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THE JOURNAL O F CONSUMER AFFAIRS
MOHAMED ABDEL-GHANY AND DEANNA L. SHARPE
Consumption Patterns Among the Young-Old
and Old-Old
Data on 2,810 elderly households were drawn from the Bureau of
Labor Statistics 1990 Consumer Expenditure Survey. Multivariate
Tobit analysis was used to examine spending pattern differences
between households with a reference person aged 65-74 (young-old)
and households with a reference person aged 75 and older (old-old).
Significant differences in spending were found for expenditures on
food at home, food away from home, alcohol and tobacco, housing,
apparel and apparel services, transportation, healthcare, entertainment, personal care, and personal insurance. The impact of sociodemographic factors on expenditures by either age group was not
uniform.
America is aging. Between 1980 and 1990, the number of elderly
(those aged 65 and over) grew by 22 percent compared with an eight
percent increase for the population under age 65 (National Institute
on Aging 1992; Taeuber and Ocker 1992). The proportion of elderly
in the total population was 11.3 percent in 1980, 12.6 percent in 1990,
and is projected to be 14 percent by 2010. Dramatic change in this
proportion is expected following 2010 when the baby boomers (those
born between 1946 and 1964), who comprised one-third of the American population in 1990, begin reaching age 65 (Hollman 1990;
Taeuber and Ocker 1992).
Age distribution of the elderly is also changing. From 1980 to 1990,
the American population aged 65 to 84 increased by 20 percent, while
those aged 85 and over increased 38 percent, and the number of centenarians more than doubled (National Institute on Aging 1992).
Given current birth and mortality rates, by 2030 there will be more
people over age 65 than under age 18. By 2050, almost one-fourth of
the total American population will be over the age of 65 and nearly
25 percent of these elderly will be 85 or older (Atchley 1991; National
Institute on Aging 1992; U.S. Department of Commerce 1986).
Mohamed Abdel-Ghany is Professor and Director of International Affairs, Department of
Consumer Sciences, University of Alabama, Tuscaloosa; and Deanna L. Sharpe is Assistant
Professor, Consumer and Family Economics Department, University of Missouri, Columbia.
The Journal of Consumer Affairs, Vol. 31, No. 1 , 1997
0022-0078/0002-090
1.50/0
1997 by The American Council on Consumer Interests
SUMMER 1997
VOLUME 31, NUMBER 1
91
To effectively meet the consumer needs of this large and growing
segment of the American population, both government policymakers
and business interests must be informed about the spending patterns
of the elderly. Most previous expenditure studies have treated those
aged 65 and older as a homogeneous group. Recent research findings, however, challenge this assumption. Educational levels, marital
status, gender ratios, race, ethnicity, economic resources, health
status, attitudes, and values among the elderly have been found to
vary widely (Atchley 1991; Crispell and Frey 1993; Moschis 1992;
Schwenk 1995; Taeuber 1988; Taeuber and Ocker 1992; Zitter 1988).
Although the life cycle hypothesis and the permanent income
hypothesis posit consumption patterns remain relatively stable over
the life span, the differing characteristics, life experiences, needs and
resources of older versus younger elderly may lead to significant differences in spending patterns between these two groups (Friedman
1957; Modigliani and Brumberg 1954; Walker and Schwenk 1991).
Thus, given the growing proportion of elderly in the population,
their changing age distribution, and their diversity, examination of
their expenditure patterns becomes important.
Researchers do not agree on the age one is classified as “elderly.”
Ages 60, 62, and 65 have been used (Axelson and Penfield 1983;
Moehrle 1990; Schwenk 1995). Two to four age groupings among the
elderly have been employed (Harrison 1986; Taeuber 1983). In this
article, elderly households are divided into two groups based on the
age of the reference person, defined herein to be the husband in married couple families and the household head in other family types: 65
to 74 (young-old) and 75 and older (old-old). Age 65 is selected
because it is the common age of retirement in the United States. The
sample is further divided at age 75 in an attempt to balance the sample size of the two elderly categories and t o recognize differences in
marital status, health status, and financial status that tend to emerge
at this age (Crispell and Frey 1993; Culter 1991). The purpose of this
article is to test whether there are differences in spending patterns
between these two groups of elderly while controlling for the influence of selected sociodemographic variables and to examine the
influence of these sociodemographic variables on the significantly
different expenditure categories.
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T H E JOURNAL OF CONSUMER AFFAIRS
REVIEW OF LITERATURE
Most studies of spending patterns of the elderly have contrasted
their spending patterns with the spending patterns of those under age
65. Different bases of comparison have been used. When comparing
the expenditures of those over age 65 to those of the nonelderly as a
percentage of income, it is found that those over age 65 spend a
larger percentage of their budgets on food, furnishings, household
operations, fuel and utilities, and medical care while they spend less
on transportation, apparel, reading, recreation, and education
(Borzilleri 1978). However, when examining the impact of age on
absolute expenditure levels, research findings indicate that those 65
and older spend relatively less on food away from home, clothing,
recreation, household furnishings and equipment, education, auto
purchase and operation, alcoholic beverages, and tobacco (Blisard
and Blaylock 1994; Chen and Chu 1982; Chung and Magrabi 1990;
Dardis, Derrick, and Lehfeld 1981; Ketkar and Cho 1982; Lazer and
Shaw 1987; Neal, Schwenk, and Courtless 1990).
Zitter (1988) found expenditures for food, transportation, housing, and healthcare comprised, on average, over four-fifths of the
elderly’s budget. Compared to nonelderly, the elderly spent relatively
more on food at home, health-related expenditures, and charitable
giving (Ambry 1990; Axelson and Penfield 1983; Blisard and Blaylock 1994; Chen and Chu 1982; Ketkar and Cho 1982). In contrast to
previous research findings, using the 1986 Consumer Expenditure
Survey, Chung and Magrabi (1990) did not find significant differences in spending on food at home and food away from home between elderly and nonelderly households.
While this previous work gives little insight into spending pattern
differences among the elderly, it indicates a relationship between age
(as a proxy for life cycle stage) and spending patterns exists. Given
recent growth in the number, diversity, and longevity of the elderly,
there is reason t o believe spending patterns of younger and older
elderly may also differ. The few available studies suggest such differences exist.
Using the 1984 Consumer Expenditure Survey and grouping the
elderly into those 65 to 75 years of age and those over age 75, Harrison (1986) noted that the younger elderly group spent more on transportation and housing and property taxes, had a higher level of
homeownership, and was more likely to pay mortgage payments. The
SUMMER 1997
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93
older elderly group allocates a greater expenditure share to fuel, utilities, and healthcare (Harrison 1986). Walker and Schwenk (1991)
examined consumer units with a reference person age 70 to 79 and 80
or older using the 1987 Consumer Expenditure Survey. They found
the older group was generally female, widowed, nonblack, with relatively lower levels of education, employment experience, and income.
The older group was more likely to live in an urban area and reside in
public or subsidized housing and less likely to own a home. This finding on homeownership agrees with Harrison’s (1986) findings.
Walker and Schwenk (1991) also found that total expenditures and
per capita total expenditures for the over 80 age group were significantly lower than those for the 70 to 79 age group and the older
group spent significantly less in each expenditure category except
healthcare, education, miscellaneous, and cash contributions. Consistent with earlier work, housing, food, transportation, and healthcare constituted, in that order, the largest shares of the household
budget for each age group.
Moehrle (1990) examined the impact of work status on expenditures of working and nonworking elderly aged 62 to 74. Using data
from the 1986-1987 Consumer Expenditure Survey, he found that
regardless of income level, nonworking elderly spent more on food
prepared at home and healthcare whereas the working elderly spent
more on transportation and pensions. Similarly, using 1972-1973
Consumer Expenditure data and econometric methods, McConnel
and Deljavan (1983) discovered that, on average, retired households
spent relatively more on food at home, housing, and medical care
and relatively less on transportation and food away from home than
did the nonretired. Because of retirement practices in the United
States, these studies focused only on the young-old among the
elderly.
Although informative, these studies of expenditure patterns
among the elderly have typically used descriptive statistics, examined
a limited age range of the elderly, o r employed limited controls for
sociodemographic differences among the elderly. This study uses
multivariate analysis to test the null hypothesis that there are no significant differences in expenditure patterns of those aged 65 to 74
and those aged 75 and older, controlling for several sociodemographic differences.
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THE JOURNAL OF CONSUMER AFFAIRS
METHODOLOGY
Data
Data for this study are from the 1990 Consumer Expenditure Interview Survey (U.S. Department of Labor 1992), the most extensive
national household expenditure data available in the United States.
This survey focuses on consumer units, defined to be all members of
a particular housing unit related by blood, marriage, adoption, or
other legal arrangement.
A national sample of consumer units is interviewed once each
quarter for five consecutive quarters; the first interview is used for
bounding purposes. Using a rotating sample design, one-fifth of the
sample is replaced by new units each quarter. The rotating sample
design means multiple quarterly observations from the same consumer units are included in the dataset. While this presents a concern
for the independence of observations, other methods of handling the
data have limitations of equal concern (Schwenk 1986). Thus, merging the four quarters of data was employed in this study.
Sample
The Consumer Expenditure Survey collects data only from consumer units that have independent living status. Residents of retirement communities are included in the survey but long-term care facility residents are excluded.
Cases selected for this study were those where the husband in married-couple families or the household head in other family types was
65 years of age or older and reported race as either white or black.
Statistical Method
Multivariate Tobit analysis was used to control for the impact of
selected sociodemographic variables on each of 13 expenditure categories for each age group. Tobit analysis was selected because large
numbers of zero expenditures existed in several categories. Over 50
percent of the sample reported no expenditures on alcohol and
tobacco, cash contributions, personal insurance, and miscellaneous.
Approximately 25 percent of the sample reported no expenditures for
food away from home, apparel and apparel services, entertainment,
SUMMER 1997
VOLUME 3 1, NUMBER 1
95
personal care, and reading materials and education. Five percent of
those aged 65 to 74 and 15 percent of those aged 75 and older reported no expenditure for transportation. Less than three percent of
the sample reported no expenditures for food at home, housing, and
healthcare. Given these large numbers of zero expenditures, ordinary
least squares regression is inappropriate because the estimated coefficients are generally biased toward zero (Maddala 1983).
Consistent with neoclassical consumer theory, in this study consumer expenditure is deemed to be a function of economic resources
as well as consumer tastes and preferences (Bryant 1990). The regression model used in this study can be represented as follows
Where C i is the annual expenditure on the ithconsumption category,
a is a constant, PI to P12are unknown coefficients, X is total annual
expenditure, R1 is the Northeastern urban region, R2 is the Midwest
urban region, R3 is the Southern urban region, R4 is the Western
urban region (rural is the omitted category), El is a reference person
with a high school degree, E2 is a reference person with some college
education, E3 is a reference person with a college degree (reference
person with less than a high school education is the omitted category), N is household size, B is a black reference person (white reference person is the omitted category), F indicates an unmarried
female-headed household, M indicates an unmarried male-headed
household (married couple is the omitted category), and p is an error
term.
The null hypothesis in this research of no significant differences in
spending patterns between the two age groups was based on the
assumption in the life cycle hypothesis and the permanent income
hypothesis that consumption patterns remain relatively constant over
time. To test for statistical differences in spending by the young-old
and the old-old for each of the 13 expenditure categories while controlling for the influence of selected variables, a dummy variable for
age group was constructed and added to equation (1). Then, for
those consumption categories where significant differences were
found, equation (1) was used to estimate the consumption functions
for each age group and the relationship between sociodemographic
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THE JOURNAL OF CONSUMER AFFAIRS
variables and annual expenditure on the i"' consumption category
was examined. Weights, adjusted to avoid inflation of the test statistics, were used in the Tobit regression analysis so that the results
apply to the total population.
Equation (1) can be summarized as follows
ci
= a
ci =
0
+ xp + p
ifa+Xp+p>O;
ifa
+ Xp + p
(2)
I0;
where X is a vector of independent variables, p is a vector of unknown coefficients, and Ci and p are defined as previously.
The marginal propensity to consume derived from the model is
where E(Ci) is the expected expenditure on a certain category of all
observations, E(CT) is the expected expenditure on a certain category
for observations with expenditures greater than zero (above the
limit), and F(Z) is the probability of having expenditures greater than
zero for all cases. The marginal propensity to consume is the change
in expenditure for a given commodity per unit change in total expenditures, ceteris paribus.
The marginal propensity t o consume for cases above the limit is
dE(CT)/dX, and dF(Z)/aX is the cumulative probability of being
above the limit associated with total expenditures (for calculations of
the terms and their derivatives see Maddala (1983, 149-160); McDonald and Moffitt (1980)).
To derive a total income elasticity measure for all cases, the lefthand side of equation (3) is multiplied by 8/E(Ci) where X is the
mean of total expenditures for the sample. Elasticity indicates the
percentage change in expenditure on a specific category, given a one
percent change in total expenditures. Multiplying 8/E(Ci) by the
first term on the right-hand side of equation (3) yields the income
elasticity for cases above the limit, whereas multiplying it by the second term on the right-hand side yields the elasticity of the probability
of spending on an item with change in income (entry/exit elasticity)
for those who had no expenditure on an item (at the limit) (Kinsey
1984).
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97
Dependent Variables
The expenditure categories used as dependent variables in this
study are food at home, food away from home, alcohol and tobacco,
housing, apparel and apparel services, transportation, healthcare,
entertainment, personal care, reading materials and education, cash
contributions, personal insurance, and miscellaneous. Each expenditure category is the result of summing several related expenditures.
Specific components of each expenditure category used in this study
are outlined in the U.S. Department of Labor (1992) Interview
Survey Public Use Tape Documentation.
Independent Variables
The independent variables include total expenditures, region of
residence, education of reference person, household size, race of reference person, and family type. Total expenditures have been used as
a proxy for income in this study for several reasons. First, the permanent income hypothesis suggests consumption is determined more by
permanent than by actual income (Friedman 1957). Second, not only
can families better control expenditures versus income in the short
run, but they are often more willing to accurately report expenditure
data than they are income data. Finally, precedent for the use of total
expenditures as an income proxy exists in the literature (Houthakker
and Taylor 1970; Ketkar and Ketkar 1987).
Climate and cultural differences in each region of the country
influence expenditure patterns (Ketkar and Cho 1982; Ketkar and
Ketkar 1987). In this study, region is a categorical variable divided
into urban Northeast, urban Midwest, urban South, and urban West.
The omitted category, rural, is not specified further due to data
limitations.
Level of education is divided into four categories: less than high
school, high school graduate, some college, and college graduate.
Higher levels of education can influence consumer tastes and preferences and can alter valuation of time allocation, consequently
affecting expenditures for time-related goods and services (Ketkar
and Cho 1992; Ketkar and Ketkar 1987).
Household size is the actual number of persons in the consumer
unit. As household size increases, expenditures on consumer goods
are also likely to increase.
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THE JOURNAL OF CONSUMER AFFAIRS
Race of reference person and family type variables are used to capture differences of taste and preferences in consumption that might
influence expenditures across households (Abbott 1977; Taeuber
1988; Zitter 1988). In this study, very few survey participants reported a race other than white or black. Because combining these few
cases with other racial groups could mask consumer behavior differences due to race, this study focused on the cases reporting their
race as either white or black. Those reporting other races,were
excluded from this study. Given the large percentage of whites in the
sample, white was selected as the reference category.
Many elderly, especially those over age 74, may have experienced
the death of a spouse. Because consumer tastes and preferences of
widows and widowers may differ (Taeuber 1988), family type was
classified as married couple, unmarried female head, and unmarried
male head. Married couple was selected as the reference category.
Characteristics of the Sample
Sociodemographic characteristics
Table 1 gives an overview of household characteristics in the two
age groups. Average household size was slightly smaller for the oldold group than the young-old group.
Close to one-fourth of each group lived in the urban South. The
Midwest was the second most popular urban region for the two age
groups. Fifteen percent of both age groups lived in rural areas.
A larger percentage of reference persons in the young-old age
group achieved higher levels of education compared with reference
persons in the old-old group. These percentages may reflect increased
access to and emphasis on educational opportunity for individuals
over time.
The sample is predominantly white; 92 percent for each of the two
groups. The percentage of unmarried female-headed households was
higher in the old-old group compared to the young-old group, reflecting a higher percentage of widows. These findings are consistent
with Walker and Schwenk (1991). Married couples accounted for 58
percent of the households of the young-old group, but only 45 percent of the old-old group.
SUMMER 1997
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VOLUME 3 1, NUMBER 1
TABLE 1
Descriptive Statistics for Selected Sociodemographic Characteristics
Age Group
Household Characteristics
Number of cases
Household size
65-74
75 and Older
1,674
2.0
1,136
1.7
Percent
Region of residence
Urban Northeast
Urban Midwest
Urban South
Urban West
Rural
21
23
25
16
15
18
21
30
16
I5
Education of respondent
Less than high school
High school graduate
Some college
College graduate or more
38
32
14
16
54
21
13
12
Race of respondent
Black
White
8
92
8
92
Family type
Married couple
Unmarried female head
Unmarried male head
58
32
10
45
47
8
Expenditure levels
Average dollar expenditures per year for 1991 for each of the two
groups are reported in Table 2. Total expenditures were higher at
$21,333 for the young-old group, compared with $15,985 for the oldold group. Expenditures for food at home, housing, transportation,
and healthcare comprised the largest share of the budget for both
groups. Housing was the largest expenditure for young-old and oldold households alike. Transportation was the second top expenditure
for the young-old, whereas healthcare was the second top expenditure for the old-old. Healthcare expenditures as well as cash contributions were higher for the 75 and older group than for the 65 to
74 group. These results are consistent with previous research findings
(Harrison 1986; Walker and Schwenk 1991; Zitter 1988).
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THE JOURNAL OF CONSUMER AFFAIRS
TABLE 2
Average Annual Dollar Expenditures b y Age Group
Age Group
Expenditure Category
65-74
Total expenditures
Food at home
Food away from home
Alcohol and tobacco
Housing
Apparel and apparel services
Transportation
Healthcare
Entertainment
Personal care
Reading materials and education
Cash contributions
Personal insurance
Miscellaneous
21,333
2,982
95 3
366
6,310
825
4,117
2,263
9%
274
232
303
1,314
3 98
75 and Older
($1
15,985
2,290
646
204
5,562
524
2,240
2,571
48 1
217
21 3
381
34 1
3 15
FINDINGS AND DISCUSSION
To test for differences in spending patterns between the two age
groups while controlling for sociodemographic differences, a dummy
variable for age group was added to the Tobit regression for each
consumption category. The two age groups differed significantly in
their spending on all consumption categories except cash contributions, reading materials and education, and miscellaneous. Thus, for
most expenditure categories, the null hypothesis of no significant difference in spending patterns between the young-old and the old-old
was rejected.
Tobit regression was also used to examine the effect of selected
sociodemographic variables on the spending by each age group on
the ten significantly different expenditure categories while controlling for total expenditures (as a proxy for income). Marginal propensities to spend and expenditure elasticities were derived from the
regression equations.
Table 3 presents the coefficients and summary statistics for each of
the expenditure regressions, with significant variables indicated.
Note, Tobit regression coefficients indicate the direction, and not the
magnitude, of differences between groups. The likelihood ratio test
statistics were used to test the overall significance of the set of varia-
SUMMER 1997
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VOLUME 31, NUMBER 1
TABLE 3
Tobit Expenditure Regressions of Householh Headed by Persons Aged 65-74
and 75 and Older
Variables
Total expenditure
Region of residence
Urban Northeast
Urban Midwest
Urban South
Urban West
Education of respondent
High school graduate
Some college
College graduate or more
Household size
Black
Family type
Unmarried female head
Unmarried male head
Constant
- Log-likelihood
Total expenditures
Region of residence
Urban Northeast
Urban Midwest
Urban South
Urban West
Education of respondent
High school graduate
Some college
College graduate or more
Household size
Black
Family type
Unmarried female head
Unmarried male head
Constant
- Log-likelihood
65-74
75 and Older
Food at Home
0.036***
0.028***
458.10***
21.32
204.28
396.70**
-5.10
49.25
223.23
639.36***
75.13
96.63
323.91 *
123.12
562.73***
86.65
264.79*
244.93
470.85**
-145.43*
-709.26***
-360.26**
-3 86.43 *
789.53***
1 5 ,O19.25
-364.90***
-188.16
754.93***
9,704.09
-302.58*
567.42**
-693.88***
12,104.93
Alcohol and Tobacco
0.012***
0.01 1***
255.65**
4 19.58***
116.23
334.93***
-22.38
-263.38*
-47.92
23.41
75 and Older
Food Away from Home
0.065***
0.045**'
334.37*
164.12
196.68
5 24.95 * *
84.51
262.39
109.98
218.48
-302.68
34.78
207.43
-37.82
331.09**
699.39***
513.47***
92.96
-828.94***
-282.52*
230.93
-728.87***
6,676.78
Housing
0.232* **
0.343* * *
1,476.00***
-297.59
-347.71
5.69
1,856.00***
37.20
480.25
345.69
120.21
28.93
253.45**
116.25***
-317.64**
-57.61
350.91**
404.01**
208.00***
33.49
64.74
301.07
179.68
-1,331.50**
1,610.90***
-178.24
-445.83***
-489.13*
47.33
283.61
-284.42***
171.00
-740.31***
7,583.00
-467.85***
278.56*
-875.02***
3,645.89
221.57
1,047.40**
-600.35
-535.78
1,790.70***
27.13
16,383.95
11,173.06
Apparel and Apparel Services
Total expenditures
Region of residence
Urban Northeast
Urban Midwest
Urban South
Urban West
Education of respondent
High school graduate
Some college
College graduate or more
Household size
Black
65-74
0.032***
358.14***
130.70
-55.36
123.04
126.82
260.79* *
277.99**
63.95
9.09
0.034***
-54.55
158.32
16.95
-58.58
333.71***
288.89**
392.38***
155.76**
-363.69**
Transportation
0.402***
-1,723.80**
96.32
-48.88
-631.90
-508.%
-2,149.50***
-2,539.60***
-504.25**
-256.85
0.278***
-2,666.70***
-1,076.50
-1,380.10* *
-1,261.80*
735.24
1,665.80**
-304.09
286.45
-497.08
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THE JOURNAL OF CONSUMER AFFAIRS
TABLE 3 (continued)
Variables
65-74
75 and Older
Apparel & Apparel Services
Family type
Unmarried female head
Unmarried male head
Constant
- Log-likelihood
140.35*
-105.95
-348.75**
12,167.99
213.36**
-90.02
-761.68***
7,021.71
Healthcare
Total expenditures
Region of residence
Urban Northeast
Urban Midwest
Urban South
Urban West
Education of respondent
High school graduate
Some college
College graduate or more
Household size
Black
Family type
Unmarried female head
Unmarried female head
Constant
- Log-likelihood
*p < .05.
**p < .01.
***p < ,001.
98.00
318.23
-2,540.20***
16,270.14
0.05O* * *
-724.01***
-468.10**
-350.19*
-607.18**
-278.97
845.21*
450.65
1,617.10***
334.35*
313.52*
400.25**
931.14***
4.07
233.78
-205.74
39.95
-578.43**
-377.14
-168.70
-560.89
-355.59*
-690.54
-764.97*** -1,264.30***
-1,255.20***
395.17
2,297.70*** 1,288.90**
14,782.99
10,823.42
211.17
453.88**
418.38**
-85.14
413.63**
0.026***
208.89
231.61
221.21*
222.99
239.93**
368.91* **
572.87***
255.88***
-636.88***
436.22***
-56.44
328.50*
55.44
-1 ,oO8.60***
-453.98*
12,910.60
6,392.55
Personal Insurance
0.005***
0.006***
91.03**
58.17*
72.16**
56.06
42.56
76.42*
73.53**
111.47***
-68.99**
- 135.09***
-1.18
10,315.05
-783.72
-33.16
-2,088.50**
9,539.17
Entertainment
0.134***
49.76*
147.83***
135.87***
31.26**
22.09
75 and Older
Transportation
0.033***
Personal Care
Total expenditures
Region of residence
Urban Northeast
Urban Midwest
Urban South
Urban West
Education of respondent
High school graduate
Some college
College graduate or more
Household size
Black
Family type
Unmarried female head
Unmarried male head
Constant
- Log-likelihood
65-74
79.55 ** *
150.70***
134.5 1** *
15.34
-1 80.83***
-56.73**
-65.56
-26.26
6,264.52
0.120***
10.95
-309.48
-130.32
-854.83*
0.026***
25.57
53.31
4.77
-597.16**
218.77
861.97*
290.25
981.83***
860.83'
142.69
-206.45
-416.29*
620.63***
776.45 * * *
134.10
838.48*
-4,805.30***
10,882.92
587.54***
622.96**
-2,451.00***
4,096.31
SUMMER 1997
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103
bles included in each expenditure regression. The resulting chisquared values were statistically significant at the .01 level. This indicated that the regression models explained the variation in the dependent variables. More specifically, all of the coefficients with the
exception of the intercept were significantly different from zero for
all the regressions considered.
Total expenditures as a proxy for household income had a significant and positive effect on all expenditure categories for the two age
groups. However, the impact of sociodemographic factors on the
various expenditure categories for each age group was not uniform.
Note, for each factor discussed, it is understood that all other factors
are held constant.
Evidence of regional differences in spending patterns was found.
In the urban Northeast, both young-old and old-old age households
spent significantly more on food at home and housing and less on
transportation compared to elderly households in rural areas. The
young-old also spent significantly more on alcohol and tobacco,
apparel and apparel services, entertainment, and personal care and
less on healthcare services than did their counterparts residing in
rural areas.
Urban Midwest young-old households spent significantly more on
alcohol and tobacco, entertainment, and personal care and less on
healthcare than young-old households in rural areas. Interestingly,
some of the same expenditure categories appear for the old-old, but
with different signs. For example, urban Midwest old-old households
spent significantly more on healthcare and less on alcohol and
tobacco compared to their rural counterparts.
Both young-old and old-old households in the urban South spent
significantly more on entertainment and personal care than did elderly rural residents. Young-old households in the urban South spent
less on healthcare whereas old-old households in the urban South
spent less on transportation than rural households.
Urban West elderly residents spent significantly more than elderly
'The test statistic is x2 = - 2 (log-likelihood R minus log-likelhood U). The log-likelihood
function for the restricted model, signified by R, is obtained when the function is maximized
with respect to the intercept only. The log likelihood of the unrestricted model, U, is obtained
when the function is maximized with respect to all the coefficient estimates corresponding to
the intercept and all explanatory variables. The statistic is asymptotically chi-squared, distributed with the degrees of freedom equal to the number of coefficients set equal to zero
(Jacobs, Shipp, and Brown 1989).
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THE JOURNAL OF CONSUMER AFFAIRS
rural residents on food at home and less on personal insurance.
Young-old urban West residents also spent significantly more on
alcohol and tobacco and entertainment and significantly less on
healthcare than rural young-old. In contrast, old-old urban West
elderly spent significantly more on healthcare and personal care and
significantly less on transportation compared to rural old-old.
In general, elderly residents of the four urban areas spent relatively
more than rural elderly for items such as food at home, housing,
entertainment, and personal care and relatively less for transportation and healthcare. Further, residence in the urban Northeast and
urban West was a significant explanatory factor for expenditures in
the various categories more often than was residence in the urban
Midwest and urban South. Such differences in urban and rural residence and in coastal versus central or southern residence may reflect
differential access to and substitutability among consumer goods and
services. For example, while rural residents may grow some of their
own food, reducing expenditures for food at home, urban residents
may use public transportation and have a shorter distance to travel
when shopping, reducing transportation costs.
Note that use of the regional categories in the expenditure survey
suggests states within a given region are homogeneous when, in fact,
they may not be. The western region, for example, includes Alaska,
Hawaii, New Mexico, and Utah (U.S. Department of Labor 1992)states which differ in climate, population, and culture. When differences among states within a given region are significant, the
regional dummies do not function well as controls for regional differences in such things as climate, culture, and consumer taste and
preference. Unfortunately, however, the data set does not permit further refinement of the regional categories.
Elderly who completed a college degree spent significantly more on
food away from home, alcohol and tobacco, apparel and apparel services, entertainment, and personal care compared to elderly who did
not have a high school degree. The young-old with a college degree
also spent significantly more on housing and significantly less on
transportation while college graduates among the old-old also spent
significantly less on personal insurance, compared to the young-old
and the old-old who did not finish high school, respectively.
Elderly households whose reference person had completed some
college but did not finish a degree spent relatively more on apparel
and apparel services, entertainment, and personal care compared to
SUMMER 1997
VOLUME 3 1, NUMBER 1
105
elderly households whose reference person did not have a high school
degree. Reference persons in young-old households who had some
college reported significantly higher expenditures for personal insurance and less on transportation compared to young-old households
whose reference person had not completed high school. Old-old
households whose reference person had some college spent significantly more on food at home, food away from home, alcohol and
tobacco, and transportation and less on housing compared to their
counterparts without a high school degree.
Expenditures for food away from home and personal care were
significantly higher for elderly households where the reference person
had completed high school compared to those households where the
reference person did not have a high school degree. High school
graduates among the old-old also spent relatively more than their
counterparts who did not complete high school on apparel and
apparel services and entertainment.
In general, higher levels of education were significantly associated
with relatively larger numbers of expenditure categories than were
lower levels of education. An interesting question, but beyond the
scope of this research is why, for either age group, college graduates
spent relatively more than those who did not complete high school on
items associated with an active social life: food away from home,
alcohol and tobacco, apparel and apparel services, entertainment,
and personal care. This finding implies education may influence consumer taste and preference in some definite ways. Given greater emphasis on and access to higher education over time, cohort differences in spending patterns among the elderly might be found in
future research.
Expenditures by households in either age group varied positively
with household size for food at home, alcohol and tobacco, and personal insurance and varied negatively with housing. The reason for
this counterintuitive latter result is a matter for further research. It
may be the elderly scale down housing in anticipation of life cycle
changes such as departure of adult children from the parental home.
Among the young-old, expenditures on personal care also varied
positively while expenditures on food away from home and transportation varied negatively with household size. Among the old-old, expenditures on apparel and apparel services and entertainment varied
positively and expenditures on healthcare varied negatively with
household size. Household size in the young-old age group had no
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THE JOURNAL OF CONSUMER AFFAIRS
effect on expenditures for apparel and apparel services, healthcare,
and entertainment. Among the old-old, household size had no effect
on expenditures for food away from home, transportation, and personal care.
Racial differences in spending among the elderly were found.
Compared to white elderly households, black elderly households
spent significantly more on personal insurance and significantly less
on food away from home and entertainment. Young-old black
households also spent significantly less on alcohol and tobacco and
healthcare, whereas old-old black households spent significantly less
on apparel and apparel services and personal care compared to
young-old or old-old white households, respectively. Lack of sufficient data to examine the expenditure patterns of other ethnic groups
is a limitation of this study.
Expenditure pattern differences were associated with family type.
Compared to elderly married couple households, both young-old and
old-old unmarried female-headed households spent significantly
more for apparel and apparel services and significantly less on food
at home, food away from home, alcohol and tobacco, healthcare,
and personal care. In contrast, spending by elderly unmarried male
household heads differed by age. Young-old unmarried male-headed
households spent relatively more on food away from home, entertainment, and personal insurance and significantly less on food at
home, healthcare, and personal care than young-old married couples.
For this group of unmarried males, purchased meals may substitute
for home-prepared meals, due to lack of meal preparation skills or a
preference for eating out versus preparing a meal for one. Also,
expenditures for eating out and entertainment may provide an opportunity to socialize with others. Old-old unmarried male households
spent significantly more on alcohol and tobacco and personal insurance than old-old married couple households.
The marginal propensities to spend calculated from the Tobit
analysis are reported in Table 4. The marginal propensities to spend
for all cases include the cases above the limit as well as those at the
limit. These marginal propensities imply the change in expenditure
on a specific category given a change in total expenditures, ceteris
paribus.
The marginal propensities to spend for cases above the limit (those
who spent on a particular category) show the increase in expenditures
on a specific category that is associated with an increase in total
SUMMER 1997
107
VOLUME 3 1, NUMBER 1
TABLE 4
Murginaf Propensity to Spend f o r Expenditure Categories b y Age Group
Age Group
65-74
Marginal Propensity
Expenditure Category
Food at home
Food away from home
Alcohol and tobacco
Housing
Apparel and apparel services
Transportation
Healthcare
Entertainment
Personal care
Personal insurance
75 and Older
Marginal Propensity
All
Cases
Above
Limit
At
Limit
All
Cases
Above
Limit
At
Limit
.031
.048
.007
.195
.025
.336
.016
.035
.003
.lo3
.024
.034
.009
.I46
.019
,249
.012
,025
.002
.078
.004E-03
.001E-02
.004E-03
.001E-02
.007E-03
.002E-02
.006E-03
.008E-03
.004E-03
.006E-03
.025
.033
.007
.303
.028
.205
.083
,022
.004
.023
.019
.004E-03
.001E-02
.004E-03
dOlE-02
.008E-03
.002E-02
,001E-02
.006E-03
.007E-03
.004E-O3
.023
.005
.235
.021
,146
.059
.016
.003
.018
expenditures by one dollar. As an example, those aged 65 to 74 increased expenditures on transportation by about 25 cents when their
total expenditures increased by one dollar. For the 75 and older age
group, an additional dollar increase in total expenditures was associated with spending almost 15 cents more on transportation.
The marginal propensities to spend for cases at the limit indicate
the probability of spending on a specific category by cases that did
not spend on such a category. As an example, the marginal propensity for those at the limit for transportation in the young-old age
group is .00002.Thus, after multiplying this figure by 100 to convert
to a percentage, each additional dollar in total expenditures implies a
.002 percent greater probability of spending on transportation. Alternatively, each additional $1,000 in total expenditures implies a two
percent greater probability of spending on transportation for those
households that did not spend on such a category.
The young-old age households had higher marginal propensities to
spend for food at home, food away from home, alcohol and tobacco,
transportation, entertainment, and personal insurance and lower for
housing, apparel and apparel services, healthcare, and personal care
compared to the old-old age group.
Expenditure elasticities for the ten expenditure categories are
shown in Table 5 . Those categories with expenditure elasticities
greater than one are generally known as luxury goods whereas those
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THE JOURNAL OF CONSUMER AFFAIRS
TABLE 5
Elasticity for Expenditure Categories by Age Group
Age Group
65-74
Elasticity
Expenditure Category
Food at home
Food away from home
Alcohol and tobacco
Housing
Apparel and apparel services
Transportation
Healthcare
Entertainment
Personal care
Personal insurance
75 and Older
Elasticity
All
Cases
Above
Limit
Entry/
Exit
All
Cases
Above
Limit
Entry/
Exit
.22
1.06
.54
.66
.66
1.77
.17
.76
.39
.49
.49
1.31
.ll
.76
.18
1.33
.29E-04
.23E-03
.23E-03
.43E-04
.19E-O3
.84E-04
.54E-04
.18E-03
.32E-03
.llE-03
.17
.84
.54
.87
.88
1.48
.51
.78
.33
1.12
.13
.60
.38
.68
.66
1.05
.36
.59
.24
.86
.31E-04
.26E-03
.32E-03
.43E-04
.25E-03
.12E-03
.80E-04
.20E-03
.51E-03
.18E-03
.15
.54
.26
1.75
categories with expenditure elasticities less than one are generally
called necessities (Layard and Walters 1978). Transportation and
personal insurance could be classified as luxury goods for all cases
and above limit cases among the young-old and for all cases among
the old-old. Almost all other elasticities are less than one.
SUMMARY AND IMPLICATIONS
The proportion of elderly in the population is increasing. Many
elderly are living to an advanced age. Given these demographic
trends will continue as the sizable baby boom generation reaches
their elder years, it is important to examine spending pattern differences among the elderly.
Most previous studies of expenditure patterns of the elderly
assumed those aged 65 and older were alike, an assumption current
research findings dispute. The few studies of expenditure patterns
among the elderly have relied on descriptive statistics, focused on a
limited age range, or used few sociodemographic controls. This study
extended previous work by using econometric methodology to compare the spending patterns of two groups of elderly while controlling
for differences in total expenditures (as a proxy for household income), region, educational levels, and consumer unit characteristics.
Significant differences in spending patterns between young-old
and old-old households were found for ten expenditure categories
SUMMER 1997
VOLUME 3 1, NUMBER 1
109
after controlling for economic and sociodemographic differences.
Thus, the null hypothesis of no significant difference was, in general,
rejected.
Marginal propensities to spend and expenditure elasticities were
calculated for the ten significantly different expenditure categories
for each age group. Comparison of results for the two age categories
revealed higher marginal propensities to spend among the young-old
for food at home, food away from home, alcohol and tobacco, transportation, entertainment, and personal insurance; while lower marginal propensities to spend were found for housing, apparel and
apparel services, healthcare, and personal care. Calculation of expenditure elasticities indicated that a one percent increase in total expenditures would likely result in a greater than one percent increase in
expenditures on transportation and personal insurance by either age
group.
Total expenditures had a significant and positive effect on each of
the ten expenditure categories examined. However, the impacts of
region of residence, education level, household size, race, and family
type on the various expenditure categories were not uniform. Two
types of differences were present. One, the set of significant sociodemographic variables was not the same across all expenditure categories. For example, for both age groups, region of residence was not
significantly associated with expenditures for food away from home,
whereas race and family type were not significantly associated with
transportation expenditures. Two, the set of significant sociodemographic variables was not the same for each age group. For the
expenditure categories examined, region of residence and family type
were more often significant explanatory factors for the young-old
compared to the old-old; whereas education was more often a significant explanatory factor for the old-old. Household size and race
seemed to perform equally well as explanatory factors for either age
SOUP.
To effectively meet the consumer needs of the elderly, designers of
programs, policies, goods, or services for the elderly should note the
spending pattern differences between young-old and old-old and the
differential impact of region of residence, educational level, household size, race, and family type on the various expenditures made by
either age group. Recognizing spending pattern differences between
the young-old and old-old in areas such as food, housing, transportation, and healthcare can facilitate development of useful public
110
THE JOURNAL OF CONSUMER AFFAlRS
policy and programs by government or community agencies. For
example, the relatively higher spending on healthcare by the old-old
suggests programs which focus on effective and economical healthcare will become increasingly important as the population ages. The
relatively higher spending on transportation by rural elderly compared to urban elderly implies development of less expensive means
of conveying goods and services to rural elders would be beneficial.
Helping the rural elderly increase their use of mail-order services or
charging them reduced fares for private or public taxi services are
examples of reducing their transportation costs.
Business can use the results of this study as a guide for market segmentation in areas such as food away from home, apparel and
apparel services, entertainment, and healthcare. As the young-old
spend relatively more on eating out, clothing, and entertainment,
businesses that effectively target this segment of the elderly may
increase their market share. Also, opportunity exists for new project
development. For instance, the old-old might spend more on clothing
if apparel manufacturers designed stylish, comfortable garments that
took the physical limitations of this group into account. Recognizing
diversity among the growing proportion of elderly in the population,
business can develop new goods and services and adapt existing
goods and services to better meet the differing needs of those aged 65
to 74 and those aged 75 and older.
REFERENCES
Abbott, J. (1977), “Socioeconomic Characteristics of the Elderly: Some Black-White Differences,” Social Security Bulletin, 4O(July): 16-42.
Ambry, M. (1990), “The Age of Spending,” American Demographics, 12(November): 16-23,
52.
Atchley, R. C. (1991), Social Forces and Aging: An Introduction to Social Gerontology,
Belmont, CA: Wadsworth Publishing Company.
Axelson, Marta L. and Marjorie P. Penfield (1983), “Factors Associated with Food Expenditure of Elderly Persons Living Alone,” Home Economics Research Journal, 12(2,
December): 228-236.
Blisard, N. and J. R. Blaylock (1994), “Food Expenditure Projections: 1990-2010,” Agribusiness, 10: 105-114.
Borzilleri, Thomas C. (1978), “The Need for a Separate Consumer Price Index for Older
Persons: A Review and New Evidence,” The Gerontologist, 18(3): 230-236.
Bryant, W. Keith (1990), The Economic Organization o f the Household, New York: Cambridge University Press.
Chen, Yung-Ping P. and Kwang-wen W. Chu (1982), “Household Expenditure Patterns: The
Effect of Age of Family Head,” fournaf of Family Issues, 3(2, June): 233-250.
Chung, Young S. and F. M. Magrabi (1990), “Age-Related Changes in Expenditure Patterns,”
in Proceedings o f ihe 36th Annual Conference o f the American Council on Consumer
Interests, Mary Carsky (ed.), Columbia, MO: ACCI: 200-206.
SUMMER 1997
VOLUME 31, NUMBER 1
111
Crispell, Diane and William H. Frey (1993), “American Maturity,” American Demographics,
15(March): 32-43.
Culter, N. E. (1991), “Happy Birthday t o the Gerontology 5,000,” Journal of the American
Society of CLU and ChFC, 44: 23-25.
Dardis, Rachel, Frederick Derrick, and Alane Lehfeld (1981), “Clothing Demand in the
United States: A Cross-Sectional Analysis,” Home Economics Research Journal, 1 q 2 ,
December): 212-221.
Friedman, Milton (1957), A Theory of the Consumption Function, Princeton, NJ: Princeton
University Press.
Harrison, Beth (1986), “Spending Patterns of Older Persons Revealed in Expenditure
Survey,” Monthly Labor Review, 109(10): 15-17.
Hollman, F. W. (1990), “United States Population Estimates by Age, Sex, Race, and Hispanic
Origin: 1980 t o 1988,” Current Population Reports, Population Estimates and Projections, Series P-25, No. 1045, U.S. Department of Commerce, Bureau of the Census.
Houthakker, H. S. and L. D. Taylor (1970), Consumer Demand in the United States: Analyses
and Projections, Cambridge, MA: Harvard University Press.
Jacobs, Eva, Stephannie Shipp, and Gregory Brown (1989), “Families of Working Wives
Spending More on Services and Nondurables,” Monthly Labor Review, 112(February):
15-23.
Ketkar, Suhas L. and Whewon Cho (1982), “Demographic Factors and the Pattern of Household Expenditures in the United States,” Atlantic Economic Journal, lO(3, September):
16-27.
Ketkar, Kusum W. and Suhas L. Ketkar (1987), “Population Dynamics and Consumer
Demand,” Applied Economics, 19(November): 1483-1495.
Kinsey, Jean (1984), “Probit and Tobit Analyses in Consumer Research,” in Proceedrngs of
the 30th Annual Conference of the American Council on Consumer Interests, Karen
Goebel (ed.): Columbia, MO: ACCI: 155-161.
Layard, P. R. G. and A. A. Walters (1978), Microeconomic Theory, New York: McGraw-Hill.
Lazer, William and Eric H . Shaw (1987), “How Older Americans Spend Their Money,”
American Demographics, 9(September): 36-41.
Maddala, G. S. (1983), Limited Dependent and Qualitative Variables in Econometrics, New
York: Cambridge University Press.
McConnel, Charles E. and Firooz Deljavan (1983), “Consumption Patterns of the Retired
Household,” Journal of Gerontology, 37(July): 480-490.
McDonald, J. F. and R. A. Moffitt (1980), “The Uses of Tobit Analysis,” The Review of
Economics and Statistics, 62: 318-321.
Modigliani, F. and R. Brumberg (1954), “Utility Analysis and the Consumption Function: An
Interpretation of Cross-section Data,” in Post-Keynesian Economics, K. K. Kurihara
(ed.), New Brunswick, NJ: Rutgers University: 388-436.
Moehrle, Thomas (1990), “Expenditure Patterns of the Elderly: Workers and Nonworkers,”
Monthly Labor Review, 113(May): 34-41.
Moschis, G. P . (1992), “Gerontographics: A Scientific Approach to Analyzing and Targeting
the Mature Market,” Journal of Services Marketing, 6: 17-26.
National Institute on Aging (1992), Profiles of America’s Elderly: Growth of America’s
Oldest-Old Population, Bethesda, MD: Office of the Demography of Aging of the
National Institute on Aging.
Neal, Edith G., Frankie N . Schwenk, and Joan C. Courtless (1990), “Apparel Expenditures of
Older Consumers,” Family Economics Review, 3(4): 1217.
Schwenk, Frankie N. (1986), “The Consumer Expenditure Survey: Data Management and
Analysis Decisions,” in Proceedings of the 32nd Annual Conference of the American
Council on Consumer Interests, K. P. Schnittgrund (ed.), Columbia, MO: ACCI:
326-327.
Schwenk, Frankie N. ( 1 9 9 9 , “Assets of Elderly Households,” Family Economics Review,
8(1): 13-19.
Taeuber, Cynthia (1983), America in Transition: An Aging Society, U.S. Department of Commerce, Bureau of the Census, Special Studies Series P-23, No. 128, Washington, DC:
U.S. Government Printing Office.
112
THE JOURNAL OF CONSUMER AFFAIRS
Taeuber, Cynthia (1988), “Diversity: The Dramatic Reality,” in Diversity in Aging, S . A. Bass,
E. A. Kutza, and P. M. Torres-Gill (eds.), Glenview, IL: Scott Foresman and Co.: 1-45.
Taeuber, Cynthia and Barry Ocker (1992), Efderfyin the United States, Bethesda, MD: Office
of the Demography of Aging of the National Institute on Aging.
U.S. Department of Commerce, Bureau of the Census (1986), Age Structure o f t h e U.S. Population in rhe 2Ist Century, SB-1-86, Washington, DC:U.S. Government Printing Office.
U.S. Department of Labor, Bureau of Labor Statistics (1992), Interview Survey Public Use
Tapes and Documents, Washington, DC: Bureau of Labor Statistics.
Walker, R. S. and Frankie N. Schwenk (1991), “Income and Expenditure Patterns of Consumer Units with Reference Person Age 70 to 79 and 80 or Older,” Family Economics
Review, 4(1): 8-13.
Zitter, Mark (1988), “Older Population Most Diverse of All American Age Groups,” Healthcare Financial Management, 42( 12, December): 38-42.