Gender Differences in Attitudes toward Debt and Financial Position:
The Impact of the Great Recession
Julie Lyn Routzahn
McDaniel College
and
Mary Eschelbach Hansen
American University
This version: October 16, 2014
Keywords: Gender, attitudes towards debt, household debt, Great Recession, feminist methods
JEL Codes: B54, D12, J16
Acknowledgements: Comments from Tom Husted, Robert Feinberg, and Martha Starr are much
appreciated. Tiffany Chang provided research assistance.
1
Abstract
We use the 2007 and 2010 Surveys of Consumer Finances (SCF) and a difference-in-differences
approach to investigate whether the Great Recession differentially affected the attitudes of men
and women towards taking on debt, as well as whether it affected the relative financial position
of women. In order to separate the influence of gender per se from the influence of household
composition and intra-household bargaining, we compare never-married women to nevermarried men. We also compare never-married women to all households. We find that the Great
Recession gave never-married women the confidence to use debt to mitigate short-term threats to
living standards while making them more cautious about taking on debt for non-essentials. The
financial position of women in 2010 improved relative to the position of men and relative to all
households
2
Gender Differences in Attitudes toward Debt and Financial Position:
The Impact of the Great Recession
Introduction
We investigate gender patterns in the use of consumer debt and attitudes towards it. Alternatives
for consumer borrowers have been expanding since the 1980s (Ryan, Trumbull, and Tufano
2011), and the share of household income going towards required payments on debt has tripled
between 1983 and 2007 (Dynan 2009). On the eve of the Great Recession, households had more
debt than they had before previous downturns (Moore and Palumbo 2010), but there was no
growth in total debt during the recession. Pay-down of mortgage debt was significant, while
there was a small increase in average non-mortgage debt (Becker and Shabani, 2009; Chakrabarti
et al. 2011). Poorer consumers and younger consumers, in particular, continued to increase their
leverage (Wolff 2011). We find that patterns differ among women and men. We show that, after
controlling for family composition, women did not leverage up and their debt-to-income ratios
improved.
We argue that a central reason why women’s financial position improved relative to men’s is that
the experience of the Great Recession made women more cautious about borrowing for nonessentials. We offer a new strategy for identifying such gender differences. We abstract from
the “blackbox” of household decision-making by comparing the attitudes and financial positions
of never-married women to those of never-married men. This strategy minimizes the impact of
partners on attitudes and financial positions. We thereby establish a baseline of behavior to
which families, in all their complexity, can be compared.
3
Framework
Like most work on personal finance, our investigation has at its foundation the permanent
income hypothesis and life cycle models: A person maximizes lifetime utility by saving and
borrowing to smooth consumption (Friedman 1957, Modigliani and Brumberg 1954).
Preferences such as “impatience” lead some people to borrow more than others, although
creditors are not always willing to lend as much as a borrower desires (Deaton 1992).
Additionally, the stream of income and expenses is uncertain, so some cushion of savings may
be desirable, even during stages of the life-cycle when borrowing dominates behavior (Carroll
and Summers 1991, Carroll 1992, Deaton 1991). In a fully-fleshed-out buffer-stock model,
when people with “prudent” preferences (that is, people who prefer to ensure that consumption
does not fall below some floor) face a long-lasting increase in uncertainty, they increase their
desired level of savings at all stages in the life cycle.
The empirical work that follows is motivated by such a buffer-stock model of behavior, in which
preferences such as “impatience,” “prudence,” and attitudes about the acceptability of using debt,
or debt “tolerance,” are considered to be closely intertwined. Preferences and attitudes are
allowed to change over time as a result of macroeconomic phenomena, personal experience, and
cultural change. Preferences and attitudes are also allowed to differ by gender at a point in time
and across time (Giannetti 2014). An improvement in financial position is expected to result
from increased prudence or reduced tolerance for debt, all other things equal.
The Tolerance of Women for Debt
The empirical literature on the determinants of debt has documented life-cycle patterns of
borrowing. Being in the middle stages of the life-cycle—with higher income, homeownership,
and a larger number of children at home—is associated with more debt. But the level of debt
4
differs widely across people at the same stage of the life cycle (Crook 2001). Risk preferences
and impatience matter (Bertaut and Starr-McCluer 2002, Brown et al. 2005, Meier and Sprenger
2010). Expectations seem to contribute to within-stage differences: Optimism about one’s
financial future is associated with higher levels of debt (Brown et al. 2005, 2008).
The literature on debt tolerance as a determinant of borrowing behavior is surprisingly thin,
particularly in light of the importance of consumer credit to the economy (Glick and Lansing
2009, Mian and Sufi 2013) and the concerns of regulators about high levels of consumer debt
(for example, Durkin et al. 2014). The few studies that directly evaluate the role of attitudes
towards debt yield mixed results. In both American and British cross-sections, tolerance of debt
is correlated with higher debt-to-asset and debt-to-income ratios (Livingstone and Lunt 1992,
Schooley and Worden 2010). People seem to acclimate to debt—they develop positive attitudes
towards debt when they successfully use it (Davies and Lea 1995). However, these findings are
inconsistent with the observation that, as consumer debt grew in the 1980s and 1990s, attitudes
towards debt in U.S. became more negative (Bloom and Steen 1987, Goodwin 1997).
Our paper contributes by measuring the extent to which gender influences attitudes towards debt.
Previous studies of gender differences in financial matters have examined relative risk aversion
and its influence on investment behavior (for example, Prince 1993, Jianakoplos and Bernasek
2007) and gender differences in financing among entrepreneurs (examples include Coleman and
Robb 2009: Madill, Riding, and Haines Jr. 2006). However, little is known about how gender
influences attitudes toward debt. There is emerging evidence that women feel more burdened by
debt after it is incurred (Keese 2012) and that women use part of newly-acquired welfare
payments to pay down debt while men do not (Lyons and Fisher 2006; Shaefer, Song, and
5
Shanks 2013). These findings lead us to suspect that women’s responses to the Great Recession
may be different from men’s.
Strategy for Detecting Gender Differences in Tolerance for Debt
We are particularly interested in separating the effects of gender per se from other influences on
women’s attitudes and behavior. Among potential influences, the most difficult and important to
separate from gender is the influence of family dynamics and intra-household bargaining.
Within families money matters have strong gender dimensions. For example, Goode (2010)
demonstrates that women are more likely to manage household finances in poor families where it
is a burden rather than a source of power. Similarly, Thorne (2010) finds that financial chores of
financially distressed households are almost always performed by women. Zagorsky (2003)
even finds that husbands report more assets and less debt than their wives. As noted by Doss et
al. (2008), to conduct the best possible gender research, it would be desirable to survey
extensively all members of the household about assets, debts, and attitudes. However, no such
survey of U.S. households is available.
Building on Routzahn (2011), we suggest that it is possible to separate family dynamics from
other aspects of gender by measuring the differences between women and men who have never
been married and who are not currently cohabiting. While our strategy cannot remove the
influence of past relationships or childhood experiences, it does eliminate the direct impact of
spousal opinions and decisions. It establishes a baseline of behavior among never-married
individuals to which future research can be compared. We apply this strategy to data from the
2007 and 2010 Surveys of Consumer Finances (SCF).
6
Data from the Surveys of Consumer Finances
The Surveys of Consumer Finances (SCF) are among the most comprehensive financial surveys
available (Lindamood et al. 2007; Board of Governors 2009, 2012). The surveys solicit
responses from the one member of the household that the interviewer establishes as the most
financially knowledgeable. From this respondent, the interviewer collects detailed information
about all forms of household assets and liabilities, as well as responses to questions on the
attitudes towards debt of the respondent himself or herself.1
To study gender differences and changes in attitudes toward credit, we use answers of
respondents to questions about how they feel about borrowing to finance different kinds of
expenditures. Respondents answered “yes” or “no” to each part of the following question:
Tell me whether you feel it is all right for someone like yourself to borrow money:
 To finance the purchase of a car.
 To finance educational expenses.
 To cover living expenses when income is cut.
 To finance the purchase of a fur coat or jewelry.2
 To cover the expenses of a vacation.
1
Because the SCF questions involve sensitive financial data that respondents are often reluctant to
provide, many variables have missing information. If the respondent does not give an exact answer,
interviewers try to get an approximation using ranges of values, but some respondents still do not provide
answers to some questions. To account for missing information and to ensure the privacy of respondents,
each response is replicated five times. For details, see Rubin (1987). We use the procedures for using the
replicates described in Board of Governors (2009, 2012). Additionally, since the survey oversamples the
wealthy, all reported results are weighted using the population weight assigned to each observation by the
SCF.
Use of multiple imputations is gaining popularity as researchers need a reliable way to deal with missing
data and ensure privacy. However, , some concepts that are well-defined in simple regression analysis do
not have a comparable in multiple imputation analysis (StataCorp 2009). Goodness-of-fit statistics are
one such example. We report the average of the goodness-of-fit indicator over all five imputations (see
for example, Lee and Carlin 2010).
2
Previous research using this question for example, Chien & Devaney (2001) interprets the question as
asking about attitudes about borrowing to finance the purchase of a luxury, but the specific wording may
be gender-biased. For example, a male respondent might feel differently about borrowing to finance
jewelry than he would feel about borrowing to finance a boat or motorcycle.
7
Figure 1 summarizes the responses. The majority of all respondents in both the 2007 and 2010
SCF believed it was acceptable to borrow money for a vehicle and for education, but only singledigit percentages believed it was acceptable to borrow for a luxury. The most positive attitudes
about any kind of debt were those of never-married women regarding debt-finance of education:
In 2007 over 88 percent found student debt acceptable. In 2010, 86 percent found it acceptable.
There was only one other statistically significant increase in debt tolerance between 2007 and
2010: never-married men became more tolerant of debt-finance of vacations.
[Figure 1 about here]
Of course, part of the difference between the attitudes of never-married respondents—men or
women—and married respondents may be due to differences in their average ages. The average
age of married respondents was just shy of 50, while the average age of never-married men and
women was about 38. The average age of respondents in 2010 did not differ meaningfully from
the age of respondents in 2007. Education levels, which also may be related to preferences, also
did not differ appreciably between respondents of the two surveys. Never-married women and
men had a fraction of a year more education on average than married respondents. However, the
household income of never-married respondents was less than half the household income of
married households. Descriptive statistics of demographics, socio-economic status, and other
controls included in the analysis below appear in Appendix A.
To study changes and differences in the financial positions of households, we use the SCF data
to compute standard financial ratios including loan balances relative to total assets, loan balances
relative to total income, and monthly loan payments relative to monthly income. We use the
definitions that are used in the Bulletin (Board of Governors 2009, 2012) in order for this work to
8
be comparable to the greater literature that uses the SCF. Assets include both financial and nonfinancial assets. Financial assets include certificates of deposit, stocks, bonds, savings accounts,
money market accounts, and so on. Non-financial assets include residential real estate, other real
estate investments, and business assets. All income sources are included: wage income, interest
and dividend income, self-employment and business income, pension income, realized capital
gains, government program income (social security and food stamps), and alimony.
The means of the major financial ratios, by respondent group, are shown in Figure 2.
Households headed by never-married women do not have high debt-to-income ratios, but they
are the most leveraged by far. This mainly reflects their low levels of assets. Women tend to
hold fewer assets than men, resulting in a gender asset gap (Deere and Doss 2006). Assets are
important to households because they can appreciate in value, generate income, and serve as a
source of collateral. Additionally, assets provide a level of security in that they buffer against
emergencies. They can also serve as a way to increase productivity and improve the ability of
the owner to earn a living. With less income and fewer assets, women are more financially
vulnerable than men.
The asset gap is partly explained by gender differences in preferences: Women exhibit more
risk-averse behavior than men in the choice of their asset portfolio, which results in lower returns
than men (Jianakoplos and Bernasek 2007). It also appears that women experience statistical
discrimination (Phelps 1972) when they receive financial and legal advice for investments,
insurance, loans, and bankruptcy, since they are viewed as the “safer sex” (Lefgren and McIntyre
2009). Women are more likely to use financial planners than men found, either because of their
lack of financial education or because of greater concern about the future (MacGregor and Slovic
9
1999). Men feel more competent in financial matters than women, and consequently they are
willing to take risks to amass wealth (Prince 1993).
The main take-away from Figure 2 is that the average household of a married couple or of a
never-married man in the 2010 survey was in worse position than the corresponding households
had been in 2007. In contrast, the households of never-married women were less leveraged in
2010 than their counterparts had been in 2007. The average income-to-debt ratio of nevermarried women remained about the same, but in 2010 the average never-married man had a
higher monthly debt-to-income ratio than he had in 2007. Moreover, in 2010 monthly debt
burden of the typical never-married man was higher than the burden carried by typical nevermarried woman that year.
[Figure 2 about here]
This is a dramatic reversal in financial status of never-married women relative to men. It further
supports research showing that women fared better during the most recent recession. For
example, they had relatively favorable employment outcomes and higher labor force
participation than men (Elsby, Hobijn, and Sahin 2010; Starr 2014). These favorable
employment outcomes are attributed to gender differences in industry and occupation (Hoyes et
al. 2012, Albanesi and Sahin 2013). That is, women were employed in areas that were not hurt
as much by the recession, whereas mean were employed in occupations that were more affected
by it.
10
Gender Differences in Attitudes toward Debt
We use a difference-in-difference approach to evaluate whether the Great Recession had a
differential impact on the attitudes towards debt of never-married women and men. Call a
positive response to a question about attitudes P. We estimate by probit
𝑃𝑟𝑜𝑏(𝑃𝑖𝑡 ) = 𝛽0 + 𝛽1 𝐹𝑖 + 𝛽2 𝑌𝑒𝑎𝑟𝑡 + 𝛽3 𝑌𝑒𝑎𝑟𝑡 ∗ 𝐹𝑖 + 𝛾𝑋𝑖 + 𝑢𝑖𝑡 ,
where F indicates that the respondent is a never-married female and Year indicates whether the
observation is from the 2010 SCF. The estimate of particular interest is 𝛽3, the coefficient on the
interaction between being a never-married female and being observed in 2010, after the Great
Recession. If 𝛽3 is negative and statistically significant, it we conclude that the attitudes towards
credit of never-married women grew more conservative than did the attitudes of never-married
men over the course of the recession.
The vector X includes respondent-specific and household-specific controls. In addition to sex and
marital status of the respondent, we control for age, education, race, and number of children. We
also control for expectations about the future. Expectations are represented by self-reported life
expectancy, current health status, opinion on future economic conditions, and respondent’s
prediction of the direction of change of interest rates. Additionally, we control for employment
status (whether the respondent has been unemployed in the last 12 months), credit worthiness
(whether the respondent has been turned down for credit in the last five years), and job quality
(proxied by access to employer-sponsored retirement and health insurance plans).
In the top panel of Table 1 we show the marginal effects when the estimation sample is restricted
to never-married respondents. Again, this is our strategy for isolating the effects of gender and
gender norms from the effects of the dynamics of the respondent’s adult relationships. In the
11
bottom panel we show results comparing the households of respondents who are never-married
women to the households of all other respondents in the SCF.
[Table 1 about here]
Consider first the top panel of Table 1. At baseline, never-married women were about as
accepting of loans for cars, vacations, and living expenses as never-married men, but more
accepting of student loans and less accepting of loans for luxuries. Attitudes towards credit were
more conservative in 2010 than they had been in 2007, which is consistent with the work of
Malmendier and Nagel (2011) who find that an experience of economic hardship reduces
acceptance of borrowing. The probability that a respondent approved of loans for cars and living
expenses fell by 7 to 8 percentage points. Only attitudes towards educational debt were
unaffected by the recession.
The recession did not have a gender-specific effect on attitudes about borrowing for cars,
education, or luxuries. However, compared to never-married men, never-married women
became more tolerant of debt to finance living expenses when income is cut and less tolerant of
debt to finance vacations. Women’s experiences during the recession gave them the confidence
to use debt to mitigate short-term threats to living standards, but made them more cautious about
financing non-essentials. This result mirrors the employment success women experienced during
the recession as compared to men.
This change in attitudes is not driven by the composition of the sample: the demographic and
socio-economic attributes of never-married women in the 2007 sample was not different from
the attributes of women observed in the 2010. (See Appendix A.) We know that documented
gender differences exist in wage income, investment strategy, risk propensity, asset ownership,
12
availability of health insurance and retirement benefits, just to name a few. Men and women also
differ in their responsibilities to care for children and older family members. Ultimately these
differences result in a disparity of available resources between never-married women and nevermarried men. Therefore, it is not possible to capture every component of possible difference in
the regression analysis.
Turning to the results from the full sample shown in the bottom panel of Table 1, we see that
never-married women were more accepting of debt in 2007 than were all respondents. This may
be because never-married women must rely on the marketplace to a greater extent than married
and cohabitating respondents, who may have more secure safety nets through larger extended
families, the possibility of having two incomes, and greater access to better employment benefits,
such as health insurance and retirement plans. The trend towards more conservative attitudes
was not limited to never-married respondents, but the size of change in attitudes between
samples is smaller when all respondents are considered than when only never-married
respondents are considered. Again, this likely reflects the younger age of the never-married
group, who were among the most hard-hit during the recession.
The diff-in-diff coefficient in the bottom panel is negative in four of the five regressions, though
it is statistically significant in only the regression for attitudes about borrowing for luxuries. This
reinforces the results from the restricted sample: the experience of the recession caused nevermarried women to become more cautious about borrowing for non-essentials.
Gender Differences in Financial Position
To investigate whether the changes in attitudes that we observe resulted in changes in the
financial position of never-married women, we use OLS to estimate a difference-in-difference
13
specification similar to the logit specification above, but with household financial ratios as the
dependent variables and attitudes towards debt as explanatory variables. Table 2 shows results
for the leverage ratio and debt-to-income ratios. Again, we follow the SCF variable definitions
to make our work comparable to the greater literature. These dependent variables are calculated
as ratios and are shown as percentages in the summary tables. Table 3 shows results for three
types of debts: car loans, student loans, and credit card debt. In addition to the demographic and
socio-economic controls used in the probit models of Table 1, the models and also control for
credit-worthiness.
[Table 2 about here]
As we saw in Figure 2, at baseline never-married women were in worse financial health than
never-married men. This is true whether we consider leverage, overall debt-to-income, or
monthly obligations-to-income. The overall de-leveraging in response to the Great Recession
that has been noted elsewhere (Becker and Shabani 2010; Chakrabarti et al. 2011) is not evident
for never-married men, but it is evident in the full sample and for never-married women. These
households have been ridding themselves of debt through bankruptcy and the short sale of
homes. At the end of 2009, household debt stood at 122.5% of disposable income, down from
its peak of 130.6% (Whitehouse 2010).
Never-married women reduced both outstanding obligations relative to annual income and
monthly required payments relative to monthly income by an economically significant extent.
Because never-married women have few assets, the debt-to-income measures most accurately
14
reflect their relative financial position.3 The reduction of debt can come from two sources:
Individuals can pay down the debt or they can rid themselves of the asset that resulted in the
debt. We know that many households rid themselves of debt associated with housing. When we
take both gender and the gender*recession contribution into account, we find that never married
women reduced their debt to income ratio by 40%. For the average never married female, that
amounts to a drop in debt of approximately $13,000. Income changed very little.
The majority of deleveraging came from the reduction of mortgage debt. This has both positive
and negative effects. While it is true that women improved their household balance sheets by
decreasing debt it was a type of debt directly tied to an asset that (historically) would have
appreciated in value over the long term.
When we look more closely at the monthly debt to monthly income ratio, we see that nevermarried women reduced their monthly debt payments by 2 percent of income. This is not as
large as one may expect considering the drop in the leverage ratio. However, it was probably the
case that never married-women were at an early stage in their mortgage amortization. They were
likely paying down very little debt each month, with the majority of the payment going to
interest. It will be a matter for future work to discover whether the short run gain from smaller
monthly payments was worth the sacrifice of the underlying housing assets for these women.
To control for attitudes in the regressions for overall household financial ratios, we sum the
individual attitudes responses to create an index of debt tolerance. We consider a respondent
with one or zero positive responses to the attitudes questions to have conservative attitudes
3
Given that asset values, especially real estate and stock values, dropped substantially during the
recession, more analysis is necessary to determine the overall financial health of the household balance
sheet. Such an analysis of changes in asset portfolios is beyond the scope of the current paper.
15
towards debt. Those with two or three positive responses have moderate attitudes; those with
four or more positive responses have liberal attitudes. Although it is intuitive to expect more
liberal attitudes towards credit to be associated with higher debt relative to assets and income
(see, for example, Rutherford and DeVaney 2009), we find no statistically significant
relationships for the sub-sample of never-married respondents. The signs of the coefficients are
negative, however, indicating that never-married respondents with moderate-to-liberal attitudes
are most likely credit-constrained: they might like more credit, but cannot obtain it. Among all
respondents, liberal credit attitudes have the expected positive effect.
Lastly, Table 3 considers the types of debt most relevant to the greatest number of never-married
respondents: car loans, student loans, and credit card debt. At baseline, never-married women
had greater obligations than never-married men in all three categories, with the exception of car
loans to total assets. Compared to all respondents, never-married women had fewer obligations
in all three areas at baseline.
[Table 3 about here]
By 2010, however, never-married women had slightly larger car loans than never-married men
or other respondents. Note that these loans did not represent more of a drain on income.
Primarily, this reflects the lower income of never-married men in 2010 compared to 2007, since
attitudes about the acceptability of auto loans became more conservative among all groups
between 2007 and 2010.
Never-married women observed in 2010 did not have more student loan debt compared to assets.
Moreover, women’s student loans in 2010 placed less of a drag on their incomes than had been
the case in 2007. The differential is quite large—21 to 26 percent of income. It is important to
16
note again that the average years of education among women surveyed did not fall; in fact, the
distribution of years of education shifted slightly to the right. We see that education debt to
assets increased for all three groups, as many individuals returned to school or continued their
education during the recession. It appears that the entry of recently college graduates into the
labor force and into sample was more than offset by the pay-down of student debt by other
women.
In Table 1, we saw that the Great Recession caused never-married women to become more
accepting of using credit to smooth essential consumption, but less accepting of using it for nonessentials. The final set of regressions in Table 3, which show differences and changes in credit
card debt, show that women’s increased conservatism dominated their monthly budgeting
decisions. Never married women and men both decreased their dependence on credit card debt
relative to their assets and total. Moreover, although never-married women were more burdened
by credit card debt in 2007, they decreased their credit card obligations relative to total income
and monthly income. Never-married men did not. Never married women even reduced their
monthly obligations by more than all households did.
In sum, we find that the relative position of the finances of never-married women improved over
the course of the Great Recession. These women’s incomes changed little, but they paid down
their debt obligations significantly.
Among households generally, the story is less rosy. Overall, the debt-to-income ratio fell as
income fell, but the debt-to-asset ratios did not change. This implies that many households used
assets to pay down debt. Fewer assets means less insurance against future risk, whether those
risks are idiosyncratic (such as health risks) or systemic (such as unemployment).
17
Conclusion
We have shown that the Great Recession increased the tolerance of never-married women for
borrowing to meet living expenses but decreased their tolerance for borrowing to obtain nonessential items. The latter finding is reinforced by comparison of the attitudes of never-married
women to all households. A reduced tolerance for debt-finance of luxuries, combined with the
relatively favorable employment outcomes and higher labor force participation of women during
the Great Recession , led to an improvement in the relative financial position of never-married
women from 2007 to 2010.
These results are important on several fronts. First, they illustrate how essential gender analysis
is for the understanding economic outcomes. Our work adds to a growing body of literature that
reinforces the need for additional research through a gender-sensitive lens. Second, the results
make an important contribution to gender-sensitive research in financial economics. Previous
research has emphasized that women are more vulnerable to economic shocks because they own
fewer assets, are more likely to be underemployed, take less risk, and take on more caring
responsibilities. This research shows that, despite these vulnerabilities, women are able to
leverage opportunities to improve their balance sheets relative to men when circumstances
permit.
The results have several implications for women’s future status. The improvement in financial
stability that we observe for women has the potential to lead to more favorable outcomes for
them in a variety of areas. For example, we expect that improved financial stability will make it
easier for women to acquire assets in the future, thus reducing financial insecurity in old age.
We also expect that improved financial stability will increase the bargaining power of never-
18
married women entering into marriage, thus reducing domestic abuse and divorce while
improving outcomes for children.
Finally, the methodology used in this paper is important for feminist economics. We
demonstrate that it is possible to establish a baseline for measuring gender influences on
behavior by comparing never-married women to never-married men. This method can be used
to move other applied gender research forward while we await the development of gender
disaggregated surveys and the dissemination of their results.
Future research may focus more directly on gender differences in the debt-finance of assets. The
Survey of Consumer Finances, for example, includes information on installment debt, revolving
debt and other debt (which consists of loans against pensions, life insurance, and margin loans).
Identifying gender differences in financing will be critical for future recommendations on credit
market regulation and for financial education.
19
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23
Table 1. Gender Differences in Attitudes towards Debt
Car
Educa-tion
OK to Borrow for
Living
Exp. if
Income
Cut
Luxury
Vacation
Never-married Respondents (n=1,431)
Never-married Female
2010
2010 * Never-married Female
Pseudo R2
0.0008
0.0297 **
0.0008
-0.0255 **
0.0193
0.0149
0.0232
0.0111
-0.0668 ***
-0.0023
-0.0794 ***
-0.0219 **
0.0170
0.0125
0.0204
0.0101
0.0899 ***
0.0205
0.0189
0.0466
0.0165 **
0.0364
-0.0086
-0.0117
-0.0614
0.0229
0.0180
0.0273
0.0138
0.0221 **
0.0438
0.0778
0.0664
0.0483
0.0360
0.0189
0.0344 **
0.0552 ***
0.0130 *
0.0261 **
All Respondents (n=10,910)
Never-married Female
2010
2010 * Never-married Female
Pseudo R2
0.0132
0.0142
0.0179
0.0075
0.0123
-0.0166 ***
-0.0122 ***
-0.0208 ***
-0.0022
-0.0031
0.0046
0.0042
0.0061
0.0028
0.0045
-0.0115
-0.0117
0.0202
-0.0284 ***
-0.0124
0.0153
0.0163
0.0212
0.0094
0.0145
0.0573
0.0539
0.0412
0.0302
0.0170
Notes: Reported standard errors (in italics) are linearized.
Level of significance, p <.10 - *, p<.05 - **, p <.01 - ***
Adjusted R2 is averaged over five imputations.
24
Table 2. Gender Differences in Household Financial Ratios
Debt/
Assets
Debt/
Income
Monthly Debt/
Income
Never-married Respondents
(n=1,431)
Never-married Female
2010
2010 * Never-married Female
Moderate Credit Attitudes
Liberal Credit Attitudes
2
R
0.0045
***
0.4898
***
0.0139
0.0118
0.1610
0.0140
0.0027
-0.1143
-0.0025
0.0125
0.1007
0.0092
-0.0188
-0.8932
0.0153
0.2140
0.0179
0.0014
-0.0902
-0.0142
0.0108
0.1367
0.0135
-0.0146
-0.0937
-0.0024
0.0130
0.1801
0.0164
0.8031
0.1721
0.1942
-0.0311
-0.0571
0.0084
-0.1895
0.0148
0.0037
-0.1919
0.0024
0.0360
-0.0094
-0.8030
0.0109
0.1932
***
-0.0336
*
All Respondents (n=10,910)
Never-married Female
2010
2010 * Never-married Female
Moderate Credit Attitudes
0.0194
0.0163
**
***
0.0027
Liberal Credit Attitudes
2
R
0.0221
-0.0986
***
0.3113
***
-0.0201
0.0149
***
-0.0046
0.0036
*
0.0358
0.0046
0.1179
0.0118
0.7061
0.0414
0.1909
Notes: Reported standard errors (in italics) are linearized.
Level of significance, p <.10 - *, p<.05 - **, p <.01 - ***
Adjusted R2 is averaged over five imputations.
25
***
0.0037
0.0378
***
-0.0257
***
***
Table 3. Gender Differences in Financial Ratios by Type of Debt
Car Loans
Debt/
Assets
Never-married Respondents (n=1,431)
Never-married Female
-0.0107 ***
0.0032
2010
-0.0037
0.0026
2010 * Never-married Female
0.0169 ***
0.0037
OK to borrow for car?
0.0056 ***
0.0021
OK to borrow for education?
Debt/
Income
0.0168 **
0.1442
0.0098 **
0.0043
-0.0093
0.0084
-0.0090
0.0055
Student Loans
Monthly
Debt/
Income
0.0109 ***
0.0030
-0.0003
0.0015
-0.0062
0.0040
-0.0099 ***
0.0031
Debt/
Assets
Monthly
Debt/
Income
Debt/
Income
0.0197 *** 0.1977 ***
0.0063
0.0692
0.0073 ** -0.0023
0.0037
0.0188
-0.0168 *
-0.2626 ***
0.0090
0.0964
-0.0009
0.0011
0.0013
0.0009
-0.0021 *
0.0018
-0.0008
0.0031
-0.0001
0.0006
0.0131
0.0231
OK to borrow for living exp.?
OK to borrow for luxuries?
OK to borrow for vacation?
R2
All Respondents (n=10,910)
Never-married Female
2010
2010 * Never-married Female
OK to borrow for car?
0.5182
-0.0101 ***
0.0023
0.0019 ***
0.0004
0.0127 ***
0.0034
0.0006
0.0005
0.6531
0.6590
-0.0340 *** -0.0014
0.0084
0.0035
-0.0013
-0.0007
0.0020
0.0007
-0.0026
-0.0052
0.0092
0.0038
0.0025
-0.0003
0.0022
0.0008
OK to borrow for education?
0.0009
0.1299
0.4865
0.0010
0.1466 *
0.0065
0.0768
0.0060 *** 0.0004
0.0016
0.0075
-0.0142 *
-0.2139 **
0.0078
0.0826
-0.0009
0.0008
0.0006 ***
0.0002
-0.0011
0.0009
0.0097 ***
0.0017
0.0002 *
0.0001
0.0341 ***
0.0066
OK to borrow for living exp.?
OK to borrow for luxuries?
OK to borrow for vacation?
2
R
0.4926
0.4412
0.4229
0.0884
0.0691
0.3561
Notes: Reported standard errors (in italics) are linearized.
Level of significance, p <.10 - *, p<.05 - **, p <.01 - ***
Adjusted R2 is averaged over five imputations.
See text for control variables.
26
Debt/
Assets
Credit Card Debt
Monthly
Debt/
Debt/
Income
Income
-0.0008
0.0046
-0.0118 **
0.0057
0.0056
0.0048
0.0295 *** 0.0085 ***
0.0112
0.0032
-0.0114
-0.0028
0.0079
0.0023
-0.0345 ** -0.0098 **
0.0158
0.0045
0.0069 **
0.0027
0.0012
0.0024
0.0024
0.0028
0.0314 *** 0.0091 ***
0.0084
0.0024
-0.0101
-0.0027
0.0080
0.0024
0.0200 *** 0.0059 ***
0.0073
0.0021
0.0928
0.0143
0.0151
-0.0012
-0.0235 ** -0.0069 **
0.0026
0.0117
0.0033
0.0019 *** 0.0233 *** -0.0060 ***
0.0006
0.0023
0.0007
-0.0021
-0.0203
-0.0059 *
0.0031
0.0124
0.0036
0.0048 *** 0.0223 ***
0.0005
0.0024
0.0031 *** 0.0088
0.0014
0.0061
0.0055 *** 0.0401 ***
0.0009
0.0042
0.0066 ***
0.0007
0.0026
0.0018
0.0116 ***
0.0012
0.0864
0.0786
0.0785
Figure 1. Attitudes towards Debt in 2007 and 2010 by Respondent Gender and Marital
Status
OK to Borrow for Education
OK to Borrow for Car
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Never-married
Women
Never-married
Men
Married
Households
Never-married
Women
All Respondents
Never-married
Men
Married
Households
All Respondents
OK to Borrow for Living Expenses
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Never-married
Women
Never-married
Men
Married
Households
All Respondents
OK to Borrow for Vacation
OK to Borrow for Luxury
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Never-married
Women
Never-married
Men
Married
Households
All Respondents
2007
Never-married
Women
2010
27
Never-married
Men
Married
Households
All Respondents
Figure 2. Household Financial Ratios in 2007 and 2010
Debt/Assets
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
Never-married
Women
Never-married
Men
Married
All Households
Debt/Income
180%
160%
140%
120%
100%
80%
60%
40%
20%
0%
Never-married
Women
Never-married
Men
Married
All Households
Monthly Debt/Monthly Income
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Never-married
Women
Never-married
Men
2007
Married
2010
28
All Households
Appendix A. Descriptive Statistics of Control Variables
Dichotomous Variables
2007
%
2010
C.I.
n
%
C.I.
n
Race of respondent - White
Never-married Women
52%
49%
55%
248
46%
44%
48%
497
Never-married Men
68%
66%
71%
238
70%
68%
72%
448
Married
83%
82%
83%
2984
78%
77%
78%
4072
All
80%
79%
80%
4418
73%
73%
74%
6492
Race of respondent - Black/African American
Never-married Women
36%
33%
39%
248
35%
33%
37%
497
Never-married Men
16%
14%
19%
238
15%
13%
16%
448
Married
6%
5%
6%
2984
8%
7%
8%
4072
All
9%
9%
10%
4418
12%
12%
13%
6492
Never-married Women
8%
7%
10%
248
15%
14%
17%
497
Never-married Men
8%
7%
10%
238
10%
8%
11%
448
Married
7%
7%
8%
2984
10%
9%
10%
4072
All
7%
7%
7%
4418
10%
10%
10%
6492
Never-married Women
4%
3%
5%
248
4%
3%
5%
497
Never-married Men
7%
5%
8%
238
6%
5%
7%
448
Married
4%
4%
5%
2984
5%
5%
5%
4072
All
4%
4%
4%
4418
5%
4%
5%
6492
Never-married Women
2%
1%
2%
248
4%
3%
4%
497
Never-married Men
5%
4%
7%
238
8%
7%
9%
448
Married
6%
6%
6%
2984
5%
5%
5%
4072
All
5%
5%
5%
4418
5%
4%
5%
6492
Race of respondent - Hispanic
Race of respondent - Asian/Other
Take substaintial financial risks
Household had any debt payments more than 60 days past due in last year
Never-married Women
7%
6%
8%
248
10%
9%
11%
497
Never-married Men
5%
4%
6%
238
6%
5%
7%
448
Married
3%
3%
4%
2984
7%
6%
7%
4072
29
All
4%
4%
4%
4418
7%
7%
10%
6492
Never-married Women
6%
5%
8%
248
7%
6%
9%
497
Never-married Men
8%
7%
10%
238
5%
4%
6%
448
Married
8%
8%
9%
2984
11%
10%
11%
4072
10%
9%
10%
4418
11%
11%
12%
6492
Ever bankrupt
All
Been turned down for credit in last 5 yrs
Never-married Women
25%
22%
27%
248
25%
24%
27%
497
Never-married Men
17%
15%
19%
238
21%
19%
23%
448
Married
15%
14%
15%
2984
20%
19%
20%
4072
All
16%
15%
16%
4418
20%
20%
20%
6492
Continuous Variables
2007
Mean
2010
C.I.
n
Mean
C.I.
n
Age of head of household
Never-married Women
37.51
36.66
38.37
248
37.72
37.12
38.31
497
Never-married Men
38.72
37.87
39.57
238
37.87
37.25
38.48
448
Married
48.54
48.29
48.79
2984
49.84
49.63
50.06
4072
All
50.02
49.79
50.24
4418
50.53
50.34
50.71
6492
Never-married Women
33,245
31,326
35,164
248
32,335
30,504
34,165
497
Never-married Men
47,447
36,222
58,673
238
44,310
37,677
50,944
448
Married
119,185
111,710
126,659
2984
105,683
101,089
110,277
4072
All
88,162
83,134
93,191
4418
78,332
75,258
81,407
6492
Total Income
30
Years of Education
Never-married Women
13.58
13.44
13.72
248
13.66
13.57
13.76
497
Never-married Men
13.81
13.66
13.96
238
14.00
13.90
14.10
448
Married
13.38
13.33
13.42
2984
13.49
13.45
13.53
4072
All
13.26
13.22
13.30
4418
13.41
13.39
13.44
6492
Never-married Women
0.73
0.67
0.78
248
0.71
0.66
0.75
497
Never-married Men
0.11
0.08
0.14
238
0.06
0.05
0.07
448
Married
1.06
1.04
1.08
2984
1.09
1.07
1.11
4072
All
0.83
0.82
0.85
4418
0.84
0.83
0.85
6492
Total Number of Children
N is number of direct observations.
Binomial confidence intervals are at 95%.
Confidence intervals are at 95%.
Married households includes both legally married and co-habitating households.
31