J Fam Econ Iss (2007) 28:305–320
DOI 10.1007/s10834-007-9055-x
ORIGINAL PAPER
Predictors of Holding Consumer and Mortgage Debt
among Older Americans
Yoon G. Lee Æ Jean M. Lown Æ Deanna L. Sharpe
Published online: 7 March 2007
Springer Science+Business Media, LLC 2007
Abstract Using data from the 2000 Health and Retirement Study (HRS), this study
examined the probability of older Americans holding consumer and mortgage debt.
The specific objectives of this study were to identify to what extent individuals aged
65 and over hold consumer or mortgage debt and to investigate factors that influence
the probability of holding consumer or mortgage debt in old age. The results of
logistic regression analysis indicated that older individuals who had larger households, had higher levels of education, were aged 65–74, were married, were Black,
and were employed, were more likely to hold consumer or mortgage debt. This study
concluded that holding one kind of debt is associated with probability of holding
another kind of debt.
Keywords Aging Æ Consumer debts Æ Mortgages Æ Economic well-being
Introduction
An aging population, uncertainties regarding future availability of Social Security,
and the shift toward personal responsibility for retirement fund adequacy have increased the importance of research related to economic well-being in later life. As
the fastest growing age group in America, seniors age 65 and older are a concern to
public policy makers regarding their financial status and ability to support themselves in ever lengthening periods of retirement. In a widely disseminated news
Y. G. Lee (&) Æ J. M. Lown
Department of Family, Consumer, and Human Development, Utah State University,
308B Family Life, 2905 Old Main Hill, Logan, UT 84322-2905, USA
e-mail: yoonlee@cc.usu.edu
e-mail: lown@cc.usu.edu
D. L. Sharpe
Personal Financial Planning Department, University of Missouri-Columbia, Columbia, MO,
USA
e-mail: SharpeD@missouri.edu
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article, Dugas (2002) revealed the growing problem of debt and bankruptcy among
older Americans, reporting that seniors are filing for bankruptcy in record numbers.
Findings from the Consumer Bankruptcy Project indicated that seniors (65 and over)
are the fastest growing age group in bankruptcy, up 213% from 1991 to 2001
(Sullivan, Thorne, & Warren, 2001). Credit card debts, often incurred for necessities,
along with home equity loans and second mortgages are increasing dramatically
among the older population (Dugas, 2002).
While recent research has explored the expenditure patterns of older consumers
(Paulin, 2000; Paulin & Duly, 2002), debt among the rapidly growing older population is a seldom-studied topic that should be of growing concern to financial
educators, advisors, and public policy makers. This study examines the consumer
and mortgage debt holding behavior of older Americans. The specific objectives of
this study are to identify to what extent individuals aged 65 and over hold consumer
or mortgage debt, to examine the debt patterns of older Americans, and to investigate factors that influence the probability of having consumer or mortgage debt in
later life.
Related Literature
To date, few researchers have explored the debt holding behavior of older
Americans. In some respects, this lack of attention is not particularly surprising. The
life cycle hypothesis, a common theoretical tool for understanding household level
saving and consumption behavior, proposes that savings accumulated during the
working years funds consumption during the retirement years, allowing individuals
to maintain approximately the same level of consumption over time (Modigliani &
Brumberg, 1954). Given the existence of rather limited credit markets when the
hypothesis was developed as compared with today, the focus on saving and spending
rather than on credit as a means of intertemporal consumption smoothing is
understandable. Further, until recently, debt was not commonly observed among the
elderly. Indeed, many in generation that survived the Great Depression were
strongly debt-adverse.
There are indications that today’s elderly are less debt-adverse than their predecessors. Gist and Figueiredo (2002a) examined relative debt burdens among
Americans age 50 and older between 1989 and 1998. They reported that, for
American seniors who owed any consumer or mortgage debt, median total debt
levels doubled over these 9 years. More specifically, for seniors in the bottom income quartile, median debt levels increased from $1,600 to $3,500 (in constant 1998
dollars) between 1989 and 1998. For the middle 50%, median debt more than
doubled from $10,500 to $22,000, while debt levels for the top income quartile rose
from $43,000 to $80,000. During that time period, housing debt as a percentage of
income doubled for study participants.
In a follow-up study, Gist and Figueiredo (2002b) reported that there was little
change in consumer debt burdens for the age 50 and older population but the level of
housing debt had more than doubled from 1989 to 1998. However, both non-housing
and housing debt were lower for older individuals; persons age 75 and older had
about one-fourth the debt of persons ages 50–64. The group with the heaviest debt
burden was the 50–64 years olds in the bottom income quartile. This growing
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housing debt may be a concern if servicing that debt prevents adequate retirement
preparation for those aged 50–64.
Two recent studies focused on change in the debt level of older households that
occurred between 1992 and 2001. Copeland (2004) found that during that time
frame, average credit card debt among seniors age 65 and older increased 89% to
$4,041. The percentage change was most pronounced for the youngest cohort.
Among those aged 65–69, average debt increased 217% to $5,844. Average credit
card debt in female-headed senior households grew 48% to $2,319 from 1992 to
2001. Among households with heads age 65 and older with incomes of less than
$50,000 (70% of 65+ households), 20% report spending more than 40% of their
income on total debt repayment, including mortgage debt.
Draut and McGhee (2004) examined debt among families with a head age 55 or
older. In both 1992 and 2001, a larger percentage of those reporting debt were
younger. About three-fourths of household heads aged 55–64 had debt as compared
with 29% of household heads aged 75 and older. Although the percentage of families
reporting some debt remained stable at about 56%, average debt grew from $27,517
in 1992 to $38,771 in 2001, while median debt increased from $13,611 to $23,000.
Note that medians were below means in both years, implying that few households
had relatively large amounts of debt. Household heads age 55–64 committed a larger
proportion of income to debt repayment (10.5%) than household heads age 75 and
older (3.6%). In addition, the composition of debts changed over the decade;
housing debt grew from 56.5% to 62.5% of total debt. Draut and McGhee (2004)
concluded there was no change in the ratio of debt-to-income or assets from 1992 to
2001. However, the percentage of families age 75 and older with debt burdens
exceeding 40% of income increased substantially. Further, there was a significant
increase from 1992 to 2001 in older families with debt levels exceeding 40% of
income.
In general, this line of research suggests that the debt holding behavior of seniors
is changing. Although existing research did not investigate reasons for increased
debt among seniors, findings of this research suggest that some seniors, especially
those with relatively low income, may be using debt as a means to increase their
purchasing power. Others may be responding to broad market forces. As an
example, high housing prices could necessitate high mortgage balances. Cohort effects may also be a factor; younger group of seniors could be more accustomed to
and comfortable with holding debt as compared with older seniors.
Empirical support for the life cycle hypothesis among older Americans has been
mixed. Using all six waves of the Longitudinal Retirement History Survey, Hogarth
(1991) assessed the applicability of the life cycle savings theory to the retirement
years. Five different savings/dissavings patterns were identified for older persons up
to age 75. The largest group (43.5% of the sample) had periods of savings and level
consumption, whereas the smallest group (4.3%) combined level consumption
(no changes in assets) and dissaving periods. Hogarth concluded that only threefifths of the households in the sample followed consumption behaviors consistent
with the life cycle theory of dissaving during retirement and recommended developing educational programs that would help retired persons devise an appropriate
dissavings plan. On the other hand, Kennickell and Starr-McCluer (1997) used
Survey of Consumer Finances (SCF) panel data for 1983 and 1989 and identified a
clear financial life-cycle pattern with older households consuming assets and retiring
debts.
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Mathur and Moschis (1994) posit that older Americans may use credit for reasons
other than consumption smoothing, per se, contending that decreased mobility and
lack of family or social resources may lead to greater use of credit to make purchases
by telephone or Internet. It is also possible that low resource elderly could use credit
as a way to purchase necessities that they could otherwise not afford, focusing on
their short term needs versus the long term consequences of holding debt.
Hypotheses
This study uses socio-demographic characteristics to explore potential influences on
dissaving behavior in later life. Family size and age are used to measure life cycle
stage. Education, gender, marital status, race, employment status, and health status
of older individuals are considered as either measures or proxies of needs, tastes, and
preferences. Net worth and income are included as measures of resources as well as
controls for accustomed levels of living that the survey respondent presumably
wishes to maintain over time.
Since consumption needs usually increase with additional household members,
family size can be an important determinant of holding debts in later life. Given a
negative relationship between family size and household wealth (Xiao, 1995), debt
may be used as a way of funding additional consumption needs. In this study, it is
expected that those with larger households are more likely to hold consumer and
mortgage debt than those with smaller households.
According to the 2001 Survey of Consumer Finances, 32.0% of individuals aged
65–74 hold mortgage debt, while only 9.5% of those aged 75 or above have a
mortgage (Aizcorbe, Kennickell, & Moore, 2003). Median debt could decline for
those aged 75 and older because they paid off mortgages on a primary residence
(Aizcorbe et al., 2003). Similarly, 30% of older individuals aged 65–74 hold consumer debt, but only 18.4% of those aged 75 or above reported consumer debt.
Based on this evidence, debt level could decline with age among older individuals as
mortgage debt is paid. Therefore, it is hypothesized that as compared with individuals aged 65–74, those aged 75–84 or those aged 85+ are less likely to hold
consumer and mortgage debt.
Chien and DeVaney (2001) reported that, across all adult age categories, those
with an education level of 13 years or above had a positive attitude toward borrowing. Extending their findings to the older individuals, this study hypothesizes that
older individuals with higher levels of education are more likely to hold consumer
and mortgage debt than those with lower levels of education.
Single older women are more likely to live in poverty and the economic wellbeing of women is generally lower than that of men (Hong & Swanson, 1995; Ozawa,
1993). Women have often worked fewer years and typically were not employed in
stable pension-earning positions (Glass & Kilpatrick, 1998). Keith (1986) reported
that married women and men are better off financially than their unmarried counterparts; both women and men are at greater risk for economic hardship when they
become widowed, divorced, or separated at all stages of the life course. Given these
findings, it is proposed in this study that among unmarried respondents, male older
individuals would be less likely to hold consumer and mortgage debt than their
female counterparts, whereas married individuals would be less likely to hold consumer and mortgage debt.
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It is presumed that those whose culture accepts borrowing money as a common
practice would be more likely to be debtors. In this study, cultural influences on
borrowing or using consumer credit are proxied by race. About 32% of Blacks
report that they do not save compared with 23% of other Americans. Blacks also
hold much less wealth than other Americans (AmericaSaves, 2002). Thus, it is
expected that those who are Black would be more likely to hold consumer and
mortgage debt than their counterparts.
Lee, Lown, and Hong (2002) found that when older individuals held lower levels
of wealth, they participated in the labor force to supplement their income. It is
speculated that the decision to participate in the labor force in later life could be the
result of running into financial problems or needs; therefore, it is expected that those
who are in the labor force in later life would be more likely to hold consumer and
mortgage debt than their counterparts.
Since an older person might borrow when he or she encounters unexpected
medical expenses, health status could affect debt behavior of the elderly. Using an
earlier release of the survey of Asset and Health Dynamics among the Oldest-Old
(AHEAD), Smith (1997) found that new health events resulted in out-of-pocket
medical expenses that depleted wealth. In this study, health status is used as a proxy
for medical expenses in later life. As medical expenses increase, older individuals
may resort to credit as a means of making the payment. It is therefore expected that
those who have poor health would be more likely to hold consumer and mortgage
debt than their counterparts.
Net worth can measure the ability to retire or leverage debts; it is expected that
older persons with higher net worth would be less likely to hold consumer and
mortgage debt in later life than those with less net worth. Lower income consumers
are more likely to experience financial risk in retirement (Butrica, Iams, & Smith,
2003; Tucker et al., 2001); whereas, higher income individuals are more likely to be
financially prepared for retirement (DeVaney & Chiremba, 2005; Xiao, 1995). Aizcorbe et al. (2003) reported that median debt declined for older household heads
from 1998 to 2001; whereas, it rose with income for other demographic groups. Thus,
it is expected that those with higher income levels are less likely to hold consumer
and mortgage debt than those with lower income levels.
Methods
Data and Sample
Data for the study are drawn from the 2000 Health and Retirement Study (HRS),
which is principally funded by the National Institute on Aging (NIA). The main goal
of the HRS is to provide panel data that facilitates policy-relevant research and
analysis on retirement, saving, and the economic well-being of older households. The
survey elicits information about income, income sources, assets, physical and mental
health, out-of-pocket costs for all health-care services, family structure and care
giving, intergenerational transfers, job status, and demographic characteristics. The
HRS is administered by the Institute for Social Research (2005) at the University of
Michigan. The HRS is described in greater detail elsewhere (http://hrsonline.isr.
umich.edu).
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This study utilizes the RAND HRS data file, which is a cleaned and easy-to-use
version of the Health and Retirement Study (HRS). The RAND Corporation is a
non-profit institution that helps improve policy and decision-making through
research and analysis. The RAND HRS file is developed by the RAND Center for
the Study of Aging with funding from the National Institute on Aging (NIA) and the
Social Security Administration (SSA). The RAND HRS data file is based on 1992,
1993, 1994, 1995, 1996, and 1998 final releases and the 2000 early release. The sample
for this study was households headed by those aged 65 or older in the 2000 RAND
HRS data (N = 9,996). RAND HRS data are weighted to create a nationally
representative sample.
Statistical Analysis
Univariate and frequency analyses generated descriptive statistics for the sample
(see Table 1). Percentages, means, standard deviations, and medians were calculated
to show the distribution of consumer and mortgage debt of older individuals (see
Table 2). Logistic regression analyses were conducted to identify factors associated
with the likelihood of holding consumer or mortgage debt in old age (see Tables 3
and 4). SAS software, version 8.2, was used for statistical analyses.
Empirical Models
Dependent Variables
The descriptive analyses examine debt levels. Total debt level is the sum of the
dollar value of both mortgage and consumer debt levels. Consumer debt represents
the dollar value of any debt other than housing debt. In this study, consumer debt
levels are the sum of the dollar value of credit card balances, medical debts, loans
from relatives, and other debts. Mortgage debt levels represent the sum of the dollar
value of mortgage debt and other home loans.
The dependent variables in the three multivariate analyses are the probability of
holding any debt (Model 1), probability of holding consumer debt (Model 2), and
probability of holding mortgage debt (Model 3). In multivariate logistic regression
analyses, a binary variable (1 if having total debt; 0 otherwise) was created for Model
1 (total debt), a binary variable (1 if having consumer/other debt; 0 otherwise) was
created for Model 2 (consumer debt), and a binary variable (1 if having mortgage
debt; 0 otherwise) was created for Model 3 (mortgage debt). Measurements of all
variables included in the multivariate analyses are presented in Table 1.
Independent Variable
Socio-demographic characteristics including net worth, household income, family
size, age, education, gender, marital status, race, employment status, and self-reported health were considered independent variables in the three empirical models.
In Model 1, two dummy variables, homeowner and non-owner, were entered to
control for housing tenure. A dummy variable for holding mortgage debt was
included in Model 2, and a dummy variable for holding consumer debt was included
in Model 3.
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Table 1 Measurement of variables and summary statistics of elderly (N = 9,996)
Variables
Measurement
Net worth
Household income:
<$15,001
$15,001–$35,000
$35,001–$55,000
$55,001–$75,000
>$75,000
Log of net worth, total of all assets—liabilities
Family size
Age:
Young-old
Middle-old
Old-old
Education:
Less than high
school
High school
education
Some college
College education
Gender:
Female
Male
Marital status:
Separated/divorced
Widowed
Never married
Married
Race:
Whites
Blacks
Hispanic/others
Employment status:
Full-time
Part-time
Not working
Self-reported health:
Fair/poor
Good
Very good
Excellent
Housing tenure
Homeowner
Non-owner
1
1
1
1
1
if
if
if
if
if
income £$15,000, 0 if otherwise
income $15,001–$35,000, 0 if otherwise
income $35,001–$55,000, 0 if otherwise
income $55,001–$75,000, 0 if otherwise
>$75,000, 0 if otherwise
Percent Mean (Median)
$354,226 (153,200)
$38,177 (25,554)
25.6
39.5
17.5
7.1
10.3
Continuous, number of people in household
1.9 (2.0)
75 (74)
1 if age 65–74, 0 if otherwise
1 if age 75–84, 0 if otherwise
1 if age 85 and older, 0 if otherwise
53.2
34.9
11.9
1 if less than high school, 0 if otherwise
32.2
1 if high school graduate, 0 if otherwise
33.7
1 if some college, 0 if otherwise
1 if college graduate or higher, 0 if otherwise
17.4
16.7
1 if female, 0 if otherwise
1 if male, 0 otherwise
56.7
43.3
1
1
1
1
6.4
30.7
2.4
60.5
11.5 (12.0)
if
if
if
if
sep/div, 0 if otherwise
widowed, 0 if otherwise
never married, 0 if otherwise
married, 0 if otherwise
1 if White, 0 if otherwise
1 if Black, 0 otherwise
1 if Hispanic/others, 0 if otherwise
86.7
10.8
2.5
1 if work full-time, 0 otherwise
1 if work part-time, 0 if otherwise
1 if not in the labor force, 0 if otherwise
6.0
2.2
91.8
1
1
1
1
32.9
31.4
26.0
9.6
if
if
if
if
fair/poor, 0 if otherwise
good, 0 if otherwise
very good, 0 if otherwise
excellent, 0 if otherwise
1 if own home, 0 if otherwise
1 if do not own home, 0 if otherwise
82.1
17.9
The net worth variable is the total of all assets minus all liabilities. Preliminary
descriptive analysis indicated that the variance of net worth was unevenly distributed. Thus, the log of net worth variable was included in the three empirical models.
Total annual household income was categorized into five dummy variables: (a) income1 (less than $15,001), (b) income2 ($15,001–$35,000), (c) income3 ($35,001–
$55,000), (d) income4 ($55,001–$75,000), and (e) income5 (greater than $75,000).
The category with annual household income greater than $75,000 was the reference
category in the models.
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Table 2 Distribution of consumer and mortgage debt among elderly (N = 9,996)
Consumer debt
Percentages
Percentages
Mean (SD)
Median
Zero balance
82.5%
Zero <
17.5 (100%)
(64.4)
(17.3)
(10.7)
(3.4)
(2.3)
(1.9)
$1,177 (9,013)
$1,460 (1,197)
$6,283 (1,306)
$11,996 (2,375)
$21,876 (2,601)
$34,217 (4,824)
$96,497 (108,329)
0
$1,000
$6,000
$11,000
$20,000
$31,300
$67,931
Zero balance
80.3%
Zero <
19.7 (100%)
(39.3)
(24.7)
(24.1)
(7.3)
(2.6)
(2.0)
$9,671 (34,931)
$11,345 (6,979)
$35,218 (6,781)
$67,410 (13,857)
$115,634 (13,918)
$169,647 (15,055)
$337,100 (234,912)
0
$11,000
$35,000
$65,000
$113,500
$170,000
$245,000
$1–4,999
$5,000–9,999
$10,000–19,999
$20,000–29,999
$30,000–49,999
$50,000+
Mortgage debt
$1–24,999
$25,000– 49,999
$50,000–99,999
$100,000–149,999
$150,000–199,999
$200,000+
Family size, measured as a continuous variable, represented the total number of
people in the household. Age was a categorical variable: (a) young-old (ages 65–74;
reference category), (b) middle-old (ages 75–84), and (c) old-old (ages 85+).
Similarly, levels of education were coded as dummy categorical variables: (a) less
than high school (reference category), (b) high school graduate, some college education, and (c) college graduate or advanced degree.
Other socio-demographic characteristics included (a) gender (male [reference
category], female); (b) marital status (married [reference category], separated/
divorced, widowed, and never married); and (c) race (White [reference category],
African-American, and Hispanic/others). Employment status indicated whether or
not respondents were currently employed in the labor force (full-time, part-time, and
not working [reference category]). Health status was categorized according to selfreported health (fair/poor, good, very good, and excellent [reference category]).
Results
Sample Characteristics of Elderly
Descriptive statistics for the sample (N = 9,996) are presented in Table 1. The
average net worth was $354,226; whereas, the average annual household income was
$38,177. Family size was about two, on average. The average age of the respondents
was 75. About 53% of the individuals were between 65 and 74. The average educational attainment was approximately 12 years. Close to one-third of the sample
household heads had either less than a high school education (32%) or a high school
degree (34%). A smaller proportion had either some college education (17.4%) or
had a college degree or higher (16.7%).
More respondents were female (56.7%) than male (43.3%). The largest portion of
the sample were married (60.5%); 30.7% were widowed, 6.4% were separated or
divorced, and 2.4% were never married. The majority of the sample was White
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Table 3 Logistic regression results of the probability of holding debt in later life (N = 9,996)
Variable
Log net worth
Income:
<$15,001
$15,001–$35,000
$35,001–$55,000
$55,001–$75,000
($75,000<)
Family size
Age: (Young-old, 65–74)
Middle-old (75–84)
Old-old (85+)
Education: (Less than high)
High school graduate
Some college
College education
Gender: (Male)
Female
Marital status: (Married)
Separated/divorced
Widowed
Never married
Race: (Whites)
Blacks
Hispanic/others
Employment status: (Not working)
Full-time
Part-time
Self-reported health: (Excellent)
Fair/poor
Good
Very good
Housing tenure: (Non-owner)
Homeowner
Intercept
Log likelihood
v2
Model 1: Total debt
Parameter estimate
Odds ratio
–0.3486***
0.706
***
–0.7135
–0.3218***
–0.0301
0.2087*
0.490
0.725
0.970
1.232
0.2006***
1.222
–0.7287***
–1.6749***
0.483
0.187
0.2881***
0.6580***
0.7067***
1.334
1.931
2.027
–0.1679***
0.845
***
1.380
1.079
0.645
0.6453***
0.3645**
1.907
1.440
0.4738***
0.6743***
1.606
1.963
0.3221
0.0758
–0.4383**
–0.1194
–0.1075
–0.1413
2.0639***
0.887
0.898
0.868
7.876
1.3782***
10596.716
1666.4262***
* p < .05; ** p < .01; *** p < .001
Note. Reference categories in models are presented in parentheses
(86.7%), 10.8% were Black, and 2.5% were Hispanic/others. Approximately 8% of
respondents were employed and 33% of the sample perceived their health as poor or
fair. Most respondents (82.1%) owned a home.
Distribution of Consumer and Mortgage Debt
The distribution of consumer debt and mortgage debt among older individuals aged
65 and over is presented in Table 2. The majority of the sample reported no consumer debt or mortgage debt. Close to 83% of respondents had no credit card
balances, medical debts, other loans, or loans from relatives; 80.3% had paid off their
mortgage debt prior to retirement.
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Some in the sample were debtors, however. For most, the outstanding balances
were relatively modest. Of those holding consumer debt, almost two-thirds (64.4%)
had outstanding balances of $4,999 or less. Close to a fifth of the sample (17.3%) had
consumer debt levels between $5,000 and $9,999. A small portion of retirees had a
relatively large amount of consumer debt. Almost 2% held consumer debts of
$50,000 or more.
Of those holding mortgage debt, most had relatively low balances. Slightly over
one-third of the sample (39.3%) owed a mortgage debt of less than $25,000; about
one-fourth of the sample (24.7%) owed mortgage debt between $25,000 and $49,999.
Although it is a small proportion, it is striking to note that approximately 3% of the
sample had a mortgage between $150,000 and $199,999, and 2.0% reported mortgage
debts of $200,000 or more.
The mean and median values of consumer and mortgage debt-by-debt categories
are also presented in Table 2. About 64% of the sample reported an average consumer debt balance of $1,460, while 3.3% owed an average consumer debt balance of
$6,283. A smaller proportion had higher debt balances; 10.7% had average consumer
debt balance of $11,996 and 3.4% had an average $21,876 consumer debt balance.
Moreover, about 2.3% of the sample held an average of $34,217 and 1.9% had an
average of $96,497 consumer debt balances. Regarding mean and median values of
mortgage debt, 39.3% reported an average of $11,345 in mortgage balances, while
24.7% owed an average of $35,218. About 24.1% of the sample reported an average
mortgage balance of $67,410. Table 2 indicates that 2.0% of the sample reported an
average mortgage debt of $337,100.
Logistic Results
Total Debt
Table 3 presents factors affecting the probability of holding debt in later life. In this
study, housing tenure was controlled when estimating the probability of holding debt
in later life. As expected, the effect of housing tenure was significant and positive,
indicating that homeowners were more likely to hold debt in later life than were
non-owners.
The results of the logistic regression analysis also indicated that net worth,
household income, family size, age, education, gender, marital status, race,
employment status, and housing tenure were all statistically significant predictors of
the probability of holding debt in old age. The relationship between log of net worth
and the probability of holding debt in later life was negative, indicating that the
probability of holding debt in old age decreased as the level of net worth increased.
It is interesting to note that the effect of income on the probability of holding any
debt was significant; however, the coefficients associated with each income category
indicate different effects. For example, those with income levels between $15,000
and $35,000 and those with incomes less than $15,001 were less likely to hold debt as
compared with those whose income was greater than $75,000. Respondents with
incomes of $55,001–$75,000 were more likely to report debt in later life than those
whose income was greater than $75,000.
Results were generally consistent with the hypotheses. As expected, the probability of holding debt in later life increased as the number of family members increased. The relationship between age and the probability of holding debt in later
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life was negative. The odds ratio shows that as compared with those aged 65–74,
individuals aged 75–84 and those aged 85 and above were 52% and 81% less likely to
hold any debt, respectively. Those with at least a high school diploma were 33%
more likely to hold debt in later life as compared with those with less than high
school education, while those with some college or a college degree were 93% and
103% more likely to hold debt, respectively.
Female older Americans were less likely to report debt than male older Americans. Whereas divorced individuals were more likely to hold debt than married
individuals, never married older individuals were less likely to report any debt than
their married counterparts. As compared with Whites, Blacks, and Hispanic/Others
were 91% and 44% more likely to hold any debt in later life. The results show that as
compared with unemployed individuals, those who were working full-time or parttime were, respectively, 61% and 96% more likely to report having debt. Contrary to
expectations, health status was not a significant factor in holding debt.
Consumer Debt
The results of the logistic regression analysis indicated that log of net worth,
household income level, family size, age, education, gender, marital status, race, and
employment status were all statistically significant in predicting the probability of
holding consumer debt in later life (see Table 4). Specifically, individuals with lower
levels of assets, lower levels of income, or larger households, those aged 65–74, the
more highly educated, males, married, those who were Black, working full-time or
part-time or holding a mortgage balance were likely to report having consumer debt
in later life.
The relationship between the log of net worth and the probability of holding
consumer debt was negative, indicating that the probability of having consumer debt
decreased as the level of net worth increased. The dummy variables associated with
five income levels were significant. Individuals whose income was (a) $15,001–
$35,000, (b) $35,001–$55,000, or (c) $55,001–$75,000 were more likely to hold
consumer debt, as compared with those with incomes greater than $75,000. Middleincome groups were more likely than those with incomes greater than $75,000 to owe
credit card balances, medical debts, loans from relatives, and other debts.
As expected, the probability of holding consumer debt increased as the number of
family members increased. Consistent with the life-cycle hypothesis, the relationship
between age and the probability of holding consumer debt was negative. The odds
ratio shows that as compared with those aged 65–74, individuals aged 75–84 and aged
85 and above were, respectively, 36% and 75% less likely to report having consumer
debt. Those with at least a high school diploma were 25% more likely to hold
consumer debt than those with less than a high school education. Those with some
college or a college degree were, respectively, 51% and 33% more likely to hold
consumer debt than those with less than a high school education.
As compared with males, females were 11% less likely to have consumer debt.
Never married individuals were 35% less likely to report consumer debt than
married counterparts. As compared with Whites, Blacks were 74% more likely to
hold consumer debt. As compared with those who were not in the labor force,
individuals who were working full-time or part-time were 43% and 54% more likely
to hold consumer debt, respectively, than those who were not in the labor force.
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Table 4 Logistic regression results of the probability of holding consumer and mortgage debt
among elderly (N = 9,996)
Variable
Log net worth
Income:
<$15,001
$15,001–$35,000
$35,001–$55,000
$55,001–$75,000
($75,000<)
Family size
Age: (Young-old, 65–74)
Middle-old (75–84)
Old-old (85+)
Education: (Less than high)
High school graduate
Some college
College education
Gender: (Male)
Female
Marital status: (Married)
Separated/divorced
Widowed
Never married
Race: (Whites)
Blacks
Hispanic/others
Employment status: (Not working)
Full-time
Part-time
Self-reported health: (Excellent)
Fair/poor
Good
Very good
Holding consumer/other debt
Holding mortgage debt
Intercept
Log likelihood
v2
Model 2: Consumer debt
Model 3: Mortgage debt
Parameter
estimate
Odds
ratio
Parameter
estimate
Odds
ratio
–0.1720***
0.842
0.0584**
1.060
0.1721
0.3366***
0.3570***
0.4050***
1.188
1.400
1.429
1.499
–0.6114***
–0.2632**
0.0668
0.0791
0.543
0.769
1.069
1.082
0.0749***
1.078
0.2229***
1.250
***
–0.4473
–1.3720***
0.639
0.254
***
–0.7364
–1.7206***
0.479
0.179
0.2228***
0.4090***
0.2868***
1.250
1.505
1.332
0.0890
0.3736***
0.4882***
1.093
1.453
1.629
–0.1127
0.893
–0.0877
0.916
–0.1115
–0.1262
–0.4381*
0.895
0.881
0.645
0.1134
0.0321
–0.7748***
1.120
1.033
0.461
0.5525***
0.0642
1.738
1.066
0.7192***
0.3773**
2.053
1.458
0.3512***
0.4332***
1.429
1.542
0.3653***
0.6524***
1.441
1.920
0.1867
0.1715
0.0250
1.205
1.187
1.025
n/a
1.0512***
–0.3463
8376.580
893.308***
2.861
–0.2123*
–0.2317**
–0.1579
1.0504***
n/a
–2.3686***
8567.630
1360.537
0.809
0.793
0.854
2.859
* p < .05; ** p < .01; *** p < .001
Note. Reference categories in models are presented in parentheses
As expected, having mortgage debt was a significant and positive factor in the
probability of holding consumer debt. Thus, it can be said that holding one kind of
debt is associated with the probability of holding another kind of debt. Neither
gender nor health status were significant factors in the probability of holding consumer debt.
Mortgage Debt
Table 4 also indicates the factors that influence the probability of holding mortgage
debt. The results of the logistic regression analysis for the mortgage debt model were
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317
similar to the findings of the consumer debt model. The complementarity of debt
holding is again noted with consumer debt a positive and significant factor in the
probability of mortgage holding.
The results of logistic regression analysis indicated that the effect of log of net
worth on the probability of holding mortgage debt was significant and positive; as the
level of net worth increased, the probability of having mortgage debt increased.
Those with incomes less than $15,001 and those with incomes between $15,001 and
$35,000 were less likely to hold mortgage debt as compared with those having incomes greater than $75,000.
As hypothesized, the probability of holding mortgage debt in later life increased
as the number of family members increased. Those aged 75–84 were 52% less likely
and those aged 85 and above were 82% less likely to have mortgage debt than those
aged 65–74. That result implies that most respondents aged 75 and older have been
able to pay off their mortgage. Those with some college were 45% more likely to
hold mortgage debt than those with less than a high school education. Those with a
college education were 63% more likely to have mortgage debt as compared with
those with less than a high school education. Thus, the hypothesis that those with
higher levels of education are more likely to be mortgage debtors in later life was
supported.
As hypothesized, Blacks were more likely to hold mortgage debt than Whites. In
fact, compared with Whites, Blacks were 105% more likely to report mortgage debt,
while Hispanic or others were 46% more likely to owe mortgage debt. Those who
were working full-time or part-time were 44% and 92% more likely, respectively, to
have mortgage debt than those not employed. Thus, the hypothesis was supported.
On the other hand, never married individuals were 54% less likely to have mortgage
debt than married individuals; therefore, this hypothesis was not supported. Individuals who perceived their health as good were 19% less likely to hold mortgage
debt compared with those who perceived their health as excellent.
Summary, Conclusions, and Implications
This study used the 2000 Rand HRS data file to examine the debt holding patterns of
Americans age 65 and older and to investigate which factors influenced the likelihood of holding consumer or mortgage debt in later life. Although more than 80% of
the respondents had no consumer or mortgage debt, a small portion of seniors
reported substantial consumer and mortgage debt balances. About 18% of these
seniors owed consumer debts from credit card balances, medical debts, or loans from
relatives, and 19.7% of the sample reported that they still have balances from
mortgage debt or other home loans. Among those with consumer debt, 1.9% owed
more than $50,000 in consumer debt; whereas, of those with mortgage debt, 11.9%
reported mortgage balances exceeding $100,000. Of course, whether these relatively
high levels of debt are a problem for the respondents and should concern policy
makers depends on the resource level of the debt holders. The fact that, all else
equal, the odds of holding either consumer or mortgage debt were relatively lower
for those with incomes above $75,000 as compared with those with less income
indicates that, for some seniors, the debt burden may be non-trivial.
Logistic regression results present some interesting contrasts. All else equal,
consumer debt and mortgage debt appear to be complementary goods. A positive
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and statistically significant relationship was found between consumer debt and
mortgage debt (and vice versa). At the same time, there is evidence that, after
controlling for other factors, resources and perhaps different tastes and preferences
may underlie use of mortgage debt versus consumer debt. Older individuals with
higher levels of net worth were less likely to report total debt and consumer debt.
Similarly, income level was negatively associated with the probability of holding
consumer debt. Conversely, however, those with higher levels of net worth were
more likely to report having mortgage debt; whereas, having relatively higher income was positively associated with the probability of owing mortgage debt. It may
be that, at least for some individuals, mortgage debt may be viewed as a way to
acquire an appreciating asset. In this sense, mortgage reduction would be a form a
saving that garners a larger share of asset ownership over time. In contrast, these
same individuals may view consumer debt as a drain on economic resources and
consider it unnecessary in light of their relatively higher economic resources.
Overall, study findings indicate that those who are more highly educated, married,
and Black were more likely than their counterparts to hold consumer debt as well as
mortgage debt in later life. As family size increased, the probability of holding total
debt, consumer debt, and mortgage debt increased, underscoring the fact that
financial demands and resources play an important role in the levels of debt carried.
In addition, older employed persons were more likely to hold consumer debt and
mortgage debt than were those who were not employed. It is unclear whether those
who are employed continue to work because they still owe consumer and/or mortgage debt or whether they choose to continue working for non-economic reasons.
The extent to which these debts are a matter of convenience or a burden due to
insufficient resources is a topic for future study.
Persons represented in this cross-sectional analysis are part of the greatest generation. From an economic perspective, this cohort, survivors of the Great
Depression and World War II, may be the most fortunate senior generation in recent
history. They were more likely than previous or subsequent generations to retire
with a company pension, inflation-adjusted Social Security payments, Medicare, and
retiree health benefits. President Lyndon Johnson’s War on Poverty during the 1960s
succeeded in moving many elderly Americans out of poverty. Older Americans are
now the wealthiest age group in America. More than 80% of this cohort was debtfree in this study; however, this situation may be changing.
Findings of this study indicate that a substantial minority of older Americans may
be living their golden years in debt. It could be argued that debt levels among older
persons may be simply a natural manifestation of the life cycle hypothesis applied
with an optimistic view of time remaining before death. In this scenario, results of
miscalculations of that time frame may not be problematic. Debts owed at death are
not passed on to heirs but paid out of the deceased’s estate. Claims that exceed assets
result in losses to lenders. Thus, owing consumer or mortgage debt may be one form
of informal financial planning among indebted older persons.
Other scenarios exist, however. The Retirement Security Projection Model
(RSPM) developed by the Employee Benefit Research Institute (2004) points to
greater financial insecurity for future retirees due to growing longevity and rising
health care costs at a time when employer-provided health care coverage for both
workers and retirees is declining. A growing national retirement income shortfall of
$57 billion by 2030 is projected. In this study, there was no way to identify how much
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debt among the elderly is due to health care expenses. Future research should
explore the reasons for incurring debt in later life.
The baby boom generation is unlikely to share the economic fortune of their
parents. With longer life spans and soaring medical costs, Medicare, Medicaid, and
Social Security are likely to be less generous for future retirees (Aizcorbe et al.,
2003). Further, the baby boom generation may be less frugal than their parents and
more inclined to carry consumer and mortgage debt into old age (Draut & McGhee,
2004). The different money management patterns of the baby boom generation call
for more research on the impact of debt on the long-term financial well-being of the
coming cohorts of older Americans.
Baby boomers need to be educated about the potential risks of entering later life
with substantial debts. Although a combination of debt and employment in later life
may work well for some individuals, continued employment income is not guaranteed. Disability, poor health, and layoffs are more likely to strike as the years roll on.
Death of a spouse may leave a widow or widower with fewer financial resources than
anticipated. Further, debt in later life may result in reduced access to essential health
care and restrictions on activities.
With limited historical data on existence of debt among the 65 and older age
group, it is difficult to determine if the debt burdens of older Americans are growing.
However, data on trends in bankruptcy filings shows that the largest cohort filing for
bankruptcy is the baby boomers and that the fastest growing age group among
bankruptcy petitioners is age 65 and older (Sullivan et al., 2001). With boomers
approaching retirement, more attention needs to be directed to the debt burden of
this group.
Financial professionals and policy makers can play an important role in helping
increase the financial security of retirees. Older persons may be hesitant to reveal or
discuss their debts with a financial counselor. There is a need to train financial
counselors to deal with the sensitive issues surrounding older Americans and debt.
Future retirees may need to invest a larger percent of their incomes more aggressively in order to build sufficient resources for future financial security. Now is the
time to address the issue of growing debt burdens among pre-retirees who still have
time to make decisions on delaying retirement in order to pay down debt.
References
AmericaSaves (2002). Black Americans hold much less wealth than other Americans. Retrieved
August 31, 2002, from http://www.americasaves.org/back_page/savinginamerica_first.cfm
Aizcorbe, A. M., Kennickell, A. B., & Moore, K. B. (2003). Recent changes in U.S. family finances:
Evidence from the 1998 and 2001 survey of consumer finances. Federal Reserve Bulletin, 1–32.
Retrieved September 24, 2004, from http://www.federalreserve.gov/ pubs/bulletin/2003/
0103lead.pdf
Butrica, B., Iams, H., & Smith, K. (2003). It’s all relative: Understanding the retirement prospects of
baby boomers. Center for Retirement Research at Boston College, working paper #2003-21.
Executive Summary. Retrieved February 25, 2004, from http://www.bc.edu/centers/ crr/
wp_2003–21.shtml
Chien, Y., & DeVaney, S. (2001). Using credit to cover living expenses: A profile of a potentially
risky behavior. Journal of Consumer Affairs, 34(1), 162–179.
Copeland, C. (2004). Debt of the elderly and near elderly, 1992–2001. Employee Benefit Research
Institute Notes, 25(4), 1–13. Retrieved January 10, 2004, from http://www.ebri.org/notepdfs/
0404notes.pdf
123
320
J Fam Econ Iss (2007) 28:305–320
DeVaney, S. A., & Chiremba, S. T. (2005). Comparing the retirement savings of the baby boomers
and other cohorts. Retrieved March 29, 2005, from http://www.bls.gov/opub /cwc/print/
cm20050114ar01p1.htm
Draut, T., & McGhee, H. C. (2004). Retiring in the red: The growth of debt among older Americans.
New York: Demos. Borrowing to make ends meet Briefing Paper #1. Retrieved November 14,
2004, from http://www.demos-usa.org/pubs/ Retiring_In_The_Red_WEB.pdf
Dugas, C. (2002). American seniors rack up debt like never before. USA today. Retrieved September
24, 2004, from http://www.usatoday.com/money/perfi/retirement/2002-04-25-elderly-debt.htm
Employee Benefit Research Institute (2004). American’s future retirement security: Implications of
the EBRI-ERF retirement security projection model. EBRI Issue Brief, 16, Retrieved August 1,
2005, from http://www.ebri.org
Gist, J., & Figueiredo, C. (2002a). Deeper in debt: Trends among mid-life and older Americans.
AARP Public Policy Institute. Retrieved September 24, 2004, from http://research.aarp.org/
econ/dd70_debt.html
Gist, J., & Figueiredo, C. (2002b). Deeper in debt redux: Housing and nonhousing debt burdens.
AARP Public Policy Institute. Retrieved September 24, 2004, from http://research.aarp.org/
econ/dd75_debt.html
Glass, J.C. Jr., & Kilpatrick, B. (1998). Financial planning for retirement: An imperative for baby
boomer women. Educational Gerontology, 24, 595–617.
Hogarth, J. M. (1991). Asset management and retired households: Savers, dissavers, and alternators.
Financial Counseling and Planning, 2, 97–121.
Hong, G. S., & Swanson, P. (1995). Comparison of financial well-being of older women: 1977 and
1988. Financial Counseling and Planning, 6, 129–138.
Institute for Social Research (2005). Health and retirement study (HRS). Retrieved May 10, 2004,
from http://hrsonline.isrumich.edu
Keith, P. (1986). The social context and resources of the unmarried in old age. International Journal
of Aging and Human Development, 23(2), 81–96.
Kennickell, A. B., & Starr-McCluer, M. (1997). Household saving and portfolio change: Evidence
from the 1983–1989 SCF panel. Review of Income and Wealth, 4, 381–399.
Lee, Y., Lown, J., & Hong, G. (2002). Financial security of single older women aged 70 and older:
How much worse off are they than single older men? Proceedings of the Association for
Financial Counseling and Planning Education, 34–44.
Mathur, A., & Moschis, G. P. (1994). Use of credit cards by older Americans. Journal of Service
Marketing, 8(1), 27–36.
Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function: An interpretation of cross-section data. In K. Kurihara (Ed.) Post-Keynesian economics. New Brunswick:
Rutgers University Press.
Ozawa, M. (1993). Solitude in old age: Effects of female headship on elderly women’s lives. Journal
of Women & Social Work, 8(2), 136–157.
Paulin, G. (2000). Expenditure patterns of older Americans, 1984–97. Monthly Labor Review,
123(5), 3–28.
Paulin, G., & Duly, A. (2002). Planning ahead: Consumer expenditure patterns in retirement.
Monthly Labor Review, 125(7), 38–58.
Smith, J. (1997). Wealth inequality among older Americans. Journal of Gerontology, 52(B), 74–81.
Sullivan, T. A., Thorne, D., & Warren, E. (2001). Young, old, and in between: Who files for
bankruptcy? Norton Bankruptcy Law Advisor, 9A, 1–10.
Tucker, J. A., Lawrence, F. C., Garrison, B. M. E., Summers, T., Hill, R. C., & Bruin, M. J. (2001).
An examination of the baby boom generation’s financial preparations for retirement. Consumer
Interests Annual, 47, 1–3.
Xiao, J. J. (1995). Patterns of household financial asset ownership. Financial Counseling and Planning, 6, 99–106.
123