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What Explains the Gender Gap in Financial Literacy? The Role of Household Decision Making


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Using newly collected data from the RAND American Life Panel, we examine potential explanations for the gender gap in financial literacy, including the role of marriage and who within a couple makes the financial decisions. Blinder-Oaxaca decomposition reveals the majority of the gender gap in financial literacy is not explained by differences in the characteristics of men and women-but rather differences in coefficients, or how literacy is produced. We find that financial decision making of couples is not centralized in one spouse although it is sensitive to the relative education level of spouses.
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What Explains the Gender
Gap in Financial Literacy?
The Role of Household Decision-
une 2010
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What Explains the Gender Gap in Financial Literacy?
The Role of Household Decision-Making
Raquel Fonseca, Kathleen Mullen, Gema Zamarro, and Julie Zissimopoulos1
June 2010
Research has shown that financial illiteracy is widespread among women, and that many women
are unfamiliar with even the most basic economic concepts needed to make saving and
investment decisions. This gender gap in financial literacy may contribute to the differential
levels of retirement preparedness between women and men. However, little is known about the
determinants of the gender gap in financial literacy. Using data from the RAND American Life
Panel, we examined potential explanations for the gender gap including the role of marriage and
division of financial decision-making among couples. We found that differences in the
demographic characteristics of women and men did not explain much of the financial literacy
gap, whereas education, income and current and past marital status reduced the observed gap by
around 25%. Oaxaca decomposition revealed the great majority of the gender gap in financial
literacy is not explained by differences in covariates - characteristics of men and women - but
due to coefficients, or how literacy is produced. We did not find strong support for specialization
in financial decision-making within couples by gender. Instead, we found that decision-making
within couples was sensitive to the relative education level of spouses for both women and men.
1 We gratefully acknowledge funding from RAND’s Roybal Center for Financial Decision Making.
Correspondence: Kathleen Mullen, RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA
90407-2138. Tel: 310-393-0411 x6265. E-mail:
1. Introduction
Women tend to live longer than men, have shorter work experiences, lower earnings and levels
of pension or survivors’ benefits. These factors put women at a higher risk than men of having
financial problems (e.g. Weir and Willis, 2000) and of approaching retirement with little or no
savings. Indeed, unmarried, particularly divorced, women near retirement age have substantially
lower wealth levels than married couples and unmarried men and the difference is only partially
explained by lower levels of permanent earnings and labor force attachment (Levine et al., 2002;
Zissimopoulos et al., 2008). A contributing factor to low wealth levels of divorced women
compared to men near retirement may be a lack of adequate financial literacy.
There is a burgeoning literature documenting low levels of financial literacy population-
wide and the relationship between literacy and savings behavior (e.g. Bernheim and Garrett
2003, Bernheim et al. 2001, Lusardi and Mitchell 2006, 2007). Lusardi and Mitchell (2006)
document that only about one-half of adults near retirement age in the United States were able to
answer basic questions about compound interest and inflation. Financial illiteracy, however, is
even more widespread among women than men, particularly familiarity with basic economic and
financial concepts (Lusardi and Mitchell, 2008; Zissimopoulos et al., 2008, Chen and Volpe,
2002).2 Zissimopoulos et al., (2008) find that less than 20 percent of middle-aged college-
educated women were able to answer a basic compound interest question compared to about 35
percent of college-educated males of the same age group. Chen and Volpe (2002) find similar
gender difference among women at younger ages that was unexplained by differences in majors,
class rank, work experience and age.
Changing demographic trends and types of financial decisions being made increase the
importance of understanding what accounts for the low levels of financial knowledge and
literacy among women and what role financial literacy plays in determining savings behavior.
Increasing rates of divorce and lower remarriage rates imply higher rates of unmarried women at
retirement age than in the past. Moreover, shifts in pension plan types from defined benefit to
defined contribution imply individuals are taking more responsibility for their retirement
security. At the same time the growing number of financial instruments available for financing a
2 Lusardi and Mitchell (2008) also found women were less likely to have undertaken retirement planning than men.
Clark et al. (2004) found that women were more likely to respond to financial education programs with increased
confidence in attaining retirement goals than men.
home or extracting equity from an existing home imply some key decisions are becoming more
Although there is general agreement in the empirical literature that women have lower
levels of financial knowledge than men, less is understood about the magnitude of this
difference, the factors associated with the difference, and how this translates into behavior. In
this paper, we contribute to our understanding of the gap in financial knowledge between women
and men by investigating the socio-economic and demographic factors associated with the gap
and quantifying the gap using Oaxaca Decomposition techniques. We investigate the role of
division of labor in financial decision-making within a household.3 That is, we investigate
decision-making within a couple by gender and how this correlates with levels of financial
literacy and education level of each partner.
For the analysis we use existing data on financial literacy from RAND American Life
Panel (ALP) and data we collected on decision making within the household. We found that
differences in the demographic characteristics of women and men did not explain much of the
financial literacy gap, whereas education, income and current and past marital status reduced the
observed gap by around 25%. Oaxaca decomposition revealed the great majority of the gender
gap in financial literacy is not explained by differences in covariates - characteristics of men and
women - but due to coefficients, or how literacy is produced. We also found that among couples,
there was no discernible pattern of financial decision-making along gender lines and one’s own
financial responsibilities increased as his or her education level increased relative to his or her
spouse’s education level for both men and women. Finally, only among men was more financial
decision-making correlated with higher financial literacy.
The rest of the paper is organized as follows. Section 2 describes our dataset and
variables of interest. The main summary statistics are also presented in this section. Section 3
describes quantifications of the gender gap on financial literacy and studies which factors
mitigate the observed gender differences. Section 4 presents our results of the analysis of the role
of household specialization and the division of labor among couples. Finally, Section 5 presents
our conclusions.
3 There is an extensive literature on division of labor within households (see Becker, 1985, among others).
2. Data
2.1. The RAND American Life Panel
To conduct this research, we used data from the RAND American Life Panel (ALP). The
ALP consists of over 2,500 respondents ages 18 and over who are interviewed periodically over
the Internet. The ALP respondents are recruited from respondents of the University of
Michigan’s Survey Research Center Monthly Survey (MS). Respondents do not need their own
Internet access to participate in the panel; those without Internet access (less than 17% of the
sample) are provided with Internet access by RAND through the provision of a WebTV and an
Internet subscription (which allows them to open an email account). This eliminates the bias
found in many Internet surveys which include only computer users. The setup of the ALP is
similar to the long-running CentERpanel in the Netherlands.
Roughly once a month, respondents receive an email with a request to fill out a
questionnaire on the Internet. Response rates average 70 to 80 percent. Data are available in real
time; that is, after each respondent completes the survey, the data for that interview are
immediately uploaded to the database, to which the researcher has access. Upon joining the
panel, respondents to the ALP complete an initial survey collecting individual socio-
demographic information, work history and household composition information. They are also
asked to update their background information each time they log in to respond to a new module.
We designed a module survey that was administered to ALP respondents last June 2009.
Apart from already collected socio-demographic and work status information, our module
included detailed questions regarding current and past marital statuses including number of years
in the current or past relationship and years passed since a marital status change. In addition, for
those married or cohabiting with a partner we asked a series of questions aiming to understand
how financial responsibilities are divided in the household. These questions asked the
respondents to state who is primarily responsible for the following activities: paying the bills,
preparing taxes, tracking investments and insurance coverage, making short-term
spending/saving plans and making long-term spending saving plans. The respondents could
answer “mostly me,” “mostly my partner,” or “both equally.” This survey was merged with
financial literacy measures collected in a previous module designed by Hung et. al. (2009a).
2.2. Measuring Financial Literacy
The definitions and measures of financial literacy that have been considered in the
literature vary considerably across researchers and studies and have included specific knowledge,
the ability or skills to apply that knowledge, perceived knowledge, good financial behavior, or
even certain financial experiences. We utilize a measure of financial literacy developed by Hung
et al. (2009a, 2009b). It is a comprehensive measure of multiple dimensions of financial literacy
and measures underlying financial literacy well, as measured by reliability of the index (Hung et
al, 2009b).
The index is based on answers to 23 questions on basic financial concepts, investing, life
insurance, and annuities. Specifically, the index included the 13-item scale from Lusardi and
Mitchell (2006). These included measuring knowledge on: numeracy, compound interest, and
inflation (five items); and stock market, stocks, bonds, mutual funds, and diversification (eight
items). It also included six additional items measuring knowledge on the definition of stock,
bond, and mutual funds and four items measuring respondent’s knowledge about life insurance
and annuities. The financial literacy index is constructed using a structural unidimensional model
of financial literacy, taking into account the distributional characteristics of the variables. In
particular, the model specified the probability of answering each of the test items as a function of
the underlying true but unobserved financial literacy. Optimal estimates of the true financial
literacy were then obtained maximizing the log pseudo-likelihood function after assuming that
the unobserved financial literacy trait was standard normally distributed.4
Thus, this financial literacy index is well-suited for our study’s goals of quantifying the
gender gap of a comprehensive measure of financial literacy and investigating how financial
literacy relates to decision-making within a household. Utilizing this index also allows us to
avoid problems of multiple inference from many separate measures and simplifies interpretation
of our results since we analyze changes in a continuous, normally distributed summary measure
of financial literacy. A limitation is that it does not allow us to separately quantify the gender
gap in a particular concept of financial literacy. In the research presented here, we normalized
the financial literacy index so it has mean zero and standard deviation equal to one.
4 See Hung (2009b) for a detailed description of how this index measure is constructed.
2.3. Descriptive Statistics
Members of the ALP tend to have more education and income than the broader U.S.
population, so we report results weighted to be representative of the U.S. population ages 18 and
older. Approximately two-thirds of the ALP respondents provided information necessary for
construction of the financial literacy index. Although correlated with the different socio-
demographic variables, the missing status on this variable was not correlated with gender once
we condition on the socio-demographic information. Therefore, we think that the prevalence of
missing information on financial literacy does not alter the interpretation of our results.
Table 1 shows weighted summary statistics, by gender, for the respondents with non-
missing values of the financial literacy index. The financial literacy index for women is about 0.7
standard deviations lower than for men (p < 0.01). Women in our sample are younger, are more
likely to belong to minority ethnic groups, and they have lower levels of education and income
than men. Women are also are more likely to be divorced, widowed or never married than men,
and they remain unmarried longer than men. Women are slightly less likely to work than men,
and a higher proportion of women have partners with lower education. These differences in
demographic characteristics as well as socio-economic characteristics of women compared to
men likely explain some of the difference in the financial literacy index.
3. What Factors Mitigate Gender Differences in Financial Literacy?
3.1. Determinants of Financial Literacy
Table 2 reports the results of multivariate regression analysis of a number of potential
factors associated with financial literacy. The dependent variable in each case is the normalized
index of financial literacy described above, so that the estimated coefficients represent the effects
of covariates in terms of standard deviation increases in financial literacy. Column 1 presents the
results of a simple regression of financial literacy on a dummy variable for female. Thus, the
coefficient on female represents the raw gender difference in financial literacy, equal to the
difference reported in Table 1. Specification 2 adds age and race dummies; although these
variables are for the most part statistically significant, they do not have a large effect on the
magnitude of the gender difference – reducing it by roughly 9%. Adding socioeconomic
characteristics (i.e., education and family income) reduces the coefficient on gender an additional
13.5% (specification 3).
Specifications 4 and 5 explore the role of marital status in explaining gender differences
in financial literacy. Note that simply including a dummy for whether the respondent is in a
couple (married or cohabiting) does not significantly effect the coefficient on female
(specification 4). When couple status is disaggregated into finer categories (i.e., couples broken
into married or cohabiting; non-couples broken into separated, divorced or widowed), the
coefficient on female is further reduced: there is a 25 percent decline in magnitude of the
estimate from specification 1 (no covariates) to specification 5. Although current marital status
is not strongly correlated with financial literacy, the length of time you have spent in the
relationship may be important. Specification 6 adds covariates on length of time in the
relationship for those currently married or co-habitating and years since marital disruption for
those currently divorced or widowed. We find no effect of years in the relationship on the
financial literacy of married individuals relative to never married. Divorced individuals,
however, are about 0.30 standard deviations less financially literate than their never married
counterparts, and 0.42 standard deviations less financially literate than currently married
respondents. Moreover, divorced respondents gain 0.02 standard deviations in financial literacy
for every year since their last relationship (making up for their initial deficit in roughly 13.7
years). The negative coefficient on divorced is consistent with marital selection: individuals with
lower “ability” are less likely to stay married. On the other hand, the positive coefficient on
years since divorce is consistent with marital specialization: previously married respondents with
low levels of financial literacy gain knowledge over time as they learn to make financial plans
without a partner.
The marital specialization hypothesis has ambiguous predictions for the coefficient on
years in current relationship. This is a result of the fact that, among couples that specialize, one
partner will develop financial literacy while the other’s financial skills will deteriorate. If men
tend to specialize in handling finances, then we might expect the coefficient on years in a
relationship to be positive for men and negative for women. In the next section we allow
coefficients on determinants of financial literacy to differ for men and women. Furthermore, we
employ Oaxaca decomposition in order to decompose gender differences into differences due to
endowments and differences due to coefficients or production technology (Oaxaca, 1973).
3.2. Oaxaca Decomposition
As noted above, if men and women tend to take on different specialized roles within the
household, specifically with respect to financial decision-making and planning, then it is
important to allow different effects of marital status and history by gender. More generally, men
and women might have different production technologies for financial literacy, so allowing for
differential effects may be important for other covariates as well. Panel A of Table 3 presents
estimates of a fully interacted version of specification 6 from Table 2. Importantly, including the
interaction terms reduces the estimated gender gap in financial literacy to -0.31 standard
deviations (the difference between the two constant terms) and the gap is no longer statistically
different from zero.
Some surprising findings emerge. For example, the effects of age, race and income on
financial literacy are not statistically different for men and women. However, men benefit much
more from education than women; indeed, there is no discernible gain to women in terms of
financial literacy from graduating high school or attending some college (compared with
dropping out of high school). Only college-educated women are more financially literate than
women without a high school degree, whereas all levels of education above no high school are
associated with higher financial literacy for men. Turning to marital status, married women are
significantly more financially literate than unmarried women, which is not the case for married
men. In addition, married women are financially more literate on average than married men.
Divorced women and men are no less financially literate than never married women and men nor
is there a significant difference between the financial literacy levels of divorced men and women.
Similar to what we saw in Table 2, specification 6, years since divorce are associated with
increases in financial literacy for both men and women. Somewhat surprising is the finding that
widowed men are more financially literate than never married men although this declines with
years married and years since the widowing occurred. Because the sample of widowed men is
small (20 men total), we do not put much weight on these estimates or place an interpretation on
Finally, Panel B of Table 3 presents the results of a Oaxaca decomposition of the gender
gap in financial literacy into variation due to (a) endowments (e.g., characteristics such as age,
education and income), (b) coefficients (i.e., differential effects of characteristics such as age,
education and income), and (c) the interaction of these two terms. Thus, if we estimate the
following regression:
[| ,] (1 )
Ey Xd dX dX
denotes financial literacy,
is a vector of socioeconomic characteristics and d is a
dummy variable for female, then we can compose the gender gap as follows:
[| 0] [| 1] [ | 1]
Ey d Ey d X EX d X
' ' '',
where [| 0] [| 1]XEXd EXd' and MF
' . The first term captures how much of
the gender gap is due to differences in characteristics among men and women (e.g., average
education) assuming the same “production technology” (here, that of women). This is often
referred to as the “explained” part of the decomposition. The second term captures how much of
the gender gap is due to differences in coefficients (production technology) assuming men and
women tend to have the same characteristics (here again, that of women). The final term is the
part of the gap arising from the interaction between endowments and coefficients. Often these
last two terms are referred to as the “unexplained” part, but sometimes the interaction term is
included within the “explained” part when the decomposition is viewed from the perspective of
men serving as the baseline.
Regardless of interpretation, it is clear that the great majority of the gender gap is due to
differences in coefficients rather than differences in characteristics between men and women.
Thus, for whatever reason, men and women have very different production processes for
financial literacy. In the next section, we explore one possible explanation: restricting our
attention to couples, we investigate how division of labor for financial decisions within the
household is correlated with financial literacy for men and women.
4. How Do Households Determine Division of Labor for Financial Decision-Making?
4.1. Gender Differences in Division of Labor Among Couples
We asked married and cohabiting respondents who in their household is responsible for
the following activities: paying the bills, preparing taxes, tracking investments and insurance
coverage, making short-term spending/saving plans (e.g., monthly budget), and making long-
term spending/saving plans (e.g., planning for retirement). Response choices were: mostly me,
both equally and mostly my partner/spouse. The ALP only surveys one respondent per household
thus we cannot not match respondents’ reports with those of their spouses. Table 4 presents the
division of labor reported by coupled respondents for men and women separately. Note that,
since both men and women were randomly sampled from the population, then on average an
objective measure would reveal the fraction of men’s reports of “mostly me” to match the
fraction of women’s reports of “mostly my partner,” and vice versa. Not surprisingly, however,
both men and women are more likely to say “mostly me” than “mostly my partner.”
Beyond these differences, however, there is a great deal of agreement on who is
responsible for what among couples. The proportion of respondents reporting that they share
responsibilities equally with their partners is roughly the same for men and women. Moreover,
both men and women agree that women are more likely to be responsible for paying the bills. In
addition, about half of respondents say they make short- and long-term spending/saving
decisions together (with slightly more women saying they are primarily responsible for short-
term spending, which may be hard to differentiate from paying bills). On the other hand, there is
some disagreement on where responsibility for paying taxes and tracking investments lies; about
half of men say they are primarily responsible, but these responsibilities seem more spread out
among couples according to women.
4.2. How Does Division of Labor Reflect Differences in Financial Literacy?
Table 5 presents results for the average financial literacy of men and women by division
of labor within the household for various activities. An immediately striking result is that the
gender gap in financial literacy persists across division of labor categories. For example, among
respondents who report that they are primarily responsible for paying the bills, men outperform
women by almost three-quarters of a standard deviation on the financial literacy index. The gap
tends to be smaller, and in some cases disappear, among men and women who report that their
partner is responsible for financial activities.
Table 5 also reports p-values for standard F tests of equality within gender. If women and
men sort into responsibility for financial activities based on financial literacy, then we would
expect financial literacy to decrease as one moves from “mostly me” to “mostly my partner.”
This is clearly the case for men, and the p-values for the F tests are all less than 0.03 (and in all
but one case less than 0.001). However, for women financial literacy does not appear to play a
role in their perception of their financial responsibilities. Only two p-values are less than 0.10 –
preparing taxes and making long-term plans – and the differences in financial literacy do not
follow the expected pattern. If anything, less financially literate women are taking on
responsibility for those activities.
A possibility is that assortive matching between men and women is confounding
correlations between financial responsibility and literacy. That is, what really matters is relative
differences in financial literacy within a couple. For example, highly financially literate women
may tend to marry highly financially literate men, so these relative differences are not reflected
in the raw correlations5. We cannot observe relative differences in financial literacy among
couples, but we can examine the role of education – both in absolute and relative terms – in
determining division of labor in financial decision-making within couples.
4.3. The Role of Education
Table 6 displays the average number of financial responsibilities (out of the five activities
we presented) taken on mostly by respondents and their partners, respectively, by gender and
education. Panel A presents means by absolute level of education, whereas Panel B presents
means by education of the respondent relative to his or her partner (more, the same or less). For
example, women who completed high school or less on average were responsible for 1.86
financial activities, compared to 1.36 for men of similar education. This pattern is reversed for
higher education categories; that is, women who completed at least some college were
responsible for fewer activities on average than similarly educated men. Table 6 also reports p-
values for standard F tests of equality within gender. As before, on average men are responsible
for more financial activities as their education increases, whereas no such pattern is discernible
for women.
However, when we consider relative education levels within couples, as opposed to
absolute education levels, these results do not hold. In fact, women and men with similar
education levels relative to their partner tend to take on the same number of financial
responsibilities on average. Additionally, both men and women are responsible for more
financial activities as their education increases relative to their spouse or partner. This suggests
5 The phenomenon that couples sort by wealth, education and other characteristics has been long studied in the
literature (see e.g. Becker (1973)).
that relative education differences may trump traditional gender roles when couples determine
how to divide up financial responsibilities.
5. Conclusion
This paper uses data from the RAND American Life Panel to examine potential explanations for
the gender gap in financial literacy including the role of household marital specialization and
division of labor among couples. We found that women perform almost 0.7 standard deviations
lower than men on our financial literacy index, and the difference is highly significant. We then
examined a number of potential factors affecting the observed financial literacy gap. We found
that demographic characteristics had a limited effect on the financial literacy gap, whereas
controlling for socio-demographic characteristics, current and past marital status reduced the
observed gap by around 25%. We found marital selection may be important in explaining the
observed gender gap, as well as marital specialization. Finally, we allowed for men and women
to have different financial literacy production functions and performed an Oaxaca decomposition
analysis. This analysis showed that the great majority of the gender gap is due to differences in
coefficients rather than differences in characteristics between men and women. Thus, men and
women seem to have very different production processes for financial literacy. Further research
is needed to understand why this could be the case.
We did not find strong support for specialization by gender for the financial decisions we
study and only a positive correlation between decision-making and financial literacy for males.
Instead, we found that decision-making within couples, with regards to paying bills, preparing
taxes, tracking investments and making short and long term savings plans, is sensitive to the
relative education level of spouses for both women and men. In fact, women and men with
similar education levels relative to their partner on average take on the same number of financial
responsibilities and both men and women are responsible for more financial activities as their
education increases relative to their spouse or partner.
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Table 1. Summary Statistics by Gender
Female Male
N Mean Std. dev. N Mean Std. dev.
Financial literacy index 844 -0.537 0.965 678 0.158 0.978 -0.695***
18-35 844 0.199 0.400 678 0.159 0.366 0.040**
36-50 844 0.355 0.479 678 0.338 0.473 0.017
51-65 844 0.257 0.437 678 0.275 0.447 -0.018
66+ 844 0.189 0.391 678 0.228 0.420 -0.039
White 844 0.750 0.433 678 0.834 0.372 -0.084***
Black 844 0.137 0.344 678 0.088 0.284 0.048***
Other 844 0.016 0.125 678 0.009 0.097 0.006
High school dropout 844 0.050 0.219 678 0.040 0.197 0.010
High school graduate 844 0.356 0.479 678 0.281 0.450 0.075***
Some college 844 0.250 0.433 678 0.262 0.440 -0.013
College graduate 844 0.344 0.475 678 0.416 0.493 -0.073***
< $35K 844 0.273 0.446 678 0.211 0.408 0.063***
$35K-$60K 844 0.278 0.448 678 0.248 0.432 0.029
$60K-$90K 844 0.272 0.445 678 0.305 0.461 -0.033
> $90K 844 0.177 0.382 678 0.236 0.425 -0.059***
Marital status
In a couple 844 0.524 0.500 678 0.664 0.473 -0.140***
Married 832 0.481 0.500 674 0.628 0.484 -0.147***
Cohabiting 832 0.047 0.211 674 0.039 0.195 0.007
Separated 832 0.013 0.112 674 0.013 0.113 0.000
Divorced 832 0.156 0.363 674 0.115 0.319 0.041**
Widowed 832 0.076 0.265 674 0.035 0.183 0.041***
Never married 832 0.227 0.419 674 0.170 0.376 0.057***
Marital history
Years in current relationship 519 20.930 14.485 490 23.277 15.637 -2.347**
Years in last relationship 199 17.468 13.747 105 17.787 15.492 -0.319
Years since last relationship 201 14.707 10.448 105 11.412 10.424 3.295***
HH size not incl. self/partner
No dependents 844 0.465 0.499 678 0.535 0.499 -0.007***
Number of dependents (if >0) 402 2.178 1.333 290 2.142 1.120 0.036
Working for pay 820 0.642 0.480 651 0.693 0.461 -0.051**
Education relative to partner
Partner has more 519 0.169 0.375 490 0.189 0.392 -0.020
Both same 519 0.590 0.492 490 0.637 0.481 -0.047
Partner has less 519 0.242 0.429 490 0.174 0.380 0.068***
Notes: *** p<0.01, ** p<0.05. Data are weighted. Financial literacy index is standardized. Summary statistics
limited to those with non-missing financial literacy (62% of females, 70% of males).
Table 2. Financial Literacy Regressions
(1) (2) (3) (4) (5)
Yrs in rel. Yrs since
Female -0.695*** -0.632*** -0.539*** -0.532*** -0.522*** -0.538***
(0.050) (0.047) (0.042) (0.043) (0.043) (0.043)
Age 36-50 0.500*** 0.339*** 0.328*** 0.317*** 0.324***
(0.068) (0.062) (0.062) (0.065) (0.065)
Age 51-65 0.724*** 0.587*** 0.580*** 0.566*** 0.563***
(0.072) (0.065) (0.065) (0.069) (0.075)
Age 66+ 0.830*** 0.822*** 0.815*** 0.822*** 0.790***
(0.077) (0.070) (0.070) (0.077) (0.099)
White 0.113 0.311*** 0.312*** 0.311*** 0.303***
(0.081) (0.074) (0.074) (0.074) (0.074)
Black -0.341*** 0.00675 0.0157 0.0154 -0.0113
(0.100) (0.093) (0.094) (0.094) (0.094)
High school graduate 0.270** 0.267** 0.239** 0.213
(0.110) (0.110) (0.110) (0.110)
Some college 0.459*** 0.458*** 0.424*** 0.399***
(0.110) (0.110) (0.110) (0.110)
College graduate 0.844*** 0.854*** 0.828*** 0.807***
(0.110) (0.110) (0.110) (0.120)
Income $35-60K 0.282*** 0.267*** 0.262*** 0.287***
(0.061) (0.062) (0.062) (0.063)
Income $60-90K 0.459*** 0.435*** 0.414*** 0.417***
(0.062) (0.064) (0.065) (0.065)
Income > $60K 0.675*** 0.642*** 0.634*** 0.635***
(0.071) (0.075) (0.076) (0.076)
In a couple 0.063
Married 0.0693 0.119 -0.002
(0.062) (0.077) (0.003)
Cohabiting -0.188 -0.135 -0.008
(0.110) (0.150) (0.013)
Divorced 0.043 -0.302** 0.001 0.022***
(0.078) (0.140) (0.007) (0.006)
Widowed -0.143 0.213 -0.003 -0.021
(0.110) (0.310) (0.007) (0.012)
Constant 0.158*** -0.463*** -1.482*** -1.502*** -1.448*** -1.412***
(0.037) (0.092) (0.130) (0.130) (0.130) (0.130)
Observations 1,522 1,522 1,522 1,522 1,506 1,504
0.11 0.22 0.38 0.38 0.39 0.40
Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05. Data are weighted. Dependent variable is
standardized financial literacy index. We also control for separated but do not report due to the very small
number of observations (5 men and 9 women).
Table 3. Oaxaca Decomposition of Gender Gap
(A) Regressions by Gender
Level Interactions w/ yrs in rel. Interactions w/ yrs since
Female Male Diff. Female Male Diff. Female Male Diff.
Age 36-50 0.380*** 0.312*** 0.068
(0.086) (0.100) (0.133)
Age 51-65 0.674*** 0.463*** 0.211
(0.100) (0.110) (0.152)
Age 66+ 0.946*** 0.645*** 0.301
(0.140) (0.140) (0.202)
White 0.271*** 0.246** 0.0246
(0.095) (0.120) (0.154)
Black 0.0486 -0.0845 0.133
(0.120) (0.160) (0.196)
High school grad. -0.0644 0.550*** -.615***
(0.140) (0.180) (0.227)
Some college 0.158 0.710*** -.553**
(0.150) (0.180) (0.229)
College graduate 0.589*** 1.108*** -.519**
(0.150) (0.180) (0.234)
Income $35-60K 0.226*** 0.315*** -0.090
(0.082) (0.099) (0.128)
Income $60-90K 0.363*** 0.475*** -0.112
(0.086) (0.099) (0.131)
Income > $60K 0.521*** 0.748*** -0.227
(0.100) (0.110) (0.152)
Married 0.237** -0.0832 .320** -0.000 -0.002 0.001
(0.100) (0.120) (0.156) (0.004) (0.003) (0.005)
Cohabiting -0.0467 -0.411 0.364 -0.015 0.007 -0.022
(0.170) (0.290) (0.337) (0.016) (0.026) (0.030)
Divorced -0.162 -0.467 0.305 0.005 -0.005 0.010 0.019*** 0.025*** -0.007
(0.180) (0.240) (0.297) (0.008) (0.011) (0.014) (0.007) (0.010) (0.012)
Widowed -0.129 2.638*** -2.766*** -0.004 -0.043** 0.039 -0.000 -0.079*** 0.079**
(0.330) (0.980) (1.029) (0.007) (0.019) (0.020) (0.015) (0.028) (0.031)
Constant -1.817*** -1.506*** -0.311
(0.160) (0.210) (0.266)
Observations 830 674
R-squared 0.34 0.36
(B) Oaxaca Decomposition
Variation due to
Total diff. Endowments Coefficients Interaction
-0.694 -0.181 -0.602 0.088
(0.051) (0.033) (0.049) (0.033)
Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05. Data are weighted.
Table 4. Division of Labor Among Couples, Reported by Gender
Female Male
Me Equal Partne
Me Equal Partne
Paying bills 51.2% 22.1% 26.7% 36.9% 22.1% 41.1%
Paying taxes 36.5% 29.0% 34.5% 48.6% 24.6% 26.8%
Tracking investments/insurance 32.8% 34.8% 32.4% 49.2% 32.2% 18.6%
Making short-term spending/saving plans 43.2% 44.2% 12.6% 24.6% 47.5% 27.8%
Making long-term spending/saving plans 26.2% 51.5% 22.3% 33.8% 49.2% 17.0%
Notes: N=827 females, 699 males. Data are weighted, include those with missing financial literacy index.
Table 5. Mean Financial Literacy by Gender & Role in Household Decision-Making
Female Male Diff.
Paying the bills
Mostly me -0.366 0.380 -0.746***
Both equally -0.512 0.129 -0.641***
Mostly my partner -0.281 0.143 -0.423***
F test of equality 0.144 0.025
Preparing taxes
Mostly me -0.394 0.486 -0.880***
Both equally -0.529 -0.048 -0.481***
Mostly my partner -0.225 -0.099 -0.126
F test of equality 0.014 0.000
Tracking investments and insurance coverage
Mostly me -0.442 0.522 -0.964***
Both equally -0.390 0.036 -0.426***
Mostly my partner -0.270 -0.376 0.106
F test of equality 0.217 0.000
Making short-term spending/saving plans
Mostly me -0.396 0.422 -0.818***
Both equally -0.341 0.277 -0.618***
Mostly my partner -0.441 -0.071 -0.370**
F test of equality 0.707 0.000
Making long-term spending/saving plans
Mostly me -0.639 0.515 -1.154***
Both equally -0.289 0.220 -0.509***
Mostly my partner -0.247 -0.558 0.312
F test of equality 0.000 0.000
Notes: *** p<0.01, ** p<0.05. Data are weighted. Financial literacy index is standardized.
Table 6. Division of Labor by Gender & Education
(A) Absolute Education Female Male Diff.
Mean count "mostly me"
Less than/equal to high school 1.861 1.363 0.498**
Some college 1.842 2.131 -0.289
College graduate 2.009 2.425 -0.416***
F test of equality (p-value) 0.574 0.000
Mean count "mostly my partner"
Less than/equal to high school 1.234 1.785 -0.551***
Some college 1.423 1.051 0.372***
College graduate 1.208 0.939 0.269**
F test of equality (p-value) 0.260 0.000
(B) Relative Education
Mean count "mostly me"
Partner has more education 1.357 1.393 -0.036
Partner has same education 1.822 1.936 -0.114
Partner has less education 2.881 2.518 0.363
F test of equality (p-value) 0.000 0.000
Mean count "mostly my partner"
Partner has more education 1.690 1.492 0.197
Partner has same education 1.319 1.391 -0.072
Partner has less education 0.592 0.781 -0.190
F test of equality (p-value) 0.000 0.001
Notes: *** p<0.01, ** p<0.05. Count is out of 5 items. Data are weighted, include those missing
financial literacy index.
... We additionally note male participants outperformed female participants. While we had not previously considered or hypothesized these sex related differences, they are consistent with research observing that females appear to trail males in financial literacy (Fonseca et al., 2012;Mottola, 2013). ...
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Introduction The results of the United Kingdom’s 2016 referendum on European Union (EU) membership have highlighted deep societal divides. In six studies, we examined the role of personality traits, cognition and cognitive biases in relation to referendum voters’ choices. Methods A total of 11,225 participants completed questionnaires and controlled experiments, which assessed differences in personality traits, levels of authoritarianism, numeracy, thinking styles, and susceptibility to cognitive biases including ideologically motivated numeracy and reasoning, framing, and the Dunning-Kruger effect. Results Participants expressing an intent to vote to leave the EU reported significantly higher levels of authoritarianism and conscientiousness, and lower levels of openness and neuroticism than voters expressing an intent to vote to remain in the EU. When compared with Remain voters, Leave voters displayed significantly lower levels of numeracy and appeared more reliant on impulsive System 1 thinking. In the experimental studies, voters on both sides were found to be susceptible to the cognitive biases tested, with a general trend for Leave voters to show more bias than Remain voters. Discussion These results raise important questions regarding the use and framing of numerical and non-numerical data for public consumption.
... without) a romantic partner reported greater preference for social decision-making. In part, spouses' social decision-making may reflect household decisions, with about one third of spouses reported shared responsibility for household investment decisions (Fonseca et al., 2012). Household decision-making responsibilities may also be divided according to individuals' abilities (Ward & Lynch, 2019). ...
Decision-making often occurs in a social context but is typically studied as if it were an individualistic process. In the present study, we investigated the relationships between age, perceived decision-making ability, and self-rated health with preferences for social decision-making, or making decisions with others. Adults (N = 1,075; ages 18-93) from an U.S. online national panel reported their preferences for social decision-making, perceived changes in decision-making ability over time, perceived decision-making ability compared to age peers, and self-rated health. We report on three key findings. First, older age was associated with being less likely to prefer social decision-making. Second, older age was associated with perceiving one's ability to have changed for the worse over time. Third, social decision-making preferences were associated both with older age and perceiving one's ability to make decisions was worse than age peers. Additionally, there was a significant cubic function of age, such that older age was associated with lesser preferences for social decision-making until around age 50. Preferences then increased slightly with age until about age 60, after which older age was once again associated with lesser preferences for social decision-making. Together, our findings suggest that compensating for perceived lack of competence compared to other people one's age may motivate preferences for social decision-making across the life span. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
... Student financial knowledge is relatively low compared to financial attitude and financial behavior because most respondents are female (85.5%), and most are second-semester students (56.5%) who have not received much lecture material on finance. Examination the gender gap in financial literacy found that the gender gap in financial literacy was not explained by differences in male and female characteristics but rather by differences in how literacy was generated [23]. Financial literacy by gender can be seen in Table VI as follow. ...
... For gender, majority of studies reviewed indicated that women are more likely to possess low debt literacy and overall financial literacy (Lusardi & Mitchell, 2011;Fonseca, Mullen, Zamarro & Zissimopoulos, 2012;Garg & Singh, 2018;Cupak, Fessler, Schneebaum & Silgoner, 2018). Chen and Volpe (2002) concluded that women generally have less knowledge about personal finance topics and are less enthusiastic and less confident in financial matters. ...
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Recently, demographic factors that influence financial literacy have become increasingly important for policymakers than ever before. The primary purpose of this study was to establish the relationship between demographic factors and financial literacy among adults in a rural and low-income areas of South Africa. The objectives were to determine which gender uses a budget, which age cohort keeps copies of financial documents, which category of education level is saving for retirement, and the correlation between education level and saving for retirement. Descriptive statistics and correlation analysis were used to analysed data. The results showed that males used a budget more than females by a low margin. Adults struggled to keep copies of financial documents; however, adults between the ages of 41-50 kept copies of financial documents the most. Those with matric were better savers for retirement than other levels of education, besides the fact that most adults were not saving for retirement. The study also revealed a negative correlation between education level and retirement savings. Thus, the relationship between demographic factors and financial literacy was negative. The study concludes by suggesting interventions that could help adults improve their financial literacy and manage and sustain their financial well-being.
... Algunos ejemplos de dichos factores son: los estereotipos de género (Driva, et al., 2016), la autoconfianza (Al-Bahrani, et al., 2020), el conocimiento y uso de la tecnología (Hernández Rivera & Rendón Rojas, 2021), la situación familiar (Al-Bahrani, et al., 2020), entre otros. Estos factores han ido evolucionando a través del tiempo, siendo para los primeros estudios un común denominador estudiar el estado civil (Fonseca, et al., 2012) y los roles de género (Banerjee & Roy, 2020) y, con el pasar de los años se han tenido en cuenta otros, tales como el mercado laboral, la industria en la que se trabaja, la afiliación sindical, la ocupación (Preston & Wright, 2019), además de otros. ...
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... Bhushan & Medury [34], Krechovská [9], Tóth et al. [35] in their research sample found higher financial literacy of men than women. Fonseca [36] argue that gender has in the United States affect the level of financial literacy because men usually carried out within the household financial decisions, while women are generally focus on other responsibilities in the home. Also Franczek and Klimontowicz [37] and Mancebón et al. [38] concluded that gender has an effect on financial decision according to their research. ...
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This book explores how behaviour affects individual decisions on retirement planning. It seeks to provide plan sponsors, retirement service providers, and policymakers with new insights on designing retirement plans, to encourage more savings and better preparation for retirement. The book is divided into four parts. Part I presents studies on retirement planning decisionmaking. Part II deals with retirement plan design. Part III examines the impact of retirement education. Part IV studies the implications of retirement payouts.
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Regressions explaining the wage rates of white males, black males, and white females are used to analyze the white-black wage differential among men and the male-female wage differential among whites. A distinction is drawn between reduced form and structural wage equations, and both are estimated. They are shown to have very different implications for analyzing the white-black and male-female wage differentials. When the two sets of estimates are synthesized, they jointly imply that 70 percent of the overall race differential and 100 percent of the overall sex differential are ultimately attributable to discrimination of various sorts.
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