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J Consum Aff. 2012 ; 46(1): 90–106. doi:10.1111/j.1745-6606.2011.01221.x.

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

Household Decision Making
RAQUEL FONSECA [Associate Professor],
Universite du Quebec a Montreal and Affiliate Adjunct at RAND
KATHLEEN J. MULLEN [Economist],
RAND
GEMA ZAMARRO [Economist], and
RAND and Professor of Economics at Pardee RAND Graduate School of Public Policy
JULIE ZISSIMOPOULOS [Associate Director]
Schaeffer Center for Health Policy and Economics and Research Associate Professor at
University of Southern California
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RAQUEL FONSECA: fonseca.raquel@uqam.ca; KATHLEEN J. MULLEN: kmullen@rand.org; GEMA ZAMARRO:

gzamarro@rand.org; JULIE ZISSIMOPOULOS: zissimop@healthpolicy.usc.edu

Abstract
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
substantially lower wealth levels than married couples and unmarried men, and the
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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 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, 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 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 differences at younger ages.

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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
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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 understood 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
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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 representative 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 American 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
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
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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 provided 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 questionnaire.
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 expectations (Delavande and Rohwedder 2008; Manski and Molinari

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2010), life satisfaction (Kapteyn, Smith, and van Soest 2010) and financial literacy (Bruine
de Bruin et al. 2010; Lusardi and Mitchell 2007b).1
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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
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
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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. Estimates 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
summary 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.
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Approximately 93% (1,547 out of 1,667 respondents in MS64) answered all 23 questions
necessary to construct the index. Of these, 1,504 respondents provide complete information
on demographic and socioeconomic characteristics, marital status and marital history. These
respondents comprise the first analysis sample, which we use to examine what
characteristics 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 cohabiting 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 an additional

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

2Full Questionnaires can be found at https://mmicdata.rand.org/alp/index.php/Data.
3See Hung et al. (2009) for a detailed description of how the index is constructed.

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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 financial decision making within the household. Of these, 91% are
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married and 9% are cohabiting. Note that in most cases only one member of the couple 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
respondents. 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 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.
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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 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
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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

4Men are more likely to die earlier, and to remarry after divorce or widowhood (Zissimopoulos, Karney, and Rauer 2008).

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and wealthier individuals have higher levels of financial literacy, and Whites have higher
literacy than racial/ethnic minorities.
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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 married
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 relationship—making up for their initial deficit in roughly 13.7 years. These
findings 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, 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
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allowing for differential effects may be important 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 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 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
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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:

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

dummy variable for male. βF and βM correspond to the coefficients for females and males,
respectively. Then we can decompose the gender gap as follows:

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where ΔX = E [X|d = 0] − E [X|d = 1] and Δβ = βM − βF.

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
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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 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
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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”

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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.”
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Beyond these differences, however, the patterns are generally consistent with agreement on
who is responsible for what among couples. In an analysis of the subsample of matched
spouses, we find that couples generally agree on the division of financial decision making
within 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 respondents say that 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 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
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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)
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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,
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

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relative education differences may trump traditional gender roles when couples divide
financial responsibilities.
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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 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.
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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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References
Bernheim, B Douglas; Garrett, Daniel M. The Effects of Financial Education in the Workplace:
Evidence from a Survey of Households. Journal of Public Economics. 2003; 87(7/8):1487–1519.
Bernheim, B Douglas; Garret, Daniel M.; Maki, Dean M. Education and Saving: The Long-Term
Effects of High School Financial Curriculum Mandates. Journal of Public Economics. 2001; 80(3):
435–465.
Blinder, Alain S. Wage Discrimination: Reduced Form and Structural Estimates. The Journal of
Human Resources. 1973; 8:436–455.
Bruine de Bruin, Wandi; van der Klaauw, Wilbert; Downs, Julie S.; Fischhoff, Baruch; Topa, Giorgio;
Armantier, Olivier. Expectations of Inflation: The Role of Demographic Variables, Expectation
Formation and Financial Literacy. The Journal of Consumer Affairs. 2010; 44(2):381–402.

J Consum Aff. Author manuscript; available in PMC 2012 October 02.

 FONSECA et al. Page 9

Chen, Haiyang; Volpe, Ronald P. Gender Differences in Personal Financial Literacy Among College
Students. Financial Services Review. 2002; 11:289–307.
Delavande, Adeline; Rohwedder, Susann. Eliciting Subjective Probabilities in Internet Surveys. Public
NIH-PA Author Manuscript

Opinion Quarterly. 2008; 72(5):866–891. [PubMed: 20862271]

Hung, Angela; Meijer, Erik; Mihaly, Kata; Yoong, Joanne. RAND Working paper WR-712. 2009.
Building Up, Spending Down: Financial Literacy, Retirement Savings Management, and
Decumulation.
Hung, Angela; Parker, AM.; Yoong, Joanne. RAND Working paper WR-708. 2009. Defining and
Measuring Financial Literacy.
Kapteyn, Arie; Smith, James P.; van Soest, Arthur. Life Satisfaction. In: Diener; Helliwell, John F.;
Kahneman, Daniel, editors. International Differences in Well-being. Vol. Chapter 4. Oxford
University Press; 2010. p. 70-104.
Levine, PB.; Michell, Olivia S.; Phillips, John WR. Worklife Determinants of Retirement Income
Differentials Between Men and Women. In: Bodie, Z.; Hammond, PB.; Mitchell, OS., editors.
Innovations in Financing Retirement. University of Pennsylvania Press; 2002. p. 50-76.
Lusardi, Annamaria; Mitchell, Olivia S. Financial Literacy and Planning Implications for Retirement
Wellbeing. Wharton School, University of Pennsylvania; 2006. Pension Research Council
Working Paper no. 1
Lusardi, Annamaria; Mitchell, Olivia S. Financial Literacy and Retirement Preparedness: Evidence
and Implications for Financial Education. Business Economics. 2007a; 42(1):35–44.
Lusardi, Annamaria; Mitchell, Olivia S. MRRC Working Paper 2007-157. 2007b. Financial Literacy
NIH-PA Author Manuscript

and Retirement Planning: New Evidence from the RAND American Life Panel.
Lusardi, Annamaria; Mitchell, Olivia S. Planning and Financial Literacy: How Do Women Fare?
American Economic Review. 2008; 98(2):413–417.
Manski, Charles F.; Molinari, Francesca. Rounding Probabilistic Expectations in Surveys. Journal of
Business and Economic Statistics. 2010; 28(2):219–231. [PubMed: 20368764]
Oaxaca, Ronald. Male-Female Wage Differentials in Urban Labor Markets. International Economic
Review. 1973; 14:693–709.
Smith, James P.; McArdle, John J.; Willis, Robert. Financial Decision Making and Cognition in a
Family Context. The Economic Journal. 2010; 120:F363–F380. [PubMed: 21116477]
Weir, David R.; Willis, Robert J. Prospects for Widow Poverty in the Finances of Married Couples in
the HRS. In: Mitchell, O.; Hammond, PB.; Rappaport, A., editors. Forecasting Retirement Needs
and Retirement Wealth. Philadelphia: University of Pennsylvania Press; 2000. p. 208-234.
Zissimopoulos, Julie; Karney, Benjamin; Rauer, Amy. MRRC Working Paper WP2008-645, 2008.
2008. Marital Histories and Economic Well-Being.
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FIGURE 1.
Kernel Density Plots of Financial Literacy by Gender
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TABLE 1
Variables Used in the Financial Literacy Index
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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
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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)
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TABLE 2
Summary Statistics by Gender

Female Male
N Mean SD N Mean SD Diff. p-value
FONSECA et al.

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

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>$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
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Female Male
N Mean SD N Mean SD Diff. p-value
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
FONSECA et al.

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, χ2 tests for
categorical variables.

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TABLE 3A
Financial Literacy Regressions, Overall and by Gender

(1) Level (2) Interactions with Years in Relationship (3) Interactions with Years Since Relationship

All Female Male All Female Male All Female Male

FONSECA et al.

Female −0.538*** (0.043)

Age
36–50 0.324*** (0.065) 0.380*** (0.086) 0.312*** (0.100)
51–65 0.563*** (0.075) 0.674*** (0.100) 0.463*** (0.100)
66+ 0.790*** (0.099) 0.946*** (0.140) 0.645*** (0.140)
White 0.303*** (0.074) 0.271*** (0.095) 0.246** (0.120)
Black −0.0113 (0.094) 0.0486 (0.120) −0.0845 (0.160)
High school graduate 0.213 (0.110) −0.0644 (0.140) 0.550*** (0.180)
Some college 0.399*** (0.110) 0.158 (0.150) 0.710*** (0.180)
College graduate 0.807*** (0.120) 0.589*** (0.150) 1.108*** (0.180)
Income
$35–60K 0.287*** (0.063) 0.226*** (0.082) 0.315*** (0.099)
$60–90K 0.417*** (0.065) 0.363*** (0.086) 0.475*** (0.099)
>$60K 0.635*** (0.076) 0.521*** (0.100) 0.748*** (0.110)
Married 0.119 (0.077) 0.237** (0.100) −0.0832 (0.120) −0.002 (0.003) −0.000 (0.004) −0.002 (0.003)

Cohabiting −0.135 (0.150) −0.0467 (0.170) −0.411 (0.290) −0.008 (0.013) −0.015 (0.016) 0.007 (0.026)
Divorced −0.302** (0.140) −0.162 (0.180) −0.467 (0.240) 0.001 (0.007) 0.005 (0.008) −0.005 (0.011) 0.022*** (0.006) 0.019*** (0.007) 0.025*** (0.010)

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Widowed 0.213 (0.310) −0.129 (0.330) 2.638*** (0.980) −0.003 (0.007) −0.004 (0.007) −0.043** (0.019) −0.021 (0.012) −0.000 (0.015) −0.079*** (0.028)
Constant −1.412*** (0.130) −1.817*** (0.160) −1.506*** (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.
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p < .05.
***

**
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TABLE 3B
Blinder-Oaxaca Decomposition of Gender Gap
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Total diff. Endowments Coefficients Interaction

−0.694 (0.051) −0.181 (0.033) −0.602 (0.049) 0.088 (0.033)
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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

FONSECA et al.

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.

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TABLE 5
Mean Financial Literacy Index by Gender, Type of Financial Decision Making and Level of Responsibility
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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***
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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.
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***
p < .01,
**
p < .05.

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TABLE 6
Division of Labor by Gender and Education
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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”
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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.
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