AN EXAMINATION OF SPENDING PATTERNS OF FAMILIES RECEIVING FORMS OF PUBLIC

ASSISTANCE

William D. Passero, U. S. Department of Labor

Bureau of Labor Statistics, Bldg. PSB, Room 3985, 2 Massachusetts Ave., NE, Washington, DC 20212

Key Words: Expenditures, Welfare, Public

Assistance

I. Introduction

This paper uses 1992-93 data from the Quarterly

Interview Survey component of the Consumer Expenditure Survey (CE) to analyze spending patterns of

families receiving forms of public assistance. Comparisons of demographic characteristics and expenditures

are made between families receiving assistance and

those not receiving assistance. Among families receiving assistance, those with working members are

compared with those with no working members.

Weighted means and percent distributions of

selected family characteristics and weighted annual

means and shares of expenditures are calculated.

Statistical tests demonstrate that expenditure shares for

most item categories are significantly different between

the compared groups. The analysis then considers

factors related to transportation expenditures. A

regression equation is specified and estimated by the

ordinary least squares (OLS) technique. Results from

the regression are examined.

II. Methodology

A. Sample

Different approaches can be used to identify

“poor” families. A poor family could be identified as

one in which a member participates in a government

program, providing general income maintenance or

specific assistance for food, housing, or medical care.

Another is to compare the family’s income or

expenditures to some threshold, below which they

would be considered poor.

The former approach is chosen for a number of

reasons. The CE data base contains variables

positively identifying families in many of these

programs. The processing of income variables in the

CE introduces limitations such that one cannot

determine conclusively that a family is below or above

a poverty threshold. While it is possible to create a

poverty line based on expenditures which avoids this

problem, neither a sample of “income-poor” nor

“expenditure-poor” families would necessarily include

all families receiving program assistance

The sample constructed for this study consists of

6,307 interviews from qualifying families. A family

can qualify for the sample in three ways. The family

can report receiving welfare or public assistance,

Supplemental Security Income (SSI), or Food Stamps.

It can live in public housing or receive housing subsidy

payments. Finally, any member of the family can be

enrolled in Medicaid. This is not an exhaustive list of

assistance programs available to families, but covers

those collected in the CE survey instruments.

B. Data source

The objective of the Interview survey is to obtain

one year’s worth of expenditures from each sample

family. A questionnaire is administered by a Census

Bureau interviewer. Each sample family undergoes

five interviews. In the first interview, housing unit

characteristics, demographic characteristics for each

member, and an inventory of durables are collected.

Expenditure data are collected over a one-month recall

period and are used for bounding purposes only. The

remaining four interviews are conducted at threemonth intervals, wherein families report expenditures

that have been made during that time. At the second

and fifth interviews, income data are collected for the

12-month period prior to the interview. Overall, about

90 percent to 95 percent of all expenditures are covered

by the Interview survey.1

Families are interviewed using a rotational

sampling procedure. Each month new families begin

the interviewing cycle, replacing old families that have

completed their participation. The Interview survey is

designed to replace 20 percent of the sample every

three months.

C. Reference period

For this study, data from interviews completed

between January 1992 and March 1994 are analyzed.

These interviews provide data for 1992 and 1993,

supplying a sufficient sample of qualifying families.

Expenditures reported over the 1992-93 period do not

manifest significant shifts in spending behavior that

would render combining data from the two years

suspect. While exceptions exist for some categories,

overall expenditures increase less than 3 percent from

1992 to 1993.

 

1

Expenditures for postage, housekeeping supplies, personal care

products, and nonprescription drugs are not collected.
D. Variables

Demographic variables in the analysis include

number of persons in the family, age, sex, race, and

education of the reference person, housing tenure,

number of children under 18, family composition,

number of persons over 64, worker composition,

number of earners, number of owned vehicles, and

types of assistance received. These variables reflect the

characteristics of the family at the time of each

interview.

Expenditure variables in this analysis identify total

expenditures, food, housing2

, apparel, transportation,3

health care, entertainment, and personal insurance and

pensions. Expenditures for alcoholic beverages,

personal care, reading, education, tobacco,

miscellaneous expenditures, and cash contributions are

combined into a category of all other expenses.

Expenditure variables aggregate all purchases

made during the three-month reference period of the

interview. They are multiplied by four for

annualization prior to computation of weighted means.

In this study, each interview is treated as an

independent observation. Each family in the CE

sample is assigned a population weight. This weight

can change from quarter to quarter for any family,

depending on the characteristics of the families

interviewed during that quarter. All means and

percent distributions are computed using these weights.

III. Statistical comparisons

Means are often inadequate for analysis in that

large differences in expenditure levels between groups

hides variations in the allocation of the expenditure

dollar. Thus, mean expenditures have been converted

to shares of total spending. T-statistics are calculated

to determine if the shares allocated to each category are

significantly different.

A. Families receiving public assistance vs. 

families not receiving public assistance

Tables 1 and 3 show that families receiving public

assistance are different than families not receiving such

assistance, both in demographic characteristics and

expenditure patterns. Table 1 shows that families

receiving assistance are larger and have more children

than families not receiving assistance. The reference

person in recipient families is only slightly younger,

running counter to the perception that families

receiving assistance are much younger than the

 

2

This variable does not include the principal portion of mortgage

payments, considered a loan repayment, thus a reduction of liabilities.

3

This variable contains the net outlay of a vehicle purchase, that is, the

amount paid after trade-in allowance and any cost paid by an employer.

population at large. Many of the families receiving

assistance have working members and own a vehicle.

Families not receiving assistance own twice as many

vehicles and have more than one earner.

Table 1. Selected family characteristics by receipt

of public assistance, 1992-1993

Family 

Family receives

receives no

assistance assistance

Age of reference person 46.4 47.9

Number of:

 Persons 3.0 2.4

 Children under 18 1.3 0.6

 Vehicles 1.0 2.1

 Earners 0.9 1.4

Percent:

 Female 58.9 32.4

 Black 30.1 8.4

 Homeowner 29.4 67.7

 Renter 67.1 30.0

 Husband/wife families 32.7 56.0

 Single parent, at least one

 child under 18 22.0 4.3

 Single person 25.0 29.5

 All other families 20.3 10.1

The ratio of homeowner to renter for nonrecipient

families is the reverse of the ratio for recipient

families. Almost 60 percent of the families obtaining

assistance are female-headed while three in 10 have a

black reference person. About one-third of the families

not receiving assistance have a female reference person, while over 90 percent of the reference persons are

white. Husband/wife families comprise just over onethird of those receiving assistance. Single person families make up 25 percent, while single-parent families

account for another fifth. On the other hand, husband/

wife families comprise the majority of nonrecipient

families. About 3 in 10 are single person families,

while just five percent are single-parent families.

Families can receive one or more forms of public

assistance. Table 2 shows no one combination

dominates. The most common combination is

Medicaid only. Food Stamps is a common component

of the next three most frequent combinations. No other

combination is received by more than about 6 percent

of the sample.
Table 2. Percent distribution of types of assistance

received, 1992-1993

Medicaid only 14.6%

Welfare, Food Stamps, and Medicaid 11.0

Food Stamps and Medicaid 9.1

Food Stamps only 9.0

SSI and Medicaid 6.1

All other combinations 41.6

Table 3 reveals that families receiving public

assistance spent $15,304 on average during 1992-93,

just over one half of the $29,800 average for

nonrecipient families.

Table 3. Shares of average annual expenditures by

receipt of public assistance, 1992-1993

Family 

Family receives

receives no

assistance assistance

Total expenditures $15,304 $29,800

Food** 22.4 15.3

Housing** 37.1 31.6

Apparel 5.0 4.9

Transportation** 15.3 19.3

Health care** 4.3 5.8

Entertainment** 4.1 5.4

Personal insurance and

 pensions** 5.5 10.6

All other expenses** 6.4 7.2

______________________________

**Share difference is significant at 99-percent level.

About three-fifths of total spending for recipient

families is allocated to food and housing.

Transportation takes up the next largest share at about

15 percent of total spending. The shares of total

spending apportioned to entertainment, apparel, health

care, and personal insurance and pensions hover

between four and five percent. All other expenses

combine to make up the remainder, just over 6 percent.

Housing and transportation account for over 50

percent of total spending among families not receiving

assistance. Food is relegated to third place at 15

percent. Over 10 percent is directed to personal

insurance and pensions. Expenditure shares for health

care, entertainment, and apparel hover between five

and six percent, while all other expenses account for

the final seven percent.

With the exception of apparel, the share

apportioned to each category by families receiving

assistance is significantly different from the share

apportioned by nonrecipient families.

B. Recipient families with working members

vs. recipient families with no working members

In Tables 4 and 6, expenditure patterns and

demographic characteristics of families in which there

are no working members are compared with families

with one or more working members. To be considered

“working”, a family member must be over 15 years of

age and have been employed full-time or part-time for

at least 27 weeks over the previous 12 months.

Each subgroup is limited to families with a

reference person under age 65. Families headed by

reference persons 65 and over are found primarily in

the nonworking subgroup and unduly affect the

distribution of expenditures of that subgroup,

particularly for health care and housing.

Referring to Table 4, the average age of the

reference person of the subgroups is almost identical.

Families in the working subgroup are larger than

families in the nonworking group, yet the number of

children is about the same in both subgroups.

Table 4. Selected family characteristics for families

with reference person under 65 receiving public

assistance by presence of working members, 1992-

1993

One or 

No more

working working

members members

Age of reference person 38.9 38.3

Number of:

 Persons 2.9 3.8

 Children under 18 1.5 1.6

 Vehicles 0.5 1.6

 Earners 0.3 1.7

Percent:

 Female 69.0 47.2

 Black 39.7 23.7

 Homeowner 15.1 35.2

 Renter 80.1 61.7

 Husband/wife families 19.4 50.7

 Single parent, at least one

 child under 18 41.6 15.7

 Single person 24.6 7.5

 All other families 14.5 26.0
By design of the subgroups, the average number of

earners is different between the subgroups. It is

noteworthy that so many of the families in the working

subgroup have more than one earner.4

A female reference person is found in almost 70

percent of the families in the nonworking subgroup,

compared to less than half in the working subgroup.

About 2 in 5 nonworking families have a black

reference person, dropping to about 1 in 4 among

working families. Renters predominate among

nonworking families. While renting is also the most

common option among working families, over onethird are homeowners.

The prevalence of female-headed families is

reflected in the distribution of family types in the

nonworking subgroup. Two in five families are made

up of a single parent with at least one child under 18.

An additional 25 percent are single-person families.

Less than 20 percent contain a husband and wife. By

contrast, over one-half of the working subgroup

families are husband/wife. Single parent families

make up 1 in 6 families.

Table 5. Percent distribution of types of assistance

received by families with reference person under 65

by presence of working members, 1992-1993

One or

No more

working working

members members

Welfare, Food Stamps,

 & Medicaid 18.8% 9.3%

Medicaid only 4.6 21.2

Food Stamps & Medicaid 8.3 11.2

Food Stamps only 7.0 11.0

Welfare, other gov’t housing

 support, Food Stamps, &

 Medicaid 7.5 2.0

SSI only 2.5 7.5

SSI, Food Stamps, &

 Medicaid 6.3 1.8

All other combinations 45.0 36.0

Differences also appear in Table 5 among the most

frequent combinations of public assistance each

subgroup collects. In the nonworking subgroup, the

combination of welfare, Food Stamps, and Medicaid

 

4

An earners in the CE data base is not the same as the “working”

member defined here. An “earner” is over 14 years old and has worked

any number of weeks, for pay, over the previous 12 months.

tops the list. That combination ranks fourth in the

working subgroup. Medicaid only is received by the

largest portion of the working subgroup, reported by

over four times as many families as in the nonworking

subgroup. Food Stamps and Medicaid and welfare,

other government housing support, Food Stamps and

Medicaid round out the top three among the

nonworking subgroup. Food Stamps and Medicaid is

also the second most frequent combination for working

families, while welfare, other government housing

support, Food Stamps and Medicaid is seldom reported. In this subgroup, Food Stamps only holds third

place, while it ranks fourth for nonworking families.

The impact of additional earners can be seen in the

disparity in total expenditures between the subgroups

displayed in Table 6. Working families report total

expenditures over double that of nonworking families.

The allocation of the expenditure dollar varies

markedly for each subgroup as illustrated by the

preponderance of statistically significant share

differences.

Table 6. Shares of average annual expenditures for

families with reference person under 65 receiving

public assistance by presence of working members,

1992-1993

One or

No more

working working

members members

Total expenditures $10,771 $21,664

Food** 28.5 19.4

Housing** 43.2 33.9

Apparel 5.6 5.1

Transportation** 9.5 19.1

Health care** 2.1 3.4

Entertainment** 3.7 4.6

Personal insurance and

 pensions** 1.3 7.9

All other expenses 6.1 6.5

______________________________

**Share difference is significant at 99-percent level.

The nonworking subgroup apportions over 70

percent to food and housing versus 53 percent by its

working counterpart. The working subgroup, on the

other hand, allocates more to transportation and

personal insurance and pensions. Transportation

makes up twice the share of total spending of the

working subgroup compared to the nonworking

subgroup. Vehicle purchases drive the disparity in
share between the subgroups. Personal insurance and

pensions command but one percent of the expenditures

of the nonworking subgroup, but rank fourth at almost

eight percent for the working subgroup. The difference

in share can be explained almost entirely by payroll

deductions for Social Security and pension

contributions - outlays contingent on being employed.

While expenditure shares for apparel, health care,

entertainment, and all other expenses appear very

similar, share differences for health care and

entertainment prove to be statistically significant.

C. Regression analysis of transportation 

expenditures

Regression analysis is employed to ascertain the

relationship of selected demographic variables with

expenditures for transportation. OLS is the technique

chosen for the regression equation. Statistical tests are

run on expenditure variables for transportation and

total expenditures to determine if data transformation

is appropriate. Regressions results for food, housing,

and apparel appear in the full paper.

1. Dependent variables

It was decided to limit regression analysis to food,

housing, transportation, and apparel, as they represent

a significant part of total expenditures. The dependent

variables represent the log of annualized quarterly

expenditures for each item category.

2. Independent variables

Most of the independent variables selected apply to

all four regression equations. An income variable is a

natural choice for inclusion where a consumption

expenditure is the dependent variable. Income

variables on the CE data base suffer shortcomings, in

that no imputation is done for invalid nonresponse. A

total expenditures variable can be used as a proxy for

income. It is reported for all families and represents

“permanent income”.

Potential exists for simultaneous equations bias in

this model as transportation makes up a sizable portion

of total expenditures. However, this may not be a

concern as OLS is being used for the regression.

Independent variables capturing demographic

characteristics are developed for the regression

analysis. These variables are posited as dummy

variables and follow the customary structure of being

coded ‘1’ if the condition is true and ‘0’ if it is not.

To account for the effects of marriage, a dummy

variable based on family composition assigns a code of

‘1’ to any husband-wife family. All other families are

coded ‘0’. The number of children under 18 is likely to

have an impact on expenditures and is included as a

continuous, right-side variable in the equations. The

number of owned vehicles certainly affects

transportation expenditures and is described by a

continuous variable in the regression.

Four variables assess the impact of working members. A continuous variable contains the number of

earners in the family. A dummy variable denotes the

presence of working members in the family. A code of

‘1’ indicates no working members in the family. An

interaction variable combining this dummy variable

with the total expenditures variable carries a value of

total expenditures for families with no working

members and 0 for families with working members.

The parameter estimates for this variable and the total

expenditures variable measure differences in the

marginal propensity to consume transportation of the

working family and nonworking family subgroups. A

dummy variable for age of reference person is added in

case the effects of having nonworking members is

really an age effect, in that nonworking members are

often the retired elderly. A code of ‘1’ for this variable

signifies the reference person is over 64.

Six dummy variables assess the effects of each type

of assistance families can obtain. What the dummy

variables signify subtly differ due to the way the data is

collected and stored. For welfare, Food Stamps, and

SSI, a code of ‘1’ indicates the family reported a dollar

amount. For Medicaid, a code of ‘1’ means someone

in the family was enrolled in Medicaid. Residence in a

structure owned by a local housing authority or other

public agency satisfies the “true” condition for public

housing. A code of ‘1’ for other government housing

support signifies the family does not reside in public

housing, but receives a government housing subsidy.

Since education may affect expenditures, a dummy

variable representing education level of the reference

person is crafted. Families whose reference person is

not a high school graduate are coded ‘1’.

3. Data transformation

Before estimating the regression, statistical testing

is done to determine whether variables for

transportation and total expenditures are amenable to

conversion to natural log form. With this

transformation, the parameters associated with the total

expenditure variable and the interaction variable can

be interpreted as the income elasticity of the working

and nonworking subgroups for transportation.

A Box-Cox transformation of the form, (yλ

 - 1) / λ,

is estimated and suggested values for λ range from -

0.125 to 0.1875. As these values are all very close to

0, it is deemed appropriate to transform expenditures

from y to log(y) in the regression.
4. Results for the transportation regression

Total transportation proves to be income elastic for

both the working and nonworking subgroups. Logged

total expenditures indicates the working subgroup has

an income elasticity of 1.08 for transportation, which

increases to 1.27 for the nonworking subgroup.

From the parameter estimate for the working

member variable, it can be inferred that families with

no working members are likely to spend much less on

transportation than families with working members.

Families with working members would require some

mode of transportation to commute to work. The

negative sign and magnitude of the coefficient for the

age variable bear out the expectation that the elderly

spend a smaller share on transportation as they are

more likely to hold on to a vehicle longer and make

more infrequent purchases of a replacement vehicle.

Replacement vehicles are more likely to be smaller

since the average size of elderly families is smaller.

The number of vehicles is positively correlated

with transportation expenditures as anticipated. Each

additional vehicle would lead to an expected increase

of 32 percent in transportation spending. The number

of children, however, is negatively correlated with an

expected decline of about 5 percent with each

additional child.

Three of the six variables for public assistance

yield significant results. The welfare variable behaves

as anticipated, as the sign for the coefficient is

negative. Families receiving welfare income are

expected to spend about 14 percent less on

transportation than families not receiving welfare.

Food Stamps and public housing both display

significant positive coefficients, from which it can be

inferred that families receiving Food Stamps or

residing in public housing are expected to spend about

9 percent and 12 percent more respectively on

transportation than families not receiving such

assistance. Families receiving welfare income tend to

be poorer and allocate a relatively small share to

transportation. As their incomes increase and they can

afford to allocate relatively more to transportation, they

also become ineligible for continued welfare assistance.

Eligibility for Food Stamps and public housing may

not be less affected by rising income, so families can

maintain these benefits, even as their increased

incomes allow them to purchase more transportation.

The R2

 for this model is a very respectable .5709.

Table 7. Parameter estimates for regression on

transportation expenditures for families receiving

public assistance, 1992-1993

Intercept **-3.573

Total expenditures (log) **1.079

Total expenditures X no working members **0.191

Family has no working members **-2.008

Number of children under 18 **-0.048

Number of vehicles **0.324

Reference person is over 64 years old **-0.243

Family receives welfare **-0.129

Family receives Food Stamps *0.082

Family resides in public housing *0.116

______________________________

** Significant at the 99-percent level.

* Significant at the 95 percent level.

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