Final Report 

of the 

Interagency Technical Working Group on 

Evaluating Alternative Measures of Poverty 
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Table of Contents Executive Summary................................................................................................................................1

Background ............................................................................................................................................3

Creation of the Technical Working Group .........................................................................................3

Goals and Elements of a Poverty Measure........................................................................................4

Goals of a Poverty Measure...........................................................................................................4

Elements of a Poverty Measure.....................................................................................................5

The Official Poverty Measure ................................................................................................................6

History of the Official Poverty Measure ............................................................................................6

Uses of the Official Poverty Measure ................................................................................................7

Concerns with the Official Poverty Measure.....................................................................................8

Resource Measure Does Not Account For In-Kind Transfers And Taxes.......................................9

Income Misreporting......................................................................................................................9

The Resource Sharing Unit May Not Capture Actual Sharing Arrangements..............................10

The CPI-U Used to Adjust the Official Poverty Thresholds May Not Measure Inflation 

Accurately.....................................................................................................................................10

Thresholds Do Not Account for Changes in Living Standards......................................................11

Thresholds Do Not Account For Geographic Price Differences...................................................11

The Adjustment for Family Size and Composition.......................................................................11

Association with Other Measures of Material Well-Being ..........................................................11

The Supplemental Poverty Measure ...................................................................................................12

History of the Supplemental Poverty Measure as Implemented....................................................12

Current Uses of the Supplemental Poverty Measure......................................................................13

Concerns with the Supplemental Poverty Measure........................................................................13

Resources.....................................................................................................................................13

Association with Other Measures of Material Well-Being ..........................................................14

Updating Mechanism for Thresholds...........................................................................................15

Alternative Measures of Poverty Recommended by the Working Group ..........................................15

Considerations and Consensus Recommendations for Income- and Consumption-based 

Resource Measures..........................................................................................................................16

Income-based Resource Measures Using the CPS ASEC and ACS ...............................................17

Consumption-based Resource Measures Using the CE Interview Survey...................................46

Advantages/Disadvantages of Income- and Consumption-based Resource Measures..............52

Considerations and Consensus Recommendations for Thresholds ................................................57

Setting Thresholds in a Baseline Year ..........................................................................................58
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Updating Thresholds Over Time ..................................................................................................60

Sharing and Adjusting for Different Sharing Unit Sizes ...............................................................62

Adjusting Poverty Thresholds Across Geographic Areas.............................................................65

Accounting for Units With Child Support Obligations and Work And Childcare Expenditures...68

Other Elements of a Poverty Measure ............................................................................................69

Consensus Recommendations Worthy of Further Research by the Federal Statistical System.........69

Additional Research on Income and Consumption Poverty............................................................69

Other Measures of Well-being Beyond Income and Consumption.................................................70

Material Hardship ........................................................................................................................70

Multidimensional Poverty............................................................................................................71

Populations Included in Poverty Measures.....................................................................................72

Accounting for Other Types of Services...........................................................................................73

Identifying Poverty Status of Individuals within a Sharing Unit ......................................................74

Timeliness of Income, Consumption, Poverty, and Other Well-being Measures...........................74

Summary of and Responses to Public Comment.................................................................................76

Questions Posed in Federal Register Notice for Public Comment ..................................................76

Definitions....................................................................................................................................76

Resource measures......................................................................................................................76

Thresholds....................................................................................................................................76

Summary of and Responses to Public Comments...........................................................................77

References ...........................................................................................................................................87

Appendix A. Census Bureau Surveys .................................................................................................114

Current Population Survey Annual Social and Economic Supplement (CPS ASEC).......................114

American Community Survey (ACS)...............................................................................................114

Survey of Income and Program Participation (SIPP) .....................................................................114

Appendix B. Bureau of Labor Statistics Surveys................................................................................116

Consumer Expenditure Survey (CE) ...............................................................................................116

Appendix C. Choices for Geographic Cost Adjustments....................................................................117

Median Rent Index (MRI)...............................................................................................................119

Regional Price Parities (RPP)..........................................................................................................119

Regional Price Parities for Food, Apparel and Rent (RPP-FAR) .....................................................120

Comparable Wage Index (CWI)......................................................................................................120

Appendix D. List of Working Group Recommendations....................................................................122
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Executive Summary Since the establishment of the U.S. official poverty measure (OPM) more than fifty years ago, there has 

been continuing research on poverty measurement. Alternative estimates of poverty have been 

published for more than three decades by the Census Bureau, and in 2011 the Census Bureau in 

cooperation with the Bureau of Labor Statistics (BLS) began publishing the Supplemental Poverty 

Measure (SPM). Existing and previous measures of poverty produced by the Federal government are 

income based and rely on surveys to capture the income data. Guidance issued by the Commission on 

Evidence-based Policymaking, National Academy of Sciences reports, and the Office of Management and 

Budget (OMB) has recommended combining administrative data with survey data to improve national 

statistics. In recent years, evidence has shown that there is survey misreporting of many income 

sources. Recognizing the changing landscape and that alternative statistics can provide useful 

information, the Chief Statistician of the United States (Chief Statistician) formed the Interagency 

Technical Working Group on Evaluating Alternative Measures of Poverty (Working Group) to evaluate 

possible alternative measures of poverty, how such measures might be constructed, and whether to 

publish those measures along with current measures.

To provide context for the Working Group's recommendations for alternative measures of poverty, this

report begins by discussing the history of poverty measurement in the U.S., including the development 

and implementation of the OPM and SPM. In addition, the Working Group identified some of the uses of 

the OPM and SPM, as well as noted some of the known concerns with each of the measures.

The Working Group primarily focused on unidimensional poverty measurement. Unidimensional poverty 

measures have two key parts: the resource measure (such as income or consumption) and the 

thresholds (the cutoffs to which the resource measure is compared). The Working Group's consensus 

recommendations for unidimensional poverty measurement provide for detailed recommendations for 

the resource measures and recommendations for future expert input and research on thresholds.

The Working Group recommends adopting both extended income-based and consumption-based 

resource measures, and it identifies other areas worthy of future research by the Federal Statistical 

System. For an extended income resource measure, the Working Group recommends expanding beyond 

pre-tax cash income to include at least some in-kind transfers and accounting for taxes and tax credits, 

much like the SPM resource measure. In addition, the Working Group recommends producing resource 

measures with and without a value for health insurance with some direction provided on how to do so. 

The Working Group discussed how to value implicit flows from non-financial assets (e.g., vehicles, owner 

occupied housing, other properties), and how to value net flows from financial assets for inclusion in 

both income- and consumption-based resources. An extended income resource measure would also 

integrate administrative data with household survey income information when appropriate, taking 

advantage of recent research on the use and the increased availability of potentially more accurate 

administrative data. The Working Group considered other approaches for adjusting survey data for 

misreporting as well.

A consumption-based resource measure may more directly capture the resources available to a family if 

it records the consumption that was actually achieved. These measures begin by summing most 

categories of expenditures on goods and services. The Working Group recommends beginning in this

way, but recommends excluding certain categories of expenditures that are often thought of as 

enhancing future consumption, such as pension contributions and education expenses. As with the 

extended income-based resource measure, the Working Group recommends that any consumptionbased resource measure be produced with and without a value of health insurance. The Working Group 
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also recommends that a flow of consumption resources be attributed to owned vehicles and owneroccupied homes and that current expenditures on these items are excluded from consumption.

The final report of the Working Group also considers implementation issues with, as well as other 

advantages and limitations of, proposed measures. The Working Group discussed many implementation 

issues. These included the choice of survey data most appropriate for use in developing the measure. 

For example, for an income resource measure, choosing between the Current Population Survey Annual 

Social and Economic Supplement (CPS ASEC) or the American Community Survey (ACS) would have an 

effect on the ability to produce estimates at different geographic levels. The Working Group suggests

using the CPS ASEC as the survey base for an extended income-based resource measure and 

recommends the Consumer Expenditure (CE) Interview Survey for a consumption-based resource 

measure. In addition, the Working Group has identified some advantages and limitations of extended 

income- and consumption-based resource measures. For example, for an income-based resource 

measure, annual income will not capture the standard of living of individuals who draw upon savings or 

borrow to fund their consumption. However, an income-based resource measure captures the flow of 

new resources available for consumption, and household income data are available in more datasets 

than are household expenditures.

The Working Group also discussed thresholds and notes that poverty thresholds are a key component of 

a poverty measure. Individuals with resources that fall below the poverty threshold are counted as poor, 

and individuals with resources at or above the poverty threshold are not counted as poor. In the end, 

the Working Group recommends additional expert input and research on several key considerations for 

determining poverty thresholds, and provides a discussion of each of these considerations within the 

report.

Finally, while the Working Group is focused on the extended income-based and consumption-based 

measures, the Working Group provides recommendations for future research by the Federal Statistical 

System on other topics, including multidimensional poverty measurement and individual indicators of 

well-being, individuals excluded from poverty measures such as most of those experiencing 

homelessness, and timely poverty measurement.

 
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Background In early 2019, the Chief Statistician of the United States established the Interagency Technical 

Working Group on Evaluating Alternative Measures of Poverty (the Working Group) in order to 

evaluate possible alternative measures of poverty, how such measures might be constructed, and 

whether to publish those measures along with the measures currently being published.1 This

Working Group includes members from 11 Federal agencies. After much deliberation throughout 

2019, the Working Group finalized a consensus interim report that was published on February 14, 

2020, alongside a Federal Register Notice for public comment on the work of the Working Group 

(85 FR 8610).2

Since the publication of the Working Group’s consensus interim report, the Working Group has 

continued to meet and advance its work. This final report of the Working Group builds upon the 

interim report and details the Working Group's considerations and consensus recommendations. 

Creation of the Technical Working Group 

Since the U.S. official poverty measure (OPM) was first established in 1969, there has been 

continuing debate about alternative approaches to the measurement of poverty. The Census 

Bureau has considered many alternative measures. Starting in 1982, the Census Bureau published 

poverty measures that accounted for in-kind benefits and in 1983 estimates that accounted for 

taxes. Between 1999 and 2010, the Census Bureau published poverty estimates that 

implemented many of the recommendations of a 1995 National Research Council report

(hereafter referred to as Measuring Poverty).

3 The Census Bureau also reported poverty 

measures using an alternative inflation adjustment.4

Recognizing that alternative statistics can provide useful information, the Chief Statistician of the 

United States (Chief Statistician) formed an Interagency Technical Working Group on Developing 

a Supplemental Poverty Measure (the SPM Development Working Group) in 2009 to develop 

initial starting points for producing a Supplemental Poverty Measure (SPM).5 The SPM 

Development Working Group issued their observations in 2010 drawing on the recommendations 

from Measuring Poverty and other sources.6 The SPM was designed to be an experimental 

measure that complements, but does not replace, the OPM. The SPM incorporates estimates of 

taxes and many in-kind benefits, which are not incorporated in the OPM. In addition, the SPM 

thresholds (poverty cutoffs) are based on recent expenditure patterns for a limited set of goods 

and services, not all spending. The SPM is described in detail in the section titled The 

Supplemental Poverty Measure. The initial release of the SPM was in the fall of 2011, and this 

experimental measure has been published annually thereafter. Research on the SPM continues 

and the Interagency Technical Working Group on the Supplemental Poverty Measure (the SPM 

Implementation Working Group) provides advice on improvement of the SPM. 

 1 This means that the poverty measures recommended by the Working Group are not intended to replace the OPM or 

the SPM.

2 The Federal Register Notice and the Working Group’s Interim Report are available online at: 

https://www.regulations.gov/docket?D=OMB-2019-0007. 3 National Research Council (1995).

4 See U.S. Census Bureau 1989 or U.S. Census Bureau 2007. 

5 For information on the creation of this Working Group, see Blank, R.M., Fall 2011. The Supplemental Poverty 

Measure: A New Tool for Understanding U.S. Poverty. Pathways. 6 https://www.bls.gov/pir/spm/spm_twg_observations.pdf
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Twenty-five years have passed since Measuring Poverty and a decade since the SPM observations 

document. Since that time, evidence has accumulated on the extent of survey misreporting of 

many income sources, along with evidence indicating a decline over time in the accuracy of 

survey reports of these income sources.7 In the last few years, the Commission on Evidencebased Policymaking, and several National Academy of Sciences reports have recommended 

combining administrative data with survey data to improve national statistics. This activity builds 

on requirements of the Paperwork Reduction Act of 1995 and guidance in OMB M-14-06.8 Recent 

research suggests that administrative data can be combined with survey data to produce poverty 

statistics.9 Building on earlier work, including by the Census Bureau and BLS, recent research has 

also examined the possibility of using expenditure data to measure material well-being and 

poverty.10 Families may more accurately report expenditures than income,

11 and conceptually, 

consumption may better reflect their material circumstances because it could reflect the actual 

consumption a family achieved, rather than what income is available to them to purchase those 

goods and services.

12 In addition, there may be value in producing additional absolute and purely 

relative poverty measures, alongside the current OPM and SPM.

With this background in mind, the Chief Statistician established the Working Group to evaluate 

possible alternative measures of poverty, how such measures might be constructed, and whether 

to publish those measures along with the measures currently being published.13 The Working 

Group includes members from 11 Federal agencies.

Goals and Elements of a Poverty Measure 

Goals of a Poverty Measure 

In order to consider the goals of a poverty measure, it is necessary to first define what is meant 

by poverty in broad terms. A helpful working definition comes from Measuring Poverty (National 

Research Council 1995, p. 19) “We define poverty as economic deprivation. A way of expressing 

this concept is that it pertains to people's lack of economic resources (e.g., money or near-money 

income) for consumption of economic goods and services (e.g., food, housing, clothing, 

transportation). Thus, a poverty standard is based on a level of family resources (or, alternatively, 

of families' actual consumption) deemed necessary to obtain a minimally adequate standard of 

living, defined appropriately for the United States today.”

 7 For example, see Coder and Scoon-Rogers (1996), Moore et al. (2000), Roemer (2000), Wheaton (2007), Iams and 

Purcell (2013a, 2013b), Gathright and Crabb (2014), Meyer, Mok and Sullivan (2015) and Rothbaum (2015). 8 This memorandum gives guidance on providing and using administrative data for statistical purposes. Available at: 

https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/memoranda/2014/m-14-06.pdf. 9 See Bee and Mitchell (2017), Nicholas and Wiseman (2010), and Meyer, Wu, Mooers and Medalia (2019).

10 For joint Census Bureau-BLS work, see U.S. Census Bureau (2003); a goal of this report was to provide information to 

initiate discussions to examine issues involved in supplementing income-based measures of poverty with those based 

on consumption and material hardship. For early research using expenditures and or/or consumption to measure 

material well-being, see Cutler and Katz (1991), Garner (1993), Poterba (1991), Slesnick (1993, 2001); for more recent 

research, see Johnson et al. (2005), Meyer and Sullivan (2012), Fisher et al. (2015), and Meyer and Sullivan (2018).

11 For example, see Meyer and Sullivan 2003, 2011. 12 A family could achieve this consumption through using income, savings, accruing debt, or other means. See UNECE 

(2017). 13 This means that the poverty measures recommended by the Working Group are not intended to replace the OPM or 

the SPM.
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The appropriateness of a poverty measure will differ depending on the goals it is designed to 

achieve. In this report, the Working Group focused on the following three goals: 1) identify who 

in the population is the most disadvantaged, 2) determine how the degree of economic 

deprivation has changed over time, and 3) assess the impact that public policies and broad social 

and economic trends have on economic deprivation.14 Some uses of poverty measures include 

targeting transfers to individuals or areas most in need and providing a benchmark for eligibility 

for means-tested programs.15 However, the Working Group’s mandate is to address only the first 

three measurement goals and not consider programmatic uses.

Elements of a Poverty Measure 

Unidimensional vs. Multidimensional Measures of Well-Being 

Most poverty measures in the U.S. reflect a single dimension of deprivation such as income or 

consumption. An attraction of such measures is that researchers and policymakers in the U.S. 

have more experience with these than with multidimensional measures. While work on 

multidimensional poverty measures (which could include dimensions such as health, education, 

work, or housing) has been done internationally, given their complexity, the limited research with 

U.S. data, and the limited time of the Working Group, the Working Group decided to focus on 

unidimensional poverty measurement. Multidimensional poverty measures are discussed more 

fully in the section titled Consensus Recommendations Worthy of Further Research by the 

Federal Statistical System in this report.

Components of a Unidimensional Poverty Measure

The design of a unidimensional poverty measure typically entails the following: 

1) A resource measure: How should the resources available to people for consumption be 

defined? Typically, resources are measured using income or consumption, but there is 

debate about how to define income and consumption. One must also decide the time 

period over which to measure resources—should poverty be measured over a year or 

over a shorter or longer time period? Finally, a measure needs to specify who should be 

included in the resource-sharing unit: only related family members, those pooling income 

and making joint purchases, or another unit.

2) Thresholds: To determine poverty, unidimensional measures compare resources to some 

specified threshold. In addition to setting the baseline threshold, one must also designate 

how this threshold will be adjusted over time (to account for inflation or changing living 

standards), across sharing units of different compositions (to account for different needs), 

and perhaps even across geographic areas (to account for geographic cost of living 

differences). Thresholds can be set at an absolute level, relative to some standard, based 

on subjective assessments, or a combination of these approaches.

16

 14 This focus accords very closely with the goals stated in Measuring Poverty: a “. . .measure that will more accurately 

identify the poor population today...” and “. . .will more accurately describe changes in the extent of poverty over time 

that result from new public polices and further social and economic change.” (National Research Council 1995, p. 1-2). 15 The Working Group recognizes that measures targeted to meet specific policy needs; such as the Department of 

Health and Human Service’s Poverty Guidelines, the Census Bureau’s Small Area Income and Poverty Estimates, and 

the National Center for Education Statistics’ in-development National Assessment of Educational Progress 

socioeconomic status indices and experimental School Neighborhood Poverty Estimates; would still be needed. 

16 The official poverty measure of Ireland is a combination measure: monetary and nonmonetary indicators of 
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In application, some poverty measures identify whether an individual’s resources fall above or 

below a specified threshold, which is usually called a head count measure. Alternatively, a 

measure could capture how far the resources of units fall below the threshold, as is done by a 

poverty gap measure, which sums the differences between the resource measure and the 

threshold for all units with resources below the thresholds. 

The Official Poverty Measure 

History of the Official Poverty Measure 

The development of the OPM had been underway, but came to the fore in January 1964 when 

President Lyndon B. Johnson called for a national war on poverty in his State of the Union 

address to Congress. Since no official poverty measure existed at the time of his call for a War on 

Poverty, for his terms of engagement President Johnson relied on a poverty measure developed 

by his Council of Economic Advisers (CEA). In the 1964 Economic Report of the President, CEA 

estimated that in 1962 approximately 20 percent of all Americans were poor (CEA 1964).17 The 

CEA poverty measure used pre-tax money income as its resource measure and extended its 1962 

thresholds back historically based only on inflation (CEA 1964). In June 1965, the Census Bureau 

released a report estimating the number of “low-income families and unrelated individuals” as of 

1963 using the CEA poverty measure (U.S. Census Bureau 1965).

The Office of Economic Opportunity was established in 1964 to coordinate efforts in 

implementing President Johnson’s War on Poverty (HHS 2000). The Office of Economic 

Opportunity adopted different poverty thresholds based on the work of Mollie Orshansky, a staff 

economist in the Social Security Administration. Orshansky published in 1965, “Counting the 

Poor: Another Look at the Poverty Profile,” intended to improve on the CEA poverty measure by 

adjusting poverty thresholds based on the number of people in a family and whether the family 

lives on a farm (Orshansky 1965). Orshansky based her poverty thresholds on the cost of either 

the lower “economy food plan” (now, the Thrifty Food Plan) or the higher “low-cost food plan” 

for a family of a given size, as determined by the U.S. Department of Agriculture (USDA). 

Orshansky multiplied the cost of each of these food plans by three to obtain two different sets of 

poverty thresholds because the average family spent about one-third in after-tax money income 

on food based on a USDA survey conducted in 1955 (Orshansky 1965). 

The Office of Economic Opportunity decided to use the lower thresholds based on the economy 

food plan, and the Census Bureau released reports in 1966 and 1968 based on this poverty 

measure (U.S. Census Bureau 1966, 1968). Fisher (2008) argues that the Office of Economic 

Opportunity chose these lower thresholds because they produced a poverty rate similar to the 

poverty rate CEA had determined for 1962, which had been used as President Johnson’s baseline 

for evaluating progress in his War on Poverty. While Orshansky herself may have preferred the 

 

deprivation are combined in a measure of “consistent” poverty to determine the status of the population living in 

household that are both economically disadvantaged and below the monetary relative poverty line (see Atkinson 

2019). Also see the recent World Bank (2018) report on poverty and shared prosperity in which absolute and relative 

concepts are combined into what is referred to as a societal poverty line. 

17 In multiple speeches in 1964, President Johnson referred to this CEA standard as his baseline for assessing progress 

in his War on Poverty, referring to the “one-fifth of our people” living in poverty and the “20 percent that earns less 

than $3,000 per year” (Johnson 1965, p. 287, p. 376). As Fisher (1992) notes, the development of the CEA poverty 

thresholds preceded the publication of Mollie Orshanky’s poverty estimates in “Children of the Poor” published in July 

1963, and thus, Fisher concludes that the CEA thresholds were ultimately not modeled after Orshanky’s thresholds.
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higher thresholds (see Fisher 2008), Orshansky recognized that the thresholds she developed 

were arbitrary, stating, “The measure of poverty thus developed is arbitrary. Few could call it too 

high. Many might find it too low” (Orshansky 1965, p. 10). As Fisher (1997) notes, Orshansky used 

the word arbitrary not to emphasize that the poverty thresholds were random, but rather, that 

they were a value judgement about societally appropriate standards.

On August 12, 1969, the Census Bureau released another report on poverty entitled, “Revision in 

Poverty Statistics, 1959 to 1968,” but this time, its poverty thresholds differed in a fundamental 

way from those in its previous poverty reports. Based on the guidance of the Poverty Level 

Review Committee, an interagency group created by the Bureau of the Budget (today the Office 

of Management and Budget) to review poverty thresholds, the Census Bureau adjusted 

thresholds each year based on inflation (using the Consumer Price Index or CPI), rather than 

changes in the cost of the food plan. This choice was consistent with the CEA approach (CEA 

1964, 1966) as well as the initial Census Bureau report on low-income populations in 1965 (U.S. 

Census Bureau 1965). 

On August 29, 1969, based on the recommendations of the Poverty Level Review Committee, the 

OPM was officially established. On that date, the Bureau of the Budget issued a notice (Definition 

of Poverty for Statistical Purposes n.d.) that all federal agencies should use the poverty statistics 

in the 1969 Census Bureau report (U.S. Census Bureau 1969) for years 1959 through 1968 (HHS 

2000). In addition, future reports of the Census Bureau in the same series would be the official 

poverty statistics for future years. This notice both endorsed the methodology the Census Bureau 

used in its 1969 report, and it stated explicitly that the CPI should be used to adjust thresholds 

each year (Bureau of the Budget 1969). OMB later formalized this notice as “Statistical Policy 

Directive 14” in 1978 (U.S. Census Bureau 2017).18

Since its inception on August 29, 1969, only minor changes have been made to the OPM (CRS 

2010). In 1979, the CPI for Urban Wage Earners and Clerical Workers (CPI-W) was replaced by the 

CPI for All Urban Consumers (CPI-U) for updating thresholds annually (CRS 2010). In 1981, farm 

families no longer were assigned a lower poverty threshold than non-farm families, femaleheaded families were no longer assigned a special threshold adjustment, and the maximum 

threshold based on family size was increased from seven to nine family members (CRS 2010).

Uses of the Official Poverty Measure 

The OPM is an indicator of economic well-being relied upon by policy makers and advocates, 

analysts, and the general public. Each September the Census Bureau releases the estimates for 

official poverty rates from the CPS ASEC for the previous calendar year with a webinar, printed 

report, and thousands of detailed internet tables. The primary focus of that report and webinar 

are the changes in poverty rates from the previous year, for the nation as a whole as well as 

many specific demographic groups. Public use microdata are also released that permit analysts 

and policymakers to do their own poverty tabulations. Later in September, official poverty 

estimates from the ACS provide annual estimates for states, Congressional districts, and any 

geographic area with a population greater than 65,000 people. In December, 5-year poverty and 

income statistics are released for all geographies in the United States using the ACS.

 18 https://www.census.gov/topics/income-poverty/poverty/about/history-of-the-poverty-measure/omb-stat-policy14.html
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The OPM from the CPS ASEC provides a time series of fairly consistent estimates back to 1959. 

Trends in poverty rates over time and differences in poverty rates across population groups are 

often cited as reasons a particular policy is, or is not, needed. The poverty measure also plays a 

role in evaluating government programs for low-income people and, more generally, the effects 

of government policies and economic growth on the distribution of income. There is a large 

literature on the characteristics of the poor, factors leading to poverty and other kinds of 

deprivation, and the effects of poverty on other behaviors and outcomes. Most of these studies 

use official poverty estimates. 

It is worth noting that various fractions or multiples of the official poverty thresholds are also 

frequently used in both government and academic reports as well as policy discussions. The 

Census Bureau regularly reports the share of the population below half the poverty line. Higher 

cutoffs are also examined, such as near poverty (often 125 percent of the poverty line).

19

Although the original statistical directive was clear, stating “these [poverty] 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” (Bureau of the Budget

1969), the current official U.S. poverty measure is not only an important statistical indicator; it is 

also used in government programs that are designed to help low-income families. Although many 

programs have their own need standard for eligibility, a significant number link their standard to 

the poverty guidelines (or a multiple of them), which are based on OPM thresholds.20 For 

example, the gross income standard used for eligibility for the Supplemental Nutrition Assistance 

Program (SNAP, formerly the food stamp program) is set at 130 percent of the poverty 

guidelines).21 Poverty estimates are also used for allocation of Federal funds to states and 

localities. Funding formulas use the official poverty rates from the CPS ASEC, but many formulas 

use poverty estimates from the ACS. The Department of Education uses poverty estimates from 

the Small Area Income and Poverty Estimates (SAIPE) program to distribute funding under Title I 

of the Elementary and Secondary Education Act.

Concerns with the Official Poverty Measure 

The methods for calculating the current OPM, largely unchanged since its inception in 1969, have 

been criticized by many researchers. In response, the Census Bureau has led an almost fourdecade process of research and discussion of poverty measurement with an eye to revising the 

official measure and developing alternative measures. The process has involved hundreds of 

papers, dozens of official Census Bureau publications, and two National Academy of Sciences 

reports (National Research Council 1995 and National Research Council 2005). The concerns with 

the official measure focus primarily on eight topics: in-kind transfers and taxes, income 

misreporting, the sharing unit, updating of the thresholds, changes in living standards, geographic 

 19 For example, one of the main detailed tables published each year, “POV-01. Age and Sex of All People, Family 

Members and Unrelated Individuals Iterated by Income-to-Poverty Ratio and Race” provides estimates for 16 distinct 

income-to-poverty ratios ranging from “Below 50 percent of poverty” to “Below 600 percent of poverty”. 

https://www.census.gov/data/tables/time-series/demo/income-poverty/cps-pov/pov-01.html 20 The poverty guidelines are published by the U.S. Department of Health and Human Services and are based on the 

Census Bureau official poverty measure thresholds (https://aspe.hhs.gov/poverty-guidelines). 21 For additional programs that use the poverty guidelines see: https://aspe.hhs.gov/frequently-asked-questionsrelated-poverty-guidelines-and-poverty.
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cost of living differences, adjustments for family size and composition, and its correlation with 

other measure of economic well-being.

Resource Measure Does Not Account For In-Kind Transfers And Taxes 

The OPM defines resources as pre-tax money income,

22 thus not reflecting the full resources at a 

family’s disposal. Money income does not subtract tax liabilities (and even poor workers must 

pay payroll taxes for Social Security and Medicare), nor does it include the Earned Income Tax 

Credit and other tax credits or in-kind benefits such as SNAP, housing or school meal subsidies, 

public health insurance, or employer-provided health insurance contributions. Thus, many of the 

major anti-poverty initiatives of the last few decades are not reflected in poverty rate trends, 

because policies like a rise in the Earned Income Tax Credit, a more generous Child Tax Credit, 

and expansions of Medicaid and SNAP do not show up as pre-tax money income. Since these 

benefits are not counted as resources in the OPM, it cannot be used to determine the 

contribution of these benefits to lifting people out of poverty.

Income Misreporting 

Income in the CPS ASEC and the ACS appears to be substantially under-reported, especially for 

categories of income important for those with few resources, and the extent of under-reporting 

has worsened over time. Roemer (2000); Wheaton (2007); Meyer, Mok, and Sullivan (2015); and 

Rothbaum (2015), among others, report comparisons of weighted micro-data from household 

surveys to administrative aggregates for government transfers and tax credits. Focusing on 

transfers important to those with low income, Meyer, Mok, and Sullivan (2015) find that the 

share of the administrative totals that appears in the CPS ASEC and ACS tends to be substantially 

below one and to have declined sharply over time. Over the available years between 2000 and 

2012, they find that the CPS ASEC is missing 50 percent of TANF dollars, 42 percent of SNAP 

dollars, 32 percent of unemployment insurance dollars, and 54 percent of workers’ compensation 

payments. While lower, the missing dollars in the ACS are still substantial. Furthermore, 

underreporting has sharply worsened over time in the CPS ASEC, even when including dollars that 

are imputed (Meyer, Mok, and Sullivan 2015). These patterns are supported in studies that link 

individual program records to surveys such as Gathright and Crabbe (2014) who examine Old Age 

and Survivor Disability Income (OASDI) and Supplemental Security Income (SSI) reporting in the 

Census Bureau’s Survey of Income and Program Participation (SIPP) and Meyer, Mittag and 

Goerge (2020) who examine SNAP reporting in the ACS, CPS ASEC, and the SIPP. Meyer and 

Mittag (2019) link CPS ASEC micro-data to New York administrative micro-data for four 

government transfer programs for the same individuals and find that unreported transfers 

exceed the recorded cash income of those with pre-tax income below half the poverty line. Bee 

and Mitchell (2017) find that approximately half of pension payments are not reported in the CPS

ASEC and that accounting for this and other underreporting reduces the elderly poverty rate by 

over two percentage points (about twenty-four percent). Meyer and Wu (2018) examine the 

poverty reduction of several transfer programs in the SIPP, which has the least underreporting of 

the major surveys, finding that the survey data miss more than half of the poverty reduction of 

several of the programs received by single parents. 

In terms of its implications for poverty, the misreporting of earnings is even more important than 

program receipt. Meyer, Wu, Mooers, and Medalia (2021) find that earnings among those at the 

 22 For the Census Bureau definition of pre-tax cash money income, see www.census.gov/topics/incomepoverty/poverty/guidance/poverty-measures.html
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very bottom of the distribution are sharply underreported in both the CPS ASEC and the SIPP.

Some past work has found over-reporting of earnings when comparing survey reports to the 

Detailed Earnings Record (DER) file (Hokayem, Bollinger, and Ziliak 2015). Recent work has 

pointed out that the DER is only intended to capture the Social Security taxable part of selfemployment income and omits unauthorized immigrants with Individual Taxpayer Identification 

Numbers. Collins et al. (2019) find that a meaningful percentage of tax units have labor earnings 

on Internal Revenue Service (IRS) Form 1040s that are not included in Form W2s (from which DER 

wage and salary income is derived), indicating that the earlier result of over-reporting may be

misleading. However, there are also concerns with the accuracy and completeness of 

administrative records. A share of the survey reports that are not corroborated by administrative 

earnings reports are likely to be cases where earnings were not recorded in the administrative 

sources. Reporting is not required for some income and the IRS estimates that tax compliance is 

not perfect (Internal Revenue Service 2016).

The Resource Sharing Unit May Not Capture Actual Sharing Arrangements 

The OPM treats the resource-sharing unit as those related by birth, marriage, or adoption. 

Cohabitors and their children are treated in the official measure as a separate family unit within 

the household even though they live together and may share resources. Unrelated children under 

age 15 are not included in the poverty universe for the OPM. Analytically, the sharing unit should 

be more aligned with those who share resources, regardless of familial connections. Information 

on resource sharing across cohabitors is not collected in the CPS ASEC, although resources or cost 

sharing provided to a family by cohabitors may be substantial. The treatment of cohabitors has 

become more important in recent years as the fraction of households with cohabitors present 

has risen (Lundberg, Pollak, and Stearns 2016). In the CE Survey, the main U.S. source of 

consumer expenditures, the reference unit includes all those related by blood, marriage,

cohabitors, and those who report sharing two of the three following: housing, food, or other 

living expenses.23 This definition arguably more directly aligns with the idea of the unit that 

shares resources.

The CPI-U Used to Adjust the Official Poverty Thresholds May Not Measure Inflation 

Accurately 

As described above, the thresholds used in the OPM are adjusted each year for inflation using the 

CPI-U. This policy of using the CPI-U for the adjustment has not been reevaluated since the 

introduction of new measures of consumer inflation, such as the Chained CPI-U. As stated in 

Federal Register Notice Vol. 84, No. 88, “OMB is currently reevaluating the appropriateness of 

the use of the CPI-U for annual adjustment in the OPM.”24 A separate Interagency Technical 

Working Group convened by OMB took up this work. In addition to this specific issue, that group 

is evaluating the strengths and weaknesses of the methodologies of different consumer inflation 

measures, including the CPI-U, and is expected to provide a report to the Chief Statistician. As 

that report has yet to be delivered to the Chief Statistician, the Evaluating Alternative Measures 

of Poverty Working Group was unable to use their work as an input to the discussion. Further 

information on the Evaluating Alternative Measures of Poverty Working Group’s considerations 

on price indices is available later in the report (see Updating Thresholds Over Time).

 23 https://www.bls.gov/opub/hom/cex/pdf/cex.pdf 24 Available at: https://www.govinfo.gov/content/pkg/FR-2019-05-07/pdf/2019-09106.pdf
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Thresholds Do Not Account for Changes in Living Standards 

The experience with the OPM has shown that a threshold chosen at one point in time tends to 

remain unchanged decades later (indexing notwithstanding), whether living standards change 

dramatically or not. A commonly expressed concern with the OPM poverty thresholds is that they 

have not adequately accounted for the improvements in living standards that have occurred in 

the half century since they were set.

Thresholds Do Not Account For Geographic Price Differences 

The OPM poverty thresholds are the same in all 50 states, though the poverty guidelines that 

determine benefit eligibility for many programs are higher for Alaska and Hawaii.25 A largely 

uniform set of thresholds does not account for the differential level (or change) in cost of living 

across geography. For example, there is considerable geographic variation in housing costs, with 

areas such as New York City and the San Francisco Bay Area having housing costs several times 

those of many other areas. The OPM poverty thresholds do not adjust for geography. Whether 

poverty thresholds should adjust for these differences is the subject of debate, in part because 

these adjustments do not account for variation in local amenities.

26

The Adjustment for Family Size and Composition 

The relationship between poverty thresholds for families with different numbers and ages of 

people, known as the equivalence scale, is widely criticized in the OPM. The scale implicit in the 

official poverty thresholds suggests children are costlier than adults in some cases and does not 

exhibit diminishing marginal increments for additional individuals over the whole range of family 

sizes. For example, the second child in a two-parent family adds much more to the OPM 

thresholds than the first or third child. Not only are the adjustments for increasing family size 

irregular in pattern, they actually increase as family size rises, implying that families of four or 

more, for example, are able to take advantage of fewer economies of scale than are families of 

two or three.

27

Association with Other Measures of Material Well-Being 

Past work has often found that income poverty is only weakly associated with measures of 

material well-being. Mayer and Jencks (1989) found a weak relationship between the level of 

income relative to poverty thresholds and whether families could afford food, housing and 

medical care. A difficulty with interpreting this evidence is that it is unclear how strong a 

relationship should be expected between economic well-being measures and income poverty. To 

interpret the strength or weakness of the associations, Meyer and Sullivan (2003, 2007, 2011a, 

2012a), Charles et al. (2006), Bavier (2008) and Fisher et al. (2009) compare the association 

between income and other measures of material well-being and the association between 

consumption and the same measures of material well-being. The papers using CE data find that

the association between low consumption and low material well-being is stronger than the 

association between low income and low material well-being, while the evidence from other 

sources is mixed. A more complete discussion is provided in a later section. The most relevant 

comparisons come from Meyer and Sullivan (2012a) who use the CE Interview Survey with fairly 

complete measures of income and consumption. They find that households added to poverty

 25 The poverty guidelines can be found at https://aspe.hhs.gov/poverty-guidelines 26 See National Research Council (1995), Kaplow (1995), Glaeser (1998, 2011) and Renwick (2019). 27 For example, see Ruggles 1990, p. 72.
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counts by the consumption-based measure are less likely to have health insurance, and have less 

education, smaller and less expensive cars, fewer household appliances and amenities, are less 

likely to own a home, have smaller homes and lower assets. However, it is worth noting that 

while the range of material well-being indicators available in the CE is broad, it is far from 

comprehensive. These results are also not informative about how low income or low 

consumption is related to later educational or health outcomes, crime or earnings, for example.

Comparisons of association with well-being measures are not the only way of validating a poverty 

measure. In certain situations that are particularly relevant for these analyses, decisions that 

improve a poverty measure may nonetheless lead to a weaker association with well-being 

measures. Means-tested transfer program application and recertification procedures are 

specifically designed to identify the disadvantaged either through their rules and screening 

process or through the self-selection process in individuals’ decision to apply (Nichols and 

Zeckhauser 1982). Many in-kind benefits like SNAP and housing benefits are examples. In this 

case, recipients of these programs should be expected to be worse off than other people who 

may seem similar based on the imperfectly measured information on their income or 

consumption. There also may be confounding variables that need to be accounted for such as 

age. These factors should be carefully considered when determining whether or how to validate a 

component of a poverty measure based on comparisons of association with well-being measures.

The Supplemental Poverty Measure 

History of the Supplemental Poverty Measure as Implemented 

Between 1999 and 2010, the Census Bureau produced estimates based on the recommendations

of Measuring Poverty that included estimates for eight variations of an alternative poverty 

measure with thresholds derived from CE Interview Survey data and resources that included 

many in-kind benefits and taxes (e.g., Short et al. 1999). In 2009, the SPM Development Working 

Group was formed and in 2010 issued a series of suggestions to the Census Bureau and the 

Bureau of Labor Statistics (BLS) on how to develop the SPM.28 Their suggestions drew on the 

recommendations of Measuring Poverty and the extensive subsequent research on poverty 

measurement. The SPM had as a key goal consistency between thresholds and resources. These 

suggestions were published in the Federal Register and the Census Bureau and BLS reviewed 

comments from the public.

In 2011, the Census Bureau released the SPM. The SPM is an income-based poverty measure that 

incorporates certain in-kind benefits such as food stamps and housing assistance (but not the 

value of health insurance), reflects a post-tax measure of income, and subtracts certain expenses 

like medical out-of-pocket expenses (MOOP), work and childcare expenses. The SPM accounts for 

health insurance by subtracting spending for it from resources. The SPM thresholds are set at 1.2 

times the average over five years of CE Interview Survey data for the 30th to 36th percentiles of 

the expenditure distribution for food, clothing, shelter, and utilities by consumer units with two 

children. The 1.2 multiplier is intended to account for other basic goods and services, like 

household supplies, personal care, and non-work-related transportation. Equivalence scales and 

geographic adjustments for housing costs are applied to the reference thresholds for use in the 

production of poverty statistics. The thresholds also differ depending on whether a family is a 

renter, a homeowner with a mortgage, or a homeowner without a mortgage and they change 

 28 For more information, see https://www.bls.gov/pir/spm/spm_twg_observations.pdf.
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over time as expenditures over the past five years change. The Census Bureau has released an 

annual report (separate from the annual report on income and official poverty rates) estimating a 

poverty rate under the SPM since 2011. Another interagency technical working group is tasked 

with making improvements to the measure. In September 2020, the National Academy of 

Sciences began a Consensus Panel on Evaluation and Improvements to the Supplemental Poverty 

Measure, sponsored by the Census Bureau. The consensus report is expected in July 2022.

Current Uses of the Supplemental Poverty Measure 

The SPM is designed to provide information on aggregate levels of economic need at a national 

level or within large subpopulations or areas. It was not designed to replace the OPM but rather 

to provide an additional indicator of economic well-being to better understand economic 

conditions and trends. For a variety of reasons, the SPM is not intended to be the measure used 

to estimate eligibility for government programs or allocation of federal funds. 

One of the most common uses of the SPM has been to measure the marginal impact of particular 

income sources, government benefits and/or certain expenses on poverty rates holding all else 

the same and assuming no behavioral changes. Each September the Census Bureau publishes a 

report with tables that estimate the change in the number of people in poverty after including 

Social Security, refundable tax credits, SNAP, SSI, housing subsidies, child support received,

school lunch, TANF/general assistance, unemployment insurance, workers’ compensation, 

Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), Low Income 

Home Energy Assistance Program (LIHEAP), child support paid, federal income tax, FICA (Federal 

Insurance Contributions Act tax), work expenses and medical expenses. 

The SPM has been used extensively in more recent academic research on poverty in the United 

States. Although Census Bureau estimates of the SPM go back only as far as 2009, outside 

researchers have extended SPM estimates to 1967.

29

Concerns with the Supplemental Poverty Measure 

Resources 

The SPM resource definition includes not only money income, but also tax credits such as the 

Earned Income Tax Credit and the child credit, and the value of some in-kind benefits. In addition, 

the resources concept subtracts income tax liabilities, payments for child support, childcare and 

other work expenses, and out-of-pocket medical expenses.30 Thus, conceptually these changes 

lead the measure of resources to more closely approximate resources available for consumption 

than pre-tax money income does. Also, by including tax credits and in-kind transfers, the SPM is 

intended to gauge more accurately the effectiveness of anti-poverty efforts.

While the conceptual advantages of these changes are clear, other changes have been 

challenged, particularly the subtraction of MOOP from income. Even the authors of Measuring 

Poverty reported, “The issue of how to best treat medical care needs and resources in the 

poverty measure has bedeviled analysts since the mid-1970s . . . Yet after almost two decades of 

experimentation, there is still no agreement on the best approach to use.” (National Research 

 29 Fox et al. (2015). 30 In 2010, the CPS ASEC added questions so that it could estimate these expenses subtracted from income, but this 

information is not available historically.
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Council 1995, p. 223-4). On the one hand, large out-of-pocket medical expenses resulting from 

poor health can drain family resources. On the other hand, these expenses can arise because 

families choose to allocate resources towards health, purchasing expensive health insurance or 

electing to have procedures that are not fully covered by insurance (see the dissent to Measuring 

Poverty, National Research Council 1995, p. 385-390). Empirically, MOOP is roughly a constant 

share of overall spending (Aguiar and Bils 2015)—on average it rises with income at the same 

rate as other spending so it is not clear that it should be treated differently. Thus, MOOP does not 

have the characteristic commonly used to define a necessity, a low-income elasticity. In addition,

the SPM does not, nor was it designed to, capture the large and growing value of health 

insurance provided to the low-income population, aside from its effect on out-of-pocket spending 

(CEA 2014, Korenman et al. 2016, Burkhauser et al. 2019, CEA 2019, National Academies of 

Sciences 2019).

While the conceptual advantages of including in-kind benefits are clear, the SPM misses a 

substantial share of these in-kind benefits due to misreporting.31 Shantz and Fox (2018) compare 

administrative records for the SNAP and TANF to CPS ASEC survey responses. They find that 43 

percent of SNAP recipients and 62 percent of TANF recipients in the administrative data do not 

report those benefits in the survey. They calculate how much misreporting biases estimates of 

the SPM, which includes cash welfare (such as TANF) and in-kind benefits (such as SNAP, which is 

not included in official poverty estimates). Despite the high false negative rate, they do not find a 

statistically significant effect on the SPM rate from TANF under-reporting. However, SNAP underreporting biases the SPM estimate upward by 0.5 percentage points or about 4 percent.

Association with Other Measures of Material Well-Being32 

As noted in the section titled The Official Poverty Measure, past work has often found that 

income poverty is only weakly associated with measures of material well-being. However, various 

measures of poverty may highlight different dimensions of economic deprivation. In addition, 

results may suggest that deprived individuals may live in households that are neither income nor 

consumption poor. Subject to these caveats, Meyer and Sullivan (2012a), using the CE, found the 

SPM classifies the population into poor and not poor relative to the OPM (holding the size of the 

poor population the same by rescaling the thresholds) in a way that the group it calls poor is 

more advantaged on the basis of a wide range of measures.33 They report that the SPM poor 

consume more goods and services, are more likely to be covered by health insurance, especially 

private health insurance, are more likely to be a homeowner, own a car, live in a large house or 

apartment, be in a family headed by a college graduate, have substantial assets, have many

household appliances and other household amenities. Research using the SIPP suggests that this 

finding could be due to the inclusion of in-kind transfers that are a marker for substantial 

 31 Many papers have examined SNAP (or Food Stamp Program) underreporting including Moore et al. (2000), while 

Meyer, Mok and Sullivan (2015) also examine school meals and Meyer and Mittag (2019) examine public and 

subsidized housing. 

32 Comparisons of association with well-being measures is not the only way of validating a poverty measure. See the 

paragraph in the section titled Association with Other Measures of Material Well-Being for more information. 33 Since Meyer and Sullivan were using the CE they were not able to fully implement the SPM measure. The SPM-like 

measure used in their analysis lacks some in-kind benefits and did not include geographic adjustments. This result that 

the SPM poor are more advantaged than the OPM poor was also confirmed in analyses using recent data from the SIPP 

that also did not incorporate geographic adjustments (Curran et al. 2020). For the association of the SPM geographic 

adjustment and other geographic adjustments with economic well-being, see the geographic adjustment section 

below.
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disadvantage, while not correcting for errors in other income sources (Meyer, Wu, Mooers, and 

Medalia, 2021).

Updating Mechanism for Thresholds 

The SPM updates the thresholds based on recent spending on food, clothing, shelter and utilities 

(FCSU), as opposed to all spending, or to changes in income or consumer prices only. Thus, the 

SPM is not an absolute measure of poverty because the value of the thresholds changes in real 

terms over time. It is also not a purely relative measure of poverty, because the value of the 

poverty thresholds does not change proportionally with a change in a point in the distribution of 

income (like the median). In practice, this updating could result in changes in the poverty rates 

that result from changes in income or changes in the thresholds. To understand what role 

economic conditions or policy had on the poverty rate, one needs to understand how these 

factors affect the underlying distribution of FCSU expenditures, as well as how they affect 

income. There is also concern that like any relative measure, in a deep recession, SPM poverty 

thresholds might fall and result in declining poverty rates during a time of increasing economic 

hardship.34 The SPM attempts to mitigate this problem by using a five-year average of changes in 

spending on FCSU rather than changes in overall consumption expenditures or income.35

Alternative Measures of Poverty Recommended by the Working 

Group This section of the final report of the Working Group details the Working Group’s considerations 

and consensus recommendations for resource measures and thresholds. At a high level, the 

Working Group recommends: 

Recommendations:

1. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics begin development of the recommended measures as soon as possible and 

that the research measures be published as soon as possible thereafter. 

2. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics engage with stakeholders and other experts throughout the development of 

the recommended measures, including soliciting additional public comment as needed. 

In particular, the Working Group recommends expert input through a National 

Academy of Sciences panel. 

3. The Working Group recommends that an advisory structure, such as a new Interagency 

Technical Working Group, be established to engage with the Census Bureau and the 

 34 The Measuring Poverty panel acknowledged that the updating method, using several years of FCSU spending data, 

could exacerbate the problem when there are downturns in the economy, but argued that this updating method better 

represents more recent standards of living than an absolute threshold set in the distance past and only updated by 

price changes (National Research Council 1995, p. 103, 329; also see Blank 2011 regarding this issue). 

35 Research is underway at the BLS to understand how changes in the economy, among other factors, impact the 

expenditures underlying the thresholds. 
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Bureau of Labor Statistics on issues related to implementation of the detailed 

recommendations throughout this report and other new issues that arise.

More detailed recommendations for resource measures and thresholds are within the following 

sections. 

Considerations and Consensus Recommendations for Income- and Consumptionbased Resource Measures 

This section details the Working Group’s considerations for income- and consumption-based 

resource measures, as well as the consensus recommendations. At a high level, the Working 

Group recommends:

Recommendations:

4. The Working Group recommends that the Census Bureau develop and publish two new 

sets of research measures of income-based resources, one that includes a value of 

health insurance and one that does not.

5. The Working Group recommends that the Bureau of Labor Statistics develop and 

publish two new sets of research measures of consumption-based resources, one that 

includes a value of health insurance and one that does not. 

6. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics use, where available and when appropriate, administrative data to 

supplement or replace the use of survey data for developing the recommended 

measures. 

7. The Working Group recommends that the Census Bureau should attempt to produce 

the new income-based resource measures within 12 months of survey data collection. 

For example, estimates for calendar year 2020, for which data are collected in 

February-April 2021, should be made available by March 2022. The Working Group 

recognizes that in the first few years of implementation these time lags may be 

greater, but expects that over time the lags will decrease. The Bureau of Labor 

Statistics may be able to produce consumption resource measures with less of a lag.

Detailed recommendations are available throughout the rest of this section within the context of 

the considerations of the Working Group.

In considering how to define income and consumption for poverty measurement, research as 

well as national and international guidance and recommended standards have been considered.

Specific research studies are highlighted later in this report. Regarding national and international 

guidance and standards, documents consulted include those produced by the International 
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Labour Organization (2003, 2004), Organization for Economic Cooperation and Development

(2013), United Nations (UN) Canberra Group (2001), and UN Economic Commission for Europe

(2017).

The Working Group recommends income measures that expand beyond pre-tax cash income to 

include some in-kind transfers and account for taxes and tax credits, much like the SPM resource 

measure. These measures should also integrate administrative data with household survey 

income information, taking advantage of recent research on the use and the increased availability 

of potentially more accurate administrative data. There are many potential versions of such 

resource measures depending on how the administrative data are used: whether they are used 

to replace survey reports, supply missing survey reports, combined with survey reports, or used 

in imputation. The strategies will likely differ across income sources. While the Working Group

discussed other data sources for income, the Working Group focused on the CPS ASEC and the 

ACS. In addition to monetary income and in-kind transfer benefits, the Working Group 

recommends incorporating implicit income flows from owner-occupied housing and vehicles, as 

well as from assets and liabilities while recognizing the limited data available in the CPS ASEC and 

ACS to derive estimates of these flows.

The Working Group also recommends poverty measures that are based on consumption. Such 

measures may more directly capture the resources available to a family if they record the 

consumption that was actually achieved. These measures begin by summing expenditures on 

most categories of goods and services. A flow of consumption resources is also typically 

attributed to owner-occupied housing and vehicles. Since it is the only comprehensive and 

nationally representative source of U.S. consumption data, the Working Group has focused on

the CE Interview Survey.

In evaluating alternative resource measures, the Working Group has been guided by how well 

existing and potential alternatives may achieve the goals of a poverty measure described earlier.

In particular, how well do the measures capture economic deprivation at a point in time, changes 

in deprivation over time, and the effects of policy? In addition, the Working Group implicitly was 

guided by some of the criteria noted in Measuring Poverty, including using scientific evidence, 

best judgement, understandability to the public, that the concepts underlying thresholds and the 

definition of resources be consistent, and feasibility of implementation.36

Income-based Resource Measures Using the CPS ASEC and ACS 

An alternative income-poverty measure should include a more expansive definition of income 

than the pre-tax cash concept currently used for the OPM. The Working Group has considered

whether and how a resource measure should account for the following: taxes paid; tax credits 

received; many of the in-kind benefits such as SNAP, WIC, housing assistance, and energy 

assistance provided to disadvantaged families and individuals; the incorporation of health 

insurance benefits; and implicit flows from non-financial assets (i.e. vehicles and owner occupied 

housing), as well as annuity-type flows of income from the stock of other assets and liabilities (as 

 36 “Our recommended changes are based on the best scientific evidence available, our best judgement, and three 

additional criteria. First, a poverty measure should be acceptable and understandable to the public. Second, a poverty 

measure should be statistically defensible. In this regard, the concepts underlying the thresholds and the definition of 

resources should be consistent. Third, a poverty measure should be feasible to implement with data that are available 

or can fairly readily be obtained.” (National Research Council 1995, p. 3).
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opposed to only the interest earned on savings, for example, which is already included in 

monetary income).

In addition to concerns about what to include in resources, there is growing concern about the 

quality of survey-based estimates of cash and in-kind benefits. The Working Group explored

methods to correct for the potential misreporting of money income and in-kind benefits. One of 

the most promising of these methods is the incorporation of administrative data. Another issue is 

to determine whether current surveys provide sufficient data to produce implicit income flows 

from non-financial assets, as well as from financial assets and liabilities. When they do not, 

recommendations are needed for the collection of such data, for example, by adding questions to 

the surveys or from administrative records. 

Adjustment to resources for child support obligations 

The last half century has seen an increase in the number of noncustodial parents living in 

households away from their children and in the number of persons making child support 

payments. Resources are transferred from one household to another when child support 

payments are made. These transfers potentially create accounting inconsistencies if child support 

payments are included in the resource totals for the recipient households and are not deducted 

from the resource totals of the providers of those payments. This results in the double counting 

of resources because once the child support payments are transferred away from the 

nonresident parents those transfers are no longer available for consumption by the households 

making the child support payments. One way to avoid double counting these resources is to 

subtract child support payments from the resource totals of the noncustodial parents and to 

include the payments in the income totals of the recipient households. This subtraction is 

currently being made with the SPM but is not made with the OPM.

37

Recommendations:

8. The Working Group recommends subtracting child support payments from resource 

totals for income-based measures. No adjustments are required for child support 

payments for consumption-based measures because the transfer of income from one 

family to another is indirectly captured through increased consumption of families 

receiving the transfers and decreased consumption among families with nonresident 

parents making child support payments.

Adjustment to resources for work and childcare expenditures 

Another important methodological issue for poverty metrics is the treatment of expenditures 

needed for employment. Since its implementation in 1969 the OPM has not made any 

adjustments for childcare and other work-related expenditures in its calculations but these costs 

are sometimes steep enough to discourage work among low-income families. Measuring Poverty

noted that almost all jobs require workers to use a part of their earnings to pay for expenditures 

such as union dues, licenses, permits, tools, uniforms, and transportation to and from places of 

employment. Many families with young children also need to purchase childcare to enter the 

 37 The Working Group supports including child support payments received in the resource totals as is currently done in 

both the OPM and SPM calculations.
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workforce. Out-of-pocket expenditures for childcare vary considerably across situations, but can 

be very high for many families.

As stated in Measuring Poverty, “Just as income used for taxes is not available for consumption, 

neither is the amount of earnings devoted to work expenses; hence, such expenses should not be 

counted as family resources” (National Research Council 1995, p. 70). The Census Bureau collects 

data on work-related expenditures on the SIPP and uses this information to impute these 

expenditures onto the CPS ASEC to be used as a part of its calculation of SPM poverty. To 

compute the SPM poverty rate, a flat deduction representing 85 percent of median weekly workrelated expenses is subtracted from family resources for all individuals based on the number of 

weeks they reported working over the year in the CPS ASEC. Additional questions were added to 

the CPS ASEC to directly collect information on childcare payments. These expenditures are 

subtracted from the resources used to calculate the SPM poverty rate. The combined value of 

childcare and work expenditures is capped at the reported earnings of the lowest earning 

reference person or his or her spouse/partner.38

Recommendations:

9. The Working Group recommends that expenditures needed to work including childcare

expenditures be subtracted from family resources for any new recommended poverty 

measures and that total childcare expenditures be capped because it is difficult to 

distinguish between childcare necessary to work versus other childcare. 

10. The Working Group recommends continued work to improve measurement of 

childcare and other work-related expenditures and methods to cap them.

Income should be defined more broadly than pre-tax cash income currently used for the OPM 

Post-tax income

While the OPM uses pre-tax cash income as the resource definition, there is a strong case that 

after-tax income should be used in an alternative resource measure because taxes paid are not 

available for consumption and tax credits received are available for consumption. In fact, many of 

the federal anti-poverty resources are delivered through the tax system, such as the Earned 

Income Tax Credit (EITC). For example, in tax year 2016 the U.S. spent $61 billion on the 

refundable portion of the EITC and $20 billion on the refundable portion of the Additional Child 

Tax Credit (ACTC), which are mainly targeted at families with children that have low to moderate 

earnings (Falk et al. 2018). The SPM subtracts Federal and State income taxes and payroll taxes 

from income, accounting for tax credits. It would be possible to consider subtracting estimates of 

property and sales taxes paid but it could only be done with substantial error.

 38 Fox (2019) notes that “Some analysts have suggested that this cap may be inappropriate in certain cases, such as if 

the parent is in school, looking for work, or receiving types of compensation other than earnings.”
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Recommendations:

11. The Working Group recommends that income be adjusted, as feasible, for federal, 

state and local income and payroll taxes and credits in a new income-based resource 

measure.

Incorporating in-kind transfers including SNAP, WIC, housing assistance, school meals, LIHEAP

Two of the measurement goals this Working Group considered were to capture the most 

economically disadvantaged people at a point-in-time and to indicate how the number of 

economically disadvantaged people changes over time. To address these goals, it is often agreed 

that all resources available to an individual should be included. Otherwise, individuals who 

benefit from omitted resources will be more likely counted as poor, holding constant total 

resources. This concern was clearly articulated by the United Nations Statistics Division’s 

Canberra Group’s international guidelines for income definitions, which stated that “despite the 

attraction and convenience of using cash income data only, this measure falls short of valuing the 

economic resources enjoyed by the household. Of particular concern is the fact that the relative 

mix of cash and non-cash income may differ significantly across population groups” (Canberra 

Group 2001, p. 25). Moreover, changes in the availability of the omitted resources to the 

population over time will not be reflected in poverty trends.39

While cash welfare assistance to low-income families has shrunk in the past 25 years, in-kind 

benefits have grown and are now much more important for low-income families than cash 

assistance. In 2016, the U.S. spent $70 billion on the SNAP and $36 billion on housing assistance 

(Section 8 Housing Choice Vouchers, Section 8 Project-Based Rental Assistance and Public 

Housing) compared to $8.7 billion for the TANF cash assistance program (Falk et al. 2018). For 

income and poverty measurement, the challenges are assigning an appropriate dollar value to 

these in-kind benefits and making decisions about precisely which benefits to include. The SPM 

includes in its resource measure benefits from SNAP, the National School Lunch Program, WIC, 

LIHEAP, and rental housing assistance.

Recommendations:

12. The Working Group recommends that a new income-based resource measure include 

as resources in-kind benefits from SNAP, school meals, WIC, LIHEAP, and rental 

housing assistance.

Valuing health insurance and the treatment of MOOP

Note: This section on valuing health insurance provides for the considerations of inclusion for 

both an income- and consumption-based resource measure. 

 39 Recognizing practical measurement issues of measuring in-kind transfers in some countries, the operational 

definition of income from the Canberra group excludes these transfers while encouraging countries to still value them

at times since these transfers are important for welfare analysis (see Canberra Group Handbook, 2nd Edition, p. 16 and 

46, https://www.unece.org/fileadmin/DAM/stats/groups/cgh/Canbera_Handbook_2011_WEB.pdf).
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This section (i) reviews possible approaches for valuing health insurance, (ii) reviews possible 

approaches for the treatment of medical out of pocket expenditures (MOOP), and (iii) presents 

the Working Group’s recommendations on the valuation of health insurance.

Valuing health insurance

In 2017, health insurance covered $2.6 trillion in health expenditures (CMS 2019). Despite the 

large and increasing amount of resources spent on health insurance, the value of employer and 

government provided health insurance is not captured in the OPM. It is also not captured in the 

SPM, except to the extent that health insurance changes out-of-pocket expenses on health care

and health insurance premiums. Experimental measures produced by the Census Bureau in the 

1980s (e.g., Smeeding 1982; U.S. Census Bureau 1988) and recent research has incorporated a 

value of health insurance in poverty measures (Short and Garner 2002; Meyer and Sullivan 

2012b; Korenman and Remler 2016; Winship 2016; Burkhauser et al. 2019; CEA 2019; National 

Academies of Sciences 2019).

40 In addition, the Congressional Budget Office (CBO) and academic 

researchers have included a value of health insurance for the related (but not identical) exercise 

of analyzing income distribution trends (CBO 2018; Burkhauser et al. 2012, 2013; Armour et al. 

2013; Larrimore et al. 2015; and Kaestner and Lubotsky 2016).

One challenge when incorporating health insurance into a poverty measure is determining its 

value. The Working Group explored different ways to reflect the ex-ante value of health 

insurance, or its value prior to knowledge about injury or sickness in a given year. This approach 

helps to avoid the problem of making the injured and sick or those with greater medical needs 

look less poor, which is a larger concern with an alternate approach of simply including as a 

resource all medical spending incurred on behalf of an individual through insurance in a given 

year. However, even the ex-ante value of health insurance can make individuals with disabilities 

or longer-term health conditions look less poor since they require more health care and thus 

typically consume more health insurance. Thus, the Working Group further explored ways to 

ensure health insurance values were not conditioned on disability status or other health 

conditions. 

The Working Group considered various approaches for valuing health insurance. The key 

approaches considered included: (i) using the full market value of health insurance; (ii) using a 

lower value of health insurance reflecting research that estimates a willingness to pay below 100 

percent of its full market value; (iii) using a fungible value of health insurance; and (iv) placing a 

zero value on health insurance. Each of these measures could be implemented with an exclusion 

from resources of all or some MOOP to reflect that such amounts are not available for non-health 

consumption. It is worth acknowledging that none of these approaches would perfectly capture 

the situation of all individuals—it is a question of choosing a strategy that approximates the value 

of health insurance well for most people. The Working Group also discussed recommending 

multiple poverty measures incorporating different values of health insurance. 

 40 For Census Bureau reports in the 1980s that incorporated a value of health insurance, see for example the report of 

the Conference on the Measurement of Noncash Benefits held in Williamsburg, Virginia in December 1985.

https://www.census.gov/library/working-papers/1985/demo/measurement-conf.html. There were a series of technical 

working papers (50, 51, 52, 55, 56, 57, and 58) published between 1982 and 1988 that discussed alternative 

approaches. After 1988 the Census Bureau began to publish the Research and Development (P-60-RD) series that 

introduced the concept of the fungible value approach for valuing Medicare and Medicaid benefits. See for example, 

https://www2.census.gov/library/publications/1988/demographics/p60-164-rd-1.pdf.
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Market Value

One possible value is the full “market value” of health insurance. The market value is the total 

price of health insurance (or comparable health insurance) in the private market. An income 

resource measure would include only the value of the employer paid premium for employmentbased coverage, and the full value of government-provided coverage for Medicaid, Medicare and 

subsidized direct purchase plans (net of premiums). A consumption resource measure would 

include both the employer paid premium and employee paid premium for employment-based 

coverage, and the full value of government provided coverage along with any individual paid 

premium for Medicaid, Medicare and direct purchase plans.

Some past research assigned market values using estimates based on insurance claims payments 

(net certain other payments and costs) (Smeeding 1984; Ward 1985). More recently, Meyer and 

Sullivan (2012b) and Burkhauser, Larrimore, and Simon (2013) estimate the market value of 

public health insurance (Medicaid and Medicare) as the average cost of providing the insurance 

for broad risk classes, and they separately estimate the market value of employer coverage based 

on external surveys covering health insurance costs. The specific approach taken by Burkhauser, 

Larrimore and Simon (2013) has been used since 2012 by CBO for analysis of income distribution 

trends, and has been used by Burkhauser et al. (2019) and CEA (2019) for the analysis of poverty 

trends.41 Other recent research has used the value of “benchmark” plans (Scholz, Moffit, and 

Cowan 2009; Korenman and Remler 2016). For example, Scholz et al. (2009) use the average cost 

for a fee-for-service plan (for Medicare) and the cost of a typical HMO policy (for Medicaid) as the 

base plan prices for measuring the market value of health insurance. Alternatively, Korenman and 

Remler (2016) define the value of Medicaid and private health insurance coverage using the 

second lowest-cost Basic Silver Plan on the Affordable Care Act exchange and define the value of 

Medicare as the average cost of Medicare Advantage-Prescription Drug (MA-PD) plans. Remler 

and Korenman's (2020) alternative full-income poverty measure limits health resources to 

covering health needs alone and shows that the absolute size of health need over time (and 

trends) is heavily dependent on the choice of price index.

Advantages/Disadvantages

The primary advantage of the market value approach is that it places a value on public health 

insurance that is equal to what people could obtain in the private market. This is consistent with 

the approach for valuing housing and food subsidies. In addition, the market value approach can 

accommodate “community rating” under the Affordable Care Act (ACA) of health insurance plans 

purchased on the health insurance exchanges, which allocates to recipients the same health 

insurance value regardless of their health conditions and thus, does not make people who need 

more care look less poor. This can be implemented by tying health insurance values to ACA 

exchange plans, or by assigning to recipients the average costs of public health insurance without 

conditioning on disability status or other health conditions. 

 41 Until the series was discontinued in 2015 due to data availability issues, the Census Bureau provided estimates of the 

market value of health insurance for those covered by Medicaid or Medicare (U.S. Census Bureau 2015) using six risk 

classes. Estimates of the market value of employer-provided health insurance were also removed from the CPS ASEC 

file effective 2019 as part of a larger overhaul of the CPS ASEC processing system. Based on research (Janicki, O’Hara, 

and Zawacki, 2013; Berchick and O’Hara, 2017), the analysts determined that the quality of this synthetic variable was 

not sufficient for a Census Bureau data product so the series was discontinued. For more information see 

https://www.census.gov/programs-surveys/cps/data-detail/fungible-values.html.
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Valuing public health insurance at its market value, however, also has disadvantages. First, the 

market value likely does not reflect the true value individuals place on public health insurance.42

People may be willing to pay substantially more or substantially less than market value for health 

insurance and market value estimates may be unrealistically high relative to the coverage lowerincome recipients can or would purchase (U.S. Census Bureau 1988). Others may choose to forgo 

purchasing insurance and health care altogether based on health status, tolerance for risk, or 

other factors and preferences (Kaestner and Lubotsky 2016). 

There are also practical hurdles for calculating a market value of health insurance. In the case of 

public health insurance this approach relies on obtaining a value of comparable health insurance 

plans in the private market. For example, Korenman and Remler (2016) assume that Medicaid has 

comparable quality to the second lowest cost silver plan on the health insurance exchanges, and 

Scholz, Moffit and Cowan (2009) value Medicaid at the cost of a “typical” HMO policy and 

Medicare at the average cost of a fee-for-service plan. These assumed values do not necessarily 

equal the market value of Medicaid or Medicare coverage, because for example, the quality of 

Medicaid coverage may differ from the quality of the second lowest cost silver plan. Another 

approach is to use the average cost of providing public health insurance for different risk classes, 

such as in Meyer and Sullivan (2012b) and Burkhauser et al. (2013). This approach may not reflect 

the actual (unsubsidized) price recipients would face for comparable plans available for purchase 

on the private market, in part because such plans may not exist. Employer premiums are not 

currently available in the relevant household income and expenditure surveys and so they would 

need to be imputed. Some research imputes these premiums based on worker or firm 

characteristics (U.S. Census Bureau 1993, Burkhauser et al. 2013, CBO 2012, Janicki et al. 2013).

In other research the total value of health insurance is based on some common baseline plan and 

employer premiums are calculated by netting out premiums paid by the employee, 

operationalized in the same way as public health insurance (Korenman and Remler 2016). 

Willingness to pay

Valuing health insurance based on a willingness to pay approach starts from the idea that many 

individuals would not be willing to pay the full market price for insurance if they had to pay for it 

out of pocket. Willingness to pay estimates reflect the amount of money a consumer would be 

willing to pay to have health insurance coverage, which may be much lower or much higher than 

market value. For example, survey-based research shows that the uninsured value health 

insurance much lower than the insured (Friedberg et al. 2008), and the uninsured, younger 

adults, Hispanics, adult males, and those with lower incomes were more likely to agree with the 

statement “health insurance is not worth the money it costs” (Cohen 2013). To the extent that 

these groups already have or gain health insurance, using the full market value may overstate 

their willingness to pay for the health insurance coverage they receive. Alternatively, a large 

 42 There is more reason to believe that private health insurance is valued near the market value. If the average 

employee valued insurance at less than the market value, the employer could forego offering insurance and instead 

provide employees with higher wages that are above the employees’ average valuation but below the employer’s 

average costs, and both would be better off. The fact that in general employers do not forego offering health insurance 

that on average employees do value insurance at or above the market value. At the same time, tax advantages and 

employer incentives to promote employee wellness could allow for employee valuation of health insurance to fall 

below its market value and, if given the choice, some employees still may prefer higher wages over health insurance 

coverage suggesting that some may value the health insurance coverage below market value.
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family may value employer-provided health insurance above the market cost if their family 

coverage costs do not rise with the number of children. 

Empirical estimates of the willingness to pay for health insurance have been based on: 

randomized evaluations from the 2008 Oregon Health Insurance Experiment (Finkelstein, 

Hendren, and Luttmer 2019a); regression discontinuity analysis of data from the Massachusetts’ 

Subsidized Insurance Exchange (Finkelstein, Hendren, and Shepard 2019b); structural models of 

labor supply in response to changes in Medicaid policies (Meyer and Rosenbaum 2000, Gallen 

2015); and a survey experiment conducted to elicit willingness to pay for hypothetical plan 

offerings among the uninsured (Krueger and Kuziemko 2013).

Studies have consistently found that recipients value Medicaid below the amount it costs the 

government to provide the insurance (Smeeding 1982, Gallen 2015, Finkelstein et al. 2019a, and 

Finkelstein et al. 2019b) and research on the individual exchange market finds that across the 

lower end of the income distribution (individuals up to 300 percent of poverty) willingness to pay 

for private insurance is lower than both the expected cost of an individual beneficiary and the 

average cost over all beneficiaries (Finkelstein et al. 2019b). Overall, researchers have found that 

the value recipients place on Medicaid and plans on the exchanges falls below its market value at 

approximately $0.30 to $0.50 on the dollar (Smeeding 1982; Gallen 2015; Finkelstein et al. 2019a; 

and Finkelstein et al. 2019b). 

Additional studies of employer-provided insurance analyze willingness to pay by weighing the 

fundamental tradeoff between the cost of health insurance benefits and foregone wages. Royalty 

(2008) finds that recipients (both those with individual plans and family plans) are not willing to 

trade off a dollar in wages to receive an additional dollar in health insurance. More relevant for 

valuing health insurance as a whole, Freiberg et al. (2008) finds that those employees who 

currently have coverage are willing to pay more than its cost, and those without coverage are 

willing to pay less than its cost (Friedberg et al. 2008). 

Overall, among the lower-income population, willingness to pay below market value is likely 

based on multiple factors including: overall income constraints among the lower-income 

population; an undervaluing based on uninsured individuals paying less than full price for 

consumed health care (Herring 2005; Kaestner and Lubotsky 2016); and because some uninsured

individuals can access informal care such as the use of emergency rooms for acute care at no cost 

to themselves (Garthwaite et al. 2018). In fact, Finkelstein et al. (2019b) find that for the lowerincome population (those below 300 percent of poverty), estimates of uncompensated care for

the uninsured are roughly close in magnitude to the gap between the full value of health 

insurance coverage and estimates of the willingness to pay for health insurance coverage. 

However, Herring (2005) and LoSasso and Meyer (2006) find considerable variation in availability 

of highly subsidized and uncompensated care across markets in the U.S., and so incorporating 

into a resource measure the value of implicit insurance due to the availability of uncompensated 

care would be difficult.

Existing studies have rarely incorporated a health insurance value based on a willingness to pay 

concept into poverty measures. However, Meyer and Sullivan (2012b) capture a fundamental 

property of the willingness to pay concept that health insurance values should be lower for those 

with fewer resources. In fact, lower-income individuals and families tend to spend less on almost 

all purchased goods, including health care, compared with those at higher income levels (Besanko 
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and Braeutigam 2011, Aguiar and Bils 2015). Thus, when individuals at the lower end of the 

income distribution receive a large subsidy for any good (such as health insurance), the Working 

Group would expect them to value it less than individuals with higher income, as long as the 

subsidy is larger than the amount they would have paid for the good on their own. The Meyer 

and Sullivan (2012b) approach was to cap the market value of health insurance at a set fraction of 

total resources. Specifically, they limited health insurance to be one-third of total resources, 

defined by these researchers as consumption. This approach meant that the cap on the share of 

total resources attributed (usually imputed) to health insurance coverage rose with consumption, 

but never could be the bulk of resources. There is nothing intrinsically meaningful about the onethird cap—another share could be chosen—and another resource measure could be chosen, like 

income, to determine the share.43

Advantages/Disadvantages

A key advantage of the willingness to pay approach is that it reflects the actual value individuals 

place on having health insurance coverage, as opposed to the full market value of insurance 

which may overestimate its value for lower-income individuals. Because willingness to pay 

estimates tend to be lower for lower income individuals, this is particularly relevant for poverty 

estimates and analyses. Another advantage is that due to the availability of “natural 

experiments” this approach includes empirically rigorous studies for some populations and 

programs.

In practice, however, willingness to pay can be difficult to estimate with accuracy because it 

requires establishing a counterfactual that is not always directly available: namely, the amounts 

individuals would be willing to pay for health insurance coverage when in fact they do not 

purchase it. Further, while empirical results are available for the specific insurance coverage 

programs/options, subpopulations, and geographic locations described above, these estimates 

may not generalize to the broader U.S. population as a whole, or to future years when the nature 

of medical care and public policies may change. In addition, willingness to pay may 

underestimate the value of public health insurance to recipients if they believe they can access 

uncompensated care when uninsured, which as noted above, may be difficult to incorporate.

The specific approach taken by Meyer and Sullivan (2012b) has its own advantages and 

disadvantages. The advantage of this approach is that it is straightforward and captures the idea 

that the value of health insurance should grow with resources, but that individuals when given 

the choice, consume many other goods and services in addition to health care. The disadvantage 

of this approach is that a body of evidence does not currently exist to support any specific 

functional relationship between the value of health insurance and the level of income or nonhealth consumption.

Fungible value

Another approach for valuing health insurance is to use its “fungible value,” or the amount of 

resources assumed to be freed up for other, non-medical consumption due to health insurance 

 43 The one-third fraction was chosen by Meyer and Sullivan because health spending was about one-sixth of total 

consumption at the time, so they took twice that to be a reasonable cap. The Current BEA PCE data indicates that this

now have gone well beyond a one-sixth share; health spending (public and private) was 21.1 percent of total personal 

consumption expenditures in 2019. See 

https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=2#reqid=19&step=2&isuri=1&1921=survey.
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coverage. The fungible value approach attempts to capture the extent to which individuals who 

are covered would have purchased coverage on their own, and thus, the extent to which the 

provision of insurance frees up these resources for other non-medical consumption. This 

approach caps the value of health insurance in a manner that depends on income. It typically 

assumes that low-income individuals receive no value from health insurance if they lack 

additional resources to fully cover their non-health basic needs, such as food and shelter. 

The fungible value approach has generally been implemented by comparing a family’s total 

income (excluding health insurance) to some overall budget level for food and housing deemed 

minimally adequate according to some specified standard. If the family’s income exceeds the 

total specified budget for food and housing, then the valuation process assumes that 

government-provided insurance would free up the difference for other non-health purposes, 

under the assumption that the family would have otherwise purchased health insurance on their 

own with every dollar of income that exceeds the budget for food and housing. That amount of 

money (up to the market value of the health insurance) is called the fungible value. For example, 

in the former Census Bureau implementation, the fungible value of health insurance was zero for 

a family of four until the family’s income reached $25,430, the combined food and shelter budget 

deemed minimally adequate for a family of four.

44 Then as income rose, the fungible value 

approach increased the value of health insurance dollar for dollar until it reached its full market 

value. The CBO previously used the Census Bureau’s fungible value estimates for income 

distribution statistics. However, concerns that the fungible value approach undervalues health 

insurance for the lower-income population contributed to the CBO decision to shift to a market 

value approach for its income distribution trends starting in 2012 (CBO 2012).

Advantages/Disadvantages

The main advantage to using the fungible value approach is that it acknowledges the value health 

insurance has in freeing up resources for other consumption, while also reflecting that health 

insurance does not free up resources on a dollar for dollar basis for those with fewer overall 

resources. A disadvantage of this approach, however, is the implication of zero or little value of 

health insurance for households with resources near or below the amount deemed minimally 

adequate for food and housing. Kaestner and Lubotsky (2016) argue that even for those 

households with very low incomes, the true value of health insurance should be higher than its 

fungible value since health insurance makes these households better off by increasing their 

consumption of health insurance.

Zero value

The Working Group also considered poverty measures that exclude the value of health insurance, 

consistent with the approach currently used in the OPM and SPM. The SPM was designed this 

way with the intention of separating the measurement of economic poverty from the 

measurement of medical care needs and the adequacy of resources to meet those needs.45

 44 The Census Bureau’s previously used fungible value approach assumed the value of health insurance is zero for 

individuals whose sharing unit income is less than the annualized sum of the national average Fair Market Rent and the 

USDA published cost of the “Thrifty Food Plan.” In 2018, the national average annualized Fair Market Rent for a threebedroom unit was $17,726, and the annualized cost of the Thrifty Food Plan was $7,705, for a total of $25,430.

45 This is the approach adopted by the authors of Measuring Poverty but they coupled their decision with a 

recommendation that appropriate agencies should work to develop one or more “medical care risk” indexes that 

measure the economic risk to families and individuals of having no or inadequate health insurance coverage (National 

Research Council 1995, p. 225). 
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Advantages of the zero-value approach are that it avoids the major unresolved challenges of 

estimating appropriate health insurance values for different individuals and it is simple to explain.

The major disadvantage of the zero-value approach is that it understates the value of health 

insurance for most people. A family with health insurance will not be categorized as better off 

than the family with the same level of resources but no health insurance. 

Treatment of Medical Out of Pocket Expenses

In addition to valuing health insurance, another important issue is the treatment of medical out 

of pocket expenses (MOOP). MOOP includes all payments by an individual for medical goods and 

services and thus excludes health insurance premiums paid by the government or their 

employers. MOOP includes employee paid premiums for health insurance, as well as co-pays and 

deductibles required to utilize health insurance, prescription drugs, medical equipment and other 

medical goods and services. The Working Group divides MOOP into two components: premium 

MOOP and non-premium MOOP. Premium MOOP includes only employee or other individually 

paid premiums for health insurance. Non-premium MOOP includes all other medical expenses 

(e.g., co-pays, deductibles, prescription drugs, and OTC items).

The treatment of MOOP depends on the definition of resources. Consumption poverty measures 

that include a value for health insurance should conceptually treat premium MOOP as 

consumption, since premium MOOP represents consumption of health insurance. In the case of 

an income-based poverty measure, premium MOOP would be subtracted from income after 

adding the total value of health insurance (or equivalently, only the employer/government paid 

premium, and not premium MOOP, would be added to income in the first place). However, 

treatment of non-premium MOOP is less clear. On the one hand, non-premium MOOP may 

reflect unavoidable costs incurred to avoid or recover from illness or other adverse health 

conditions. Someone who becomes sick and must pay for treatment in order to return to health 

has fewer resources available for non-health consumption on items such as food and shelter. On 

the other hand, some non-premium MOOP may be discretionary such as some plastic surgery or 

cosmetic dentistry.

In an income poverty measure that does not include a value of health insurance, both premium 

and non-premium MOOP could potentially be subtracted from income to reflect what is spent on 

MOOP is not available for non-health related consumption. And in a parallel fashion, for a 

consumption poverty measure, both premium MOOP and non-premium MOOP could be 

excluded from resources. However, as MOOP often increases when people gain health insurance 

(since they may pay a premium and incur other out-of-pocket costs), the receipt of health 

insurance can lead to an increased likelihood someone is counted as poor. Meyer and Sullivan 

(2012a) find that subtracting MOOP from income without accounting for the value of health 

insurance causes a poverty measure to perform less well in identifying the most disadvantaged 

based on markers of hardship. 

When considering how to treat MOOP in a poverty measure, the Working Group considered 

concerns about discretionary spending on MOOP, inconsistent treatment of people who suffer 

the same adverse health shock but choose to consume different amounts of care, and 

inconsistent treatment of people with different insurance plans (e.g. low-premium/high-copay vs 

high-premium/low-copay plans). While concerns may exist about discretionary MOOP 

expenditures, it may be difficult or infeasible to separate out unavoidable costs from avoidable 

ones in existing datasets. Additional research is needed on these issues.
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Recommendations

The Working Group reviewed and discussed the available literature and research on health 

insurance valuation and MOOP as presented above. The Working Group had consensus 

agreement on multiple broad aspects of measuring the value of health insurance coverage as a 

resource in the context of one or more potential new poverty measures. Treatment of MOOP 

requires further consideration.

Recommendations:

13. Two sets of income resource measures and two sets of consumption resource 

measures should be produced. For each type of resource measure, one set should not 

include a value of health insurance, and the other set should include some value of 

health insurance.

The recommendation to produce alternative resource measures – one with a value of health 

insurance and one without – reflects both the importance of health insurance as a resource for 

lower-income people and the lack of academic consensus about how to value it if at all. The 

Working Group agreed that for poverty measures that include a value of health insurance, 

thresholds should be either explicitly adjusted (e.g. Korenman and Remler 2016, Remler and 

Korenman 2020) or implicitly adjusted (e.g., Meyer and Sullivan 2012b, Burkhauser et al. 2019) to 

account for the additional resources.

Recommendations:

14. The value of health insurance should not depend on the disability or health status of 

individuals.

This would ensure that those with potentially greater health needs would not be deemed “richer” 

by having insurance coverage values that were substantially higher than those who may have 

fewer health needs. Importantly, the Working Group agreed that if community-rating is not 

available at some point in the future, health insurance values assigned to individuals should still 

not depend on their health or disability status.46 For simplicity, throughout the rest of the 

recommendations, the Working Group will refer to the full market value of health insurance 

without adjustments for health or disability status as simply “market value”.

 46 Currently under the Affordable Care Act, community-rating is a rule that prevents insurers from charging different 

premiums based on disability status or other preexisting conditions. Premiums do vary based on age, geography, family 

vs. individual coverage, and tobacco usage.
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Recommendations:

15. The value of health insurance should ideally depend on a person’s resources but the 

precise method to implement this should be the subject of further research. In the 

interim, the market value of health insurance (with values that do not depend on the 

health status of the recipient) should be capped at some share of total resources.

While the Working Group agreed that health insurance values should ideally depend on a 

person’s resources, the Working Group did not come to consensus agreement on whether there 

currently exists a suitable approach for doing so. Some Working Group members concluded that 

a zero value of health insurance should be used for one version of a poverty measure, and the full 

market value of health insurance should be used for another, in recognition that trends in 

absolute poverty using a resource-adjusted value of health insurance would fall somewhere in 

between. Other Working Group members preferred using a zero value of health insurance for 

one version, and for the other measure adjusting downward the market value of health insurance 

based on resources using a method developed by an appropriate agency. The latter approach 

also recognizes the “true” value of health insurance would likely be somewhere in between a full 

market value and zero value of health insurance, acknowledging that using the full market value 

of health insurance as a resource may be much too high for many at the low-end of the resource 

distribution but that people with very few resources still place some value greater than zero on 

their health insurance.

The Working Group unanimously agreed that the appropriate agencies should research the 

feasibility of developing a method to downwardly adjust the market value, and if/when it is 

ready, it should be used for the poverty measure that includes a value of health insurance. There 

was disagreement about the appropriate interim steps. A majority of the Working Group voted 

that the best interim approach was to cap the market value of health insurance at some share of 

total resources. This approach follows the methodology developed by Meyer and Sullivan (2012b) 

discussed earlier in this section, but it would not necessarily use the same one-third share. The 

exact share would be determined by the statistical agencies producing the poverty measure or a 

future advisory Working Group. A minority voted that the best interim approach was to use the 

market value. A smaller minority voted that the best interim approach was to not produce a 

poverty measure with a nonzero value of health insurance.

Recommendations:

16. The value of health insurance should ideally not constitute a majority of resources for 

people near the poverty threshold.

The group agreed that health insurance alone should not be able to lift a family out of poverty. 

Notably, capping the value of health insurance as a share of total resources (which is the interim 

approach described in Recommendation 3) automatically guarantees this condition. As long as 
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the cap is no higher than 50%, the capping approach ensures that health insurance does not 

constitute a majority of resources for people at any fraction or multiple of the poverty threshold.

Treatment of Education

Note: This section on the treatment of education provides for the considerations for both an 

income- and consumption-based resource measure. 

In the United States during the 2016-2017 school year, $739 billion was spent on public 

elementary and secondary schools alone, excluding expenditures on private and post-secondary 

education (Hussar et al. 2020). Given the large amount of spending on education and its impact 

on students, an important question is whether it should be considered a resource in an extended

income- or consumption-based resource measure.

The question of whether to consider education as current consumption or investment in human 

capital (or a combination of the two) determines the treatment of education as a resource in an 

income or consumption measure.

47 If education is viewed as an investment in human capital, 

education would not be included in an extended income-based resource measure nor a 

consumption-based resource measure. However, if education is viewed as current consumption, 

it might be included in an extended income-based resource measure or a consumption-based 

resource measure.

Traditionally, education has been viewed by economists as an investment in human capital 

(Becker 1964; Mincer 1974; and Schultz 1961). Education is regarded as an investment in human 

capital because benefits accrue to an educated individual over a lifetime of activities (Jorgenson 

and Fraumeni 1989). A recurring theme in the literature is that the benefits of investment in 

education, such as greater earning power, are recorded in transactions within the labor market.48

On the other hand, some researchers have asserted that education may also be considered, at 

least in part, as a consumption good based on the satisfaction of those obtaining an education, 

particularly at the University level.49 If education were partially or totally considered as a 

consumption good, there is a significant challenge in valuing it in a poverty measure.50

In theory, an income resource measure would deduct out-of-pocket education expenditures on 

tuition and other educational items. However, appropriate data to do so are currently

unavailable or not of sufficient quality for this purpose. Similarly, the total value of education 

undertaken – either from education provided by others or through one’s own spending on 

education such as for private school or tuition for public colleges – is typically excluded from 

consumption resource measures.51

 

 47 Ruggles (1991) and National Research Council (1995) do not consider the inclusion of the value of education as a 

resource. Neither the OPM nor the SPM include a value of education in their resource measures.

48 These labor market transactions include increased salaries and benefits as a direct result of education. 49 See Pseiridis, Lianos, and Agiomirgianakis (2016), Alstadsaeter (2009), Akerlof and Kranton (2002), Dolton et al.

(1989), and Weisbrod (1962). 50 Determining the value of expenditures directly paid for education is more straightforward for a consumption-based 

resource measure based on data from the CE Survey. Data are more limited for determining the value of education for 

an extended income measure.

51 For example, in their consumption-poverty measure, Meyer and Sullivan (2012a) exclude educational expenditures 

from their resource measure because these expenditures are assumed to reflect investment.
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Recommendations:

17. The Working Group recommends that expenditures on education be excluded from the

recommended extended income-based and consumption-based resource measures 

because education is generally considered an investment in human capital.

18. The Working Group recommends that, at this juncture, personal educational expenses 

not be subtracted from the extended income-based resource measure. However, the 

Working Group recommends a future advisory structure revisit the issue of deducting 

out-of-pocket education expenditures on tuition and other educational items if the 

data quality makes it feasible.

19. The Working Group recommends continued research and additional stakeholder and 

expert engagement on whether and how to treat education within resource measures.

Valuing Key Service Flows and Other Asset Flows

Current income, as measured in either the survey data or administrative data, does not capture 

the income or net service flows that households derive from home ownership or vehicles, nor 

from nonmarket domestic services (i.e. home production for the consumption of one’s sharing 

unit members). Nor does it include service flows from other assets and debt. Including the service 

flows for all of these elements was considered by the Working Group.

The conceptual framework, underlying the idea that the income or service flows from owneroccupied housing (including second homes) and vehicles be included, is that if two households 

have identical incomes but one owns a home or vehicle and the other does not, the household 

with the home or vehicle is often better off than the household without. A household would be 

considered better off as long as the implicit income flows from these are greater than the sum of 

the financing costs, maintenance and repair expenses, and the opportunity costs of investing in 

the house or vehicle versus other assets. The 1995 Measuring Poverty report concluded that “the 

next regular review of the poverty measure should give serious consideration to revising the 

income definition to include imputed net implicit rental values in homeowners’ income (National 

Research Council 1995, p. 246).” Including the net value of owner-occupied housing services in 

income is consistent with international standards of household income (see ILO 2003; Canberra 

2001, 2011). Also, including a value for the net service flows from household durables and 

including the value of unpaid domestic services (i.e. home production for the consumption of 

one’s sharing unit) are included in the conceptual definition of income.

52 At the present time, due 

to data quality considerations, the Working Group decided not to consider including the value of 

unpaid domestic services. 

 52 Yet, the Canberra Group recommendation was that the flow of services from durables would be excluded from 

income due to practical considerations. However, with detailed data on vehicles (e.g., make, model, and year, plus year 

of purchase) collected in household surveys and the growing market of leased vehicles, this concern seems less of an 

issue than in 2011. In contrast, the Canberra Group determined that for most countries, even in 2011, there was 

sufficient data available to produce values of unpaid domestic services and thus this element should be included in 

measures of income.
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The service flows from the ownership of other assets and liabilities can also be counted in an 

extended measure of income. Currently the Census Bureau official measure of income, and that 

recommended by the Canberra Group (2011), includes returns from the ownership of assets as 

property income (e.g., dividends, interest, and rent). However, an alternative is not to include 

this property income but instead to convert the stock of assets into income for consumption, 

taking into account debt. For this, an option is to follow the example of Wolff and Zacharias 

(2003, 2009)53 who produce the Levy Institute Measure of Economic Well-Being (LIMEW). This 

measure includes income flows from both housing and financial assets and debt. In addition to 

money income (but subtracting out property income), Wolff and Zacharias include imputed rent 

on owner-occupied housing less the annuitized value of mortgage debt, and the annuitized value 

of non-home wealth less the annuitized value of other debt. To obtain the annuity values, they 

assume that a household’s net worth is spent over the period of the household’s remaining life 

using life expectancy data. For the LIMEW, durables (like vehicles) were not considered to be 

assets because durables cannot be readily converted to cash without compromising current 

consumption (Wolff and Zacharias 2009). Also excluded from the LIMEW is the value of future 

retirement income such as from Social Security benefits or defined-benefits private pension 

plans; these were excluded since the household does not have direct control over these and they 

cannot be marketed.

Possible Accounting for Owner-Occupied Housing and Vehicle Ownership in Income

Implicit income can be derived from the ownership of housing and vehicles,54 and thus can be 

included in an extended measure of income. This implicit income can come from these being 

considered as assets alone (like other non-financial assets like undeveloped property and 

valuable works of art, jewelry, gold, etc.) and providing a return on capital investments, or from 

these being considered assets that additionally provide flows of shelter and vehicle services.

Regardless of the assumption one makes, owner-occupied housing and owned vehicles provide 

implicit income to households; and thus, these owner households could be better off than others 

who are not owners of these.55

Treatment of Owned Housing and Vehicles as Assets Alone

If owned housing and vehicles were to be considered assets only, a simple approach would be to 

value the implicit income in terms of returns to capital held in the assets (the equity).56 The 

 53 Also see Zacharias et al. (2018). 54 The flow of services from other major durables could also be considered, but those from vehicles are usually the only 

ones counted since there is an active rental and lease market for vehicles as opposed to other durables like furniture.

The flow of services from major appliances, those that would normally be included in rental units, would already be 

accounted for in the flow of services from owner-occupied housing. 55 An alternative method for incorporating housing costs and benefits to homeownership is to subtract housing costs 

from post-tax income. This approach, sometimes referred to as “shelter poverty,” categorizes a household as “in 

poverty” if after paying for their housing, they can’t meet their non-shelter needs. This type of a post-housing poverty 

measure is published in the UK and has been explored in research for the United States. See 

https://www.gov.uk/government/statistics/households-below-average-income-199495-to-201617 and Stone (2004) 

and Pelletiere (2008).

56 Another approach would be to annuitize the value of owner-occupied shelter and owned vehicles over the life cycle 

of the individual who owns the asset, or in the case of vehicles, over the product life-cycle. This is comparable to the 

possible inclusion of the annuitized values of other financial and non-financial assets and liabilities that are available to 

the household to meet its consumption needs; such an approach was presented earlier in this report. Either of these 

two approaches, return to equity or annuitizing the assets, would be preferred when there is limited information (e.g., 
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return to capital approach focuses on implicit income as a function of equity in the asset and the 

rate of return that one might receive from investing in an alternative asset as opposed to putting 

one’s money in the ownership of a home or a vehicle. This approach is recommended when 

rental markets are limited (Canberra 2011) and when little information is available (e.g., only the 

market value of equity). Smeeding et al. (2013) used this approach to impute implicit income 

from the return to home equity for households. In the past the Census Bureau followed the 

return to capital approach to derive implicit income from home equity based on the rate of 

return equal to the current year’s return to municipal bonds (U.S. Census Bureau 1993), but no

longer produces this measure.57 Others using this approach for owner occupied shelter have used 

mortgage interest rates, the rate on short-term bonds, or mortgage rates plus a risk premium. 

The Canberra Group suggests that an interest rate would be chosen to represent a safe private 

market rate of return (Canberra Handbook on Household Income Statistics 2011, p. 39).

Treatment of Owned Housing and Vehicles as Assets Providing Service Flows

Based on international standards for economic statistical measures (see Canberra Report 2011, 

ILO 2003), both owner shelter and vehicles are durables from which owning households receive 

benefits that those who are not owners do not receive (Canberra 2011, ILO 2003). The 

assumption is that, in the language of the System of National Accounts (SNA 2008), owners of 

homes, and extrapolating to vehicle owners, are unincorporated enterprises that lease their 

houses and vehicles back to themselves, like landlords for rental units and lessors of vehicles. The 

value of the flow of services from the house or the flow of services from the vehicle is set at the 

market rent for a similar house or at the rental or lease value for a similar vehicle. However, to 

derive the net imputed income from owning a house or vehicle, one needs to subtract from these 

flow values the expenses that go into the production of the housing and vehicle services. The 

subtracted expenses are those that are incurred by the household in its role as the landlord of 

the owned home or as the lessor of the owned vehicle. Thus, the net implicit income from owing 

a house or vehicle is the economic benefit that the household derives from these after paying for 

the costs of producing shelter or vehicle services.

Including the net implicit income derived from the flow of services from owner-occupied shelter

and owned vehicles is based on two facts: (1) these capital or non-financial assets produce both 

flows of shelter and vehicle services; and (2) the economic benefits received exceed their costs of 

production. For household income statistics, the net implicit income from these would be 

included in a comprehensive income measure. To produce net implicit income, one needs both 

the value of service flows produced, and value of shelter landlord and vehicle lessor expenses.

 

at least current market value and balance owned on mortgage or vehicle loan if financed) about the asset or rental 

markets are limited.

57 The new processing system, used at the Census Bureau, is no longer matching to the American Housing Survey (AHS) 

to pick up the variables needed to create HOUSRET or PROP_TAX on the CPS ASEC file. Under the earlier processing 

system, imputation for presence of a mortgage and property value-related questions was done with a match to the 

AHS using a cell hot deck approach. This was done to impute data from the AHS for questions not asked in the CPS 

ASEC. However, this resulted in a data lag where data from an earlier year’s AHS was used (AHS conducted biennially in 

odd-numbered years). In the new processing system, the AHS variables needed to create presence of mortgage 

(HPRES_MORT needed for SPM) are no longer needed as questions on this topic were added to the CPS ASEC but not 

questions on property taxes or return on equity. Because of a redesign of the AHS in 2015, Census Bureau staff have 

been matching to the 2013 AHS. This resulted in the variables needed from AHS to create HOUSRET and PROP_TAX 

being increasingly out-of-date. In order to remove the processing dependency on another survey and eliminate the lag 

that would be present in the imputed data, the new system creates HPRES_MORT from respondents in the CPS ASEC 

but does not create HOUSRET or PROP_TAX. 
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The value of service flows from owner-occupied housing and durable goods like vehicles is 

referred to as the “consumption” value; this will be visited again in the Consumption section of 

this report. 

Imputing the Value of Service Flows from Owner-occupied Shelter and Owned Vehicles

Before net implicit income from owner-occupied housing and vehicles can be produced, values 

for the flow of shelter and vehicle services derived from these need to be imputed. The literature 

focuses on imputing values for owner-occupied housing, with an implication that the same or 

similar methods can be used for durables like vehicles (see ILO 2003, paragraph 292). To estimate 

the values for the flow of services (also referred to as the market or consumption value), two 

main valuation approaches are used internationally: user costs and rental equivalence. Both user 

costs and rental equivalence approaches are designed to measure the change over time in the 

value for the flow of services consumed by owner-occupants or vehicle owners. In theory, both 

approaches should produce equivalent values.58 However, there is a distinction in the two 

approaches: one focuses on the costs of producing the service while the other focuses on the 

selling price of the service. Specifically, “the user cost approach attempts to measure the changes 

in the cost to owner-occupiers of using the dwelling; user costs account for both recurring costs 

and the opportunity cost of having money tied up in the dwelling rather than being used for 

some other purpose” (ILO 2004, p. 179). In contrast, “the rental equivalence approach attempts 

to measure the change in the price of the housing service consumed by owner-occupants by 

estimating the market value of those services” (ILO 2004, p. 180); in). In other words, rental 

equivalence is how much owner-occupants would have to pay to rent their dwellings if they did 

not own them. In trying to understand the relationship between the two approaches, think about 

how a landlord decides what rent to charge for a housing unit. She would consider all the costs 

that she incurs in order to keep the shelter unit in the rental market; these include recurring costs 

like mortgage interest payments, property insurance, and routine maintenance and repairs 

(including those for major appliances included in the shelter unit and covered by the rental 

contract), but also the opportunity costs of investing in this shelter property as opposed to 

another asset plus perhaps expected appreciation. Using this information, she will set the market 

rent of the shelter unit based on her production costs or in other words, user costs. In theory, the 

market rent she sets will be comparable to the market rents, or rental equivalence, set by other 

landlords for comparable properties. The market rent that she sets will be comparable to what 

owner occupants would pay to rent their own homes when the units have the same 

characteristics and are in the same rental markets. Implicit in market rents, and by extension in 

imputed rents for owners, are the service flows from major appliances that are included in 

comparable rental units; in the U.S., usually refrigerators and stoves are included in rental units 

unlike in many European countries, for example.

There is a large literature that presents different approaches to impute the value of owneroccupied shelter service flows (e.g., Aten 2018; Balcazar et al. 2014; Canberra 2011; Diewert 

2008a, 2008b; Garner and Short 2009; Garner and Verbrugge 2009a, 2009b; Gillingham 1983; ILO 

2003, 2004; Katz 2004, 2017; Verbrugge 2008). As noted by the Canberra Group (2011), when 

there are established rental markets, the rental equivalence approach, as opposed to the user 

 58 As noted by Garner and Verbrugge (2009), “In standard frictionless Jorgesonian capital theory with competitive 

markets, a durable good’s rental costs will equal its ex ant user cost, suggesting that these alternative measurements of 

the value of the flow services should be roughly equivalent.” However, empirically, values based on the two 

approaches diverge.
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costs approach, is the recommended valuation approach to use since it is easier to implement 

compared to the user cost approach. 

User Costs

The user cost approach has been applied to approximate the imputed flow of services (or 

imputed rent) from owned housing. Based on the ILO report (2003), similar methods to those 

from the housing literature can be used to derive implicit values of the service flows from 

vehicles. As noted earlier, the user cost of capital approach seeks to capture the opportunity cost 

of funds invested, the depreciation of the capital from use, the change in the market price of the 

asset, and expected appreciation or capital gains (ILO 2004). Depreciation is normal wear and 

tear, but it can include obsolescence. For a study that considers all of these costs in the 

estimation of user costs, see Garner and Verbrugge (2009). Another option to produce owner 

shelter user costs by applying rent-to-value ratios (also referred to as capitalization rates) based 

on the rents of rented properties to the market value of comparable rented properties. This is the 

method currently in use by the BEA for the National Income and Product Accounts (NIPA) (see 

Katz 2017).59 The BEA is investigating alternatives to the current rent-to-value ratio approach that 

are more rental equivalence- as opposed to user costs-based (see Aten 2018).

All user cost approaches to value the service flow values from owner-occupied housing depend 

on the market value of the owned dwelling at a minimum. Market values can be obtained from 

tax records, multiple listings from real estate agents, recent sales, data sources like Zillow or 

CoreLogic, or self-reports of home values from surveys. As reported in Carter (2012), comparisons 

of appraisal data or sale prices with self-reports of home market values suggest that home 

owners may overvalue the current market values of their homes; overestimates have ranged 

from about 4 percent to 8.4 percent (see Kish Lansing 1954; Kiel and Zabel 1999). In contrast, 

research by Benítez-Silva et al. (2008) suggests that respondents who purchase their homes 

during soft housing markets (when sellers outnumber buyers) are more likely to report home 

market values that are more accurate. However, even with the market values of owned shelter 

and vehicles, additional data are needed to identify the inputs to derive user costs (as noted 

earlier, for example, interest and depreciation rates), and may involve significant assumptions 

regarding rates of return and expected appreciation. As a result, according to the ILO (2004), no 

statistical office was using the full user costs approach to derive the value of the flow of services 

from owner-occupied shelter as of the publication date of that report.60

User costs approaches have also been used to produce values of service flows from vehicles. 

Fisher and Johnson (2013) used an interest rate to represent the opportunity costs of investing in 

vehicles versus other assets, and a depreciation rate to represent what essentially is the wear and 

tear on the vehicle over time. In contrast, Meyer and Sullivan (2012b) produce imputed flows of 

services from owned vehicles based on derived depreciation rates using the market value of cars 

over time. In both cases, these researchers produced these flow values for inclusion in 

consumption measures as opposed to income measures. 

 59 For a discussion and application of the user costs approach compared to rental equivalence, see Garner and 

Verbrugge (2009); in their user cost calculations, they make an adjustment for the tax advantage of owning one’s 

home. See Yates (1994) and Crone, Nakamura, and Voith (2004) for a discussion of capitalization rate methods. 60 A noted in the ILO report, statistical offices are reluctant to use the measure “partly due to conceptual and 

methodological complexity of the measure, which may also make it difficult to obtain widespread public support” (ILO 

2004, p. 180).
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Rental Equivalence

The other approach to derive implicit values of the flow of services from owner-occupied 

housing, or the implicit rents, is based on what is referred to as rental equivalence. However, as 

noted earlier, rental equivalence is also an estimate of imputed rents, just like imputed rents that 

result from application of the user cost approach. The intuition underlying rental equivalence 

methods, like that of the user cost approaches, is to assign some value to living in one’s own 

home that is consistent with what a landlord would charge as rent. Or in the case of an owned 

vehicle, the goal is to assign some value to using one’s own vehicle that is consistent with what a 

lessor of the vehicle (comparable to a landlord for shelter) would charge the owner to lease her 

own vehicle. Or one could think of this as answering the following question: For how much would 

you pay in rent to use your vehicle if you did not own it?

Rental equivalence can be derived from regression hedonic models, matches of renter and owner 

units by characteristics, or direct reports. Researchers primarily have used the hedonic and direct 

report approaches as alternatives to user costs (see, for example, Arévalo and Ruiz-Castillo 2004; 

Frick et al. 2010; Garner and Short 2009; Norris and Pendakur 2013).

61 However, more recently, 

Aten (2018) produced imputed rents through the matching of renters and owners by housing unit 

characteristics, with an adjustment to owners’ imputed rents that constitutes an owners’ 

premium based on the market value of the owned homes. For regression-based models, the 

preferred approach is to apply a Heckman selection model of owners versus renters before 

estimating the rent equation. Using Canadian data, Norris and Pendakur (2013) reported an 

increase in the imputed rents of owners based on a Heckman selection model of about 30 

percent over rents based on the standard OLS model that did not account for selection. The 

second most frequently used rental equivalence approach is to use owners’ direct estimates of 

rental equivalence for their own shelter. This method relies on the expectation that owners can 

estimate rental equivalence even when there are not comparable rental dwellings in their 

geographic area if they know the rents in other areas. As noted by Lanjouw (2009), this is less of a 

problem in areas with well-developed rental markets. Garner and Short (2009) reported average 

imputed rents for the U.S. based on a selection/hedonic model were about 83 percent of 

respondent rental equivalence for the U.S. in 2005. In contrast, Arévalo and Ruiz-Castillo (2004) 

reported that self-reported rents are very similar to imputed rents estimated using a hedonic 

model of the same dwellings for Spain.62

In theory, rental equivalence estimates for vehicle services could be derived from the information 

on vehicle leases. However, as far as the Working Group knows, there are no estimates of the 

flow of services from vehicles that are based on rental equivalence approaches; this is likely due 

to the limited amount of data on long-term vehicle agreements, the fact that leases cover more 

than the value of the service provided, and that the stock of leased vehicles tend to be newer 

than the stock of owned vehicles by consumers.

Household Survey Data Available to Value the Flow of Services from Owner-Occupied Housing and 

Vehicles

Information collected in the ACS, CE, and CPS ASEC can be used to derive the service flow values 

for owner-occupied housing based on the market value of the housing unit, the rent of renters, 

 61 For a review of the capitalization rate approach, a hedonic model with sample selection correction, and reported 

rental equivalence with applications to the U.S., see Garner and Short (2009). 62 Also see the work on Frick et al. (2010) from a European perspective.
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and shelter unit characteristics of both renters and owners. The ACS asks homeowners “how 

much do you think this house and lot, apartment or mobile home and lot, would sell for if it were 

for sale?” Respondents are given categories and asked to “specify” the amount if the amount is 

$250,000 or more. The CPS ASEC asks a similar question, “About how much do you think this 

(house and lot/apartment/mobile home) would sell for if it were for sale?” This information could 

be combined with the housing unit characteristics of owner units to match rent and owner units 

(similar to the current BEA approach). Another alternative is that hedonic methods with Heckman 

selection to imputed owner rents. The Aten model could also be used. The CE Interview Survey 

asks a similar question for the market value of the owned home along with shelter unit 

characteristics of owners and rents, and various other owner shelter expenses that could be used 

in a user cost approach. However, in addition, the CE Interview Survey asks the owner to assess 

how much the unit would rent for, unfurnished and without utilities, in the current market. Thus, 

using the CE data, imputed rents could be estimated using both user costs and various rental 

equivalence approaches; this is what Garner and Short 2009 and Garner and Verbrugge (2009) 

have done previously. 

Assigning dollar values to the service flows from vehicles requires data that are not currently 

available in the CPS and ACS, but are available in the SIPP and CE. For flows of services from 

vehicles, the CE Interview Survey and SIPP ask sufficient information about vehicle ownership to 

assign a value. The CPS ASEC does not ask about vehicle ownership. The ACS asks only how many 

vehicles are “kept at the home for use by members of the household”. Estimates of service flows 

from vehicles have been produced by Fisher and Johnson (2012) and Meyer and Sullivan (2012b) 

using data form the CE Interview Survey using user cost approaches. As noted earlier, Fisher and 

Johnson apply a set depreciation rate for all vehicles and interest rates to reflect the opportunity 

costs of investing in vehicles. In contrast, Meyer and Sullivan derive depreciation rates using 

vehicle characteristics but do not account for the interest rate. Neither accounts for the 

additional income that could be derived from the sale of the vehicle during the reference period. 

Operating Expenses to Subtract from the Value of Service Flows from Owner-Occupied Housing 

and Vehicles

The final step to derive net implicit income from owning one’s home and vehicles is to subtract 

expenses from the imputed value of the service flow from owner-occupied shelter and owned 

vehicles. For owner-occupied housing, the net implicit income from owning is the difference in 

the value of the flow of shelter services (based on either a user cost approach or a rental 

equivalence approach) and the expenses that the owner as landlord would incur as operating 

expenses. These expenses are those which the landlord would not have incurred had she not 

placed the property in the rental market. These operating expenses could include, for example, 

mortgage interest payments, property taxes, routine repair and maintenance expenses (again, 

including those of major appliances), insurance costs, and depreciation. Some of these same 

costs would have entered the estimation of the shelter service flows when a user costs approach 

is used, but user costs (as opposed to operating expenses) also includes the opportunity costs of 

capital and expected appreciation. For example, suppose a landlord rents the housing unit out for 

$1000 per month and her operating expenses are $700 per month; her net implicit income is 

$300 per month. The net implicit income from a similar owner -occupied housing unit would also 

be $300 per month.

To derive net implicit income from owning vehicles, if one were to use the costs of renting 

vehicles, the operating expenses to be subtracted would include those that are similar to those of 
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landlords. However, if one were to use the costs of leasing vehicles to value the flow of services 

from vehicles, operating expenses deducted would be limited to lessor-type expenses. These 

expenses would include depreciation and interest payments (reflecting the finance charge on the 

amount of money the leasing company has tied up in the vehicle), assuming the vehicles are 

financed. Unlike for owner-occupants and landlords, vehicle lessors usually are not responsible 

for vehicle insurance and routine maintenance and repairs. These expenses are paid by the 

person who leases the vehicle, and thus would not be deducted to derive the net implicit from 

owning one’s vehicle. The expected net income from owning a vehicle could be adjusted upward 

in any year in which the vehicle is both consumed for part of the year but sold by the end of the 

year (similar to the value of the vehicle at the end of the lease period).

Household Survey Data Available on Owner Expenses to Derive Net Implicit Income from Owneroccupied Housing and Owned Vehicles

As noted earlier, to derive the implicit net income from owned housing and from vehicles, 

landlord- or lessor-like expenses need to be deducted from the imputed rents in the case of 

shelter and the imputed lease values in the case of vehicles. For owned housing, the ACS asks 

about the amount of regular mortgage payments as well as real estate taxes and insurance 

payments. The CPS ASEC does not ask about owner expenditures for shelter. Neither survey asks 

about the amount of “equity” held in the home but both ask whether or not the homeowner has 

a mortgage and/or a home equity loan.63 The CE Interview Survey asks detailed questions about 

mortgage payments including interest, homeowner’s insurance, property taxes, and maintenance 

and repairs; equity in the owned house can also be ascertained from data available in the CE. 

Neither the ACS nor CPS ASEC includes information about vehicle ownership. In contrast, the CE 

Interview collects information about the stock of vehicles owned, vehicle make, model and year, 

and financing information. This information can be used to estimate lessor-types of expenses that 

would be deducted to derive the implicit net lease value from owning one’s vehicle.

Recommendations:

20. The Working Group recommends that further research be undertaken to evaluate 

alternative methods to estimate the net value of service flows from owner-occupied 

shelter and the net value of the service flows from owned vehicles to be included in the 

income resource measures, including the possibility of imputing such values using 

statistical methods and data from the CE Survey. Such research should consider the 

availability of data from the CPS ASEC and ACS.

Correcting Survey Data for Misreporting and Improving Tax Estimates

There is growing evidence that survey-based estimates of cash income, in-kind benefits, and 

taxes suffer from misreporting that can bias poverty estimates and bias the estimated poverty 

reduction of government programs and taxes. There are three major approaches currently used 

to correct survey data for misreporting (that most often takes the form of underreporting): (1) 

rules-based approaches; (2) statistical- or regression-based modeling; and (3) direct substitution 

 63 HUD’s American Housing Survey asks about outstanding balance and value, which could be used to calculate equity 

with the caveats related to the estimation of value.
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of survey reports with administrative records. These three approaches could be used 

independently or in combination.

Rules-based approaches 

One approach to correcting for misreporting and missing data, particularly for program 

participation, is to use program rules to impute participation. For example, in some states any 

person receiving public assistance is categorically eligible for Medicaid and SNAP. Knowledge of 

these rules could therefore be used to assign Medicaid participation to all those who are 

automatically eligible.64

Microsimulation models, such as the TRIM3 model, use a database of program eligibility rules and 

benefit levels to correct survey reports.65 TRIM3 uses administrative data on program aggregates 

and the characteristics of program recipients to impute additional program recipients to align 

with the total number of recipients recorded in program data. Microsimulation models have not 

generally been used in surveys to correct for misreporting of earnings, the largest component of 

total income for most people. The TRIM3 model focuses on correcting survey reports of certain 

transfers and in-kind benefits. 

Statistical- or regression-based modeling

Statistical- or regression-based modeling has been used by CBO as well as by Census Bureau 

researchers to improve the imputation of specific income elements and to correct for 

misreporting of means-tested benefits.

66 The CBO’s analyses of the distribution of household 

income rely on the Census Bureau’s CPS ASEC for information about receipt of government 

transfers, particularly means-tested transfers. CBO uses a probit regression to estimate a 

probability that people receive transfer income. CBO then imputes additional recipients on the 

basis of those probabilities so that the total number of people receiving a transfer in the model 

matches the actual number of recipients recorded in administrative program data.67 Like TRIM3, 

the CBO models have focused on in-kind benefits and transfers rather than earnings from wages 

and salaries or self-employment. 

Other research has considered statistical modeling to improve Census Bureau imputation of 

missing income data. The SIPP uses a sequential regression multivariable imputation (SRMI) 

method to impute missing data. A recent paper (Hokayem et al. 2015) evaluates the use of SRMI 

to impute missing income data in the CPS ASEC. The SRMI method allows for greater flexibility 

than the hot deck procedure68 currently used to impute missing data in the CPS ASEC and the ACS 

and allows for the inclusion of additional covariate variables. Hokayem et al. compare modelbased imputation with and without the use of administrative data from the Social Security 

 64 However, it is also important to consider and understand uptake. For example, see https://aspe.hhs.gov/basicreport/understanding-participation-rates-medicaid-implications-affordable-care-act. 65 The Transfer Income Model, version 3 (TRIM3) is a comprehensive microsimulation model that simulates the major 

governmental tax, transfer, and health programs that affect the U.S. population, and can produce results at the 

individual, family, state, and national levels. TRIM3 is developed and maintained at the Urban Institute under primary 

funding from Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation 

(HHS/ASPE). For more information, see http://trim3.urban.org/T3Welcome.php. 66 Several academic papers have also corrected for benefit underreporting, for example Scholz et al. (2009) and BenShalom et al. (2012).

67 https://www.cbo.gov/system/files?file=2018-07/54234-workingpaper.pdf 68 See here for more information about the hot deck method used in the CPS: https://www.census.gov/programssurveys/cps/technical-documentation/methodology/imputation-of-unreported-data-items.html
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Administration’s Detailed Earnings Record (DER) file. They find that for nearly all recipiency and 

value models, the inclusion of DER data in the model improves the prediction of survey values. 

Other research at the Census Bureau has looked at using this type of model-based approach to 

reduce respondent burden by reducing (or eliminating) income questions in the survey (O’Hara, 

Bee, and Mitchell 2016).

Direct substitution of survey reports with administrative records

In addition to statistical modeling, administrative records have been linked directly to survey 

reports at the individual level to evaluate survey responses and measure the impact of survey 

misreporting on poverty and other income estimates, and to replace modeled estimates of tax 

payments and credits with administrative reports. The Census Bureau is permitted under Title 13 

§6 of the United States Code to acquire and use records, reports, and other material instead of 

conducting direct inquiries.69 The Census Bureau is building an inventory of administrative 

records from federal agencies (IRS, SSA, HHS, HUD and others), the state agencies that deliver 

SNAP, WIC, TANF and other benefits, as well as some local agencies and commercial data 

providers.

70 In January 2019, the Foundations for Evidence-based Policymaking Act of 2018 was 

enacted.71 It includes a statutory provision called the Presumption of Accessibility which provides 

authority, similar to the authority of the Census Bureau, for statistical agencies and units to 

request data assets of any agency. 

At the Census Bureau, both survey and administrative record data are linked at the individual 

level using unique anonymized identifiers called Protected Identification Keys (PIKs). These PIKs 

are assigned either probabilistically or deterministically, depending on the amount of information 

available.72, 73 Once PIKs are assigned and any Personally Identifiable Information (PII) is removed, 

the data are provisioned to Census Bureau employees and Special Sworn Status employees in a 

restricted research environment for use on approved projects. 

There have been a number of studies that have employed this direct linkage methodology.

Gathright and Crabbe (2015) examine OASDI and SSI reporting in the SIPP. Meyer, Mittag and 

Goerge (2020) examine SNAP reporting in the ACS, CPS and the SIPP. Meyer and Mittag (2019) 

link CPS micro-data to New York administrative micro-data for four government transfer 

programs. Bee and Mitchell study income and poverty estimates for the population age 65 and 

older linking a wide variety of administrative records on retirement income. Meyer and Wu 

(2018) examine the poverty reduction of several transfer programs in the SIPP. Meyer, Wu, 

Mooers, and Medalia (2021) link a wide variety of administrative records to the SIPP and CPS to 

look at extreme poverty. Shantz and Fox (2018) estimate SPM rates replacing survey reports with 

administrative data on TANF and SNAP for a subset of states. 

 69 See https://www.law.cornell.edu/uscode/text/13/6 70 For up-to-date information on the current inventory, see www.census.gov/datalinkage. 71 Pub. Law 115-435. Available at https://www.congress.gov/115/plaws/publ435/PLAW-115publ435.pdf. 72 See Wagner and Layne (2014) for a detailed description of the process used to assign PIKs. 73 Since not all survey respondents or individuals within the administrative records can be assigned a PIK, researchers 

often use inverse probability weighting to reweight the estimates and make the linked sample representative of the 

target population. The inverse probability weights are created by dividing the survey sample weight by the predicted 

probability of the individual having a PIK.
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These three approaches to correcting for misreporting and missing data each have advantages 

and disadvantages. The rules-based approach has the advantages of (i) being easier to implement 

than other approaches by requiring a significantly shorter time horizon for research and 

development, (ii) being easily adapted to analyzing impacts of various current or potential policy 

changes, and (iii) requiring no new inter-agency agreements or regulation changes prior to 

implementation. On the other hand, this approach cannot adjust for missing or misreported 

income/program receipt when rules are not available, such as for most earned income and other 

non-rules-based income sources. Even when rules are available, people who are eligible for a 

particular program may not enroll.74 In addition, the rules-based approach is dependent on 

timely reported aggregated totals, which may be needed for calibration. Assuming totals are 

timely, while this approach may be correct at the aggregate level, the assigned income/program 

receipt may be incorrect at the micro or subgroup level, since it could be applied to the wrong 

record and the wrong part of the resource distribution. If not applied carefully, this could result in 

further erosion of the quality of survey data by replacing missing data with erroneous data. 

The regression-based/modeling approach affords some of the same advantages as the rulesbased approach and can overcome many of the disadvantages. This approach requires no new 

agreements or regulation changes (when based on publicly available data), is flexible to the 

timing of implementation and thus does not largely and adversely affect the timing of data 

release because modeling can use the latest data available, even if it does not correspond to the 

survey reference year. Since this approach is applied to each record and is not reliant on detailed 

program rules for its application, it can be implemented for any resource component that has 

missing or misreported information. Further, it affords the ability to take advantage of both 

individual-level and aggregate administrative data when such information is available. The 

statistical modeling approach can be combined with the rules-based approach, since known 

program enrollment rules can be built into the statistical models. This approach requires

investments in research to develop appropriate models. It is sensitive to the specifications of the 

model chosen, assumes missing or misreported data are similar to data that are accurately 

reported, and is often hard to explain to the public.

Using administrative data to replace missing data and/or misreported data has several unique 

advantages that overcome limitations of other approaches. The public may more easily 

understand this approach. This approach overcomes the concerns of mismatched 

income/program assignment to an observation as it only assigns values when such information is 

directly observed. It affords the ability to capitalize on the quality of administrative data, when 

such quality is deemed high. Further, like any approach that uses administrative data, it is 

consistent with the recommendations from the Foundations for Evidence-Based Policymaking Act

of 2018.

75

There are several key disadvantages to using administrative data that must be considered.

Administrative data are not all of uniform quality, so research is needed to evaluate each source 

of administrative data and their fitness for use in a poverty measure. Administrative data are not 

collected, reported, or disseminated on the same timeline as many survey questions and release 

dates, so these timing issues must be addressed before implementation could occur. Similarly, 

the reference period for which administrative data are collected may not align with the reference 

 74 See for example https://www.census.gov/library/visualizations/interactive/snap-profiles.html. 75 Pub. Law 115-435. Available at https://www.congress.gov/115/plaws/publ435/PLAW-115publ435.pdf.
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period for a survey, further requiring reconciliation before implementation. Program agencies 

may change the administrative data forms or other attributes about the collection, potentially 

causing breaks-in-series to statistical estimates of poverty using these data. There are issues 

surrounding how to handle data that are not linkable or are erroneously linked. In cases where 

data are linked, there are issues with how to reconcile differences between survey and 

administrative data. Administrative data are only available for some income sources, program 

receipts, geographic regions, and years; therefore, this approach would need to be used in 

combination with other approaches to address missing data and misreporting. And finally, there 

could be a significant cost associated with the need to modify agreements and/or regulations to 

permit the use of administrative data for use in the production of official statistics. Many of these 

key concerns are discussed below.

Implementation issues 

Choice of survey

The Census Bureau reports income and poverty estimates from three major national household 

surveys: the CPS ASEC, the ACS, and the SIPP. These surveys differ in the length and detail of 

questionnaires, the number of households included (sample size), and the methodology used to 

collect and process data. Choice of which survey to use depends on the level of geographic 

disaggregation for the estimates, the timeframe for release of the estimates and preferences for 

level of detail for income and program participation reporting. The target population for income 

and poverty estimates from each of these household surveys is the resident civilian 

noninstitutionalized population. Therefore, people in institutions, such as prisons, long-term care 

hospitals, and nursing homes are not included in current poverty estimates, and people who are 

homeless are generally not included in the samples. All three surveys have been shown to suffer 

from misreporting of income with aggregate income reported well below estimates from the 

national accounts.76 For more details on these potential data sources, see Appendix A. Based on 

the strengths and disadvantages of each survey, both the OPM and SPM are based on the CPS 

ASEC.

Recommendations:

21. The Working Group recommends that the Census Bureau use CPS ASEC for an 

alternative income-based resource measure. 

Administrative Data Use Considerations

Availability and Permission to Use Administrative Records in Production

Using administrative records comes with a unique set of challenges that would need to be 

addressed before linking to survey data and producing official poverty estimates. Importantly, the 

Census Bureau is restricted to using administrative records for approved projects, per 

agreements with the data providers. At this time, most administrative records are currently not 

permitted for use in the production of official income and poverty estimates. Before using the 

 76 See Table 7-6 of 2018 National Academies of Sciences assessment of the SIPP Redesign that compares income 

aggregates from the SIPP and the CPS ASEC to NIPA. Rothbaum (2015) has done a similar analysis including ACS 

estimates. See https://www.census.gov/content/dam/Census/library/working-papers/2015/demo/SEHSD-WP2015-

01.pdf. See also Meyer, Mok and Sullivan (2015).
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linked administrative records and survey data to produce official income and poverty estimates, 

the new uses of data would have to be approved by the data source agency, potentially including 

renegotiating agreements. 

The availability and accessibility of administrative data sources differs for each of the elements 

included in cash income (23 in the CPS, 8 in the ACS and 81 in the SIPP) and for in-kind benefits.

These administrative records also differ in quality.

For earnings, which is the single largest component of income for most people (about 80 percent 

of CPS ASEC income, but only about half of income of those below the official poverty line), the 

Census Bureau has extracts from IRS Form W-2, Form 1040, and the Detailed Earnings Record 

(DER) from the Social Security Administration.77 These extracts do not contain all the information 

necessary to measure gross earnings, and do not include unreported earnings.

For other income sources, the administrative data sources vary. From SSA it is possible to obtain 

information on Social Security retirement income, disability compensation and survivor income

and SSI payments.

78 IRS data on self-employment income are limited, do not include losses, and 

can be subject to underreporting (Erard and Ho 2003; Slemrod 2017). IRS administrative records

are also available on interest and dividends (excluding tax-preferred accounts) and rent and 

royalty income. The rent and royalty data currently accessible include only gross rent.

Absent timely and complete administrative data, after-tax income requires the use of tax 

modeling to estimate tax obligations and tax credits. For the CPS ASEC, the Census Bureau 

maintains its own tax model.79 External tax models are also available, including the NBER TAXSIM 

model80 and the Bakija model.81, 82 These tax models require assumptions and rules regarding the 

formation of tax units and model-based estimates of itemized deductions. However, recent 

research finds that the tax imputations introduce substantial error in overall taxes, as well as tax 

credits. Errors in both measures can be sharply reduced even with a limited set of administrative 

tax data (Meyer, Wu, Finley, Langetieg, Medalia, Payne, and Plumley 2020). Research from the 

Census Bureau comparing CPS ASEC-simulated EITC payments to actual EITC payments suggests 

that these tax simulators overestimate the antipoverty effects of the EITC (Jones and Ziliak 

2019).83 Other research finds that the CPS tax model inaccurately distributes the EITC across the 

income distribution (Meyer and Wu 2018).

 77 The W-2s can be linked to all household surveys, and the DER can currently be linked to the CPS ASEC and the SIPP. 78 Social Security and SSI data are available for the CPS ASEC and SIPP but not currently for the ACS, while the other 

income sources are available for the CPS ASEC, ACS and SIPP. 79 U.S. Census Bureau, “CPS ASEC 2005 Tax Model Documentation”, SEHSD Working Paper, 2004. 

https://www.census.gov/content/dam/Census/library/working-papers/2005/demo/cpsasec2005taxmodeldoc.pdf. 

O’Hara, Amy, “New Methods for Simulating CPS Taxes” Amy O’Hara, SEHSD working paper, 2004 

https://www.census.gov/library/working-papers/2004/demo/oharataxmodel.html. 80 The TAXSIM model was developed by the National Bureau of Economic Research (NBER). More information about 

the model can be found at http://users.nber.org/~taxsim/feenberg-coutts.pdf. 81 The Bakija model was developed by John Bakija with support from various organizations, including the Congressional 

Budget Office. For more information see the model documentation at 

https://web.williams.edu/Economics/wp/BakijaIncTaxCalcDoc.pdf. 82 For a summary of the advantages and disadvantages of each tax model, see Wheaton and Stevens (2016), The Effect 

of Different Tax Calculators on the Supplemental Poverty Measure. https://www.census.gov/library/workingpapers/2016/demo/wheaton-stevens-2016.html 83 https://ideas.repec.org/p/cen/wpaper/19-14.html
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Currently, in-kind benefits, SNAP, WIC, and Public Assistance84 are available for some states in 

some years. Data on housing assistance are available for those receiving assistance from the U.S. 

Department of Housing and Urban Development. Households receiving housing assistance from 

USDA or state/local programs are not included in the HUD data set. Data on LIHEAP are available 

for only one state. Data on Medicare and Medicaid receipt are available from the Department of 

Health and Human Services and subject to HIPAA constraints on their use. Data on means-tested 

benefits received from the Department of Veteran’s Affairs are available at the Census Bureau for 

approved research projects.

The Census Bureau is continuing to develop its administrative records acquisition program, and 

future acquisitions could improve the ability to accurately measure income and poverty. For 

example, the Census Bureau is considering requesting more detailed data from the IRS, which

would provide more complete reports of gross earnings, federal tax obligations and credits, and 

limited information on state, local and property tax obligations, information that excludes 

depreciation from rental income, and information on unemployment compensation, educational 

assistance, capital gains, alimony, moving expenses, and gambling winnings. Future data 

acquisitions could improve the coverage of in-kind benefits administered at the state-level. Other 

data gaps may or may not be addressed, such as data on worker’s compensation payments, and 

employer contributions to retirement plans and health insurance. 

Administrative record quality and other issues

While administrative records can dramatically improve income estimates from survey data, the 

value of administrative records depends on the income component measured and the quality of 

the administrative data. For some administrative records, considerable work is required to 

“clean” the data, remove duplicates, and decipher the metadata.

For example, administrative data on Social Security benefits, SSI, and retirement income could be 

directly substituted for survey reports. On the other hand, use of administrative data on wage 

and salary earnings85 and income from self-employment is less straightforward and decisions 

must be made when survey responses do not match estimates from administrative data, and 

when data from one administrative source do not align with another.

Administrative records may not be available for the same reference period as the survey data, 

presenting another challenge to researchers. For example, the ACS reference period is the 

“previous 12 months,” whereas the reference period of IRS records is the tax year. A related issue 

is that the unit of analysis may vary across sources. For example, a family or household unit may 

not align with a tax unit.

As previously described, many sources of administrative records derive from state-administered 

programs where data availability varies. Decisions would have to be made as to how to treat 

respondents from states and programs with missing data. The Census Bureau is currently 

undertaking some research to evaluate an approach that would model responses for missing 

states using the states for which data are available.

 84 Not all cash assistance is covered by these data. 85 Some sources of earnings (e.g. tips, under the table payments) may be reported in the survey but not in the 

administrative records.
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Trade-offs between timeliness and accuracy of estimates

Administrative records are usually delivered to the Census Bureau at a lag compared to the 

survey data collected directly by the Census Bureau. This lag would likely mean that some (or 

many) administrative records may not be usable via direct substitution within the timeframe of 

the official publication of income and poverty estimates in September each year. Decisions would 

need to be made with respect to how to use these linked data, such as whether to delay 

publication of an alternative poverty measure until most administrative records are available, or 

to use the older administrative data to model current income sources. In addition, contingency 

plans would need to be developed to produce poverty estimates if an administrative data source 

was delayed in delivery/processing, became unavailable, or changed as a result of a policy 

change.86

Recommendations:

22. The Working Group recommends, taking into consideration the advantages and 

disadvantages for each of the above approaches for correcting for missing or 

misreported data, the application of all three approaches, where appropriate, in the 

following order:

Methods that combine administrative data with survey data are the preferred approach 

for adjusting survey data to correct for misreporting and missing data. These methods may 

involve direct replacement of survey responses with administrative reports when research 

supports the quality of the administrative records relative to survey reports. The 

administrative data need to be available for use in production and the administrative data 

must also be available in a timely fashion.

When survey reports conflict with administrative records for particular individuals, 

research should examine criteria to determine which source to use for the poverty 

estimates.

Consistent with Foundations for the Evidence-based Policymaking Act of 2018, efforts to 

encourage and facilitate data sharing across government agencies should be 

strengthened.

Research to assess ways in which survey data might be made more comparable to 

administrative data (e.g. changing the reference period for income from the previous 12 

months to the previous calendar year) should be encouraged. 

Regression-based modeling (with or without individual-level or aggregate administrative 

data) can also improve the quality of estimates of income, expenditures, and program 

participation. These regression-based techniques can be used, for example, when sharing 

 86 For example, data available from the IRS on employer contributions to health plans are the result of provisions of the 

Affordable Care Act (ACA). If there are changes to the ACA requirements the data available from the IRS could change.
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agreements do not allow for direct substitution, there are significant lags in the availability 

of administrative data, or administrative data are not available for all geographies or 

years. Regression-based modeling such as Sequential Regression Multiple Imputation 

(SRMI) should replace hot deck imputations where feasible and continued research should 

be conducted to improve these methods as new tools and techniques become available.

Some rules-based adjustment may be necessary for some programs and income sources.

For example, if program rules assign automatic eligibility for Medicaid to all TANF and SSI 

recipients, it could be logical to assign program receipt to all survey respondents who are 

known to participate in either of these two programs. In a similar vein, if there are school 

districts in which all students are deemed eligible for free school lunch, they should be 

assigned participation in free school lunch if they report that their children regularly eat 

school meals. 

23. The Working Group recommends that the advisory structure recommended previously 

should vet decisions about data sources, adjustment strategies, and other 

assumptions. This advisory structure should consider and discuss continued research 

into availability and applicability of administrative data sources.

24. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics continue to research, and possibly implement, ways to reduce survey burden 

and improve the quality of resulting data through increased access and use of 

administrative data in surveys, including the CPS ASEC, ACS, and CE. 

Consumption-based Resource Measures Using the CE Interview Survey 

There is a long history of constructing consumption poverty or inequality measures from CE 

Interview Survey expenditure data (Cutler and Katz 1991; Garner 1993; Slesnick 1993; Meyer and 

Sullivan 2012a,b, 2017, 2018; Fisher et al. 2015). The Working Group has considered the 

approaches that researchers have typically followed. Researchers begin by summing most 

categories of expenditures, such as food eaten at home, food eaten away from home, rent, 

utilities, transportation, clothing and other goods and services. Expenditures that are made 

through SNAP food assistance program would also be included. Some other social transfers inkind such as WIC would also be included in consumption measures as they would be in 

comprehensive income measures. Certain categories of expenditures have been thought of as 

enhancing future consumption and thus, have been typically excluded, such as pension 

contributions and education expenses. (See, for example, Cutler and Katz 1991; Slesnick 1993; 

Meyer and Sullivan 2012b; Fisher et al. 2015.) Health expenditures are less uniformly excluded, 

since they have both substantial investment and immediate consumption features. The Working 

Group proposes a variant on this approach where a value of any public or private health 

insurance coverage that an individual has is included. Current expenditures on owned vehicles 

and homes are also typically excluded. Instead, the value of service flows is calculated for these, 

using methods like those previously described in an earlier section, and included as part of 

consumption.

The main source of consumption data in the U.S. is the CE Interview Survey. See Appendix B for 

an overview of the CE Interview and Diary surveys. 
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Recommendations:

25. The Working Group recommends funding support of the work to develop the new 

recommended measures, including funding to support BLS to research the nature and 

construction of a potential consumption-based poverty measure and improve the CE 

program in support of improved poverty measurement. A proposal requesting $7.1 

million was included in the fiscal year 2021 President’s Budget.

26. The Working Group recommends that the Bureau of Labor Statistics use the CE 

Interview Survey data to research and develop a consumption-based resource 

measure. 

Value of Rental Housing 

For most renters, the value of rental shelter is just the rent they pay and thus is assumed to be 

the consumption value of shelter.

87 For others, methods to derive the consumption value of rents 

must be used. The rent paid by those living in public, subsidized, or rent-controlled housing, or 

living in a unit without paying any rent usually does not approximate the consumption value of 

that shelter. One can impute a market rent or rental equivalent for consumers in the CE Interview 

Survey in non-market valued shelter using information available in the CE Interview Survey on 

housing unit characteristics and geography. Hedonic models can be used to impute these rents.

Value of Flow of Services from Owner-occupied Housing 

The value of the flow of services from owner-occupied shelter (including second homes) can be 

derived using user costs or rental equivalence approaches. These were presented and discussed 

in the income section of this report that focuses on including a net implicit income from owneroccupied housing as well as owned vehicles (both taking into account the flow value of services 

as well as the landlord and vehicle lessor-type expenses). Most researchers using the CE

Interview Survey have used self-reported rental equivalence for owner-occupied housing as 

collected in the questionnaire. However, other approaches to estimate imputed rents have been 

used by the BEA for the NIPA as discussed earlier. For a consumption measure of resources based 

on reported rental equivalence, out-of-pocket spending on owner-occupied housing (e.g.,

mortgage interest payments, property tax payments, spending on insurance, and maintenance 

and repairs) would not be included in consumption. However, such expenses could be used to 

 87 To the extent consumption amounts measure the quantity and quality of goods or services measured, they are valid 

individual measures of well-being. To the extent that consumption amounts are determined by factors other than 

quantity and quality, their validity as individual measures of well-being may be less clear. For example, if two 

households live in identical housing units in the same geographic area and the landlord for one household raises the 

rent for one unit, but the landlord for the other does not, does it follow that the one for whom rent is raised is better 

off? The interpretation of rent increases for consumption poverty depends on the reasons for the increases as well as 

the tradeoffs that households must make when those increases occur. Is the reason for the rent increase related to 

inflation, increased amenities (attributes) in an area, or is the rent increase being used as a strategy for making tenants 

leave the housing unit or because the tenant is new to the unit? The rents paid by tenants living in a unit for a longer 

period of time tend to represent under-estimates of their consumption while those who recently moved into their 

rental units, the rent reported will be an over-estimate (see Gallin and Verbrugge 2019). When rent is raised are 

households able to move easily to another more affordable housing unit or must they reduce expenditures in other 

consumption domains?
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support the derivation of imputed rents using a user costs approach (see, Garner and Verbrugge 

2009). It needs to be pointed out that, regardless of the approach used to derive a measure of 

the consumption value of the flow of services from owner-occupied housing, expenditures for 

the purchase and repairs of major appliances that are typically included in rented housing units 

would also not be counted among the other expenditures used to derive a consumption 

measure. 

Value of Flow of Services from Owned Vehicles 

Regarding vehicles, because vehicle expenditures occur very unevenly over time, to account for 

the consumption value of the flow of services from the stock of vehicles owned, one would not

include the spending on new or used vehicle purchases, or expenditures associated with owning 

a vehicle (e.g., routine maintenance and repairs, insurance, interest paid if financed) in a 

consumption measure. Instead, an estimate of the value of the flow of services consumed from 

owned vehicles would be included. As noted in the income section, this flow of services from 

owing vehicles could be estimated as a function of an interest rate that reflects foregone interest 

by investing in the vehicle, estimated depreciation rates that reflect differences by age of the 

vehicle, and the current market value of the vehicle. The current market value of the vehicle is 

determined from the reported purchase price of the vehicle or an imputed current market value.

To estimate the current market value of each car owned (if not purchased within the reference 

period), researchers can use detailed information on vehicles including make, model, and year 

available in the CE Interview Survey data. Other sources of data for current market values of new 

and used vehicles include those from aggregators like the National Automobile Dealers 

Association (NADA); using these data, interest and depreciation rates would still need to be 

selected. Another approach to derive the value of the flow of services from the stock of vehicles 

owned is to use data based on leased vehicles or short-term vehicle rentals.

Recommendations:

27. The Working Group recommends that the value of service flows from owner-occupied 

shelter and the value of the service flows from owned vehicles be included in the 

consumption resource measures.

Implementation Issues for Consumption-based Resource Measures Using the CE Interview Survey 

As discussed in greater detail in Appendix B, the CE consists of estimates derived from two 

separate surveys, the Interview Survey and the Diary Survey. The Interview Survey is designed to 

collect data on large and recurring expenditures that consumers can be expected to recall for a 

period of 3 months or longer, such as rent and utilities, and the Diary Survey is designed to collect 

data on small, frequently purchased items, including most food and clothing. Together, the data 

from the two surveys cover the complete range of consumers’ expenditures. Although the CE 

includes a Diary Survey component with a separate sample, the CE Interview Survey component 

is more suitable for analyses of poverty (and inequality more generally) because its longer 

reference period (a calendar quarter) as compared to the Diary Survey (two weeks) yields a more 

complete picture of spending. Additionally, the Interview Survey’s coverage of the universe of 

expenditures approximates that of the open-ended diary, with about 65 percent of all spending 

represented by detailed questions and another 25 percent by global questions.
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Accounting for Expenditure Misreporting 

There is concern that some expenditures in the CE Interview Survey are misreported at the 

micro-level and that this misreporting is thus carried over into aggregate statistics. To assess the 

degree of differences at the aggregate level, CE Interview Survey data have been compared to 

data from many sources, but the most extensive and heavily cited comparisons are to the 

Personal Consumption Expenditure (PCE) data from the NIPA, with CE estimates being lower for 

most commodity groups (see, for example, Passero et al. 2015, as well as continuing comparisons 

completed by the CE program). However, there are reasons for differences in estimates from the 

CE Survey and the PCE including different population coverage, goods and services included, and 

concepts. Unfortunately, unlike for income, very few components of expenditures can be 

compared to administrative data at the household level; one exception is rent in public and 

subsidized housing, as noted in Christensen et al. (2019).

Focusing on the comparisons that separate Interview Survey and Diary Survey data, Bee, Meyer, 

and Sullivan (2015) report ratios of CE Interview Survey data to PCE aggregate data for 1986 and 

2010. They divide the largest categories of expenditures for which comparable CE Interview 

Survey and PCE data are available into what they consider “well-measured” and “poorly 

measured” categories. Among the eight largest comparable categories of expenditures, six are 

reported at a high rate in the CE Interview Survey and that rate has been roughly constant over 

time. These well-measured categories are the imputed rent on owner-occupied housing, rent and 

utilities, food at home, gasoline and other energy goods, communication and new motor 

vehicles. In 2010, the ratio of CE Interview Survey to PCE is 0.95 or higher for imputed rent, rent 

and utilities, and new motor vehicles. It is 0.86 for food at home, 0.80 for communication, and 

0.78 for gasoline and other energy goods.

When interview aggregated expenditures are compared to PCE aggregates, the categories that 

represent the largest shares of expenditures in what they consider “poorly measured” are food 

away from home with a ratio of 0.51, furniture and furnishings at 0.44, clothing at 0.32, and 

alcohol at 0.22, and for all of these measured categories, the ratio has fallen noticeably since 

1980.88

While under-reporting is generally a concern for recall-based household surveys, recent research 

results have also provided evidence of over-reporting for certain expenditure categories. In a 

records verification study, Tseng et al. (2012) noted that “With regards to the distribution of 

report values, participants’ underestimated costs for 37 percent of items reported and 

overestimated costs for the remaining 33 percent of the items.” The authors found overestimates 

of expenses in categories such as subscription/membership fees, home furnishings, and rent. 

Other studies with findings of CE over-reporting include Christensen et al. (2019), who reported 

that in a matched sample of CE-HUD administrative records, subsidized renters over-report rent 

payments by $100 on average, when compared to administrative data. Additionally, in the most 

recent comparisons of CE-PCE for rental equivalence, the 2017 CE-PCE ratio for imputed rental of 

owner-occupied nonfarm housing was 1.10. 

 88 To address concerns about potential measurement error in consumption based on CE data, Meyer and Sullivan 

(2012b, 2017) build upon the evidence noted above and construct a measure of economic well-being that is based on 

what they identify as “well-measured consumption.” This they define as food at home, rent plus utilities, gasoline and 

motor oil, the rental value of owner-occupied housing, and the rental value of owned vehicles.
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Time Period 

An important issue in constructing a consumption resource measure is deciding on the time 

period. The natural choices are either 3 months, relying on a single CE interview (as is done in 

Meyer and Sullivan 2012b, 2017) or 12 months, relying on four consecutive interviews (as is done 

in Fisher et al. 2015, and Aguiar and Bils 2015). There are advantages to each choice. Using 3 

months has the advantage of relying on the largest sample and reduces the potential bias due to 

attrition over the course of the survey. Using 12 months provides a longer-term measure of 

consumption. A shorter time period will lead to a more dispersed measure of the consumption 

distribution and a higher poverty level if thresholds are just scaled by the length of the time 

period.

According to Coibion et al., a potential concern about a short time period is the potential for 

shopping frequency to change over time which could affect the ability to make valid comparisons 

over time, but the evidence suggests that the difference between 3 and 12 months is small, 

unlike the difference between 2 weeks and 3 months which appears to yield very different 

indicators of inequality (Coibion et al. 2017).

There is also nothing particularly compelling about looking at a longer-term measure of 

consumption. Over either 3 or 12 months, consumption should in principle provide a longer-term 

measure of well-being than would income over the same period assuming some consumers can 

borrow or draw from savings to partially fund their consumption.

Sample Design 

The CE sample design is a two-step process in which a first-stage representative sample of 

geographic areas is selected from the U.S., and then a second-stage representative sample of 

household addresses is selected from those areas. This method is designed to produce unbiased 

weighted expenditure estimates at the national, U.S. Census Region, and U.S. Census Division 

level, but not at the state level (King and Wilson 2018). Of note, data users cannot generate 

statistically valid estimates at the state level since CE weights are calibrated to household and 

person population totals at the U.S. Census Region (prior to 2017) or the U.S. Census Division 

(2017 and later) level. Additionally, not all states are included in CE’s sample design. 

The use of state-level CE estimates for a consumption poverty measure would require changes in 

CE’s first-stage sampling areas (both urban and rural) and sample sizes. First, the sample design 

must be updated to expand to an additional 4 states, which would ensure coverage in all 50 

states and the District of Columbia. Second, in order to support reliable state-level estimates, CE 

Interview Survey sample sizes need to be substantially increased from the current annual level of 

approximately 13,000 unique household addresses. These addresses yield on average 22,000 

interviews in a calendar year, which includes respondents who participated in multiple quarterly 

interviews. For comparison purposes, in the construction of state-level estimates, the 

Supplemental Poverty Measure relies on three years of CPS ASEC data, with about 90,000 

household addresses per year (Fox 2019, Semega et al. 2020). Conversely, under the current 

sample design, CE estimates are calculated from a relatively small sample of predominantly urban 

areas. Within these areas, the CE program surveys only a small percentage of household 
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addresses. For example, in New York State the CE program successfully interviewed roughly 1,400 

consumer units for the Interview Survey in 2019. 

Additional Considerations 

There are additional considerations related to CE’s sample design and availability of 

administrative data for use in a consumption-based resource measure. 

CE sampling, weighting, and coverage

First, if 12 months of respondent data are required, the sample size must be increased even 

further, since only approximately 60 percent of respondents complete all quarterly interviews. 

Second, a longitudinal weight must be produced. Third, the Interview Survey is not exhaustive in 

the sense that not all expenditure or consumption categories are covered; the Interview Survey 

covers approximately 90 percent of spending, excluding items like non-prescription drugs and 

postage stamps (BLS 2018).

Recommendations:

28. The Working Group recommends that the CE Interview serve as the primary data 

source for the production of the consumption resource measures, with estimates 

produced at the state level. 

29. The Working Group recommends that the current CE Interview Survey serve as the 

interim data source for the production of the consumption resource measures, with 

estimates produced at the Census Division level.

Availability of administrative data

As discussed in the earlier section Administrative Data Use Considerations within the discussion 

of an income-based resource measure, it is important to consider whether data are available for 

use for these purposes. The Working Group recognizes that the strategies to correct for 

misreporting and impute missing data described earlier in the section titled Correcting Survey 

Data for Misreporting and Improving Tax Estimates are more applicable to income surveys than 

to the CE survey given the current paucity of reliable administrative data on expenditures. 

However, the Working Group also recognizes that current research at BLS using administrative 

data from HUD on public and subsidized housing is promising (see Christensen et al 2019). 

Another promising strategy for estimating consumption poverty with existing expenditure data is 

to use only those components of expenditures that research finds to be well-measured. 

Valuing Health Insurance 

The same choices that one faces for valuing health insurance with an income resource measure 

also apply to a consumption resource measure. See the section titled Valuing health insurance

and the treatment of MOOP for more information. The Working Group’s consensus 

recommendations on valuing health insurance for both an income- and consumption-based 

resource measures are available within that section.
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Treatment of Education 

The treatment of education for a consumption resource measure, similar to for an income 

resource measure, relies on whether education is considered current consumption or an 

investment. See the previous section titled Treatment of Education for more information. The 

Working Group’s consensus recommendations on valuing education for both income- and 

consumption-based resource measures are available within that section.

Advantages/Disadvantages of Income- and Consumption-based Resource Measures 

Conceptual Issues 

Conceptual limitations of income resource measures have influenced a large literature that looks 

at consumption-based measures of well-being and discusses their advantages (Cutler and Katz 

1991; Fisher and Johnson 2012; Fisher, Johnson, and Smeeding 2015; ILO 2003; National 

Research Council 1995; Meyer and Sullivan 2003, 2011b, 2012a,b; Poterba 1991; Slesnick 1993; 

UNECE 2017; U.S. Census Bureau 2003; World Bank 2018). Consumption is often thought to 

better estimate a families’ long-term or “permanent” income. Annual income will not capture the 

standard of living of individuals who smooth consumption by drawing upon savings or borrow to 

fund this consumption. Also, income-based measures of well-being will not capture differences 

over time or across households in wealth accumulation, ownership of durable goods such as 

houses and vehicles, or access to credit. In addition, many anti-poverty programs such as 

Medicaid provide an insurance value to households, freeing up resources otherwise set aside for

an emergency, that would be reflected in consumption but not income.

Some researchers have argued that income may have some conceptual advantages over 

consumption. One reason is that individuals can choose to have low consumption, while income 

reflects access to resources that can be used for consumption, and as such is not driven by 

consumption decisions (Atkinson 1991). However, individual choices affect the level of income as 

well through education, employment, occupation, and hours worked choices. Another potential 

advantage to income is that current consumption fails to capture the benefits to donors of 

leaving bequests. 

In their Poverty Guidebook, the United Nations Economic Commission on Europe (2017, see p. 

38) lists several conceptual advantages of an income measure: income measures a household’s 

command over resources, there is a direct link between transfer program policies and income, 

and income measures allow for disaggregation by income source components. From a conceptual 

perspective, income allows people to satisfy their needs and pursue many other goals that they 

deem important to their lives. In particular, disposable income is an effective proxy for the 

resources that are available to an individual or household for either consumption (if they so wish) 

or saving. In addition, income-based poverty measures are often appealing to policy makers due 

to direct policy levers that exist through, for example, the targeting of welfare payments or tax 

credits to families below the poverty line. And, in general, it is possible to break down income by 

source (wages, pensions, social protection receipts, intra-household transfers, etc.) when 

analyzing poverty. This provides advantages in terms of understanding poverty within a certain 

group, and as a quality check for the data, via possible comparisons with other data sources.

A conceptual difference between consumption and income is the treatment of savings and debt. 

A consumption measure of resources might indicate a high level of consumption for a family that 
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has gotten itself heavily into debt. In such a case, a consumption measure would record the high 

level of spending while borrowing, but in the future, it would also record a low level of 

consumption if the family must sharply cut back to repay loans. Income in such a situation might 

miss both the period of high spending when the family is enjoying more goods and services and 

also miss the period of a tighter budget. In general, consumption should capture the extent to 

which people are able to maintain their living standard when they experience loss of employment 

or plan to work less out of choice. How this differs across population groups (possibly depending 

on the ability to obtain credit) or over time would be missed by an income measure.

Measurement Issues 

Both income and consumption resource measures are subject to misreporting. However, on the 

income side there are many more sources available to use to understand the extent of the 

misreporting than on the consumption side. 

Income misreporting

Income in the CPS ASEC appears to be misreported, especially for categories of income important 

for those with few resources, and the extent of under-reporting has worsened over time as 

discussed in detail above.

Comparisons of income and expenditures at the bottom of the distribution provide additional 

evidence that income is under-reported. Reported expenditures exceeds reported income at the 

bottom of the distribution, even for those with little or no assets or debts (Meyer and Sullivan 

2003, 2011a).89 For recent years, the fifth percentile of the expenditures distribution in the CE

Interview Survey is more than 40 percent higher than the fifth percentile of the income 

distribution in the CPS ASEC. For families in the CE Interview Survey in the bottom five percent of 

the income distribution, expenditures exceed income by more than a factor of seven (Meyer and 

Sullivan 2011b). While expenditures could be overreported at the bottom of the distribution 

rather than income underreported, expenditures on average are underreported.90 Furthermore, 

one of the main lessons of Meyer, Wu, Mooers, and Medalia (2021) is that the income 

distribution at the very bottom after incorporating administrative data becomes much closer to 

the consumption distribution, suggesting that much of the difference is due to income data that 

was in error originally.

Expenditure Misreporting

There is also evidence that at least some types of expenditures are misreported in the CE 

Interview Survey and that this misreporting has increased over time. However, as discussed 

above, among the eight largest comparable categories of expenditures, Meyer and Sullivan 

(2011b) consider six to be well reported: rent and utilities, food at home, gasoline and other 

energy goods, communication, and new motor vehicles.

However, Sabelhaus et al. (2015) find that the income reported in the CE Interview Survey, either 

because high-income people are missing or because income is under-reported at the top, does 

not match well to other sources such as the Survey of Consumer Finances and tax records.

 89 These comparisons have been questioned by Bavier (2008) based on the quality of income data in the CE from which 

these comparisons are derived. 

90 Comparisons of aggregated survey data expenditures to NIPA consumption indicate under-reporting of expenditures 

on average as noted above.
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Furthermore, reported spending relative to income is very low at the top. The finding that much 

of the under-reporting of expenditures occurs at the very top of the income distribution means 

that the aggregate under-reporting of consumption statistics may overstate the weakness of the 

CE Interview Survey for a typical low-income person. 

Of note, respondents at the low end of the income distribution may misreport income that is 

significantly lower than expenditures for a variety of measurement related reasons, including 

inaccurately reported means tested transfer program participation, and other recall-related 

under-reporting. However, certain population subgroups, such as students and retirees, may be 

accurately reporting low income and relying on assets and debt to support relatively higher than 

expected expenditures. As reported in Crain and Wilson (2017), “... 13 percent of the reference 

persons in the lowest 20 percent income quintile identified themselves as college students, and 

an additional 33 percent of the reference persons reported that they were retirees. Accordingly, 

46 percent of the households ranked in the lowest income quintile are at stages in the life-cycle 

where one would assume lower incomes might prevail... Given the proportion of college students 

and retirees, who are assigned to the lowest 20 percent, one can assume that not all households 

within the lowest quintile represent the usual image of a “poor” household.”

Income vs. Consumption

International statistical agencies have expressed mixed recommendations on this issue. On the 

one hand, the World Bank and the ILO have stated preferences for consumption-based measures.

The ILO states that final consumption is the most appropriate concept for welfare analysis as it 

takes into account all consumer goods and services available to the household for the satisfaction 

of needs and wants of its consumers (ILO 2003, paragraph 26). The World Bank in its 2016 report 

of the Commission on Global Poverty stated, “consumption per capita is the preferred welfare 

indicator for the World Bank’s analysis of global poverty.”

On the other hand, the Canberra Group (2011 p. 3) favors income-based measures. In the 2011 

update of its report, it states, “For most people, household income is the most important 

determinant of economic well-being.” However, the group does acknowledge that there are 

cases for which consumption expenditure may represent a better estimate of the household’s 

sustainable standard of living. They conclude, “In fact, the choice between the income or the 

consumption expenditure approach to measuring economic wellbeing is often made for the 

analyst by the fact that, at least in developed countries, income data may be more frequently 

available than data on consumption expenditure.”

The UNECE Handbook on Poverty (2017, p46) lists the advantages and disadvantages of each 

concluding “where both income and consumption expenditures data are available for a given 

population, there is value in utilizing poverty measures based on both approaches. For 

international comparisons of poverty across the UNECE region, it is recommended that income 

be the main welfare measure, given its widespread usage among EU and OECD countries as well 

as increasing availability in other areas of the region”. The UNECE handbook (p. 48) also includes 

another recommendation that “where data availability allows it, compilers of poverty statistics 

consider examining poverty measures based on income and expenditures as well as their 

intersection.”

In their evaluation of poverty measurement for the United States, Measuring Poverty notes that

“On balance, many members of the panel find more compelling the arguments in favor of a 
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consumption definition that attempts to assess actual levels of material well-being”. The panel’s 

final recommendation, however, called for an income-based measure because of concerns about 

adequate consumption data noting, “in the United States today, adequate data with which to 

implement a consumption-based resource definition for use in the official poverty measure are 

not available." (National Research Council 1995, p. 213) One important concern was that small 

samples in current consumption datasets make it difficult to construct poverty statistics at the 

subnational level, rather than national statistics as well as concerns with response rates and 

recall bias. Other concerns regarding data quality are discussed below.

A case for the accuracy of income would note that, for most people, income is easier to report 

given that much of it is recorded in tax records and that it typically comes from a small number of 

sources (predominantly earnings). However, for analyses of families with few resources this 

argument is less valid, as these families tend to have many, often sporadic, income sources (Edin 

and Lein 1997). Additionally, while income may be easier to report for some, it is likely to be a 

more sensitive topic for survey respondents than spending for consumption. 

There is empirical evidence that consumption and income measures are very different. For those 

with few resources, spending on consumption exceeds income. Using the PSID, this is evident 

even for those with little or no assets or debts, so the discrepancy may not be due to borrowing 

or drawing down assets. This pattern is particularly evident the closer to the very bottom of the 

distribution examined (Rogers and Gray 1994; Jencks 1997; Sabelhaus and Groen 2000; Meyer 

and Sullivan 2003, 2011; Hall and Rector 2018). For families near the very bottom of the income 

distribution, these authors find that expenditures are a multiple of income. Evidence from other 

countries is consistent with that for the U.S. that shows that those at the very bottom in income 

data are often close to average in their spending. In their paper, "Why are Households That 

Report The Lowest Incomes so Well-Off?", Brewer, Etheridge, and O'Dea (2017) show that on 

average in the UK spending is much greater among families with extremely low income than 

those with just very low income. The authors conclude that this is mostly due to under-reporting 

of income (also see Brzozowski and Crossley 2011).

While both income and consumption surveys suffer from misreporting, there is some evidence 

that, as discussed above, components of consumption that are particularly important for the 

poor are fairly well-captured in surveys while many components of income important to the 

poor, especially transfer payments, may not be captured well (Roemer 2000; Wheaton 2007; 

Gathright and Crabb 2014; Meyer and Sullivan 2015; Rothbaum 2015; Meyer, Mittag and Goerge 

2018; Meyer and Mittag 2019).

Some argue that consumption is more highly correlated with many measures of material wellbeing than income, in that material hardship and other adverse family outcomes are more severe 

for those with low consumption than for those with low income. This issue is discussed in the 

next section. 

On the other hand, many would argue that there are important advantages to an income poverty 

measure over a consumption measure. First, there exist administrative records for many income 

sources, but for few spending categories. As described in this report, administrative records are 

useful for benchmarking survey estimates at the aggregate level, and improving survey responses 

at the individual level. Second, income questions are more likely to appear on other surveys, 

including other federal surveys, than consumption questions. This is important because 
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researchers may use these surveys to examine the relationship between poverty and housing, 

health, and other topics. Being able to benchmark a survey’s income poverty estimates to other 

income poverty estimates could be valuable. Third, the survey data required to measure income 

poverty are available at the sub-national level, including at the state-level and even at the Census 

block level (when using the ACS). This is important because analysts can examine trends in 

poverty that may vary across sub-national geographies and examine the effect of state-specific 

policies.

Capturing economic disadvantage 

The first two goals of a poverty measure outlined in the Background section of this report were 

assessing economic disadvantage at a point in time and over time. The Working Group discussed 

the possibility of placing the choice of a poverty measure on a firm footing by examining

empirical evidence on how well different poverty measures capture economic well-being. This 

approach to assessing measures is not perfect as available indicators of economic well-being will 

not be comprehensive, and one measure may be associated more highly with some indicators, 

while another measure may be more highly associated with others.

The Working Group begins by assessing how well consumption and income measures capture 

disadvantage at a point in time. The comparisons from the CE Interview Survey have found that 

consumption poverty is more highly associated with available indicators of material well-being 

than income poverty at a point in time (Meyer and Sullivan 2003, 2007, 2011, 2012; Fisher et al. 

2009). These studies look at a wide range of indicators including car ownership, housing 

characteristics, appliance ownership, education and assets. The evidence from other surveys such 

as the SIPP, and the University of Michigan’s Panel Study of Income Dynamics and Health and 

Retirement Study is mixed, but slightly favors consumption (Meyer and Sullivan 2003, 2007, 

2011; Charles et al. 2006; Bavier 2008). The SIPP studies examine hardship measures, the PSID 

studies examine housing and car characteristics, health and food insecurity measures, while the 

HRS study examines physical health, wealth, housing characteristics, mental health and food 

hardship.

A difficulty in interpreting these comparisons is that the definitions, comprehensiveness and 

accuracy of income and consumption differ across dataset. In addition, different researchers have 

defined income and consumption differently in different studies as well. Most of the CE Interview 

Survey studies used data from before 2004 that did not include imputed income values and that 

has led some authors to question the early comparisons (Fisher 2006; Bavier 2008). However, the 

strongest evidence comes from the most recent study, Meyer and Sullivan (2012), discussed 

above, that uses the income data without the drawback of the earlier data.91 However, even the 

pre-2004 CE Interview Survey data are more comprehensive and come from a larger battery of 

questions than the consumption data used in the other surveys. In each of the other surveys that 

have been used for comparisons of income and consumption, income collection is a primary 

focus of the survey, while consumption data is at best a brief section or one-time short 

supplement, so that uneven data quality has given the income data an advantage in the 

comparisons.

 91 Another potential concern with some of the earlier studies is that they examined cutoffs well above or below typical 

poverty cutoffs. This was not the case with the Meyer and Sullivan (2012) study that used the SPM level of poverty. 
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Second, the Working Group examined the short literature comparing how different poverty 

measures capture changes in economic well-being over time. Meyer and Sullivan (2011) 

conducted a thorough examination of the short-term relationship between income and 

consumption poverty and economic conditions, such as the unemployment rate and GDP, over 

five decades. They examined the relationship between income and consumption poverty and 

economic conditions at both the national and regional level, specifying economic conditions 

several different ways. They found that both income and consumption poverty were sensitive to 

macroeconomic conditions. One might expect that income would be more strongly related to the 

business cycle than consumption given that consumption reflects more long-term prospects and 

is known to be less subject to shocks than income. However, the evidence on whether income is 

more responsive to the business cycle than consumption is mixed. Income poverty does appear 

to be more responsive using national level variation, but consumption poverty is often more 

responsive to unemployment when using regional variation.

There is even more limited work looking at other indicators. Shaefer and Rivera (2018)examine 

several outcomes using a very small number of annual observations (usually 6, but sometimes as 

many as 18). The authors conclude that the time patterns of consumption poverty do not match 

up well with other indicators of well-being, while income poverty does quite well. However,

Meyer and Sullivan (2018) find that after allowing for a time trend, all of the Shaefer and Rivera 

coefficients in the consumption poverty equations change sign. 92

It should be recognized that some proposed measures have not been evaluated through 

comparisons to economic well-being. For example, income measures improved by the 

incorporation of administrative data have not been evaluated because the required 

administrative data have become available only recently. It may be that an income measure that 

partially or fully incorporates administrative data may do a better job of capturing disadvantage 

than a consumption resource measure, but that is not known at this time.

Considerations and Consensus Recommendations for Thresholds 

Poverty thresholds are a key component of a poverty measure. In this report, poverty is defined 

as economic deprivation and measured in terms of income or consumption.93 Individuals with 

income or consumption that falls below the poverty threshold are counted as poor, and 

individuals with income or consumption at or above the poverty threshold are not counted as 

poor.

94 An implicit consideration of the Working Group for recommending new measures (see 

section titled Considerations and Consensus Recommendations for Income- and Consumptionbased Resource Measures) is that thresholds and resources be consistently determined, either 

explicitly or implicitly. In the case of thresholds defined in terms of basic needs, an explicit 

adjustment to resources would be needed. For example, if childcare is included in basic needs, 

subsidies for childcare would be added to resources. If health insurance is included in thresholds, 

 92 Granger and Newbold (1974) argue that to analyze the short-term association between trending variables one 

should include a time trend to prevent spurious correlation.

93 Poverty thresholds could also be produced that are based on other indicators of deprivation, for example, food 

insecurity or a lack of health insurance, or a multitude of indicators (see the Future Research section).

94 In this section the Working Group highlights choices regarding setting poverty thresholds in a base year and 

updating them over time. However, poverty thresholds employed by national and international statistical offices and in 

policy discussions are often referred to as “absolute”, “relative”, and “subjective” (for example, see: National Research 

Council 1995; Ravallion 2016; World Bank 2018; UNECE 2017). 
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health insurance would be counted in resources. How these adjustments to resources are made 

is dependent upon whether an income- or consumption-based measure is assumed.95 In contrast, 

when thresholds are derived from the resource measure (the two non-basic needs thresholds 

described in the next section), thresholds and resources are by definition consistently defined 

and thus no additional adjustment is needed.

There are several key decisions to be addressed when developing a poverty threshold: (i) where 

to set the threshold in a baseline year, (ii) how to adjust the threshold over time, and (iii) how the 

threshold varies across geographic locations.

The Working Group has discussed a number of considerations to understand and evaluate these 

key decisions for thresholds. Those considerations are articulated in the following sections. The 

Working Group recommends the following: 

Recommendations:

30. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics seek additional stakeholder and expert engagement on the key decisions 

required in setting poverty thresholds. 

31. The Working Group recommends that this engagement should occur in parallel with 

the Census Bureau and the Bureau of Labor Statistics developing research income- and 

consumption-based resource measures. 

32. The Working Group recommends that by the time the proposed resource measures are 

ready to be published, BLS and the Census Bureau should work to identify an interim 

solution for each set of the measures for applying thresholds to produce a full poverty 

measure. BLS and the Census Bureau should consider input from experts (per the 

previous recommendation) as available to inform the applied methodology. The 

interim methodology applied need not be the final methodology chosen for application 

to the resource measures. The intention of this recommendation is to ensure the ability 

to publish poverty measures using the proposed resource measure recommendations.

Setting Thresholds in a Baseline Year 

While the poverty threshold can be set at any dollar value in a baseline year, there are three 

general approaches for determining this dollar value: 

1. The threshold can be set at a chosen fraction of median resources.

96

2. The threshold can be set at a chosen percentile of the resource distribution.

3. The threshold can be set to reflect a view of the amount of resources needed to meet basic 

needs in terms of a specified bundle of goods and services.

 95 For a discussion of issues and approaches that could be used to account for health insurance in a resource measure, 

see the Health Insurance section. 96 Some countries use other measure of central tendency like a mean, but this is less common than the median (see 

Ravallion, 2016).
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Setting Thresholds at a Chosen Fraction Of Median Resources 

Thresholds can be set at a chosen fraction of median resources. Countries in the European Union 

(EU) and the OECD employ this approach. Most EU countries set what are referred to as “at-riskof-poverty” thresholds at 60 percent of national median equivalized income (Eurostat 2020). In 

contrast, the OECD sets poverty thresholds equal to 50 percent of median equivalized income for 

each country (OECD 2019). Setting thresholds in this way ties the poverty threshold to resources 

at a focal point of the resource distribution (i.e., the median). When baseline thresholds are set at 

a chosen fraction of median resources, poverty measures based on different resource measures 

will have different poverty rates in the baseline year.

Notably, any change in the resource definition would automatically adjust the thresholds, 

because median resources would change.

Setting Thresholds at a Chosen Percentile Of The Resource Distribution 

Thresholds can alternatively be set at a chosen percentile of the resource distribution. For 

example, if the 20th percentile is chosen, then the poverty rate will be 20 percent. Based on the 

historical account of Fisher (2008), the OPM baseline thresholds for 1963 were implicitly set in 

this fashion. Fisher argues that during the development of the OPM during the 1960s, the Office 

of Economic Opportunity chose the lower of two threshold options produced by Mollie 

Orshansky because the lower threshold produced a poverty rate approximately equal to the 20 

percent poverty rate estimated by CEA (1964) and used by President Johnson as his baseline for 

the War on Poverty. Meyer and Sullivan (2012), CEA (2019) and Burkhauser et al. (2019) followed 

this approach as well, anchoring the threshold at the percentile in the income distribution that 

was equal to the official poverty rate in a particular year. In other words, they set thresholds such 

that the poverty rate under their own poverty measure was equal to the poverty rate under the 

OPM in a reference year. Setting thresholds at a chosen percentile of the resource distribution 

facilitates the comparison of poverty trends across different resource measures because they can 

all begin at the same poverty rate in the baseline year.

Notably, any change in the resource definition would automatically adjust the thresholds, 

because the value of the chosen percentile of the resource distribution would change.

Setting Thresholds Defined in Terms Of Basic Needs 

Basic needs thresholds are set to reflect the costs of buying a bundle of goods and services 

needed to satisfy basic needs to attain a specific standard of living. These thresholds vary 

according to household composition, and the cost of the threshold bundle within the geographic 

area where a household lives. Basic needs standards usually are defined based on some target 

level relative to some reference group such as a subsistence level or a level that allows 

achievement of such statuses as social inclusion, self-sufficiency, or social acceptance. The quality

and nature of items included in the bundle are influenced by current norms and these are 

determined by judgments influenced by time and place and can be empirically informed. 

Budgets can be descriptive, prescriptive (normative), or a combination of the two. Descriptive 

budgets represent observed expenditures for particular family or household types, for example, 

based on median spending for a specific family type; two examples are the set of Prevailing 

Family Budgets published by the BLS in the 1960s through 1980s (see Johnson et al. 2001) and 

the SPM thresholds (Interagency Technical Working Group 2010). Prescriptive budgets can be 
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based on multipliers (e.g., if food expenditures reflect 1/3 of income, food spending times three 

equals the threshold; an example is the set of Orshansky thresholds), in terms of self-declared

basic needs and associated income,97 or bundle or budget building. Examples of the latter 

thresholds are those developed by the BLS in the early 1900s (see Johnson et al. 2001) and 

Renwick and Bergmann (1993). Examples of more recently developed bundle or budget-built 

basic needs thresholds include the following: Asset Limited, Income Constrained, Employed

(ALICE) thresholds produced for the United Way;

98 Economic Policy Institute Family Budgets;99

Self-Sufficiency Standards;

100 the Living Wage Calculator;101 and basic needs thresholds calculated 

for Canada and Italy.

102

Updating Thresholds Over Time 

There are three common approaches for updating thresholds over time. 

1. Updating proportionally with the annual growth in median resources,

2. Updating with inflation each year, holding the value of the thresholds constant in real 

terms, and

3. Re-estimating each year the value of goods and services needed to meet basic needs and 

their associated costs in the geographic area for which the thresholds are produced.103

Updating Proportionally 

Updating proportionally ties the poverty threshold to changes in median resources. For example, 

when median resources grow by five percent in a given year the poverty threshold is increased by 

five percent as well. This means that thresholds automatically vary with changes in living 

standards over time, and it avoids the need to select a price index. It also means that thresholds 

over time cannot be used to ascertain how many people are lifted over a fixed living standard. 

For example, if median resources fell but the resources of those previously in poverty remained 

unchanged, the poverty rate would fall, despite the population with few resources being no 

better off.

 97 Subjective thresholds can be determined, for example, using reports to questions regarding minimum needs and 

actual income or spending. Examples using U.S. data include the following: Garner and Short (2003, 2004); De Vos and 

Garner (1991); and Garner and De Vos (1995). Recent international examples include, for example, Bishop et al. (2014); 

Buttler (2013); Carbonnier (2019). Much of this work depends of that of Goedhart et al. (1977). Kapteyn et al. (1988), 

Van Praag et al. (1980). 98 See Hoopes et al. (2020) and available at https://www.unitedforalice.org 99 https://www.epi.org/resources/budget 100 For example, see The Self-Sufficiency Standard for Washington State 2020 produced by Diana M. Pearce, August, 2020.

http://www.selfsufficiencystandard.org/sites/default/files/selfsuff/docs/WA2020_SSS.pdf

101See Nadeau (2019) and available at https://livingwage.mit.edu/ 102 For the Canadian official measure of poverty, basic needs thresholds are produced; see Heisz (2019) and STATCAN 

(2016). For the Italian example, see Cutillo et al. (2019).

103 Basic needs budgets can be updated to also reflect differing quantities and qualities of the basic bundle of goods 

and services in the budgets, thus prices alone are not the only factor that influence the updating. For example, if the 

quality of childcare within an area has increased this will be reflected implicitly in the cost for childcare included in the 

budget. In addition, basic needs budgets are expected to be rebased periodically to reflect differing combinations of 

goods and services considered to be essential. For example, a basic needs budget created for 1990 would likely not 

have included internet while one based on 2020 would.
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Updating By Inflation 

Holding constant a fixed standard of living by updating thresholds with inflation each year allows 

a poverty measure to be used to evaluate progress in lifting people above a fixed living standard

over time without “moving the goalpost.” When updating for inflation, an index must be chosen

that represents the price change over the comparison periods of interest. Identifying an index 

that allows one to hold the living standards constant over substantial periods of time and that 

accurately measures inflation is difficult. 

The choice of the inflation adjustment should be based on the concept underlying the living 

standards threshold. If the threshold to which resources are compared is assumed to represent 

some fixed standard of living, then an inflation adjustment based on cost of living and consumer 

utility theory would be ideal.104 How that living standard is defined is relevant for the selection of 

an inflation adjustment. For example, if the living standard is defined such that the flow of 

services derived from owned vehicles and owned shelter and in-kind benefits like Medicaid and 

free and reduced priced school meals are included, the inflation adjustment should reflect the 

changes in prices associated with these items in addition to price changes for consumer 

purchased goods and services. If the living standard selected does not account for education or 

medical goods and services, these would not be included in the inflation adjustment.

The threshold in the Official Poverty Measure (OPM) is based on a fixed standard of living. 

Accordingly, the All-Items Consumer Price Index – Urban series (CPI-U), published by the BLS, is 

used to adjust the value of the baseline thresholds to account for changes in prices from year to 

year.105 However, research suggests the CPI-U does not fully account for changes in quality and 

substitution of goods and services to attain a constant living standard (see Moulton 2018).106 Thus, 

the CPI-U is an upper bound to a cost of living adjustment for the general population. As a result, 

adjusting the OPM thresholds by the CPI-U could result in “moving the goalpost” over time. The 

CPI-U has undergone improvements in recent years (see BLS 2020) that partly address these issues. 

Alternative price indices including the Chained CPI-U (C-CPI-U), also produced by the BLS, and the 

Personal Consumption Expenditures (PCE) price index, produced by the Bureau of Economic 

Analysis, more extensively address these issues, though still not fully.

It is important to note that the CPI-U, C-CPI-U, and PCE indexes reflect price increases for the total 

population (in the case of the PCE) or total urban population (in the case of the CPIs). When 

adjusting poverty thresholds, there could be a compelling justification to develop and use a price 

index restricted to a lower income subpopulation for an income-based measure and a price index 

restricted to a lower consumption subpopulation for a consumption-based measure if these 

subpopulations face different rates of inflation than the general population at large.

While some research has found that lower income subpopulations have experienced similar or 

lower rates of price inflation than the broader population (e.g., Garner et al. 1996; McGranahan 

and Paulson 2006; Broda, Leibtag and Weinstein 2009), other research comes to the opposite 

 104 Under consumer utility theory, a cost-of-living (COLI) based index reflects a fixed “standard of living” because 

consumers can shift between variates of goods – and even different bundles of goods – to achieve the same 

“standard.” (see ILO CPI Manual 2004).

105 The CPI is assumed to be a very close an approximation to a cost of living index. For more information on the CPI

and additional BLS produced consumer price indexes, see https://stats.bls.gov/cpi 106 A separate Interagency Technical Working Group has been focusing on this issue with respect to the OPM and other 

income measures produced by the Census Bureau; see OMB (2019) for the Federal Register Notice requesting public 

input.
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conclusion that lower income subpopulations face higher rates of inflation (e.g., Kaplan and 

Schulhofer-Wohl 2017; Jaravel 2019). This research raises questions regarding whether the 

spending pattern among the poor is sufficiently different from the population at large to warrant 

specifically designed price indexes for the poor.

An alternative approach to using an all-items basket in the above inflation measures is to restrict 

the set of goods and services to those corresponding to a baseline thresholds that represents the 

budget needed to afford specific, basic needs. Statistics Canada and the Italian Statistical Office 

update their official poverty thresholds in this way, updating each component of the fixed basket 

of goods and services for inflation each year. The price index adjustment factors differ for each 

geographic area based on the assumption that price trends may differ.107

Recommendations:

33. The Working Group recommends that the BLS conduct a study of price indexes 

appropriate for use in updating thresholds that would be used in combination with

consumption and income as defined in this report.

Updating through Re-Estimation 

Updating thresholds through re-estimation is the approach most often used when adopting basic 

needs thresholds. For example, the SPM thresholds are updated each year through re-estimation.

In the case of most budget-built thresholds, geographically specific monthly or annual costs of 

components, for example center-based childcare, are used to set and update the budgets. 

However, when cost or price data are not available for the threshold year, area-specific (e.g., for 

the Washington metropolitan area) and commodity specific prices indexes (for example, those 

for food, housing, health) are used. Once the value of each budget component is updated to 

threshold year dollars, the sum of the components represents the new threshold year budget.108

Sharing and Adjusting for Different Sharing Unit Sizes

The OPM uses the family as the resource sharing unit, while the SPM includes cohabitors and 

non-relative or foster children as well in the resource sharing unit. The CE Survey defines the 

sharing unit as the consumer unit,109 which in most cases is the OPM family and SPM unit, but it 

 107 Intra-area price indexes, not inter-area indexes, are used. For Canada, see STATCAN (2016), and for Italy, see Cutillo 

et al. (2019). 

108 This approach is used for geographically defined basic needs thresholds. For example, the costs of the components 

that comprise the Economic Policy Institute (EPI) Family Budgets Calculator (Gould et al., 2018), Living Wage Calculator 

(see Nadeau 2019), and United Way ALICE budgets (see Hoopes et al. 2020) are updated each year based on data for 

that year and geographic area when available, and when not available by item or commodity group specific consumer 

price indexes for components applied to earlier year values. The Self-Sufficiency Standard, or thresholds are generally 

re-based every three years, with cost components that apply to all states updated on an annual basis for all states (see 

Pearce 2020). 

109 A consumer unit comprises either: (1) all members of a particular household who are related by blood, marriage, 

adoption, or other legal arrangements; (2) a person living alone or sharing a household with others or living as a 

roomer in a private home or lodging house or in permanent living quarters in a hotel or motel, but who is financially 

independent; or (3) two or more persons living together who use their income to make joint expenditure decisions. 

Financial independence is determined by the three major expense categories: Housing, food, and other living expenses. 
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is not identical. The approach for defining a resource sharing unit assumes equal sharing across 

the unit; however, whether that is true or not, is difficult to determine. The assumption of equal 

sharing is the subject of research on the intra-household allocation of resources, often with 

reference to poverty measurement.

110

In order to ascertain the poverty status of individuals in a society, data from the resource sharing 

unit need to be converted to the individual level in order to obtain comparable measures of 

resources across units of differing compositions (e.g., numbers of adults and children, age of 

children, disability status). This conversion is most often accomplished through the application of 

an equivalence scale. In its classic form, the equivalence scale question is "How much does it cost 

for a sharing unit with composition X to get to the same welfare level as a sharing unit with 

composition Y?"111 Desirable properties of equivalence scales, in general, are that needs increase 

with sharing unit size, marginal needs decrease with increases in size, differing needs of adults 

and children are considered, and economies of scale for larger sharing units are incorporated.

Larger sharing units require more resources to maintain a constant standard of living because 

they contain more people who consume those resources. However, larger sharing units may 

require fewer resources on average per person due to differences in need, buying in bulk, 

economies of scale in the production of certain commodities like home produced meals, within 

the household nature of “public” commodities like shelter and utilities, and whether home 

production is accounted for consumption.112 Whether and how to account for these factors 

should be considered in the development and selection of an equivalence scale for poverty 

measurement.

Equivalence scales should reflect time and place.113 Food now accounts for a smaller share of 

consumption relative to housing than in the past. This means that equivalence scales have 

become flatter (scale elasticity closer to zero) and the marginal cost of adding one more person 

to the household is less than what it would have been in the 1960’s when food accounted for a 

 

To be considered financially independent, at least two of the three major expense categories have to be provided 

entirely, or in part, by the respondent. See: https://stats.bls.gov/cex/csxgloss.htm. 110 Researchers have noted that the assumption of equal sharing of living conditions is a very restrictive one open to 

criticism (see Jenkins, 1991, and Guio and Van den Bosch, 2020) For much of the poverty literature, a key assumption is 

that all members within a consuming unit are equally well-off. However, it does not consider alternative explanations 

of human behavior which result in some members being better off while others are worse off; for example, one adult 

consumes most of the resources for a family with little left over for any other adult or children. If there is not equal 

sharing of living conditions, an assumption of equal sharing could result in a downward bias in estimations of the 

extent of poverty especially among some subgroups, such as women and children. In addition, by masking potential 

differences in poverty among consuming) unit members, the effectiveness of poverty programs targeted to individual 

consuming unit members may be biased. For additional empirical research, see Bargain et al. (2014); Basu (2006); 

Botha and Ribar (2020); Calvi et al. (2020); Cherchye et al. (2015, 2016, 2017); De Vreyer et al. (2020); Dunbar et al. 

(2013); Lechene et al. (2020); Lewbel and Pendakur (2020); Penglase (2020); World Bank (2018). 111 At one extreme is that each additional person costs the same as the first, a per capita equivalence scale adjustment 

with a scale elasticity of one. At the other extreme is no adjustment for sharing unit composition, with a scale elasticity 

of zero. The per capita scale does not account for economies of scale and the no adjustment option assumes perfect 

economies of scale. Neither accounts for the differing needs of adults and children or for other compositions. 

Alternatives that account for the differing needs of adults and children are those of the OECD (2013), and for adults 

and children plus an economies of scale factor are those recommended in the Measuring Poverty report (National 

Research Council 1995) and the ITWG SPM (2010).

112 See Nelson (1993) Folbre et al. (2018). 113 For a discussion of this point see Mysíková et al. (2020).
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larger share of consumption.

114 Various approaches to derive equivalence scales exist in the 

literature. For example, there are utility or behavior-based approaches from analysis of 

expenditure survey data, expert judgment of what is considered reasonable, and approaches 

derived from individual responses to questions about minimum income and spending.115 Varying 

equivalence scales have been produced for different countries (e.g., Daley et al. 2020, Mysíková 

et al. 2020) and based on differing commodities or commodity bundles (e.g., Phipps and Garner 

1994, Renwick and Garner 2020).

There is no consensus on the best equivalence scale for poverty measurement, nor is there 

consensus on whether the same equivalence scale should be used for both an income resource 

measure and a consumption resource measure. Empirical evidence can help determine the limits 

of what makes sense, but the setting of scales is more a matter of judgement than the application 

of scientific theory (National Research Council 1995; Nelson 1993). Questions to guide the 

selection of equivalence scales include the following: 

• Would a sharing unit equivalence scale that accounts for the differing needs of adults and 

children and for economies of scale be used for monetary income but a per capita scale 

be applied to in-kind transfers like Medicaid and school meals? Would there be a 

different scale for WIC, LIHEAP, and subsidized housing? 

• For an income resource measure that accounts for health insurance, would there be 

different equivalence scales to account for economies of scale in the purchase of a private 

family health insurance plan (e.g., a family plan for two adults and one child costs the 

same as for two adults and five children) as opposed to the provision of a public plan at 

the individual level? 

• For a consumption resource measure that accounts for health insurance, would one 

account for economies of scale in purchasing a family plan or for per capita consumption 

of health insurance? 

• For a consumption resource measure, would one apply different equivalence scales to 

various commodities as opposed to depending on a general equivalence scale to reflect 

differences in economies of scale across all consumption?

• And in addition, would the equivalence scale selected differ if the adults are related (i.e., 

by blood, marriage or other legal arrangement) or not, or by gender? For example, low 

economies of scale in shelter would be assumed when two adults are not related since 

each would require a separate bedroom, and when the gender of children differs since 

parents often prefer separate bedrooms for boys and girls.

 114 Differences by housing tenure have also been reported. See Goedemé et al. (2017) who reported that housing costs 

are the primary driver of economies of scale and, in Europe, there is a difference between economies of scale for 

owners and renters. When housing costs increase, the relative cost of additional household members decreases, 

resulting in a flatter implicit equivalence scale. They note that the OECD equivalence scale neglects differences in 

economies of scale by tenure status, and across countries. In addition, the OECD scales appear to underestimate the 

additional cost of children, especially for families with older children and with low housing costs (outright owners or 

those who benefit from subsidized rent).

115 For an overview of equivalence scales, see Buhmann et al. (1988). For examples of model-based scales, see: Daley et 

al. (2020), Merz et al. (1994), Johnson and Garner (1994/95), Nelson (1993), Phipps and Garner (1994), and Renwick 

and Garner (2020). For scales based on responses to minimum income or spending questions, see: Bishop et al. (2014, 

2017), Garner and Short (2003), Mysíková et al. (2020). And for expert scales see: OECD (2013) and ITWG SPM (2010). 
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Recommendations:

34. The Working Group recommends that an expert panel conduct a study of and make a 

recommendation regarding the application of equivalence scales that would be most 

appropriate for the income and consumption resource measures recommended in this 

report. In the interim, the Working Group recommends that for any resource measures 

produced an equivalence scale be applied that accounts for the potentially differing 

needs of adults and children and economies of scale.

Adjusting Poverty Thresholds Across Geographic Areas 

When setting poverty thresholds, it must be determined whether thresholds should vary over 

different geographic areas, and if so, how. This section summarizes factors to consider when 

determining whether to geographically adjust poverty thresholds. It then describes some 

methods for implementing any potential geographic adjustment.

Factors to Consider When Determining Whether to Adjust Poverty Thresholds Across Geographic 

Areas 

The cost of housing and other goods and services varies across geographic areas. The average 

market rent in New York City far exceeds the average market rent in rural Mississippi. As a result, 

when comparing two families with the same income, the family renting a home in New York City 

will generally have fewer resources available to meet other needs such as for food and other 

goods and services than a family renting a home in rural Mississippi. These differences in costs 

are reflected in other government policies. For example, “School districts in high-cost 

environments must spend more than school districts in low-cost environments just to provide the 

same level of educational services” (Cornman, Nixon, Spence, Taylor, and Geverdt 2019 p.1).

While the OPM does not vary poverty thresholds by geography, the SPM accounts for these 

differences in prices by setting thresholds higher in high-cost places like New York City and lower 

in low-cost places like rural Mississippi (see Appendix for more on this method). Another 

approach is basic needs thresholds that tie thresholds to the budget needed to afford a specific 

subset of goods and services in a local area to account for these geographic price differences as 

well.

At the same time, price differences generally reflect differences in the attributes of a location,

sometimes called amenities. Home prices are higher on average in locations with more costly 

attributes because consumers are willing to pay a premium to have access to those attributes. 

Such attributes include any characteristic of a location that affects the desire to live there such as

employment opportunities, the quality of public schools and other services, and clean air and 

water.116 A full geographic adjustment of thresholds by price alone does not account for the 

differences in these attributes across areas that may have important benefits for lower income 

people.

 116 Attributes that have been found to be associated with home prices or rents include wages and employment 

opportunities (Roback 1982; Ruggles 1990; National Research Council 1995; Hirsch 2011); the presence, number, and 

quality of public goods such as schools, parks, libraries, fire and police (Tiebout 1956, Oates 1969, Black 1999, Epple 

2008; Bayer et al. 2019); pollution (Davis 2004, Chay and Greenstone 2005); and safety net programs such as TANF 

(Glaeser 1998).
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To the extent that people can choose their locations, it follows that when people live in high-cost 

areas, the value they place on that location’s attributes exceeds the higher cost they must pay to 

live there. However, people, and in particular those with low incomes, may experience 

constraints on their choices to live in a particular area or to move to another, as well as their 

access to the attributes where they live. For example, high moving costs,117 strong social ties

(Dawkins 2006),118 and historic and current racial discrimination and housing segregation (Turner 

et al. 2012; Massey and Denton 1993; Krysan and Crowder 2017) contribute to lower levels of 

long-distance mobility among low-income households (Gimpel 1999). As a result, lower-income 

people “often bear the costs of amenities through higher housing prices, regardless of whether or 

how much they actually value them” (Curran 2008, p.3). On the other hand, when costs prevent 

families from leaving low cost areas with undesirable attributes, geographic adjustments could 

remove from the poverty rolls those in a particularly unfortunate situation; those particularly 

disadvantaged who are unable to move.

To help inform the decision over whether to geographically adjust poverty thresholds, some 

researchers have examined whether geographic adjustment of thresholds would lead to a 

poverty measure that better identifies the most economically disadvantaged than would 

thresholds that are not geographically adjusted. For example, Meyer, Wu and Curran (2020) 

found that people near a non-geographically adjusted threshold in high-cost areas were 

significantly better off using many deprivation indicators than people near the threshold in lowcost areas.119 This finding implies that geographically adjusting thresholds by raising them in highcost areas and lowering them in low-cost areas could lead to a poverty measure identifying a less 

deprived group of people. Other research is less conclusive but suggests that same pattern of 

higher well-being in high-cost areas (Renwick 2018, Baker et al. 2018).

 117 Moving costs include the transportation of households, their belongings and, in some cases, the storage of those 

belongings (Amundsen 1985; Edin and Englund 1991; Nordvick 2001; Tunstall et al. 2013). Faced by these costs, lowincome households are least likely to have the savings and wealth to afford them (Brown and Braga 2019). Using data 

from the 2017 American Housing Survey (AHS), Zamboni and Martin (2020) found respondents with lower incomes and 

lower levels of education were less likely to have financial resources to evacuate from disaster areas. In the Urban 

Institute’s Well-Being and Basic Needs Survey, almost half (49.5%) of families below the Federal Poverty Line said they 

were not confident they could come up with $400 to meet an unexpected expense (Brown and Braga 2019: 5). Barriers 

to mobility are higher for African-Americans who both have lower amounts of wealth than white households and face 

more housing discrimination (Oliver and Shapiro 1995; Conley 1999).

118 Social ties may play role in migration decisions when families must contend with losing economic and social 

supports of their extended families and communities in their origin location and weaker ties in their destination 

locations (Dawkins 2006). In a study of the role of social ties in migration processes during the Great Migration, 

Carrington (1996) found that migration is more likely when social ties are already established in the receiving location. 

He found that large numbers of individuals and families moving at one time and increases social ties between 

originating and receiving locations provided more places for new arrivals to stay as they searched for work and shelter. 

For more information on the history and causes of the Great Migration, see Tolnay (2003). 119 This research examined nine domains of material well-being. Across the two poverty measures used, seven of these 

domains had multiple statistically significant indicators that suggested that those identified as poor were better off 

with a geographic adjustment. These domains included measures of health; measures of assets and net worth; food 

security; housing problems like leaks or holes in floors; education; mortality; and a long-term income measured using 

tax records. One domain, material hardships, such as inability to pay bills, phone or electricity disconnected, or 

eviction, did not have any statistically significant indicators. The last domain, appliance ownership including 

dishwashers, computers, cell phones and the like, had multiple significant coefficients pointing in each direction.
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Methods for Implementing Any Geographic Adjustments

If it were determined that a geographic adjustment should be applied to thresholds, methods for 

implementing the adjustment would need to be considered.

Across the federal government, as well as other places, and for a variety of different purposes, 

geographic cost adjustments are used. After reviewing these, the ITWG determined that there 

are two general categories of geographic cost adjustments: those which would be more 

appropriate for an alternative poverty measure, and those which would not. Appendix Table C1

summarizes the geographic cost adjustments that are better suited to a federal agency producing 

an alternative poverty measure; Appendix Table C2summarizes several other geographic cost 

adjustments that the ITWG determined were inappropriate for the application at hand.

The geographic cost adjustments that are potentially useful when considering developing an 

alternative poverty measure include: Median Rent Index (MRI), Regional Price Parities (RPP), 

Regional Price Parities for Food, Apparel and Rent (RPP-FAR), and Comparable Wage Index (CWI).

The MRI, produced by the Census Bureau, is used to geographically adjust the housing portion of 

nationally defined poverty thresholds for the SPM. It only covers rent and utilities,120 and is 

therefore considered a partial cost adjustment. It is calculated based on median gross rents using 

the ACS (see Appendix C. Choices for Geographic Cost Adjustments). RPPs are produced by BEA 

and used for price-adjustments of personal income. RPPs cover all goods and services measured 

in the CPI.121 The RPP-FAR is similar to the RPPs but focuses on expenditures included in the SPM 

threshold (namely, food, apparel and rent). Finally, the CWI, produced by the Census Bureau, 

could be utilized to geographically adjust the entire threshold based on wage differentials. The 

CWI is a modification of the Comparable Wage Index for Teachers (CWIFT), which is used to 

facilitate comparisons of educational expenditures across school districts by the National Center 

for Education Statistics.122

Another possibility that one could consider is some partial adjustment of thresholds to prices.

Both proportional scaling of adjustments (Hirsch 2011, Renwick 2018) and nonproportional 

adjustments have been suggested (Kaplow 1995, Glaeser 1998). However, there are difficulties 

with estimating the appropriate nonproportional adjustments that some have argued are 

daunting (Greenstone 2017).

If adjusting poverty thresholds by geography, it is necessary to determine which level of 

geography is appropriate. One may not want to adjust at such a local level of geography that 

would result in a different threshold in every neighborhood. On the other hand, adjusting at an 

aggregated level of geography (like state or region) would mask cost of living differences within 

those areas. An example of a possible level of geography for which to produce thresholds is the 

 120 Calculated based on renter units, and then applied to both renter- and owner-occupied housing. 121 Some (see World Bank 2017) have argued that a Poverty Purchasing Power Parity (PPPP) could improve upon 

currently produced Purchasing Power Parity (PPP) for use in calculating poverty statistics. The idea is that if the 

spending patterns among the poor are different from the population on average, and that these differences are 

relevant across areas, then PPPPs could be used to adjust poverty thresholds. As a reference, see the work of Allen 

(2017) who has proposed a new basis for an international poverty measurement based on PPP methodology. Such a 

methodology could be applied to the U.S. if geographic adjustment of thresholds is deemed appropriate for poverty 

measurement.

122 The National Center for Education Statistics applied the CWI to data in the Congressionally mandated report entitled 

the Study of the Title I, Part A Grant Program Mathematical Formulas (NCES 2019-016).
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one used to produce the SPM – the Metropolitan Statistical Area (MSA) level which mirrors labor 

market areas. For more information on this, see Appendix C. Choices for Geographic Cost 

Adjustments.

Other considerations in deciding whether to geographically adjust poverty thresholds include 

those related to whether implementing an adjustment is feasible. For example, the difficulty of 

appropriately measuring prices has frequently been given as a reason to not adjust thresholds. 

Additionally, producing a consistent historical time series could be challenging if the data and/or 

methodology underlying the geographic adjustment changes. 

Recommendations:

35. The Working Group recommends that expert review be sought on all of the 

considerations described above because whether and if so how to adjust poverty 

thresholds across geographic areas is a complex topic. 

Accounting for Units With Child Support Obligations and Work And Childcare Expenditures 

With regard to the thresholds, the Working Group considered the specific circumstances of 

several subpopulations including noncustodial parents making child support payments and 

families paying for childcare, commuting costs, or other expenditures required to obtain 

employment. Theoretically, poverty metrics could make adjustments for these groups by creating 

population-specific thresholds or by altering the resource totals. 

An influential commentary on this topic was included in Measuring Poverty, which recommended 

that out-of-pocket payments for child support and work expenditures (including childcare) be

subtracted from available resources instead of adjusting the thresholds for these perceived needs 

of various types of families. One reason for this decision was that the creation of many additional 

thresholds was believed to complicate their presentation and add too much complexity to 

capture all variants. Moreover, the average expenditures123 for these categories varied 

considerably even among families with similar characteristics, and the sample sizes in the CE

Survey—the primary source of consumption measurement in the United States—were too small 

to support reliable estimates of multiple thresholds.

One potential disadvantage of accounting for these expenditures in resources instead of creating 

alternative thresholds based on each families’ estimated needs is that efforts to provide public 

services for these efforts—through policies such as subsidies and tax credits—are only captured 

to the extent to which they reduce the out-of-pocket costs for these items. Subtracting these 

expenditures from resources instead of creating different thresholds cannot fully capture public 

investments such as: transportation vouchers, childcare subsidies, and educational scholarships 

or reductions in tuitions at public institutions. It can also produce some unintuitive outcomes 

when the introduction of public benefits intended to assist the poor result in increases in 

statistical poverty rates. For instance, expansions in public programs providing subsidized 

 123 For example, while some families require full-time paid childcare, other families utilize uncompensated care from 

relatives for their care needs. For work-related expenditures some persons are able to work at or near their homes 

while other persons have long commutes to and from their places of employment.
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childcare sometimes increase poverty rates as measured by the SPM because the small out-ofpocket contributions that the families make for these childcare plans are just enough to decrease 

their incomes to below the thresholds. This concern could theoretically be mitigated by 

estimating the values for these investments and adding them to resources.

Other Elements of a Poverty Measure 

Other elements of a poverty measure must be specified as well, including the time interval, and 

whether to use a headcount or poverty gap measure. 

The time interval is the period of time during which resources are aggregated and poverty status 

is determined by comparing resources against thresholds. Poverty measures based on annual 

time intervals are the most common and benefit from the current availability of annual data from 

most potential data sources. The Working Group discussed other possible time intervals as well. 

Headcount poverty measures, such as the OPM, count the number of people in sharing units 

whose resources fall below their poverty thresholds. Poverty gap measures calculate the 

difference between resources and thresholds for each poor sharing unit, and sum these 

differences across all poor sharing units. The Working Group discussed the advantages and 

disadvantages of headcount and poverty gap measures.

Consensus Recommendations Worthy of Further Research by the 

Federal Statistical System As this Working Group’s time is limited, the Working Group finds it important to focus on the 

alternative measures previously discussed more extensively, but to acknowledge additional 

research areas worthy of further research by the Federal Statistical System. Additional 

information on these additional research areas follows.

Additional Research on Income and Consumption Poverty 

Throughout this report the Working Group has identified numerous areas in the measurement of 

income and consumption poverty that will require additional research. While these are described 

in the body of the final report, some of the major areas requiring additional research are listed 

here:

• Continued work to improve measurement of childcare and other work-related expenses 

and methods to cap them. 

• Additional research on how to treat MOOP. 

• Research on a precise method to tie the value of health insurance to a person’s resources: 

specifically, the feasibility of developing a method to downwardly adjust the market 

value. 

• Continued research and additional stakeholder and expert engagement on whether and 

how to treat education within resource measures, including whether to deduct out-ofpocket education expenditures on tuition and other educational items. 

• Research to impute the net value of service flows from owner-occupied shelter and the 

net value of service flows from owned vehicles given current CPS ASEC and ACS data 

limitations.
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• Research to assess ways in which survey data might be made more comparable to 

administrative data (e.g. changing the reference period for income from the previous 12 

months to the previous calendar year). 

• Continued research to improve regression-based modeling such as Sequential Regression 

Multiple Imputation (SRMI) as new tools and techniques become available. 

• Continued research into availability and applicability of administrative records. 

• Research on ways to reduce survey burden and improve the quality of resulting data 

through increased access and use of administrative data in surveys including the CPS 

ASEC, ACS, and CE. 

• Research to develop a consumption-based resource measure by BLS using the CE 

Interview Survey

• Research to identify an interim solution for each set of the measures for applying 

thresholds to produce a full poverty measure by the time the proposed research 

measures are ready to be published. 

• Study by an expert panel regarding the application of equivalence scales for use with for 

the income and consumption resource measures recommended in this report. 

• Expert review on the entire topic of geographically adjusting thresholds with all of the 

considerations described in the report.

• Study of price indexes appropriate for use in updating thresholds that would be used in 

combination with consumption and income as defined in this report by BLS.

To investigate these areas as well as others, the Working Group recommends that federal 

agencies seek expert input through the establishment of a National Academy of Sciences panel 

and potential a new Interagency Technical Working Group. A list of recommendations is available 

in Appendix D. List of Working Group Recommendations.

Other Measures of Well-being Beyond Income and Consumption 

There are other poverty measurement approaches that may be worthy of further research by the 

Federal Statistical System. One approach is to directly measure hardships (Mayer and Jenks 1989)

such as food insecurity or rent burden. Another approach is to examine multiple dimensions of 

well-being beyond income and consumption, including material well-being, education, and 

health.124 Yet another would be the approach on social exclusion described in the recent UNECE 

report.

Material Hardship 

There have been many studies of material hardship indicators, completed inside and outside the 

federal statistical system. (See Bauman 2003; Short and Shea 1995; U.S. Census Bureau 2003, 

2005; Short 2005; Carle et al. 2009; Short 2015; Mykyta and Pilkauskas 2016; Mayer and Jencks 

1989; Meyer and Sullivan 2003, 2011, 2012.)

 124 See Federman et al. (1996) for an approach to consider a number of related but independent measures. Also see a

recently published UNECE report on social exclusion (UNECE 2020) that identifies financial wellbeing as one aspect of 

social exclusion (a term some refer to as multidimensional poverty), but is not the only nor necessarily the most 

important concern. Yet, in Europe most definitions of poverty include social exclusion as an underlying concept. For 

example, the poverty definition adopted by the European Union in 1975 states that “people are said to be living in 

poverty if their income and resources are so inadequate as to preclude them from having a standard of living 

considered acceptable in the society in which they live” (Nolan and Whelan, 2010). 
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In 2004, the U.S. Department of Health and Human Services Office of the Assistant Secretary for 

Planning and Evaluation released a final report125 of their 2002 Roundtable Meeting on 

Measuring Material Hardship attended by over 35 researchers and experts from both inside and 

outside the government (Isaacs 2004). This report:

• Discusses the ways in which material hardship had been conceptualized and 

operationalized by researchers;

• Highlights where there was consensus and differences across material hardship 

definitions and measurement approaches;

• Summarizes what they knew about the material hardship measures that had been used to 

date in domestic research;

• Presents new analyses of the SIPP for the purpose of furthering the understanding of 

material hardship measurement among families and children.

Multidimensional Poverty 

The United Nation’s Sustainable Development Goals recognize that poverty has multiple 

dimensions and include a target to reduce at least by half the proportion of men, women, and 

children of all ages living in poverty in all its dimensions by 2030. The Global Multidimensional 

Poverty Index (MPI) developed by the Oxford Poverty and Human Development Initiative (OPHI) 

and released by the United Nations Development Program (UNDP) in the Human Development 

Report since 2010 is an internationally comparable measure of acute multidimensional 

poverty.126The MPI developed at Oxford University with the UNDP’s Human Development Report 

Office uses ten indicators to measure three critical dimensions of poverty at the individual level: 

education, health, and material living standards. 

Following this trend, in October 2018, the World Bank presented its own multidimensional 

poverty measure (World Bank 2018). The World Bank’s Atkinson Commission Report “Monitoring 

Global Poverty” (World Bank 2017), advised to use a six-dimensional measure of overlapping 

deprivations. The report advised that this measure should cover health, nutrition, education, 

work, living standards, and violence.

Multidimensional poverty indices (MPIs) have also been developed by many countries as official 

national poverty statistics.127 The MPIs that are in place include a range of indicators such as 

health, education, living standards, social inclusion, violence, and employment, among others.

National MPIs reflect national priorities and are constructed using national datasets. However, 

they cannot be compared. Some examples of dimensions used in official multidimensional 

poverty measures include health, education, work, housing, living standards, basic services, 

environment, personal security (safety from violence), food security, and childhood and youth. 

Suggestions for several new indicators surfaced to build national MPIs based on updates in the 

EU-SILC surveys after 2013. For example, an indicator for education could include assessments of 

 125 Available at https://aspe.hhs.gov/report/measures-material-hardship-final-report 126 See the Oxford Poverty & Human Development Initiative’s Global Multidimensional Poverty Index (Oxford 

University, 2014), or UNICEF’s Child Poverty Report (Fanjul, 2014) for other examples of multidimensional poverty. 127 The governments of Colombia, Mexico, Bhutan, Chile, El Salvador, Costa Rica, Honduras, Malaysia, Vietnam, South 

Africa, Ecuador, Pakistan, Armenia, Mozambique, Panama, Nepal, Dominican Republic, Nigeria, Sierra Leone, the 

Philippines, and Afghanistan, among others, have implemented and included nationally adapted multidimensional 

measures of poverty in their long term social development strategies.
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an individual’s obtained skills (e.g., via adult education, national education schemes, vocational 

training, open learning). The previous indicator consisted of the level of education.128

Some work has been done to develop these types of measures for the U.S., including two 2019 

reports using ACS data, one from the Census Bureau (Glassman 2019) and a paper by Dhongde 

and Haveman (2019).129 Indicators used in these studies include income poverty status, 

incompletion of high school education, lack of health insurance, number of disabilities 

experienced by an individual, overcrowding, housing costs, English fluency, and neighborhood 

characteristics but there are important differences in the specific dimensions selected and the 

definition of deprivation for each dimension across studies. For example, Glassman includes 

official poverty status and neighborhood characteristics as dimensions while Dhongde and 

Haveman do not. On the other hand, Dhongde and Haveman examine English fluency while 

Glassman does not. The ACS does not include any questions on health status so these studies 

have either omitted health status or attempted to impute it from other surveys. The Census 

Bureau is continuing to refine the work on its multidimensional measure, presenting working 

papers at conferences and consulting with academics, including Dhongde and Haveman, engaged 

in this research. 

While these multidimensional approaches offer many advantages, this interim report does not 

cover them in any depth given their complexity, the limited research with U.S. data, and the 

limited time of the Working Group. The federal statistical system may want to convene another 

Working Group to examine the challenges and opportunities for developing this type of 

multidimensional measure for the United States.

Recommendations:

36. The Working Group recommends continued research and additional stakeholder and 

expert engagement on whether and how to develop multidimensional measures. Focus 

should be on operationalizing such a measure in the policy context, ideally with 

indicators that are directly linked to policy levers.

Populations Included in Poverty Measures 

Another topic that merits further evaluation is the exclusion of certain individuals. In particular, 

those experiencing homelessness are generally excluded from the frames of most of the 

household surveys the Working Group has considered.

130 If the purpose of the poverty measure 

 128 UNECE (United Nations Economic Commission for Europe), 2017, Guide on Poverty Measurement, United Nations, 

New York.

129 See Dhongde et al. (2019) for an examination of well-being and deprivation in a multidimensional framework using 

American Community Survey data from 2008 through 2017 and Glassman, Brian, “Multidimensional Deprivation in the 

United States: 2017 American Community Survey Report (U.S. Census Bureau, 2019). Earlier work used the National 

Health Interview Survey (Alkire and Foster, 2011), the CPS ASEC (Mitra and Brucker, 2016), the American Community 

Survey (Reeves et al., 2016), the Survey of Income and Program Participation (Short, 2005), the Panel Study of Income 

Dynamics (Ciula and Skinner, 2015), and the General Social Survey (Wagle, 2014) to measure multidimensional 

deprivation in the United States.

130 The ACS and the CPS ASEC includes those experiencing homelessness who are staying in shelters, but not those who 

are un-sheltered. The number of noninstitutional group quarters in the CPS ASEC sample tends to be small and is not 

nationally representative. 
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is to count the number of individuals and families experiencing extreme hardship, measures that 

exclude some of the least privileged members of society are problematic. Each year, HUD 

publishes an Annual Homelessness Assessment Report (AHAR) to Congress. Part 1 of the AHAR 

reports Point-in-Time (PIT) estimates of sheltered and unsheltered homelessness collected by 

Continuums of Care (CoC) across the United States on a single night in January, and Part 2 reports 

annual estimates of homeless prevalence using data from Homelessness Management 

Information Systems (HMIS). When the data collection time periods align with those of 

household surveys, PIT estimates provide a potentially useful way to quantify undercoverage of 

homeless persons in household surveys. HUD’s 2019 AHAR Part I reported that roughly 568,000 

people experienced homelessness on a single night in 2019, including 396,000 persons 

experiencing homelessness as individuals and 172,000 persons experiencing homelessness in 

families with children (U.S. Department of Housing and Urban Development 2020). The number 

of persons experiencing homelessness may be even higher with several factors potentially 

contributing to underestimates, including difficulties inherent in locating and obtaining 

cooperation from persons experiencing unsheltered homelessness, logistical issues of collecting 

in-person point-in-time data over large geographic areas, and underestimates of persons 

experiencing homelessness on Indian reservations (U.S. Department of Housing and Urban 

Development 2007, Link et al. 1994, National Academies of Sciences, Engineering, and Medicine 

2018).

131 The AHAR does not report on the incomes of homeless individuals and families, but 

research by Burt et al. (1999) using the National Survey of Homeless Assistance Providers and 

Clients (NSHAPC) found that mean incomes of homeless individuals and families were about half 

of the Official Poverty Line.

In addition, other excluded populations would also benefit from additional research. These 

excluded populations include unrelated individuals under age 15 (i.e. Foster Children), persons 

living in military barracks, incarcerated individuals, people living in nursing facilities and those 

living in college/university housing. In addition, research on the impact of undercoverage and 

nonresponse bias on poverty estimates would be useful. At a minimum, the Census Bureau and 

other federal statistical agencies should explicitly acknowledge the limitations of their household 

survey frames and explore ways to examine the well-being of these excluded groups. 

Accounting for Other Types of Services 

Not being considered as part of income or consumption are what are often referred to as “unpaid 

domestic services.” These include, for example, own-produced services such as laundry, cooking 

meals, caring for adults and children, housekeeping and management, and growing one’s own 

food. The production of these services clearly impacts the well-being of the household (see 

Zacharias 2017; Folbre et al. 2018; Suh and Folbre 2016; Wolff et al. 2012). Although time use 

data can be used to value these, this information is not currently collected in conjunction with 

available income and consumption data, so the Working Group is not currently including these 

types of services in the alternative measures more extensively examined.132

 131 Continuums of Care are asked to use sampling and extrapolation methods consistent with HUD standards to address 

undercount issues when a census (complete coverage) is not conducted. (U.S. Department of Housing and Urban 

Development 2014: 8, U.S. Department of Housing and Urban Development 2018a: 22).

132 However, modeling can be used to produce such measures, for example, see Fernando Rios-Avila (2018) for a 

statistical match of CPS ASEC, American Time Use, and Survey of Consumer Finance data for the Levy Institute Measure 

of Economic Well-being (LIMEW).
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Failure to take into account unpaid domestic services is a concern for both income and 

consumption poverty measures. For a consumption measure, if childcare services are included as 

consumption, families purchasing childcare will appear to be better off than families with a stayat-home parent providing these services. For an income measure, families paying for childcare 

may be deemed “not in poverty” despite the fact that the resources available to purchase other 

basic necessities may be constrained by their childcare outlays.133 Similar arguments can be made 

about other unpaid domestic services.

Identifying Poverty Status of Individuals within a Sharing Unit 

The Working Group also acknowledges that there are limited data available about how a defined 

sharing unit actually shares their resources, but that it may be important to better understand 

whether individuals may be deprived within sharing units that are not identified as poor.134 In 

their recent report, “Piecing Together the Poverty Puzzle,” the World Bank devotes an entire 

chapter to concerns about intrahousehold sharing (Work Bank 2018, p.125). While there has 

been some work done with the EU-SILC and by UNICEF to look at individual level hardship 

indicators, the Working Group agreed that for the United States, there are not currently sufficient 

data to pursue these types of measures. 

Timeliness of Income, Consumption, Poverty, and Other Well-being Measures 

The COVID-19 pandemic brought to the fore the value of timely poverty measurement. In such 

times of rapid economic change, it is especially valuable to track the income of the American 

population overall and by demographic group. Unfortunately, official estimates of income and 

poverty from the CPS ASEC lag at least 9 months and up to a year and 9 months behind a given 

12-month reference period. These official statistics are of little use to policymakers who would 

like to understand the dimensions of an economic crisis and respond. To address this gap, realtime information is needed. The Household Pulse Survey (HPS) was designed by the U.S. Census 

Bureau in conjunction with numerous other federal agencies, and fielded during the coronavirus 

pandemic and to meet such a need; the focus is on how households across the country have been 

impacted economically and socially.

135 The HPS collects direct indicators of well-being including, 

for example, food insecurity and insufficiency, not being able to pay one’s rent or mortgage, 

eviction from one’s home, difficulty in meeting expenses, lack of internet access, loss of 

employment income, interruptions in education, loss or lack of health insurance, and mental 

health concerns.

136 Such data provide insights regarding how individuals and families are dealing 

with the pandemic and have been reported on in the popular press and by policy makers. 

However, information on levels of income and income poverty is not collected. For monitoring 

large changes, it is also helpful to have a long baseline of prior history to interpret and validate 

measures. While some of the HPS questions have been asked in prior surveys (supplemental 

collections of the CPS ask about, for example, food insecurity and insufficiency, and the National 

Health Interview Survey asks about mental health concerns), some questions are being asked for 

the first time and the survey itself is new.

 133 The Supplemental Poverty Measure handles this issue by subtracting work-related out-of-pocket child-care 

expenses from resources before evaluating poverty status. 134 For example, see Piccoli, (2017). 135 https://www.census.gov/programs-surveys/household-pulse-survey.html 136 See the following for tabulations of data for these and additional variables: 

https://www.census.gov/data/tables/2020/demo/hhp/hhp20.html#tables
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There are many models for a timely poverty measurement program that are income based. A 

convenient approach with some attractive properties has been employed by Han, Meyer and 

Sullivan (2020, 2021) relying on CPS Monthly data.137 Several important features of these data 

should be noted. The income data needed to construct poverty measures are available with a lag 

of only a few weeks after the end of the 12-month reference period. While the sample is 

representative of U.S. families and individuals, only one-quarter of the full CPS Monthly sample is 

asked the income question. Finally, the income information is from a single global question and is 

recorded in intervals (that are narrow at low incomes).

Combining timeliness and accuracy presents a number of challenges and considerations. Is the 

CPS Monthly the best source? If so, could the sample be easily expanded? Should any significant 

changes be made to current CPS Monthly survey questions without hurting the availability of 

baseline and validation data? Should timely consumption poverty measurement be pursued as 

well? These and other questions seem appropriate for future discussion.

 137 Another approach was employed by researchers from Columbia University (Parolin et. al. 2020) who do not use 

timely income measures, but instead predict poverty using employment status and other variables.
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Summary of and Responses to Public Comment On February 14, 2020, OMB issued a Federal Register Notice seeking public comment on the 

Working Group’s interim report, and in particular, the Working Group requested public comment 

on the following questions. For reference those questions are listed below, followed by the 

summary of and responses to the public comments received.

Questions Posed in Federal Register Notice for Public Comment 

Definitions 

1. How should a sharing unit be defined? 

Resource measures 

2. What standards should the group use to determine which resource measures should 

be preferred? Specifically, to what extent should the group consider the following 

standards: (i) association with other measures of material hardship, (ii) conceptual 

advantages, (iii) simplicity, (iv) feasibility (including data availability), (v) 

reproducibility?

3. Should the value of health insurance be incorporated? And if so, how? 

4. Should the value of education be incorporated? And if so, how? 

For a potential income resource measure: 

5. What income sources should be included (aside from health insurance, which is 

addressed by question 3)? If so, how? 

6. What expenses, if any, should be subtracted from income? For example, how should 

medical out of pocket (MOOP) expenditures be treated in a new measure? Should 

other expenses such as childcare and commuting costs be subtracted? 

7. How should the Working Group address the problem of survey misreporting of 

income in household surveys?

For a potential consumption resource measure: 

8. What types of spending should be included as consumption (aside from spending on 

health care or insurance, which is addressed by question 3)? If so, how? 

9. How should vehicles and housing be included?

10. How should the Working Group address the problem of survey misreporting of 

consumption in household surveys? Should the group consider using only those types 

of consumption that are reported with greater accuracy, while excluding less 

accurately measured types of consumption? What are the tradeoffs in using only wellmeasured consumption versus full consumption?

Thresholds 

11. How should the thresholds be set initially? 

12. How should they be updated over time? 

13. Should thresholds be adjusted for geographic areas? If so, how? 

14. How should a sharing unit’s size and composition be accounted for?
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Summary of and Responses to Public Comments 

A 60-Day Federal Register Notice (FRN) was published on February 14, 2020 and asked for 

comments to 14 questions on the Interim Report of the Interagency Technical Working Group on 

Evaluating Alternative Measures of Poverty.

138 The 14 questions centered around five themes: 

definitions, resource measures, “for a potential income resource measure,” “for a potential 

consumption resource measure,” and thresholds. This summary is structured following these 

themes and includes additional comments regarding general concepts and implementation.

By the closing date of April 14, 2020, almost 25,000 comments were received, with most of those 

being solely requests to extend the 60-Day comment period. Additionally, an extension was 

discussed by 37 of the 71 other commenters. Dozens of commenters stated, “The public 

comment period on this notice and interim report should be extended until after the current 

National Emergency is over.” The researchers believed that adequate attention could not be 

given to the FRN with the overwhelming disruption the coronavirus pandemic has created. 

Response: 

While a formal extension of the comment period was not provided during the 

deliberations of this Working Group,

139many of the Working Group’s consensus 

recommendations call for further public input in the process of developing new measures.

For example, the second recommendation of the report reads:

The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics engage with stakeholders and other experts throughout the development of the 

recommended measures, including soliciting additional public comment as needed. In 

particular, the Working Group recommends expert input through a National Academy of 

Sciences panel.

Below is a summary of the 71 responses that mentioned more than an extension of the 60-Day 

FRN comment period. The commenters usually included multiple criticisms or suggestions in their 

emails, letters, and responses.

Many commenters noted the profound implications of the measurement of poverty and noted 

that how poverty is measured has many lasting ramifications for how public policy is developed, 

administered, and assessed.

Counting the number of responses that mentioned a particular issue or position is difficult. For 

example, three Columbia University researchers sent a letter of comments co-signed by 30 other 

academic researchers and these comments were also repeated almost word-for-word by 10 

other researchers who were not signatories to the letter and instead sent separate submissions.

Another group of very similar responses was received from 14 organizations, mostly advocating 

on behalf of children.

 138 https://www.federalregister.gov/documents/2020/02/14/2020-02858/request-for-comment-on-considerations-foradditional-measures-of-poverty 139 Comments received after April 14, 2020, were also considered by the Working Group.
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Recommendations made by the Working Group are available throughout the report within 

discussion context, as well as in Appendix D. List of Working Group Recommendations. 

Definition

1. How should a sharing unit be defined? A sharing unit is meant to reflect the set of people 

sharing resources in a household. 

Only three commenters addressed this issue directly. Two suggested that the sharing unit reflect 

those people who jointly purchased core goods used to determine the thresholds (food, housing, 

clothing). A third argued against broadening the sharing unit to the whole household because this 

would not capture the impact of families forced to double up with non-relatives and suggested a 

NAS panel consider this issue in more depth. 

Response: 

The Working Group considered various possible sharing units. Further discussion of the 

Working Group’s considerations is available in the section titled Sharing and Adjusting for 

Different Sharing Unit Sizes.

Resource Measures

2. What standards should the Working Group use to determine which resource measures 

should be preferred? Specifically, to what extent should the Working Group consider the 

following standards: (i) Association with other measures of material hardship, (ii) 

conceptual advantages, (iii) simplicity, (iv) feasibility (including data availability), (v) 

reproducibility? 

Only a handful of commenters addressed this question directly. One commenter explicitly rejected the 

criterion of association with other measures of material hardship while others endorsed this criterion.

Comments were made in support of conceptual advantages, reproducibility and feasibility as criteria. 

One commenter expressly opposed the criteria of simplicity. The comments in support of the criterion of 

association with other measures of material hardship emphasized the importance of looking at trends in 

these measures over time and whether or not the proposed poverty measure exhibits the same trend 

over time as measures of material hardship.

Most commenters responded to this question by giving their opinions on whether the Working Group 

should recommend any new measure, a new income measure, a consumption measure, or both. Most 

felt that income measures were preferable although some supported improvements to the 

Supplemental Poverty Measure (SPM) rather than a new measure noting that the SPM could 

incorporate administrative records.

There were more reservations expressed about a consumption measure, mostly focusing on 

concerns about the CE, specifically data quality concerns and the inability of the existing survey to 

support estimates for states or smaller geographies and/or smaller demographic groups. There 

was also concern about the failure of consumption measures to take into account debt. Some 

also expressed concerns regarding the sensitivity of consumption measures to methodological 

choices pointing to existing research that different consumption measures yield very different 

poverty levels. Others expressed concern that consumption measures currently produced by 
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outside researchers show poverty declining during the Great Recession when other measures of 

material hardship were on the rise. One comment argued that consumption measures of poverty 

do not reflect the lived experience of scarcity and expressed concern that consumption on 

housing rises, making one appear less poor, when a household’s rent rises.

Many commenters noted that a consumption measure should only be produced as an experimental 

alternative and should not replace income measures. One commenter strongly favored a consumption 

measure because it was more closely correlated to material hardship.

Most commenters seemed to favor the development of both income and consumption measures. 

There were also some who encouraged the development of a multidimensional measure. 

Response: 

The final report discusses these advantages and disadvantages of the income versus 

consumption measures of poverty in the section titled Advantages/Disadvantages of 

Income- and Consumption-based Resource Measures. The Working Group’s 

recommendations call for the production of both as research measures. The Working 

Group also recommends funding support to develop these measures, such as the proposal 

to provide that BLS be given funding to improve the CE, including sufficient funding to 

support state-level poverty estimates. The section on future research urges continued 

research into the development and implementation of a multidimensional measure as 

well as direct measures of material hardship.

140

3. Should the value of health insurance be incorporated? And if so, how? 

Most responses to this question focused on the importance of consistency between the resource 

measure and the poverty threshold (the value of health insurance should not be added to resources 

unless the cost of health insurance is added to the thresholds) and that no person should be taken out 

of poverty by the value of health insurance if the person does not have enough cash income to meet 

other basic needs. 

The Columbia commenters stated that a “poverty threshold implicitly or explicitly represents a 

set of basic needs, and only those resources that a family could use to meet those needs should 

 140 The report does not directly address the concern that under inflationary periods, tenant’s who’s are locked into 

fixed rent leases in multi-month rental agreements, will spend less than the current market rental value of the shelter 

service they are consuming. That is, the consumption value of the housing unit could be higher (or lower) than the 

transactional expenditure of the tenant who is in a multi-month fixed rental lease, when general market conditions are 

inflationary (or deflationary). The Working Group notes here that the price index in an absolute poverty measure 

adjusts for changes in nominal consumption in relation to changes in the price index. However, price indexes usually 

measure average price changes for an area or group, which are different than the specific price changes that individual 

households experience. Rents paid by tenants living in a unit for a longer period of time also tend to represent underestimates of consumption (see Gallin and Verbrugge 2019 regarding rent stickiness and tenure discounts). The Working 

Group also notes that while the prices some people face may differ from the market price and that this would have 

unintended effects on poverty, this is a problem for both consumption and income measures. For example, if rent rises 

above the market rent for a particular household, the household would be no more likely to fall into income poverty 

despite having fewer resources available for other goods. Similarly, the household forced to reallocate more resources 

from other areas to rent would be no more likely to fall into consumption poverty.
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be counted and compared against that poverty threshold in determining a family’s poverty 

status.”

There was disagreement as to whether or not it was feasible to incorporate the cost of health care in the 

thresholds, one commenter deeming this impossible but many others noting the work on the Health 

Inclusive Poverty Measure (HIPM) produced by Dahlia Remler, Sanders Korenman, and their colleagues 

as a promising approach. One commenter argued that along with the value of health insurance the 

resource measure should include the implicit insurance value of uncompensated care under the 

Emergency Medical Treatment and Active Labor Act of 1986. There were several comments in support 

of using market value or the price of an insurance plan as a basis of the value incorporated and one 

commenter explicitly rejected the notion of “willingness to pay”. There were arguments for and against 

the use of fungibility. Others opposed any addition of the value of health insurance while others called 

for additional expert guidance on this issue.

Response: 

Recognizing the conceptual and implementation challenges surrounding the inclusion of 

the value of health insurance in any poverty measure, the Working Group recommends

that both the alternative measures (income and consumption) be produced with and 

without a value of health insurance. The Working Group agreed that for poverty measures 

that include a value of health insurance, thresholds should be either explicitly adjusted 

(e.g. Korenman and Remler 2016; Remler and Korenman 2020) or implicitly adjusted (e.g., 

Meyer and Sullivan 2012a; Burkhauser et al. 2019) to account for the additional resources.

For further discussion, see the section titled Valuing health insurance.

4. Should the value of education be incorporated? And if so, how? 

On this question, two researchers felt that education should be added as a value or as a longterm benefit. One commenter argued that government education payments for education 

beyond K-12 should be considered transfer payments and income to the receiving household. 

However, four comments opposed including education as a resource, specifying that education is 

an investment and would require including education as a need and cost in the poverty 

threshold. Another commenter added that valuing education should be included, more 

specifically that technical and vocational schools should be included.

Response: 

The Working Group recommends continued research and additional stakeholder and 

expert engagement on whether and how to treat education within resource measures.

For further discussion, see the section titled Treatment of Education.

For a Potential Income Resource Measure

5. What income sources should be included (aside from health insurance, which is addressed 

by question 3)? If so, how? 

The comments addressing this question strongly supported the inclusion of in-kind benefits and tax 

credits but again emphasized that any change to the resource measure should be accompanied by a 

change to the thresholds. One commenter urged the Working Group to recommend that poverty 

estimates be provided with and without these in-kind transfers so one could compare market income 
Final Report ITWG on Evaluating Alternative Measures of Poverty

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poverty to post-transfer poverty to gauge the effectiveness of transfer programs. Another comment 

echoed this sentiment – asking for a full-income measure and a self-sufficiency measure.

Comments on including the flow of services from housing and vehicles were generally supportive but 

again emphasized the need for consistency between the resource measure and the threshold and the 

need for an expert panel to determine how to measure the values of the flows of these services. 

One commenter noted the importance of including COVID relief payments in the resource measure.

Response: 

As explained in the final report, the Working Group supports the inclusion of in-kind 

benefits and tax credits in the resource measure. While conceptually agreeing with the 

advantages of including the flow of services from housing and vehicles in both measures, 

the Working Group found that data was not currently available to do these imputations in 

an income measure. The Working Group outlined a variety of approaches to doing these 

imputations for a consumption measure, recognizing that the methodological decisions on 

these imputations should be left the discretion of the implementing agency. This would 

not preclude input from experts on the precise methodology. The final report notes that 

when any new resources are added to the resource measure, thresholds under the two 

non-basic needs threshold concepts are automatically adjusted.

6. What expenses, if any, should be subtracted from income? For example, how should 

medical out of pocket (MOOP) expenditures be treated in a new measure? Should other 

expenses such as childcare and commuting costs be subtracted? 

Several commenters believed childcare and healthcare expenditures should be deducted from 

income. On the contrary, one commenter was strongly against subtracting childcare

expenditures. Another commenter stated childcare, commuting, and other work expenses should 

be deducted from income, but capped. Another commenter stated that no expenses should be 

deducted from income. One commenter made the overall comment that actual expenditures 

reflect need, not just differences in preferences and choices.

Only a handful of comments addressed the specific issue of MOOP. Several suggested that the 

Working Group use the SPM approach of subtracting these expenses from resources, with a 

particular emphasis on “necessary” expenses. Several commenters suggested that this was such a 

complicated issue that it should be studied by a NAS panel of experts while several others 

supported the HIPM approach.

Response: 

The Working Group made the following recommendation on accounting for childcare 

expenditures: 

The Working Group recommends subtracting child support payments from resource totals 

for income-based measures. No adjustments are required for child support payments for 

consumption-based measures because the transfer of income from one family to another 

is indirectly captured through increased consumption of families receiving the transfers 
Final Report ITWG on Evaluating Alternative Measures of Poverty

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and decreased consumption among families with nonresident parents making child 

support payments.

When considering how to treat MOOP in a poverty measure, the Working Group 

considered concerns about discretionary spending on MOOP, inconsistent treatment of 

people who suffer the same adverse health shock but choose to consume different 

amounts of care, and inconsistent treatment of people with different insurance plans (e.g. 

low-premium/high-copay vs high-premium/low-copay plans). While concerns may exist 

about discretionary MOOP expenditures, it may be difficult or infeasible to separate out 

unavoidable costs from avoidable ones in existing datasets. The Working Group agreed 

that additional research is needed on these issues and did not make any specific 

recommendations. For further discussion, see the section titled Treatment of Medical Out 

of Pocket Expenses.

7. How should the Working Group address the problem of misreporting of income in 

household surveys? 

Comments in response to this question were mixed but generally supportive, again with the 

caveat that any correction for underreporting be accompanied with an increase in the thresholds.

Several favored direct substitutions of administrative records for survey reports while others 

cautioned about the quality of administrative data and recommended adjusting survey reports to 

match national and state control totals. One commenter warned the Working Group to avoid any 

method that would automatically choose the higher of the survey report or the administrative 

report. Some commenters suggested that the Census Bureau should create Working Groups to 

determine the best practices for reconciling differences between administrative and survey 

income reports and expand the administrative records acquisition program.

Response: 

The Working Group considered three approaches: rules-based approaches; (2) statisticalor regression-based modeling; and (3) direct substitution of survey reports with 

administrative records. These three approaches could be used independently or in 

combination. The Working Group made recommendations on this topic available in the 

section titled Administrative Data Use Considerations.

For a Potential Consumption Resource Measure

8. What types of spending should be included as consumption (aside from spending on health 

care or insurance, which is addressed by question 3)? If so, how? 

There were very few direct responses to this question. One commenter expressed the need to 

count technology needs of students, particularly given the nature of education during the 

pandemic. There was a discussion of the complexity of these issues resulting in a suggestion that 

any consumption measure be only experimental until these issues can be resolved by an expert 

panel. Several comments mentioned the sensitivity of consumption measures to these decisions 

as a rationale for an expert panel. 
Final Report ITWG on Evaluating Alternative Measures of Poverty

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Response: 

The Working Group considered these issues and further discussion is available in the 

section titled Consumption-based Resource Measures Using the CE Interview Survey. The 

Working Group recommends producing a consumption-based resource measure and has 

recommended additional research to support development and implementation of such a 

measure. 

9. How should vehicles and housing be included? 

There was little consensus amongst commenters regarding the inclusion of vehicles and housing. 

One commenter suggested that vehicle and housing flows should be imputed but admitted this 

would be difficult practically. Another comment simply claimed that housing value should be a 

rental-equivalence measure and that the Census Bureau, BEA, and BLS should jointly create this 

measure that avoids the upward bias from not adjusting housing stock appropriately for aging. 

This commenter further added that estimating flow of services with capital models that depend 

on interest rates are inappropriate. Finally, another comment concludes that since there is not a 

clear consensus on a method to include vehicles and house, this issue should be referred to a NAS 

panel study for further research.

Response:

The Working Group agreed that value of service flows from owner-occupied shelter and 

owned vehicles should be included in consumption resources but did not specify the 

methodology to be used to assign these values.

10. How should the Working Group address the problem of misreporting of consumption in 

household surveys? Should the Working Group consider using only those types of 

consumption that are reported with greater accuracy, while excluding less accurately 

measured types of consumption? What are the tradeoffs in using only well-measured 

consumption versus full consumption? 

There were only a handful of direct responses to this question. There was a concern about the 

need for better consumption/expenditure data and the need for funding for an expanded survey 

that would be capable of yielding reliable state-by-state poverty estimates. One commenter 

suggested that the quality of data from the CE was deteriorating. One commenter suggested that 

CE reported expenditures should be adjusted to control totals using microsimulation and 

imputation. One commenter was strongly opposed to selecting a subset of consumption items.

Response: 

The Working Group considered these issues and further discussion is available in the 

section titled Consumption-based Resource Measures Using the CE Interview Survey. The 

Working Group recommends producing a consumption-based resource measure and has 

recommended additional research to support development and implementation of such a 

measure. 
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Thresholds

11. How should the thresholds be set initially? 

The most prevalent comment on the thresholds was the need for the threshold definition to be 

consistent with the resource definition, as described in the previous sections. 

There were also numerous comments that nothing should be done that would lower the poverty 

thresholds (and thereby decrease the poverty rate) since studies of material deprivation show 

that a higher percentage of the population, particularly children, face material hardship than 

current poverty rates using either the OPM or the SPM. One comment had the opposite position 

– arguing that OPM poverty rates were too high.

Several comments suggested that thresholds be based on the money needed to purchase of a 

minimum bundle of core goods and services as is done in expert budgets. The United Ways’ ALICE 

(Asset Limited, Income Constrained, Employed)141 budgets were mentioned explicitly by at least 

five commenters. These comments expressed the importance of including health care and 

childcare in this bundle. 

Some commenters supported the use of a relative measure based on 50 or 60 percent of median 

income in addition to other alternative measures. One commenter was opposed to a relative 

measure.

At least five comments focused on the need to use public opinion to set the thresholds, 

discussing the results of polling as to what a family needs to “get by”. 

One comment was strongly opposed to the strategy of setting poverty thresholds at a level that 

would anchor the poverty rate to the current poverty rate.

Response: 

The Working Group recommended additional stakeholder and expert engagement on 

these issues. For further discussion, see the section titled Considerations and Consensus 

Recommendations for Thresholds.

12. How should they be updated over time? 

Most of the responses to this question were statements that no change to the updating 

mechanism should be made that would result in thresholds that are lower than the current 

thresholds. One commenter supported a group-specific consumer price index while another 

supported the PCEPI rather than the CPI.

A number of comments urged the Working Group to reduce reliance on any price index to update 

the thresholds but suggested that the bundle of goods and services included in the thresholds be 

updated on a regular basis – at least every five years. 

 141 See Hoopes (2020).
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Response: 

The Working Group recommended additional stakeholder and expert engagement on 

these issues. For further discussion, see the section titled Considerations and Consensus 

Recommendations for Thresholds.

13. Should thresholds be adjusted for geographic areas? If so, how? 

Nine responses commented on this question of including geographic adjustments, with six 

commenters advocating for including geographic adjustments. Several commenters suggested 

both geographically and non-geographical adjusted poverty measures should be reported. One 

commenter noted that prevailing wages should not be used to adjust poverty thresholds. Several 

suggested that differences in transportation costs (both time and cash) should be included in the 

adjustments as well as housing cost differences. Another commenter emphasized the need to 

adjust core good bundles geographically with as many goods as possible and within as small areas 

as possible. Two comments opposed any consideration of amenities in the cost index and one 

opposed using prevailing wages in the index. One commenter expressed concern that the 

geographic adjustment of the SPM thresholds did not improve their correlation with other 

measures of material hardship. As with other issues, some suggested that this be taken under 

consideration by a NAS panel.

Response: 

The Working Group recommended additional stakeholder and expert engagement on 

these issues. For further discussion, see the section titled Considerations and Consensus 

Recommendations for Thresholds.

14. How should a sharing unit’s size and composition be accounted for? (See Adjusting for 

different sharing unit sizes.)

Only two commenters addressed this directly, one arguing against the use of the OPM 

equivalence scales or the square root of family size and the other asking for more research.

Others addressed this indirectly by arguing that the thresholds need to include the special needs 

of children and the disabled, particularly the needs of infants and pre-school children and the 

extra costs of children’s development and educational needs.

Response: 

The Working Group recommended additional stakeholder and expert engagement on 

these issues. For further discussion, see the section titled Considerations and Consensus 

Recommendations for Thresholds.

Implementation

Although not specifically mentioned in the FRN, commenters discussed how to implement the 

Working Group’s recommendations. As noted in the beginning of this summary, almost all 

comments asked that the public comment period on this notice and interim report should be 

extended until after the current National Emergency is over.
Final Report ITWG on Evaluating Alternative Measures of Poverty

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Many others commented that there should be long-term experimentation and exploration with 

any new measures, with some advocating for rigorous impact analysis of proposed measures, 

disaggregated by race and gender. One commenter concluded by emphasizing the need for the 

Working Group to "go slow and get it right".

There was also overwhelming support for a NAS or similar expert panel to adjudicate the issues 

raised in the report and develop recommendations prior to any implementation.

Comments called for deliberations to include the scientific community as well as both providers 

who serve low-income families and low-income individuals themselves. 

Response: 

The Working Group recommended additional engagement with stakeholder and experts 

throughout the development process of the recommended measures. In particular, the 

Working Group recommends expert input through a National Academy of Sciences panel. 

In addition, the Working Group has recommended an advisory structure be setup to help 

the Census Bureau and the Bureau of Labor Statistics on issues related to implementation. 

Other Issues

There was a plethora of other issues raised in the comments. These included:

1) The intergenerational transmission of poverty

2) Asset poverty and student debt

3) Inequal access to public goods

4) Human capital formation

5) Concern for those left out of household surveys, particularly the homeless, the 

institutionalized and the incarcerated

6) Need to consider near-poverty

7) Economic instability and income volatility

8) Need for multidimensional poverty measures

Response: 

Some of these issues are discussed in the section titled Consensus Recommendations 

Worthy of Further Research by the Federal Statistical System, particularly the issue of 

multidimensional poverty measures and the concern for those left out of household 

surveys. 
Final Report ITWG on Evaluating Alternative Measures of Poverty

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Institute of Bard College. Available at http://www.levyinstitute.org/pubs/ppb_146.pdf

Zamboni, L. M., & Martin, E. G. (2020). Association of US households’ disaster preparedness with 

socioeconomic characteristics, composition, and region. JAMA network open, 3(4), 

e206881-e206881. doi:10.1001/jamanetworkopen.2020.6881
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Appendix A. Census Bureau Surveys 

Current Population Survey Annual Social and Economic Supplement (CPS ASEC) 

The CPS ASEC is the source of official annual estimates of national poverty levels and rates, as 

well as widely used estimates of household income, individual earnings, and the distribution of 

income. The CPS ASEC provides a consistent historical time series beginning in 1959 at the 

national level and can also be used to look at state-level trends and differences (through multiyear averages) going back to 1980. However, the relatively large sampling errors of state-level 

estimates for smaller states somewhat limit their usefulness.

The CPS ASEC is administered to about 95,000 households in February, March, and April each 

year and asks respondents about their income in the previous calendar year. Data from these 

respondents is processed throughout the summer each year with income and poverty estimates 

released typically in the first or second week of September. Estimates are released for the nation 

as a whole, major demographic subgroups, and regions. Users are encouraged to use three-year 

average estimates to look at income and poverty for individual states and smaller demographic 

groups. The questionnaire asks about income from more than 50 sources and records up to 27 

different income amounts, including receipt of numerous in-kind benefits, such as Supplemental 

Nutrition Assistance Program (formerly known as the food stamp program), subsidized school 

lunches, and housing assistance.

American Community Survey (ACS) 

The ACS is administered to approximately 3.5 million households each year on a continuous 

basis. Starting in 2006, the ACS replaced the decennial census long-form sample questionnaire. 

The ACS offers comprehensive information on social, economic, and housing characteristics and 

because of its large sample size, the ACS is exceptionally useful for subnational analyses, serving 

as the best source for survey-based state level income and poverty estimates. The ACS provides 

single-year estimates of income and poverty for all places, counties, and metropolitan areas with 

a population of at least 65,000 as well as the nation and the states, and provides estimates for all 

geographies, including census tracts and block groups using data pooled over a five-year period. 

Both one- and five-year estimates are updated every year.

ACS respondents are asked to report their income in the “previous twelve months.”142 Income 

questions in the ACS are less detailed than in the CPS ASEC with respondents asked to report 

income in eight broad income categories. The only in-kind benefit reported on the ACS is whether 

or not anyone in the household received SNAP benefits. There is no information on housing 

assistance, LIHEAP, WIC or school lunch. Since estimates from the ACS are provided for lower 

levels of geography, they are often used in funding formulas for government programs and 

eligibility for special tax credits.

Survey of Income and Program Participation (SIPP) 

SIPP is a longitudinal survey most useful for understanding changes in monthly income and 

poverty status for the same individuals or households over time, typically 3 to 4 years, and for 

examining the nature and frequency of poverty spells. Unlike the ACS and CPS ASEC, the SIPP also 

 142 The upcoming ACS content test will explore the feasibility of changing the reference period for the ACS income 

questions from the previous 12 months to the previous calendar year in order to facilitate the matching of ACS 

responses to administrative records that tend to be provided with calendar year reference periods. 
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permits researchers to look at estimates of income and poverty for periods of more than or less 

than one year, including monthly poverty rates. The SIPP is a nationally representative survey and 

since the 2004 SIPP Panel reliable state-level estimates are available in select states, generally the 

most populous 20 states.143

Whereas the CPS ASEC is a labor force survey with supplementary questions on income, the SIPP 

focuses on income and asks about income from up to 81 sources. The SIPP typically reports more 

income than the CPS ASEC does. The SIPP also contains detailed information on government 

program participation, asset-holdings, and other subject areas critical for understanding social 

and economic well-being. Most income estimates are collected at the monthly level.

Respondents are asked about receipt of all in-kind benefits covered by the CPS ASEC plus school 

breakfast and other assistance such as transportation, clothing and Meals on Wheels. Like 

income, program participation data are at the monthly level for most in-kind benefits. SIPP also 

includes a number of questions designed to estimate material hardship such as questions on 

whether or not a household had trouble paying its bills, skipped meals, or faced eviction.

Historically SIPP interviews were conducted in 4-month intervals, or “waves.” In the 2008 and 

earlier SIPP Panels, each wave included the same set of core questions about the previous 4 

months, or reference period, and a set of topical questions that varied from wave to wave. The 

2008 Panel had 16 waves spanning 6 years (May 2008 to November 2013). The SIPP was 

reengineered following the 2008 Panel, with the subsequent 2014 Panel collecting data in 12-

month intervals over 4 annual waves asking about income and program participation for the 

period January 2013 through December 2016. A new SIPP panel began in 2018 with a calendar 

year 2017 reference period. In the 2014 and 2018 panels, all questions were asked in every 

interview. SIPP interviewers return to the same household (not housing unit) and attempt to 

follow people interviewed in the first wave, even if they move. 

The 2014 SIPP panel started with a universe of 53,000 living quarters. Like other longitudinal 

surveys, SIPP has considerable attrition across waves of a panel. Survey weights are adjusted in 

an attempt to address any bias in estimates that may be introduced by attrition. 

 143 The 2014 SIPP includes a state expansion sample of 13,800 addresses selected from 16 states: Alabama, Arizona, 

Arkansas, Georgia, Illinois, Indiana, Kentucky, Louisiana, Michigan, Mississippi, New Mexico, North Carolina, Ohio, 

Pennsylvania, South Carolina, and Tennessee. The size of the expansion in each state was determined by the amount of 

sample needed in order to reach a target coefficient of variation2 (CV) of 6 percent on the estimate of low income. The 

four largest states – California, Florida, New York, and Texas – met this requirement without any additional sample. 

https://www.census.gov/content/dam/Census/programs-surveys/sipp/methodology/2014-SIPP-Panel-Users-Guide.pdf
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Appendix B. Bureau of Labor Statistics Surveys 

Consumer Expenditure Survey (CE) 

The Consumer Expenditure Survey (CE) is a nationwide household survey conducted by BLS to 

find out how U.S. consumers spend their money. It is the only Federal government survey that 

provides information on the complete range of consumers’ expenditures as well as their incomes 

and demographic characteristics. BLS publishes 12-month estimates of consumer expenditures 

twice a year with the estimates summarized by various income levels and household 

characteristics. BLS also produces annual public use microdata files to help researchers analyze 

the data in more detail.

The CE consists of estimates derived from two separate surveys, the Interview Survey and the 

Diary Survey. The Interview Survey is designed to collect data on large and recurring expenditures 

that consumers can be expected to recall for a period of 3 months or longer, such as rent and 

utilities, and the Diary Survey is designed to collect data on small, frequently purchased items, 

including most food and clothing. Together, the data from the two surveys cover the complete 

range of consumers’ expenditures. The Census Bureau collects CE data for BLS.

The CE Interview Survey is a rotating panel survey with approximately 22,000 interviews 

completed annually. Expenditures are reported for each consumer unit (CU), defined as either a 

group of individuals who are related by blood or marriage, a single or financially independent 

individual, or two or more persons who share resources. Expenditures on nearly all categories of 

goods and services are recorded in the survey, in addition to income, and the ownership of 

vehicles and housing.

Although the CE includes a Diary Survey component with a separate sample, the Interview Survey 

component is more suitable for analyses of poverty (and inequality more generally) because its 

longer reference period (a calendar quarter) as compared to the Diary Survey (two weeks) yields 

reported expenditures for more categories than do the diaries. Additionally, the Interview 

Survey’s coverage of the universe of expenditures approximates that of the open-ended diary, 

with about 65 percent of all spending represented by detailed questions and another 25 percent

by global questions. 

More detailed information about the CE surveys is available in the online BLS Handbook of 

Methods section on Consumer Expenditures and Income.
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Appendix C. Choices for Geographic Cost Adjustments Choices for geographic adjustments for poverty thresholds are described here. There are two 

general categories of geographic cost adjustments: those which may be more appropriate for an 

alternative poverty measure, and those which would not. Brief descriptions for those which may 

be more appropriate for use in an alternative poverty measure are available below the table.

Table C1. Choices for geographic cost adjustments which may be appropriate for alternative poverty measure

Name Produced by Purpose What is 

covered?

Geography Advantages Technical 

Limitations/

Disadvantag

es

Median 

Rent Index 

(MRI)

Census

Bureau

Geographica

lly adjust the 

housing 

portion of 

the 

thresholds

Rent 342 

geographies 

to match 

public use 

CPS ASEC

Transparent, 

currently in 

use for SPM

Only adjusts 

housing 

portion of 

threshold. 

Based on 

rents at 

median. 

Does not 

reflect 

differences 

in local 

attributes.

Regional 

Price 

Parities 

(RPP)

BEA Geographica

lly adjust the 

entire 

threshold 

using full

RPPs

All 

expenditure

s in CPI*

Off-theshelf: state 

level, or 

MSA level or 

state 

metro/non

metro level

Special run: 

342 

geographies 

to match 

public use 

CPS ASEC

Credible and 

widely used 

for priceadjustment

Requires 

special run 

by BEA. BEA 

methodolog

y is 

changing. 

Not based 

on spending 

at bottom of 

income of 

income 

distribution. 

Weights are 

not 

representati

ve of 

bottom of 

income 

distribution. 

Does not 

reflect 

differences 

in local 

attributes.
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Name Produced by Purpose What is 

covered?

Geography Advantages Technical 

Limitations/

Disadvantag

es

Regional 

Price 

Parities for 

Food, 

Apparel and 

Rent (RPPFAR)

BEA Geographica

lly adjust the 

entire 

threshold 

using 

restricted 

set of 

expenditure

s

Rent, 

apparel and 

food

Special run 

only: 342 

geographies 

to match 

public use 

CPS ASEC

Focused on 

expenditure

s included in 

SPM 

threshold

Requires 

special run 

by BEA. BEA 

methodolog

y is 

changing. 

Not based

on spending 

at bottom of 

income of 

income 

distribution. 

Weights are 

not 

representati

ve of 

bottom of 

income 

distribution. 

Does not 

reflect 

differences 

in local 

attributes.

Comparable 

Wage Index 

(CWI)

Census

Bureau

Geographica

lly adjust the 

entire 

threshold 

based on 

wage 

differentials

Based on 

wages

342 

geographies 

to match 

public use 

CPS ASEC

CWI reflects 

differences 

in local 

attributes as 

well as the 

cost of 

living. 

CWI is an 

estimate 

from a 

sample 

survey and 

is subject to 

the usual 

criticisms of 

samplebased 

research. 

Need to 

define which 

workers go 

into 

calculation.

*rents, food, transportation, housing, education, medical, recreation, apparel, other
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Median Rent Index (MRI) 

The MRI is the ratio of the median gross rent of a two-bedroom unit with complete kitchen and 

plumbing facilities in a specific metro area or state to the U.S. median gross rent of the same type 

of unit using the ACS (see Renwick, 2011). For the SPM, the MRI is applied to the national 

threshold values, as defined by the Consumer Expenditure Survey (CE), in proportion to the 

national average shares of housing and utility expenditures from total expenditures. The result is 

a metro area- and state-specific threshold value, and the poverty rate is given by the estimated 

population below this threshold.

TTTTTTTTTTTTTTTTTTiiiiii = [�HH HH HHHHHH HH × MMMMMMiiii) + (11 − HH HH HHHHHH HH �] × TTTTTTTTTTTTTTTTTT

• i = state 

• j=specific metro area, other metro or nonmetro area 

• t= tenure: owner with mortgage, owner without a mortgage, renter 

• MRI = Median Rent Index 

• HousingShare = percent of threshold represented by housing and utility 

expenditures

• Threshold = national average dollar value for income below which households are 

considered in poverty

Both the Threshold and HousingShare vary by housing tenure status and the overall index is not 

normalized. The MRI is currently used for geographically adjusting SPM thresholds. Separate 

medians are estimated for each of the 260 MSAs large enough to be identified on the public-use 

version of the CPS ASEC. For each state, a median is estimated for all nonmetropolitan areas (47) 

and for a combination of smaller metropolitan areas within a state (35).

Regional Price Parities (RPP) 

RPPs are spatial price indexes which measure price level differences across regions. They are 

based on rents from the ACS, combined with five-year rolling average price levels for inputs for 

other consumption expenditure classes including food, apparel, transportation, housing, 

education, recreation, medical and other goods and services from the Consumer Price Index 

(CPI). RPPs are produced by the Bureau of Economic Analysis (BEA) for either 51 states, 383 

MSAs, or 98 state metro/nonmetro areas. By special run, BEA has produced RPPs at the same 

level of geographies used by the SPM and available on the public use CPS ASEC (260 MSAs, 47 

nonmetropolitan areas and 35 smaller metro areas).

RPPs are constructed in two stages. The first stage estimates annual multilateral price level 

indexes for CPI areas and for several consumption expenditure classes such as apparel, food and 

transportation. CPI price data are available for 38 urban areas. In the second stage, price levels 

and expenditure weights are allocated from CPI areas to all counties in U.S. and then recombined 

for regions, such as states and metropolitan areas. This stage also merges data on rents from 

ACS, which are quality-adjusted using a hedonic model that controls for unit characteristics. The 

final RPPs are calculated by stacking five years of first-stage results, plus annual rent indexes, and 

calculating multilateral aggregate price index for all goods and services and rents. RPPs are then 

applied to the entire threshold (for full details see Renwick et al. 2014 and BEA 2020).

RPP TThrrrrrrhoollddiiiiii = RRRRRR × TThrrrrrrhoo

• i = state j=specific metro area, other metro or nonmetro area 
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• t= tenure: owner with mortgage, owner without a mortgage, renter 

Regional Price Parities for Food, Apparel and Rent (RPP-FAR) 

Since the RPP’s track differences in prices for a much broader array of goods and services than 

those that are included in the SPM thresholds, the Census Bureau worked with BEA to produce a 

version of the RPPS that focused only on the specific expenditures categories included in the SPM 

thresholds: Food, Apparel and Rent (FAR). RPP-FARs were developed by the BEA as a special run 

as a method to potentially geographically adjust SPM thresholds. (See Renwick et al. 2014, 2017)

Comparable Wage Index (CWI) 

Because of price differences, real resources are more expensive in some locations than in others 

and expenditures for the same services vary in different parts of the country (Cornman, Nixon, 

Spence, Taylor and Geverdt 2019 p.1). In January of 2012, an expert panel recommended that the 

National Center for Education Statistics (NCES) annually produce and release geographic 

adjustment factors for educational expenditures (Cornman et al 2018, p. 2). In May of 2017, NCES 

convened a panel of recognized experts on school finances and Title I to review the framework of 

the Study of the Title I, Part A Grant Program Mathematical Formulas on the Title I Formula. The 

expert panel reached consensus that “The American Community Survey-Comparable Wage Index 

(ACS-CWI) cost of living adjustment should be used to compare purchasing power of funding 

across states and geographic locales” (Snyder, et al 2018, p. 9).

A comparable wage index (CWI) estimates the wage level for each location using a weighted GLS 

regression analysis of annual wage and salary earnings for individual workers, controlling for 

worker characteristics, occupation, industry, and location. The predicted local wage in each 

location is the model predicted wage for a person with average characteristics. 

Researchers (Baker et al.) have suggested that similar approach be used to adjust poverty 

thresholds for differences in the cost of living. In a 2013 article in the journal of the Association 

for Education Finance and Policy, Baker et al. propose an index based on an estimate of the 

prevailing wage for individuals with the typical characteristics of the working poor. These 

estimates are created using a hedonic wage analysis that is designed to capture differences in the 

cost of living as well as access to desirable local amenities. As they summarize it, “Essentially, we 

presume that if the prevailing wage for Chicago for a worker with poverty-level characteristics is 

10 percent above the national average, then the poverty income threshold in Chicago should also 

be 10 percent above the national average” (Baker et al., 2013, p.399). They argue that this 

approach is preferable to a market-basket approach because it incorporates amenities and allows 

for geographic differences in the cost and composition of the entire bundle of goods and services. 

For example, this approach allows for the possibility that families may choose a more modest 

dwelling in amenity-rich locations like San Francisco than they would choose in other parts of the 

country. Renwick (2020) applied this approach to SPM estimates for 2018 and compared these 

results to the MRI adjustment.
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Table C2. Choices for geographic cost adjustments which may not be appropriate for alternative poverty measure

Name Produced 

by

Purpose Why not use?

Fair Market 

Rents (FMR)

HUD Determine payment 

standard amounts for 

vouchers and initial 

rents for Section 8 

and Housing 

Assistance Program

HUD does not support the use of FMRs for 

this purpose; FMRs are designed to 

measure differences in rental housing 

costs for recent movers – not homeowner 

costs, rest of consumption basket, nor 

established renters. FMRs are not 

calculated in a consistent manner across 

all geographies for which they are 

estimated. Calculation methods are 

frequently changed in response to 

program needs, so FMRs may not be 

historically consistent over time either.

Comparable 

Wage Index 

for Teachers 

(CWIFT)

NCES Compare educational 

expenditures across 

districts

Specifically applies to wages and salaries 

for education. School districts use real 

resources—teachers, principals, classroom 

materials—to produce education. The 

CWIFT is limited to measuring 

uncontrollable differences in the 

purchasing power of school districts on 

real educational resources. 

Geographic 

Practice Cost 

Indices (GPCI)

CMS Set Medicare 

Physician Fee 

Schedules

Too specific to Medicare application –

geographies are Medicare carrier-locality 

level; costs based on physician work, 

practice expenses, and malpractice 

insurance

CONUS Cost of 

Living 

Allowance 

(CONUS COLA)

DOD Provide 

compensation for 

variations in nonhousing costs in 

contiguous U.S.

Too specific to military application –

geographies are Military Housing Area; 

deductions are made for commissaries and 

exchanges

Locality Pay 

Areas

OPM Set locality pay for 

federal employees

Geographies are too broad – 46 

Metropolitan Statistical Areas and the 

“rest of U.S.” is lumped together

Cost of Living 

Index (COLI)

C2ER Local level cost of 

living index available 

for U.S.

Weights assigned to relative costs based 

on expenditure patterns for professional 

and executive households; commercial 

(need to purchase)
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Appendix D. List of Working Group Recommendations In this report, the Working Group has offered numerous recommendations in support of 

developing alternative measures of poverty. The recommendations are found throughout the 

report, placed within the context of the relevant discussion section. For ease of reference, a 

complete list of Working Group recommendations can be found below. 

1. The Working Group recommends that the Census Bureau and the Bureau of Labor Statistics 

begin development of the recommended measures as soon as possible and that the research 

measures be published as soon as possible thereafter. .....................................................................15

2. The Working Group recommends that the Census Bureau and the Bureau of Labor Statistics 

engage with stakeholders and other experts throughout the development of the recommended 

measures, including soliciting additional public comment as needed. In particular, the Working 

Group recommends expert input through a National Academy of Sciences panel............................15

3. The Working Group recommends that an advisory structure, such as a new Interagency 

Technical Working Group, be established to engage with the Census Bureau and the Bureau of 

Labor Statistics on issues related to implementation of the detailed recommendations 

throughout this report and other new issues that arise. ....................................................................15

4. The Working Group recommends that the Census Bureau develop and publish two new sets 

of research measures of income-based resources, one that includes a value of health insurance 

and one that does not. ........................................................................................................................16

5. The Working Group recommends that the Bureau of Labor Statistics develop and publish two 

new sets of research measures of consumption-based resources, one that includes a value of 

health insurance and one that does not. ............................................................................................16

6. The Working Group recommends that the Census Bureau and the Bureau of Labor Statistics 

use, where available and when appropriate, administrative data to supplement or replace the use 

of survey data for developing the recommended measures. .............................................................16

7. The Working Group recommends that the Census Bureau should attempt to produce the 

new income-based resource measures within 12 months of survey data collection. For example, 

estimates for calendar year 2020, for which data are collected in February-April 2021, should be 

made available by March 2022. The Working Group recognizes that in the first few years of 

implementation these time lags may be greater, but expects that over time the lags will decrease. 

The Bureau of Labor Statistics may be able to produce consumption resource measures with less 

of a lag..................................................................................................................................................16

8. The Working Group recommends subtracting child support payments from resource totals 

for income-based measures. No adjustments are required for child support payments for 

consumption-based measures because the transfer of income from one family to another is 

indirectly captured through increased consumption of families receiving the transfers and 

decreased consumption among families with nonresident parents making child support 

payments. ............................................................................................................................................18

9. The Working Group recommends that expenditures needed to work including childcare 

expenditures be subtracted from family resources for any new recommended poverty measures 
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and that total childcare expenditures be capped because it is difficult to distinguish between 

childcare necessary to work versus other childcare. ..........................................................................19

10. The Working Group recommends continued work to improve measurement of childcare 

and other work-related expenditures and methods to cap them. .....................................................19

11. The Working Group recommends that income be adjusted, as feasible, for federal, state 

and local income and payroll taxes and credits in a new income-based resource measure..............20

12. The Working Group recommends that a new income-based resource measure include as 

resources in-kind benefits from SNAP, school meals, WIC, LIHEAP, and rental housing assistance..20

13. Two sets of income resource measures and two sets of consumption resource measures 

should be produced. For each type of resource measure, one set should not include a value of 

health insurance, and the other set should include some value of health insurance. .......................28

14. The value of health insurance should not depend on the disability or health status of 

individuals. ...........................................................................................................................................28

15. The value of health insurance should ideally depend on a person’s resources but the 

precise method to implement this should be the subject of further research. In the interim, the

market value of health insurance (with values that do not depend on the health status of the 

recipient) should be capped at some share of total resources...........................................................29

16. The value of health insurance should ideally not constitute a majority of resources for 

people near the poverty threshold......................................................................................................29

17. The Working Group recommends that expenditures on education be excluded from the 

recommended extended income-based and consumption-based resource measures because 

education is generally considered an investment in human capital...................................................31

18. The Working Group recommends that, at this juncture, personal educational expenses not 

be subtracted from the extended income-based resource measure. However, the Working Group 

recommends a future advisory structure revisit the issue of deducting out-of-pocket education 

expenditures on tuition and other educational items if the data quality makes it feasible...............31

19. The Working Group recommends continued research and additional stakeholder and 

expert engagement on whether and how to treat education within resource measures. ................31

20. The Working Group recommends that further research be undertaken to evaluate 

alternative methods to estimate the net value of service flows from owner-occupied shelter and 

the net value of the service flows from owned vehicles to be included in the income resource 

measures, including the possibility of imputing such values using statistical methods and data 

from the CE Survey. Such research should consider the availability of data from the CPS ASEC and 

ACS. 38

21. The Working Group recommends that the Census Bureau use CPS ASEC for an alternative 

income-based resource measure. .......................................................................................................42

22. The Working Group recommends, taking into consideration the advantages and 

disadvantages for each of the above approaches for correcting for missing or misreported data, 

the application of all three approaches, where appropriate, in the following order:........................45

Methods that combine administrative data with survey data are the preferred approach for 

adjusting survey data to correct for misreporting and missing data. These methods may involve 

direct replacement of survey responses with administrative reports when research supports the 

quality of the administrative records relative to survey reports. The administrative data need to 
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be available for use in production and the administrative data must also be available in a timely 

fashion..................................................................................................................................................45

When survey reports conflict with administrative records for particular individuals, research 

should examine criteria to determine which source to use for the poverty estimates. ....................45

Consistent with Foundations for the Evidence-based Policymaking Act of 2018, efforts to 

encourage and facilitate data sharing across government agencies should be strengthened. .........45

Research to assess ways in which survey data might be made more comparable to administrative 

data (e.g. changing the reference period for income from the previous 12 months to the previous 

calendar year) should be encouraged. ................................................................................................45

Regression-based modeling (with or without individual-level or aggregate administrative data) 

can also improve the quality of estimates of income, expenditures, and program participation. 

These regression-based techniques can be used, for example, when sharing agreements do not 

allow for direct substitution, there are significant lags in the availability of administrative data, or 

administrative data are not available for all geographies or years. Regression-based modeling 

such as Sequential Regression Multiple Imputation (SRMI) should replace hot deck imputations 

where feasible and continued research should be conducted to improve these methods as new 

tools and techniques become available. .............................................................................................45

Some rules-based adjustment may be necessary for some programs and income sources. For 

example, if program rules assign automatic eligibility for Medicaid to all TANF and SSI recipients, 

it could be logical to assign program receipt to all survey respondents who are known to 

participate in either of these two programs. In a similar vein, if there are school districts in which 

all students are deemed eligible for free school lunch, they should be assigned participation in 

free school lunch if they report that their children regularly eat school meals. ................................46

23. The Working Group recommends that the advisory structure recommended previously 

should vet decisions about data sources, adjustment strategies, and other assumptions. This 

advisory structure should consider and discuss continued research into availability and 

applicability of administrative data sources........................................................................................46

24. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics continue to research, and possibly implement, ways to reduce survey burden and 

improve the quality of resulting data through increased access and use of administrative data in 

surveys, including the CPS ASEC, ACS, and CE.....................................................................................46

25. The Working Group recommends funding support of the work to develop the new 

recommended measures, including funding to support BLS to research the nature and 

construction of a potential consumption-based poverty measure and improve the CE program in 

support of improved poverty measurement. A proposal requesting $7.1 million was included in 

the fiscal year 2021 President’s Budget. .............................................................................................47

26. The Working Group recommends that the Bureau of Labor Statistics use the CE Interview 

Survey data to research and develop a consumption-based resource measure................................47

27. The Working Group recommends that the value of service flows from owner-occupied 

shelter and the value of the service flows from owned vehicles be included in the consumption 

resource measures...............................................................................................................................48

28. The Working Group recommends that the CE Interview serve as the primary data source 

for the production of the consumption resource measures, with estimates produced at the state 

level. 51
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29. The Working Group recommends that the current CE Interview Survey serve as the interim 

data source for the production of the consumption resource measures, with estimates produced 

at the Census Division level. ................................................................................................................51

30. The Working Group recommends that the Census Bureau and the Bureau of Labor 

Statistics seek additional stakeholder and expert engagement on the key decisions required in 

setting poverty thresholds...................................................................................................................58

31. The Working Group recommends that this engagement should occur in parallel with the 

Census Bureau and the Bureau of Labor Statistics developing research income- and consumptionbased resource measures....................................................................................................................58

32. The Working Group recommends that by the time the proposed resource measures are 

ready to be published, BLS and the Census Bureau should work to identify an interim solution for 

each set of the measures for applying thresholds to produce a full poverty measure. BLS and the 

Census Bureau should consider input from experts (per the previous recommendation) as 

available to inform the applied methodology. The interim methodology applied need not be the 

final methodology chosen for application to the resource measures. The intention of this 

recommendation is to ensure the ability to publish poverty measures using the proposed 

resource measure recommendations. ................................................................................................58

33. The Working Group recommends that the BLS conduct a study of price indexes 

appropriate for use in updating thresholds that would be used in combination with consumption 

and income as defined in this report...................................................................................................62

34. The Working Group recommends that an expert panel conduct a study of and make a 

recommendation regarding the application of equivalence scales that would be most appropriate 

for the income and consumption resource measures recommended in this report. In the interim, 

the Working Group recommends that for any resource measures produced an equivalence scale

be applied that accounts for the potentially differing needs of adults and children and economies 

of scale. ................................................................................................................................................65

35. The Working Group recommends that expert review be sought on all of the considerations 

described above because whether and if so how to adjust poverty thresholds across geographic 

areas is a complex topic.......................................................................................................................68

36. The Working Group recommends continued research and additional stakeholder and 

expert engagement on whether and how to develop multidimensional measures. Focus should be 

on operationalizing such a measure in the policy context, ideally with indicators that are directly 

linked to policy levers. .........................................................................................................................72