new PSUs into the sample. All late-dropping PSUs (i.e., existing PSUs scheduled to be rotated out of
the sample late in the implementation process) will be used as proxy candidates for late-rotating new PSUs, until
the complete set of new PSUs has been rotated into the sample. An ideal proxy for a given new PSU was
considered to be one of the dropping PSUs within a new PSU geographic stratum. If such dropping PSU were not
available, a proxy was identified through nearest neighbor rules, with the constraint that the proxy falls within 200
miles of the new PSU. If no eligible proxy existed, the new PSU was considered to be a “geographic hole” within
the new area structure. There were eight new PSUs with no eligible proxy. Therefore, they were given priority in
the rotation schedule.

In devising the rotation schedule, CPI determined the following field operational constraints: (1) no more than six
new PSUs could be rotated in a calendar year, and (2) no more than two new PSUs could be rotated in any of the
six BLS regional offices in a calendar year. Because there are 21 new PSUs, the new PSUs will be rotated across
four groups, or waves, over a 4-year period. Six new PSUs will be introduced in each of the first three waves, and
three new PSUs will be introduced in the fourth, and final, wave. Figure 2 shows the timeline for introducing
wave-1 PSUs into the various components of the CPI. The milestones for each successive wave begin exactly 1
year after the corresponding milestones for the previous wave have been completed; according to this schedule,

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all waves will be completed by the end of 2023. Because the publication of indexes based on the new area design
will begin in January 2018, the new PSUs for waves 2–4 will be “proxied” by a dropping PSU. All dropping PSUs
that were not designated as proxies (18 PSUs) will be dropped with wave 1. Three new PSUs are considered
geographic holes and are part of wave 2. These PSUs will be entirely imputed for the first year of the new area
design. The appendix indicates the respective wave during which each new PSU will enter the index.

Appendix
Final CPI geographic sample, 2018 revision
PSU
code (1)

PSU name

PSU definition (state and county)

Region 1—Northeast, Division 1—New England
MA: Essex, Middlesex, Norfolk, Plymouth, Suffolk
Boston–Cambridge–
S11A
Newton, MA–NH
NH: Rockingham, Strafford
Hartford–West Hartford–
N11B
CT: Hartford, Middlesex, Tolland
East Hartford, CT
N11C
Springfield, MA
MA: Hampden, Hampshire
Region 1—Northeast, Division 2—Middle Atlantic
NJ: Bergen, Essex, Hudson, Hunterdon, Middlesex,
Monmouth, Morris, Ocean, Passaic, Somerset, Sussex, Union
New York–Newark–
S12A
NY: Bronx, Dutchess, Kings, Nassau, New York, Orange,
Jersey City, NY–NJ–PA
Putnam, Queens, Richmond, Rockland, Suffolk, Westchester
PA: Pike
DE: New Castle
Philadelphia–Camden– MD: Cecil
S12B
Wilmington, PA–NJ–
NJ: Burlington, Camden, Gloucester, Salem
DE–MD
PA: Bucks, Chester, Delaware, Montgomery, Philadelphia
PA: Allegheny, Armstrong, Beaver, Butler, Fayette, Washington,
N12C
Pittsburgh, PA
Westmoreland
Buffalo–Cheektowaga–
N12D
NY: Erie, Niagara
Niagara Falls, NY
W1Rochester, NY
N12E
NY: Livingston, Monroe, Ontario, Orleans, Wayne, Yates
N12F
Reading, PA
PA: Berks
Region 2—Midwest, Division 3—East North Central
IL: Cook, De Kalb, Du Page, Grundy, Kane, Kendall, Lake,
McHenry, Will
Chicago–Naperville–
S23A
Elgin, IL–IN–WI
IN: Jasper, Lake, Newton, Porter
WI: Kenosha
Detroit–Warren–
S23B
MI: Lapeer, Livingston, Macomb, Oakland, St. Clair, Wayne
Dearborn, MI
IN: Dearborn, Ohio, Union
KY: Boone, Bracken, Campbell, Gallatin, Grant, Kenton,
N23C
Cincinnati, OH–KY–IN
Pendleton
OH: Brown, Butler, Clermont, Hamilton, Warren
N23D
Cleveland–Elyria, OH
OH: Cuyahoga, Geauga, Lake, Lorain, Medina
OH: Delaware, Fairfield, Franklin, Hocking, Licking, Madison,
N23E
Columbus, OH
Morrow, Perry, Pickaway, Union
Milwaukee–Waukesha–
N23F
WI: Milwaukee, Ozaukee, Washington, Waukesha
West Allis, WI
See footnotes at end of table.

13

Stratum
population

Percent of
index
population

4,552,402

1.57

5,005,793

1.73

4,233,926

1.46

19,567,410

6.76

5,965,343

2.06

4,065,877

1.40

3,483,174

1.20

3,925,318

1.36

3,562,332

1.23

9,461,105

3.27

4,296,250

1.48

3,395,853

1.17

3,257,953

1.12

3,758,510

1.30

3,256,494

1.12

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Final CPI geographic sample, 2018 revision
PSU
code (1)

PSU name

PSU definition (state and county)

Stratum
population

Percent of
index
population

N23G

Dayton, OH

OH: Greene, Miami, Montgomery

3,924,320

1.36

N23H

W1Flint,

MI: Genesee

3,911,189

1.35

N23I

W2Janesville–Beloit,

WI: Rock

N23J

W3Frankfort,

MI

3,745,126

1.29

IN: Clinton
IN
Region 2—Midwest, Division 4—West North Central
MN: Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Le
Sueur, Mille Lacs, Ramsey, Scott, Sherburne, Sibley,
Minneapolis–St. Paul–
S24A
Washington, Wright
Bloomington, MN–WI
WI: Pierce, St. Croix
IL: Bond, Calhoun, Clinton, Jersey, Macoupin, Madison,
Monroe, St. Clair
S24B
St. Louis, MO–IL
MO: Franklin, Jefferson, Lincoln, St. Charles, St. Louis, St.
Louis City, Warren
W2Omaha–Council
IA: Harrison, Mills, Pottawattamie
N24C
NE: Cass, Douglas, Sarpy, Saunders, Washington
Bluffs, NE–IA
W2
N24D
KS: Butler, Harvey, Kingman, Sedgwick, Sumner
Wichita, KS

3,427,365

1.18

3,348,859

1.16

2,787,701

.96

2,974,017

1.03

2,842,770

.98

N24E

3,288,318

1.14

2,947,903

1.02

5,636,232

1.95

5,564,635

1.92

5,286,728

1.83

2,783,243

.96

2,710,489

.94

3,035,149

1.05

2,642,941

.91

3,027,856

1.05

2,549,176

.88

3,094,518

1.07

WI

NE: Lancaster, Seward
MN: Wilkin
W3Wahpeton, ND–MN
N24F
ND: Richland
Region 3—South, Division 5—South Atlantic
DC: District of Columbia
MD: Calvert, Charles, Frederick, Montgomery, Prince George’s
Washington–Arlington–
VA: Alexandria City, Arlington, Clarke, Culpeper, Fairfax,
S35A
Alexandria, DC–VA–
Fairfax City, Falls Church City, Fauquier, Fredericksburg City,
MD–WV
Loudoun, Manassas City, Manassas Park City, Prince William,
Rappahannock, Spotsylvania, Stafford, Warren
WV: Jefferson
Miami–Fort Lauderdale–
S35B
FL: Broward, Miami–Dade, Palm Beach
West Palm Beach, FL
GA: Barrow, Bartow, Butts, Carroll, Cherokee, Clayton, Cobb,
Coweta, Dawson, DeKalb, Douglas, Fayette, Forsyth, Fulton,
Atlanta–Sandy Springs–
S35C
Gwinnett, Haralson, Heard, Henry, Jasper, Lamar, Meriwether,
Roswell, GA
Morgan, Newton, Paulding, Pickens, Pike, Rockdale, Spalding,
Walton
Tampa–St. Petersburg–
S35D
FL: Hernando, Hillsborough, Pasco, Pinellas
Clearwater, FL
Baltimore–Columbia–
MD: Anne Arundel, Baltimore, Baltimore City, Carroll, Harford,
S35E
Towson, MD
Howard, Queen Anne’s
NC: Cabarrus, Gaston, Iredell, Lincoln, Mecklenburg, Rowan,
W3Charlotte–Concord–
Union
N35F
Gastonia, NC–SC
SC: Chester, Lancaster, York
N35G

Lincoln, NE

W1Orlando–Kissimmee–

Sanford, FL

N35H

Richmond, VA

N35I

Raleigh, NC
Greenville–Anderson–
Mauldin, SC

N35J

FL: Lake, Orange, Osceola, Seminole
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
NC: Franklin, Johnston, Wake
SC: Anderson, Greenville, Laurens, Pickens

See footnotes at end of table.

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Final CPI geographic sample, 2018 revision
PSU

PSU name

code (1)
N35K

W3Winston–Salem,

PSU definition (state and county)

Stratum
population

Percent of
index
population

NC: Davidson, Davie, Forsyth, Stokes, Yadkin

2,637,083

.91

FL: Lee

3,091,153

1.07

N35M
N35N

NC
Cape Coral–Fort Myers,
FL
Ocala, FL
Gainesville, FL

FL: Marion
FL: Alachua, Gilchrist

2,568,744
2,913,140

.89
1.01

N35O

W2Wilmington,

NC: New Hanover, Pender

2,736,321

.94

N35P

W2Jacksonville,

NC: Onslow

3,100,604

1.07

N35Q

W1Clarksburg,

2,563,098

.89

2,529,624

.87

2,483,606
2,620,595

.86
.90

2,801,399

.97

2,550,408

.88

2,397,313

.83

6,426,214

2.22

5,920,416

2.04

2,436,095

.84

2,812,948

.97

2,543,610

.88

2,444,837

.84

2,581,037

.89

N35L

NC
NC

WV: Doddridge, Harrison, Taylor
WV
Region 3—South, Division 6—East South Central
W4Louisville/Jefferson
IN: Clark, Floyd, Harrison, Scott, Washington
N36A
KY: Bullitt, Henry, Jefferson, Oldham, Shelby, Spencer, Trimble
County, KY–IN
N36B
Birmingham–Hoover, AL AL: Bibb, Blount, Chilton, Jefferson, Shelby, St. Clair, Walker
N36C
Chattanooga, TN–GA
GA: Catoosa, Dade, Walker
TN: Hamilton, Marion, Sequatchie
W4Huntsville, AL
N36D
AL: Limestone, Madison
Florence–Muscle
N36E
AL: Colbert, Lauderdale
Shoals, AL
W1Meridian, MS
N36F
MS: Clarke, Kemper, Lauderdale
Region 3—South, Division 7—West South Central
Dallas–Fort Worth–
TX: Collin, Dallas, Denton, Ellis, Hood, Hunt, Johnson,
S37A
Arlington, TX
Kaufman, Parker, Rockwall, Somervell, Tarrant, Wise
Houston–The
TX: Austin, Brazoria, Chambers, Fort Bend, Galveston, Harris,
S37B
Woodlands–Sugar
Liberty, Montgomery, Waller
Land, TX
San Antonio–New
TX: Atascosa, Bandera, Bexar, Comal, Guadalupe, Kendall,
N37C
Braunfels, TX
Medina, Wilson
OK: Canadian, Cleveland, Grady, Lincoln, Logan, McClain,
N37D
Oklahoma City, OK
Oklahoma
LA: Ascension, East Baton Rouge, East Feliciana, Iberville,
N37E
Baton Rouge, LA
Livingston, Pointe Coupee, St. Helena, West Baton Rouge,
West Feliciana
N37F
Lafayette, LA
LA: Acadia, Iberia, Lafayette, St. Martin, Vermilion
Brownsville–Harlingen,
N37G
TX: Cameron
TX
N37H
Amarillo, TX
TX: Armstrong, Carson, Oldham, Potter, Randall
N37I

W2Russellville, AR

N37J

W3Paris,

AR: Pope, Yell

TX: Lamar
TX
Region 4—West, Division 8—Mountain
Phoenix–Mesa–
S48A
AZ: Maricopa, Pinal
Scottsdale, AZ
Denver–Aurora–
CO: Adams, Arapahoe, Broomfield, Clear Creek, Denver,
S48B
Lakewood, CO
Douglas, Elbert, Gilpin, Jefferson, Park
Las Vegas–Henderson–
N48C
NV: Clark
Paradise, NV
N48D
Provo–Orem, UT
UT: Juab, Utah
N48E
Yuma, AZ
AZ: Yuma
N48F

W3St.

George, UT

UT: Washington

See footnotes at end of table.

15

2,756,117

.95

2,620,998

.91

2,851,943

.98

4,192,887

1.45

2,543,482

.88

3,227,960

1.11

3,724,271
3,840,701

1.29
1.33

3,206,759

1.11

U.S. BUREAU OF LABOR STATISTICS

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Final CPI geographic sample, 2018 revision
PSU
code (1)

PSU name

PSU definition (state and county)

Stratum
population

Region 4—West, Division 9—Pacific
Los Angeles–Long
S49A
CA: Los Angeles, Orange
Beach–Anaheim, CA
San Francisco–
S49B
CA: Alameda, Contra Costa, Marin, San Francisco, San Mateo
Oakland–Hayward, CA
Riverside–San
S49C
CA: Riverside, San Bernardino
Bernardino–Ontario, CA
Seattle–Tacoma–
S49D
WA: King, Pierce, Snohomish
Bellevue, WA
San Diego–Carlsbad,
S49E
CA: San Diego
CA
S49F
Honolulu, HI
HI: Honolulu
S49G
Anchorage, AK
AK: Anchorage, Matanuska–Susitna
OR: Clackamas, Columbia, Multnomah, Washington, Yamhill
Portland–Vancouver–
N49H
Hillsboro, OR–WA
WA: Clark, Skamania
W1Santa Rosa, CA
N49I
CA: Sonoma

Percent of
index
population

12,828,837

4.43

4,335,391

1.50

4,224,851

1.46

3,439,809

1.19

3,095,313

1.07

1,360,301
523,154

.47
.18

5,208,366

1.80

5,163,670

1.78

N49J

Chico, CA

CA: Butte

4,623,339

1.60

N49K

W4Moses

WA: Grant

4,363,676

1.51

Lake, WA

Notes:
(1) PSU code (1st character: S—self–representing or N—nonself–representing; 2nd character: region number; 3rd character: division number; 4th character:

A–Q, depending on number of PSUs within a Census division).
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.

SUGGESTED CITATION

Steven P. Paben, William H. Johnson, and John F. Schilp, "The 2018 revision of the Consumer Price Index
geographic sample," Monthly Labor Review, U.S. Bureau of Labor Statistics, October 2016, https://doi.org/
10.21916/mlr.2016.47.
NOTES
1 Because of resource constraints, the U.S. Bureau of Labor Statistics (BLS) did not rotate the 2008 geographic sample, which was
based on the 2000 decennial census.
2 The continuous rotation plan for geographic areas includes the following CPI component surveys: the Housing Survey, the
Telephone Point-of-Purchase Survey, and the Commodity and Services survey. The latter two surveys moved to 4-year within-PSU
rotation cycles as part of the 1998 CPI revision. In 2010, CPI began a multiyear effort to continually update the rent sample for the
Housing Survey. (See Frank Ptacek, “Updating the rent sample for the CPI Housing Survey,” Monthly Labor Review, August 2013,
https://www.bls.gov/opub/mlr/2013/article/updating-the-rent-sample-for-the-cpi-housing-survey.htm.) In January 2015, the Consumer
Expenditure Survey (CE) switched to a geographic sample design based on the 2010 decennial census.
3 Janet L. Williams, Eugene F. Brown, and Gary R. Zion, “The challenge of redesigning the Consumer Price Index area sample,”
Proceedings of the Survey Research Methods Section, vol. 1 (American Statistical Association, 1993), pp. 200–205.
4 Sample allocation divides the total sample into smaller samples for each classification variable.

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5 Because of a lack of population, PSUs were not selected for the Northeast region’s urban nonmetropolitan index areas (size class
D100) used in the 1998 area design.
6 Revised delineations of metropolitan statistical areas, micropolitan statistical areas, and combined statistical areas, and guidance on
uses of the delineations of these areas, Bulletin No. 13-01 (Office of Management and Budget, February 28, 2013), https://
obamawhitehouse.archives.gov/sites/default/files/omb/bulletins/2013/b-13-01.pdf.
7 Ralph Bradley, “Analytical bias reduction for small samples in the U.S. Consumer Price Index,” Journal of Business & Economic
Statistics, vol. 25, no. 3, 2007, pp. 337–346.
8 Ibid.
9 A self-representing area represents only its own area definition. A nonself-representing area stands for multiple area definitions.
10 John F. Schilp, “Simulated statistics for the proposed by-division design in the Consumer Price Index,” Proceedings of the
Government Statistics Section (American Statistical Association, 2014).
11 The 1998 area design, used currently, includes seven “Census region by size class” groups for the nonself-representing areas,
because it has no sample allocated to the C-sized areas in the Northeast region.
12 Under the 2018 area design, the total population residing in each county included in a CBSA, both metropolitan and micropolitan,
is defined as urban. Under the 1998 design, the total population in A- and B-sized PSUs was defined as urban, but only the population
residing within the political boundaries of an “urban core” in the C-sized PSUs was defined as urban. The population outside of an
urban core, but inside a county defining a C-sized PSU, was considered rural under the 1998 design.
13 The CE additionally covers rural areas. These areas are out of scope for the CPI.
14 The original design based on the 2000 decennial census had 86 urban PSUs for the CE and CPI surveys. CPI did not receive
funding for its initiative in time to make the switch to that design. The CE program did implement the design, but then had to cut 11
PSUs because of insufficient funding in 2006.
15 The variance models are for 6-month percent-change standard errors, because the main purpose of the models is to allocate
outlets and items to areas where the samples are rotated every 6 months.
16 An overview of the ACS data products can be found at https://www.Census.gov/programs-surveys/acs/.
17 The 3-year product was discontinued in 2015.
18 Education level for those age 25 and over.
19 A price relative is the ratio of an item’s current-period price to its previous-period price.
20 Median household income and median property value were derived from 2010 5-year ACS estimates for the final stratification
model.
21 H. P. Friedman and J. Rubin, “On some invariant criteria for grouping data,” Journal of the American Statistical Association, vol. 62,
1967, pp. 1159–1178.
22 Susan L. King, John Schilp, and Erik Bergman, “Assigning PSUs to a stratification PSU,” Proceedings of the Survey Research
Methods Section (American Statistical Association, 2011), pp. 2235–2246.
23 William H. Johnson, Steven P. Paben, John F. Schilp, “The use of sample overlap methods in the Consumer Price Index redesign,”
Proceedings of the Fourth International Conference of Establishment Surveys, June 11–14, 2012, Montréal, Canada (American
Statistical Association, 2012).
24 See Walter M. Perkins, “1970 CPS redesign: proposed method for deriving sample PSU selection probabilities within 1970 NSR
strata,” memorandum to Joseph Waksberg (U.S. Bureau of the Census, August 5, 1970); and Lawrence R. Ernst, “Maximizing the

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overlap between surveys when information is incomplete,” European Journal of Operational Research, vol. 27, no. 2, 1986, pp. 192–
200.
25 SOCSLP is written in SAS by Sun Wong Kim, Steven G. Herringa, and Peter W. Solenberger of the University of Michigan. It
should remain useable in the future, because SAS will continue to be supported at BLS. For details on the methodology used in
SOCSLP, see Kim, Herringa, and Solenberger, “Optimizing solution sets in two-way controlled selection problems” (Institute for Social
Research, University of Michigan), ftp://ftp.isr.umich.edu/pub/src/smp/socslp/socslp_paper.pdf.
26 Cincinnati, Cleveland, Milwaukee, Pittsburgh, and Portland were reselected as nonself-representing areas.

RELATED CONTENT

Related Articles
Comparing the Consumer Price Index with the gross domestic product price index and gross domestic product implicit price deflator,
Monthly Labor Review, March 2016.
Explaining the 30-year shift in consumer expenditures from commodities to services, 1982–2012, Monthly Labor Review, April 2014.
The first hundred years of the Consumer Price Index: a methodological and political history, Monthly Labor Review, April 2014.
Updating the rent sample for the CPI Housing Survey, Monthly Labor Review, August 2013.

Related Subjects
Consumer price index

Sampling

Prices and Spending

18

Statistical programs and methods

Geographic areas