90 THE JOURNAL OF CONSUMER AFFAIRS

RAQUEL FONSECA, KATHLEEN J. MULLEN, GEMA ZAMARRO,

AND JULIE ZISSIMOPOULOS
What Explains the Gender Gap in Financial Literacy?
The Role of Household Decision Making
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.

Women tend to live longer than men, have shorter work tenures, lower
earnings and levels of pension or survivors’ benefits. These factors put
women at higher risk than men of having financial problems (e.g., Weir
and Willis 2000) and of approaching retirement with insufficient savings.
Unmarried, particularly divorced, women near retirement age have sub-
stantially 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, Michell, and Phillips 2002;
Zissimopoulos, Karney, and Rauer 2008). Contributing to low wealth lev-
els 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 lit-
eracy population-wide and the relationship between literacy and savings
behavior (e.g., Bernheim and Garrett 2003; Bernheim, Garret, and Maki
2001; Lusardi and Mitchell 2006 2007a). Lusardi and Mitchell (2008)
document that financial illiteracy is even more prevalent among women
than men. Zissimopoulos, Karney, and Rauer (2008) found that less than
Raquel Fonseca is Associate Professor at Universite du Quebec a Montreal and Affiliate Adjunct
at RAND (fonseca.raquel@uqam.ca), Kathleen Mullen is Economist at RAND (kmullen@rand.org),
Gema Zamarro is Economist at RAND and Professor of Economics at Pardee RAND Graduate
School of Public Policy (gzamarro@rand.org), and Julie Zissimopoulos is Associate Director of the
Schaeffer Center for Health Policy and Economics and Research Associate Professor at University
of Southern California (zissimop@healthpolicy.usc.edu).

The Journal of Consumer Affairs, Spring 2012: 90–106

DOI: 10.1111/j.1745-6606.2011.01221.x
Copyright 2012 by The American Council on Consumer Interests
SPRING 2012 VOLUME 46, NUMBER 1 91

20% of middle-aged college-educated women were able to answer a basic

compound interest question compared to about 35% of college-educated
males of the same age. Chen and Volpe (2002) find similar gender differ-
ences at younger ages.
Understanding how and why men and women have different levels of
financial literacy is crucial to developing policies aimed at reducing the
gender gap and improving the saving and investing decisions of women.
Changing demographic trends and changes in the types of financial
decisions being made further increase the importance of understanding
what accounts for the low levels of financial knowledge and literacy
among women. Higher rates of divorce and lower remarriage rates have
increased over time the percent of women who approach retirement age
unmarried. Moreover, individuals are offered a large number of financial
products (i.e., different retirement plans, investment products, etc.) and
financial products are becoming more complex. For example, there are a
growing number of financial instruments available for financing a home
or extracting equity from an existing home. Individuals have greater
responsibility for their retirement income security with the advent of
defined contribution pension plans (e.g., 401k plans) and declines in
employer-offered defined benefit pension plans. These trends imply that
financial choices may require higher levels of financial knowledge.
Although there is general agreement in the empirical literature that
women have lower levels of financial knowledge than men, less is under-
stood about what factors contribute to these differences. In this article, we
investigate the socioeconomic and demographic factors associated with the
gender gap in financial literacy using multivariate regression analysis and
Blinder–Oaxaca decomposition. Furthermore, we examine the division of
labor in financial decision making within couples as an explanation for the
gender gap in financial literacy. If, within couples, men tend to specialize in
the handling of finances, then married, divorced and widowed women are
less likely to develop their financial knowledge. In this respect, previous
research by Smith, McArdle, and Willis (2010) found that within couples
men are more likely to be chosen in surveys as the financial representa-
tive of the household, and that husband’s education and cognitive scores
(memory, numeracy and mental status) are bigger predictors of this choice
than wife’s education and cognition.
We use existing data on financial literacy combined with new data we
collected on decision making within the household from RAND Ameri-
can Life Panel (ALP). Using Blinder–Oaxaca decomposition we find that
the great majority of the gender gap is not explained by differences in
covariates—characteristics of men and women—but rather differences in
92 THE JOURNAL OF CONSUMER AFFAIRS

coefficients, or how literacy is produced. There is no discernible pattern

of financial decision making along gender lines and one’s own financial
responsibilities increase as his/her education level increases relative to
his/her spouse’s education level for both men and women. Finally, greater
financial decision making responsibility within couples is correlated with
higher financial literacy for men, but not women.
DATA

To conduct this research, we use data from the RAND ALP. The ALP
consists of over 2,500 respondents aged 18 and over who are interviewed
periodically over the Internet. Respondents do not need Internet access to
participate; those without access (less than 17% of the sample) are pro-
vided RAND via WebTV and an Internet subscription, eliminating the
bias found in many Internet surveys which include only computer users.
Upon joining the panel, respondents complete an initial survey collecting
individual sociodemographic 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 module. Roughly once a
month, respondents receive an e-mail with a request to fill out a question-
naire. Response rates average 70%–80%. Since 2003 researchers have
fielded over 200 modules in the ALP and published papers using these
data on a wide variety of topics (e.g., subjective probabilities and expecta-
tions (Delavande and Rohwedder 2008; Manski and Molinari 2010), life
satisfaction (Kapteyn, Smith, and van Soest 2010) and financial literacy
(Bruine de Bruin et al. 2010; Lusardi and Mitchell 2007b).1
We designed a module survey (MS73) that was administered in June
2009. The module included detailed questions regarding marital status
and history. For those married or cohabiting with a partner, we also
posed questions regarding how financial responsibilities are divided in the
household. We then merged this survey with financial literacy measures
collected in a previous module (MS64) designed by Hung, Parker, and
Yoong (2009) and fielded in March 2009. Sampling weights are provided
by the ALP to adjust for sample selection.
Definitions and measures of financial literacy 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 use an index measure
developed by Hung, Parker, and Yoong (2009) and Hung et al. (2009) that

1. The data collected in the ALP is publicly available at https://mmicdata.rand.org/alp/index.

php/Data.
SPRING 2012 VOLUME 46, NUMBER 1 93

combines multiple dimensions of financial literacy. The index is based

on answers to 23 questions on basic financial concepts, investing, life
insurance and annuities, and includes the 13-item scale used by Lusardi
and Mitchell (2006): five items measuring numeracy and understanding
of compound interest and inflation and eight items measuring knowledge
of the stock market, stocks, bonds, mutual funds and diversification.
The index also includes six additional items measuring knowledge of
stocks, bonds and mutual funds and four items measuring knowledge
about life insurance and annuities based on different questionnaires (e.g.,
the FINRA Investor Survey, Survey of Financial literacy in WA State,
etc.). Table 1 lists the variables used to construct the index.2
The index is constructed using estimates from a structural one-
dimensional latent variable model of financial literacy. In particular, the
model specifies the probability of answering each test item correctly as
a function of the underlying true but unobserved financial literacy. Esti-
mates of respondents’ latent financial literacy (their scores on the index) are
obtained by maximizing the log pseudo-likelihood function after assuming
that the unobserved financial literacy trait is standard normally distributed.
Hung et al. (2009) provide additional details on the index and sensitivity
analysis.3 Using the financial literacy index allows us to avoid problems of
multiple inferences, and simplifies considerably the interpretation of our
results since we analyze gender differences of a financial literacy sum-
mary measure that is continuous and normally distributed. We normalize
the financial literacy index so that it has mean 0 and standard deviation
(SD) 1. This transformation simplifies the interpretation of the estimated
coefficients as they will represent the effects in terms of SD increases in
financial literacy.
Approximately 93% (1,547 out of 1,667 respondents in MS64) answered
all 23 questions necessary to construct the index. Of these, 1,504 respon-
dents provide complete information on demographic and socioeconomic
characteristics, marital status and marital history. These respondents com-
prise the first analysis sample, which we use to examine what characteris-
tics are correlated with financial literacy by gender.
Our second analysis sample is expanded to include individuals with
missing financial literacy scores, but is restricted to married or cohab-
iting respondents who participated in our module on financial decision
making within households. All coupled respondents in the first sample
(N = 1, 009; 519 females and 490 males) also responded to MS73, and

2. Full Questionnaires can be found at https://mmicdata.rand.org/alp/index.php/Data.

3. See Hung et al. (2009) for a detailed description of how the index is constructed.
94 THE JOURNAL OF CONSUMER AFFAIRS

TABLE 1
Variables Used in the Financial Literacy Index

Group 1: Basic Financial Concept

1. Numeracy question about savings and interest rates
2. Numeracy question about compound interest
3. Question about the effect of inflation
4. Question about the value of money over time
5. Question capturing understanding of money illusion
Group 2: Investment
6. Question about the main function of the stock market
7. Question measuring knowledge of mutual funds
8. Question about the relation between interest rates and bond prices
9. Question comparing the safety of the return of company stocks and mutual funds
10. Question comparing the risk level of stocks and bonds
11. Question identifying assets with longer period returns
12. Question identifying assets with highest fluctuations over time
13. Question about risk diversification
14. Question about what happens when someone buys company stocks
15. Question about what happens when someone buys company bonds
16. Question about withdrawing money from a stock mutual fund
17. Question to assess stock mutual funds knowledge
18. Question to assess stock mutual funds annual fees knowledge
19. Question to assess mutual fund rate of return knowledge
Group 3: Life insurance and Annuities
20. Question to assess knowledge of the saving feature of whole life insurance
21. Question to assess understanding of the cash value of a life insurance policy
22. Question to assess knowledge of the annuity payments structure (yearly payments)
23. Question to assess the annuity payments structure (lump sum)

an additional 517 new coupled respondents were recruited to join the

ALP between the two waves. Combining these two groups gives us 1,526
respondents (827 females and 699 males) reporting information on finan-
cial decision making within the household. Of these, 91% are married and
9% are cohabiting. Note that in most cases only one member of the cou-
ple is an ALP respondent, who reports information (e.g., education) for
both respondents. Our sample includes 1,318 unique couples. Of the 1,526
respondents 208 are the spouse of a respondent (1, 526 − 208 = 1,318
unique couples). In order to maximize power, we use data from all respon-
dents. Disagreement within couples on who bears responsibility for given
tasks will affect the interpretation of our results as we discuss further below.

WHAT FACTORS MITIGATE GENDER DIFFERENCES

IN FINANCIAL LITERACY?

Table 2 shows weighted summary statistics, by gender, for the

respondents with non-missing values of the financial literacy index. The
SPRING 2012 VOLUME 46, NUMBER 1 95

TABLE 2
Summary Statistics by Gender

Female Male
N Mean SD N Mean SD Diff. p-value

Financial literacy index 844 −0.537 0.965 678 0.158 0.978 −0.695 <.001
Age 0.331
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
Race 0.047
White 844 0.750 0.433 678 0.834 0.372 −0.08
Black 844 0.137 0.344 678 0.088 0.284 0.048
Other 844 0.114 0.318 678 0.078 0.268 0.036
Education 0.168
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
Income 0.049
<$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
In a couple 844 0.524 0.500 678 0.664 0.473 −0.140 <.001
Marital status 0.003
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
Years in current relationship 519 20.930 14.485 490 23.277 15.637 −2.347 0.014
Years in last relationship 199 17.468 13.747 105 17.787 15.492 −0.319 0.853
Years since last relationship 201 14.707 10.448 105 11.412 10.424 3.295 0.009
No dependents 844 0.465 0.499 678 0.535 0.499 −0.070 0.007
Number of dependents (if >0) 402 2.178 1.333 290 2.142 1.120 0.036 0.705
Working for pay 820 0.642 0.480 651 0.693 0.461 −0.051 0.039
Education relative to partner 0.151
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: Data are weighted. Financial literacy index is standardized. Summary statistics limited to
those with non-missing financial literacy. p-values are for t tests for independent variables, χ 2 tests
for categorical variables.
96 THE JOURNAL OF CONSUMER AFFAIRS

FIGURE 1
Kernel Density Plots of Financial Literacy by Gender
.5
.4.3
Density
.2 .1
0

-4 -2 0 2 4
Financial literacy index

Female Male

financial literacy index for women is about 0.7 standard deviations lower
than that for men (p < .001). Figure 1 gives a more complete picture of
the differences in financial literacy levels between men and women. The
distribution of the financial literacy index for women is shifted to the
left of that for men. While the range of financial literacy levels is similar
across the two groups, for much of the distribution the gap between men
and women is relatively fixed at around 0.7 and only becomes compressed
in the tails.
Table 2 compares characteristics of men and women, and finds that
more women in our sample belong to minority ethnic groups than men.
Fewer women are currently married or cohabiting, and more women are
divorced, widowed or never married, and they remain unmarried longer
than men.4 While education status is not jointly significantly different
across the two groups, fewer women advanced past high school than
men (p = .028). Women in our sample have lower household income
than men on average, and fewer women report working for pay. These
differences in demographic characteristics alone may explain some of the
difference in financial literacy, and we explore this explanation first.
Table 3A reports the results of multivariate regression analysis of a
number of potential factors associated with financial literacy, overall

4. Men are more likely to die earlier, and to remarry after divorce or widowhood (Zissimopoulos,
Karney, and Rauer 2008).
SPRING 2012 VOLUME 46, NUMBER 1 97

TABLE 3A
Financial Literacy Regressions, Overall and by Gender

(2) Interactions with Years in (3) Interactions with Years

(1) Level Relationship Since Relationship

All Female Male All Female Male All Female Male

Female −0.538∗∗∗
(0.043)
Age
36–50 0.324∗∗∗ 0.380∗∗∗ 0.312∗∗∗
(0.065) (0.086) (0.100)
51–65 0.563∗∗∗ 0.674∗∗∗ 0.463∗∗∗
(0.075) (0.100) (0.100)
66+ 0.790∗∗∗ 0.946∗∗∗ 0.645∗∗∗
(0.099) (0.140) (0.140)
White 0.303∗∗∗ 0.271∗∗∗ 0.246**
(0.074) (0.095) (0.120)
Black −0.0113 0.0486 −0.0845
(0.094) (0.120) (0.160)
High school 0.213 −0.0644 0.550∗∗∗
graduate (0.110) (0.140) (0.180)
Some college 0.399∗∗∗ 0.158 0.710∗∗∗
(0.110) (0.150) (0.180)
College 0.807∗∗∗ 0.589∗∗∗ 1.108∗∗∗
graduate (0.120) (0.150) (0.180)
Income
$35–60K 0.287∗∗∗ 0.226∗∗∗ 0.315∗∗∗
(0.063) (0.082) (0.099)
$60-90K 0.417∗∗∗ 0.363∗∗∗ 0.475∗∗∗
(0.065) (0.086) (0.099)
>$60K 0.635∗∗∗ 0.521∗∗∗ 0.748∗∗∗
(0.076) (0.100) (0.110)
Married 0.119 0.237** −0.0832 −0.002 −0.000 −0.002
(0.077) (0.100) (0.120) (0.003) (0.004) (0.003)
Cohabiting −0.135 −0.0467 −0.411 −0.008 −0.015 0.007
(0.150) (0.170) (0.290) (0.013) (0.016) (0.026)
Divorced −0.302** −0.162 −0.467 0.001 0.005 −0.005 0.022∗∗∗ 0.019∗∗∗ 0.025∗∗∗
(0.140) (0.180) (0.240) (0.007) (0.008) (0.011) (0.006) (0.007) (0.010)
Widowed 0.213 −0.129 2.638∗∗∗ −0.003 −0.004 −0.043** −0.021 −0.000 −0.079∗∗∗
(0.310) (0.330) (0.980) (0.007) (0.007) (0.019) (0.012) (0.015) (0.028)
Constant −1.412∗∗∗ −1.817∗∗∗ −1.506∗∗∗
(0.130) (0.160) (0.210)
Observations 1,504 830 674
R2 0.40 0.34 0.36

Notes: Standard errors in parentheses. Data are weighted. Dependent variable is standardized financial
literacy index. We also control for being separated but do not report due to very small sample size.
p-values for joint tests of significance for age, race, education and income groups were all less than 0.01
in all specifications. Marital status was jointly significant overall and for men only (p < .01), but not for
women (p = .13). Interactions in (2) were jointly significant (p = .091) for men only and insignificant
overall (p = .92) and for women only (p = .50). Interactions in (3) were jointly significant for all groups:
p < .01 overall and for men only, and p = .05 for women only.
∗∗∗
p < .01, ∗∗ p < .05.
98 THE JOURNAL OF CONSUMER AFFAIRS

and separately by gender. 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
estimated coefficients for demographic characteristics (age and race
dummies), socioeconomic characteristics (education and family income)
and marital status dummies. Since we are particularly interested in the
role of the household in explaining financial literacy differences, columns
2 and 3 add interactions between current marital status and length of the
most recent relationship and years since marital disruption, respectively.
Within each column, results for regressions estimated using the entire
sample, and for women and men, respectively, are presented in sub-
columns.
When we focus on the combined regression specification (i.e., where
covariates are not interacted with gender), we find that, even though
most of the covariates are statistically significant, they do not have a
large effect on the gender gap—0.54 standard deviations, compared to
0.7 standard deviations (the raw difference without any covariates, from
Table 2). In alternative specifications that sequentially add covariates,
we find that education and income has the biggest impact on the
gender gap. Demographic and socioeconomic variables are correlated
with financial literacy in the expected ways: older, more educated and
wealthier individuals have higher levels of financial literacy, and Whites
have higher literacy than racial/ethnic minorities.
When aggregating men and women together, married and cohabiting
individuals do not have significantly higher levels of financial literacy
than their never-married counterparts. Divorced individuals, however,
are 0.3 standard deviations less financially literate than the never married
and 0.4 standard deviations less financially literate than currently mar-
ried respondents. Length of time in the most recent relationship does not
appear to have any effect on financial literacy levels of current or formerly
married respondents. However, divorced respondents gain 0.02 standard
deviations in financial literacy for every year since their last relation-
ship—making up for their initial deficit in roughly 13.7 years. These find-
ings are consistent with selection out of marriage: individuals with lower
“ability” are less likely to stay married. The findings are also consistent
with “learning.” Divorced individuals gain financial knowledge over time
as they learn to make financial plans without the help of a partner.
It may be the case that one spouse specializes in financial decision
making and the other does not invest time or effort in making financial
decisions. For example, if men tend to specialize in handling finances,
SPRING 2012 VOLUME 46, NUMBER 1 99

TABLE 3B
Blinder-Oaxaca Decomposition of Gender Gap

Total diff. Endowments Coefficients Interaction

−0.694 −0.181 −0.602 0.088

(0.051) (0.033) (0.049) (0.033)

then we might expect a positive relationship between years of marriage

and financial literacy for men and zero or negative for women. More
generally, men and women might have different production technologies
for financial literacy, so allowing for differential effects may be impor-
tant for other covariates as well. The last two sets of subcolumns present
estimates of the first specification in Table 3 fully interacted with gender.
Importantly, including the interaction terms reduces the estimated gen-
der 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 findings emerge from the model with gender interactions with
all covariates. The effects of age, race and income on financial literacy
are not statistically different between men and women. However, men
benefit 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 any education increase
is 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 men. Indeed, married
women are financially more literate than married men. Divorcees are
no less financially literate than never-married individuals, nor is there a
significant difference between the financial literacy of divorced men and
women. Similar to what we saw in the specification without interactions,
years since divorce are associated with increased financial literacy for
both men and women.
Table 3B presents the results of a Blinder–Oaxaca decomposition of
the gender gap into variation due to endowments, coefficients and their
interaction (Blinder 1973; Oaxaca 1973). Note that we estimated the
following conditional expectation function (CEF) using ordinary least
squares regression:

E [y|X, d ] = dX β M + (1 − d )X β F ,
100 THE JOURNAL OF CONSUMER AFFAIRS

where y denotes financial literacy, X is a vector of socioeconomic

characteristics and d is a dummy variable for male. β F and β M
correspond to the coefficients for females and males, respectively. Then
we can decompose the gender gap as follows:
E [y|d = 0] − E [y|d = 1] = Xβ M − βE [X|d = 1] + Xβ,
where X = E [X|d = 0] − E [X|d = 1] and β = β M − β F .
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 men). 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 men). The final term is the part of the gap arising from the interaction
between endowments and coefficients. Often the 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 women serving as the baseline.
The decomposition suggests that the great majority of the gender gap is
due to differences in coefficients rather than differences in characteristics
between men and women. For whatever reason, men and women have
very different production processes for financial literacy. The interaction
effect is statistically significant and has the opposite sign, suggesting that
the endowment and coefficient effects together account for more than
the total effect. Intuitively, in the case of a scalar X, this can happen
when the CEF for females is steeper and well below the CEF for males
over the support of X. In a sensitivity analysis of the Blinder–Oaxaca
decomposition (not shown and available upon request), we find that
inclusion of the marital status and marital history variables account
for this pattern. Next we explore one possible explanation for the
production process difference between men and women: division of labor
for financial decisions within couples.

HOW DO HOUSEHOLDS DIVIDE FINANCIAL

DECISION MAKING?

A possible mechanism through which men and women “produce”

different levels of financial literacy may arise through a process by which,
within the household, men specialize in acquiring financial knowledge
and women specialize in other household functions. If so, married women
SPRING 2012 VOLUME 46, NUMBER 1 101

will have lower levels of financial literacy than men all else equal because
men are investing in this form of human capital. Previously married
women may not have invested in understanding complex financial
decisions while married if the husband, and not the wife, specialized
in financial decision making. To shed some light on this hypothesis
as a possible explanation, we examine how households make financial
decisions and study the correlation between decision making and financial
literacy. A finding of a positive correlation, however, does not indicate
a causal mechanism: it may be the case that men have higher levels of
financial literacy for other reasons and thus they are more likely to make
the financial decisions.
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. Table 4
presents self-reported division of labor for coupled men and women
separately. Since both men and women were randomly sampled from
the population, if both partners agree on who is responsible for a given
task then an objective measure should reveal that the fraction of men
reporting “mostly me” matches the fraction of women reporting “mostly
my partner,” and vice versa. Yet both men and women are more likely
to report “mostly me” than “mostly my partner.”
Beyond these differences, however, the patterns are generally con-
sistent with agreement on who is responsible for what among couples.
In an analysis of the subsample of matched spouses, we find that cou-
ples generally agree on the division of financial decision making within

TABLE 4
Percent of Women and Men Reporting Mostly Me, Equal, Mostly Partner for Financial
Tasks

Female (%) Male (%)

Mostly Me Equal Partner Me Equal Partner

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: number of observations = 827 females, 699 males. Data are weighted and include those with
missing financial literacy index.
102 THE JOURNAL OF CONSUMER AFFAIRS

the household. 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 report that women are more
likely to be responsible for paying the bills. About half of the respon-
dents say that they make short- and long-term spending/saving decisions
together (with slightly more women saying they are primarily respon-
sible for short-term spending, which may be hard to differentiate from
paying bills). On the other hand, there are differences in reporting of
responsibility for paying taxes and tracking investments; half of men say
they are primarily responsible, but women report these responsibilities
are more evenly distributed.
Table 5 presents estimates of 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 persists
across categories. For example, among respondents who report primary
responsibility for paying 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 disappears, among those who
report their partner is responsible for financial activities.
Table 5 also reports p-values for standard F -tests of equality within
gender. If individuals sort into responsibility for financial activities based
on financial literacy, then we would expect financial literacy to decrease
moving 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 .03 (and in all
but one case less than .001). However, for women financial literacy does
not appear to play a role in their perception of financial responsibilities.
Only two p-values are less than .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 these activities.
A possibility is that assortative 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. While we cannot observe relative differences in financial
literacy among couples for the vast majority of couples in our data, we
can examine the role of education—both in absolute and relative terms.
Table 6 displays the average number of financial responsibilities (out
of the five activities) adopted mostly by respondents and their partners,
respectively, by gender and education. Panel A presents mean values by
absolute education, whereas Panel B presents mean values by education
relative to the respondent’s partner (more, the same or less). For example,
SPRING 2012 VOLUME 46, NUMBER 1 103

TABLE 5
Mean Financial Literacy Index by Gender, Type of Financial Decision Making and Level
of Responsibility

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 (p-value) 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 (p-value) 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 (p-value) 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 (p-value) 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 (p-value) 0.000 0.000

Notes: number of observations = 519 females, 490 males. Data are weighted. Financial literacy
index is standardized.
∗∗∗
p < .01, ∗∗ p < .05.

women who completed high school or less on average handle 1.86

financial activities, compared to 1.36 for men of similar education.
This pattern is reversed for higher education categories; women who
completed at least some college graduation are 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 activities as their education
increases, whereas no such pattern appears for women. However, if we
consider relative education levels we find that women and men with
education similar to their partners tend to take on the same number
of financial responsibilities. Additionally, both men and women are
responsible for more financial activities as their education increases
relative to their spouse. These findings suggest that relative education
104 THE JOURNAL OF CONSUMER AFFAIRS

TABLE 6
Division of Labor by Gender and Education

Female Male Diff.

(A) Absolute education

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: number of observations = 827 females, 699 males. Count is out of five items. Data are
weighted and include those missing financial literacy index data.
∗∗∗
p < .01, ∗∗ p < .05.

differences may trump traditional gender roles when couples divide

financial responsibilities.

CONCLUSION

We utilize newly collected data from the RAND ALP to examine

potential explanations for the gender gap in financial literacy: differences
in the characteristics of men and women and differences in how these
characteristics “produce” financial literacy. We specifically examine
a mechanism by which gender differences may be produced: within
households, men more often than women specialize in financial decisions
thereby acquiring more financial knowledge. Similar to past studies,
we find gender differences in financial literacy utilizing a single
comprehensive measure that is a combination of multiple measures of
financial literacy. We find that women perform 0.7 standard deviations
lower than men on our financial literacy index, and the difference is
SPRING 2012 VOLUME 46, NUMBER 1 105

statistically significant. Controlling for sociodemographic characteristics

has only a limited effect on the gap. On the other hand, our estimated
coefficient of the correlation between these characteristics and financial
literacy reveals that men and women have different production processes
for financial literacy. A possible mechanism through which gender
differences are produced is household specialization: men specialize
in making household financial decisions thereby acquiring financial
knowledge and women specialize in other household functions.
We, however, find little support for financial decision specialization by
gender within couples although we do find a positive correlation between
decision making and financial literacy but only for males. We need a
better understanding of why the intensity of decision making is related to
financial literacy for men and not women. This may be in part explained
by the limited set of financial decisions we study. Importantly, we find
that decision making within couples depends on the relative education of
spouses. Women and men with similar education 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. In 2010, slightly more
women than men over age 25 have achieved a bachelor’s degree or higher
(U.S. Census Bureau, Current Population Survey, 2010 Annual Social
and Economic Supplement). Our results suggest that with approximately
equal education achievement by gender, financial decision making should
also be approximately equal by gender, although within an individual
household it will depend upon the relative levels of the spouses. More
research is needed to understand how the intensity of involvement in
financial decisions increases financial knowledge and importantly, how
this knowledge is used. As the number of financial products and their
complexity continue to grow, so will the importance of acquiring financial
knowledge and the ability to use it to achieve income security.

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