Report on Experimental Poverty Measures: 1990 to 1997

Kathleen Short and John Iceland, Census Bureau, Washington DC, 20233

Richard Bavier, Office of Management and Budget

Thesia I. Garner and Patricia Rozaklis, Bureau of Labor Statistics

Donald J. Hernandez, SUNY at Albany

I. Overview

In July of this year the Census Bureau

released the report entitled Experimental Poverty

Measures 1990 to 1997. This report responded to

criticisms of the current official measure and to

recommendations of the National Academy of

Sciences Panel on Poverty and Family Assistance.

This Census Bureau report presents a

series of experimental poverty measures that serve

to illustrate the effect on the perception of who is

poor if the official measure were revised.

The experimental measures presented in

the Census Bureau report are based on the work of

a National Academy of Sciences (NAS) panel

contained in their report which was published in

1995, Measuring Poverty: A New Approach (see

Citro and Michael, 1995). The NAS panel

identified several major weaknesses of the current

poverty measure, including the definitions of both

thresholds and incomes (or resources), that have

become more apparent and problematic during the

past three decades.

• The current income measure does not reflect

the effects of key government policies that

alter the disposable income available to

families and, hence, their poverty status.

Examples include payroll taxes, which reduce

disposable income, and in-kind public-benefit

programs, such as the food stamps program,

which free up resources to spend on nonfood

items.

• The current poverty thresholds do not adjust

for rising income levels and standards of

living that have occurred since 1965, despite

evidence suggesting that the trend in the

income level commonly thought necessary to

lift a family out of poverty follows the trend in

overall consumption expenditures.

• The current measure does not take into

account variation in expenses that are

necessary to hold a job and to earn income--

expenses that reduce disposable income.

These expenses include transportation costs

pertaining to work, and, perhaps more

importantly, the increasing costs of child care

for working families resulting from the

increased labor force participation of mothers.

• The current measure does not take into

account variation in medical costs across

population groups, which are a function of

differences in health status and insurance

coverage.

• The current poverty thresholds use family size

adjustments that are anomalous and do not

take into account important changes in family

situations, including payments made for child

support and increasing cohabitation among

unmarried couples.

• The current poverty thresholds do not adjust

for geographic differences in the cost of living

across the nation, although there are

significant variations in prices across

geographic areas.

There have been many attempts to revise the

current official poverty measure in the past. Some

of those are listed below.

1969-- Revision of poverty thresholds

1971-72 -- Technical Committee on Poverty

Statistics

Interagency Subcommittees on Cash Income, on

Non-Cash Income, and on updating the Poverty

Threshold

1976 -- Poverty Studies Task Force under HEW

1980 -- Expanded content of CPS to include inkind benefits

1981 -- Revision of poverty thresholds

1982 -- Census Bureau published first

experimental poverty measures

1992 -- Congressional funding of National

Academy of Sciences (NAS) Panel on Poverty and

Family Assistance

Most relevant for our work here are the

last two in the list. Much of the work in the latest

report is based on work originally published by the

Census Bureau in 1982. These experimental

measures have been published annually with the
official poverty measure since that time. But these

measures differ from those in the current report in

that they use the official thresholds with varying

definitions of income. The result in many cases

being that the measures are not consistently defined

in terms of comparing a measure of need against a

measure of resources available to meet those needs.

The last item in the list, the 1992 funding

of the National Academy of Sciences work resulted

in the report we refer to here and represents the

impetus behind the research described in our

report.

The experimental measures presented in

the Census Bureau report are based on the work of

a National Academy of Sciences (NAS) panel.

Their recommendations fall into four general

categories:

1. Threshold recommendations. The panel

recommended that the thresholds should represent

a dollar amount for food, clothing, shelter

(including utilities), and a small additional amount

to allow for other common, everyday needs (e.g.,

household supplies, personal care, and non-workrelated transportation). One threshold should be

developed for a reference family type using

Consumer Expenditure Survey (CE) data, and the

reference family threshold should be adjusted to

reflect the needs of different family types and

geographic differences in the cost of living. The

reference family should consist of two adults and

two children. Adjustments to thresholds should be

made over time to reflect changes in real growth in

basic consumption expenditures.

2. Family resource recommendations.

The panel recommended that family resources

should be defined as the value of money income

from all sources, plus the value of near-money

benefits that are available to buy goods and

services covered by the new thresholds, minus

expenses that divert money that can no longer be

used to buy these critical goods and services.

Near-money benefits include non-medical in-kind

benefits, such as food stamps, subsidized housing,

school lunches, and home energy assistance.

Expenses to be subtracted include income taxes,

Social Security payroll taxes, childcare and other

work-related expenses, child support payments to

another household, and household contributions

toward the costs of medical care and health

insurance premiums (i.e., medical out-of-pocket

costs or MOOP).

3. Data recommendations. Several of the

panel’s recommendations dealt with survey

methodology. Most significantly, the panel

recommended that the Survey of Income and

Program Participation (SIPP) should become the

basis of official income and poverty statistics,

replacing the March income supplement to the

Current Population Survey (CPS). In this

recommendation, the panel recognized that the

SIPP asks more relevant questions than the March

CPS and obtains income data of higher quality. The

panel also encouraged a review of the Consumer

Expenditure Survey (CE) to improve the quality

and usefulness of the data for poverty

measurement. Finally, they recommended that

consideration should be given to the practical

problems of implementing fully an improved

measure of poverty when using other surveys that

do not collect the detailed information that is

needed.

4. Research recommendations. There are

several elements in the proposed poverty measure

for which the panel recommended additional

research. Among them are improved estimation of

the geographic cost-of-living differences, an

assessment of the extent of resource sharing among

non-family household members for the purpose of

broadening the unit of analysis, development of

methods to value the benefits of owning a home,

and development of one or more medical care risk

indexes (separate from the measure of economic

poverty) that would measure the risk of having

inadequate or no health insurance coverage.

In our report we constructed six basic

experimental measures. The first measure we refer

to as the NAS measure. We calculated it by closely

following the methods outlined in the NAS panel’s

report. While there are a few minor differences

from the measure the panel recommended, they are

computational rather than conceptual in nature. In

both the panel’s report and here this measure is

constructed in the following wayi:

Thresholds:

• Thresholds are set at the midpoints of the

ranges recommended by the NAS panel –

averaged over the three most recent years –

i.e., data for 1995, 1996, and 1997 are

averaged for the 1997 threshold

• The equivalence scale is a two-parameter

version

• Geographic indexes are those listed in the

panel report

Resources:

• Include the value of food assistance programs

• Include the value of housing subsidies
• Include the value of energy assistance (only

heating assistance)

• Subtract work-related and childcare expenses

using the panel’s childcare model

• Take account of taxes as modeled in the CPS

• Subtract medical out-of-pocket expenses

(MOOP), modeled and calibrated to spending

totals

The second and third experimental measures

we report use a different method of valuing

childcare expenses. These measures are referred to

as DCM1 (Different Childcare Method 1) and

DCM2 (Different Childcare Method 2). The DCM1

measure uses a percent of median childcare

expenditures estimated from the SIPP, while

DCM2, uses the amounts based on deductions for

necessary childcare in the former Aid to Families

with Dependent Children program (AFDC) and

Food Stamp programs. DCM2 is similar to the

panel’s method in its effect on poverty estimates

but is easier to implement.

The fourth experimental measure we refer to

as the DES-DCM2 measure. This measure is

constructed like the DCM2, but, in addition to

changing the childcare computation, we also use a

Different Equivalence Scale. For this measure we

use the three-parameter equivalence scale and

AFDC allowances to value childcare expenses. We

include it here because it is arguably a more refined

equivalence scale than the two-parameter one that

the panel used.

Finally we show the NAS and the DES-DCM2

measures without a geographic adjustment. These

measures are referred to as NGA and NGA-DESDCM2, respectively. These two measures are

calculated exactly as the NAS and the DES-DCM2

measures but the thresholds are not adjusted for

differences in the cost of housing in different parts

of the country. The geographic adjustment is

excluded because, as the panel noted, this element

requires more research and better data sources.

These measures, then, adopt the assumption that

the cost of meeting basic needs does not vary by

geographic area.

The new experimental poverty measures that

are presented in the report paint a different picture

of the nation’s poor. According to the new

measures, people who receive more from certain

types of government assistance programs have

relatively lower poverty rates, while families with

high work-related and medical expenses have

relatively higher poverty rates. The experimental

measures incorporate the effects of government

policies aimed at the most needy families in the

United States. They use an after-tax income

measure, and add the value of in-kind benefits C

such as food stamps C to income. They also take

into account variations in expenses that are

necessary to hold a job or to obtain medical care.

Key findings:

Χ Due to the Earned Income Tax Credit,

deducting taxes from income on balance

reduces the percent of people who are viewed

as being poor.

Χ Adding in-kind benefits to income reduces

poverty rates, but the reductions from any

single program are generally quite small.

Χ Under the experimental poverty measures,

children, while still having higher poverty

rates than other age groups, would make up a

smaller proportion of the total poor than they

do under the official poverty measure. The

elderly would make up a larger proportion of

the total poor under the new measures.

ΧPeople in families in which there are no

workers are less likely to be classified as poor

under the experimental measures than they are

under the official measure. This is due to the

greater likelihood of receiving in-kind benefits

and incurring no work-related expenses.

Χ Experimental measures that account for

geographic differences in the cost of housing

show higher standardized poverty rates for

people in the Northeast and the West C as well

as suburban areas C than poverty rates based

on the official measure.

One of the panel’s recommendations is to

make the Census Bureau=s Survey of Income and

Program Participation the official source for

measuring income or resources in poverty

statistics. The Current Population Survey is used in

this report and for official poverty estimates

published annually by the Census Bureau.

In the final section of the report we discuss

additional research that should be done to refine

improved poverty measures. Among the various

items that require additional work are developing

better geographic adjustments for the thresholds,

improving the method for valuing medical out-ofpocket expenditures, taking account of the fact that

some people live in housing that they own

themselves and therefore have much lower housing

costs then others, and finally, researching whether

or not the family is the most appropriate unit of

analysis for poverty measurement. These issues are

described below in Sections II through IV of this

paper. A different author as noted presents each

section.
II. Variation in need for shelter and medical

out-of-pocket spending

Richard Bavier

(The author is a policy analyst at the Office of

Management and Budget. The views expressed are

the author's personal views and do not represent

the views of OMB or the Administration.)

The NRC panel recommended that

medical needs not be included in the poverty

budget, and that medical out-of-pocket spending

(or moop) be subtracted from income before

poverty status is determined.ii The controversial

nature of the panel's approach and the difficulty

surveys will have in implementing it have left

many people wishing for a variation on the panel's

proposed poverty measure that would include

something for medical out-of-pocket needs in the

thresholds. However, those analysts who would

like to see Census include a variation with moopin-the-thresholds (or moopitt) in its experimental

poverty series need to address the reasons that led

the panel to reject such an approach.

The panel's principal argument against

including anything in the poverty thresholds for

medical needs is that such needs are, “...much

more variable than needs for food and housing.”

(224). And, consequently, much “erroneous

poverty classification” would result.

For the panel, a good indication of

variation in medical needs, at least out-of-pocket

medical needs, is variation in spending. For

poverty measurement purposes, we should regard

all moop as "nondiscretionary." (102, 235-6) So,

to illustrate the variability of medical needs, the

panel presented a table (Table 4-1) that showed the

variability of medical out-of-pocket spending.

A 1994 paper by Eva Jacobs, David

Johnson, and Stephanie Shippiii showed that shelter

spending contributed much more than health

spending did to total inequality of expenditures.

Their analysis included all households, not just

low-income households. To minimize the effects

of both resource constraints and discretionary

income on expenditures for basic needs, I selected

1992-95 Consumer Expenditure Survey units with

four quarters of expenditure data and total annual

expenditures between 100 percent and 150 percent

of their NRC poverty thresholds, with results as in

Table 1.

The coefficient of variation of moop is

much larger than for shelter spending. The tail of

the moop distribution includes more extreme

values. By that measure, moop is much more

variable.

Table 1. moop shelter +

utilities

Median 987 3,727

Mean 1,399 4,001

Variance 2,203,048 7,094,186

Coefficient of

variation

106 67

However, saying that moop is more

variable than shelter spending is not the same as

saying that moop varies too much to include in the

poverty budget. To see this, consider a two-item

poverty budget made up of needs for shelter and

moop. If they were independent random variables

each need would contribute exactly its variance to

the variance of the total budget that combines the

two needs.iv

variancethreshold = varianceshelter + variancemoop

Assuming for the moment that the variation in

spending shown in Table 1 reflects differences in

need, shelter plus utilities would account for

around three-fourths of the variation in need in this

two-item poverty budget.v Looked at this way,

spending data don't seem to support the contention

that moop varies too much to include in a poverty

budget. It appears that including moop wouldn't

introduce nearly as much variation into the poverty

budget as including shelter does.

The panel's report offers two lines of

counter-argument. The first acknowledges that

there is a lot of variation in shelter needs, but that

this variation can be captured by variations in the

thresholds. The panel proposed 50 threshold

variations to reflect differences in the costs of

shelter by census division and metro size.

However, when it comes to moop, even allowing

for “... a large number of thresholds to reflect

different levels of medical care need ..,” the report

observes:

... the predictor variables used to develop the

thresholds (e.g., age or self-reported health status)

may not properly reflect an individual’s medical

care needs during any one year: some people in a

generally sicker group may not be sick that year

and vice versa for people in a generally healthier

group. (224)

In other words, within any class of

families with a similar risk of incurring high

medical costs, the actual incidence of those costs in
any year will have a practically random character

that no threshold variations could capture.

This seems true. Next I'll mention a

similar feature in the variation of shelter needs.

But first a caution against confusing the statistical

measure of poverty with the measure of poverty or

need employed in means-tested assistance

programs. For means-tested programs, “erroneous

poverty classification” may mean that an eligible

family is denied assistance, and real deprivation

goes unalleviated. Consequently, variation of

needs among individual families, including random

variation of medical needs, is of first importance.

Such programs seek to accommodate exceptional

needs by allowing a range of deductions before

income is tested against eligibility criteria that

apply to all families of the same class. (For

example, the Food Stamp Program and rental

assistance programs like public housing allow

some actual medical expenses to be deducted from

income prior to determining assistance amounts.)

The statistical measure of poverty,

however, is not concerned with meeting the needs

of individual families. Rather, it is a measure of

the economic status of groups, how they compare

to other groups, and how they change over time.

Failure to correctly order the prevalence and degree

of low economic resources among relevant

subgroups would be a serious flaw in a poverty

threshold. By comparison, failure to identify those

individual families that experience essentially

random economic shocks is not a flaw in a

statistical measure of poverty as long as the relative

prevalence and degree of low economic resources

among groups are represented accurately.

The second counter-argument to Table 1

starts with the panel's characterization of only

moop, among all basic needs, as

"nondiscretionary." The panel would argue that

nearly all the variance of moop on Table 1 reflects

differences in need, but not all the variance in

shelter spending does. So, if we could measure

need directly, as opposed to spending, we would

find that, after controlling for family composition,

region, and metro size, the remaining variance of

need for medical out-of-pocket spending would be

much greater than the remaining variance of shelter

needs.

We don't have a direct empirical measure

of need as opposed to spending, so we can't settle

this once and for all. But even after controlling for

family composition, region, and metro size,

considerable variation in shelter spending by lowincome families remains, and some of it clearly is

not discretionary. A shortage of affordable

housing within a metro area can also drive the kind

of variation in shelter spending by low-income

families displayed on Table 1. Every year the

Department of Housing and Urban Development

reports that the supply of affordable housing for

low-income families falls well short of need. For

1997, HUD reported that for every 100 families

with gross incomes below half the area median,

there are only 69 affordable units available. This

ratio of affordable units varies from a low of 49 in

the West to a high of 84 in the Midwest.vi

In light of the fact that the local supply of

housing at poverty budget prices may fall short of

demand, it is not a surprise that there appears to be

significant variation in shelter spending near the

bottom of the distribution, even controlling for

region and metro size. In the 1993 American

Housing Survey, among families not receiving

rental subsidies who rented two-bedroom

apartments in cities of one-million or more, the

range of shelter costs in the bottom quartile was

close to the median in the quartile in every Census

region. The standard deviation in the bottom

quartile was between $800 and $1,300 per year.

(This compares to a standard deviation of $1,484

for moop on Table 1 with no controls for medical

risk group.)

The effects of a shortfall of affordable

housing within a locality occur unpredictably

among families with the same composition and

needing the same number of rooms. (So do the

effects of the availability of some housing at very

low or no cost. Among households classified as

poor and not receiving rental assistance, the 1993

American Housing Survey found 9 percent

occupying their dwelling with no charge for shelter

or utilities.) Like the incidence of illness or

accident among families in the same medical risk

group such variation in shelter spending due to

intra-area supply factors cannot be fully captured

beforehand in threshold variations.

So, in sum, there appears to be a lot more

variation in shelter needs than the NRC thresholds

do or could capture. And because shelter makes up

a much larger share of basic needs than moop does,

it is not evident that moop would introduce more

variation into the poverty budget than shelter

already does. On balance, the argument that moop

is too variable to include in the poverty budget

does not seem compelling.

One commenter noted that it would not be

a good idea to have separate poverty thresholds for

those who happened to be sick in a given year, a

view with which the foregoing agrees. To the

extent that a class of families with high medical

needs due to the presence of members with chronic
conditions can be identified, threshold variations to

capture such predictable differences in need could

be considered. Unpredictable intra-group variation

in medical needs, or other needs, is the proper

concern of poverty programs rather than poverty

statistics.

Another commenter speculated that, even

allowing for the kinds of supply-driven differences

in shelter costs adduced, a larger share of medical

spending probably was nondiscretionary. The

discussion above does not dispute this. But if only

one-third of the variance of shelter spending on

Table 1 were found to represent nondiscretionary

variation, the argument that moop is too variable to

include in the poverty budget still would not be

supported by these data.

It also should be acknowledged that

neither the NRC thresholds nor the moopitt

thresholds I'm suggesting will tell us whether

families can afford or have access to medical care,

just as neither one can tell us whether families can

afford the housing that is available when and where

they need it. To answer questions about the

availability, affordability, and consumption of

health care, special measures, like the medical care

risk index proposed by the panel, are needed. They

would join an array of special measures already in

use to assess affordability of housing, sufficiency

of food consumption, and health insurance

coverage. These measure living standards, and

supplement the measure of minimally adequate

economic resources represented by the poverty

thresholds.

III. Measuring Poverty Using Alternative Units

of Analysis

John Iceland, U.S. Census Bureau, and Don

Hernandez, SUNY-Albany

The unit of analysis used in poverty

measurement continues to receive critical scrutiny.

The debate revolves around how the “family”

should be defined. The National Academy of

Science Panel on Poverty and Family Assistance

offered two recommendations concerning the

current official unit of analysis (Citro and Michael,

1995). First, the “definition of ‘family’ should be

broadened for the purposes of poverty

measurement to include cohabiting couples” (Citro

and Michael, 1995, p. 13). Second, research

should be conducted “on the extent of resource

sharing among roommates and other household and

family members to determine if the definition of

the unit of analysis should be modified in the

future” (Citro and Michael, 1995, p. 13). The

panel further recommended an assessment of the

effects of changing the unit of analysis on poverty

rates.

The panel noted that insofar as cohabitors,

roommates, and other household members share

resources and benefit from economies of scale, the

current measure likely overstates the poverty rate

for such people. The panel also noted that

cohabiting couples typically pool resources, and

many exhibit considerable stability in their living

arrangements, so that it makes sense to treat them

like married-couple families for purposes of

poverty measurement.

This analysis presents results on poverty rates

using three alternative units of analysis: 1) the

official family, 2) the cohabiting couple, and 3) the

household. Specifically, we estimate 1997 poverty

rates by both unit of analysis and poverty measure

(official versus experimental), and examine who is

most affected by using different units of analysis.

Issues

Most people would agree that official statistics

should take into account the realities of changes in

society. Over the past few decades we have

witnessed a growing number of persons living in

cohabiting relationships. Also, more generally,

many people continue to live in households with

non-family members. Consequently, some argue

that these cohabiting families and/or households

should be considered a single unit because persons

living in them benefit from economies of scale, and

many also share resources.

Yet some issues remained unresolved. First,

most would agree that there should be some

stability in arrangements for a group a persons to

be considered a unit. Some recent research on this

issue by Bauman (1999) suggests that there is a

modest degree of stability in the cohabiting and

household units-- a majority of both types of

arrangement last for over a year. This favors

considering cohabiting persons or households as

the appropriate unit in poverty measurement.

A second issue that remains unclear is the

extent to which people share resources in various

types of units. Most would agree that people in

larger households benefit from economies of scale,

and this argues for adopting a more inclusive unit

of analysis. However, due to lack of good data,

there is little research which directly examines the

extent of resource sharing within households.

Bauman’s study (1999), which uses an indirect

method, suggests that while there is some resource

sharing, non-family members, as traditionally

defined, unsurprisingly do seem to share less.

Keeping in mind that more research on these issues

is needed before making a definitive choice on
which unit of analysis to adopt, this analysis is an

empirical examination of resulting poverty rates

when using different units, as described below.

Defining Alternative Units of Analysis

We use three alternative units of analysis here:

the official family, the cohabiting couple, and the

household. These three units represent viable

alternatives, as discussed in the Panel’s report.

The official family currently used in poverty

measurement basically consists of persons related

to one another by birth, marriage, or adoption. This

definition includes siblings and other kin.

According to this definition, there may be multiple

families within a household.

In the cohabiting couple unit of analysis,

families in households where no person is

identified as an unmarried partner are defined as in

the official family measure. However, in

households where a person is identified as an

unmarried partner, the householder’s family and

the unmarried partner’s family are combined into a

single unit with pooled resources and a different

threshold based on the size and composition of the

combined unit. Finally, the third unit of analysis--

households-- consists of all persons who occupy a

housing unit. So in addition to family members and

cohabiting couples, this unit includes all

housemates, roommates, boarders, and foster

children who share the housing unit.

Results and Conclusion

We estimated 1997 poverty rates by unit of

analysis and poverty measure, and examined who

was most affected by using different units of

analysis.

We find that when using either the official

measure of poverty or the National Academy of

Science Panel’s recommended measure (here

referred to as the NAS measure), poverty rates are

moderately lower the more inclusive the housing

unit (see Table 2). As expected, regardless of unit

of analysis used, experimental poverty rates

continue to be higher than official ones.

Table 2. Poverty Rates by Unit of Analysis and

Poverty Measure, 1997

Official

family

Cohabiting

couple

Household

Official

measure

13.3 12.7 11.6

NAS

measure

15.4 14.9 14.0

While the overall effect of moving to a more

inclusive unit of analysis is moderate, the effect

differs by a person’s relationship to the

householder. The effect of using a more inclusive

unit of analysis is smallest for persons who are

identified as core household members—

householders, spouses, children, and other relatives

of the householder. Poverty rates for these persons

tend to change little because they often live in

households where they are part of the only family

unit present.

However, we observe, as expected, a

substantial effect of using more inclusive units of

analysis on persons identified as unmarried

partners, non-relatives, housemates, roomers,

boarders, and foster children. For example, the

poverty rate for unmarried partners with families

declines from 47.9 percent when using the official

family unit of analysis to 17.5 percent when using

the cohabiting unit of analysis, which pools the

unmarried partner’s resources with the

householder’s.

For persons who are identified as housemates,

roommates, or boarders, the poverty rate declines

from 30.9 percent when using the official family

definition to 13.5 when using the household unit of

analysis. Finally, among foster children, the

poverty rate declines from 68.6 percent when using

the official family definition to 20.9 percent when

using the household unit of analysis. It is important

to note that foster children under the age of 15 are

not even in the official poverty universe. In

contrast, all foster children are in the poverty

universe in the household unit of analysis, with

their resources pooled with other members in the

household.

Overall, results show that people benefit from

both pooling of resources and economies of scale,

as assumed in the different unit of analysis

definitions. As expected, the difference is largest

for persons living in nontraditional household

arrangements. These findings hold whether using

the official measure of resources and thresholds or

the experimental poverty measures.

IV. Accounting for Owner Occupied Housing in

Poverty: Focus on Thresholds

Thesia I. Garner and Patricia Rozaklis

Bureau of Labor Statistics

The National Academy of Sciences Panel stated

that the poverty thresholds should represent a

budget for food, clothing, shelter (including

utilities), and a small amount to allow for other

needs (e.g., household supplies, personal care, non-
work-related transportation). To obtain this

budget, the Panel used the U.S. Consumer

Expenditure Survey (CE) data. Out-of-pocket

expenditures were used. However, the updating

mechanism to update the thresholds was to be

based on changes in consumption. Out-of-pocket

expenditures for food and utilities are likely to

represent consumption. Such expenditures are less

likely to represent the consumption of clothing, but

may. The consumption of rental housing also is

likely to be fairly well represented by rental

expenditures.

Out-of-pocket expenditure for owner housing is

not likely to be a good proxy for consumption. For

example, if most housing were owner occupied and

the owners had low or no mortgages, the

expenditure approach would imply that these

owners have no consumption of housing. If the

Panel were attempting to provide a threshold based

on the cost of the consumption, the out-of-pocket

approach would not be a good model to follow.

The Panel acknowledged this by stating that that

their approach was used for processing

convenience only. The implicit cost of owned

housing should be accounted for in the measure. If

on the other hand, the purpose of the threshold

were to provide an estimate of the expenditure that

would be needed to meet the basic expenditures of

the family, the out-of-pocket approach would be

appropriate. In the Panel's report, expenditures,

consumption, and needs are interchanged.

However these are not the same. Until a decision is

made concerning the focus of the thresholds (and

corresponding resources) - expenditures,

consumption, or needs - confusion will remain

concerning the measure, especially with regard to

the treatment of owner occupied housing.

In this presentation we present thresholds based

on the out-of-pocket approach, a hedonic

regression approach (estimated rental shelter costs

were produced), and reported rental equivalence

from consumer units participating in the CE. The

thresholds are produced for the reference family of

two adults with two children. First, to get an idea

of the magnitude of the issue, we note that about 75

percent of all reference families are owners, and 64

percent of all reference families have mortgages.

Owners without mortgages represent 10.4 percent

and renters represent 25.3 percent of the reference

families. The 1997 threshold for two adults and

two children families based on out-of-pocket

expenditures is about $16,000. The threshold

based on the hedonic approach is slightly less than

$16,000. The reported rental equivalence

approach results in a threshold that is $2,000

higher, at $18,000. Given the rental-owner

occupied housing mix for the reference families,

the out-of-pocket approach and the hedonic

approach result in similar thresholds. However, if

this mix were to change, the same may not result.

Again, if the purpose of the threshold is to present

the cost of consumption, conceptually, the hedonic

approach is the most appropriate since it models

the costs of housing consumed. Also, if

consumption is the focus of the thresholds, then a

consistent approach to valuing some implicit

income from owner occupied housing would be

desirable as an addition to the resources.

Another alternative noted by the Panel but not

produced by them was to produce separate

thresholds for owners with mortgages, owners

without mortgages, and renters. If this were done,

no flow income amount would need to be added to

resources. The highest thresholds result for owners

with mortgages, first based on reported rental

equivalence (over $20,000), followed by out-ofpocket expenditures (over $18,000), and then based

on the hedonic model (about $17,000). The

thresholds for owners without mortgages also were

highest when reported rental equivalence was used

(about $16,000).

However, in contrast to the owners with

mortgages, those without mortgages had higher

thresholds based on the hedonic approach

($14,000) as compared to the out-of-pocket

approach (slightly above $11,000). The threshold

for renters is about $13,500.

We also asked whether the approach to valuing

housing in the thresholds had an effect on poverty

rates overall; we compared rates based on the

different approaches to the overall poverty rate for

all persons in 1997 using the official measure

(thresholds were not geographically adjusted for

any of the thresholds). The official poverty rate in

1997 was 13.3 percent. If we only changed the

threshold from the official one to one based on outof-pocket expenditures, the rate would fall to 12.4

percent. If we used the threshold based on the

hedonic approach, the rate fell to 12.2 percent.

Using an hedonic based threshold and a resource

measure that accounted for the implicit income

from owner occupied housing (i.e., current money

income plus net return on home equity), the

poverty rate dropped to 11.0 percent. Using this

last poverty measure, the elderly would be quite

affected; their poverty rate would drop from 10.5

percent using the official measure to 6.3 percent

using this latter measure. If the net flow income to
the elderly were capped, the poverty rate for them

would be higher however.

There are many questions that remain with

regard to the treatment of owner occupied housing

in a revised poverty measure. For example, should

the focus of the poverty measure be based on the

expenses that people face and the income that they

have to meet those expenses? Or should the

measure be based on the costs of consumption or

some basic needs and the resources available to

provide for that consumption or to meet those

needs? Are the out-of-pocket expenditures that the

Panel used too high due to the fact that there is no

accounting for the deduction of mortgage interest

when one estimates their income taxes? Would the

thresholds based on the hedonic model be higher if

the model better accounted for differences in

amenities such as quality of neighborhoods and

dwellings? If the cost of housing is included in the

thresholds, what is the best approach to account for

the flow of income on the resource side? For the

elderly, who many say may be "over-housed,"

should the flow income implicit from owner

occupied housing be capped? These and other

issues will be examined in future research.

References

Bauman, Kurt J. 1999. “Shifting Family

Definitions: The Effect of Cohabitation and

Other Nonfamily Household Relationships on

Measures of Poverty.” Demography 36, 3

(August): 315-26.

Citro, Constance F., and Robert T. Michael (eds.),

Measuring Poverty: A New Approach,

Washington, D.C.: National Academy Press,

1995.

Dalaker, Joseph, Poverty in the United States:

1998, U. S. Census Bureau, Current Population

Reports, Series P60-207, Washington, D.C.,

September 1998.

Short, Kathleen, Thesia Garner , David Johnson,

and Patricia Doyle, Experimental Poverty

Measures: 1990 to 1997, U. S. Census Bureau,

Current Population Reports, Series P60-205, U.S.

Government Printing Office, Washington, D.C. ,

1999.

 

i All measures use the family as the unit of analysis.

See Short et al. (1999) for more details on the

construction of these measures.

ii Citro, Constance F., and Robert T. Michael (eds.),

Measuring Poverty: A New Approach, National

Academy Press, Washington DC, 1995. Page

references will appear parenthetically in the text.

iii Johnson, David, Stephanie Shipp, and Eva

Jacobs, "Expenditure Trends and Measures of

Inequality: 1960 to 1992," paper prepared for the

23rd General Conference of the International

Association for Research on Income and Wealth,

July 1994.

iv For sample units with spending between 100 and

150 percent of NRC poverty, the variance of the

sum of moop and shelter equaled 99 percent of the

sum of the variances of these spending classes.

v

 Shelter represents an even larger share when only

unsubsidized renters are selected in order to reduce

the confounding effects of home-buying

expenditures. When only units with a member

aged 65 or older are included, shelter plus utilities

accounts for 60 percent of the variation.

vi Nelson, Kathryn P., et.al., “Rental Housing

Assistance – The Crisis Continues, The 1997

Report to Congress on Worst Case Housing

Needs,” U.S. Department of Housing and Urban

Development,

http://www.huduser.org/publications/affhsg/worstc

ase, Last revised: 3/31/99, Tables A-4, A-14.