BLS WORKING PAPERS 

 U.S. Department of Labor 

U.S. Bureau of Labor Statistics

Office of Prices and Living Conditions 

Setting and Updating Modern Poverty Thresholds

Thesia I Garner, U.S. Bureau of Labor Statistics

David Betson, University of Notre Dame

Working Paper 435

March 2010

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the

U.S. Bureau of Labor Statistics.
Garner and Betson, March 2010 Page 1 

Setting and Updating Modern Poverty Thresholds 

Thesia I Garner1 and David Betson2 

1

Bureau of Labor Statistics (BLS) 

Division of Price and Index Number Research 

Massachusetts Ave NE, Washington, DC 20212 

Phone: (202) 691-6576 

Email: Garner.Thesia@BLS.gov 

2

University of Notre Dame 

Department of Economics and Policy Studies 

404 Decio Faculty Hall Notre Dame, IN 46556 

Phone: (574) 631-5068 Email: David.M.Betson.1@nd.edu 

Submitted to BLS Working Paper Series: March 3, 2010

Keywords: poverty measurement, expenditures, rental equivalence, subsidized housing 

ABSTRACT 

This research compares median-based thresholds with ones based on the 33rd percentile using Consumer 

Expenditure Interview Survey (CE) data from 2004 quarter one through 2009 quarter one. Thresholds for 

reference families are produced using two different approaches: (1) calculating the thresholds based on the 

expenditure records of reference families composed of two adults with two children; and (2) calculating the 

thresholds using expenditures from all consumer units participating in the CE, but first converting their 

expenditures into adult equivalent value using the three-parameter equivalence scale before identifying the 

median and percentile values. Different updating mechanisms examined include: (1) an annual 

recalculation of the 33rd percentile of the reference family’s outlays on FCSU, and (2) changes in the 

reference family’s median spending or consumption based on needs. Thresholds, based on spending and 

consumption concepts to value needs for food, clothing, shelter, and utilities (FCSU), are produced. 

______________________________________________________________________________________ 

Acknowledgements and Disclaimer 

An earlier version of this paper was presented at the Annual Meeting of the Allied Social Science 

Associations (ASSA) in Atlanta, Georgia, January 3, 2010 during the Society of Government Economists 

Session (SGE) on “Measuring Poverty in the U.S.: Changing the Official Statistic.” We thank Barbara 

Bergmann and Sheldon Danziger, as discussants, and other participants in the SGE session for their helpful 

comments and suggestions. We also appreciate the comments of attendees at the2009 Brookings Meeting 

on poverty measurement, Randy Verbrugge of the Bureau of Labor Statistics (BLS), Tony Yezer of George 

Washington University, and many others who have shared their thoughts and ideas with us as we explore 

how to optimally account for non-market valued shelter in poverty thresholds. All views expressed in this 

paper are ours and do not reflect the views or policies of the BLS, the views of other BLS staff members, or 

the view or policies of the University of Notre Dame or staff therein. We, the authors, are responsible for 

any errors in this work.
Garner and Betson, March 2010 Page 2 

Introduction

Determining whether someone is poor is conceptually simple. The poor are those individuals whose 

economic resources are insufficient to meet a socially determined level of need. However, determining the 

level of need is complicated, with choices to be made -- choices regarding what to include in the needs 

measure, how to set the threshold, and how to update the threshold. Each choice involves subjective 

judgment. The same is true of resources but less so since once the decisions regarding the threshold are 

made, a measure of resources that is consistent conceptually with the threshold follows. 

As is well known by now, in 1995, the National Academy of Science (NAS) Panel on Poverty 

Measurement and Family Assistance issued their report calling for revisions in the US official poverty 

measure. They made recommendations regarding how to determine the threshold and how to define an 

appropriate resource measure. In 2004 the NAS held a Workshop to review the research conducted since 

the report’s release. 

 

Since the release of NAS report, many of the Panel’s recommendations have been questioned and tested. 

Attention has been placed on the study of: child care (see Short 2009, 2010); medical care (Banthin et al. 

2001; Bavier 2001; Short and Garner 2002), geographic adjustment (Renwick 2009) and shelter. The work 

on shelter began in earnest shortly after the Panel’s report was published. The consumption value of owner 

occupied housing was included in alternative poverty measures first by Johnson, Shipp, and Garner (1997), 

followed by Short et al. (May 1998) and in the first Census Bureau report released on an alternative poverty 

measure in 1999 (Short et al. 1999) Also in the 1999 Census report, outlays or out-of-pocket (OOP) 

spending based thresholds were produced. Garner and Rozaklis (1999, 2001) and Rozaklis and Garner 

(1999) presented poverty thresholds that accounted for the rental equivalence value of owner occupied 

housing and the market value of subsidized rental housing. The value of subsidized housing accounted for a 

rise in the experimental threshold by $1 (Rozaklis and Garner 1999). In 2005, Garner computed and 

compared thresholds based on three different definitions of shelter: CE-defined expenditures, OOP 

spending, and OOP spending for food, clothing, utilities, rents and maintenance and repairs for renters, and 

the rental equivalence value of owner occupied housing (replacing owners’ shelter expenditures) for 

homeowners. Short (2005) used the OOP and shelter consumption based thresholds produced by Garner 

(2005) and created consistently defined resources measures; using this measure Short produced poverty 

rates. Renwick (2009, 2010) focused on resources and how to account for subsidizes for rental units in 

resources. In more recent work, Betson (2009) presented alternative ways to account for the consumption 

needs of owners without mortgages and renters living in subsidized housing using regression methods. 

Garner (2009a, b) also produced thresholds using OOP spending and rental equivalence. 

Beginning in 2008, conversations and meetings with Congressional staff from the Joint Economic 

Committee, staff within the BLS and Census Bureau and outside experts were held to learn more about the 

research that had been conducted on the NAS-based poverty measure. On June 17, 2009, the “Measuring 

American Poverty Act of 2009” was signed and introduced to the House of Representatives. From 

henceforth, we refer to the Act as MAP. The Act was introduced to amend title XI of the Social Security 

Act. According to the proposed legislation, 

....this Act is to provide for an improved and updated method for measuring the extent to 

which families and individuals in the Unites states have sufficient income to allow a 

minimal level of consumption spending that meets their basic physical needs, including 

food, shelter (including utilities) clothing, and other necessary items, in order to better 

assess the effects of certain policies and programs in reducing the prevalence and depth 

of poverty, to accurately gauge the level of economic deprivation, and to improve 

understanding of targeting of public resources, without directly affecting the distribution 

of, or eligibility for, any Federal benefits or assistance. (page 3, lines 21 through page 4, 

lines 1-8).

The MAP specified that modern poverty thresholds are to be based on a distribution of consumption 

expenditures that includes food, clothing, shelter and utilities. The threshold is to be produced for a 

reference family with the threshold equal to 120 percent of the 33rd percentile of the distribution of FCSU 

consumption expenditures, or a limited band converging on this percentile. Four or more of the most recent 
Garner and Betson, March 2010 Page 3 

years of CE data, or a combination with other data, are to be used to produce the thresholds. The thresholds 

are to be updated no less often than annually using this method (page 6, lines 5-20). 

Due to differences in the out-of-pocket expenditures of owners with and without mortgages the MAP 

further specifies that the calculation of the threshold “shall be made separately” for (1) families who own 

their primary residence and do not have a mortgage secured by the residence, and (2) all other families such 

that they can “purchase similar quality shelter” (page 8, lines 1-17).

In response to the MAP proposed legislation, Garner (2009a, b) produced reference family thresholds for 

consumer units composed of two adults and two children that accounted for differences in housing payment 

status as specified in the MAP. Thresholds for 2007 were produced based on the 33rd percentile of FCSU 

out-of-pocket expenditures with a multiplier of 1.20. Reference family thresholds were produced for: 

(1) all reference families; (2) reference family homeowners without mortgages (and those with no rent 

payment---this did not impact the thresholds as such renters are minimal); and (3) owners with mortgages 

plus renters with rent payments. No adjustments were made for subsidized renters in thresholds 1 or 3. 

The 2007 all reference person threshold was $25,179, the owners with mortgages and renters threshold was

higher, $26,732, and the homeowners without mortgages threshold was $14,833. These are in contrast to 

an all reference family threshold based on a percentage of the median rather than the 33rd percentile; this 

threshold was estimated to be $25,865 for 2007.

The purposes of this paper are four: 

 

1. to continue and extend our examination of the sensitivity of poverty thresholds to the 

definition of shelter

2. to examine the impact of different updating mechanisms

3. to study the change in the real value of the thresholds overtime, and 

4. to examine the impact of basing the thresholds on the behavior of reference families only 

or on all consumer units. 

Our motivation in paying particular attention to shelter is that shelter accounts for the largest single share of 

the threshold and this share has been increasing over time. Garner and Short (2008) reported that shelter 

(not including utilities) accounted for 29 percent of OOP expenditure-based threshold based on FCSU plus 

medical care spending in 1996 but 32.9 percent by 2005. A rough adjustment to the FCSUM threshold to 

remove medical care expenditures resulted in shelter accounting for 31.3 percent of FCSU thresholds in 

1996 and 35.6 percent by 2005. 

The updating mechanisms are based on the Panel’s proposal and the MAP proposed legislation. In none of 

the previous research has the MAP legislation updating mechanism been tested. 

The real value of thresholds over time has not been raised since the Panel first proposed these in 1992. The 

real value of the current official poverty threshold has not changed essentially since the threshold was first 

introduced. Official poverty thresholds are updated each year only by changes in the CPI.

Our motivation in producing thresholds using the behavior of reference families as opposed to all consumer 

units is due to the relatively small percentage of consumer units represented by the reference family. As 

reported by Garner and Short (2010), in 2005, a reference family composed of two adults and two children 

represented 8.45 percent of household types. The Panel selected this type of household unit as it 

represents the largest proportion of people in households with children, approximately 14 percent of the 

U.S. population in 2005. Since children have historically made up a large portion of the poverty 

population, it seemed reasonable that the selected reference family would represent spending patterns for 

families with children. 

While the definition of a family’s resources should be made consistent with any concept of need, this paper 

is limited to presenting various choices in setting and updating the concept of economic need or poverty 

threshold. The paper highlights the methods used to produce thresholds and addresses some advantages 

and disadvantages; however, it does not suggest a specific choice as optimal. In addition, the paper does not 
Garner and Betson, March 2010 Page 4 

present differences in the choices in terms of the percentage of people who are poor. To do that, consistent 

resources measures would need to be developed, and the development of the resource measures is beyond 

the scope of this research.

Background

The official U.S. poverty measure follows the logic of comparing economic resources to a threshold 

representing needs and is based on subjective judgment. The current poverty measure’s concept of needs 

(expressed in the poverty threshold) reflects the cost of a nutritionally adequate basket of food commodities 

multiplied by three. These thresholds reflect the empirical observation that in the 1960’s the average 

family, with children, spent roughly one-third of their budget on food. Since their inception, these 

thresholds have been annually updated to maintain their real purchasing power by inflating their nominal 

value by the CPI-U. The current poverty measure utilizes a concept of available resources that reflects the 

before tax post transfer income of a family.

The Panel’s new approach to poverty measurement did not abandon the logic of the current methodology 

but made fundamental departures from how needs and resources are defined. In their evaluation of the 

current official measure, the Panel concluded that while the current official approach may have been 

adequate for poverty measurement in the 1960’s, it is inadequate to determine who is poor in today’s 

society. While the poor may have, in large part, been exempt from taxation, today they face a significant 

tax burden from payroll taxes. While the poor in the 1960’s may have receive the majority of assistance in 

the form of means tested assistance programs, assistance today is provided through in-kind assistance 

programs or through the tax system in the form of refundable tax credits. With more mothers entering the 

work force, ignoring work related expenses and in particular child-care expenses became a significant 

problem. By focusing solely upon pre-tax post-transfer cash income, The Panel concluded that the actual 

available resources to meet the family’s needs are being misrepresented with the currently official poverty 

measure. 

While many concerns have been raised about the current measure’s concept of resources, the adequacy of 

the poverty threshold to reflect a family’s needed outlays in today’s society has also been a concern. The 

real value of the thresholds has not increased since they were first adopted. The Panel voiced concern that 

the thresholds were out of step with today’s society and called for a uniquely new approach to how to set 

the thresholds. The Panel called for abandoning the approach of setting needs based upon what nutritional 

experts considered appropriate consumption levels for food and relying upon a large multiplier (3.0) to 

capture other needs of the family. The approach favored by the Panel was to base the choice of the poverty 

threshold on the spending patterns of people living in the U.S. on a core group of necessities and then to 

use a relatively small multiplier to capture the other non-medical and non work related needs of the family. 

In the Panel’s report, recommendations 2.1-2.4 state that a poverty threshold with which to initiate a new 

series of official U.S. poverty statistics should be derived from Consumer Expenditure Survey data for a 

reference family of four persons (two adults and two children). The basic bundle was defined to include 

food, clothing, shelter and utilities (FCSU). A percentage of the median, plus a multiplier to account for 

other basic needs, would to be applied to derive the threshold. The needs of other families would be derived 

using an equivalence scale. The threshold was to be updated each year based on changes in median 

spending on the core group of necessities. Updating of the threshold would be made according to changes 

in median FCSU expenditures. This updating would allow for changes in the real changes in the 

consumption of the bundle of necessities (see Citro and Michael 1995).

This method has been used within BLS (under the umbrella of a memorandum of understanding for 

research on a NAS-based poverty measure by Garner and Short) to produce thresholds that have been used 

by the Census Bureau since 2005. From 2000 to 2005, the thresholds were produced as part of joint 

research conducted by Garner and Short, on-site at the BLS. In 1999, the 1998 NAS-based thresholds were 

produced at BLS by Garner for the Census Bureau under a contract between the BLS and Census Bureau. 

The most recent research NAS thresholds, based on CE data, can be found on the Census Bureau web site: 

http://www.census.gov/hhes/www/povmeas/tables.html. 
Garner and Betson, March 2010 Page 5 

The first series of NAS-based thresholds introduced used Consumer Expenditure Survey (CE) defined 

expenditures (see BSL 2010); for this definition, owner shelter expenditures did not include principal 

payments. Another series was introduced in 2008 that added mortgage principal payments to owners’ 

shelter expenditures and thus were based on outlays or out-of-pocket or outlay (OOP) expenditures (see 

Rogers and Gray, 1994). Consistently defined thresholds, using the OOP definition of FCSU expenditures 

were produced by Garner (2005), and resources (Short (2005) were produced for the American Statistical 

Meetings in 2005, and later updated for a BLS working paper (Garner and Short 2008). That work has 

subsequently been accepted for publication in the Review of income and Wealth (Garner and Short 2010). 

In the Panel’s production of an alternative poverty threshold, and in the first series of CE-based research 

thresholds used by the Census Bureau, the CE-defined definition of food, clothing, shelter, and utilities 

expenditures was used to produce the threshold. By using the CE-defined definitions, the Panel was 

implicitly assuming that spending equaled what it would cost to attain the core consumption bundle. While 

expenditures for food, clothing, and utilities are fair approximations of the costs to meet one’s consumption 

needs, expenditures for shelter are not. The consumption value of non-market shelter, such as owner 

occupied housing, subsidized rental housing, and rent controlled housing, is undervalued by spending. The 

Panel acknowledged this problem but did produce a measure to account for this in their report. 1

 

Methods and Procedures

As stated earlier, the purposes of this paper are four: 

 

1. to continue and extend our examination of the sensitivity of poverty thresholds to the 

definition of shelter

2. to examine the impact of different updating mechanisms

3. to study the change in the real value of the thresholds overtime, and 

4. to examine the impact of basing the thresholds on the behavior of reference families only 

or on all consumer units. 

The following sections provide details regarding the methods employed in this research.

Impact of Changes in Shelter Definitions

Both spending and consumption underlie the official and NAS recommended thresholds. The official 

thresholds and the NAS thresholds presented in the Panel’s report are based on spending, yet both are 

assumed to reflect needed consumption (e.g., Citro and Michael, 1995, pp. 1, 4, 148). Thus, both spending 

and consumption based thresholds are presented. Thresholds are produced for five definitions of shelter. 

Shelter is defined as:

1. CE-defined expenditures for all reference families (CE), 

2. CE outlays or out-of-pocket OOP expenditures for all reference families (OOP), 

3. CE OOP expenditures for reference families who are owners with mortgages or paying 

market rent only (OOP-Drop), 

4. CE OOP expenditures for all reference families with adjustments for reference families 

who are owners with mortgages or who pay market rent (OOP-Adjust), and 

5. CE OOP expenditures for renters paying market rent, imputed market rent for renters 

receiving housing subsidies, and rental equivalence valuations for owners 

(Consumption).

Details regarding each definition and implementation in a threshold are presented later in the paper.

 

1 The Panel noted that using the CE definition of shelter for owner-occupied housing was for “processing 

convenience; a preferable definition would include actual outlays for mortgage payments, taxes, insurance, 

and maintenance and repairs, together with an imputed amount for the estimated rental value of the home 

net of such out outlays” (Citro and Michael 1995, p. 148). For the threshold measure, the Panel’s 

recommendation is equivalent to replacing shelter expenditure for owners-occupiers in the thresholds with 

the implicit rent of this shelter (Garner 2005).
Garner and Betson, March 2010 Page 6 

Threshold Specification

In the NAS report, the Panel recommended that the poverty threshold should reflect what families of four 

(two adults and two children), living in the United States, spend on food, clothing, shelter and utilities 

(FCSU). Specifically the Panel stated that the poverty threshold should be set so that the amount of 

spending on these necessities would reflect 78 percent to 83 percent of median spending on food, clothing, 

shelter, and utilities (FCSU) by the reference family. The Panel preferred the use of percentiles of the 

median so that changes below the median will not affect the poverty rate. The percentages chosen by the 

Panel corresponded to 30th and 35th percentiles of the FCSU spending distribution in 1992 using Bureau of 

Labor Statistics (BLS) Consumer Expenditures Survey (CE) data. Garner (2005) reported that these 

percentages remained relatively stable from 1993 to 2003; however, when the shelter definition changed to 

reflect the rental equivalence of homeowners, the percentages corresponding to the 30th the 33rd percentiles 

were higher. To account for other needed goods (non-medical and non-work related needs), the Panel 

recommended that a multiplier between 1.15 and 1.25 would be applied to choice of spending needs on 

FCSU. Once the poverty threshold for the reference family was set then the thresholds would be updated by 

changes in the median spending by families of four on the core necessities of FCSU. The equation used for 

the Census Bureau CE-defined FCSU thresholds is below:

 BLS Threshold = (1.15*0.78* ) (1.25*0.83* )

2

Median + Median (1)

Updates to the threshold have been made to reflect changes in median FCSU expenditures. The assumption 

is that the percentages of the median are held constant at the values that were used by the Panel for the 

1992 thresholds presented in the NAS report. 

According to the MAP, the threshold would be determined using the following equation:

 MAP Threshold = 1.20*33 rd percentile (2)

Updated to the threshold would be made by re-estimating the 33rd percentile of FCSU expenditures each 

year using.

For this study, we use equation (2) to specify the initial threshold and then using two different methods to 

update the thresholds: (1) by changes in the median, and (2) by re-estimating the 33rd percentile each year. 

Updating and Different Samples

In order to study the impact of different updating mechanisms and different samples, four sets of thresholds 

were produced. The first two sets were based on the behavior of reference families and the second two 

were based on the behavior of all consumer units, with an equivalence scale adjustment to reflect reference 

family thresholds. For both reference family-based thresholds and all consumer unit-based thresholds, 

thresholds were updated by changes in the median and also by changes in the 33rd percentile of FCSU 

expenditures. See below. 

1. Reference family based thresholds

a. Updated by changes in the median

b. Updated by changes in the 33rd percentile

2. All consumer unit based thresholds

a. Updated by changes in the median

b. Updated by changes in the 33rd percentile

Changes in Real Consumption Over Time

The NAS Panel noted that the current official poverty thresholds had not been updated for real growth in 

consumption, only price change since 1965, with a few minor exceptions. The Panel stated that new 

thresholds were needed that reflect changes in spending needs. (See Citro and Michael, 1995). In the 1995 

report, the Panel recommended a procedure to calculate poverty thresholds that would, by design, be 

updated on a continuous basis and would reflect changes in levels of living over time that are relevant to a 
Garner and Betson, March 2010 Page 7 

poverty budget rather than for changes in total expenditures. There was broad agreement at the 2004 NAS 

Workshop that the Panel’s quasi-relative approach for annually updating the thresholds continue. The 

Panel’s original recommendation was that the most recent three years of CE data be used, with earlier 

years’ data updated to current dollars. This approach would allow for “...changes in real consumption but 

in a conservative manner” (Citro and Michael, 1995, p. 154). The three-year approach was recommended to 

increase the sample size and also to smooth out year-to-year changes in the thresholds.2

CPI-U.

Using three years 

of data however produces thresholds that lag somewhat behind changes in real consumption. Yet, such 

thresholds are more reflective of current consumption that the official threshold that is updated by the 

We followed the NAS recommendation to use 3 years of CE data for setting and annually updating the 

thresholds. We produced a series of thresholds for each of the five spending concepts listed earlier. We 

initially set the threshold by using the twelve quarters of data – quarter one 2004 through quarter four 2006. 

For each spending concept, we determined the 33rd percentile of FCSU spending and set the initial 

threshold at 120 percent of this amount. For the next time period, quarter two 2004 through quarter one

2007, we computed the median of FCSU spending for reference families then adjusted the threshold by the 

ratio of the current median relative to the median in the base period quarter one 2004 through quarter four

2006). This quarterly updating continued until the last twelve quarter period contained in our data –quarter 

two 2006 through quarter one 2009. In all we were able to construct a quarterly series for each spending 

concept containing ten observations. This resulted in annual thresholds for 2006, 2007, and 2008 and for 

intervening periods between the threshold years. This series of thresholds enabled us to examine the impact 

of changes in expenditures, and methods introduced into the CE Interview data by the BLS. 

See below for the data series used to create each threshold: 

1. 2004 quarter 1 through 2006 quarter 4

2. 2004 quarter 2 through 2007 quarter 1 2006 Annual Threshold

3. 2004 quarter 3 through 2007 quarter 2

4. 2004 quarter 4 through 2007 quarter 3

5. 2005 quarter 1 through 2007 quarter 4

6. 2005 quarter 2 through 2008 quarter 1 2007 Annual Threshold

7. 2005 quarter 3 through 2008 quarter 2

8. 2005 quarter 4 through 2008 quarter 3

9. 2006 quarter 1 through 2008 quarter 4

10. 2006 quarter 2 through 2009 quarter 1 2008 Annual Threshold

Series 2 represents the 2006 annual threshold, series 6 represents the 2007 threshold, and series 10 

represents the 2008 threshold.

In this study, we examine how thresholds reflect changes in real growth in consumption over time. To do 

this we converted values for FCSU into December 2006 dollars. As a result of this adjustment, thresholds 

from this study are not comparable to those presented in previous work. 

The Reference Family

Another area of research has focused on the equivalence scale applied to produce thresholds for nonreference families. The Panel recommended using a two-parameter equivalence scale; however, at a 2004 

NAS Workshop on poverty measurement, consensus was reached that a three-parameter scale, that 

accounts for the differences in two-parent and single-parent households, better approximates spending and 

consumption needs in the U.S. The three-parameter scale was applied in this research.

Thresholds were produced for a reference family. For this study, a reference family is defined to include 

two adults and two children; these individuals do not need to be related. This definition of the reference 

 

2 As noted in the Panel’s report (Citro and Michael 1995, p. 147), “For actual use in updating the reference

family poverty threshold, however, we believe it would be preferable to aggregate quarterly amounts for 

those units with complete data, making an appropriate adjustment to the weights to account for other units.”
Garner and Betson, March 2010 Page 8 

family is like a consumer unit as defined by the Bureau of Labor Statistics (BLS) for the CE data. In the 

Panel’s report and much of the NAS-based poverty work prior to 2009, the reference family was defined as 

two married adults with related children. 

Details Regarding Shelter

As noted earlier, a purpose of this paper is to continue and extend our examination of the sensitivity of 

poverty thresholds to the definition of what constitutes the core necessities, FCSU, in particular, shelter. 

1. CE-defined Expenditures

The Panel used the CE definition of shelter expenditures for its report.3

1. Interest paid on reference families’ principal residences 

 The primary series produced on the 

Census Bureau web site also is based on this definition. Thus, for historical purposes, we produce 

thresholds based upon this definition. The CE-definition of shelter for includes: 

2. Property taxes

3. Expenses for maintenance, repairs, insurance, other expenses

4. Rents paid 

The CE-definition of shelter for owners is based on the old method used to value homeowner shelter for the 

Consumer Price Index (CPI) prior to 1983. That method reflected the purchasing and financing costs of 

base-period homebuyers and operating expenses of all base-period homeowners. The old method failed to 

distinguish the investment aspect of home owning from the consumption aspect. In 1983, the BLS change 

the way that the CPI for All Urban Consumers (CPI-U) had been measured to account for consumption 

costs. Shelter spending was replaced by rental equivalence (BLS 1983). 

For a CE-defined expenditures threshold, no additions would be added to resources for housing subsidies or 

the implicit net rental income for homeowners.

2. Outlays or Out-of-pocket (OOP) Expenditures 

An OOP alternative definition of shelter spending was first produced by Garner and Short (2001) and again 

by Garner (2005) and Garner and Short (2008, 2010) to account for all shelter spending by reference 

families. The only difference between CE-defined shelter expenditures and OOP expenditures is that 

mortgage principal payments are now included. The reason these payments have been added for the 

poverty threshold is that once a commitment to live in a mortgaged housing unit is made, such payments 

are not discretionary and must be paid by the homeowner to live there (Garner and Short 2008, 2010). Thus 

the CE-OOP definition of shelter includes: 

1. Mortgage interest payments

2. Interest paid on reference families’ principal residences

3. Property taxes

4. Expenses for maintenance, repairs, insurance, other expenses

5. Rents paid 

 

3 Betson, in a recent manuscript (Betson 2009) and a member of the NAS Panel, noted, “The reason for 

using the expenditure perspective on shelter expenses is perhaps historical. The BLS provided tabulations 

of the 1989 to 1991 CE data for use in the Panel’s report that only included mortgage interest (principal 

was not included). I don’t recall any discussion by the Panel on this point but clearly the Panel didn’t 

directly ask for the principal payments to be included either. Consequently, the Census has since then 

routinely utilized the threshold based up on the exclusion of principal payments in their reports,” (p. 12). 

Danziger, as a discussant at the 2010 ASSA meetings and also a member of the NAS Panel, also 

acknowledged that he could not remember any discussion of how shelter was defined in the measure that 

the Panel produced. In a 1995 manuscript, Betson referred explicitly to mortgage principal payments as 

being part of the shelter expenditures of owners with mortgages (see footnote 2 and see page 3). 
Garner and Betson, March 2010 Page 9 

The outlays or OOP concept gained support as a preferred measure to the CE definition with the housing 

crisis and increases in homeownership home mortgages. In 1992, homeowners represented 61 percent of 

all consumer units, by 2005 they represented 67 percent and by 2008, 66 percent. Homeowners with 

mortgages increased from 59 percent in 1992 to 63 percent in 2005 and 64 percent in 2008. Four-person 

consumer units have much higher rates of homeownership and mortgage holdings than all consumer units 

on average: in 1992, 72 of all four-person consumer units were home owners and 83 of these held 

mortgages; in 2005, 74 person were homeowners and 87 of these had mortgages; by 2008, homeownership 

rates rose to 75 percent but those with mortgages fell to 84 percent (BLS web site 

http://stats.bls.gov/cex/csxstnd.htm 2009). 

Based on unpublished CE data, renters with subsidies also increased in number over the 1992 to 2008 

period. They represented 3.5 percent of all consumer units (CUs) in 1992 and 3 percent of four person 

CUs. By 2005, 5.2 percent of all CUS living in the U.S. were receiving housing subsidies; 3.6 percent of 

all four-person units received subsidies. By 2008, the percentage of CUs with housing subsidies decreased 

among all consumer units (representing 4.9 percent of all CUs) and four-person units (representing 4.2 

percent of four-person units).4

 

When thresholds are based on OOP spending, they reflect heavily the spending of homeowners with 

mortgages and renters not receiving housing subsidizes. 

For an OOP expenditures threshold, no additions would be added to resources for housing subsidies or the 

implicit net rental income for homeowners.

3. OOP Drop

The OOP definition is fine if the focus is on spending needs given the current mix of renter and owner 

housing in the U.S. If one assumes that individuals and families choose to live in owner or renter housing, 

owners’ needs should be the same and renter needs should be the same. The FCSU needs of owners with 

and without mortgages would be the same and the FCSU needs of renters in subsidized or not subsidized 

housing would be the same. Thus, there would be no difference in the shelter needs of owners and no 

differences in the shelter needs of renters. With the OOP spending approach, the shelter needs of

homeowners without mortgages and rented by families receiving housing subsidizes are undervalued. 

One approach to rectify this problem would be to drop reference family homeowners without mortgages 

and those receiving housing subsidies before determining the poverty threshold and applying the annual 

updating process. In this study we refer to this option as OOP-Drop. A caution with this method is that by 

dropping these two groups, who may have low spending on FCSU, from the determination of the poverty 

threshold will likely “bias” upward the poverty thresholds. 

For an “OOP Drop” threshold, additions could be added to resources to equal to the implicit shelter 

expenditures added to the threshold for homeowners without a mortgage and for renters receiving housing 

subsidies.

4. OOP-Adjust

A fourth option alleviates the potential bias from dropping these families before producing the FCSU 

threshold. For this option, we adjust their OOP spending on FCSU to reflect levels of spending as if 

homeowners without a mortgage had a mortgage and subsidized renters did not receive a subsidy. We refer 

to this option as OOP-Adjust. 

For renters receiving housing subsidies, rent regressions are estimated to impute the market value of their 

housing. These replace the reported rents of renters receiving subsidies in the OOP measure. The 

regression includes a dummy variable and interaction terms for subsidy and income. An earlier model 

specification, based on market rents only resulted in predicted imputed market rents for subsidized housing 

 

4 CE tabulations were produced by Jeffrey Crilley of the Division of Consumer Expenditure Surveys using 

internal BLS data, December 29, 2009.
Garner and Betson, March 2010 Page 10

that were less than what subsidizers renters reported in expenditures. See Tables 1-5 for results of the rent 

regressions that underlie our results. 

Betson (2009) proposed a method to adjust homeowners FCSU expenditures to a level of FCSU that would 

be spent if they had mortgages. Application of this method would place all homeowners on equal footing. 

To formalize how the adjustment could be made, let FCSU denote the 33rd percentile of FCSU of the 

reference family and m (.20 or 20 percent) represents the “little bit more” reflected in the threshold. 

Consequently, the threshold for the reference family would equal: 

Threshold = FCSU (1+m) (3)

and would represent the needed outlays for all households that rent, own their homes but have a mortgage 

payment, or receive a government subsidy. Families who own their homes without a mortgage will still 

have outlays for shelter (property taxes, maintenance, insurance and other expenses) but will not have any 

outlays for mortgage interest or principal. If θ is the percent of FCSU reflecting total shelter outlays and 

ρ is the percent of shelter outlays reflecting mortgage payments then a household that does not have a 

mortgage payment would need outlays equal to: 

 

Threshold + Outlays for Mortgage Payments = FCSU (1+m) + θ ρ FCSU (4)

Thus, thresholds for homeowners without mortgage payments will be higher byθ ρ/(1+m) percent. For 

example, if shelter outlays are 50 percent of FCSU needed outlays, mortgage payments are 70 percent of 

shelter outlays, and we employ a 20 percent value for m, the threshold for homeowners without a mortgage 

should be 29.2 percent higher. 

To implement this approach for this study, Betson estimated how the FCSU budget share varies with total 

outlays in families who own their home (with and without a mortgage). He estimated the relationship using 

the logistic transformation of the FCSU budget share. Betson controlled for the log of real total outlays 

(lntot), its square (lntot2), and a dummy variable indicating whether the unit was a homeowner without a 

mortgage (nomort). The sample represented all reference families who own their home (with and without a 

mortgage). “Nomort” is the variable indicating that the homeowner did not have a mortgage. 

The regression model was run for each set of data used to produce the thresholds. Following is the result of 

the analysis for the 2007 threshold. 

 Source | SS df MS Number of obs = 6294 

-------------+------------------------------ F( 3, 6290) = 572.70 

 Model | 764.113245 3 254.704415 Prob > F = 0.0000 

 Residual | 2797.41584 6290 .444740198 R-squared = 0.2145 

-------------+------------------------------ Adj R-squared = 0.2142 

 Total | 3561.52909 6293 .565950912 Root MSE = .66689 

------------------------------------------------------------------------------ 

 loddshr | Coef. Std. Err. t P>|t| [95% Conf. Interval] 

-------------+---------------------------------------------------------------- 

 lntot | -1.6825 .3687852 -4.56 0.000 -2.405445 -.9595551 

 lntot2 | .0479825 .0168933 2.84 0.005 .0148659 .0810992 

 nomort | -.7065383 .029568 -23.90 0.000 -.7645016 -.648575 

 _cons | 13.03327 2.012272 6.48 0.000 9.088529 16.97801 

------------------------------------------------------------------------------ 

The figure below illustrates how the FCSU budget share varies with total outlays for both two groups. For 

this study, we used the estimated budget shares to determine the difference in the budget share spent on 

FCSU by homeowners with and without a mortgage holding total outlays constant. 
Garner and Betson, March 2010 Page 11

The reported amount of FCSU expenditures for homeowners without mortgages were adjusted by 

multiplying their reported FCSU by the ratio of the expected FCSU budget share if they had a mortgage 

(ωw) to the expected FCSU budget share if they did not have a mortgage (ωwo). The two expected budget 

shares would be computed using the estimated relationship between the FCSU budget share, total outlays 

and whether the family had a mortgage or not. Consequently, the family’s adjusted FCSU would equal

 

ωW

ωWO

×FCSU = 1+exp ( ) −PV −μ

1+exp( −PV )

×FCSU (5)

where μ is the estimated effect on the budget share of not having a mortgage (-.7065) for 2007 and 

PV = 13.0333 – 1.6825 lntot + .0480 lntot2 (6)

where lntot and lntot2 are the log of total outlays and the square of the log of total outlays.

The adjustment factor is predicted to increase with the total outlays of the reference family. The following 

figure depicts the relationship between the adjustment factors that were used as a function of the family’s 

total outlays. To be clear, the amount of FCSU that was adjusted was the family’s reported outlays on 

FCSU.
Garner and Betson, March 2010 Page 12

For an OOP Adjust threshold, additions would not need to be added to resources.

. 

5. Housing Consumption

The OOP-Drop and OOP-Adjust concepts are attempts to estimate the FCSU spending needs if all owners 

were assumed to be like the majority of owners and if all renters were assumed to be like all renters. 

However with none of the spending concepts for shelter represented by options 1-4 above are renters and 

homeowners treated equivalently. The OOP-Adjust for homeowners without mortgages accounts for 

additional spending needed for shelter with the implicit additions added to resources; however, it does not 

account for the fact that homeowners are both renters and owners, and thus, receive implicit net rental 

income from homeownership. To place both homeowners and renters an equivalent footing, economists 

have long stressed the use of rental equivalence for homeowners. By defining needs in terms what it would 

costs for the consumption of FCSU, rather than current or adjusted spending, market rental values are 

needed both for all owner occupied housing and rental units. For renters receiving rental subsidies, imputed 

rents reflect rental payments had they not received subsidies. For all owners, the reported rental 

equivalence is used. Rental equivalence estimates the cost of renting housing services equivalent to those 

provided by owner-occupied homes. (The change in these rents, represents the change in the amount that 

homeowners would have to pay each month to live in homes like the ones they own. The CPI-for Owner’s 

equivalent rent reflects this change.)

By including the consumption value of shelter in FCSU, the threshold is nearer to becoming consumptionbased rather than spending-based. The implicit assumption in a consumption-based threshold is that there is 

a basic level of consumption that is needed so as not to be poor. A consumption-based threshold would 

include the value of shelter services regardless of who paid for them (e.g., they could have been paid for by 

a person not living in the household or another entity) or if there were very low expenditures for the 

services (e.g., there is no mortgage and the homeowner only pays for property insurance). For a fully 

consumption based threshold, the value of transfers in-kind, gift received, and the home production of these 

goods and services for own consumption would need to be added to expenditures for food, clothing, and 

utilities, fairly good proxies of the value of the consumption of FCU goods and services. 

The resource measure consistent with such a threshold would include the subsidy value for renters and for 

homeowners, the net implicit rental income from the owner-occupied housing (see Garner and Short 2009 
Garner and Betson, March 2010 Page 13

JHE).. Implicit net rental income could be defined as the rental value of the owned shelter services minus 

the user costs of producing the housing services. User costs depend on interest rates, depreciation, 

maintenance, property taxes, insurance, capital gains, and the marginal federal income tax rate of the 

owner. Accrued capital gain decreases the cost of owner-occupied housing when positive. Homeowner-user 

costs would be expected to be lower than those of a landlord offering the same level of housing services 

since homeowners have additional tax advantages that landlords do not have. Garner and Verbrugge (2006) 

found that user costs sometimes lie above the implicit rents of owners, depending on the geographic market 

of the property, although in the last several years, rents have exceeded user costs. (For research on rents 

and user costs of homeowners, see for example, Diaz and Luengo-Prado 2008, Garner and Verbrugge 

2009a,b, Verbrugge 2008, Green and Malpezzi 2003.) 

See Garner and Verbrugge (2009a, b) for a comparison of estimated user costs and rental equivalence. See 

Garner (2005) and Garner and Short (2001) for out-of-pocket spending and consumption-based thresholds 

using homeowners rental equivalence. 

Results

Thresholds are presented in Tables 6-9 Charts 1-4. Table 6 and 7 and Charts 1 and 2 include thresholds 

based on the behavior of reference families. Tables 8 and 9 and Charts 3 and 4 include thresholds for 

reference families but with the thresholds derived from the spending behavior of all consumer units with

the conversion of FCSU values for all CUS to single-adult equivalents first using the three-parameter 

equivalence scale. The FCSU single-adult equivalents are ranked to obtain the 33rd percentiles and medians. 

These percentiles and medians are then converted to referenced family thresholds by applying the threeparameter equivalence scale to the single person adult equivalent 33rd percentiles and medians. All 

thresholds are presented in December 2006 U.S. dollars.

First looking at the trends over time, it is clear that the real value of consumption has increased over the 

time period with greater increases between 2006 and 2007 than between 2007 and 2008. This pattern holds 

with both updating approaches, using the median and or the 33rd percentile, and for all definitions of FCSU. 

The spread in thresholds over time using the different shelter definitions is greater when thresholds are 

updated each year by the 33rd percentile than by the median. Chart 10 shows the ratios of FCSU 

expenditures to the median over the time period. Unlike the Panel’s thresholds and those presented in 

earlier work, the CE-defined ratio is closer to 82.5 percent of the median, closer to the upper range of the 

Panel’s and earlier threshold percentages of the median, 78 percent and 83 percent. The OOP ratios are 

lower than the other four and are in the middle range of the Panel’s suggested range from 1992. The dip in 

the 2007 ratio for the CE-defined measure is a reflection of the change in wording for food away from 

home expenditures by the CE (see Brookings presentation by Garner 2009). Ratios vary by FCSU 

definition. The ratios based on the housing consumption shelter measure were second lowest in 2006 (80.8 

percent) but were the highest by 2008 (over 83 percent) 

As expected, the different ways to account for shelter in the thresholds impacts both the level but not the 

trends as much. The CE-defined threshold includes the least amount for shelter needs, followed by the OOP 

thresholds. The only difference between these two is the addition of mortgage principal payments. 

Adjusting shelter expenditures for homeowners without mortgages and for subsidized renters, allowing 

their for food, clothing, and utilities to remain as they would have if shelter were not adjusted, increases the 

thresholds but not by as much as if these two groups of references families were dropped from the 

threshold sample. The shelter consumption based threshold is the highest for all years. In the latter period, 

as represented by series 8, the housing consumption thresholds are almost exactly the same as the OOPdrop thresholds when based on reference families alone (Chart 6). These two thresholds are the same by 

2008 when the thresholds are updated by the 33rd percentile rather than the median equivalence scale (Chart 

7). 

Using all consumer units (CUs), applying the three-parameter equivalence scale to derive single adult 

thresholds and then applying again the three-parameter equivalence scale results in reference family 

thresholds that differ from those based on reference families only (see charts 8 and 9). When all CUs are 

used, the CE-defined reference family thresholds are lower than those based on a reference family sample 

only, and the housing consumption thresholds are higher. These results are not surprising since the FCSU 
Garner and Betson, March 2010 Page 14

expenditures are more diverse for the full U.S. population than for families composed of two adults and two 

children alone. A striking difference between the thresholds based on the reference families only and those 

based on all CUs, regardless of the updating mechanism, is that the level and trends are almost exactly the 

same when FCSU expenditures for all CUs are used. This is likely due to the fact that the 33rd percentileto-median ratios are almost constant when all consumer units are in the sample (see Chart 11). 

Conclusions

The thresholds presented in this study, unlike the official thresholds, reflect recent spending and 

consumption needs in levels and patterns. They account for changes in living standards over time, unlike 

the official measure. A focus on meeting spending needs versus consumption needs dictates which 

threshold is more appropriate. Both types have been produced in this study. The results presented reveal 

thresholds that have been increasing at a faster rate than official poverty thresholds. This is not surprising 

given the different assumptions underlying the NAS approach and the official measure. 

Shelter accounts for the largest percentage of the FCSU thresholds. Therefore the treatment of shelter in the 

thresholds is of upmost importance. In this research, we have moved beyond the spending based thresholds 

used by the Panel (CE-defined expenditures) and those based strictly on out-of-pocket expenditures (OOP) 

to account for the needs of all owners and all renters, regardless of whether they have a mortgage or not or 

live in subsidized housing or not. 

The OOP-drop and OOP-adjust thresholds made adjustments to spending such that the cost to live in an 

owned home would be the same for all owners and the costs to live in a rental unit would be the same for 

all renters. We noted that the OOP-drop thresholds were biased upward since owners without mortgages 

and renters with subsidies were excluded from the estimation sample. To obtain a measure of resources that 

is consistent with the OOP-adjust threshold, only the subsidies for renters would be added to resources. For 

the other OOP thresholds, nothing extra would be added to resources.

The fifth option moves from spending-based thresholds closer to a consumption based threshold. The 

housing consumption threshold reflects what it would cost to meet shelter consumption needs, regardless of 

housing status, and out-of-pocket needs for food, clothing, and utilities. When using consumption based 

thresholds, the subsidies received by renters would be added to resources and the implicit net rental income 

from owning one’s home would be added to resources. 

This research highlights:

1. the importance of the definition of shelter used for the thresholds,

2. that spending and consumption needs change over time in real terms,

3. that the base period for the initial threshold matters, and

4. the sample underlying the identification of the distribution of FCSU expenditures matters. 

Regardless of the shelter definition selected for the modern threshold, the resource measure must be 

consistently defined, including additions to resources for shelter or not. As one of the main uses of a new 

poverty measure could be to study the impact of government transfers and other programs on poverty rates, 

it seems natural that the spending and consumption needs of renters with and without subsidizes would be 

the same and accounted for in the thresholds. OOP-adjust and housing consumption thresholds meet this 

criterion. 

The question of homeowners without mortgages remains a challenge for poverty measurement. Given the 

current mix of owned housing with and without mortgages, one might want to account for the increase in 

spending needs so that all owners are treated the same, as mortgages holders. Another option is to develop 

a threshold that would allow us to account for the implicit transfers families receive from owning their own 

homes. Three options were presented in this research: dropping this group from the threshold sample, 

adjusting their spending on shelter so that it would look like that of homeowners with mortgages, or treat 

all homeowners the same by using report rental equivalence values from the CE survey. The OOP-drop 

option results in thresholds that are biased upwards due to the fact that homeowners without mortgages 

have different spending patterns for food, clothing, and utilities. OOP-adjust thresholds were proposed as 

a way to allow these homeowners to maintain their spending for these other goods and services, while 
Garner and Betson, March 2010 Page 15

bumping their shelter expenditures up. This option is appealing; however, the relationship between FCSU 

spending and total outlays, with and without mortgages differs over consumer unit types (see Betson 2009) 

and likely over time. Before applying this method in production, additional research is needed. The fifth 

option, the housing consumption threshold, treats all owners and renters the same. The BLS uses owners’ 

equivalent rents for the CPI; thus, using rental equivalence for poverty measurement would be consistent 

with this other major economic statistic. In addition, the internationally poverty thresholds often used in 

World Bank studies (with price adjustments based on Purchasing Price Parities) are based on consumption 

and are designed to account for the rental equivalence of owner-occupied housing (gleaned from Angus 

Deaton’s speech given at the ASSA 2010, January 4). Also, implementing a modern poverty measure based 

on rental equivalence would be much easier than adjusting homeowners without mortgages expenditures 

using the method proposed by Betson (2009).

 

In selecting the approach to develop the FCSU thresholds, it is necessary to keep in mind the properties that 

the Panel deemed desirable for a modern poverty measure for the United States: consistency in the 

construction of thresholds and resources; statistical defensibility; understandability; broad acceptance by 

the public; and operational feasibility. As we have shown, all of the methods used to produce the thresholds 

dictate the use of resources that are consistently defined. Some are more statistically defensible than others 

and the operational feasibility varies as well. We know that the first two threshold measures, the CEdefined and OOP thresholds, are acceptable to the public (and presumably understandable) as they have 

been used for several years now by the public. Consumption based thresholds have also been used. The 

other options, the OOP-drop and OOP-adjust, are still to be vetted by researchers and by the statistical 

agencies responsible for the possible implementation of a new poverty measure for the United States. 
Garner and Betson, March 2010 Page 16

References

(Many of the unpublished working papers and presentations listed here are available at:

http://www.census.gov/hhes/www/povmeas/papers.html

Banthin, Jessica, Thesia I. Garner, and Kathleen Short, “Medical Care Needs in Poverty Thresholds: 

Problems Posed by the Uninsured.” Agency for Healthcare Research and Quality, Presented at the 

American Economic Association Meetings, January 2001.

Bavier, Richard, “Do the Current Poverty Thresholds Include Any Amount for Health Care”, Poverty 

Measurement Working Paper, March 2001.

Betson, David, “Effect of Home Ownership on Poverty Measurement,” unpublished manuscript, University 

of Notre Dame, November 1995.

Betson, David, “Homeownership and Poverty Measurement,” paper prepared for presentation at the 

Brookings Institution/ Census Bureau Conference on Improved Poverty Measurement, Washington, DC, 

(October) 2009.

Bureau of Labor Statistics (BLS), U.S. Department of Labor, Questions and Answers on Homeownership 

Costs, BLS News Releases, January 1983.

Bureau of Labor Statistics (BLS), U.S. Department of Labor, 

https://www.bls.gov/cex/csxgloss.htm#housing, 2010.

Citro, Constance F., and Robert T. Michael (eds.), Measuring Poverty: A New Approach, Washington, 

D.C.: National Academy Press, 1995.

Díaz, Antonia, Luengo-Prado, María J., “On the User Cost and Homeownership,” Review of Economic 

Dynamics, 11 (3), 584–613, 2008. 

Garner, Thesia I., “Developing Poverty Thresholds 1993-2003,” 2005 Proceedings of the American 

Statistical Association, Social Statistics Section [CD-ROM], Alexandria, VA: American Statistical 

Association: pp-pp. (revised September 18, 2006)

Garner, Thesia I., “National Academy of Sciences--Based Poverty Thresholds: The Details of Alternatives 

and Choices in Specification,” presented at the Joint Statistical Meetings, American Statistical Association, 

Social Statistics Section, Washington, DC, (August) 2009a. 

Garner, Thesia I., “Poverty Thresholds Alternatives/ Choices,” presented at the Brookings Institution/ 

Census Bureau Conference on Improved Poverty Measurement, Washington, DC, (October) 2009b. 

Garner, Thesia I. and Patricia Rozaklis, “Accounting for Owner Occupied Housing in Poverty: Focus on 

Thresholds,” American Statistical Association (ASA) Proceedings of the Section on Government Statistics 

and Section on Social Statistic, 1999. 

Garner, Thesia I. and Patricia Rozaklis, “Owner-Occupied Housing: An Input for Experimental Poverty 

Thresholds,” paper presented at session organized by the Society of Government Economists at the annual 

meeting of the Allied Social Sciences Associations, January 6, 2001.

Garner, Thesia I., Stephanie Shipp, Geoffrey Paulin, Kathleen Short, and Charles Nelson, “Poverty 

Measurement in the 1990s,” Monthly Labor Review, March 1998, pp. 39-61.

Garner, Thesia I. and Kathleen S. Short, “Accounting for Owner-occupied Dwelling Services: Aggregates 

and Distributions,” Journal of Housing Economics, Volume 18, Issue 3, September 2009, pp. 233-248, 

Special Issue on Owner Occupied Housing in National Accounts and Inflation Measures. 
Garner and Betson, March 2010 Page 17

Garner, Thesia I. and Kathleen S. Short, “Creating a Consistent Poverty Measure Over Time Using NAS 

Procedures: 1996-2005,” Bureau of Labor Statistics Working Paper Number 417, 2008.

Garner, Thesia I. and Kathleen S. Short, “Creating a Consistent Poverty Measure Over Time Using NAS 

Procedures: 1996-2005,”Review of Income and Wealth, forthcoming 2010.

Garner, Thesia I. and Kathleen S. Short, “Owner-Occupied Shelter in Experimental Poverty Measures,” 

paper presented at the Annual Meeting of the Southern Economic Association, Tampa, Florida, November 

1, 2001.

Garner, Thesia I. and Randal Verbrugge, “The Puzzling Divergence of Rents and User Costs, 1980-2004: 

Summary and Extensions,” chapter 8, in Price and Productivity Measurement: Volume 1 – Housing (W.E., 

Diewert, B.M. Balk, D. Fixler, K.J. Fox and A.O. Nakamura, eds.), Trafford Press, 2009a, pp. 125-146. 

Garner, Thesia I. and Randal Verbrugge, “Reconciling User Costs and Rental Equivalence: Evidence from 

the US Consumer Expenditure Survey,” Journal of Housing Economics, Volume 18, Issue 3, (September) 

2009b, pp. 172-192, Special Issue on Owner Occupied Housing in National Accounts and Inflation 

Measures. 

Green, Richard K. and Stephen Malpezzi, A Primer on U.S. Housing Markets and Housing Policy, 

Washington, DC: The Urban Institute, 2003.

Iceland, John. "The CNSTAT Workshop on Experimental Poverty Measures, June 2004." Focus 23, 3 

(Spring) 2005, pp. 26-30.

Johnson, David, Stephanie Shipp, and Thesia I. Garner, “Developing Poverty Thresholds Using 

Expenditure Data,” in Proceedings of the Government and Social Statistics Section. Alexandria, VA: 

American Statistical Association, August 1997, pp. 28-37.

National Research Council. Workshop on Experimental Poverty Measures: Summary of a Workshop. 

Washington, D.C.: National Academy Press, 2005.

Renwick, Trudi, “Improving the Measurement of Family Resources in a Modernized Poverty Measure,”

paper prepared for presentations at the Allied Social Sciences Associations (ASSA) meetings, Society of 

Government Economists (SGE) session, Atlanta, GA, 2010. 

Renwick, Trudi, “Experimental Poverty Measures: Geographic Adjustments from the American 

Community Survey and BEA Price Parities,” 2009 Proceedings of the American Statistical Association, 

Social Statistics Section [CD-ROM], Alexandria, VA: American Statistical Association: pp.-pp. Presented 

at the conference in Washington, DC, August 2009. 

Rogers, John M. and Maureen B. Gray, “CE Data: Quintiles of Income versus Quintiles of Outlays,” 

Monthly Labor Review, Vol. 117, No. 12, December 1994.

Rozaklis, Patricia and Thesia I. Garner, “Subsidized Renters: Thresholds,” presented during the BLS 

Division of Price and Index Number Research Seminar Series, May 5, 1999.

Short, Kathleen, “Cohabitation and Child Care in a Poverty Measure,” 2009 Proceedings of the American 

Statistical Association, Social Statistics Section [CD-ROM], Alexandria, VA: American Statistical 

Association: pp.-pp. Presented at the conference in Washington, DC, August 2009. 

Short, Kathleen, “Estimating Resources for Poverty Measurement: 1993-2003,” 2005 Proceedings of the 

American Statistical Association, Social Statistics Section [CD-ROM], Alexandria, VA: American 

Statistical Association: pp.-pp. Presented at the conference in Minneapolis, Minnesota, August 10, 2005.
Garner and Betson, March 2010 Page 18

Short, Kathleen, Experimental Poverty Measures: 1999, U.S. Census Bureau, Current Population Reports, 

Consumer Income, P60-216, U.S. Government Printing Office, Washington, DC, 2001.

Short, Kathleen, “Experimental Modern Poverty Measure 2007,” paper prepared for presentations at the 

Allied Social Sciences Associations (ASSA) meetings, Society of Government Economists (SGE) session, 

Atlanta, GA, 2010.

Short, Kathleen and Garner, Thesia I. “Experimental Poverty Measures Under Alternate Treatments of 

Medical Out-of-Pocket Expenditures.” Monthly Labor Review, August 2002, pp. 3-13. 

Short, Kathleen et al., 1998.

Short, Kathleen, Thesia Garner, David Johnson, and Patricia Doyle, Experimental Poverty Measures: 1990 

to 1997, U.S. Census Bureau, Current Population Reports, Consumer Income, P60-205, U.S. Government 

Printing Office, Washington, DC, 1999.

Verbrugge, Randal, ”The Puzzling Divergence of Aggregate Rents and User Costs, 1980–2004,” The 

Review of Income and Wealth 54 (4), 671–699, 2008.
Garner and Betson, March 2010 Page 19

Table 1. Rent Regression to Impute Rents for Renters Receiving Subsidies.

Sample: All Renters. Data: 2004Q1-2005Q1 CE Interview. _____________________________________________________________________________ 

Source | SS df MS Number of obs = 11805 

-------------+------------------------------ F( 38, 11766) = 179.57 

 Model | 2199.04611 38 57.8696344 Prob > F = 0.0000 

 Residual | 3791.88312 11766 .322274615 R-squared = 0.3671 

-------------+------------------------------ Adj R-squared = 0.3650 

 Total | 5990.92923 11804 .507533822 Root MSE = .56769 

------------------------------------------------------------------------------ 

 lnrent | Coef. Std. Err. t P>|t| [95% Conf. Interval] 

-------------+---------------------------------------------------------------- 

 detached | .1242012 .0280863 4.42 0.000 .0691475 .179255 

 rowhouse | .1720682 .0322084 5.34 0.000 .1089344 .2352019 

 endrow | .1573149 .0441066 3.57 0.000 .0708586 .2437711 

 duplex | .1758895 .0303875 5.79 0.000 .1163249 .235454 

 numplex | .176296 .0316019 5.58 0.000 .1143511 .238241 

 garden | .1769723 .0301893 5.86 0.000 .1177963 .2361484 

 hirise | .3251252 .0343593 9.46 0.000 .2577753 .392475 

 aptflat | .196757 .0267823 7.35 0.000 .1442594 .2492547 

 room | .0039405 .0054536 0.72 0.470 -.0067494 .0146303 

 room2 | .0001078 .0001742 0.62 0.536 -.0002337 .0004494 

 bath | .1231849 .0401506 3.07 0.002 .0444831 .2018868 

 bath2 | .0033287 .0112864 0.29 0.768 -.0187945 .0254519 

 hbath | .1196439 .0374303 3.20 0.001 .0462744 .1930134 

 hbath2 | -.0435747 .02557 -1.70 0.088 -.093696 .0065466 

 ldwellage | -.000769 .0011839 -0.65 0.516 -.0030896 .0015516 

 ldwellage2 | 4.71e-06 .0000132 0.36 0.721 -.0000211 .0000305 

 age100 | -.1906575 .0560891 -3.40 0.001 -.3006014 -.0807137 

missdwellage | -.0658015 .0240413 -2.74 0.006 -.1129264 -.0186765 

 pool | .0555228 .0165976 3.35 0.001 .0229888 .0880568 

 haveporch | .008578 .011975 0.72 0.474 -.0148949 .032051 

 centralair | .0102884 .0131321 0.78 0.433 -.0154528 .0360295 

 yes_apl | .0329902 .0142718 2.31 0.021 .0050152 .0609653 

 energy_rent | .0466686 .0126273 3.70 0.000 .021917 .0714202 

 water_trash | .0448854 .0148248 3.03 0.002 .0158264 .0739444 

 crowd | .1152696 .0153837 7.49 0.000 .0851151 .1454242 

 insmsa | .284777 .016324 17.45 0.000 .2527793 .3167747 

 south | -.2425786 .0183897 -13.19 0.000 -.2786255 -.2065317 

 midwest | -.2140218 .0180187 -11.88 0.000 -.2493415 -.1787022 

 west | -.0347507 .0172534 -2.01 0.044 -.0685703 -.000931 

 income | .1137067 .003791 29.99 0.000 .1062758 .1211376 

 income2 | -.0029356 .0002022 -14.52 0.000 -.0033319 -.0025393 

 sub | -.8562419 .0297046 -28.83 0.000 -.9144679 -.7980158 

 sincome | .2248147 .021247 10.58 0.000 .1831671 .2664622 

 sincome2 | -.0166483 .0024774 -6.72 0.000 -.0215044 -.0117922 

 y1 | .021157 .0163617 1.29 0.196 -.0109146 .0532287 

 y2 | .0161869 .0164938 0.98 0.326 -.0161437 .0485174 

 y3 | .0164718 .0165188 1.00 0.319 -.0159078 .0488514 

 y4 | .0124247 .0164054 0.76 0.449 -.0197326 .0445821 

 _cons | 6.498652 .0507308 128.10 0.000 6.399211 6.598093 

------------------------------------------------------------------------------ 
Garner and Betson, March 2010 Page 20

Table 2. Rent Regression to Impute Rents for Renters Receiving Subsidies.

Sample: All Renters. Data: 2005Q2-2006Q1 CE Interview. ______________________________________________________________________________ 

Source | SS df MS Number of obs = 9354 

-------------+------------------------------ F( 37, 9316) = 136.32 

 Model | 1870.69615 37 50.5593553 Prob > F = 0.0000 

 Residual | 3455.29897 9316 .370899417 R-squared = 0.3512 

-------------+------------------------------ Adj R-squared = 0.3487 

 Total | 5325.99512 9353 .569442437 Root MSE = .60902 

------------------------------------------------------------------------------ 

 lnrent | Coef. Std. Err. t P>|t| [95% Conf. Interval] 

-------------+---------------------------------------------------------------- 

 detached | .11836 .0323083 3.66 0.000 .0550288 .1816913 

 rowhouse | .126765 .0391369 3.24 0.001 .0500481 .2034818 

 endrow | .2216177 .0551793 4.02 0.000 .1134543 .3297811 

 duplex | .1362315 .0368666 3.70 0.000 .063965 .2084981 

 numplex | .1197765 .0384027 3.12 0.002 .0444988 .1950542 

 garden | .1175732 .0387282 3.04 0.002 .0416574 .193489 

 hirise | .2836297 .0426777 6.65 0.000 .1999721 .3672872 

 aptflat | .1576171 .0312327 5.05 0.000 .0963943 .21884 

 room | .0052695 .0063983 0.82 0.410 -.0072726 .0178116 

 room2 | .0002523 .0001683 1.50 0.134 -.0000776 .0005821 

 bath | -.0167867 .0616998 -0.27 0.786 -.1377318 .1041584 

 bath2 | .0322265 .019552 1.65 0.099 -.0060996 .0705526 

 hbath | .1290902 .0320312 4.03 0.000 .066302 .1918784 

 hbath2 | -.0305009 .0188063 -1.62 0.105 -.0673654 .0063636 

 ldwellage | -.0025268 .0013531 -1.87 0.062 -.005179 .0001255 

 ldwellage2 | .000015 .0000148 1.01 0.312 -.0000141 .0000441 

 age100 | .0101305 .0614537 0.16 0.869 -.1103322 .1305932 

missdwellage | -.1263478 .0284945 -4.43 0.000 -.1822032 -.0704923 

 pool | -.0157419 .0201098 -0.78 0.434 -.0551614 .0236776 

 haveporch | .023453 .0140052 1.67 0.094 -.0040002 .0509063 

 centralair | .0325792 .0155903 2.09 0.037 .0020189 .0631395 

 yes_apl | .1027375 .0174393 5.89 0.000 .0685526 .1369224 

 energy_rent | .1003197 .0152401 6.58 0.000 .0704458 .1301935 

 water_trash | .0281015 .0176037 1.60 0.110 -.0064056 .0626087 

 crowd | .1042168 .0172555 6.04 0.000 .0703924 .1380413 

 insmsa | .3084223 .0229428 13.44 0.000 .2634495 .3533952 

 south | -.2770879 .0215546 -12.86 0.000 -.3193397 -.2348362 

 midwest | -.2532286 .0225416 -11.23 0.000 -.2974151 -.209042 

 west | .0034923 .0215212 0.16 0.871 -.038694 .0456787 

 income | .1036481 .0040461 25.62 0.000 .0957168 .1115793 

 income2 | -.0023339 .0001827 -12.77 0.000 -.002692 -.0019757 

 sub | -.8766729 .0369926 -23.70 0.000 -.9491864 -.8041593 

 sincome | .2738198 .0289028 9.47 0.000 .217164 .3304756 

 sincome2 | -.0248658 .0039115 -6.36 0.000 -.0325333 -.0171983 

 y6 | .0325614 .0179335 1.82 0.069 -.0025923 .067715 

 y7 | .0285056 .0178827 1.59 0.111 -.0065484 .0635595 

 y8 | .0542008 .0175786 3.08 0.002 .0197428 .0886587 

 _cons | 6.667222 .0655047 101.78 0.000 6.538819 6.795626 

------------------------------------------------------------------------------ 
Garner and Betson, March 2010 Page 21

Table 3. Rent Regression to Impute Rents for Renters Receiving Subsidies.

Sample: All Renters. Data: 2006Q2-2007Q1 CE Interview. ______________________________________________________________________________ 

Source | SS df MS Number of obs = 8566 

-------------+------------------------------ F( 37, 8528) = 144.70 

 Model | 1886.97735 37 50.9993879 Prob > F = 0.0000 

 Residual | 3005.64489 8528 .352444289 R-squared = 0.3857 

-------------+------------------------------ Adj R-squared = 0.3830 

 Total | 4892.62225 8565 .571234355 Root MSE = .59367 

------------------------------------------------------------------------------ 

 lnrent | Coef. Std. Err. t P>|t| [95% Conf. Interval] 

-------------+---------------------------------------------------------------- 

 detached | .0212705 .0345685 0.62 0.538 -.0464921 .0890331 

 rowhouse | .0509333 .0401931 1.27 0.205 -.0278549 .1297214 

 endrow | .0474356 .0566255 0.84 0.402 -.063564 .1584352 

 duplex | .0455546 .0396787 1.15 0.251 -.0322252 .1233344 

 numplex | -.0251913 .0414043 -0.61 0.543 -.1063538 .0559711 

 garden | .0722565 .0417767 1.73 0.084 -.0096359 .154149 

 hirise | .2052629 .0429578 4.78 0.000 .1210551 .2894706 

 aptflat | .068702 .0331737 2.07 0.038 .0036736 .1337304 

 room | .0130305 .0061854 2.11 0.035 .0009056 .0251554 

 room2 | -.0002809 .000112 -2.51 0.012 -.0005004 -.0000614 

 bath | -.1060445 .0548703 -1.93 0.053 -.2136035 .0015145 

 bath2 | .0558163 .0167801 3.33 0.001 .0229233 .0887094 

 hbath | .2331286 .043432 5.37 0.000 .1479914 .3182657 

 hbath2 | -.1021477 .028609 -3.57 0.000 -.1582283 -.0460671 

 ldwellage | -.0043214 .001416 -3.05 0.002 -.0070971 -.0015458 

 ldwellage2 | .0000456 .0000153 2.98 0.003 .0000156 .0000756 

 age100 | -.1074236 .0589889 -1.82 0.069 -.2230561 .008209 

missdwellage | -.1263331 .0308007 -4.10 0.000 -.1867099 -.0659564 

 pool | .0146843 .0210143 0.70 0.485 -.0265087 .0558774 

 haveporch | -.0267299 .014416 -1.85 0.064 -.0549887 .001529 

 centralair | .022319 .0161024 1.39 0.166 -.0092456 .0538835 

 yes_apl | .1102647 .0175029 6.30 0.000 .0759547 .1445747 

 energy_rent | .0902665 .0156795 5.76 0.000 .0595308 .1210021 

 water_trash | .0358134 .0176943 2.02 0.043 .0011283 .0704985 

 crowd | .095355 .0182547 5.22 0.000 .0595714 .1311386 

 insmsa | .3226028 .022975 14.04 0.000 .2775662 .3676394 

 south | -.2239311 .0214109 -10.46 0.000 -.2659018 -.1819605 

 midwest | -.2599636 .0215778 -12.05 0.000 -.3022614 -.2176658 

 west | .0087352 .0209536 0.42 0.677 -.032339 .0498095 

 income | .0963199 .0039345 24.48 0.000 .0886073 .1040325 

 income2 | -.0020726 .0001751 -11.84 0.000 -.0024158 -.0017295 

 sub | -.9521857 .0345182 -27.59 0.000 -1.01985 -.8845218 

 sincome | .2374149 .0237073 10.01 0.000 .1909429 .2838869 

 sincome2 | -.014644 .0023868 -6.14 0.000 -.0193227 -.0099652 

 y10 | -.0005898 .0182318 -0.03 0.974 -.0363286 .035149 

 y11 | .0282164 .0180944 1.56 0.119 -.007253 .0636859 

 y12 | .0318903 .0180991 1.76 0.078 -.0035884 .0673691 

 _cons | 6.892635 .0639251 107.82 0.000 6.767326 7.017944 

------------------------------------------------------------------------------ 
Garner and Betson, March 2010 Page 22

Table 4. Rent Regression to Impute Rents for Renters Receiving Subsidies.

Sample: All Renters. Data: 2007Q2-2008Q1 CE Interview. ______________________________________________________________________________ 

Source | SS df MS Number of obs = 8450 

-------------+------------------------------ F( 37, 8412) = 148.58 

 Model | 1859.51499 37 50.2571619 Prob > F = 0.0000 

 Residual | 2845.2704 8412 .338239468 R-squared = 0.3952 

-------------+------------------------------ Adj R-squared = 0.3926 

 Total | 4704.78539 8449 .556845235 Root MSE = .58158 

------------------------------------------------------------------------------ 

 lnrent | Coef. Std. Err. t P>|t| [95% Conf. Interval] 

-------------+---------------------------------------------------------------- 

 detached | .0684705 .0326599 2.10 0.036 .004449 .132492 

 rowhouse | .115226 .0393017 2.93 0.003 .038185 .192267 

 endrow | .0889048 .0538403 1.65 0.099 -.0166354 .1944451 

 duplex | .1053194 .0375867 2.80 0.005 .0316403 .1789985 

 numplex | .0169334 .039947 0.42 0.672 -.0613726 .0952395 

 garden | .1033141 .0416512 2.48 0.013 .0216675 .1849607 

 hirise | .2761108 .0443574 6.22 0.000 .1891593 .3630623 

 aptflat | .1162814 .0314637 3.70 0.000 .0546047 .1779581 

 room | .0025404 .0071347 0.36 0.722 -.0114454 .0165262 

 room2 | -.000054 .0003063 -0.18 0.860 -.0006543 .0005464 

 bath | .0250103 .0505957 0.49 0.621 -.0741698 .1241904 

 bath2 | .0228511 .0144366 1.58 0.113 -.0054481 .0511504 

 hbath | .1726426 .0402818 4.29 0.000 .0936804 .2516049 

 hbath2 | -.0767877 .0266366 -2.88 0.004 -.129002 -.0245734 

 ldwellage | .000028 .0013913 0.02 0.984 -.0026992 .0027552 

 ldwellage2 | -8.50e-06 .0000151 -0.56 0.572 -.000038 .000021 

 age100 | .1293428 .063125 2.05 0.040 .0056023 .2530833 

missdwellage | -.0567568 .029919 -1.90 0.058 -.1154054 .0018918 

 pool | .0352005 .020529 1.71 0.086 -.0050414 .0754423 

 haveporch | -.0115303 .0143207 -0.81 0.421 -.0396024 .0165419 

 centralair | .0376282 .0157517 2.39 0.017 .006751 .0685053 

 yes_apl | .1119291 .0167214 6.69 0.000 .079151 .1447073 

 energy_rent | .0588517 .0154011 3.82 0.000 .0286617 .0890417 

 water_trash | .0354276 .0172752 2.05 0.040 .0015639 .0692913 

 crowd | .1313947 .0185485 7.08 0.000 .0950351 .1677543 

 insmsa | .3793788 .0219136 17.31 0.000 .3364228 .4223349 

 south | -.2628589 .0212206 -12.39 0.000 -.3044566 -.2212612 

 midwest | -.2051163 .0213718 -9.60 0.000 -.2470102 -.1632223 

 west | .0531038 .0208989 2.54 0.011 .0121367 .0940708 

 income | .0885976 .0039789 22.27 0.000 .0807979 .0963973 

 income2 | -.0018328 .0001806 -10.15 0.000 -.0021869 -.0014788 

 sub | -1.068349 .035461 -30.13 0.000 -1.137861 -.9988368 

 sincome | .3405218 .0217285 15.67 0.000 .2979286 .383115 

 sincome2 | -.0214266 .0016837 -12.73 0.000 -.024727 -.0181261 

 y14 | .0088459 .0179224 0.49 0.622 -.0262864 .0439782 

 y15 | .0049323 .0178432 0.28 0.782 -.0300448 .0399094 

 y16 | .0312681 .0179813 1.74 0.082 -.0039797 .0665158 

 _cons | 6.681166 .0619022 107.93 0.000 6.559823 6.80251 

------------------------------------------------------------------------------ 
Garner and Betson, March 2010 Page 23

Table 5. Rent Regression to Impute Rents for Renters Receiving Subsidies.

Sample: All Renters. Data: 2008Q2-2009Q1 CE Interview. ______________________________________________________________________________ 

 Source | SS df MS Number of obs = 8512 

-------------+------------------------------ F( 37, 8474) = 147.35 

 Model | 1929.58865 37 52.1510446 Prob > F = 0.0000 

 Residual | 2999.14665 8474 .353923371 R-squared = 0.3915 

-------------+------------------------------ Adj R-squared = 0.3888 

 Total | 4928.7353 8511 .579101785 Root MSE = .59491 

------------------------------------------------------------------------------ 

 lnrent | Coef. Std. Err. t P>|t| [95% Conf. Interval] 

-------------+---------------------------------------------------------------- 

 detached | .164759 .033565 4.91 0.000 .0989633 .2305547 

 rowhouse | .2856371 .0406355 7.03 0.000 .2059817 .3652925 

 endrow | .0906281 .0543283 1.67 0.095 -.0158686 .1971248 

 duplex | .187639 .0396763 4.73 0.000 .1098637 .2654143 

 numplex | .1440219 .0413876 3.48 0.001 .062892 .2251518 

 garden | .1987008 .043173 4.60 0.000 .1140712 .2833303 

 hirise | .2949598 .044225 6.67 0.000 .2082681 .3816515 

 aptflat | .227994 .0329749 6.91 0.000 .1633553 .2926328 

 room | -.0111437 .0068083 -1.64 0.102 -.0244896 .0022022 

 room2 | .0007401 .0001853 3.99 0.000 .0003769 .0011033 

 bath | .1581032 .0354747 4.46 0.000 .0885642 .2276423 

 bath2 | -.015561 .0081785 -1.90 0.057 -.0315929 .0004709 

 hbath | .0838336 .0373953 2.24 0.025 .0105297 .1571375 

 hbath2 | -.0280153 .0238652 -1.17 0.240 -.074797 .0187664 

 ldwellage | -.0010068 .0013659 -0.74 0.461 -.0036843 .0016707 

 ldwellage2 | 2.41e-06 .0000142 0.17 0.866 -.0000255 .0000303 

 age100 | .046852 .0619721 0.76 0.450 -.0746285 .1683325 

missdwellage | -.0626632 .0300031 -2.09 0.037 -.1214767 -.0038497 

 pool | -.0186042 .0214136 -0.87 0.385 -.06058 .0233716 

 haveporch | -.0308105 .0147686 -2.09 0.037 -.0597605 -.0018606 

 centralair | .0116985 .0157194 0.74 0.457 -.0191153 .0425123 

 yes_apl | .1264954 .0168499 7.51 0.000 .0934655 .1595253 

 energy_rent | .0391883 .0158278 2.48 0.013 .008162 .0702146 

 water_trash | .0396695 .0173774 2.28 0.022 .0056056 .0737334 

 crowd | .104088 .0199449 5.22 0.000 .0649911 .143185 

 insmsa | .4134749 .0225587 18.33 0.000 .3692543 .4576956 

 south | -.2146186 .021657 -9.91 0.000 -.2570715 -.1721657 

 midwest | -.1633603 .0222127 -7.35 0.000 -.2069027 -.1198179 

 west | .0380344 .0213727 1.78 0.075 -.0038613 .0799301 

 income | .1020135 .0040153 25.41 0.000 .0941425 .1098844 

 income2 | -.0021504 .0001719 -12.51 0.000 -.0024874 -.0018135 

 sub | -.7776943 .0314448 -24.73 0.000 -.8393338 -.7160549 

 sincome | .1303728 .0151497 8.61 0.000 .1006757 .1600698 

 sincome2 | -.0049828 .0008009 -6.22 0.000 -.0065528 -.0034128 

 y18 | -.0110155 .0183705 -0.60 0.549 -.0470262 .0249951 

 y19 | .0427997 .0182591 2.34 0.019 .0070074 .078592 

 y20 | .0530409 .0181977 2.91 0.004 .017369 .0887128 

 _cons | 6.516084 .0611129 106.62 0.000 6.396288 6.635881 

------------------------------------------------------------------------------ 
Garner and Betson, March 2010 Page 24

Table 6. Reference Family (Two Adults with Two Children) Thresholds Updated by the Median

(All Thresholds in December 2006 U.S. Dollars)

 Expenditure Concept 

Series

12 quarters of data for 

each threshold

Annual 

Threshold CE OOP OOP-Drop OOP-Adjust

Housing 

Consumption 

1 2004Q1-2006Q4 $21,864 $23,302 $24,828 $24,028 $25,667

2 2004Q2-2007Q1 2006 $22,076 $23,522 $25,006 $24,237 $25,825

3 2004Q3-2007Q2 $22,388 $23,847 $25,265 $24,525 $26,122

4 2004Q4-2007Q3 $22,745 $24,173 $25,441 $24,763 $26,363

5 2005Q1-2007Q4 $23,028 $24,432 $25,701 $25,014 $26,426

6 2005Q2-2008Q1 2007 $23,244 $24,634 $25,894 $25,177 $26,491

7 2005Q3-2008Q2 $23,331 $24,798 $26,146 $25,330 $26,488

8 2005Q4-2008Q3 $23,519 $24,987 $26,399 $25,589 $26,543

9 2006Q1-2008Q4 $23,535 $24,945 $26,384 $25,552 $26,426

10 2006Q2-2009Q1 2008 $23,586 $24,990 $26,434 $25,595 $26,326

Source: U.S. Consumer Expenditure Interview Survey public use data. 

Table 7. Reference Family (Two Adults with Two Children) Thresholds Updated by the 33rd Percentile (All 

Thresholds in December 2006 U.S. Dollars)

 Expenditure Concept

Series

12 quarters of data for 

each threshold

Annual 

Threshold CE OOP OOP-Drop OOP-Adjust

Housing 

Consumption 

1 2004Q1-2006Q4 $21,864 $23,302 $24,828 $24,028 $25,667

2 2004Q2-2007Q1 2006 $22,034 $23,558 $25,015 $24,150 $25,841

3 2004Q3-2007Q2 $22,300 $23,814 $25,331 $24,428 $26,200

4 2004Q4-2007Q3 $22,579 $24,181 $25,601 $24,804 $26,561

5 2005Q1-2007Q4 $22,853 $24,474 $25,852 $25,126 $26,807

6 2005Q2-2008Q1 2007 $22,985 $24,679 $26,023 $25,274 $26,917

7 2005Q3-2008Q2 $23,173 $24,887 $26,257 $25,513 $27,011

8 2005Q4-2008Q3 $23,419 $25,164 $26,561 $25,766 $27,106

9 2006Q1-2008Q4 $23,537 $25,242 $26,698 $25,877 $27,068

10 2006Q2-2009Q1 2008 $23,600 $25,360 $26,892 $26,026 $27,125

Source: U.S. Consumer Expenditure Interview Survey public use data. 
Garner and Betson, March 2010 Page 25

Table 8. Equivalized Reference Family (Two Adults with Two Children) Thresholds Updated by the Median 

Based on All Consumer Units (All Thresholds in December 2006 U.S. Dollars)

 Expenditure Concept

Series

12 quarters of data for 

each threshold

Annual 

Threshold CE OOP

OOPDropOOPAdjustHousing 

Consumption 

1 2004Q1-2006Q4 20712 21540 25156.8 23572.8 26797.2

2 2004Q2-2007Q1 2006 $20,848 $21,685 $25,322 $23,711 $26,933

3 2004Q3-2007Q2 $21,064 $21,912 $25,516 $23,971 $27,161

4 2004Q4-2007Q3 $21,244 $22,124 $25,688 $24,175 $27,366

5 2005Q1-2007Q4 $21,446 $22,331 $25,893 $24,407 $27,530

6 2005Q2-2008Q1 2007 $21,630 $22,523 $26,074 $24,587 $27,653

7 2005Q3-2008Q2 $21,814 $22,732 $26,336 $24,803 $27,801

8 2005Q4-2008Q3 $21,949 $22,869 $26,496 $24,969 $27,852

9 2006Q1-2008Q4 $22,027 $22,993 $26,601 $25,068 $27,891

10 2006Q2-2009Q1 2008 $22,165 $23,179 $26,745 $25,245 $27,946

Source: U.S. Consumer Expenditure Interview Survey public use data. 

Table 10. Equivalized Reference Family (Two Adults with Two Children) Thresholds Updated by the 33rd 

Percentile Based on All Consumer Units (All Thresholds in December 2006 U.S. Dollars)

 Expenditure Concept

Series

12 quarters of data for 

each threshold

Annual 

Threshold CE OOP

OOPDropOOPAdjustHousing 

Consumption 

1 2004Q1-2006Q4 $20,712 $21,540 $25,157 $23,573 $26,797

2 2004Q2-2007Q1 2006 $20,840 $21,670 $25,300 $23,695 $26,936

3 2004Q3-2007Q2 $21,061 $21,894 $25,506 $23,902 $27,148

4 2004Q4-2007Q3 $21,247 $22,096 $25,693 $24,106 $27,384

5 2005Q1-2007Q4 $21,446 $22,300 $25,904 $24,335 $27,584

6 2005Q2-2008Q1 2007 $21,599 $22,481 $26,087 $24,491 $27,733

7 2005Q3-2008Q2 $21,780 $22,691 $26,280 $24,671 $27,899

8 2005Q4-2008Q3 $21,929 $22,855 $26,466 $24,835 $27,976

9 2006Q1-2008Q4 $22,027 $22,986 $26,593 $24,976 $28,020

10 2006Q2-2009Q1 2008 $22,145 $23,143 $26,758 $25,133 $28,110

Source: U.S. Consumer Expenditure Interview Survey public use data. 
Garner and Betson, March 2010 Page 26

Chart 1.

Chart 2.

$20,000

$21,000

$22,000

$23,000

$24,000

$25,000

$26,000

$27,000

$28,000

$29,000

1

2006

345

2007

789

2008

Ref. Fam. Thresholds (in Dec. 2006$)

Thresholds Based on 33rd Percentile Updated by 

Median: References Families Only

Housing Consp

OOP-Drop

OOP-Adjust

OOP

CE

$20,000

$21,000

$22,000

$23,000

$24,000

$25,000

$26,000

$27,000

$28,000

$29,000

1

2006

345

2007

789

2008

Ref. Fam. Thresholds (in Dec 2006$)

Thresholds Based on 33rd Percentile Updated by 33rd 

Percentile: Reference Families Only

Housing Consp

OOP Drop

OOP Adjust

OOP

CE
Garner and Betson, March 2010 Page 27

Chart 3.

Chart 4.

$20,000

$21,000

$22,000

$23,000

$24,000

$25,000

$26,000

$27,000

$28,000

$29,000

1

2006

345

2007

789

2008

Ref. Fam. Threhsolds (in Dec. 2006$)

Threhsolds Based on 33rd Percentile Updated by 

Median: All CUs

Housing Consp

OOP Drop

OOP Adjust

OOP

CE

$20,000

$21,000

$22,000

$23,000

$24,000

$25,000

$26,000

$27,000

$28,000

$29,000

1

2006

345

2007

789

2008

Ref. Fam. Thresholds (in Dec. 2006$)

Thresholds Based on 33rd Percentile Updated by 33rd 

Percentile: All CUs

Housing Consp

OOP Drop

OOP Adjust

OOP

CE
Garner and Betson, March 2010 Page 28

Chart 10. Ratio of 33rd Percentile to Median, Reference Families Only (Based on December 2006$)

Chart 11. Ratio of 33rd Perentile to Median, All Consumer Units (Based on December 2006$)

0.78

0.79

0.80

0.81

0.82

0.83

0.84

1

2006

345

2007

789

2008

CE

OOP-Drop

OOP-Adjust

Housing Consp

OOP

0.78

0.79

0.80

0.81

0.82

0.83

0.84

1

2006

345

2007

789

2008

CE

OOP HM&MR

OOP Hadj%Rsub

Housing Consp

OOP