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REPORT TO THE OFFICE OF MANAGEMENT AND BUDGET

Consumer Inflation Measures

June 16, 2021
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Contents

EXECUTIVE SUMMARY......................................................................................................................4

Recommendation 1: Official poverty measure ................................................................................4

Recommendation 2: Need for OMB guidance .................................................................................4

SECTION 1: BACKGROUND ................................................................................................................5

1.1 Charge to the ITWG ..................................................................................................................5

1.2 Using a price index to adjust for inflation ..................................................................................6

1.3 The Official Poverty Measure....................................................................................................7

SECTION 2: ITWG ACTIVITIES.............................................................................................................9

2.1 Public comment .......................................................................................................................9

2.2 Literature review.................................................................................................................... 10

2.3 Original research on subpopulation inflation rates .................................................................. 11

SECTION 3: EXISTING FEDERAL MEASURES OF CONSUMER INFLATION............................................. 14

3.1 BEA products.......................................................................................................................... 14

3.2 BLS products .......................................................................................................................... 15

3.3 Evaluating differences between existing indexes..................................................................... 17

3.3.1 Measurement objectives................................................................................................... 17

3.3.2 Scope: Consumption and geographic coverage .................................................................. 17

3.3.3 Methodology.................................................................................................................... 18

3.3.4 Biases and other limitations of CPI measures..................................................................... 19

3.3.5 Strengths and weaknesses of specific CPI measures........................................................... 21

SECTION 4: ITWG RECOMMENDATIONS TO OMB............................................................................. 24

Recommendation 1: Official Poverty Measure .............................................................................. 24

Recommendation 2: Need for OMB guidance ............................................................................... 24

4.1 Recommendations on the index best suited for the Official Poverty Measure .......................... 25

4.1.1. The ideal index for OPM annual calculation ...................................................................... 25

4.1.2 Creating a new chained CPI for low-income households..................................................... 29

4.1.3 Provisional options for OMB to consider............................................................................ 34

4.2 OMB guidance for federal agencies on index use..................................................................... 35

SECTION 5: REMAINING ISSUES IN NAVIGATING APPROPRIATE USE OF INFLATION MEASURES......... 35

5.1 Adjusting long-running historical series................................................................................... 35

5.2 Background ............................................................................................................................ 36
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5.3 Existing indexes...................................................................................................................... 36

5.4 Proposed guidance on index selection .................................................................................... 37

Appendix A..................................................................................................................................... 40

Appendix A.1: OMB Statistical Policy 14........................................................................................ 40

Appendix A.2: Federal Register Notice, consumer inflation measures ITWG................................... 40

Appendix A.3: All purpose index decision-making flowchart .......................................................... 46

Appendix A.4: Index choice flowchart documentation................................................................... 47

Appendix B..................................................................................................................................... 50

Appendix B.1: Consumer Price Index for All Urban Consumers (CPI-U) compared to other index.... 50

measures..................................................................................................................................... 50

Appendix B.2: Poverty thresholds, family of four with two children, estimated using various......... 53

indexes, anchor year=2000........................................................................................................... 53

Appendix B.3: Evaluation of CPI-P and Chained CPI-P .................................................................... 53

Appendix B.4: Breakdowns of index and share differences by item and poverty definition............. 61

Appendix B.5: Comparing subpopulation indexes to full population indexes and the studying the.. 61

effecs of sample size .................................................................................................................... 61

Appendix B.6: Analysis of Food Costs and Updated Multipliers...................................................... 65

(Note: Document errors have been corrected subsequent to publication that reflect the intent of the ITWG.)
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EXECUTIVE SUMMARY

In 2019, the Office of Management and Budget (OMB) convened an Interagency Technical Working 

Group on Consumer Inflation Measures (hereafter ‘ITWG’) to evaluate the strengths, weaknesses, 

and best practices for the application of the primary consumer inflation measures produced by 

Federal statistical agencies. The ITWG was chartered to: (1) develop a Federal Register Notice (FRN)

to solicit public comment on its planned activities, (2) develop a recommendation on the consumer 

inflation measure most appropriate for conducting annual adjustments to the Official Poverty 

Measure (OPM), (3) recommend whether OMB guidance on the use of the various inflation measures 

by Federal agencies is feasible, (4) prepare and submit a report containing recommendations on the 

remaining issues around appropriate use of existing inflation measures, and (5) provide a report 

describing the strengths and weaknesses of the methodologies used in the consumer inflation 

measures. This report incorporates all five ITWG responsibilities and the two following 

recommendations to OMB.

Recommendation 1: Official poverty measure

The ITWG developed cohort consumer price indexes for households at the lower end of the income 

distribution, in order to explore the inflation experience of a low-income subset of the population. 

The results of this research indicate low-income cohorts tend to have different budget shares than 

the overall urban population which, when applied to the data from 2004 to 2017, resulted in higher 

estimates of inflation for these cohorts. The ITWG recommends the Bureau of Labor Statistics (BLS)

pursue development of a new consumer price index specifically designed to represent the inflation 

experience of low-income consumers, and that OMB use that new index to adjust the Official Poverty 

Measure. To that end, a formula measuring price change based on the current behavior of consumers 

is superior to one based on consumer behavior from several years ago. Therefore, an index that uses 

the Tornqvist (or a similar formula that averages spending from adjacent time periods) should be 

used in the construction of the low-income index. (See section 4.1.)

Recommendation 2: Need for OMB guidance

Evaluating a given price index’s fitness for a particular purpose requires an understanding of the 

strengths and weaknesses of the various indexes available, and also a comprehensive understanding 

of the specific use under consideration. ITWG determined that any guidance OMB could disseminate 

on the fitness for use would be of little value to agencies, because guidance could only reasonably 

consider the measures themselves in isolation from any potential specific agency use. ITWG 

recommends the BLS provide additional resources to assist users on index choice. (See section 4.2.)
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SECTION 1: BACKGROUND 

In its role as coordinator of the Federal statistical system under the Paperwork Reduction Act, OMB is 

required to ensure the system’s efficiency and effectiveness. A key way OMB fulfills this responsibility 

is by promulgating, maintaining, and overseeing Government-wide principles, policies, standards, and 

guidance concerning the development, presentation, and dissemination of Federal statistical products. 

OMB’s Office of Statistical and Science Policy (SSP), within the Office of Information and Regulatory 

Affairs, relies on public comment, stakeholder input, and subject matter expertise to identify areas 

where existing OMB policies or guidance may be out of date, lacking clarity, or insufficient for efficient 

coordination of Federal statistics.

OMB convened an interagency technical working group (ITWG) to advise OMB on the strengths, 

weaknesses, and best practices for the application of various inflation measures produced by Federal 

statistical agencies. The members of the ITWG are subject matter experts in inflation measurement 

from across Federal government. Staff from seven different departments participated on the group. 

Members of the group do not represent their respective agency in an official agency head capacity; 

members were selected based solely on their technical expertise. The Bureau of Labor Statistics (BLS) 

chaired the group alongside a non-voting representative from OMB. 

1.1 Charge to the ITWG

Specifically, OMB charged the ITWG with the following tasks:

• Develop and submit a draft Federal Register Notice (FRN) soliciting public comment on the 

strengths and weaknesses of the existing consumer inflation measures produced by the BLS

and the Bureau of Economic Analysis (BEA) to the Chief Statistician of the U.S. to be reviewed 

and then issued by SSP; 

• Deliver a final recommendation on the consumer inflation measure most appropriate for 

conducting annual adjustments to the Official Poverty Measure (OPM) that considers the 

public comments received from the FRN and provide it to the Chief Statistician of the U.S.;

• Deliver a final recommendation on whether OMB guidance on the use of the various inflation 

measures by Federal agencies is feasible;

• Prepare and submit a report containing recommendations on the remaining issues around 

appropriate use of existing inflation measures — including for adjusting Census Bureau 

historical figures — that takes into account the public comments received from the FRN; and 

• Provide a report concerning the strengths and weaknesses of the methodologies used in the 

Federal Government’s primary consumer inflation measures and make a recommendation on 

the feasibility for guidance from OMB on the appropriate use of the various indexes.
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1.2 Using a price index to adjust for inflation

Inflation is commonly defined as a rise in the general level of prices, and deflation as a decline in the 

general level of prices.1 Equivalently, inflation represents a decline in the purchasing power of 

money. As a general matter, adjusting statistics and other data for inflation better reflects 

consumers’ actual experiences over time. There are many different ways in which inflation indexes 

attempt to capture this change in the level of prices. Specifically, the inflation statistics produced by 

BLS and BEA differ in their scope, weighting, formulas, underlying assumptions, and other factors 

consistent with their measurement objectives. The definition of a price index, as stated by the 

Consumer Price Index Manual: Theory and Practice is:

A measure of the proportionate or percentage change in a set of prices over time. A consumer 

price index (CPI) measures changes in the prices of goods and services that households consume 

for personal use.2 

As prices of different goods and services do not necessarily all change at the same time and by the 

same amount, a price index aggregates their movement. There are a variety of different consumer 

inflation measures currently produced by Federal statistical agencies which vary in how they address 

three fundamental questions concerning inflation measurement: (1) what population is the index 

designed to represent, (2) exactly what set of prices should be covered by the index, and (3) what is 

the most appropriate way to aggregate their movements?

The answers to these three basic questions usually turn on the primary underlying intended use of an 

index. Indexes have a variety of uses:

• As a compensation measure (e.g., how much is needed to adjust benefit amounts for Social 

Security beneficiaries);

• As an inflation adjustment in escalation clauses of private contracts;

• Inflation-indexed Treasury bonds;

• As an adjustment to marginal tax rate income brackets to keep the tax system inflation 

neutral;

• In estimating real values — as a deflator to convert current period nominal values to prior 

period values or an inflator to convert prior period values to current period values; and

• As an inflation yardstick for macroeconomic policy makers .

The BLS publishes five major aggregate index categories: 1) the CPI for all urban consumers (CPI-U), 

which is the flagship or headline index published by BLS; 2) the CPI for urban wage earners and 

clerical workers (CPI-W), which is the historical index dating back to 1912 and the index currently 

1 Collins Dictionary of Economics, 4th ed. © C. Pass, B. Lowes, L. Davies 2005.

2 International Labour Office, et. al. Consumer Price Index Manual Theory and Practice, ILO/IMF/OECD/UNECE/Eurostat/The Word Bank, Geneva, 

Switzerland. 2004, p. 1.
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used by the Social Security Administration (SSA) for calculating cost-of-living-adjustments to Social 

Security benefits; 3) a research index estimating the inflation experience of elderly consumers (R-CPIE); 4) an index designed to capture consumer substitution across component consumption categories 

(Chained CPI-U), which is the index used since 2018 to inflation adjust federal tax brackets; and 5) a 

research series which approximates inflation retrospectively to 1978 using current CPI-U methods (RCPI-U-RS). Other indexes are available upon request (e.g., an index adopting a fixed-base aggregation 

methodology (CPI-U-X1)). Additionally, BLS researchers have occasionally created additional research 

series, including indexes based on the spending behavior of low-income households.

The BEA features five different inflation measures on its website: 1) the GDP Price Index, 2) the GDP 

Deflator, 3) the Gross Domestic Purchases Price Index, 4) the Personal Consumption Expenditures 

Price Index (PCE-PI), and 5) the PCE index excluding food and energy. In addition, the BEA publishes 

regional price parities by state and metropolitan area (indexes that measure price levels across 

geography), and an experimental health care index that measures price change of various disease 

treatments.

1.3 The Official Poverty Measure

The current OPM uses a set of thresholds that are compared to annual pre-tax income to identify 

families in poverty for statistical purposes. There are different thresholds varying by the number of 

adults and children in a family and, for some family types, by the age of the family head. The 

thresholds were originally set at the cost of a minimum food diet (USDA’s “economy food plan”) 

times three to allow for expenditures on all other goods and services. The multiplier of three 

represented the after-tax monetary income of the average family in 1955 relative to the amount 

spent on food.3

As early as November 1965, SSA policymakers and analysts began to express concern about how to 

adjust the poverty thresholds for increases in the general standard of living. In 1968, the SSA tried to 

raise the poverty thresholds in real terms to reflect increases in the general standard of living. The 

Bureau of the Budget (the predecessor of OMB) prohibited the real increase in the poverty 

thresholds, but initiated an interagency Poverty Level Review Committee to re-evaluate the poverty 

thresholds. This Committee decided to adjust the thresholds only for price changes, and not for 

changes in the general standard of living. In 1969, the Committee decided the thresholds would be 

indexed by the Consumer Price Index instead of the per capita cost of the economy food plan, and 

that farm poverty thresholds would be set at 85 percent rather than 70 percent of corresponding 

nonfarm thresholds. This effectivelymade the annual adjustment a function of overall inflation, 

rather than food price inflation. In August 1969, the Bureau of the Budget designated the poverty 

thresholds with these revisions as the federal government's official statistical definition of poverty.

The official measure gained its status through the Bureau of the Budget’s Circular A-46, issued in 

1969. OMB, the successor agency to the Bureau of the Budget, issued Statistical Policy Directive 14 in 

1978, reconfirming the measure as official and directing federal agencies to use it for statistical 

3 Fisher, Gordon, The Development and History of the Poverty Thresholds” Social Security Bulletin Vol 55, No. 4, Winter 1992.
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purposes. (See Appendix A.1.) It describes the series as a classification of income applied to a set of 

poverty thresholds developed by the SSA, subsequently modified by a Federal Interagency 

Committee in 1969. The directive explicitly states the measure was not developed for administrative 

purposes, and allows for other measures of poverty to be developed, as long as those measures are 

distinguished from the official series. 

Currently, the OPM thresholds are re-estimated annually by taking the 1978 values and adjusting 

them forward by an index of price change. They are not derived by a re-evaluation of the standards 

for minimum nutrition or subsistence, nor from direct observation of spending from household 

expenditure surveys. The index used for the inflation adjustment has varied over the history of the 

series:

 1963 to 1968: December to December change in the per capita cost of the economy (or low cost) 

food plan, published by the USDA4

 1969 to 1979: Annual change in the All-items Consumer Price Index, the index currently labeled 

the CPI-W, published by the BLS5

 1980 to current: Change in the annual average Consumer Price Index for all urban consumers, 

labeled the CPI-U and published by BLS6

A simplified equation illustrating the calculation of the official poverty threshold (P) for a given year 

(y) can be expressed as:

PPyy = PPbb �

IIyy

IIbb

�

where Iy = an index of price change (currently the CPI-U) in year y. The ITWG is narrowly charged with 

identifying and evaluating the various different indexes of price change (I) that could be used for 

indexation between a base year (b), currently 1978, and current year (y), and recommending the 

index that is most appropriate for the purpose of estimating the current year (y) poverty thresholds 

(P).

For more than 40 years, the standards in OMB’s Statistical Policy Directive 14 have provided a shared 

threshold for Federal statistical agencies to generate U.S. poverty statistics over time. 

The OPM guidelines should not be confused with the poverty guidelines produced annually by the 

U.S. Department of Health and Human Services. While the poverty thresholds are used for calculating 

official poverty population statistics, the poverty guidelines are used for administrative purposes. 

Most commonly the poverty guidelines are used by a number of federal, state, local, and non-profit 

4 Fisher, Gordon, The Development and History of the Poverty Thresholds. Social Security Bulletin Vol 55, No. 4, Winter 1992.

5 Poverty estimates in prior years were revised to reflect the 1969 adjustments. These included changes to the relative poverty thresholds for farm and 

non-farm households.

6 In 1981, the poverty thresholds were adjusted to eliminate differences between the following groups: 1) farm and non-farm households and 2) families 

with a female householder, no husband present and all other families.
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programs, such as Medicaid and the Supplemental Nutrition Assistance Program (SNAP), to 

determine income eligibility. The guidelines are based on the previous year’s poverty thresholds, and 

updated for inflation using the CPI-U, based on statutory language in the Community Services Block 

Grant Act (42 U.S.C. §9902(2)). Because of this, changes to the poverty thresholds, including how they 

are updated for inflation over time, may affect eligibility for programs that use the poverty guidelines. 

SECTION 2: ITWG ACTIVITIES 

To meet the charges described in section 1, the ITWG conducted a literature review, solicited and 

reviewed public comment, and conducted original research on the inflation experience of low-income 

consumers. 

2.1 Public comment

OMB published a Federal Register Notice (FRN) on May 7, 2019 seeking public comment on the 

differences among the primary consumer inflation measures produced by BLS and BEA and how 

those differences might influence the estimation of the Official Poverty Measure. The FRN received 

57,127 comments from the public, the vast majority of which expressed opposition to a revision to 

the Official Poverty Measure on the basis of the potential impact to eligibility for various needs-based 

programs. We also received comments from several members of Congress and other elected officials 

representing state, Tribal, and local governments. These commenters expressed concern about 

possible reductions in federal services within their communities and opposed revising the OPM on

those grounds. All public comments can be viewed at: https://www.regulations.gov/document/OMB2019-0002-0001. (See Appendix A.2 for the full text of the FRN.)

The following specific objections were raised by several commenters:

• The Chained CPI-U does not represent the low-income population because:

o Low-income consumers spend a larger proportion of their income on high-inflation 

expenditures, like energy and housing, and thus have different spending patterns than 

the urban population at large

o Low-income consumers have limited opportunity to substitute across items

• Revising the OPM makes it less valuable as a long-standing and consistent statistical measure.

• The OPM already fails to keep up with living standards.

• The OPM has several flaws that should be prioritized over updating the method of inflation 

adjustment, including:

o Geographic differences 

o Changes to the demographic composition of the workforce

o Changes to employment-necessary expenses, such as internet access
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• No changes should be made to the OPM without an analysis of potential program impacts.

Commenters supporting a revision to the OPM cited the likely overestimation of inflation by the CPI-U

and CPI-W due to the failure to account for upper-level substitution bias, which occurs when 

component indexes for item groups and areas are aggregated to form the all-items measure. The BLS 

uses a fixed quantity weighted index for this purpose (with weights derived from the Consumer 

Expenditure Survey (CE), a survey of household expenditure patterns), and hence ignores 

substitutions of chicken for beef, apples for oranges, etc. 7 

Several commenters identified publications for the ITWG’s literature review discussed in the 

upcoming section.

2.2 Literature review

BLS economists examined the inflation experience of the poor nearly 25 years ago. Recognizing the 

possibility that consumers at different ends of the income distribution might have different 

experiences of price change and that this could be relevant for adjustments of thresholds, Garner, 

Johnson, and Kokoski (1996) presented results from experimental BLS indexes for different income 

groups. The results generally concluded that the price change experience of the poor was similar to 

that of the broader population over the period researched (1984-94), though different formulas and 

aggregation methods affect the exact results. 

Work by the Chicago Federal Reserve Bank continued along these lines, using BLS data to estimate 

price indexes for different demographic groups. McGranahan and Paulson (2006) presented the 

Chicago Fed Income Based Index (IBEX). The IBEX included inflation estimates by income quartile 

from 1983-2005, as well as inflation for food stamp vs. non-food stamp recipients, among other 

breakdowns. The data was subsequently extended through 2013. The results are hard to generalize 

but the long-term differences between groups are small. The lowest income quartile did have higher 

inflation than the population as a whole by just under 0.1 percentage point per year over the span. 

Curiously, though, food stamp recipients experienced a slightly lower inflation rate than non-food 

stamp recipients over the same period.

Studies such as Broda and Romalis (2009); Broda, Leibtag, and Weinstein (2009); Argente and Lee 

(2017); Jaravel (2017); and Kaplan and Schulhofer-Wohl (2017), have used AC Nielsen’s (ACN) grocery 

and drug store transactions data to measure inflation rates for lower-income consumers versus other 

groups. These studies cover different time periods, and the data for the earlier time period is no 

longer available for replication. However, these studies are fairly consistent, showing a lower inflation 

rate for lower-income consumers from 1994 to about 2006, and then a higher inflation rate through 

2015. They consistently find that lower income consumers substitute more in response to relative 

price changes, as measured by the spread between Paasche and Laspeyres indexes, which measures 

7 Michael J. Boskin, E. Dulberger, R. Gordon, Z. Griliches, and D. Jorgenson, “Toward a More Accurate Measure of the Cost of Living,” Final Report to 

the Senate Finance Committee, December 4, 1996, http://www.ssa.gov/history/reports/boskinrpt.html/. 8 IRI data also covers this, but it comes from 

the same survey and respondent sources.
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the differences in inflation from using the period t versus t+1 choices, and also by the estimated 

elasticities of substitution, which is a more direct, though stylized measure. 

However, the ACN data is the only major data source currently available to study these effects at the 

level of individual goods as opposed to broad aggregates of goods, and it only covers about 8 percent 

of CPI expenditures, as measured by BLS samples of this data.8 Since the relative inflation rates of 

lower-income consumers are also found to change over time, it is impossible to extrapolate any 

conclusions to the entire index for the indefinite future.

2.3 Original research on subpopulation inflation rates

Group members and public comment on the FRN brought up the issue of whether the inflation 

experiences of low-income consumers are the same as the population overall. Consumers with 

relatively lower income might have a higher budget share on goods with a different average inflation 

rate, or they might substitute between goods due to relative price changes to a different extent. The 

2002 National Academies of Sciences report on cost-of-living and price measures At What Price

stated:

The existence of heterogeneity (in purchasing patterns and shopping behaviors) raises... 

important questions: First, are the rates of inflation experienced by different groups or by people 

in different geographic locations sufficiently different so that separate indexes should be 

constructed for each group or location? This issue is particularly important when indexes are used 

for adjusting taxes, social security benefits, and other public transfer payments. 8

This section describes the ITWG’s efforts to answer this question regarding the inflation experience of 

the poor.

The ITWG reviewed the existing economics literature to see if evidence existed of a different inflation 

experience among the poor. Since the indexes are all aggregated at two levels, first combining prices 

of goods into elementary item-area cells, and then combining cells weighted by expenditure shares, 

there are two ways to have different inflation rates. One is by different purchases within a cell, across 

goods and/or outlets, and the second is by different expenditure shares across cells. The literature 

only addressed the first possibility with consumer survey data from grocery and drug store 

transactions data, which represents only about 8 percent of the index. 9 The literature’s conclusions 

are inconsistent over time, with the earlier period showing lower inflation for the poor and the later 

period showing higher inflation. For the second possibility of differences at the upper level of 

aggregation, the literature generally shows little difference, with at most a 0.1 percentage point

higher average annual inflation for the lower-income consumers.

However, directly checking whetherthe upper level differences were significant was something the 

BLS was best able to do. The BLS created concurrent and lagged weighted indexes for a low-income 

8 National Research Council, At What Price? Conceptualizing and Measuring Cost-of-Living and Price Indexes. Panel on Conceptual, Measurement, and 

Other Statistical Issues in Developing Cost-of-Living Indexes, Charles L. Schultze and Christopher Mackie, Editors. Committee on National Statistics, 

Washington, DC. 2002. p. 246.

9 This number is from internal BLS research using the same datasets as the papers referenced in the literature, comparing the data to Consumer 

Expenditure Survey data.
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subgroup of the population, a chained consumer price index for poverty (C-CPI-P) and consumer price 

index for poverty (CPI-P), to compare to the C-CPI-U and CPI-U. This was done by using only a 

subpopulation of CE respondents to calculate different expenditure shares to weight the same 

elementary cell indexes. The same elementary indexes were used as for the CPI-U population, 

because for all component items, the BLS input data are not available to produce elementary indexes 

scientifically designed for the low-income subgroup.

Indexes were made for multiple definitions of the low-income population, including only those with 

CE reported income below the current OPM threshold, or targeting the OPM threshold itself with an 

income interval centered on the OPM threshold in different ways. In total, the BLS created three 

different experimental indexes for three different subgroup definitions for poverty. 

The ITWG found that all three experimental chained indexes showed higher inflation than the CPI-U 

from 2002 to 2018, which of course is higher than the C-CPI-U. A major reason was the higher weight 

for the poor on rent, which rose faster than the all items index over this span. This was partly offset 

by a lower weight for the poor on owners’ equivalent rent, which also rose more quickly, but the net 

effect was positive. Other contributors included a higher weight on college tuition, textbooks and 

college housing, and energy and transportation. (See Appendix B.4 for a table showing the 

contributions to the differences by item. 10)

The sample sizes of respondents were quite small, however, ranging from 12.5 percent of 

respondents to 25 percent depending on the definition of the poverty cohort used. Due to the small 

household sample size, most elementary item-area cells had no reported expenditures for the one

week (Diary) or 3-month (Interview) survey period, especially durable goods and infrequently 

purchased nondurable goods. Therefore, to see if the low sample sizes were driving the results 

through either high variance or some other bias, confidence intervals for the indexes were created by 

bootstrapping the CE population samples. Confidence intervals showed that the differences were 

statistically significant. (See Appendix B.5.) Defining the population by expenditure rather than 

income made the differences larger; see Chart 1.11

10 Differences by item are reported between income and expenditure definitions for the poor, using the bottom 25% of the distribution, and between 

those populations and the entire population, for both index and shares. These are the sum of log differences summed over areas and months for the 

period 1999-2017. They are sorted by the highest to lowest of the difference between the income definition and the whole population, in an embedded 

Excel table.

11 Research has shown that income, and the implied poverty rate, is very poorly measured by survey data, and expenditure can be a better proxy. See 

Meyer, Mok, and Sullivan (2015).
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Chart 1

The group concluded there was sufficient evidence of different inflation between the average urban 

consumer and low-income urban consumers. The group also unanimously agreed that a newly 

created C-CPI-P type index specifically targeting a low-income subgroup and having purchases 

concurrent with prices would be the best index for the OPM. Therefore, the group recommends the 

BLS pursue the development and production of a new C-CPI-P and that the OPM be indexed to it.

The research conducted by the ITWG provides a proof of concept and feasibility for using existing 

data and resources to construct a C-CPI-P. It also identified several questions that BLS will need to 

further research and decide on as part of the process of constructing an official BLS statistical 

product, which are described in section 4.1.2. One issue is exactly what method of sample selection 

or weighting would be used to estimate the inflation rate for the OPM cutoff itself. Another issue is 

whether to identify the sample used in this estimation by income or expenditure. Also, indexes for 

rent and owner’s equivalent rent could be constructed specifically for the OPM subpopulation, to see 

if it is feasible to use them and how best to construct them. (See section 4.1.2.) In the future, it may 

also be possible to redesign the CE to construct dedicated item-area indexes for the OPM 

subpopulation, by collecting data on individual purchases of specific items at specific outlets across 

different household income cohorts. 

The other main charge of the group was to resolve remaining issues on index use, and to address 

historical index use including Census median income. This was done by incorporating the lessons and 
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decisions made by the group (see section 4.1) to propose a possible framework to aid agencies and 

private entities in choosing an appropriate index, which included Census median income as an 

example to support their review process.

SECTION 3: EXISTING FEDERAL MEASURES OF CONSUMER INFLATION 

Federal statistical agencies currently produce several different measures of consumer inflation. These 

measures differ in their scope, methods, and intended uses or objectives. 

3.1 BEA products

The BEA produces five measures of price change that receive much attention from macroeconomists. 

Three of them are aggregate in nature and are used to adjust nominal GDP for price change. These 

three are: the GDP Price Index, the GDP Price Deflator, and the Gross Domestic Purchases Price Index. 

The remaining indexesfocuses on the price change for Personal Consumption Expenditures, which 

constitutes about 70 percent of GDP. 

GDP Price Index: The GDP price index measures changes in prices paid for goods and services 

produced in the United States, including those exported to other countries. Prices of imports are 

excluded.

GDP Price Deflator: The GDP implicit price deflator, or GDP deflator, basically measures the same 

components and closely mirrors the GDP price index, although the two price measures are calculated 

differently. More specifically, it is derived implicitly as the ratio of the current-dollar value of GDP to 

its corresponding chained-dollar value, multiplied by 100. The GDP deflator is used by some firms to 

adjust payments in contracts. 

Gross Domestic Purchases Price Index: The Gross Domestic Purchases Price index is BEA's featured 

measure of inflation for the U.S. economy. It measures changes in the prices paid by consumers, 

businesses, and governments in the United States, including the prices of the imports they buy.

Personal Consumption Expenditures Price Index: The PCE price index is a narrower measure. It looks 

at the changing prices of goods and services purchased by consumers (and includes goods and 

services provided by non-profit institutions serving households) in the United States. It's similar to 

BLS' consumer price index for urban consumers (CPI-U). In fact, because BEA does not collect any 

price data, the PCE price index is largely made up of CPIs and PPIs for a variety of goods and services. 

However, the two indexes have different measurement objectives and are constructed differently. 

Consequently, the monthly PCE and CPI inflation rates differ. 12Also, BEA revises previously published 

PCE data to reflect updated information or new methodology, providing consistency across decades 

12 BEA provides a reconciliation of the inflation rates here in Table 9.1U. Reconciliation of Percent Change in the CPI with Percent Change in the PCE Price 

Index at: https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=2#reqid=19&step=2&isuri=1&1921=underlying. 
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of valuable data for researchers. The PCE price index is used primarily for macroeconomic analysis 

and forecasting.

Except for the PCE, BEA measures of price change are not designed to measure the price change 

experience solely of consumers, which makes them less appropriate for adjustments that affect 

consumers, including the thresholds of the OPM. While the PCE is more focused on consumers, its 

broader scope and use of some Producer Price Index data make it less appropriate for adjusting the 

OPM thresholds.

3.2 BLS products

To produce its inflation measures, BLS tracks the change in price of a scientifically selected sample of 

consumer goods and services over time. Those items are then weighted using household survey 

spending data to represent the experience of consumers in their day-to-day living expenses, with 

each of the CPI measures reflecting different item substitution rates, consumer populations, or other 

attributes. 

Consumer Price Index for all Urban Consumers (CPI-U): The all urban consumer group is 

representative of about 93 percent of the total U.S. population. It is based on the expenditures of 

almost all residents of metropolitan or micropolitan areas, which are contiguous areas containing a 

population nucleus of at least 50,000 or 10,000 residents, respectively, together with adjacent 

counties that have a high degree of economic and social integration with that core. The population 

scope includes professionals, the self-employed, the unemployed, retired persons, as well as urban 

wage earners and clerical workers. Not included in the CPI-U are the spending patterns of people 

living in rural areas (defined as outside of any metropolitan or micropolitan statistical area), those in 

farm households, Armed Forces members and their families, and those in institutions such as prisons 

and psychiatric hospitals. 

The CPI-U is used extensively (i) for a variety of statutory purposes as prescribed by Congress, (ii) in 

the calculation of other federal statistics, (iii) by federal, state, and local governments for 

administrative purposes, and (iv) by the private sector in escalation clauses of contracts. 

Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W): The CPI-W corresponds 

closely to the population used in computing the CPI from its inception during the World War I era 

through 1978. The CPI-W represents the expenditures of households included in the CPI-U definition 

that also meet two additional requirements: more than one-half of the household's income must 

come from clerical or wage occupations, and at least one of the household's earners must have been 

a full-time worker, that is employed for at least 37 weeks during the previous 12 months. The CPI-W 

population represents about 29 percent of the total U.S. population and 31 percent of the CPI-U

population. The CPI-U and the CPI-W are constructed using the same component indexes, and differ 

only in population coverage and the weights used to aggregate these components.

The CPI-W is used to calculate the annual cost-of-living-adjustment (COLA) applied to Social Security 

benefits and some other Federal transfer payments. The percentage of the population within the CPI-
Page | 16

W definition has declined over time, and the CPI-U, which was first published in 1978, has become 

more widely used. 

Chained Consumer Price Index for all Urban Consumers (C-CPI-U): Both the CPI-U and C-CPI-U are 

indexes designed to measure price changes faced by all urban consumers. The C-CPI-U is 

distinguished from the CPI-U by the expenditure weights and price index formula used to produce 

aggregate measures of price change. The C-CPI-U employs a formula that reflects the effect of 

substitution that consumers make across component item categories, for example in response to 

changes in relative prices. The formula used in the CPI-U and CPI-W does not capture consumer 

spending response to changing relative prices across the component item categories.

The C-CPI-U formula uses expenditure data that are not available until several months after the 

reference month. Due to this lag in data availability, the C-CPI-U estimates for a reference month that 

are published alongside the CPI-U are calculated using a modeled estimate of consumer spending 

response. This preliminary published value is subsequently revised 10 to 12 months later when the 

actual expenditure data are available. The C-CPI-U was first published in 2002 and covers years 2000 

to present. 

Consumer Price Index for the Elderly (R-CPI-E): The R-CPI-E is a research index product that uses the 

same price surveys and formulas as the CPI-U and CPI-W, but uses expenditure weights for 

households with a reference person or spouse aged 62 years or older. As currently produced, the RCPI-E captures the household budgets of the elderly population, which differ from the non-elderly 

population for notable items such as medical care and shelter. However, the sample of stores and 

specific goods and services used in the construction of the R-CPI-E are not scientifically selected 

based on the spending choices of the elderly population. In addition, the actual prices tracked month 

to month do not control for known third-degree price discrimination (i.e. senior citizen discount 

pricing).

The R-CPI-E is a research index and is not currently used for any known official purpose. BLS produces 

this index at the request of Congress and as mandated by statute.

Consumer Price Index Research Series Using Current Methods (R-CPI-U-RS): The Consumer Price 

Index Retroactive Series using current methods (R-CPI-U-RS) presents an estimate of the CPI for all 

Urban Consumers (CPI-U) from 1978 to present that incorporates most of the improvements made 

over that time span into the entire time series. The R-CPI-U-RS, therefore, provides an estimate of 

what the CPI-U would have looked like had current methodology been in place since 1978.

The R-CPI-U-RS is produced by adjusting the historical CPI-U series by estimates of the impact of 

methodological changes made since 1978. It does not involve applying the new methodology to the 

input micro-level sampled data.
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3.3 Evaluating differences between existing indexes

The indexes published by BEA and the BLS differ in their measurement objectives, scope of 

population, geographic coverage, types of goods consumed, and methodology and data sources used 

to calculate the index. 

3.3.1 Measurement objectives

The conceptual foundation for all BLS consumer inflation measures, referred to here as "CPI," is a 

cost-of-living index (COLI). 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 any BLS 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 bias in the CPI is defined.

The CPI-U, CPI-W, and R-CPI-E all attempt to approximate the change in the cost of living for their 

respective populations. The R-CPI-U-RS attempts to define the past change in the cost of living based 

on current CPI methods. The C-CPI-U also attempts to approximate the change in the cost of living, 

but it is less constrained than the other measures in that it is based on contemporaneous expenditure 

weights rather than fixed weights based on a past period. 

3.3.2 Scope: Consumption and geographic coverage

The CPI provides an approximation to a conditional COLI, pricing consumer goods and services. Except 

for the exclusions below, all consumer goods and services are eligible for pricing in the CPI; the CPI 

does not attempt to price only necessities or exclude certain classes of goods and services. 

Goods and services (such as raw steel, business software, or commercial space) not typically 

purchased by individuals and households are out of scope. Free goods, the quality of the 

environment, goods provided by the government at no cost, and the value of leisure time, are all out 

of scope despite affecting the cost of living as broadly defined.

Excluded goods and services: The CPI covers the consumption sector of the U.S. economy, which is 

defined as the purchase of goods and services for use by households. Consequently, it excludes 

investment items, such as stocks, bonds, real estate, and business expenses. Life insurance is also 

excluded for this reason, although health, household, and vehicle insurance are in scope. Employer 

provided in-kind benefits are viewed as part of income rather than consumption. Purchases of 

houses, antiques, and collectibles are viewed as investment expenditures and are therefore excluded. 

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

also are out of scope. Interest costs and finance charges are also out-of-scope. The CPI excludes 

illegal goods and services and the value of home-produced items because of the practical difficulties 

of collecting the data.
Page | 18

Government-provided and government-subsidized items: The CPI treats any changes to fees that the 

government charges for items, such as admission to a national park, as in-scope changes in price. 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 as a price increase. On the other hand, the CPI does not reflect changes to meanstested subsidies (dependent on the recipient’s income), such as the Supplemental Nutrition 

Assistance Program (SNAP) or section 8 housing allowances. Changes in such subsidies are treated as 

changes to the recipient’s income and are out of scope.

Taxes: The CPI excludes income tax and other direct taxes; however, it does include the effects of 

changes in sales taxes and other indirect taxes paid on consumer products. No attempt is made to 

reflect changes in the quantity or quality of government services paid for through taxes.

Items: Some specific items mentioned by respondents in the CE survey might not be priced for 

practical reasons such as difficulties in sampling; in such cases the weight of those items is moved by 

price change by other items in the same category. 

Geography: Geographically, the scope of the CPI is the entire United States, including all states but 

excluding territories. A geographic sample is drawn based on the Decennial Census; the current CPI 

geographic sample is based on the 2010 Census. 

3.3.3 Methodology

The CPI is created from a series of interrelated surveys. The CPI requires:

• The Decennial Census which is used to select a geographic sample (i.e., a set of core-based 

statistical areas where prices will be collected);

• A survey of consumer expenditures (BLS Consumer Expenditure Survey; CE) to create and 

appropriately weight a market basket of goods and services to be priced and to create a 

sample of outlets in which prices are collected;

• A survey of retail establishments for the collection of prices of consumer commodities and 

services (BLS Commodities and Services Survey; C&S); and

• A survey of rental housing units (BLS CPI Housing Survey).

Weights: Weights for all CPI measures are based on household surveys of consumer expenditures. 

This contrasts with the PCE and reflects the focus of the CPI; it is designed as a cost-of-living measure 

and attempts to measure the price change experience of consumers for items they use for everyday 

living. The weights in the CPI reflect the spending patterns of the target population. Note the scope of 

the CPI excludes investment products, changes in intertemporal prices (interest rates), and the 

purchase price of houses. (Changes in rent are used to measure changes in the opportunity cost of 

owner-occupied housing.) 

With the exception of the C-CPI-U, CPI weights are based on surveys done in the past. CPI indexes 

from 2018 and 2019, for instance, were based on BLS’ Consumer Expenditure Survey (CE) data from 
Page | 19

2015 and 2016. There is an implicit assumption that consumer behavior does not change across item 

categories; effectively the quantities of the goods and services are held constant over time.

Estimation of basic indexes: Each month, the processing of the BLS’ C&S and housing survey data 

yields a set of price relatives, a measure of short-term price change, for all component indexes. The 

CPI uses an index number formula to obtain an average price change for the sampled items in each 

component index’s sample. Most component items, termed elementary item strata in BLS jargon, use 

the geometric mean index formula, which is a weighted geometric mean of price ratios (a sampled 

item’s current price divided by its price in the previous period) with fixed weights equal to the item’s 

expenditure share within the component cell for the sampling period.

Calculations for a limited number of strata use a modified Laspeyres index number formula, which is a 

ratio of the sum of current period prices weighted by base period quantities to the sum of the 

previous month’s prices by base period quantities, with the base period corresponding to the two 

year period during which consumer expenditures were surveyed. 

The CPI-U, CPI-W, and R-CPI-E are all based on the samples of outlets and prices and differ only in the 

expenditure weights of the different item categories. 

Aggregation: Except for the C-CPI-U, basic indexes are aggregated into the broader published indexes 

using a Laspeyres formula. This holds quantities fixed and does not account for consumer substitution 

across item categories. In fact, the CPI-U, CPI-W, R-CPI-E, and R-CPI-U-RS all treat consumer behavior 

as unchanged since the period of BLS’s CE survey used to compute expenditure weights. Since 

consumers generally change their purchases in response to relative price changes, this use of a 

Laspeyres formula with weights from a base period a few years old results in a biased estimate of 

price change relative to the cost-of-living measurement objective of the CPI.

In contrast, the C-CPI-U uses a Tornqvist formula to average price changes across item categories, 

averaging the weights from the two months used in calculation (for example, the May C-CPI-U would 

be used based on weights from April and May). These weights are not obtained until 10-12 months 

after initial publication, however, so the initial C-CPI-U is estimated using a CES or Lloyd Moulton 

price index formula, and revised when the weight data are available.

The C-CPI-U is considered a more accurate measure of the change in the cost of living since it is based 

on current consumer behavior and is free of the substitution bias that arises in the other measures 

from the assumption of unchanged consumer behavior. However, the fact that the C-CPI-U is revised 

after initial publication creates complications for its official use.

3.3.4 Biases and other limitations of CPI measures

The CPI is subject to several types of bias. Most of these biases apply to all the CPI measures. 

Additionally, there are other limitations that apply to CPI measures that should be kept in mind when 

evaluating the usefulness of CPI data.

New Goods Bias: The CPI may fail to capture the increase in consumer welfare generated by new 

goods. If a price index incorporates a new good into its sample immediately upon introduction, it will 

capture some of the increased consumer welfare generated by the new good by capturing the decline 
Page | 20

in price that typically happens in the early stages of a new good. However, it is operationally difficult 

to get new goods (or meaningfully new varieties of existing goods) into the sample without some 

time lag. 

The BLS has taken steps in recent decades (including more frequent weight updates and sample 

rotation) to incorporate new goods into the CPI sample more quickly, but realistically the CPI does 

not capture the full price decline from introduction. Additionally, some new goods may generate 

welfare gains that are hard to quantify regardless of how quickly they might be introduced into the 

sample. So, it is unlikely the CPI fully reflects the welfare increases generated by new goods. 

Quality change bias: Quality change bias is the failure to properly adjust for changes in the quality of 

goods and services. Conceptually, quality change bias could be in either direction. While it is more 

often alleged that the quality change bias leads to the CPI overstating inflation because it fails to fully 

capture quality increases, some do assert that the CPI overstates quality increases, or fails to measure 

quality declines.13

While the CPI uses several methods to estimate quality change, some quality change may be difficult 

to quantify (for instance, the increased capabilities of smartphones).While there is disagreement 

over the magnitude and even the direction of quality change, the difficulty of measuring quality 

change is an important complication to be aware of in assessing CPI measures.

Substitution bias: Substitution bias arises when the CPI fails to capture the impact of consumers 

changing their purchasing habits in reaction to changes in relative price and substituting toward 

relatively cheaper goods. It can be helpful analytically to distinguish between lower level and upper 

level substitution bias. Lower level substitution bias refers to substitution between similar items 

within a category (specifically a CPI basic index, which is within a specific item and area). Upper level 

bias refers to substitution across the boundaries of these categories. So, substitution between cuts of 

beef would be lower level; substitution from beef to chicken would be upper level.

The CPI since 1999 has used a geometric means formula which assumes a degree of substitution at

the lower level (except in selected categories where such substitution is unlikely). This substantially 

eliminates lower substitution bias. However, except for the Chained CPI-U, CPI measures are 

calculated with an assumption of no upper level substitution, since the Laspeyres formula used to 

calculate upper level indexes implicitly makes that assumption. 

Outlet bias: Outlet bias refers to the failure by the CPI to capture increases in consumer welfare from 

changing outlets, usually from higher priced outlets to lower priced outlets. The CPI sample contains 

specific items in specific stores, so welfare gained by a consumer by purchasing the same items at a 

new, lower price store isn’t explicitly captured in the CPI. 

Outlet bias may be mitigated by the fact that the CPI would indirectly capture such gains if new lower 

price outlets put downward pressure on prices in existing outlets. It might also be argued that the 

13 See. e.g., Bart Hobijn, On Both Sides of the Quality Bias in Price Indexes, Staff Report 157 (Federal Reserve Bank of New York, 2002).
Page | 21

new outlets are not perfect substitutes to higher price ones, in that they may be less convenient or 

offer less service. 

Small sample bias: Small sample bias arises because, when samples are small, the expected value of 

the geometric average of price changes in the sample will tend to be higher than the actual geometric 

price change of all price changes for that item and region. Small sample bias is widely considered to 

be relatively minor compared to other types of bias.

Other limitations: In addition to the several types of bias, CPI measures have other limitations that 

users should consider. CPI measures are subject to sampling error. In 2017 the CPI-U All items 12-

month change had a median standard error of 0.07 percent. Additionally, CPI measures are affected 

by any biases or limitation of the surveys used as inputs. For example, if participants in the BLS’ CE

used to create upper level weights in the CPI underreport their tobacco purchases, the weight in the 

CPI for tobacco will not reflect actual expenditures. Some price index experts have expressed 

concerns about chain-link bias in high-frequency chained indexes, such as the monthly C-CPI-U. The 

hypothesis is that the monthly chained version will ‘drift’ over long periods of time, either 

systematically higher or lower, compared to a bimodal estimate of price change over the historical 

period.14 There is little evidence of chain link biases over the 20-year history of the BLS C-CPI-U index 

at the broad, all-items level.16

3.3.5 Strengths and weaknesses of specific CPI measures

All CPI measures are based on the same sample of outlets and prices, and generally subject to the 

same biases and limitations. The CPI-U, CPI-W, and R-CPI-E differ only in the population whose 

spending habits is used to calculate the weights of the item categories. The C-CPI-U uses the same 

population as the CPI-U but uses different time periods for its weights and a different formula than 

the other measures. The R-CPI-U-RS is the same as the CPI-U during the current time period, but is a 

different historical time series as it attempts to adjust the CPI-U from earlier periods based on the 

impact of new methodology. The C-CPI-U uses the same basic indexes as the CPI-U, but uses more 

recent weights and a different formula to aggregate the indexes.

The following is a summary of the general applicability strengths and weaknesses of the various BLS 

CPI products, in regard to use as the OPM inflator.

CPI-U (Consumer Price Index for All Urban Consumers)

Population: The CPI-U is based on the expenditures of almost all residents of urban or metropolitan 

areas, including professionals, the self-employed, the unemployed, and retired people, as well as 

urban wage earners and clerical workers. Not included in the CPI are the spending patterns of people 

living in rural nonmetropolitan areas, those in farm households, people in the Armed Forces, and 

those in institutions, such as prisons and mental hospitals. 

14 See, e.g., de Haan, Jan. Estimating chain drift in price indexes based on scanner data. Journal of Econometrics, Vol. 161, Issue 1, March 2011, pp 36-46. 16 BLS members of ITWG shared some preliminary analysis of chain drift in the Chained CPI, from a forthcoming BLS Monthly Labor Review publication 

that is planned on the subject. Expected publication is fall 2020 or winter 2021.
Page | 22

Strengths: The CPI-U is the broadest and most widely used CPI and is based on the spending patterns 

of the broadest subset of the population. Sampling and other decisions are made based on the CPI-U. 

Other CPI measures might be seen as derivations of the CPI-U, based on either the spending patterns 

of a subset of the U population (R-CPI-E, CPI-W), alternative aggregation of CPI-U basic indexes (CCPI-U), or adjustments to the CPI-U historical series (R-CPI-U-RS).

Weaknesses: It may be argued the CPI does not accurately capture the inflation experience of certain 

subgroups, including those impacted by changes in poverty thresholds. The spending data used to 

compute the weights of the CPI-U includes spending of virtually all consumers, including very wealthy 

consumers whose market basket may be very different from those at the margin of poverty.

CPI-W (Consumer Price Index for All Urban Wage Earners and Clerical Workers)

Population: The CPI-W is based on the expenditures of households included in the CPI-U definition 

that also meet two additional requirements: more than one-half of the household's income must 

come from clerical or wage occupations, and at least one of the household's earners must have been 

employed for at least 37 weeks during the previous 12 months. 

Strengths: The CPI-W is based on the spending patterns of a subset of the population that, while not 

equivalent to the poor or those near the poverty threshold, would tend to exclude most wealthy 

households and so might be a better proxy for the market baskets of those at the margin of the 

poverty threshold. The CPI-W is used to adjust Social Security COLAs and other federal payments. 

Weaknesses: The definition of the CPI-W population presents conceptual challenges. The percentage 

of the population that meets the CPI-W definition has shrunk over time and no longer corresponds to 

an economically meaningful subset of the population

R-CPI-E (Research Consumer Price Index for Americans 62 Years of Age and Older)

Population: The R-CPI-E is based on the spending patterns of households included in the CPI-U 

definition in which the reference person is 62 or older.

Strengths: As with the CPI-W, it might be argued the population of the R-CPI-E has spending patterns 

more similar to the spending of those near the margin of poverty than the broader CPI-U population.

Weaknesses: The R-CPI-E is considered a research index rather than an official index, and has never 

been used in any official capacity. It uses the same sample, prices, and outlets as the CPI-U, so it does 

not reflect the fact that the elderly might purchase different items, shop at different places, or in 

some cases pay different prices than does the non-elderly population. 

C-CPI-U (Chained Consumer Price Index for All Urban Consumers)

While the CPI-U, CPI-W, and R-CPI-E differ only in the population coverage, the C-CPI-U differs from 

the other measures in several ways. While the other measures have weights that are based on a base 

period of consumer expenditures that are a few years old, the C-CPI-U is based on spending patterns 

of the same population as the CPI-U, but (for the final version) uses weights contemporaneous to the 

months involved in the calculation. Additionally, the C- CPI-U uses a different formula to aggregate 
Page | 23

indexes than the other measures. Whereas the other measures use a Laspeyres formula, the C-CPI-U 

uses a Tornqvist formula that averages the weights from the two months involved in calculation.

The C-CPI-U, unlike the other measures, is revised. It is subject to multiple revisions and the final 

version is not available until 10 to 12 months after initial publication. The C-CPI-U is initially estimated 

using a CES (constant-elasticity-of-substitution) formula and then made final when the expenditure 

weight data for the relevant months become available.

Population: Same as CPI-U

Strengths: The C-CPI-U may be a more accurate measure of the change in the cost of living than other 

CPI measures because it is nearly free of upper level substitution bias. The use of more up-to-date 

expenditure data and the Tornqvist formula allow the C-CPI-U to reflect actual consumer behavior, 

rather than assume quantities of goods and services purchased remains constant, as the other 

indexes implicitly do. Additionally, small sample bias is less of an issue for the C-CPI-U.

Weaknesses: The Chained index is subject to multiple revisions and the final version is not available 

until 10 to 12 months after initial publication. Any official use of the C-CPI-U must wrestle with 

revision issues, which is not the case for the other measures. In addition, some may argue the use of 

the C-CPI-U is inappropriate for adjustment of poverty thresholds because those near the margin of 

poverty may have more limited ability to substitute than other consumers. This criticism is not rooted 

in the formula used in the C-CPI-U, but rather its population scope. The formula, a monthly chained 

Tornqvist, resolves to a Laspeyres in an environment of little consumer substitution. That is, if the 

spending weights used in the Tornqvist actually reflect consumer behavior which is fixed quantity 

(little substitution), then the Tornqvist and Laspeyres will closely approximate one another. However, 

if high-income households substitute a lot while low-income households do not, then an index 

representing all households along the income distribution might not be appropriate. Some might also 

argue that if low-income families experience higher inflation than the broader population, the C- CPIU will exacerbate the underestimation of price change for the low-income population, given it tends 

to run slightly lower than other CPI measures. Others might argue that even if the poor experience 

higher inflation than the broader population, the CPI-U may still overstate inflation for all groups 

(including the low-income population) due to other sources of upward bias, and thus, the C-CPI-U 

could better approximate price changes for the poor than the CPI-U.

R-CPI-U-RS (Consumer Price Index Retroactive Series Using Current Methods)

The retroactive series using current methods (R-CPI-U-RS) presents an estimate of the CPI for all 

Urban Consumers (CPI-U) from 1978 to present that incorporates most of the improvements made 

over that time span into the entire series. So, the series is a variant of the CPI-U but is different 

historically in that it is adjusted by estimates of the impact of methodological changes made since 

1978. The R-CPI-U-RS is very similar to the CPI-U for recent years but quite different before 1999.

Population: Same as CPI-U
Page | 24

Strengths: Historical CPI data from before 1999 and especially before 1983 reflect a CPI that used a 

substantially different methodology. The R-CPI-U-RS arguably provides a more accurate measure of 

historical price change since it reflects estimates of the impact of methodological improvements.

Weaknesses: The R-CPI-U-RS is considered a research index rather than an official index, and has been 

used officially only in a limited way. Most estimates are based on BLS research covering a short 

period of time and extrapolated to a longer period. Therefore, there is considerable uncertainty 

surrounding the magnitude of the adjustments. (Although the larger adjustments, notably for rental 

equivalence and geometric means, are careful estimates based on long term simulations.) 

Additionally, there have been several improvements in the CPI not incorporated into the R-CPI-U-RS, 

either because they do not represent changes in methodology, because they had negligible impacts 

on the CPI’s growth rate, or because it was impossible to systematically estimate the impacts of the 

new methods in past years.

SECTION 4: ITWG RECOMMENDATIONS TOOMB

The ITWG was asked to provide two recommendations to OMB: how should the OPM be adjusted for 

inflation and whetherthere is a need for OMB to provide guidance to federal agencies on the use of 

various inflation measures. This section presents our recommendations to OMB and discusses how 

we reached these recommendations. 

Recommendation 1: Official Poverty Measure

The ITWG developed cohort consumer price indexes for households at the lower end of the income 

distribution, in order to explore the inflation experience of a low-income subset of the population. 

The results of this research indicate low-income cohorts tend to have different budget shares than 

the overall urban population which, when applied to the data from 2004 to 2017, resulted in higher 

estimates of inflation for these cohorts. The ITWG recommends the BLS pursue development of a 

new consumer price index specifically designed to represent the inflation experience of low-income 

consumers, and that OMB use that new index to adjust the OPM. To that end, a formula measuring 

price change based on the current behavior of consumers is superior to one based on consumer 

behavior from several years ago. Therefore, an index that uses the Tornqvist (or a similar formula that 

averages spending from adjacent time periods) should be used in the construction of the low-income 

index. (See section 4.1.) 

Recommendation 2: Need for OMB guidance

Evaluating a given price index’s fitness for a particular purpose requires an understanding of the 

strengths and weaknesses of the various indexes available, and also a comprehensive understanding 

of the specific use under consideration. ITWG determined that any guidance OMB could disseminate 

on the fitness for use would be of little value to agencies, because guidance could only reasonably 

consider the measures themselves in isolation from any potential specific agency use. ITWG 

recommends the BLS provide additional resources to assist users on index choice. (See section 4.2.) 
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4.1 Recommendations on the index best suited for the Official Poverty Measure

OMB asked the ITWG to prepare a final recommendation on the consumer inflation measure most 

appropriate for conducting annual adjustments to the OPM thresholds. This section provides 

background on the OPM and on inflation adjustment in general, then describes the specific thought 

process and intermediate decisions of the ITWG. 

4.1.1. The ideal index for OPM annual calculation

With so many different indexes available, and additional indexes often requested by users, Federal 

statistical agencies face a tension between the goals of providing indexes tailored for specific 

purposes of public policy and avoiding public confusion that might result from too many indexes to 

choose amongst. We start by conceptually examining the specific purpose of the index in the OPM 

calculation and then arrive at an appropriate index tailored for OPM use with respect to population 

coverage, market basket definition, and aggregation methodology. 

We began our deliberations by asking ourselves "what features would an ideal index for adjusting the 

OPM thresholds have?" The specific purpose of the consumer inflation measure as used in the OPM is 

to convert a set of known, historical dollar value numbers into current year dollar value numbers.

That is, it is used as an inflator — a predictor of the current year dollar value of a previously 

estimated dollar value. In that context,this section examinesthe proper population, consumption, 

and aggregation scope. 

What population? The first overarching conceptual issue that must be addressed when making an 

index choice is whether the adjustment contemplated should compensate recipients for changes in 

the overall costs of living for the nation as a whole, or whether it should take account of any 

significant differences among particular groups or individuals in society. In the case of the OPM, the 

choice is between the use of a broad index representing the overall population versus a narrower 

index representing the inflation experience of the low-income population. 

According to the Committee on National Statistics (CNSTAT), if purchasing patterns diverge widely 

and if prices of items marking the big differences change at significantly different rates, the idea of 

creating group-specific sub-indexes becomes compelling. If consumption bundles are proportionally 

similar, or if price changes across group-differentiated bundles consistently balance out, index 

disaggregation may be superfluous and may reduce precision of price change estimates. In this 

scenario, a broad index tailored to represent the inflation experience of the general population would 

be sufficient as proxies for group-specific purposes.15 

In economics, Engel curves describe how household expenditures vary with income: primarily as 

income increases, the budget share spent on food decreases.16 This pattern can be seen in CE data,

indicating that purchasing patterns do diverge by income. Additionally, ITWG work has shown that 

inflation rates differ by income as well (i.e.,the indexes for different populations were also measured

15 National Research Council, At What Price? Conceptualizing and Measuring Cost-of-Living and Price Indexes. Panel on Conceptual, Measurement, and 

Other Statistical Issues in Developing Cost-of-Living Indexes, Charles L. Schultze and Christopher Mackie, Editors. Committee on National Statistics, 

Washington, DC. 2002, p. 246. 16 See https://www.britannica.com/biography/Ernst-Engel#ref86426.
Page | 26

to be statistically significantly different as described in the statistical appendix). Thus, ITWG decided a 

sub-population target is better than a full-population target based on both economic theory and

empirical verification of the criteria established by CNSTAT. 

Principle A: The target for the index to adjust the OPM threshold is the change in expenditure needed 

to purchase the standard of living as defined by the official poverty threshold from a consistent, given 

reference year. There is sufficient evidence that purchasing patterns diverge widely and the prices of 

goods and services that mark this divergence change at significantly different rates for several 

reasonable subgroup definitions constructed to represent low-income consumers, and therefore 

creation of a group-specific sub index is compelling. 

Exactly what set of prices should be included? The second overarching conceptual issue is the 

market basket scope of the index: exactly what items should be included in the measure. 

Traditionally, consumer inflation measures limit the set of prices to those paid by consumers for 

personal use or consumption. That is, prices for items purchased for investment or for business are 

excluded. As the ILO manual states: the CPI is not a measure of general inflation, as it only measures 

changes in the prices of consumer goods and services purchased by households. A CPI does not cover 

capital goods, such as the purchase of a house, or the goods and services consumed by enterprises or 

the government. Any attempt to analyze inflationary pressures in the total economy must also take 

account of other price movements, such as changes in the prices of imports and exports, industrial 

inputs and outputs, and asset prices. 

Since the ITWG recommends the target population of the OPM index inflator to be a set of 

households, this necessarily limits the consumption scope of the index to those items purchased by 

households. The term consumption is imprecise, and its interpretation could lead to a different CPI 

scope. The ILO defines consumption as: "an activity in which persons, acting either individually or 

collectively, use goods or services to satisfy needs and wants." 17

In economics, consumption is measured by the value of goods and services either wholly or partially 

used up in some period, or by the value of the goods and services acquired for eventual use. 

Households may acquire goods and services for consumption in four distinct ways: 

• By purchase in a monetary transaction; 

• By producing or growing them for their own use; 

• As a payment in-kind for remuneration, such as for work done; and 

• By receipt as a gift or transfer. 

Generally, the scope of a CPI is limited to items purchased in a monetary transaction, and excludes 

household production, in-kind transfers, and gifts. The BLS consumer price indexes adopt an 

17 International Labour Office, et. al. Consumer Price Index Manual Theory and Practice, ILO/IMF/OECD/UNECE/Eurostat/The Word Bank, Geneva, 

Switzerland. 2004, p. 39.
Page | 27

‘acquisition cost’ approach as the valuation definition of consumption: the full out-of-pocket expense 

to the household incurred to acquire the good or service, at the time of acquisition, excluding finance 

charges. The set of items to include in a CPI is usually termed the CPI market basket. The BLS market 

basket is structured on eight major groups: food and beverages, apparel, transportation, medical 

care, recreation, education and communication housing, and other goods and services. It excludes 

pensions, life insurance, taxes, gambling, and fines. 

For the purpose of the OPM, the ITWG considered two possible market baskets: all consumption 

items included in the CPI market basket and a subset basket limited to only food items. On one hand, 

the original Orshansky poverty thresholds18 were chiefly based on the cost of a subsistence food 

budget and then simply multiplied by a scalar adjustment to approximate a subsistence level for all 

consumption items. On this view, one might argue only price changes for food should be used, in 

combination with the same or updated scalar multiplier, as the mechanism to convert the prior year 

threshold values to current year. (See Appendix B.2 for an estimate using this approach). On the 

other hand, the Orshansky thresholds when viewed in their entirety, represent spending on more 

than just food – on all items needed for subsistence. Under this view, the full CPI market basket 

would be deemed appropriate. The ITWG recreated the Orshansky thresholds under two different 

conditions: using updated food costs and the same fixed multiplier as the original scalar adjustment 

(3X), and using updated food costs and updated scalar adjustments based on the percent of total 

expenditures on food reported in BLS’ CE data. (See Appendix B.6 for the results of these analyses.) 

Principle B: The OPM has been adjusted by an inflation measure representing all consumption goods 

and services since its inception. There is no strong basis for recommending a change, and therefore

an all-items CPI remains the relevant concept. 

What type of formula? The third overarching conceptual issue is how to aggregate the price 

movements of the components included in the consumption scope or market basket. 

Mathematically, there are numerous types of formulas from arithmetic to geometric, weighted and 

unweighted. As described in section 3, the BLS uses a variety of formulas. This section explains the 

economic underpinnings of those decisions.

A common approach typically used by statistical agencies to choose an index formula is the economic 

approach to price index theory. In the case of the consumer and the retail marketplace, this approach 

espouses to select the index formula that best corresponds to consumer utility maximization and 

consumer behavior. Under consumer utility theory, consumers are hypothesized to be utility 

maximizers subject to their income (resource) constraints. The term utility is generally defined as the 

satisfaction derived or expected to be derived from the consumption of goods and services.19Hence, 

it is a conceptual object that is difficult if not impossible to measure. 

18 See https://aspe.hhs.gov/historypovertythresholds#:~:text=The%20poverty%20thresholds%20were%20originally,of%20the%20Social%20Security%20Administration.&text=She%20derived%20poverty%20thre

sholds% 20for,derived%20from%20the%201955%20survey.

19 See https://financial-dictionary.thefreedictionary.com/Utility+(economics).
Page | 28

In classical economics, when faced with changing prices of goods and services, consumers are 

hypothesized to behave in such a way as to maximize utility. The traditional cost-of-living index 

concept assumes if all environmental conditions are constant and equal between two periods, with 

the exception of changing relative prices of consumer goods, then consumers will alter their spending 

behavior such that they will make the minimum outlay (expense) in the current period to achieve the 

same standard of living (utility) as they achieved in the base period. This minimum-expenditure-toobtain-a-fixed-level-of-utility theory is the assumption behind the cost-of-living approach to price 

index formula. Under this approach, the price index formula to choose is the one that best 

corresponds with the utility function of the average consumer.

In turn, a fundamental conceptual distinction may be drawn between a basket index and a cost-ofliving index. A basket index is an index that measures the change between two time periods in the 

total expenditure needed to purchase a given, fixed set or basket of consumption goods and services.

This is sometimes referred to as a cost-of-goods or COGI index. In economics, this index corresponds 

to a Leontief consumer utility function; consumers respond to changing relative prices by consuming 

the same fixed-quantity bundle of items as they purchased in the previous period. The Leontief 

function assumes in order to achieve the same base-period utility in a current period, the consumer 

must purchase the exact same items in the exact same quantities. That is, the consumer is completely 

indifferent to price change (i.e., has price inelastic demand for the items possessed, consumed, and 

used). The Laspeyresindex, used by BLS in upper-level aggregation for the CPI-U, corresponds to 

Leontief utility preferences. 

A cost-of-living index approach, or COLI index, recognizes consumers may substitute among items 

when faced with changing relative prices, in order to achieve the same level of utility at a lower 

expense. For example, if the price of a movie ticket increases relative to the price of a restaurant 

meal, a consumer may choose to substitute away from theatre going and toward dinner out on the 

town as a way to achieve the same level of ‘entertainment’ in the two pricing situations. Symmetrical 

index formula, those that make use of the prices and quantities in both periods compared, are 

deemed more appropriate and closer approximations to cost-of-living indexes when consumers 

exhibit some substitution behavior. Popular symmetrical index formulas include the Fisher Ideal (as 

used in the PCE-PI), the Tornqvist (as used in the C-CPI-U), the Sato-Vartia, and the Walsh. 20 

Nonetheless, one can interpret both a COGI and COLI as indices that aim to measure the change in 

expenditure needed to purchase either the same basket or two baskets whose composition may 

differ somewhat but between which the consumer is indifferent. If, in reality, consumers do exhibit 

Leontief or fixed-quantity behavior, then a COLI index and a COGI index would approximate one 

another very closely. For this reason, most economists prefer a COLI to a COGI formula if the purpose 

of the index is to more closely approximate a cost-of-living-index.

The C-CPI-U uses a Tornqvist formula and utilizes expenditure data in adjacent time periods in order 

to reflect current consumer behavior, meaning that expenditures correspond to the same period as

prices. The final C-CPI-U estimate of price change to a given month t from month t-1 is based on an 

20 Diewert, W.E. Exact and superlative index numbers. Journal of Econometrics. Volume 4, Issu2, May 1976, pp 115-145.
Page | 29

average of consumer expenditures in months t and t-1, which will be referred to as the current

period. Other CPI measures use a Laspeyres formula with fixed weights that are updated biennially. 

So, the measure or price change to month t from month t-1 is based on consumer expenditure data 

that are several years old. (For example, CPI-U indexes in 2018 and 2019 were based on expenditure 

data from 2015 and 2016.) 

While the choice between these approaches is often framed in terms of accounting for consumer 

substitution in reaction to changes in relative prices, or in some cases as representing a choice 

between a COLI and COGI approach, it need not be. It seems intuitively reasonable that a measure of 

current price change is best estimated based on consumer behavior facing the current array of goods 

and prices than a different array of goods and prices faced in the past. Indeed, recognizing the value 

of more recent weights in producing a more accurate measure of price change, the BLS has in recent 

years undertaken great efforts to reduce the time lag between surveys of consumers and 

expenditures and their use in the CPI, implementing new surveys more frequently and reducing the 

processing time. Still, the ITWG acknowledges the time currently required to produce a final index 

based on a COLI approach using a Tornqvist formula is a challenge that would need to be addressed

for timely release of poverty estimates using this method. 

One may note that the fixed-weight Laspeyresformula implicitly assumes consumer behavior remains 

unchanged between the time of the expenditure survey and the measure of price change. The C-CPIU makes no significant assumption about consumer behavior. If consumer behavior is in fact 

unchanged, then the formulas will yield nearly the same results; if it is not then the C-CPI-U is a more 

accurate measure of price change in the current period. 

Principle C: A formula measuring price change based on the current behavior of consumers is 

superior to one based on consumer behavior from several years ago. Therefore, an index that uses 

the Tornqvist (or a similar symmetrical formula that incorporates weights from adjacent time periods)

should be used to adjust the OPM, subject to addressing implementation timing issues.

4.1.2 Creating a new chained CPI for low-income households

The principles described in the previous section led the ITWG to conclude that existing indexes are 

not ideally suited to adjust the OPM, and that the BLS should pursue the creation of a new official 

subgroup index, a Chained CPI-P or C-CPI-P. There are many issues to be decided about how to 

construct this new C-CPI-P. The BLS should research, consult with relevant subject matter experts, 

and decide how to resolve these issues. 

The first issue is how to define the subpopulation, and what criteria will be used to identify the CE 

respondents included in the new P population definition. The target for the C-CPI-P as described

above is the inflation rate for the OPM threshold itself; however, any given CE sample will have few 

or no respondents with income or expenditure exactly equal to the current year OPM cutoffs. While 

the ITWG experimented with different ways to use the CE sample to define the P cohort, these may 

not necessarily be the most efficient or accurate ways possible. One possibility is to include 

respondents at or near the threshold itself, since that is what is being adjusted and would be the 

target consistent with the data and economics for dividing the population into higher and lower living 
Page | 30

standards. This definition would likely only be useful for this specific OPM use. A second possibility is 

to include only those respondents whose income is below the OPM thresholds, because this cohort 

definition would have broader applications and could be used for other purposes related to the 

inflation experience of low-income consumers. This approach has the drawback that it is not the

population at or near the threshold itself, and depends on an accurately adjusted threshold to define 

the below-threshold population. 

A second issue is whether a plutocratic weighted index, where each household is weighted by 

expenditures, is preferred to a democratic index, where each household is equally weighted. An index 

meant to define society’s welfare costs, market conditions, or output would imply a plutocratic index, 

while one that targets the average inflation of particular individuals would imply a democratic one. 

Making an index for the OPM would require consideration of this in deciding how to calculate it with 

the available sample. 

A third issue is whether the use of establishments, items, and housing units sampled for the urban 

population is appropriate for the new poverty population. For example, the research C-CPI-P 

developed for this report only differs from the C-CPI-U by the expenditure share weights for item

area elementary cells that are aggregated to make the indexes — not by the elementary cell indexes 

themselves. Each elementary cell index is constructed by aggregating price relatives for each sampled 

good in the cell. The goods are sampled based on a probability proportional to expenditure share. For 

most goods, it is not possible with existing data to calculate elementary cells for the OPM cutoff 

cohort. There may be ways in the future, however, if the CE is redesigned and sample expanded. The 

BLS should research and incorporate elementary cells for the OPM index as feasible given budget 

constraints. 

The BLS should explicitly explore whether housing sample data can be re-weighted to address this 

issue. For shelter services (rent and owner’s equivalent rent (OER)), it may be possible to create lowincome specific elementary indexes simply be reweighting the existing all-urban housing sample. 

Currently, both rent and OER use the same survey sample of rents, though some rents are excluded 

from use in OER. The most important cause of differential in movement between rent and OER is the 

difference in weight shares by neighborhood. The weights are the share of each neighborhood that 

rents versus owns. These weights are calculated from the CE and American Community Survey (ACS) 

based on data that are lagged by eight years or more. However, the data do exist to weight 

neighborhoods by the share of low income (or income or expenditure near the OPM) residents, thus 

creating elementary indexes for the OPM that also use more current data. Currently, the items that 

make up a large majority of the difference between the C-CPI-U and research C-CPI-P produced for 

this report, are rent and OER. Lower income households have a higher weight on rent and a lower 

weight on OER, and because for recent years rent has had higher inflation than OER, the net effect is 

a higher all-item inflation estimate as measured by the C-CPI-P relative to the C-CPI-U. This is such a 

large effect because shelter — rent and OER combined — represents the largest component part of 

the CPI, around a third of expenditures. Therefore, anything that happens with rent prices can have a 

very large effect on the difference between the all-urban and low-income cohort indexes. 
Page | 31

A fourth issue is whether income or expenditure is the best variable to define the OPM cutoff. While 

the official poverty definition is by income, research has shown income is often misreported in survey 

data, as compared to more reliable administrative data.21 The CE is designed to measure 

expenditures, and the income items are not critical to the primary purpose of the survey. Therefore, 

income could be measured especially inaccurately in the CE. The target of the C-CPI-P is the change in 

expenditure needed to purchase a given standard of living. Preliminary research by ITWG has found 

that a C-CPI-P defined by expenditure is higher than one defined by income, in part because of the 

higher weight on rent for the low-income population. This is consistent with an expenditure 

definition being a less noisy measure and thus including lower income households on average, who 

would then have even lower average rent shares. The expenditure definition also puts lower weight 

for low-income households on college tuition, which is also consistent with a definition that contains 

lower income households.(See appendix B.4.)

A fifth issue is whether to broaden the definition of the poverty subpopulation to include rural areas. 

The current urban population does not include rural consumers, but rural consumers are included in

the Census definition of poverty. BLS has developed research indexes that add weight for rural 

residents, even though no prices are sampled from rural areas. The C-CPI-P could be made using 

these weights. 

A sixth issue is to determine the specific aggregation scheme to produce reliable monthly weights for

a subpopulation. BLS has not produced chained versions of subpopulation indexes in the past 

because of CE data constraints. One reason the R-CPI-E is published as a research index is the 

relatively smaller sample of CE respondents used to construct its weights. A chained version would 

further tax the CE sample to produce monthly estimates of expenditures. Resolving this issue could 

entail an aggregation scheme that employs a Laspeyres formula across geography (employing an 

assumption about consumer substitution between cities) and a Tornqvist formula across items. 

Dealing with revisions to the Chained CPI

A primary feature of the C-CPI-Uand the proposed chained CPI for the poverty population (C-CPI-P) is 

that they are aggregated with expenditure weights that reflect the actual purchasing decisions of 

consumers. 22With this desirable feature, however, also comes a defect: publication of the C-CPI-U

arrives with a delay of 10 to 12 months, a delay that arises because of the lag in reporting and 

processing consumer expenditures.23 In contrast, the CPI-U does not have this problem because the 

aggregation weights—also known as the market baskets—are not current. For the most recent 9 to 

11 months, the BLS estimates a preliminary version of the C-CPI-U by aggregating the component 

item-area indexes (the same ones used in the monthly CPI-U) with a CES aggregation formula. This 

CES formula works fairly well, but still makes small errors that become evident only in hindsight.

21 See Meyer, Mok, and Sullivan (2015).

22 If and when the consumers buy (say) more beef and less chicken, the Tornqvist aggregation formula used in the chained indices ensures that the 

chained indexes reflect that reconfiguration of the market basket without imposing any theoretical constraints. 

23 Although prices are reported with a lag of one month, the consumer spending weights needed to aggregate them are derived from the Consumer 

Expenditure Survey, and that reporting system involves a delay.
Page | 32

For data users that do not require such timely measures of prices, this reporting lag is not important 

as they can use the final value of the chained index for their analysis. However, data users that 

require timely chained price indexes are left with a choice. They must decide whether to: 

1) use the preliminary version of the index and adjust the index each year in such a way that is 

self-correcting so that the error in the preliminary CES estimate does not accumulate or 

compound over time, or

2) use the final version of the index for a prior period and accept a mismatch between the period 

covered by the price index and the ideal period.

This problem has been recognized by previous studies that recommended use of the C-CPI.24 

We will use the example of adjusting the poverty thresholds to elaborate on these points. It is not the 

intention of the ITWG to recommend which of these methods would be most appropriate for 

adjusting poverty thresholds. In the example below, we will assume the existence of the C-CPI-P in 

the discussion for simplicity. In general, users of the various versions of the chained CPI must think 

carefully about the production schedule for the final version of the chained CPI and their proposed 

application. 

The production process for the OPM for a given year (yy) begins with the calculation of income 

thresholds by family size and number of children in January of the following year (yy + 1), and 

publication of weighted averages by family size in September of yy + 1.25 In January and September 

of yy + 1, only the preliminary estimate of the C-CPI-P would be available. Below we discuss how the 

Census Bureau could adjust the poverty thresholds.

Updating Poverty Thresholds with the Preliminary C-CPI-P

In order to update the thresholds so that the income reporting period (year yy) matches the period 

used for the price index, the Census Bureau would need to use the preliminary C-CPI-P for year yy. We 

will denote the final price index for year yy as PPyy and the preliminary index as PPyy

pp

. Because PPpp(yy) is 

preliminary, it will likely have some error eeyy, so that 

PPyy

pp = PPyy + eeyy. (Eq.1)

In the year that C-CPI-P escalation of the OPM thresholds begins, calculations could be based on the 

latest year for which C-CPI-P is finalized. Suppose, for example, that the escalation were to begin with 

the 2020 OPM—with the calculation of the 2020 income thresholds in January 2021. At that date, 

24 See pages 5-7 of Congressional Budget Office (2010) “Using a Different Measure of Inflation for Indexing Federal Programs and the Tax Code” 

https://www.cbo.gov/sites/default/files/111th-congress-2009-2010/reports/cpi_brief.pdf, or pp 10-12 of Congressional Budget Office (2013), “Using the 

Chained CPI to Index Social Security, Other Federal Programs, and the Tax Code for Inflation” https://www.cbo.gov/sites/default/files/113thcongress2013-2014/reports/44083_ChainedCPI.pdf, or conclusion 7-1 of (page 11) of National Research Council (2002), “At What Price?: 

Conceptualizing and Measuring Cost-of-Living and Price Indexes. Washington, DC, The National Academies Press. 25 The poverty thresholds can be found at https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html. 

In September, the Census Bureau releases the “Weighted Average Poverty Thresholds for Families of Specified Size,” which are used as inputs into the 

calculation of the Poverty Guidelines that are used to determine eligibility for means-tested program benefits such as Medicaid and Supplemental 

Nutrition Assistance Program (SNAP), among others. For more information on the Poverty Guidelines, see https://aspe.hhs.gov/poverty-guidelines.
Page | 33

calculations could be based on the OPM for 2019, the final C-CPI-P for 2019 (PP2019), and the 

preliminary C-CPI-P for 2020 (PP2020

pp ), as follows:

OO MM2020 = OO MM2019

PP2020

pp

PP2019

. (Eq.2)

In subsequent years, escalation of the OPM could continue to use this formula, but only if the base 

year is frozen at 2019. That is: 

OO MMyy = OO MM2019

PPyy

pp

PP2019

. (Eq.3)

Because of the nature of the preliminary estimate, the error in the PPyy

pp will affect the OPM in that 

year, but not subsequent years. To see why, we can rewrite (Eq.3) using (Eq.1) as:

OO MMyy = OO MM2019

PPyy + eeyy

PP2019

. (Eq.4)

The presence of the eeyy term in (A.4) shows the error in the preliminary estimate will also result in 

error in the poverty threshold. By keeping the base year fixed (in this case for 2019), only the error of 

each year’s preliminary estimate will affect the poverty thresholds. However, if we updated the 

thresholds from one year to the next, that would not be the case. 

To see why, suppose we can define thresholds updated each year from the thresholds in the prior 

year as:

OO MMyy

pp = OO MMyy−1

pp PPyy

pp

PPyy−1

. (Eq.5)

In this example, we use the final estimate in −1, PPyy−1, because the preliminary estimates are no 

longer available on the BLS website after the release of the final estimates. In 2020, the result would 

be the same as in (Eq.4), with the error from 2020 only. However, in 2021 the result would be:

OO MM2021

pp = OO MM2020pp PP2021

pp

PP2020

. (Eq.6)

If we replaced the OO MM2020

pp and OO MM2021pp with the versions from (Eq.4) and (Eq.1), we would get:

OO MM2021

pp = OO MM2019 �

PP2020 + ee2020

PP2019

� �

PP2021 + ee2021

PP2020

�. (Eq.7)

The important takeaway from (Eq.7) is that the error term from each year’s preliminary estimate 

affects the thresholds. When we adjust the thresholds from a baseline year as in (A.4), the updates 

are “self-correcting”, as only the error from the most recent year’s price index affects the thresholds. 

Updating Poverty Thresholds with the Most Recently Available Final C-CPI-P
Page | 34

Alternatively, the September publication schedule of the OPM could be maintained by using the Q2-

to-Q2 increase in the C-CPI-P, as the final chained price index for Q2 (April-June) is released in April 

the following year. The downside of this approach is the income used in the OPM calculation is 

reported for calendar year yy whereas the prices used to adjust the incomes would come from May in 

year yy − 1 through April in year yy. If after April there were large movements in one of the volatile 

components of the CPI, such as gasoline, these would not be reflected in the inflation adjustments.

OO MMyy = OO MMyy−1

PPQQ2,yy−1−QQ2,yy

PPQQ2,yy−2−QQ2,yy−1

. (Eq.8)

4.1.3 Provisional options for OMB to consider

Until an index with a population scope targeting low-income households constructed using a

Tornqvist aggregation formula is officially ready for use in OPM calculations, what are the 

intervening options that OMB could consider for adjusting the OPM? The ITWG did not reach a 

consensus on which of the following options is best, though a majority favored no change to the 

current methodology during the interim period (option 2). Itemized below are the three key

options we considered along with their arguments. 

Option 1 – Research version of the C-CPI-P: Use an unofficial, research version of a C-CPI-P that BLS 

could ostensibly start publishing monthly within a year or two. This product would be based on the 

methodology used in simulations produced by the ITWG, but might not sufficiently address the issues 

listed in the previous section. This is currently the best estimate available of the ideal, conceptual 

index ITWG prefers. It is also the best index for the BLS to hammer out methodological questions 

through learning-by-doing. It may be a challenge, however, to use an unofficial index product to 

revise an official Federal statistic. 

Option 2 - CPI-U:Continue to use the CPI-U. If there is a planned change to the OPM calculus in the 

near future (C-CPI-P when it is released as official), it is costly to change the methodology in the 

interim – and then to follow on the heels of that with another change. This may cause the public to 

think the government, either Census or OMB, is tinkering with the statistic for policy 

reasons. A challenge in retaining the CPI-U during an interim period may be that it does not take into 

account some improvements made to other indexes (such as the C-CPI-U) and it would not represent 

the current knowledge (based on ITWG analysis) of the inflation experience of the low-income 

population. 

Option 3 – Chained CPI-U: Switch to the C-CPI-U. It is preferred over the CPI-Uin general based on 

formula and mitigation of substitution bias. The ideal index C-CPI-P differs from the current index 

used in the OPM calculation in two basic ways: upper level formula and population target, so 

switching to the C-CPI-U gets us halfway to incorporation of the ideal index. ITWG analysis, however,

showed the experimental C-CPI-P produced by the group was closer to the CPI-U than the C-CPI-U 

over the entire study period (2004 to 2017).
Page | 35

4.2 OMB guidance for federal agencies on index use

The ITWG was charged by OMB to consider the feasibility of OMB guidance on the fitness for use of 

the different consumer inflation indexes. The ITWG does not believe formal OMB guidance is feasible. 

Evaluating fitness for use requires a good understanding of the strengths and weaknesses of the 

different indexes, but it also requires a comprehensive understanding of the specific use under 

consideration. The ITWG determined any guidance OMB could feasibly generate on the fitness for 

use would be of little value to agencies because it would only be able to consider the measures 

themselves in isolation from any potential specific uses.(See section 5.) 

Instead, we recommend the BLS provide bettersupport to public and Federal users in choosing 

among indexes. Knowledge of the uses is needed in choosing an index, as it helps guide users to an 

index based on their specific needs and circumstances. 

SECTION 5: REMAINING ISSUES IN NAVIGATING APPROPRIATE USE OF INFLATION 

MEASURES 

OMB asked the ITWG to prepare and submit a report to the Chief Statistician containing 

recommendations on the remaining issues around appropriate use of existing inflation measures, 

including for adjusting Census Bureau historical figures,that takes into account the public comments 

received from the FRN. 

The following is intended to tie together lessons learned and agreed upon principles into a more 

general framework to include the remaining issues of the use of inflation measures to answer the 

charter charge. Its purpose is also to provide aid to the Census’s review of the adjustment of the 

Census Bureau historical median income series, and to support the BLS’ intention to provide guidance 

to the public on index choices. The following provides one example of such guidance, which the BLS 

could revise as needed. It can also aid other agencies and private entities until such BLS guidance is 

available. 

5.1 Adjusting long-running historical series

The ITWG identified the challenges associated with adjusting long-running historical series as a

major remaining issue that could benefit from our input. Federal statistical agencies and 

researchers want to adjust long running data for inflation using the most appropriate methods 

available, but depending on the time frame and the goals of the adjustment, different methods may 

be appropriate.

For example,the Census Bureau uses the R-CPI-U-RS to adjust their median historical income figures 

because that index is designed to represent a consistent historical series covering the time period 

from 1978 forward by incorporating most of the improvements made over that time period into the 

entire series. The Census Bureau recently initiated a review of their use of this index to adjust their 

historical income series and to consider potential alternatives. This review process will include the 
Page | 36

release of research and working papers on the topic, presentations at professional conferences, 

expert group meetings to solicit feedback from external subject matter experts, and additional 

appendix information in the official income and poverty report. 

5.2 Background

The options currently available for adjusting historical series include the CPI-U (1913-present), the 

PCE price index (1959-present), the C-CPI-U (2000-present), the initial C-CPI-U values, which are 

published in real time but later revised (2000-present), the CPI-W (1978-present), and the R-CPI-E 

(1982-present). In addition, this report recommends the publishing of a C-CPI-P, which is not yet 

available, and other indexes that may become available in the future. 

Several of the more theoretically advanced models are relatively recent in their time range while use 

of an index might necessitate a longer time range. In these cases, combinations of the indexes could 

be used over different time ranges. For example, the CPI-U could be used from 1913-1959, the PCE 

index from 1959-2000, and the C-CPI-U from 2000 onward. Additionally, any guidance regarding 

index use should also look forward and attempt to cover the possibility of new indexes that might 

become available.

5.3 Existing indexes

CPI-U: The CPI-U(1913-present) is a Lowe index, which uses a modified Laspeyres formula with 

weights based on purchases of consumers; these purchase weights could be lagged by ten years or 

more depending on the time range (as opposed to current weights). The CPI-U is the longest running 

headline CPI, covering periods no other published index series does. Therefore, for the time range of 

1913-1959, the CPI-U may be the best index available by default.

CPI-W: The CPI-W (1978-present) is also a Lowe index with the same weight lag as the CPI-U, but it 

uses purchases only of urban wage earners. This was the headline CPI until 1978, when the historical 

CPI was renamed the CPI-U. Use of the CPI-W as an index choice might be desired for a historical 

series that simply records what was used at the time regardless of what would have been ideal.

R-CPI-E: The R-CPI-E (1982-present) is also a Lowe index similar to the CPI-U and CPI-W, but the 

purchases are for the elderly population, age 62 or older.

PCE Index: The PCE index (1959-present) aggregates price data from CPI, PPI, and BEA, and used a 

Fisher formula starting in 1978. The Fisher formula uses consumer purchases that correspond to the 

same period as the prices in the index, and the PCE index also uses the R-CPI-U-RS for many 

elementary inputs when available. The R-CPI-U-RS recalculates or extrapolates the elementary 

indexes for improvements made since the indexes were first made, and so is the current best 

estimate for those elementary indexes. The aggregation weights used are targeted toward deflating 

national accounts however, so the PCE index is most appropriate for such output-focused uses. 

However, it could be used for consumer-focused purposes if a better-suited index is unavailable given 

the time period.
Page | 37

C-CPI-U: The C-CPI-U(2000-present) aggregates elementary CPI cells by a Tornqvist formula using 

current weights from consumer purchases. The use of those weights makes it desirable to index 

measures of consumer welfare. The C-CPI-U final values are not available for the most recent month, 

however, as these weights take time to process. Instead, initial values are published with available 

data and final values are later published when ready.

5.4 Proposed guidance on index selection

Appendix A.3 is a flowchart and Appendix A.4 an accompanying overview document which details a 

decision-tree method for choosing among indexes. Several use-case examples are detailed including 

the OPM and Census Median Income.

The following list of principles is in order of priority to help users chose among the best possible 

improved indexes.

For a given time range,

1. An index with current purchases is better than one with lagged purchases (principle C). 

2. Specific population-targeted indexes are better for uses related to their targeted population 

than general indexes(principle A). 

3. A revised index is better than an unrevised index. 

4. A recalculated revised index is better than an extrapolated one. 

5. An index targeted to consumers is better for a consumer income measure (principle B). 

6. An official index is better than a research series of similar nature. 

Therefore,this implies: 

• The C-CPI-Ucould be used if consumer wellbeing is the target when available for a time range. 

• The PCE index could be used when producer output is the deflation target, or the C-CPI-U is 

not available for that time range period needed and if the range needed is not before 1978. 

• The CPI-Ucould be used when neither the C-CPI-U nor the PCE index is available for a given 

range. 

• If a new version of the index for the range that would have been used is available that targets 

the relevant subpopulation or dollar amount, that index is consistent with the principles

instead. 

• If a new version of the index for the range that would have been used is available that better 

recalculates the elementary indexes, that index be used instead. 
Page | 38

Clearly, if an agency is required by law to use a certain index, or if there is a historical series with the 

goal of only recording what was done, not what should have been done, then there is no reason to 

use this framework. 
Page | 39

REFERENCES 

Argente, David, and Munseob Lee, “Cost of Living Inequality During the Great Recession”, Journal 

of the European Economic Association (2020).

Boskin, Michael , E. Dulberger, R. Gordon, Z. Griliches, and D. Jorgenson, “Toward a More 

Accurate Measure of the Cost of Living,” Final Report to the Senate Finance Committee, 

December 4, 1996, http://www.ssa.gov/history/reports/boskinrpt.html/.

Broda, Christian, and John Romalis, “The Welfare Implications of Rising Price Dispersion”, Working 

Paper (2009).

Broda, Christian, Ephraim Leibtag, and David E. Weinstein, “The Role of Prices in Measuring the 

Poor’s Living Standards”, Journal of Economic Perspectives 23 (2009): 77-97.

de Haan, Jan. Estimating chain drift in price indexes based on scanner data. Journal of 

Econometrics, Vol. 161, Issue 1, March 2011, pp 36-46.

Diewert, W.E. Exact and superlative index numbers. Journal of Econometrics. Volume 4, Issu2, 

May 1976, pp 115-145.

Fisher, Gordon, The Development and History of the Poverty Thresholds” Social Security Bulletin 

Vol 55, No. 4, Winter 1992

Klick, Josh and Anya Stockburger, ‘Experimental CPI for lower and higher income households,’ BLS 

Working Paper #537, 2021. https://www.bls.gov/osmr/research-papers/2021/pdf/ec210030.pdf

Hobijn, Bart. On Both Sides of the Quality Bias in Price Indexes, Staff Report 157 (Federal Reserve 

Bank of New York, 2002).

International Labour Office, et. al. Consumer Price Index Manual Theory and Practice, 

ILO/IMF/OECD/UNECE/Eurostat/The Word Bank, Geneva, Switzerland. 2004

Jaravel, Xavier, “The Unequal Gains from Product Innovations: Evidence from the US Retail 

Sector”, Working Paper (2017).

Kaplan, Greg, and Sam Schulhofer-Wohl, “Inflation at the household level”, Journal of Monetary 

Economics 91 (2017): 19-38.

Kurtzon, Gregory, “How much does formula versus chaining matter for a cost-of-living index? The 

CPI-U versus the C-CPI-U”, Economic Inquiry 60 (2022): 645-667.

Meyer, Bruce D., Wallace K.C. Mok, and James X. Sullivan, “Household Surveys in Crisis”, Journal 

of Economic Perspectives 29 (2017): 199-226.

National Research Council, At What Price? Conceptualizing and Measuring Cost-of-Living and Price 
Page | 40

Indexes. Panel on Conceptual, Measurement, and Other Statistical Issues in Developing CostofLiving Indexes, Charles L. Schultze and Christopher Mackie, Editors. Committee on National 

Statistics, Washington, DC. 2002. p. 246.

AppendixA

Appendix A.1: OMB Statistical Policy 14

FEDERAL REGISTER, VOL 43, NO. 7-THURSDAY, MAY 4, 1971

Directive No. 14 Definition of Poverty for Statistical Purposes.

For the years 1959-1968 the statistics on poverty contained in the Census Bureau's Current 

Population Reports, Series P-60, No. 68, shall be used by all executive departments and 

establishments for statistical purposes. For the years 1969 and thereafter, the statistics contained in 

subsequent applicable reports in this series shall be used. A number of Federal agencies have been 

using statistical series on the number of persons and families in poverty, and their characteristics, in 

analytical and program planning work. The basis for these series has been the classification of income 

data collected by the Bureau of the Census in accordance with a definition of poverty developed by 

the SSA and revised by a Federal Interagency Committee in 1969. This definition provides a range of 

income cutoffs adjusted by such factors as family size, sex of family head, number of children under 

18 years of age, and farm-nonfarm residences. The Bureau of the Census series continues the SSA

definition for the base year, 1963, except that the differential between poverty levels for farm and 

nonfarm families is reduced from 30 percent to 15 percent. Annual adjustments in Census series are 

based on changes in the average annual total consumer Price Index (CPI) instead of changes in the 

cost of the U.S. Department of Agriculture's Economy Food Plan. The establishment of this standard 

data series does-not preclude departments and agencies from more detailed analyses or from 

publication of tabulations for specialized needs although, where applicable, totals must agree with 

totals published by the *Bureau of the Census. Other measures of poverty may be developed for 

particular research purposes, and published, so long as they are clearly distinguished from the 

standard data series. The poverty levels used by the Bureau of the Census were developed as rough 

statistical measures to record changes in the number of persons and families in poverty and their 

characteristics, over time. While they have relevance to a concept of poverty, these levels were not 

developed for administrative use in any specific program and nothing in this directive should be 

construed as requiring that they should be applied for such a purpose. 

Appendix A.2: Federal Register Notice, consumer inflation measures ITWG

OFFICE OF MANAGEMENT AND BUDGET Request for Comment on the Consumer Price Indexes 

Produced by Federal Statistical Agencies

AGENCY: Executive Office of Management and Budget (OMB)
Page | 41

ACTION: Notice of solicitation of comments

SUMMARY: Under the Budget and Accounting Procedures Act of 1950 (31 U.S.C. 1104 (d)) and the 

Paperwork Reduction Act of 1995 (44 U.S.C. 3504 (e)), the Office of Management and Budget (OMB) 

issues a request for comments on the differences among the various consumer price indexes 

produced by the Bureau of Labor Statistics (BLS) and the Bureau of Economic Analysis (BEA). 

In its role as coordinator of the Federal statistical system under the Paperwork Reduction Act, OMB, 

among other responsibilities, is required to ensure the efficiency and effectiveness of the system. A 

key method used by OMB to achieve this responsibility is the promulgation, maintenance, and 

oversight of Government-wide principles, policies, standards, and guidelines concerning the 

development, presentation, and dissemination of Federal statistical products. OMB’s Office of the 

Chief Statistician relies on public comment and subject matter expertise across the Federal 

government to identify areas where existing OMB policies or guidance may be out of date, lacking 

clarity, or insufficient for efficient coordination of Federal statistics.

Accordingly, OMB is seeking public comment on the strengths, weaknesses, and best practices for the 

application of the following consumer inflation measures: the Consumer Price Index for All Urban 

Consumers (CPI-U), the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W), 

the Chained Consumer Price Index for All Urban Consumers (C-CPI-U), the Consumer Price Index 

Research Series (CPI-U-RS), and experimental Consumer Price Index for Urban Elderly Consumers 

(CPI-E), all produced by BLS, and the Personal Consumption Expenditures price index (PCEPI) 

produced by BEA.

Issues for comment: OMB is interested in receiving comments from the public on: (1) The intended 

scope and purpose of each of the indexes under consideration; (2) The strengths and weaknesses of 

the indexes for different applications or uses; (3) The strengths and weaknesses of the use of the CPIU to make annual adjustments to the Official Poverty Measure, as established in OMB’s Statistical 

Policy Directive #14, and discussion of potential alternative indexes; (4) The strengths and 

weaknesses of the different indexes for making annual adjustments to the historical income figures 

produced by the Bureau of the Census; (5) The need for and feasibility of guidance from OMB or 

other Federal source on the appropriate or best practice use of the different indexes; (6) 

Recommendations for the use of the PCEPI and C-CPI-U for the production of official statistics, 

considering that both measures are revised after initial release. 

Dates:

Comments on the strengths, weaknesses, and appropriate uses of the various consumer inflation 

measures produced by Federal statistical agencies must be in writing. To ensure consideration of 

comments, they must be received no later than 45 days from the publication of this notice. Please be 

aware of delays in mail processing at Federal facilities due to increased security. Respondents are 

encouraged to send comments electronically via email, or through http://www.regulations.gov

(discussed in ADDRESSES below).
Page | 42

ADDRESSES: Comments may be addressed to: Nancy Potok, Chief Statistician, Office of Management 

and Budget, fax number (202) 395-7245. Email comments may be sent to 

Directive_No._14@omb.eop.gov, with the subject “Directive No. 14”. Alternatively, comments may 

also be sent via www.regulations.gov—a Federal E-Government website that allows the public to 

find, review, and submit comments on documents that agencies have published in the Federal 

Register and that are open for comment. Simply type “OMB-2019-00XX” (in quotes) in the Comment 

or Submission search box, click “Go”, and follow the instructions for submitting comments. 

Comments received by the date specified above will be included as part of the official record.

Comments submitted in response to this notice may be made available to the public. For this reason, 

please do not include in your comments information of a confidential nature, such as sensitive 

personal information or proprietary information. If you send an email comment, your email address 

will be automatically captured and included as part of the comment that is placed in the public 

docket. Please note that responses to this public comment request containing any routine notice 

about the confidentiality of the communication will be treated as public comments that may be made 

available to the public notwithstanding the inclusion of the routine notice.

Electronic Availability: This document is available on the internet on the OMB Web site at: https://[to

be inserted]

FOR FURTHER INFORMATION CONTACT:

For information about this request for comments, contact Bob Sivinski, Office of Management and 

Budget, 9257 New Executive Office Building, 725 17th St. NW, Washington, DC 20006, telephone (202) 

395-1205, email Directive_No._14@omb.eop.gov with the subject “More Info: Directive No. 14”.

Supplementary Information

Measuring Inflation

Inflation is defined as a rise in the general level of prices (and deflation a decline in the general level 

of prices). Equivalently, inflation represents a decline in the purchasing power of money. Adjusting 

statistics and other data for inflation may better reflect consumers’ actual experiences over time. The 

inflation measures produced by BLS and BEA were developed for different purposes through rigorous 

technical processes. They are continually evaluated to ensure they are objective, accurate, relevant, 

and timely, thereby maintaining the integrity of official government statistics.

Uses of Different Inflation Measures

Congress sometimes requires agencies to use a specific inflation measure for specific programs. For 

example, public law Pub.L. 115–97 directs the IRS to adjust federal income tax brackets for inflation 

with the C-CPI-U. In other instances, Congressional guidance may be absent or less specific, and 

agencies exercise their discretion in choosing an index to calculate inflation-adjusted statistics. In 

cases where Congress has not specified a specific methodology, agencies should use the measure of 

inflation most appropriate for the intended purpose of the program’s needs, consistent with 

maintaining the integrity of the inflation measure’s intent. 
Page | 43

The different indexes produced by the Federal statistical agencies differ in their scope, weighting, and 

formulas. OMB will be considering how these differences might impact the estimation of the Official 

Poverty Measure and other income measures produced by the Census Bureau. OMB will also 

consider the need for guidance to Federal agencies on the differences among the indexes.

BLS Consumer Price Indexes

In order to produce its inflation measures, BLS tracks the change in price of a sample of consumer 

goods and services over time. The sample is intended to represent the experience of consumers in 

their day-to-day living expenses, with each of the CPI measures reflecting different item substitution 

rates, consumer populations, or other attributes. OMB is seeking comment on the five following 

indexes produced by BLS. 

The Consumer Price Index for all Urban Consumers (CPI-U)

The all urban consumer group represents about 93 percent of the total U.S. population. It is based on 

the expenditures of almost all residents of metropolitan or micropolitan areas. The population scope 

includes professionals, the self-employed, the unemployed, retired persons, as well as urban wage 

earners and clerical workers. Not included in the CPI-U are the spending patterns of people living in 

rural areas, those in farm households, Armed Forces members and their families, and those in 

institutions, such as prisons and mental hospitals. 

The CPI-U is used extensively for official purposes. In addition to the official poverty thresholds, it is 

used to adjust Treasury Inflation-indexed securities, to deflate nominal values in a variety of 

measures, and until recently to adjust federal tax brackets. 

The Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W)

The CPI-W is based on the expenditures of households included in the CPI-U definition that also meet 

two additional requirements: more than one-half of the household's income must come from clerical 

or wage occupations, and at least one of the household's earners must have been a full-time worker, 

that is employed for at least 37 weeks during the previous 12 months. The CPI-W population 

represents about 29 percent of the total U.S. population and 31 percent of the CPI-U population. The 

CPI-U and the CPI-W share the same components (item strata) and differ only in population coverage 

and the weights used to aggregate these components.

The CPI-W is used to adjust Social Security benefits and many other Federal transfer payments. The 

percentage of the population within the CPI-W definition has declined over time, and the CPI-U, 

which was first published in 1978, has become more widely used. The CPI-W corresponds closely to 

the population used in computing the CPI from its inception during the World War I era through 

1978.

The Chained Consumer Price Index for all Urban Consumers (C-CPI-U)

Both the CPI-U and C-CPI-U are indexes designed to measure price changes faced by urban 

consumers. The C-CPI-U is distinguished from the CPI-U by the expenditure weights and price index 
Page | 44

formulas used to produce aggregate measures of price change. The C-CPI-U employs a formula that 

reflects the effect of substitution that consumers make across component item categories, for 

example in response to changes in relative prices. The formula used in the CPI-U and CPI-W does not 

capture consumer spending response to changing relative prices across the component item 

categories.

Because the expenditure data used in the C-CPI-U formula is not available until several months after 

the reference month, the C-CPI-U is estimated ahead of time and revised later, becoming final 10 

to12 months after initial publication. The C-CPI-U was first published in 2002. 

The Experimental Price Index for the Elderly (CPI-E)

The CPI-E uses the same price surveys and formulas as the CPI-U and CPI-W, but uses expenditure 

weights for households with a reference person or spouse aged 62 years or older. As currently 

produced, the CPI-E captures the household budgets of the elderly population, which differ from the 

non-elderly population for notable items such as medical care and shelter. However, the CPI-E does 

not capture that the elderly population might shop at different places, purchase different specific 

products and services, or in some cases receive specific price discounts. 

The CPI-E is an experimental index and not currently used for official purposes.

The Consumer Price Index Research Series Using Current Methods (CPI-U-RS)

The consumer price index research series using current methods (CPI-U-RS) presents an estimate of 

the CPI for all Urban Consumers (CPI-U) from 1978 to present that incorporates most of the 

improvements made over that time span into the entire series. The CPI-U-RS therefore provides an 

estimate of what the CPI-U would have looked like had current methodology been in place since 

1978.

The Personal Consumption Expenditures Price Index (PCEPI)

In addition to the BLS CPI measures described above, OMB also seeks comment on the PCEPI chain 

type price index produced by BEA.

The PCEPI is a measure of the prices that people living in the United States and nonprofit institutions 

pay for goods and services. The PCEPI uses mainly CPI series along with some PPI series, also 

produced by BLS, and price indexes from other Federal agencies. The PCEPI differs from the CPI in 

weighting, formula, and scope. A summary of these differences can be found at the BEA website: 

https://www.bea.gov/help/faq/555. The PCEPI is compiled monthly and quarterly and both are 

revised routinely unlike the official CPI-U and CPI-W series, which are not revised. 

More information on these measures can be found at the BLS website: https://www.bls.gov/cpi/ and 

the BEA website: https://www.bea.gov/data/personal-consumption-expenditures-price-index

The Official Poverty Measure
Page | 45

OMB’s Statistical Policy Directive 14 (SPD #14), issued in May 1978, specifies the use of the Consumer 

Price Index (CPI) in the annual adjustments of poverty thresholds (the Official Poverty Measure, or 

OPM) calculated and published by the Census Bureau. Specifically, the directive states:

Annual adjustments in Census series are based on changes in the average annual total Consumer Price 

Index (CPI) instead of changes in the cost of the U.S. Department of Agriculture's Economy Food Plan.

In practice, the All-items, U.S. City Average Consumer Price Index for All Urban Consumers (CPI-U), 

produced by the Department of Labor, Bureau of Labor Statistics (BLS), has been the index used for 

the annual adjustments. This policy has not been reevaluated since the introduction of new consumer

inflation measures, such as the Chained CPI-U (C-CPI-U). OMB is currently reevaluating the 

appropriateness of the use of the CPI-U for annual adjustment in the Official Poverty Measure. To 

assist in this reevaluation, OMB assembled an interagency technical working group to study an array 

of possible price change measures and make a recommendation to OMB on revising the current 

method for adjusting the OPM. The comments received under this Notice will be reviewed and 

considered by the technical working group in development of their recommendation to OMB. 

The poverty thresholds should not be confused with the poverty guidelines, produced annually by the 

U.S. Department of Health and Human Services. While the poverty thresholds are used for calculating 

official poverty population statistics, the guidelines are used for administrative purposes. Most 

commonly they are used by a number of federal, state, local, and non-profit programs, such as 

Medicaid and the Supplemental Nutrition Assistance Program (SNAP) to determine income eligibility. 

The guidelines are based on the previous year’s poverty thresholds, and updated for inflation using 

the CPI-U, based on statutory language in the Community Services Block Grant Act (42 U.S.C. 

9902(2)). Because of this, changes to the poverty thresholds may affect eligibility for programs that 

use the poverty guidelines. OMB is not currently seeking comment on the poverty guidelines or their 

application. More information on the poverty guidelines can be found at: 

https://aspe.hhs.gov/poverty-guidelines. 
Page | 46

Appendix A.3: All purpose index decision-making flowchart
Page | 47

Appendix A.4: Index choice flowchart documentation

This flowchart is a proposed starting point to help users decide upon the best index for them to use 

based on statistical principles and index availability at the time. It can be fine-tuned with more 

experience using it. The flowchart is currently designed only for choosing among national all items 

indexes, and not for index components. The orange boxes are questions for the user to answer, and 

purple boxes are possible end decisions (see Appendix A.3). Some examples of how the flowchart 

could be used are listed below.

A flowchart design challenge is how to allow for the possibility of superior future indexes being 

published. The current solution is to include a set of checklist boxes on the bottom row. It could be the 

case that multiple improved indexes are made, which is why the boxes are all listed in the current 

order with the priority of principles increasing to the right. This is to make sure that even if there is an 

improvement, another index which has even more improvements is not overlooked.

Also, a desired index may not be available for a time range of a historical series. In that case, the user 

is advised to restart the flowchart ignoring the unavailable option for that time range only, in order to 

pick the next best index. Then there may be only one option for an orange box regardless of the 

question, or the user may have to back up one box if there are no options. The changes in the different 

indexes can then be used in different time ranges to make a continuous series. The hybrid series 

would thus change at the rate of one index for a given time range, and then change at the rate of the 

other index for another time range. 

A glossary of terms is below to make the flowcharts clearer, followed by examples of its use including the 

Official Poverty Measure and Census Median Income. 

Glossary

CPI-U: Headline CPI for urban (U) consumers population, does not use current purchases

C-CPI-U:Chained CPI for urban (U) consumers, uses current purchases

R-CPI-U-RS: Research series for CPI-U, a revised historical series that calculates or extrapolates the 

effects of current practices on the CPI-U 1978-1999

CPI-W: A CPI for the wage earners (W) population

C-CPI-W:A proposed chained CPI-W

R-CPI-E: A CPI for the elderly (E) population

C-CPI-E: A proposed chained CPI-E

C-CPI-P: A proposed chained CPI for the poverty (P) population or the poverty line

PCE index: The personal consumption expenditures index series made by the BEA

Revised series:A series that calculates or estimates an historical series in a different way than it originally 

was, i.e. the R-CPI-U-RS is revised
Page | 48

Recalculated-revised: A revised series that recalculates it from the basic data, instead of estimating or 

extrapolating the difference with the original series through other means, i.e. the current R-CPI-URS is 

not all recalculated

Current purchases: Using data on purchases that are in the same time period and frequency that the 

prices are taken from, i.e. when June ’02 prices are used, so are June ’02 shares and/or quantities, and 

July ’02 prices are used with July ’02 shares and/or quantities 

Time range: A range of periods that form part of a series, i.e. a series could be indexed from January 

1978 to Dec. 2019 using one index for the time range of Jan. 1978 to Dec. 1999 and another for the time

range of Jan. 2000 to Dec. 2019

Examples

Consider the U.S. Census using this to decide the index to deflate historic median nominal household 

income to yield real median household income. Starting at the top left orange box, “Is the target of 

inflation ‘all items’ or relative to all items?” Since income is spent on all items, the answer would be 

“Yes.” Next, “Is the target population a demographic subpopulation?” might answer “Yes” for 

households at or near the median, but since this isn’t one the three subpopulation boxes, it would 

return a dead end and require restarting and going down the “No” line. Then “Is a monthly real-time 

never-revised index needed?” would answer “No”. “Is the focus on output or consumer welfare?” 

answers “Consumer Welfare”, leading to “Use the C-CPI-U” in a possible ending purple box. However, 

since the C-CPI-U is not available before 2000, the user would restart the process for the pre-2000 

time range excluding the C-CPI-U box and changing course at the last orange decision box. The same 

process would then lead to the “Use of PCE index”, but since the PCE index isn’t available before 1959, 

the process would be restarted for the pre-1959 time range. This time the user would have to back up 

to the “real time” question box and change course leading to “Use the CPI-U”. If an index was made 

that targeted households, the final checklist box would be “Yes”, so if the desired answer to “Is the 

target population a demographic subpopulation?” was “Yes”, then the newly made median income 

index would be chosen.

Suppose the C-CPI-U is later extended back in time, using the R-CPI-U-RS components, so that that a 

continuous C-CPI-U is available from 1978 onwards. Then the user would go to the first box on the 

bottom row, “Is a previously more preferable choice newly available for the time range?” In this 

hypothetical, the answer is yes for 1978-2000, so the user would replace the PCE index with the 

extended C-CPI-U series (the C-CPI-U-RS) from 1978-2000. The next box, “Is there a version with 

current purchases available?” would also answer yes but it is the same hypothetical new index. Next 

the “Is there a version of the current index with recalculated-revised indexes available?” box would 

answer yes assuming the C-CPI-U-RS used in the hypothetical new index was recalculated. Finally, 

assess the box “If the target is the cost of living of median income households, is there a version that 

also target the median income level itself?” The target does match, but the answer for this hypothetical 

would be “No.” (Assuming the C-CPI-U-RS was for the population as a whole just as the C-CPI-U is.)

Now consider the choice of index for deflating the Official Poverty Measure (OPM). “Is the target of 

inflation ‘all items’ or relative to all items?” The second box, “Is the target population a demographic 
Page | 49

subpopulation” would answer “Yes,” and direct along the “Poverty/low income” line to “Use the CCPI-P If 

available” box.

Next, consider the choice of index to escalate postal rates for mailing services. “Is the target of inflation 

‘all items’ or relative to all items?” The target postal rates would be examined relative to all items, so 

“Yes” leads to the next orange box on subpopulations which would answer “No”, the “Is a monthly 

real-time never-revised index needed?” would answer “No.” “Is the focus on output or consumer 

welfare?” answers “Consumer Welfare,” leading to “Use the C-CPI-U.” 

Finally, consider the choice of index for Treasury Inflation-Protected Securities (TIPS). The 

subpopulation box would answer “No”, the real time question would (likely) answer “Yes”, the official 

index question would (likely) answer “No”, and so the purple “Use the initial (unrevised) C-CPI-U” would 

be chosen. With new indexes, since this is the first choice the first appendix box would answer “No”, 

the next two would yield the same choice, and the last box would answer “No”.
Page | 50

Appendix B

Appendix B.1: Consumer Price Index for All Urban Consumers (CPI-U) compared to other index 

measures
Page | 51
Page | 52

Statistic PCEPA (SA) Chained CPI-U CPI-U-RS CPI-W CPI-E

Mean -0.31% -0.27% 0.02% -0.02% 0.12%

Standard Error 0.000262564 0.000110372 1.80227E-05 0.000197657 0.000157829

Median -0.29% -0.26% 0.02% -0.04% 0.15%

Mode -0.16% -0.34% 0.05% 0.01% -0.07%

Standard Deviation 0.0038678 0.0016259 0.0002580 0.0029117 0.0023250

Sample Variance 0.0000150 0.0000026 0.0000001 0.0000085 0.0000054

Kurtosis 0.576 1.257 -0.457 -0.019 0.197

Skewness -0.234 -0.374 -0.109 0.166 -0.628

Range 2.32% 1.00% 0.12% 1.40% 1.17%

Minimum -1.46% -0.79% -0.05% -0.67% -0.56%

Maximum 0.86% 0.22% 0.07% 0.73% 0.60%

Sum -0.66204 -0.58135 0.04714 -0.04972 0.255387

Count 217 217 205 217 217

Confidence Level(95.0%) 0.00051752 0.00021755 0.00003553 0.00038958 0.00031108
Page | 53

Statistic PCEPA (SA) Chained CPI-U CPI-U-RS CPI-W CPI-E

Number of periods smaller 175 210 39 121 61

Percentage of time smaller 81% 97% 19% 56% 28%

Appendix B.2: Poverty thresholds, family of four with two children, estimated using various 

indexes, anchor year=2000

YEAR CPI-U CPI-W CPI-E Chained CPI-U

2000 $ 17,463 $ 17,463 $ 17,463 $ 17,463

2001 $ 17,960 $ 17,939 $ 18,003 $ 17,857

2002 $ 18,244 $ 18,187 $ 18,335 $ 18,079

2003 $ 18,660 $ 18,590 $ 18,784 $ 18,456

2004 $ 19,157 $ 19,076 $ 19,347 $ 18,918

2005 $ 19,806 $ 19,748 $ 20,025 $ 19,466

2006 $ 20,444 $ 20,379 $ 20,680 $ 20,031

2007 $ 21,027 $ 20,965 $ 21,300 $ 20,537

2008 $ 21,834 $ 21,821 $ 22,113 $ 21,304

2009 $ 21,756 $ 21,674 $ 22,065 $ 21,204

2010 $ 22,113 $ 22,123 $ 22,365 $ 21,506

2011 $ 22,811 $ 22,909 $ 23,009 $ 22,163

2012 $ 23,283 $ 23,390 $ 23,482 $ 22,595

2013 $ 23,624 $ 23,710 $ 23,849 $ 22,872

2014 $ 24,008 $ 24,067 $ 24,287 $ 23,203

2015 $ 24,036 $ 23,967 $ 24,433 $ 23,175

2016 $ 24,339 $ 24,202 $ 24,820 $ 23,391

2017 $ 24,858 $ 24,716 $ 25,384 $ 23,804

2018 $ 25,465 $ 25,346 $ 26,001 $ 24,300

$ 1.46 $ 1.45 $ 1.49 $ 1.39

Total Change 45.8% 45.1% 48.9% 39.2%

Per annum change 2.12% 2.09% 2.24% 1.85%

Appendix B.3: Evaluation of CPI-P and Chained CPI-P

1. Study Period: December 1999 to December 2017
Page | 54

• Experimental indexes start with base period 2001.12=100

2. Expenditures: CE Diary and Interview Survey micro-level data, 1999 to 2017

• Households classified into U, W, E, and P population groups

• P=1 (Poverty household) if final income before taxes is less than official poverty threshold 

for corresponding calendar year; weighted average thresholds used by household size

• Monthly weights for each cohort estimated for matrix of 210 items x 38 areas

3. Price Indexes: CPI elementary item-area indexes

• Matrix of 210 items x 38 areas = 7,980 indexes each month

• Adjustments made to build continuous matrix over entire study period:

• HC09 (OER of vacation homes) mapped to HBO2 (other lodging) and elementary index 

deleted

• EDO1 (Local) and ED02 (Long distance) collapsed into ED04 (Landline telephone service

• 2010 structure for Medicinal drugs imposed retrospectively to 1999 (MA01→MF01, 

MA09→MG09, MB01→MF02, 

MB02→MG01)

• Health insurance categories collapsed into ME01, Me02, ME03, ME04 throughout

4. Research objective: produce experimental CPI indexes for a poverty cohort

• H1: Test hypothesis that inflation experience of a poverty cohort is higher than all 

consumer at large

• H2: Test hypothesis that households in poverty exhibit fixed-quantity behavior (i.e., less 

substitution compared to all consumer at large
Page | 55

Sample Diagnostics

Population subgroup sample size, 1999 to 2017, CE-Interview Survey

W = Urban wage-earners and clerical workers

Population subgroup sample size, 1999 to 2017, CE-Diary Survey

Average number of diaries per year

W = 

Urban wage-earners and clerical workers

GROUP Total

Per 

Basic 

Area

Per 

Basic 

Area 

per 

Quarter

U 

12,985 342 85

W 3,759 99 25

E 3,426 90 23

P 1,631 43 11

KEY: 

U = All urban consumers

as percent of CPI-U sample size

Average number of interviews

per year

GROUP Total

Per 

Basic 

Area

Per 

Basic 

Area

per 

Quarter

U 26,501

 697 174

W 7,641 201 50

E 7,204 190 47

P 3,743 99 25

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds

KEY: 

U = All urban consumers

as percent of CPI-U sample size

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds
Page | 56

Percent of elementary cells with no expenditure data

n = 210 items x 38 areas = 7,980 cells per month 

t = 12 months x 19 years = 228 months n x t = 

1,819,440 total item-area-month cells 

CATEGORY U W E P

Item-area-month 17.5% 41.3% 45.2% 63.4%

Item-area-month ratio 

allocated1 2.7% 9.0% 12.1% 24.0%

Item-U.S.-month 

(47,880 cells) 0.6% 1.4% 2.2% 4.5%

Item-area-biennial2 1.4% 4.8% 6.9% 13.7% KEY: NOTES:

U = All urban consumers 1. Statistical smoothing procedure used in Chained CPI-U weights.

W = Urban wage-earners and clerical workers 2. Level of aggregation for CPI-U basic weights, prior to statistical smoothing.

E = Elderly consumers (households with reference person or spouse 62 or over) 

P = Households at or below official poverty thresholds

Expenditure Categories (EC) with the least and most missing data 

percent of ec-area-month cells with missing data

CPI-Poverty cohort

Group Title % Missing Group Title % Missing

1 ED Telephone services 0.0% 55 FP Coffee, tea 10.8%

2 HF Gas and electricity 0.2% 56 HJ Furniture and bedding 11.1%

3 TB Gasoline 0.3% 57 AC Women's apparel 15.9%

4 HA Rent 0.3% 58 AF Infants apparel 16.0%

5 RA Video and audio 0.3% 59 FG Fish and seafood 16.7%

6 MF Medicinal drugs 1.4% 60 EA Educational books 17.3%

7 GD Miscellaneousservices

(laundry)

1.8% 61 AA Men's apparel 20.3%

8 MC Medical services 1.9% 62 AG Jewelry and watches 22.1%

9 GC Haircuts and personal services 2.0% 63 HE Fuel oil 28.9%

10 MD Hospital services 2.9% 64 GE Misc. personal goods 29.8%

11 HC Owner's rent 3.5% 66 AE Footwear 31.3%

12 ME Health insurance 6.5% 67 FW Alcohol home 34.8%

13 EE Internet and computer 6.8% 68 RC Sporting goods 36.0%

14 GA Tobacco 6.9% 69 EC Postage 36.1%

15 RF Recreation services 8.0% 70 FX Alcohol away 37.2%
Page | 57

Percent of elementary item-area-months cells with expenditure < $1 after ratio allocation

1999.12 to 2017.12

KEY: 

Relative importance, 1999 to 2017

KEY: 

U = All urban consumers

W = Urban wage-earners and clerical workers

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds

U = All urban consumers

W = Urban wage-earners and clerical workers

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds
Page | 58

Relative importance, 1999 to 2017

KEY: 

Relative importance, 1999 to 2017

KEY: 

U = All urban consumers

W = Urban wage-earners and clerical workers

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds

U = All urban consumers

W = Urban wage-earners and clerical workers

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds
Page | 59

Index Simulations

Summary

Historical index estimates, December 2001=100

KEY: 

Experimental low-income index

 ITWG developed spending weights for poverty households, 

using three different definitions

 P1: All households with income less than or equal to OPM 

 P2: Lowest quartile of income

 P3: 25% CE sample with mean income and minimum variance equal 

to OPM

 Weights used to calculate reweighted aggregations of CPI-U 

elementary item-area indexes

 CPI-P simulations adopt same methodology as CPI-W and CPI-E

– Exception: aggregation weights not statistically smoothed to minimize variance 

 Chained CPI-P simulations adopt same methodology as Chained CPI-U

 Simulations do not control for heterogeneity in spending below the 

elementary item-area level

– No control for specific outlets patronized by poor, unique products\services 

purchased, and actual transaction prices paid by poor

U = All urban consumers

W = Urban wage-earners and clerical workers

E = Elderly consumers (households with reference person or spouse 62 or over)

P = Households at or below official poverty thresholds
Page | 60

Experimental low-income compared to all-urban indexes

All-Items, U.S. City Average not seasonally adjusted index, December 2001 to December 2017

Chained CPI-P minus Chained CPI-U contributions

6 — U.S. B UREAU OF LABOR STATISTICS • bls.gov

43%

9%

33%

15%

Source: BLS unpublished research for ITWG, P3 cohort definition

Item contributionsto average annual 0.24% difference in Chained CPI-P minus Chained CPI-U
Page | 61

Appendix B.4: Breakdowns of index and share differences by item and poverty definition

This table reports the log differences by item in Tornqvist indexes and shares between different 

definitions of the poor and the whole population. It is sorted by the highest index difference between the 

income definition and the whole population to the lowest. The positive index contributions make the 

difference higher, while the negative contributions make the difference smaller. For example, the higher 

share on rent (HA01) vs. owner’s equivalent rent (HC01) for the poor for the income definition leads to a 

large positive contribution to the difference for HA01 (at the top) and a large negative contribution for 

HC01 (at the bottom). The net of all of these is the total difference.

Appendix B.4: Breakdowns of index and share differences by item and poverty definition(XLSX)

Appendix B.5: Comparing subpopulation indexes to full population indexes and the studying the

effects of sample size

In order to decide whether a subpopulation index was needed for the OPM, the group had to 

determine if the differences between the calculated subpopulation C-CPI-P indexes were due to the 

small CES sample sizes and the resultant higher variance of the indexes. To do this, various indexes 

were made by repeated resampling with replacement (bootstrapping) with different sample sizes. This 

yielded a distribution for each index, which was used to make means and confidence intervals to 

measure the effects of CES survey sampling. If the mean of the distribution was different from the 

non-bootstrapped index, it would imply that the sample size biased the index. If another index was 

outside of the 95% confidence interval of the distribution, then it was concluded that the difference 

was not due to variance from sampling or the small sample size. This method does not, however, 

include the variance due to price sampling, but the prices are the same for every index, only the CES 

respondent population is different.

Two kinds of sampling was done. First, the entire U population of the CES was sampled with 

replacement. This is to pick a sample that could have been chosen, using the actual sample as the 

universe to choose from. Then, depending on which definition of the P population was used, the 

subpopulation of the new sample was taken to make the new P index. This was repeated many times 

for each definition to get a distribution of chained P indexes, or C-CPI-P indexes, for each definition. 

Using more than 200 iterations was tested but made little difference. Since 12.5 % of the CES sample 

had reported income below the current official OPM, one definition was to use the bottom 12.5%, or 

1/8th, of each new sample. Since the OPM itself is the true target, it could be approximated with equal 

numbers of CES respondents above and below the current OPM, which is about the bottom 25% of 

households by reported income, or the bottom 1/4th of the new sample.

Due to time constraints, the programs calculating the indexes could not be made to exactly replicate 

the official indexes, as there are many structure changes and issues for particular items that make very 

small differences. Therefore, some of the small differences could be due to the simplified programs, 

but the scale of the differences between indexes is much larger than any replication errors.
Page | 62

Figure 1 shows the 95% confidence interval for the distribution for 200 resamples of C-CPI-P indexes 

using the bottom 25% of income definition, the mean of the distribution, and the full sample C-CPI-P for 

that definition and the C-CPI-U for comparison. The mean of the bootstrapped indexes is slightly below 

the full sample index, but the official C-CPI-U is well outside of the lower confidence interval bound. 

Thus, for this definition, the differences between the C-CPI-P and C-CPI-U are not due to the small 

sample size and sampling variation.

 

Figure 1

Figure 2 shows similar indexes above but instead uses the bottom 12.5% of income to define the P 

population. Here, the mean of the bootstrap samples is not below the full sample C-CPI-P. The 

confidence interval is wider than the 25% definition, but still excludes the C-CPI-U. Figure 2
Page | 63

Figure 3 shows the mean and confidence intervals of the resampled distribution (700 iterations) of the 

while U population, but using only 25% of the sample size. This is to measure purely the effect of 

having a smaller sample size on the index. Also included is the C-CPI-P for the bottom 25% definition 

of the poor (P2). The mean is very close to the full sample index, the interval is tight, and the C-CPI-P2 

index is outside of it.

Figure 3

Figure 4 is similar, but uses a 12.5% sample of the U population. As before, the means are similar and 

the C-CPI-P is outside the interval, which is somewhat wider. Figure 4
Page | 64

To see how much the interval changes with an even smaller sample size, a 1/16th, or 6.25% sample of 

the U population was used, presented in Figure 5. Even though the interval is significantly wider than 

the 12.5% sample, the C-CPI-P is still outside of it. Figure 5
Page | 65

 

 

Appendix B.6: Analysis of Food Costs and Updated Multipliers

Research Questions: The ITWG calculated several different poverty thresholds based on the methods 

used to develop the Orshansky Poverty Thresholds in 1965. These thresholds were derived by dividing 

the cost of a nutritionally adequate short-term or emergency-use diet by the average percent of total 

household expenditures spent on food (see Background section below for more details on the 

Orshansky Poverty Thresholds). The OPM estimates current year thresholds from base year thresholds 

by using an inflation adjustment, while these methods generate new thresholds each year by using 

current data on food costs and expenditures.

(1) What would be the current value of a poverty threshold based on the Thrifty Food Plan with a 

fixed multiplier? (Updated TFP values are used, but the expenditure share multiplier is fixed at 

3x)

 o Results:

 For 2018, the TFP-based threshold with a fixed multiplier was $23,115.  OPM 

higher than a TFP-based measure by 8.74%

 The TFP-based measure would have been above the OPM in 1973-1979

(2) What would be the poverty measure have been if based on the TFP with variable multiplier? 

(Updated TFP values are used, and the expenditure share is updated using the Consumer 

Expenditure Survey)

 o Results

 For 2018, the TFP-based measure with changing expenditure share is $63,410

 TFP-based measure higher than the OPM by 249%.

Details on data and assumptions

 o In 2018 the OPM was $25,465 for a family of four with two children.

o Expenditure share drawn from CE when available o TFP was $642.10 per 

month in 2018 

o CE Food Expenditure share was 12.2% (multiplier of 8.23) in 2018

o TFP measures based on Thrifty Food Plan Family of 4 - 2 children over 6 o

Based on historical data from CNPP 

o TFP from 1975 to present, EFP 1971-1974, Low-cost FP from 1960-1975 o No 

information on EFP (now TFP) from 1960-1970

o Use adjustment of data on Low-cost Food Plan: EFP is 80% of Low-Cost Food 

Plan
Page | 66

Background

The Four Current USDA Food Plans: The Thrifty, Low-Cost, Moderate-Cost, and Liberal Food Plans each 

represent a nutritious diet at a different cost26. 

• Revised by USDA’s Center for Nutrition Policy and Promotion (CNPP), with assistance from 

USDA’s Food and Nutrition Service (FNS), Economic Research Service (ERS), and 

Agricultural Research Service (ARS) 

o All four Food Plans are based on 2001-02 data and updated to current dollars by using 

 the Consumer Price Index for specific food items.

o There are 15 market baskets—one for each of 15 specific age-gender groups.

o Child, Male, Female (each with 5 age ranges), Family of 2, Family of 4 

The Thrifty Food Plan (TFP): The TFP provides a representative healthful and minimal cost meal plan 

that shows how a nutritious diet may be achieved with limited resources

• Assumes that all purchased food is consumed at home.

• The Thrifty Food Plan (TFP) market baskets specify the types and quantities of foods that 

people could purchase to be consumed at home to obtain a nutritious diet at a minimal cost.

• The TFP is the basis for SNAP (Supplemental Nutrition Assistance Program) allotments

• The Agriculture Improvement Act of 2018 calls for the foods that make up the basket for the 

TFP to be updated every five years and for purchase data to be collected from retail stores

• The TFP was last revised in 2006 

o Based on the 2005 Dietary Guidelines for Americans as well as the 2005 MyPyramid 

 Food Guidance System.

o Uses data from National Health and Nutrition Examination Survey (NHANES) and 

the Food Price Database to identify the prices low-income people paid for many 

foods. 

o Uses the 2001-2002 data on food consumption, nutrient content, and food prices.

o Offers a more realistic reflection of the time available for food preparation, 

especially with increased expectations for work in assistance programs. o Allows 

more prepared foods and requires fewer preparations from scratch.

The Economy Food Plan: The 1961 Economy Food Plan (EFP) was developed by Mollie Orshansky of 

the SSA as a nutritionally adequate diet for short-term or emergency use.

• EFP was priced at less than the Low-Cost Plan and served as the basis for maximum food 

stamp allotments as stipulated in the 1964 Food Stamp Program Act.

• Replaced by the Thrifty Food Plan (TFP) in 1975.

• The EFP (now TFP) was not designed to be a minimum cost food plan but a palatable food 

plan at minimal cost that met the 1958 recommended dietary allowances.

26 https://www.fns.usda.gov/cnpp/usda-food-plans-cost-food-reports-monthly-reports
Page | 67

o Designed for temporary or emergency use when funds are low.

o Included foods that require a considerable amount of home preparation. 

o Required skill in food shopping and preparation, minimal food waste.

• The cost of the food plan was treated as an adequate standard of food expenditures for a 

household with income at the poverty threshold. 

The EFP and the Orshansky Poverty Threshold: Orshansky used the EFP and 1955 Household Food 

Consumption Survey (USDA,1956) to develop a poverty threshold for income. 

• The survey showed that for all households of two or more persons, the average dollar value of 

food used during a week accounted for one-third (1/3) of after-tax cash income

• These results were used to create a poverty threshold by multiplying the cost of the EFP by 

the inverse of the expenditure share (3) to calculate the poverty threshold of income.

o The multiplier is based on a standard of adequacy for food and a food expenditure 

share, but not other necessities purchased by households. 

o Reasoning: no generally accepted standard of adequacy for essentials of living except 

for food at that time.

o Expenditures shares vary by income: Low-income families spent 47% and high-income

families spent 28%. (1955 values) 

o Different income thresholds were also set using multipliers of 2.12 for low-income and 

3.57 for high-income.

o Decided it was reasonable to use average income for all families when estimating the

food expenditure share of total income.

Subsequent Work on the Economy-Thrifty Food Plan:

• Updates to Food Plans 1983, 1999, and 2006 have maintained procedural consistency with 

the 1975 update.

• Consumption data and dietary standards for each revision were more up-to-date, and the 

model is made more complex.

• The cost equivalency is a key feature of all updates in that the cost of a new food plan is set 

equal to the cost of the previous food plan adjusted for inflation with a food plan price index. 

(See Hanson Review of Ag Econ, Fall 2008)

• The revised food plans change the mix of food groups in the food plan and the mix of items in 

a food group, which affect the food plan price index.

• Food plan costs are inflated by a price index derived from Bureau of Labor Statistics food 

prices.

• Costs of the TFP market baskets are updated monthly for each of the 15 age-gender groups.

o Uses the monthly Consumer Price Indexes (CPIs) for specific food categories to update 

prices for the food categories of the TFP market baskets. 

o Each of the 29 food categories of the TFP has a corresponding CPI or set of 

corresponding CPIs that are applied to update the appropriate cost of the TFP food 

categories for the market basket for each age-gender group. 
Page | 68

o For TFP food categories with more than one corresponding CPI, CNPP uses a weighted 

average of the appropriate CPIs. 

o USDA-food-plans-cost-food-reports-monthly-reports

Research on household food consumption and the food expenditure shares

• USDA Household Food Consumption Surveys began to examine food intake, diet quality, and 

health, moving away from data on household food expenditures

• This highlights the value of the Consumer Expenditure Survey (CE) to examine the long-term 

trends in household food expenditure shares 

o Historical note: CE for 1960-1961 found a food expenditure share of 25% 

(multiplier of 4)

o Orshansky chose to use the USDA survey with 33% share (multiplier of 3)

More detailed checklist of foods used in a week in the questionnaire

• Trend in the food expenditure share of after-tax income based on CE

 o For all families: 21.9% in 1960 (multiplier of 4.57) and 12.2% in 2018 (multiplier of 8.23) 

 o Lowest quintile 34.1% in 2017 (multiplier of 2.93) and highest quintile 8.7% in 

2017 (multiplier of 11.43)

 o Lower food exp share ==> higher multiplier ==> higher poverty threshold

o Larger share of nonfood necessities in low-income household budget

References

Carlson, A. M. Lino, W.Y. Juan, K. Hanson, P. P. Basiotis. Thrifty Food Plan, 2006. U.S. Department of 

Agriculture, Center for Nutrition and Food Policy, CNPP 19, April 2007.

Hanson, K. “Mollie Orshanky’s Strategy to Poverty Measurement as a Relationship between Household 

Food Expenditures and Economy Food Plan.” Review of Agricultural Economics, 30(5) Fall 2008, 57-580. 
Page | 69