United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

Measuring the Well-Being 

of the Poor

Demographics of Low-Income 

Households

Noel Blisard and J. Michael Harris

April 2002

Technical

Bulletin

Number

1898

Abstract

The economic well-being of the U.S. population with incomes below 130 percent of the

official poverty guideline is of special interest to policymakers and food assistance program administrators. For example, the Food Stamp Program uses gross income below

this level as one of several criteria for determining eligibility for program benefits. This

study employs alternative welfare measures, including the Sen index, to assess the economic status of the low-income population and to track changes in welfare status over

time. In general, welfare measures of households with income no greater than 130 percent of the poverty line improved slightly between 1981 and 1995. The study also

assesses which demographic characteristics that describe these low-income households

have the largest impact on the welfare measures. This demographic analysis is useful for

identifying household types that could merit special attention in designing strategies

such as job training or food stamp education and outreach. 

Keywords: Food stamps, demographics, low-income households, welfare measures.
2 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Economic Concepts of Social Welfare and Inequality . . . . . . . . . . . . . . . . . . .6

Measures of Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Methodology of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Database and Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Welfare Measures and Inequality Between 1981 and 1995 . . . . . . . . . . . . . .12

Counterfactual Impacts of Selected Demographic Factors on

Welfare Measures and Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
Summary

The Food Stamp and School Lunch Programs both

give a special status to the population with incomes

below 130 percent of the official poverty guideline.

The Food Stamp Program uses gross income below

this level as one of several criteria for determining eligibility for program benefits. It is, therefore, of particular interest to measure the welfare status of the

American population with incomes below this level,

and to track changes in this welfare status over time.

For example, measuring changes in welfare status

allows us to examine the success of the Food Stamp

Program in lessening the dispersion of income among

all poor households. 

A methodological approach well-suited for this purpose

has been developed by Amartya Sen (1992). Using a

particular poverty cutoff—such as 130 percent of the

official poverty guideline—Sen’s social welfare index

combines three other measures of welfare: (1) the number of people who are poor by this standard, (2) the

depth of their poverty, and (3) the degree of inequality

in the distribution of income within this group. Sen’s

index is particularly appropriate for social welfare measurement when the analyst wants to give a special status to the welfare of people with the lowest incomes. 

In the first main empirical section of this paper, we

report estimates for Sen’s social welfare index and its

three component parts for 1981 through 1995. In general, we find that welfare measures of households with

income no greater than 130 percent of the poverty line

improved slightly between 1981 and 1995. We also

find, using these measures, that income inequality was

less over this period for households participating in the

Food Stamp Program than for non-participating households. This indicates success in encouraging the neediest families to participate in the Food Stamp Program

versus those families at or near 130 percent of the

poverty line, even though all eligible households are

encouraged to participate.

In the second main empirical section of this report we

investigate the statistical effect of a particular household demographic characteristic on the social welfare

status of low-income Americans, as measured using the

above methods. The demographic variables we control

for are region, race, age, family size, one-person households, head of household with and without a high

school diploma, and the number of earners in a household. For this purpose, we estimate a regression model

of the demographic determinants of income, where

income is measured as a proportion of 130 percent of

the official poverty guideline. This regression model

provides estimates of the effect of each explanatory

characteristic on income status, while holding constant

all other household demographic characteristics. 

We then conduct a series of six hypothetical illustrations, called counterfactual analysis, of how social

welfare would be affected if we could redress the

income disadvantage accounted for by each of the six

demographic characteristics. For example, our regression model indicates that the demographic characteristic “household headed by a person with a high school

education or less” is associated with a measurable disadvantage in terms of household income. Suppose it

were not the case that this demographic characteristic

was associated with this income disadvantage. What,

then, would be the prevalence of poverty, the degree of

inequality, and the level of social welfare as measured

by Sen’s index? We find the number of poor households declines by almost 43 percent in the counterfactual case where “head of household without a high

school education” provides no income disadvantage. 

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 3
Introduction

This report explores the major demographic characteristics that describe households with incomes of 130

percent or less of the poverty line by statistically analyzing household data from 1981-95.1 The 130 percent

or less cut-off is of special interest because it is these

households that meet the gross income test for food

stamps. The U.S. Department of Agriculture (USDA)

administers the food stamp program in order to ensure

that American households have the opportunity to consume a nutritious and healthy diet, and food stamps

increase the real income of participating households.

USDA allocates over one-quarter of its annual budget

to food stamps for eligible households that choose to

participate in the program. 

Both Federal and State governments are concerned that

food stamp administrative dollars be allocated to maximize the expected program benefits. A better understanding of the dominant demographic characteristics that distinguish program-eligible households may assist in

designing approaches and targeting beneficiaries so as to

enhance the program’s efficiency and effectiveness. 

More specifically, the income requirement for food

stamp eligibility is that household income cannot be

greater than 130 percent of the poverty line, given

household size, for all households without elderly

members.2 In addition, households may have only

$2,000 in countable assets unless one or more members of the household are 60 years or older, in which

case they can have $3,000 in countable assets. This

asset requirement is not taken into account in our

study, and so we may overestimate the number of

households actually eligible to receive food stamps.

This overestimation is likely to be constant over time,

however, and our primary purpose in looking at households that meet the gross income test is to identify

those households that may be chronically poor. In this

regard, “eligible for food stamps,” “household income

below 130 percent of the poverty line,” and “poor

households” are synonymous in this report.

The regression technique that we use allows us to isolate the influence of any one demographic variable on

a household’s poverty status, while holding other

determining variables constant. Using this technique,

we estimate the impacts of demographic characteristics

on four different indicators of poverty, or welfare measures: the head-count ratio, which represents the percentage of poor households in a population; the

income gap, which measures the deviation of income

from the poverty line; the Gini coefficient, which measures income inequality; and the Sen index, which

combines the headcount, income gap, and Gini coefficient into one summary measure. By showing which

demographic characteristics have the largest impact on

these welfare measures, we can identify household

types that could merit special attention in designing

strategies to increase the effectiveness of welfareenhancing programs. 

Strategies could include educational or job training

opportunities, which potentially raise income levels, or

encourage food stamp participation by eligible households that have chosen for some reason not to participate. Success in enhancing participation would

increase household income by the value of the food

stamps and ensure that the household has adequate

resources to purchase a healthy and nutritious diet.

Better diets and nutrition may contribute to long-term

welfare gains for society by increasing work productivity, reducing medical costs, and promoting the

learning ability of children. 

Food stamp participation may also affect the dispersion

or disparity of income in the population. One contribution of the analysis in this report is an assessment of the

income dispersion among food stamp program participants, using the Gini coefficient measure, and of how

the value of the stamps affects this disparity measure. 

4 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

1 At the start of this study we only had data for 1981-95, which is

before reform of the Food Stamp Program.

2 Households with elderly members may have higher income, but

few participating households have this higher income. All eligible

households must have net income (gross income less defined program deductions) less than 100 percent of the poverty line.
Literature Review 

Most studies that analyze the population eligible for

food stamps fall into two categories. The first group of

studies analyzes the impact of stamps on food spending. This type of study usually concerns itself with the

marginal impact of food stamps on food spending in

eligible households. A well-known finding is that the

marginal propensity to spend from food stamps is

greater than the marginal propensity to spend from

cash for households that do not spend all of their food

stamps in a given month. That is, they will spend all

their cash first and keep some food stamps in reserve.

Why this is so has never been fully explained,

although several theories exist. An excellent review of

studies of this type can be found in Fraker (1990). 

The other type of study is concerned with food stamp

participation. These studies usually examine the determinants of participation among low-income or food

stamp-eligible households and usually employ multivariate analysis or compare the characteristics of participants and nonparticipants in the Food Stamp

Program. Such studies have generated a fairly consistent set of findings: that food stamp participation rates

are highest among nonwhite and nonelderly people living in households that are leased or rented, are low

income, include children, and are eligible for the highest food stamp benefits. Gleason et al. (1998) is an

excellent example of a study of this type.

Our study has a different focus and is a novel contribution to the food stamp literature. We look at the characteristics of the total population that meets the

income requirement for participating in the food stamp

program, and we then estimate the importance of each

demographic characteristic on several measures of

poverty using a regression technique. The exercise

allows us to identify which demographic characteristics are most typical of large numbers of the poor who

are eligible to participate in the Food Stamp Program.

In this way, private or government entities concerned

with the alleviation of poverty can identify those

household types to target with special programs, such

as worker training or food stamp outreach, so as to

have the most success in reducing poverty. 

The technique employed in this report is an advance

on the usual approach to studying income inequality.

The usual approach to measuring the impact of a variable on the level of inequality is to use a decomposable inequality index. While widespread and useful,

the technique of decomposition is not without limitations. For example, if one looks at income inequality

and decomposes the Gini coefficient by race, then one

completely ignores the correlations between race, education, age, and even region of residence. Hence, the

decomposition technique does not lead the researcher

to the “net effect” of the variable in question. As noted

by Bishop, Formby, and Smith (1997), a decomposable

index is appropriate when the intent of the researcher

is to identify “gross” effects correlated among several

variables. However, if the intent of the researcher is to

present an uncorrelated effect, then a regression-based

technique must be employed. By using regression

analysis we are able to report the net effects of selected demographic variables on, in this case, four alternative measures of inequality. 

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 5
Economic Concepts of Social

Welfare and Inequality

This section presents some relevant economic concepts

and draws heavily upon work by Deaton (1997).

Perhaps the easiest way to explain the economic background of inequality and poverty measures is to start

with the concept of a social welfare function. This is a

concept that provides a consistent way to think conceptually about welfare and inequality measures. We

can denote social welfare by W and write it as a nondecreasing function of all the income for the population denoted by x. Thus,

where n is the population size. Hence, we wish to maximize W or welfare, which is a function of V, or more

directly of household income. If we assume V to be

increasing in each of its arguments, social welfare is

assumed to be greater when at least one household is

better off and no household is worse off. Sometimes this

assumption is slightly changed, and we assume that the

social welfare function W is unresponsive to changes in

welfare among the nonpoor. In this case, we assume V

to be nondecreasing in each of the arguments, x. This

concept of nondecreasing social welfare becomes relevant in our discussion of poverty measures.

There are two other properties that may define social

welfare functions that should be noted. The first is that

the social welfare function is a list of welfare levels in

society. This simply means that welfare does not depend

on which household has what level of income in society

and is often referred to as the property of symmetry or

anonymity. Second, and perhaps most important, society

and policymakers are usually assumed to prefer moreequal distributions to less-equal distributions. If society

believed that any inequality were undesirable, then W,

as defined above, would be maximized when all

incomes were equal. Lacking a desire for complete

equality, economists usually assume that any transfer of

income from a wealthier household to a poorer household will increase social welfare. This is known as the

“principle of transfers.”

In order to transfer from welfare to inequality measures, it will be helpful if we define our welfare function. Hence, we can let: 

where μ is the average of the x’s or average income.

Defining the welfare function this way gives a separation between the average value of household income

and the distribution of that income. This allows us to

talk about changes in social welfare as changes in the

average value of income and some acceptable measure

of inequality. If we choose a functional form for W,

such that V(1,1...1) = 1, then if everyone had the mean

level of welfare, social welfare would also equal that

value. From this assumption, we can surmise that if

the income distribution is unequal, then social welfare

cannot be greater than the average of the distribution

of income. Hence, with an unequal distribution of

income, the social welfare function can be written as:

where I is some appropriate measure of inequality. One

way to think about the social welfare function as written

above is that is it the cost of inequality. I is thus the

measure of inequality, taking the value of zero when the

income is equally distributed and increasing with 

disequalizing transfers. Note that I is not a measure of

welfare. It is only part of the equation. Hence, inequality might increase even as average income becomes larger, thereby increasing social welfare.

This report will use one measure of inequality: The

Gini coefficient. This measure is desirable from the

point of view that it satisfies the principle of transfers.

The Gini coefficient can be written as:

where ρ is the rank of household i in the income distribution, with the household with the highest income

having a rank of 1. Note, that if everyone has the same

income, m, the Gini coefficient, G, is zero, while if

one person has all the income, the Gini coefficient

would be 1. Hence, low values of the Gini are associated with more equal distributions of income.

6 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

W = V (x1,x2,...xn),

W = μ (1 – I),

,... , 1 



 



 = μ μ

μ

x xn W V

( ) ∑=





 





−  −



 





−

+ =

n

i

x ixi

n n n

n

1

, 1

2

1

1 G ρ

μ
Measures of Poverty

The social welfare function we have presented above

takes into account the income of all households.

However, there is no requirement that we look at all

households. Rather, we can limit our concern to those

households at or below the poverty line, in effect, giving no weight to households above it. However, it

should be recognized that these low-income measures

are special cases of social welfare measures. In effect,

we are relaxing our definition of inequality in the

social welfare function and attempting to measure the

degree of poverty in society. This is the case alluded to

earlier where we consider our welfare function, W, to

be nondecreasing in each of its arguments. Hence, our

goal remains the same, to maximize social welfare.

The current study looks at three measures of poverty

or low income and applies them to households with no

more than 130 percent of poverty-level income. These

measures are the headcount, the minimum needs gap,

and the Sen index. The headcount is simply:

where q is the number of poor households in society

and n is the total number of households. Given a

poverty line, the headcount ratio is at best a limited

measure of poverty. Its shortcoming is that it does not

take into account the degree of poverty; that is, the

headcount does not give any indication of how severe

poverty is in terms of lack of income. In addition, the

headcount ratio, H would be unaffected by a policy

that might make the poor even poorer.

The minimum needs gap is a measurement that

attempts to overcome the shortcomings of the headcount. This measurement can be written:

where Θ is the arithmetic mean of the ratio of household income to the poverty line or other measure of

minimum needs. (In this report, P it is the ratio of

household income to 130 percent of the poverty line).

The minimum needs gap can be interpreted as the

average percentage deviation of income from the

poverty line for the poor population (Blackburn, 1990).

Hence, the minimum needs gap as defined above provides a measure of the severity of poverty. However, it

does not depend on the actual number of poor people

and will not change when the numbers of poor are

increasing or decreasing.3 In addition, transfers from

poor to nonpoor, or from poor to poor who then

become nonpoor, will increase the income gap. But

transfers among the poor that make the distribution of

income more unequal will not affect the income gap.

The fact that the income gap does not take into

account the actual numbers of the poor or income

transfers among them is a severe shortcoming.

In an attempt to rectify the above problems, Sen

(1992) proposed a poverty measure that is a combination of the headcount, income-gap, and Gini coefficient. This measure of poverty can be written:

Written this way, the Sen index, S, is shown to be the

average of the headcount and the income-gap measures

weighted by the Gini coefficient of the poor. If there

were no income inequality among the poor, G would

be zero and the Sen index would reduce to the

income-gap measure. Conversely, if only one household among the poor had all the income, G would be

equal to one and the Sen index would reduce to the

headcount. Given a population somewhere between

these extremes, the Sen index takes into account the

numbers of the poor, their shortfall in income relative

to the minimum needs line, and the degree of inequality in the distribution of their income.

The above measures of inequality and minimum needs

are not the only ones used by social scientists. Other

measures do exist, and each has both strong and weak

points for the job that it was designed to do. (For a

review of various measures of poverty, see Foster and

Sen (1997) and Cowell (1995). However, in this report,

where we look at the demographic characteristics of

the population eligible for food stamps, we make use

of the measures that we set for the above. While not

perfect, they do allow the researcher to describe the

different dimensions of poverty or minimum needs in

our society. 

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 7

S = (H x G) + P x (1 – G).

,

n

q H =

P = 1−Θ

3 A reviewer pointed out that this is not true if all households who

have zero income are removed from the data. 
Methodology of Study

In the first part of this empirical study, we look at what

has actually occurred to Sen’s welfare index and its

component parts between 1981 through 1995 for

households with income no greater than 130 percent of

the poverty line. Any household with a ratio of one or

less of household income to 130 percent of the poverty

line would meet the gross income test for food stamp

eligibility. In addition to this, we look at household

and per capita income as well as household size. In the

second half of this empirical report, we employ multivariate statistical techniques to investigate the influence of demographic factors. Using data from the

Consumer Expenditure Survey, we estimate regression

models for every other year beginning in 1981 and

ending in 1995. (See appendix A for a list of the estimated parameters by year.) Hence, we estimate a

model that can be symbolically written as:

Where:

NE = Dummy variable for the Northeast,

MW = Dummy variable for the Midwest,

S = Dummy variable for the South,

Black = Dummy variable for Blacks,

Age = Age of household head,

Age

Squared = Age of household head squared,

FS = Family size,

FH = Dummy variable for a femaleheaded household,

SP = Dummy variable for a one-person 

household other than older females,

OF = Dummy variable for single females 

50 years and older,

NOHS = Dummy variable for head of household without high school diploma,

HS = Dummy variable for head of household with a high school diploma,

NOE = Number of earners in the 

household,

ε = Error variable for the model,

and the B’s (lower-case Greek Beta in the equations)

are coefficients estimated by the regression technique.

Each B represents the net effect of that variable on the

ratio of household income to 130 percent of the poverty line, given all the other variables in the model. The

effect of any variable not included in the model is captured in the error term of the model. 

The above variables were selected a priori because they

were thought to represent the main demographic characteristics of poor U.S. households. Regions were

added to the regression under the hypothesis that particular areas of the United States may have more poor

households than others. Likewise, race was divided into

Black and non-Black households on the theory that

there are proportionally more poor Black households

than non-Black households. Age was entered in a quadratic form since this specification has been shown to

provide a good statistical fit in models with income and

household composition entered separately. Adding

household size to the regression controlled for variations in size between households. It was hypothesized

that female-headed households might represent proportionally more poor households than nonfemale-headed

households, so this variable was added to the regression

equation. Likewise, we hypothesized that one-person

households might represent a large proportion of poor

households, so this variable was added. From the oneperson household group, single females 50 years or

older were entered separately, on the theory that older

females might represent a growing proportion of the

population who might be poor and eligible for food

stamps. Household heads were classified as high school

dropouts, high school graduates, or college graduates

(including graduate degrees).4 These were entered

under the hypothesis that high school dropouts are

severely disadvantaged in terms of human capital and

more likely to be poor than those heads of household

with a high school education or more. Finally, the number of wage earners in a household was entered under

the assumption that the more wage earners there are,

the less likely the household will be poor. 

After estimating the above model, we calculate summary statistics for each year. These include the headcount ratio, the income gap ratio, the Gini coefficient

for the population eligible for food stamps, the Sen

index, household size, real household income (adjusted

for inflation), and real per capita income. Then using

8 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

NOHS 12 * HS 13* NOE ,

7 * FS 8 * FH 9 * SP 10 * OF 11*

3* 4 * 5* 6 * Squared

0 1* 2 * 130% Poverty Line

Income

β β ε

β β β β β

β β β β

β β β

+ + +

+ + + +

+ + + +

= + + +

S Black Age Age

NE MW Household

4 Household heads with some college were classified as having a

high school diploma.
our regression models, we isolate the effects of selected demographic factors on the ratio of household

income to 130 percent of the poverty line. In reality,

we are examining the counterfactual case whereby we

isolate the net influence of selected demographic variables on the dependent variable (the ratio of household

income to 130 percent of the poverty line). We do this

by using the estimated parameter values from the

regression models and then constructing a vector of

adjusted eligible households by removing the net influence of any selected demographic factor (Bishop,

Formby, and Smith, 1997). Since the estimated sign of

these selected demographic factors is negative, these

attributes really represent a disadvantage in terms of

the ratio of household income to the poverty line. To

give a concrete example, if we want to know the effect

of single-person households (which has a negative

coefficient in our models) on our welfare measures, we

can create a new variable, P*, by taking the ratio of

household income to 130 percent of the poverty line,

P, and adding the statistically estimated coefficient

from the single household variable, B9. Hence, we

would have:

The effect of creating P* is to isolate the “single person” effect, or more technically, to remove the net

income disadvantage associated with single-person

households. All other attributes of single-person households, such as education and race, remain. We then

determine the net effect of isolating this demographic

characteristic associated with an income disadvantage

by recalculating the summary statistics for this modified variable and comparing them to the original calculations. In this way, we can identify which demographic characteristics have the largest impact on the size of

the population eligible to receive food stamps. We

would like to make the point that this is not the same

as dropping one demographic group, say singles, from

the regression equation and then reestimating the equation. This is because any demographic group will be

composed of more than one demographic characteristic. Here we wish to identify those demographic characteristics most responsible for increasing the abovementioned measures of poverty. While we cannot

observe such an exercise in the “real world,” it is the

case that the regression coefficient represents the net

effect of that variable on the dependent variable, all

other things being equal.

We would like to note that income is reported here on

both a household basis and a per capita basis. Food

stamp eligibility is based on household income given

household size. And indeed, an argument can be made

that household welfare is largely determined by household income, even though some economists prefer to

discuss social welfare on a per capita basis. Both arguments have merit. However, it is problematical to compare household incomes when household size differs;

therefore, we also report income on a per capita basis

to facilitate such discussions.

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 9

P* = P + β9*(Single Person).
Database and 

Descriptive Statistics

Our statistical analysis is based upon data taken from

the Consumer Expenditure Survey (CES) for 1981-95

(every other year). The CES grew out of consumer

expenditure surveys of American households that the

U.S. Department of Labor’s Bureau of Labor Statistics

(BLS) had been conducting periodically at about

10-year intervals since 1888.

The CES is composed of two components, each with

its own questionnaire and sample. The first is an interview panel survey in which each of approximately

5,000 households is surveyed every 3 months over a

1-year period. The second is a diary survey of approximately the same sample size in which households keep

an expenditure diary for two consecutive 1-week periods. The diary survey obtains data on small, frequently

purchased items normally difficult to recall, consisting

of food and beverages, tobacco, housekeeping supplies, and nonprescription drugs, personal care products and services, fuels, and utilities. Both surveys

have a complete demographic profile of each household and its members. In this study, we use data from

the diary survey. From our original sample, we eliminated households that did not have positive income. In

addition, we subtracted the value of any food stamps

that the household received in order to determine

household income.

The Current Population Survey (CPS) is actually the

benchmark dataset to use for income and the distribution of that income. The sample size of the CES is

smaller than the CPS, and hence the CES income estimates are less accurate. Many studies have used the

CES, however, including work on income inequality

by Deaton and Paxson (1994). Furthermore,

researchers have compared the CES income estimates

with those of the CPS and the National Income and

Product Accounts (NIPA). Most have found an underreporting in income in the CES that is roughly constant over time (see Attanasio, 1998). 

The important question is whether the results found in

this study would be altered if the CPS were used

instead of the CES. To get some idea of the correlation

between the two datasets, the growth rate in reported

household income from the CES was compared with

the growth in personal income in the NIPA. For 1981-

95, the correlation coefficient between the two datasets

was 0.72, which is nearly identical to what Attanasio

(1998) found. Likewise, the correlation was calculated

for the growth in the headcount of the number of

households at or below 130 percent of the poverty line

in our study with the growth rate of the number of all

families in poverty as reported by the U.S. census.

This correlation was 0.77. These correlation coefficients indicate that the CES is a fairly representative

sample, relative to the NIPA and the families in poverty data. Hence, we doubt if the conclusions reported

below would change if we used the CPS dataset. In

any case, in the results reported below, we calculate a

base set of statistics and report percentage changes

from that base after netting out the influence of selected demographic factors.

Table 1 contains selected unweighted average values

for both the total population and those who meet the

gross income test for food stamps. Households eligible

for food stamps are broken out into those who participate in the food stamp program and those who do not.

Regional statistics are very similar for both groups,

with slightly more of the eligible-for-food-stamp population living in the South and slightly less of it living in

the West. In our sample, Blacks represent about 11 percent of the population and non-Blacks about 89 percent. However, in the eligible for food stamp population, Blacks represent about 20 percent of the population and non-Blacks about 80 percent. In households

that actually receive food stamps, about 34 percent are

Black, while about 66 percent are non-Black.

Age and family size are very similar in the two populations. The average age of the household head was 46

in the total population, with a household size of 2.6

people. This compares with an average age of 47.5 for

the household head in the eligible-for-food-stamp population, and an average household size of 2.4 people.

In households that actually receive food stamps, the

average age of the household head was about 42 years

with an average household size of 3.2 persons.

Female-headed households comprise about 6 percent

of the total population but represent almost 14 percent

of those households eligible for food stamps. However,

of households that actually receive food stamps,

almost 38 percent are female-headed.

One-person households have been sorted into two

groups noted above: Single females 50 years of age or

older and all other singles. As noted earlier, we

hypothesize that single older females may be a segment of the population that is growing proportionally

larger over time. All single households represent about

10 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA
29 percent of the total population, but they account for

45 percent of the population eligible for food stamps.

Of this total, about 21 percent are older females, while

the remainder of the group is made up of all other oneperson households. In households that actually participate in the food stamp program, about 13 percent are

older females, while all other singles represent about

24 percent of program participants. 

Approximately 22 percent of the total U.S. adult population has never received a high school diploma. This

includes people who never attended school, as well as

those who never completed the 12th grade. In the eligible-for-food-stamp population, approximately 43

percent have not completed high school. However,

fully 54.2 percent of all householders who participate

in the food stamp program do not have a high school

diploma. In the total population, 53.5 percent are high

school graduates, whereas this figure falls to about 49

percent in the population eligible for food stamps. In

households that receive food stamps, about 43 percent

are high school graduates. About 25 percent of the

population has a bachelor’s degree or higher, but this

figure falls to about 8 percent in the population eligible

for food stamps, and approximately 3 percent of food

stamp recipients are college graduates. Finally, it is not

surprising that households in the total population have

about 1.4 income earners, whereas households eligible

for food stamps or that actually receive food stamps

average less than one income earner per household.

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 11

Table 1—Comparison of selected means for the total population and the population eligible for food

stamps, 1981-95

Eligible for food stamps

Eligible for Receives Does not

food stamps total food receive

Item Total population eligible population stamps food stamps

Percent

Northeast 20.5 20.2 20.0 20.3

Midwest 25.2 26.1 28.5 25.3

South 29.0 32.0 33.9 31.4

West 25.3 21.7 17.7 23.0

Black 11.1 21.0 34.4 16.6

Non-Black 88.9 79.0 65.6 83.4

Age of head 46.1 years 47.6 years 42.2 years 49.3 years

Family size 2.6 persons 2.4 persons 3.2 persons 2.1 persons

Female-head 5.9 13.7 37.6 5.9

One-person households

(excluding older females) 19.0 24.3 8.5 29.5

Single older females 9.7 20.7 13.4 23.0

No high school diploma 21.3 42.9 54.2 39.2

High school diploma 53.5 48.8 43.1 50.7

College degree or higher 25.1 8.3 2.8 10.1

Earners per household 1.4 persons .8 person .6 person .9 person

Source: U.S. Department of Agriculture, Economic Research Service.
Welfare Measures and

Inequality Between 

1981 and 1995

Table 2 represents the baseline analysis for households

that meet the gross income test to receive food stamps.

This table contains the headcount ratio, income gap,

Gini coefficient, Sen index, real household income,

real per capita income, and household size. In addition, we have calculated the Gini coefficient for those

households that participate in the food stamp program.

This Gini coefficient is calculated after adding the

actual value of food stamps received to household

income. This statistic will then give an indication of

how effective the food stamp program is in evening

out the dispersion of income among food stamp recipients. However, the main focus of this report is still on

the demographic characteristics of the population eligible for food stamps, regardless of whether they participate in the food stamp program. We also note that a

standard error could be calculated for the statistics that

we report. The easiest way to do this would be by

bootstrapping our estimates. However, the majority of

studies that report such statistics do not usually calculate a standard error. 

The headcount ratio indicates that the percentage of

households eligible to receive food stamps declined

from 27 percent in 1981 to 21 percent in 1989, before

rising to 24 percent in 1993 and declining again to 22

percent in 1995. For the entire sample period, the

headcount averaged 24 percent. Importantly, the

income gap also declined from 44 percent in 1981 to

38 percent in 1995 and averaged 41 percent between

1981 and 1995. This statistic represents the average

percentage deviation of income from 130 percent of

the poverty line for the poor population. The Gini

coefficient measures the degree of inequality among

the poor, and this, too, fell from 0.36 to 0.32, averaging 0.33 for all years. The Sen index summarizes the

headcount, income gap, and the Gini coefficient, so it

too fell from 0.38 in 1981 to 0.33 in 1995, with an

average value of 0.36. Real income for households eligible for food stamps has increased over this period

from $4,735 in 1981 to about $5,387 in 1995. This is a

very modest increase of 14 percent, whereas per capita

income ranged from $2,058.70 in 1981 to $2,154.65 in

1995, an increase of about 5 percent. 

In order to determine if a group is better or worse off

over time, one has to look at measures of dispersion

along with a measure of average income. In looking at

the Sen index, real household income, and real per

capita income, we can conclude that poor households

in 1995 were slightly better off than in 1981. First,

there were proportionally fewer poor households, and

second, they had more income in real terms, although

the income gains were rather modest as indicated

above. However, we do need to note that those who

left the ranks of poor households, approximately 5 percent of all households, were the real winners. A reduction in the number of households at or below 130 per12 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

Table 2—Welfare measures of food stamp-eligible households (incomes less than or equal to 130 percent

of the poverty line)

Components of Sen index

Gini

coefficient Real Per 

Headcount Income Gini Sen with food household capita Household

Year ratio gap coefficient index stamps income income size

------1981 dollars------ Number

1981 0.27 0.44 0.36 0.38 0.35 4,735.02 2,058.70 2.3

1983 .26 .42 .33 .37 .31 4,832.78 2,101.21 2.3

1985 .25 .42 .33 .36 .32 4,986.12 2,077.55 2.4

1987 .22 .41 .31 .35 .30 5,039.55 2,099.81 2.4

1989 .21 .40 .31 .34 .30 5,095.61 2,123.17 2.4

1991 .23 .41 .32 .35 .30 5,094.25 2,037.70 2.5

1993 .24 .40 .32 .35 .30 5,252.13 2,100.85 2.5

1995 .22 .38 .32 .33 .31 5,386.62 2,154.65 2.5

Average .24 .41 .33 .36 .31 5,052.76 2,094.21 2.4

Source: U.S. Department of Agriculture, Economic Research Service.
cent of the poverty line would also be consistent with

an increase in average income for the total population,

and therefore, an increase in general welfare. Note too

that the Gini coefficient for households that participate

in the food stamp program is slightly below the Gini

coefficient of all households eligible for food stamps.

The Gini coefficient for households that received food

stamps was about 6 percent below the Gini coefficient

for all households eligible for food stamps in 1983 and

about 3 percent below in 1995 and averaged about 6

percent below for the entire time period. This indicates

that those households derived positive benefits from

participating in the Food Stamp Program and that

needy families other than those at or near 130 percent

of the poverty line have participated and realized higher average incomes.

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 13
Counterfactual Impacts of

Selected Demographic Factors

on Welfare Measures 

and Inequality

Tables 3-8 present the same summary statistics as table

2, except that we have isolated the impacts on the various welfare measures of one demographic factor in

each table using our estimated statistical model. Again,

we emphasize that we are statistically controlling for

one demographic characteristic at a time, which is not

the same as eliminating a specific demographic group

from the dataset, say one-person households, and then

recalculating the welfare statistics. This is because any

demographic group would embody more than one

demographic characteristic, like race and region of residence. Rather, our technique lets us statistically isolate the net effect of various demographic characteristics. We can then isolate the income disadvantage (in

the case of coefficients with a negative sign) of these

households by the technique discussed earlier. We then

recalculate our poverty statistics for those households

who remain eligible for food stamps and report the

percentage change from our base table (table 2). It is

important to note that decreasing the population eligible for food stamps is generally consistent with

increasing average household income for the total population and thus with total welfare. Hence, in this

report we emphasize the impact on the headcount ratio

of netting out a demographic effect, but we also recalculate the other welfare measures to determine the

potential economic well-being of those households

remaining in the food stamp-eligible population. 

Table 3 shows the welfare measures for food stampeligible households under the counterfactual case

where race provides no income disadvantage to the

household. In other words, if the net income disadvantage associated with Black households, all other

household characteristics held constant, could be

redressed, the welfare measures would deviate from

the base measures by the figures reported in the table.

The headcount ratio indicates that the proportion of

low-income households would decline about 11 percent for 1981 and about 9 percent for 1995, and would

average a decline of about 8 percent relative to the

baseline analysis. Likewise, the income gap for the

remaining households eligible for food stamps would

be about 9 percent less in 1981 and about 8 percent

less in 1995 and average approximately 9 percent less

over the sample period. Interestingly, the Gini coefficient for this table is about the same as the baseline

calculation in that it rises by no more than 3.2 percent

over the baseline and averages an increase of 1.2 percent. However, the Sen index does decline since both

the headcount and income gap measures declined.

Relative to the baseline, real income is lower in both

household and per capita terms. Hence, if the income

disadvantage of Black households could be isolated

from the population, only modest reductions in the

headcount ratio would be realized. 

14 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

Table 3–Welfare measures of food stamp-eligible households under the counterfactual case where race

provides no income disadvantage

Components of Sen index

Real Per

Headcount Income Gini Sen household capita Household

Year ratio gap coefficient index income income size

Percent changes from base

1981 -11.1 -9.1 0 -10.5 -5.1 -0.8 -4.3

1983 -7.7 -9.5 0 -10.8 -4.1 .3 -4.3

1985 -8.0 -9.5 0 -8.3 -4.0 -4.0 0

1987 -9.1 -9.8 3.2 -11.4 -3.5 -3.5 0

1989 -9.5 -7.5 3.2 -8.8 -3.2 1.0 -4.2

1991 -4.3 -7.3 0 -5.7 -2.7 -2.7 0

1993 -8.3 -7.5 0 -8.6 -4.0 -4.0 0

1995 -9.1 -7.9 3.1 -9.0 -3.0 -3.0 0

Average -8.4 -8.5 1.2 -9.1 -3.7 -2.1 -1.6

Source: U.S. Department of Agriculture, Economic Research Service.
Table 4 contains the welfare measures after removing

the net income disadvantage for households associated

with the characteristic “female-headed.” If the

female-headed household effect could be removed, the

number of households eligible for food stamps would

decline by approximately 10 to 14 percent for each

year in the sample and would average a decline of

about 12 percent over the entire period. The income

gap would also decline by approximately 2 to 8 percent each year and would average a decline of about 5

percent over the sample period. Again, the Gini coefficient would be little changed, although it would average an increase of about 2 percent between 1981 and

1995. However, the Sen index would decline by

approximately 5 to 9 percent each year, with an average decline of about 7 percent. While real household

income declines relative to the baseline, per capita

income increases between 3 and 7 percent each year.

These results are very similar to those for race. 

Table 5 presents the welfare measures after removing

the net income disadvantage for households associated

with the characteristic “one-person, other than older

single females.” The effect on the headcount ratio is

rather dramatic. The headcount ratio would have

declined by approximately 44 percent in 1981 and 41

percent in 1995 and would average approximately 40

percent for all years. This would be a large reduction

in the number of households eligible for food stamps.

However, the effect on the income gap is rather mixed.

In the early to mid-1980s, the income gap would have

declined by approximately 7 to 16 percent. Thereafter,

the income gap would have remained about the same

as the baseline, but would average a decline of 5 percent for all years. The Gini coefficient increases for all

years except 1985 and 1995, when it is the same as the

baseline, and averages an increase of about 5 percent.

Because of the dynamic decline in the headcount, the

Sen index also declines. This would have ranged from

approximately 26 percent in 1981 to about 9 percent in

1995, with an average decline of about 14 percent. On

a per capita basis, income was lower relative to the

baseline by an average of about 18 percent, but note

that per capita income would have increased by a modest 3 percent between 1981 and 1995 for these remaining households. In summary, if this demographic effect

could be redressed, there would be a large reduction in

the number of households eligible for food stamps, but

this would leave behind some hardcore poor households, as measured by the decline in per capita income

relative to the baseline.

Table 6 contains the welfare statistics after removing

the net income disadvantage for households associated

with the characteristic “single females 50 years or

older.” The reduction in the headcount is not as great

as that associated with all other one-person households. Yet, the headcount ratio would have fallen by

approximately 19 to 23 percent in each year, with an

average decline of about 21 percent. Interestingly, the

income gap of those who remained eligible for food

stamps would have increased by about 5 percent in

each year except for 1989 (about 8 percent). Likewise,

the Gini coefficient is larger every year relative to the

baseline, with an average increase of about 5 percent.

The end result is that the Sen index is very close to

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 15

Table 4—Welfare measures of food stamp-eligible households under the counterfactual case where femaleheaded households provide no income disadvantage

Components of Sen index

Real Per

Headcount Income Gini Sen household capita Household

Year ratio gap coefficient index income income size

Percent changes from base

1981 -11.1 -2.3 2.8 -5.3 -2.1 7.3 -8.7

1983 -11.5 -4.8 3.0 -8.1 -2.7 6.6 -8.7

1985 -12.0 -2.4 0 -5.6 -10.4 4.8 -4.2

1987 -13.6 -7.3 3.2 -8.6 .3 4.6 -4.2

1989 -9.5 -5.0 0 -5.9 .4 4.7 -4.2

1991 -13.0 -7.3 0 -5.7 2.3 6.5 -4.2

1993 -12.5 -7.5 3.1 -8.6 -1.1 3.0 -4.2

1995 -13.6 -5.3 0 -6.1 .9 5.1 -4.2

Average -12.1 -5.2 1.5 -6.7 -1.6 5.3 -5.3

Source: U.S. Department of Agriculture, Economic Research Service.
that of the baseline index, on average only 1 percent

below the baseline. Hence, the headcount would fall,

the income gap would widen, the dispersion of household income would increase, and while real household

income would increase modestly, real per capita

income would fall relative to the baseline. Thus, the

net result of removing the income disadvantage of single females 50 years or older would not be as dramatic

as for all other one-person households, but it would

still have a rather large impact on the total number of

households eligible for food stamps (the headcount). 

Table 7 contains the welfare measures for food-stampeligible households after removing income disadvantage for households associated with the characteristic

“household head does not have a high school diploma.”

Relative to the baseline, the headcount ratio would

decline between 37 and 50 percent and would show an

average decline of about 43 percent between 1981 and

1995. The income gap presents rather mixed results.

Between 1983 and 1987, the income gap for households remaining eligible for food stamps would have

risen about 2 percent, while it would have declined

16 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

Table 5–Welfare measures of food stamp-eligible households under the counterfactual case where 

one-person households (all one-person households except females 50 years or older) provide no 

income disadvantage

Components of Sen index

Real Per

Headcount Income Gini Sen household capita Household

Year ratio gap coefficient index income income size

Percent changes from base

1981 -44.4 -15.9 0 -26.3 22.7 -14.5 43.5

1983 -42.3 -11.9 9.1 -21.6 15.6 -31.2 39.1

1985 -44.0 -7.1 0 -13.9 18.1 -14.1 37.5

1987 -36.4 -2.4 12.9 -11.4 10.9 -19.4 37.5

1989 -42.9 -2.5 6.5 -8.8 16.5 -17.8 41.7

1991 -39.1 4.9 3.1 -5.7 16.3 -16.9 40.0

1993 -33.3 -5.0 9.4 -14.3 11.2 -15.8 32.0

1995 -40.9 0 0 -9.1 17.3 -16.2 4.0

Average -40.4 -5.0 5.1 -13.9 16.1 -18.2 34.4

Source: U.S. Department of Agriculture, Economic Research Service.

Table 6—Welfare measures of food stamp-eligible households under the counterfactual case where 

one-person households who are females 50 years or older provide no income disadvantage

Components of Sen index

Real Per

Headcount Income Gini Sen household capita Household

Year ratio gap coefficient index income income size

Percent changes from base

1981 -18.5 4.5 2.8 -2.6 4.9 -3.5 8.7

1983 -19.2 4.8 6.1 -2.7 4.7 -7.4 13.0

1985 -20.0 4.8 3.0 0 4.2 -7.3 12.5

1987 -22.7 4.9 6.5 0 4.8 -10.2 16.7

1989 -19.0 7.5 6.5 0 4.8 -10.2 16.7

1991 -21.7 4.9 6.3 0 5.3 -9.2 16.0

1993 -20.8 5.0 3.1 -2.9 5.1 -9.4 16.0

1995 -22.7 5.3 3.1 0 6.4 8.3 16.0

Average -20.6 5.2 4.7 -1.0 5.0 -6.1 14.5

Source: U.S. Department of Agriculture, Economic Research Service.
about 2 percent in 1991 and 1993. On average, the

income gap would increase by about 1 percent. As a

result, the Gini coefficient increases slightly each year

except for 1989 and 1995, when it is the same as the

baseline, and averages an increase of about 3 percent.

The net effect of this is to reduce the Sen index by an

average of about 10 percent for all years. Household

income for those still eligible for food stamps fell about

one percent between 1981 and 1995, whereas per capita income, which is above that of the baseline, rose

about 3 percent. Hence, if we could remove the income

disadvantage of household heads without a high school

education, we would see a rather dramatic decline in

the number of households eligible for food stamps. 

Table 8 contains the welfare measures for households

eligible for food stamps after removing the income

disadvantage for households associated with the characteristic “household head has a high school diploma.”

Once again, this causes a large reduction in the headcount ratio; in fact, it is larger than removing the

income disadvantage of households whose heads do

not have a high school diploma. Between 1981 and

1995, the head count would have declined between

approximately 45 to 54 percent, with an average

decline of about 48 percent. However, other than in

1981, the income gap appears to be very similar to the

baseline, with an average decline of about 2 percent.

Excluding 1981, the Gini coefficient ranges between a

decline of about 9 percent and an increase of about 3

percent. Over the entire period, the Gini coefficient

declines by about 5 percent. The Sen index is well

below that of the baseline, and averages a decline of

about 12 percent. Income for those households still eligible for food stamps is very mixed in terms of being

above or below that of the baseline, although on average, it is about 1 percent below the baseline on a

household basis and about 3 percent below the baseline on a per capita basis. As noted above, it is somewhat surprising that removing the income disadvantage

of this variable produces slightly larger changes in

welfare measures than removing it for households

without a high school diploma. Undoubtedly, a large

number of high school graduates are unprepared to

make an adequate living upon leaving high school.

Still, if this effect could be addressed, there would be a

very large decline in the number of households eligible

for food stamps.

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 17

Table 7—Welfare measures of food stamp-eligible households under the counterfactual case where heads

of households without a high school education provide no income disadvantage

Components of Sen index

Real Per

Headcount Income Gini Sen household capita Household

Year ratio gap coefficient index income income size

Percent changes from base

1981 -37.0 4.5 5.6 -7.9 -4.9 4.2 -8.7

1983 -46.2 2.4 3.0 -10.8 -2.7 1.7 -4.3

1985 -44.0 2.4 6.1 -8.3 -1.1 3.2 -4.2

1987 -50.0 2.4 6.5 -8.6 -1.7 -1.7 0

1989 -42.3 0 0 -8.8 -.6 3.7 -4.2

1991 -43.5 -2.4 3.1 -11.4 1.5 1.5 0

1993 -37.5 -2.5 3.1 -11.4 .6 4.8 -4.0

1995 -40.9 0 0 -9.1 -1.2 2.9 -4.0

Average -42.7 .9 3.4 -9.5 -1.3 2.5 -3.7

Source: U.S. Department of Agriculture, Economic Research Service.
18 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

Table 8—Welfare measures of food stamp-eligible households under the counterfactual case where household heads with a high school education provide no income disadvantage

Components of Sen index

Real Per

Headcount Income Gini Sen household capita Household

Year ratio gap coefficient index income income size

Percent changes from base

1981 -44.4 -18.2 -18.3 -26.3 -9.3 -9.3 0

1983 -46.2 -2.4 -6.1 -13.5 2.0 2.0 0

1985 -52.0 0 -9.1 -8.3 1.4 -2.7 4.2

1987 -45.5 -2.4 -3.2 -11.4 1.0 1.0 0

1989 -47.6 -2.5 0 -11.8 2.1 -1.9 4.2

1991 -47.8 0 -3.1 -8.6 -0.7 -0.7 0

1993 -54.2 2.5 -3.1 -8.6 -2.1 -5.8 4.0

1995 -45.5 5.3 3.1 -6.1 -0.4 -4.3 4.0

Average -47.9 -2.2 -5.0 -11.8 -0.8 -2.7 2.1

Source: U.S. Department of Agriculture, Economic Research Service.
Summary and Conclusions

Descriptive statistics indicate that welfare measures of

food stamp-eligible households improved between

1981 and 1995. In 1981, a year of recession, 27 percent of all households met the gross income test for

food stamp eligibility, but this figure declined to 22

percent by 1995, a year of economic expansion. This

represents a decrease of about 19 percent. Likewise,

the income gap, which in this report is the average percentage deviation of income from 130 percent of the

poverty line, declined about 14 percent, from 0.44 to

0.38. Even the Gini coefficient, which measures the

dispersion of income among the poor, declined almost

11 percent from 0.36 to 0.32. The Sen index, a measure of poverty that weights the headcount and the

income gap by the Gini coefficient, declined from 0.38

to 0.33. This represents a 13-percent improvement in

this statistic.

Income for households eligible for food stamps also

improved over the 1981-95 period. On a household

basis, after adjusting for inflation, it rose about 14 percent. However, on a per capita basis, average income

rose just 5 percent.

Taken together, the above measures of welfare and the

increases in real income for households eligible for

food stamps indicate that society was slightly better off

in 1995 than in 1981. First, there were proportionally

fewer households eligible for food stamps. This indicates that proportionally more households were above

130 percent of the poverty line, and this is consistent

with an increase in average income for the total population. In addition, average income for those households still eligible for food stamps also increased,

however slightly.

Our counterfactual analysis indicates that large numbers of poor households are comprised of one-person

households (and we would include single females age

50 and older) and by households headed by a person

with a high school education or less. In contrast, poor

households have a smaller proportion of Black, or

female-headed households. If policymakers wish to

encourage those households that are eligible for food

stamps but who do not participate in the program, then

our analysis indicates that they could likely recruit the

largest numbers of eligible households by trying to

influence non-Black, one-person households and those

whose heads have a high school education or less. This

of course assumes that these household types could be

targeted and that all groups that now participate in the

food stamp program do so in numbers proportional to

their representation in poor households (see our

descriptive statistics in table 1). The Gini coefficients

for the baseline indicate the success of the Food Stamp

Program in lessening the dispersion of income among

the poor households that participate in the program. 

One question not answered by this study concerns the

demographic profile of the one-person households. Are

these younger people who are just starting out in their

careers and need temporary assistance until they are

economically established? Or are the households made

up of single people of all ages who are temporarily out

of work? If this is the case, not much could be done to

ease their plight, in the sense of government intervention, other than to encourage them to participate in the

Food Stamp Program if they are eligible. However, it

could be the case that this is a group of people who

live alone and continually work in low-paying jobs. If

many of these households are of the latter type, then

this group may represent hardcore welfare recipients

who will need government assistance for an extended

period. These questions may be answered by analyzing

a dataset such as the Survey of Income and Program

Participation (SIPP).

Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 19
20 ❖ Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 Economic Research Service/USDA

Table 9—Estimated regression models 1981-95

Characteristics 1981 1983 1985 1987 1989 1991 1993 1995

Intercept 0.84 1.25 1.57 1.77 1.78 2.24 1.12 1.64

(.17) (.22) (.23) (.20) (.24) (.23) (.23) (.26)

Northeast -.34 -.26 -.14 .11 .20 .19 . 13 .07

(.06) (.08) (.07) (.06) (.08) (.07) (.07) (.08)

Midwest -.22 -.23 -.12 0 .09 -.08 .08 -.03

(.06) (.07) (.07) (.06) (.07) (.07) (.07) (.08)

South -.20 -.06 .02 .14 .07 .02 .17 .03

(.06) (.07) (.07) (.06) (.07) (.06) (.06) (.07)

Black -.43 -.43 -.38 -.46 -.48 -.26 -.42 -.35

(.07) (.08) (.09) (.08) (.09) (.08) (.08) (.09)

Age .14 .13 .13 .11 .12 .10 .13 .11

(.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01)

Age -1.37e-3 -1.21e-3 -1.20e-3 -1.10e-3 -1.09e-3 -9.61e-4 -1.19e-3 -1.07e-3

squared (7.51e-5) (9.22e-5) (9.38e-5) (8.09e-5) (9.56e-5) (8.59e-5) (8.82e-5) (9.84e-5)

Family size -.43 -.46 -.49 -.47 -.47 -.48 -.45 -.48

(.02) (.02) (.03) (.02) (.03) (.02) (.02) (.03)

Female head -.82 -.76 -1.03 -.82 -1.05 -1.03 -.78 -.90

(.09) (.11) (.12) (.11) (.13) (.12) (.11) (.13)

Single-person -.84 -.80 -.87 -.71 -.82 -.94 -.73 -.85

(.07) (.09) (.09) (.08) (.09) (.09) (.09) (.10)

Single older -1.12 -1.26 -1.31 -.97 -1.21 -1.07 - 1. 10 -1.16

female (.09) (.11) (.11) (.09) (.11) (.10) (.11) (.11)

No high school -1.20 -1.60 -1.92 -1.86 -2.00 -2.02 -1.95 -1.90

diploma (.06) (.08) (.08) (.07) (.08) (.08) (.08) (.08)

High school -.75 -1.10 -1.29 -1.19 -1.39 -1.35 -1.34 -1.23

diploma (.05) (.06) (.07) (.06) (.06) (.06) (.06) (.07)

Number of 48 .60 .55 .56 .58 .56 .58 .58

earners (.03) (.04) (.04) (.03) (.04) (.03) (.04) (.04)

Adj R2 .38 .33 .32 .34 .31 .33 .35 .33

SSE 10,165.57 11,028.23 13,298.20 13,103.09 14,356.78 13,634.25 11,934.49 10,255.64

Numbers in parentheses = Standard errors.

SSE = Sums of squares of error.

Source: U.S. Department of Agriculture, Economic Research Service.
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Economic Research Service/USDA Measuring the Well-Being of the Poor: Demographics of Low-Income Households / TB-1898 ❖ 21