Aging and Strategic Learning: The Impact of Spousal Incentives on Financial Literacy

Abstract

Women tend to be less financially literate than men, consistent with a division of labor where husbands manage finances. However, women tend to outlive their husbands. I find that older women acquire financial literacy as they approach widowhood — 80 percent would catch up with their husbands by the expected onset of widowhood. These gains are not attributable to husbands’ cognitive decline, as captured by cognition tests. The results are consistent with a model in which the division of labor collapses when a spouse dies: women have incentives to delay acquiring financial human capital, but also to begin learning before widowhood.

I. Introduction

Empirical studies have found that women tend to have lower levels of financial literacy than men (for example, Lusardi and Mitchell 2008; Fonseca et al. 2012; Kotlikoff and Bernheim 2001). This gap may reflect a division of labor within the household such that men are responsible for financial matters. However, women also tend to outlive their husbands, so they will eventually need to take over this task. Women therefore have incentives both to delay acquiring financial knowledge but also to begin learning prior to widowhood. Financial literacy is a critical form of financial knowledge that is linked to important economic outcomes, including more effective wealth management, better management of credit and debt, retirement planning, increased saving, and higher stock market participation (Hilgert, Hogarth, and Beverly 2003; Lusardi and Tufano 2009b; Lusardi and Mitchell 2007, 2009; Carlin and Robinson 2012; Van Rooij, Lusardi, and Alessie 2011). Economists view investment in human capital as a purposive process, and in this paper, I show that the acquisition of financial literacy is no different.

This paper presents a model of the human capital investment process of longer-lived spouses over the life cycle and tests its predictions using innovative new data on financial literacy and financial decision-making. The model generates three results. First, if the household finances are managed by husbands, women may rationally delay learning about finances. Secondly, investments in financial knowledge should increase as widowhood becomes more imminent; lastly, longer durations of widowhood provide additional incentives for accumulating more human capital. While I analyze the model specifically for women and financial literacy, the model is generalizable to any task specialized in by the shorter living spouse.

Using a cross-sectional sample that links husbands and wives, I use variation in the husbands’ life expectancies to analyze how women accumulate human capital relative to their husbands, who do not have the same incentive to increase learning in old age. The results, robust to controlling for four forms of cognition that decline with age, show that women acquire financial literacy as they approach widowhood. At an estimated rate of 0.04 standard deviations per year, 80 percent of women in the sample would catch up with their husbands prior to the expected onset of widowhood.

Financial knowledge is critical due to its relationship to economic outcomes and its policy implications. Given its links with financial planning and behavior, having sufficient financial literacy is becoming even more important since the responsibility for retirement planning has shifted to individuals. Wealth management has become increasingly complex as predictable streams of retirement income from pensions have been replaced by account-type plans that need to be managed both before and after retirement. In addition, financial literacy has become a prominent policy issue in the wake of the 2008 economic crisis.¹ The large numbers of foreclosures, defaults, and debt problems that arose during the housing and financial crisis highlight the costs of financial illiteracy for individuals with low and high levels of wealth. Furthermore, proposals to privatize Social Security would introduce further individual responsibility for retirement planning and require even more knowledge.

This paper makes a number of contributions. This is, to my knowledge, the first study to analyze investments in financial knowledge in a life-cycle framework. While descriptive studies have documented low levels of financial literacy, especially among women (Lusardi and Mitchell 2011, 2008; Lusardi, Mitchell, and Curto 2010, 2009; Fonseca et al. 2012; Lusardi and Tufano 2009a), they have not found an economic basis for the pattern. My finding that women acquire knowledge as they approach widowhood shows that the gender gap in financial literacy may reflect strategic responses of women to incentives over the life cycle.

Second, this paper is the first to link the financial knowledge of both members of a couple. By analyzing the spousal gap in financial literacy rather than differences between women and men in different households, I can investigate how financial knowledge relates to the division of labor over the life cycle. I also use a rich set of cognitive measures to show that the narrowing of the wife-husband gap in financial literacy reflects advances on the part of women and is unlikely to be merely an artifact of men’s cognitive decline.² This is not to say, however, that older women are sufficiently preparing for their financial future. In light of studies documenting the low levels of financial literacy among both older men and women (Lusardi, Mitchell, and Curto 2009), merely catching up with one’s husband may not be enough to prevent the poor economic outcomes common among widows.

This paper combines ideas about the household division of labor with human capital theory. Section II provides additional background on financial knowledge as human capital in the context of the household division of labor and widowhood. Section III presents a theoretical model of the timing of a woman’s investment in financial human capital over her lifetime. Section IV describes the data used, and Section V presents evidence that older women acquire financial knowledge as widowhood approaches. These effects remain even when controlling for a rich set of measures of husband’s cognition. Section VI concludes.

II. Financial Literacy, Human Capital, and Specialization

The management of household finances is an important type of non-market production that requires its own form of human capital. One major component of this human capital is financial literacy. Public and scholarly interest in financial literacy and informed financial decision-making is increasing in part because of the poor financial outcomes that are associated with low levels of financial literacy: problems with debt (Lusardi and Tufano 2009b) and lack of retirement planning (Lusardi and Mitchell 2007, 2009), among others. At the same time, studies have found that Americans tend to display low levels of financial literacy (Bernheim 1998; Hilgert, Hogarth, and Beverly 2003; Lusardi and Tufano 2009b). Financial illiteracy and financial mistakes are particularly widespread among older Americans (Lusardi and Mitchell 2011; Agarwal et al. 2009). Recent government policies, including the establishment of the Consumer Financial Protection Bureau, aim to increase financial literacy among the public.

As mentioned, descriptive studies have shown that women tend to have lower levels of financial literacy than men. This is true even for younger women (Lusardi, Mitchell, and Curto 2010; Chen and Volpe 2002), in spite of their gains in educational attainment. Low levels of financial literacy may not be problematic if one’s partner has higher literacy and specializes in managing household finances. As Becker (1985) shows, under a number of assumptions, it is efficient for members of a household to specialize in particular tasks. However, such reliance on a partner can have serious consequences when one is unable to divide tasks among household members either before forming a household or during widowhood.³

In American households, men are usually primarily responsible for household finances.⁴ In the Cognitive Economics Study used in this paper, only 16 percent of couples report that the woman is the most financially knowledgeable person in the household. A person may become the financial specialist in a couple for a number of reasons. First, the person entering marriage with a greater stock of financial knowledge might be more likely to specialize; this could favor the older member of the couple, typically the man. This advantage may arise from past experience with finances, possibly through one’s occupation. Educational sorting may play a role, if college-educated women were more likely to major in non-quantitative fields. A second factor may simply be the interest or enthusiasm of the specialist, or fear or avoidance on the part of the non-specialist. Third, the division of labor may also be a product of intra-household bargaining. Whatever the root causes, women tend to be less financially literate than men. Since women tend to outlive men, this division of labor cannot be sustained indefinitely.

Economists have shown that the expected duration of a household affects how labor is divided. Johnson and Skinner (1986) find that greater divorce risk increases the labor supply of women, and Stratton (2005) shows that cohabiting couples, whose relationships are typically shorter in duration than those of married couples, have less intra-household specialization in housework than married couples. While widowhood is a different form of relationship termination, it operates similarly by ending a person’s ability to reap the benefits of specialization. This suggests that the nature of the division of labor within a household changes over time and therefore calls for continued investment.

Widowhood is a very likely outcome for most married women, who not only face longer life expectancies than men but are also typically younger than their husbands. According to 1995 marital status life tables, 75 percent of marriages not ending in divorce end in widowhood (Schoen and Standish 2001). Furthermore, the mean duration of widowhood in the Health and Retirement Study is about nine years (author’s own calculations). Although the gender disparity in life expectancies has changed over time, widowed women still outnumbered widowed men four to one in 2009 (U.S. Census Bureau 2010). The prospect of many years without the couple’s financial specialist creates incentives for women to prepare by acquiring financial knowledge.⁵

The notion that financial knowledge is a form of human capital was introduced in Delavande, Rohwedder, and Willis (2008), which related the production of human capital to portfolio choice. Human capital accumulation is purposive based on its costs and benefits, and likewise, financial illiteracy can be costly for widows. Even a widow who plans to delegate the management of her finances to a professional or a relative needs enough knowledge to choose someone trustworthy and to recognize if she is being bilked. If she manages her own finances, she needs to be knowledgeable enough to distinguish fraudulent offers from legitimate ones. On the other hand, a widow who recognizes her lack of knowledge but does not trust any individuals or financial institutions may lose potential gains by keeping all of her money in cash. Financial illiteracy can also lead to anxiety about money. A woman with insufficient financial knowledge may find herself in widowhood without a firm understanding of how much she can afford to spend, what her holdings are, or how quickly to decumulate during widowhood.

Since investment decisions and payoffs are realized over the life cycle, an important aspect of human capital accumulation is its timing. Mincer and Polachek (1974) argue that the human capital investments and time allocation of individuals will be influenced by expectations of future family and market activities. In most applications such as formal education and on the job training (Ben-Porath 1967), it is advantageous to invest early to capture the longest stream of benefits. On the other hand, some investments (such as religious devotion as investment in the afterlife, studied by Azzi and Ehrenberg 1975) may not yield benefits until much later in life, so the payoffs to such investments should increase with age. Similarly, household specialization creates delays in the returns to investing in knowledge related to the spouse’s tasks.

In this paper, I develop a simple model to analyze the timing of human capital investments in the spouse’s tasks and the effects of differing time horizons arising from gender differences in life expectancies. Using an innovative new dataset, I study the financial knowledge of husbands and wives, and in doing so I am able to learn more about an aspect of household production that is not well understood. One theme underpinning the human capital literature is that investments are purposive, and I show that the timing of investments in financial human capital is purposive as well.

III. Theoretical Framework

This section presents a simple model to build intuition for the woman’s decision to accumulate human capital related to tasks in which she does not specialize. Consider a time span that begins with marriage (t = 0) and lasts until the woman dies in period T. The woman will outlive her husband, who passes away at time D.⁶ Therefore, widowhood spans from time D to T. Assume the husband specializes in household finances from the beginning of the marriage.

Assume further that non-wage financial resources can only be used if at least one person in the household has financial knowledge. A new widow with no financial knowledge will not be able to access any non-wage financial resources until she acquires some financial knowledge.⁷ In this case, consumption smoothing implies that a widow will want at least some financial knowledge at the time of widowhood. This is most realistic in a situation in which the husband was wholly responsible for all household financial matters.

A woman only begins to use this financial knowledge after her husband dies, after which the returns to her stock of financial human capital K are v(K) annually until her death. The present discounted value (after depreciation) of a marginal unit of financial human capital over the course of her life is then:

$$P_t=\{\begin{array}{cc}{[\beta (1-\delta )]}^{D-t}\sum _{j=0}^{T-D}{\beta }^j{v}^{\prime }(K_t)& \text{if}t<D\\ \sum _{j=0}^{T-t}{\beta }^j{v}^{\prime }(K_t)& \text{if}t\ge D\end{array}$$

where β is the subjective discount factor and δ is the depreciation rate⁸ of human capital. Prior to widowhood, the value of a marginal increase in human capital is the present value of the stream of annual benefits realized during widowhood for a total of T – D years, discounted by the number of years a woman must wait until the stream begins (D – t years). At time zero, the present value of the benefits are low due to the D-year delay until widowhood. The value increases as a woman approaches widowhood, at which point it declines because of the decreasing number of years the knowledge can be used. Assuming that units of human capital have a constant marginal product that is independent of the number of units newly acquired or of the current stock of knowledge, the time path of P_t is upward and convex until it peaks at time D, when it declines concavely. P_t can therefore be interpreted as the demand for financial human capital at time t. This demand is time variant, so a marginal cost curve is required to pin down the time-path of human capital investments. Time is allocated to acquiring financial knowledge at the expense of other activities. In its simplest form, assume that this marginal cost curve is upward sloping and fixed over time, with its position determined by underlying ability. As P_t shifts upward, a woman will acquire more human capital until widowhood (with the rate of accumulation increasing with age), after which point she will stop, as the costs exceed the benefits. She will therefore use whatever human capital she acquired by time D for the duration of widowhood.

The derivative of P_t with respect to the time to widowhood D is

$$\frac{{dP}_t}{dD}={[\beta (1-\delta )]}^{D-t}\frac{1}{1-\beta }({\beta }^{T}ln\beta +(\beta -{\beta }^{T+1-D})ln[\beta (1-\delta )])<0.$$ (1)

The negative sign of this derivative confirms the intuition that as one approaches widowhood, the marginal benefit increases. The derivative with respect to the length of widowhood (T – D), holding D constant:

$$\frac{{dP}_t}{d(T-D)}=-{[\beta (1-\delta )]}^{D-t}\frac{1}{1-\beta }{\beta }^{T+1-D}ln\beta >0.$$ (2)

Therefore, the shorter the time to widowhood, the greater the demand for financial human capital. The longer the duration of widowhood, the greater the demand for financial knowledge. The ratio of the magnitude of the two derivatives is

$$\frac{\frac{{dP}_t}{dD}}{\frac{{dP}_t}{d(T-D)}}=\frac{{\beta }^{T}ln\beta +(\beta -{\beta }^{T+1-D})ln[\beta (1-\delta )]}{{\beta }^{T+1-D}ln\beta }.$$ (3)

Assuming β = 0.97 and δ = 0.03, with D and T – D the expected time to and expected length of widowhood, respectively, the ratio in my sample ranges from 1.3 to 2.8, with a mean of 1.7.⁹ The ratio is larger the more imminent widowhood is and the longer its duration. The ratio is also larger the greater the depreciation rate of human capital and the lower the discount factor β. Therefore, the effect of the time to widowhood on the acquisition of financial literacy should be greater in magnitude than the effect of the duration of widowhood.

Lastly, a large depreciation rate δ of human capital also increases the incentive to delay the investment. In the context of financial knowledge, depreciation may take the form of specific knowledge becoming obsolete as financial institutions and rules change. In sum, the model predicts that a woman will acquire financial knowledge very slowly at the beginning of the marriage and delay larger investments in human capital. The rate of investing will increase as the expected time of widowhood approaches. After her husband dies, she takes charge of the finances and accrues payoffs to her financial knowledge. This framework is described in terms of financially specializing husbands and their wives, but it can easily apply to any couple in which one person outlives the other and the shorter living spouse specializes in at least one task. The fact that women have longer life expectancies than men and are typically younger than their husbands makes it easier to test the implications of such a model. Had the longer-living spouse specialized in household finances from the beginning, the time-path of financial human capital investments would more closely follow the Ben-Porath prediction — front-loaded investments that decline over time. ¹⁰ Other models, such as those assuming hyperbolic discounting or other behavioral models, can be used to analyze these investments in human capital, but I will show that the standard model presented here is consistent with empirical patterns.

IV. Data

The empirical analysis uses data from the Cognitive Economics Survey (CogEcon),¹¹ an innovative new survey of a national sample of persons 51 and older and their spouses regardless of age. The first wave, administered in the spring and summer of 2008, includes a 24 question battery on financial literacy, detailed measures of income, wealth and portfolio allocation plus measures of risk tolerance, self-assessed financial knowledge, and several questions on decision-making.

These respondents also participated in the Cognition and Aging in the USA study (CogUSA)¹² which includes an extremely detailed cognitive assessment. In addition, respondents were asked questions about their subjective expectations, including their subjective survival probabilities. The combined survey allows for the linking of cognitive measures and the economic measures collected in CogEcon. Furthermore, unlike many other surveys that collect financial data from only one person per household, this study collects measures of financial decision-making and financial knowledge from both husbands and wives.

Because the model predicts that women will increase their acquisition of financial knowledge prior to the death of their spouses, the empirical analysis requires measures of the expected time and length of widowhood. I compute these using survival probabilities drawn from 2004 period life tables published by the National Center for Health Statistics.¹³ As a robustness check (see the online appendix, found at http://jhr.uwpress.org/), I use alternative survival measures from subjective survival questions as well as objective survival probabilities predicted using the Health and Retirement Study (HRS), a much larger panel survey of similarly-aged individuals.¹⁴ In an additional robustness check, I also analyze longitudinal data on proxies for financial literacy on the HRS.

A. Sample and Demographics

CogEcon collects information from 748 unique households,¹⁵ defined as couples or individuals without partners. Both members of 224 couples participated in the survey (448 respondents), including the financial literacy battery. The remaining respondents are unmarried individuals or coupled respondents with partners about whom we have partial or no information due to complete or partial non-response. Further information about response rates and the derivation of the analysis sample can be found in the online appendix. Table 1 reports the demographic characteristics of all respondents with spouses in the sample. The average age of women is 60.5 years, with men about 2.5 years older. Because the analysis focuses on couples, and requires that both members of the couple complete the survey, the analysis sample is relatively highly educated, and of higher income than in general for this age group, when including single individuals such as widows and widowers.

Table 1.

Demographic Characteristics of the Analysis Sample

Individual Characteristics	Women			Men			Difference
	Mean	Standard Deviation	N	Mean	Standard Deviation	N
Age	60.5	9.44	224	62.9	10.04	224	−2.33***
Life expectancy	24	7.51	224	19.3	6.96	224	4.78***
Years of education	14.4	1.99	224	14.7	2.16	224	−0.219*
Race: white	0.946	0.225	224	0.933	0.251	224	0.013
Job status
Working now	0.522	0.501	224	0.567	0.497	224	−0.045
Disabled	0.040	0.197	224	0.031	0.174	224	0.009
Retired	0.246	0.431	224	0.371	0.484	224	−0.125***
Homemaker	0.174	0.38	224	0		224	0.174***
Other	0.018	0.133	224	0.031	0.174	224	−0.013
First marriage?	0.80	0.40	220	0.73	0.44	222	0.064 ***
Number of living children	2.65	1.62	224	2.75	1.587	221	0.061
Who is most knowledgeable about finances?
Me	0.161	0.367	223	0.491	0.501	216	−0.321***
Me OR Both of us	0.489	0.501	223	0.866	0.342	216	−0.372***
Couple Characteristics				Mean	Standard Deviation	N
Length of current marriage				38.6	11.56	220
Log household income				11.34	0.787	208

Notes:

^*p<0.1

^**p<0.05

^***p<0.01.

Data sources: CogEcon and CogUSA.

The marriage is the individual’s first in a majority of cases, and respondents have on average 2.7 children. According to life tables, the average life expectancy of women is 24 years, while it is 19 years for their husbands. Men have slightly more education than women in this sample. Only 16 percent of wives report being most knowledgeable about finances.

B. Outcomes of Interest: Financial Knowledge

The survey includes a financial literacy battery of 24 questions. Each presents a statement, and the respondent is asked whether s/he thinks the statement is true or false, and how sure s/he is of that response on a 12-point scale based on her/his degree of certainty (see Figure A1 in the online appendix). Questions are converted to the true version so that the scale can be interpreted as “0% surely (correct answer)” to “100% surely (correct answer).” The responses are re-scaled to a zero-one scale.¹⁶ An individual’s financial sophistication score is calculated by taking each respondent’s mean score across questions in the battery and normalizing across all survey respondents. A within-couple relative score is computed using the wife-husband difference in normalized mean scores.

Topics covered include interest compounding, diversification and risk, financial terms, stock market concepts, taxation, and inflation.¹⁷ To account for the fact that not all respondents participate in the stock market, some analyses will employ a basic literacy score that excludes the fifteen stock related questions. As can be seen in the summary statistics in Table 2, men have, on average, higher levels of financial literacy than women whether or not stock questions are included.

Table 2.

Summary of Regression Variables

Variable	Mean	Standard Deviation	N
Outcomes
Wife-husband diff. in normalized financial literacy	−0.367	1.10	224
Wife-husband diff. in fin. literacy (no stock questions)	−0.309	1.25	224
Key explanatory variables
Expected time to widowhood (years)	14.6	5.86	224
Expected length of widowhood (years)	13.3	2.82	224
Other regressors
Woman’s years of education	14.4	1.99	224
Husband’s years of education	14.6	2.16	224
Woman’s self-rated health (5 point scale)	3.73	0.961	224
Husband’s self-rated health (5 point scale)	3.63	1.00	224
Wealth decile	6.39	2.69	224
Woman’s Number Series	0.15	0.859	224
Husband’s Number Series	0.346	0.882	222
Woman’s Visual Matching	0.442	0.860	224
Husband’s Visual Matching	0.070	0.806	217
Woman’s Auditory Working Memory	0.357	0.646	223
Husband’s Auditory Working Memory	0.102	0.944	219
Woman’s Matrix Reasoning	0.248	0.851	224
Husband’s Matrix Reasoning	0.220	0.837	223

Data sources: CogEcon and CogUSA.

C. Cognitive Ability and Other Control Variables

While the model emphasizes the effect of a spouse’s mortality on the division of labor, a spouse’s declining cognition or health status are other factors that would similarly necessitate learning about his tasks. Summary statistics for these factors are reported in Table 2.

One of the most widely accepted theories of cognitive abilities is that of fluid and crystallized intelligence (Gf-Gc theory) (Cattell 1941; Horn 1965; Horn and Cattell 1966, 1967). Primary abilities are divided into two broad dimensions: fluid intelligence (Gf) and crystallized intelligence (Gc). Fluid intelligence represents reasoning abilities that result from biological influences on intellectual development, such as heredity or brain injuries. Crystallized intelligence refers to the use of accumulated knowledge and skill and represents the results of educational investments and experience rather than underlying ability. The distinction between fluid and crystallized intelligence is similar to the notion of ability versus human capital in labor economics.

Financial literacy can be interpreted both as a form of human capital as well as a form of crystallized intelligence. While crystallized intelligence tends to increase through the accumulation of knowledge, fluid intelligence peaks early in life and declines over the remaining life cycle. Psychologists have verified that fluid intelligence declines with age (McArdle, Fisher, and Kadlec 2007; Verhaeghen and Salthouse 1997). Other broad cognitive abilities within the Gf-Gc framework that decline strongly with age include processing speed and memory (McArdle et al. 2002). Measures of speed typically share about 75 percent of the age-related variance of other measures of cognition (Salthouse 2000). Declines in speed and memory have even been shown to precede declines in fluid intelligence (McArdle et al. 2000).¹⁸ Measures of fluid intelligence, memory, and processing speed can thus be used to control for the cognitive decline of respondents and to conduct robustness checks.

Fluid intelligence is measured using the Woodcock-Johnson III (WJ-III) Number Series test. Respondents are given a sequence of numbers with a missing number, and they are asked the value of the missing number. The WJ-III Visual Matching test, a measure of processing speed, asks respondents to locate the two identical numbers within a row of six numbers. Memory is measured using the WJ-III Auditory Working Memory test. After listening to a series of numbers and words, the respondent is asked to recall in sequential order first the objects then the numbers. Lastly, the Wechsler Adult Intelligence Scale (WAIS) Matrix Reasoning test is a measure of non-verbal fluid intelligence. The respondent is presented with an incomplete matrix or series of images or designs and is asked for the missing image. The scores used by psychologists are scaled using large external norming samples, but here I standardize the scores among all CogUSA respondents (pooling together men and women) for easier interpretation. In the analysis sample, men tend to have higher Number Series scores, but lower processing speed and memory scores, than women (see Table 2).¹⁹

Additional cognition measures can be used in place of financial knowledge as outcomes in falsification tests. In addition to the aforementioned measures of cognition, I also use measures of verbal reasoning and numeracy/mathematical skill. The WJ-III Verbal Analogies test measures the respondent’s ability to reason using lexical knowledge; it is a verbal measure of fluid intelligence. Lastly, numeracy is measured with the WJ-III Calculation test. The Number Series and Calculation tests are the two scores that are most highly correlated with financial literacy.

In addition to his cognitive decline, a husband’s poor physical health may also contribute to a woman taking over his tasks. One overall measure of health is the question, “Would you say your health is excellent, very good, good, fair, or poor?” This self-rated health measure is coded from 1 (for poor) and 5 (for excellent). Women rate their health slightly higher than men (see Table 2), though this difference is not statistically significant.

Lastly, I control for wealth using the decile of household net worth, as reported by the CFO of the household.²⁰ Because household net worth is computed as the sum of numerous questions on the survey and is subject to item nonresponse, imputation variability, and so forth, I use deciles computed at the household level (including singletons) instead of the dollar value of net worth.

V. Empirical Results and Discussion

A. Background: the CFO and the Household Division of Labor

The most direct question related to household division of labor asks “Which member of the immediate family is most knowledgeable about your family’s assets, debts, and retirement planning?” Respondents may specify “me,” “my spouse/partner,” “both me and my spouse/partner,” or “someone else in the family” as the household’s “Chief Financial Officer” (CFO). About 16 percent of women in couples report being most knowledgeable, and less than half report being at least equally knowledgeable (see Table 1).

A unique advantage of the CogEcon study is that it interviews both members of a couple whenever possible. Only 4 percent of couples provide contradictory responses; a cross-tabulation of the two members’ responses to the question about who is the household CFO is reported in Table A1 in the online appendix. Because of the small number of couples with inconsistent answers, the analysis will ignore these discrepancies and will consider the woman’s response as representative of the couple.

To verify that the question on financial knowledge provides information about the division of labor, I investigate how financial knowledge relates to financial decision-making using the question “Who (among members of your immediate family) makes the decisions about how to save for retirement and other large expenses?” Responses to the two questions are highly correlated. Among those in couples, over 60 percent of respondents name the same person(s) as the most knowledgeable as well as the decision-maker. Over one-third of respondents state that both members make decisions while only one is most knowledgeable, and 2 percent report that both are most knowledgeable but one makes the decisions.

Only about 2 percent of respondents give inconsistent answers to the two questions. Since inconsistent responses are so rare and two-thirds of respondents state that decisions are made jointly, the most knowledgeable person is a meaningful measure without incorporating additional data about who makes the major decisions.

Table 3 reports characteristics of couples, by the gender of the household CFO. Only 16 percent of couples have female CFOs. The CFO tends to be more educated, have more financial literacy, and have more fluid intelligence (as measured by the Number Series score) than his or her spouse; this is true for both male and female CFOs. These patterns are consistent with the idea that where one spouse has a comparative advantage with respect to fluid intelligence or education, s/he becomes the CFO.

Table 3.

Comparative Advantage and the Division of Labor: Proportion of Couples in which Women Have Higher Levels of Characteristics than Their Husbands (mean differences in parentheses), by Gender of CFO

CFO	Proportion of couples where wives exceed husbands in				Mean wife-husband differences in				N
	Age	Education	Number Series	Financial Literacy	Age	Education	Number Series	Financial Literacy
Wife	0.250	0.417	0.5	0.583	−1.33	0.722	0.140	0.296	36
Husband	0.134	0.223	0.295	0.259	−2.60	−0.607	−0.474	−0.712	112
Both	0.192	0.274	0.438	0.521	−2.40	−0.123	0.033	−0.157	73
Someone Else	0	0.5	1	0.5	−4	2.5	0.929	0.207	2
No Response	1	0	1	0	1	−3	0.664	−2.10	1
Total	0.174	0.272	0.383	0.397	−2.33	−0.219	−0.192	−0.367	224

Notes: The household CFO is defined as the person who is most financially knowledgeable within the household. CFOs of either gender tend to be more educated, have more financial literacy (standardized scores), and have more fluid intelligence (as measured by the standardized Number Series score) than their spouses.

Data sources: CogEcon and CogUSA.

B. Descriptive Non-parametric Analysis: Financial Literacy by Age and Potential Confounders

The cross-section can be used as a synthetic cohort to see if patterns of financial knowledge within couples change with age. My model predicts that women should increase their financial knowledge as they approach widowhood. Furthermore, if their husbands’ cognition or health deteriorate earlier than their own, women will have greater incentive to acquire more knowledge. Because longitudinal data are not currently available, there are no measurements of baseline knowledge. To measure changes in financial knowledge in the synthetic cohort, I use husbands’ knowledge as a baseline for women. An implicit assumption here is that the husband’s current financial knowledge is an appropriate level for being the CFO; wealth is included as an additional control of the different levels of knowledge “needed” to manage the family’s current finances.²¹

Figure 1a shows the age profile of the financial sophistication score based on the husband’s age. The age profile is estimated using a lowess plot (locally weighted scatterplot smoothing), which non-parametrically estimates:

$$\mathit{wife}’s\mathit{knowledge}-\mathit{husband}’s\mathit{knowledge}=f(\mathit{husband}’s\mathit{age}).$$ (4)

Figure 1. Financial Literacy and Husband’s Age.

Data sources: CogEcon and CogUSA.

Men’s financial sophistication follows a flat or upside-down U-shape, whereas women’s scores are upward sloping with respect to their husband’s age. To see if this pattern holds when women are matched to their husbands, I plot the wife-husband difference in financial literacy on Figure 1b. Within couples, the wife’s score rises relative to her husband’s score as he ages and his life expectancy shortens; this is also true when excluding stock questions (shown in Figure A2 in the online appendix). These patterns are not sensitive to bandwidth choice. Univariate regressions of the wife-husband difference in financial scores on the husband’s life expectancy show the same negative relationship, and the slopes are statistically significant at the 10 percent and 5 percent level for sophistication and the non-stock literacy regressions, respectively (not shown).

These patterns are consistent with the notion that women acquire human capital as their husbands age. Do these empirical patterns reflect active learning on the part of the women, in anticipation of their husbands’ decline in health and cognition? The age profiles in financial knowledge detailed above are also consistent with three different explanations unrelated to my theoretical model. First, the gains in women’s knowledge relative to men may not actually reflect any actual gains; women’s knowledge may remain constant while their husbands’ cognition declines. Measuring women’s financial literacy against their husband’s knowledge as a baseline implicitly assumes that the husband’s knowledge is, or at least is perceived by the household to be appropriate for handling the household’s financial tasks. But it may be the case that the husband at one time had an appropriate level of knowledge but no longer does.

Second, older women may have been in charge of finances throughout their marriages. Third, women married to older men may differentially have additional reasons to have higher financial literacy than spouses of younger men due to wealth, larger age gaps, or second marriages. These last two explanations violate the assumptions of the synthetic cohort analysis employed here, and the results may merely reflect cohort effects rather than progression as they approach widowhood.

1 Cognitive Decline of Husbands

One might be concerned that these age profiles are generated by older men paired with younger women, such that an increase in the wife-husband financial knowledge gap is driven solely by a decline in the husband’s ability, rather than a true increase in the woman’s literacy. Figure 2 shows placebo age profiles of various other cognitive scores, plotted against the husband’s age (comparable to Figure 1a). These graphs are generated by locally weighted scatterplot (lowess) smoothing. Aside from the memory score, the cognitive measures do not generally have a wife-husband gap that increases with the husband’s age. The scores for husbands and wives track each other remarkably closely by the husband’s age; in fact, for Calculations and Visual Matching (a measure of processing speed), men seem to gain on women at the oldest ages. Furthermore, the Number Series scores, which have been shown to be strong predictors of financial literacy and wealth (McArdle, Smith, and Willis 2011), have parallel profiles for both men and women.²² These patterns suggest that the age profile of financial literacy scores does not merely track underlying patterns of cognitive decline of husbands and wives in these six measures of cognition. Because the memory score is the exception, all regressions will include controls for the husbands’ and wives’ Auditory Memory score, as well as additional measures that typically exhibit declines with age. Still, for women’s gains in financial literacy relative to their husbands to be attributable to learning, the cognitive decline of husbands must be fully captured by the controls for memory, fluid intelligence, and processing speed. For further evidence of learning that does not require this assumption, I also conduct robustness checks in Section V.D.2 using a panel dataset to confirm that women also gain financial knowledge in absolute terms, and that these gains are not limited to those with the oldest husbands.

Figure 2. Cognitive Measures and Husband’s Age.

Notes: All scores are standardized using all CogUSA respondents, pooling together men and women. Unlike financial literacy, women’s cognition scores do not systematically gain on their husband’s scores. The cognition variables are detailed in Section IV.C.

Data source: CogUSA.

2 Cohort Effects

Using the cross-section as a synthetic cohort assumes that the experiences of individuals over the age distribution of the cross-section reflect the experiences of individuals as they age through each successive cohort, as if I had observed a single cohort longitudinally.²³ An alternative hypothesis consistent with my results is that older women have been household CFOs throughout the marriage, while younger women have not. This would cause the synthetic cohort to produce spurious support for the model’s predictions.

However, social changes across cohorts suggest otherwise; historical marriage and divorce patterns are likely to bias the data against my model’s predictions. Women in younger cohorts are likely to have married at an older age, as seen in the Current Population Survey and Census data in Figure 3a.²⁴ Although the median age of women married before 1949 (the 5th percentile in my data of the year of first marriage) was slightly older, there was subsequently an upward trend over time. Therefore, the younger women in my sample have had greater incentive to gain financial knowledge prior to marriage. In addition, if the dispersion of power within a couple is greater when the spousal age gap is larger, we may not expect the younger spouses of the older men to have as much control over finances. Younger couples are likely to be more “egalitarian” than older couples, and therefore older women might be less likely to be the CFO.

Figure 3. Historical Demographic Patterns in the United States.

Notes: Vertical lines indicate the 5^th and 95^th percentile of year of first marriage among women in the analysis sample. Younger women face greater incentives than older women to acquire financial literacy early in life.

Data sources: CPS, Decennial Census, and Statistical Abstract of the United States.

The prospect of divorce, which has changed considerably over time, may also lead women to learn about finances earlier in life. Historical divorce rates in the United States are shown in Figure 3b.²⁵ Although the rates were slightly higher in the mid-1940s than in the 1950s, divorce rates climbed rapidly through the 1960s and 70s. Divorce rates remained high through the 80s and declined only more recently. The sharp increase in divorce rates would create incentives for the younger women in my sample to insure themselves by acquiring more knowledge earlier in adulthood. These patterns, in addition to changing norms due to the rise of feminism, would create greater incentives for younger women (relative to older women) to learn early and/or become CFOs in the household. All of these cohort effects should produce downward bias on any estimates of the effects of time to widowhood on financial knowledge.

Another possibility is that husbands’ age is still correlated with other demographic and economic factors that increase the incentive for women to gain financial literacy, apart from the aging and widowhood incentive. Age profiles of some of these factors are shown in Figure A3 in the online appendix. If the wives of older husbands come from second marriages (perhaps through widowhood or divorce), then these women might enter these marriages with more financial knowledge from their past experience with independent financial decision-making. In the CogEcon sample, however, in general the older the husband, the more likely the woman is on her first marriage (and the less likely she was previously divorced) and the longer the duration of the current marriage. Older men are married to older women, but the age gap does increase somewhat with the husband’s age. Still, perhaps older men are wealthier, and women with high wealth may have greater incentives to learn more about finances due to the potential of greater return (or losses due to mismanagement). In the CogEcon sample, wealth follows the same hump-shaped life-cycle trajectory as expected, and the oldest men are no wealthier than those in their fifties. The correlation between husband’s age and wealth (whether measured in dollar values or deciles) is 0.08; the correlation between wealth and wife-husband age gap is -0.10. These low correlations suggest that wives of older men do not face greater incentives to acquire financial literacy due to greater wealth in the analysis sample.

In sum, cohort effects stemming from marriage and divorce shifts in the population are unlikely to drive my results. Furthermore, women married to the older men in the sample do not appear to have reasons for differentially higher past experience with independent financial decision-making, or differentially greater wealth to potentially manage, than women married to younger men. Still, as a robustness check, I will analyze some proxies for financial literacy using panel data in Section V.D.2.

C. Regression Analysis

Using matched data on husbands and wives, I estimate the effect of expected time to widowhood and expected length of widowhood on women’s financial knowledge (derivations in the online appendix). Table 2 reports summary statistics of the financial knowledge and widowhood variables used in the analysis. Women have on average −0.37 standard deviations less financial sophistication than their husbands, and −0.31 less for non-stock literacy questions alone. In this sample, the average expected time to widowhood (conditional on the woman outliving the man) is 15 years, with a mean expected duration of widowhood of 13 years.²⁶

Table 4 presents estimation results for the following equation:

$$d(\mathit{financial}\mathit{knowledge})={\gamma }_1(\mathit{expected}\mathit{time}to\mathit{widowhood})+{\gamma }_2(\mathit{expected}\mathit{duration}of\mathit{widowhood})+X\beta +\mathrm{\epsilon }$$ (5)

where d(x) designates the wife-husband difference in x.

Table 4.

Financial Literacy Regressions

	(1)	(2)	(3)	(4)	(5)	(6)
	All questions			Non-stock literacy
Expected time to widowhood	−0.038** (0.019)	−0.041** (0.019)	−0.043** (0.021)	−0.042** (0.021)	−0.047** (0.022)	−0.047* (0.024)
Expected duration of widowhood	0.044 (0.039)	0.051 (0.039)	0.055 (0.042)	0.042 (0.044)	0.048 (0.044)	0.055 (0.047)
Woman’s education		0.060 (0.041)	0.015 (0.047)		0.084* (0.047)	0.021 (0.054)
Husband’s education		−0.107*** (0.038)	−0.086** (0.042)		−0.137*** (0.043)	−0.113** (0.048)
Woman’s health		0.058 (0.079)	0.034 (0.083)		0.047 (0.089)	0.031 (0.094)
Husband’s health		−0.080 (0.075)	−0.078 (0.077)		−0.015 (0.084)	−0.006 (0.087)
Wealth decile		−0.017 (0.028)	−0.016 (0.030)		−0.028 (0.032)	−0.028 (0.034)
Woman’s Number Series			0.372*** (0.131)			0.442*** (0.148)
Husband’s Number Series			−0.249** (0.118)			−0.235* (0.133)
Woman’s Visual Matching			−0.049 (0.119)			−0.092 (0.134)
Husband’s Visual Matching			0.122 (0.118)			0.006 (0.133)
Woman’s Working Memory			0.033 (0.140)			0.079 (0.158)
Husband’s Working Memory			−0.035 (0.102)			−0.06 (0.115)
Woman’s Matrix Reasoning			−0.178 (0.126)			−0.143 (0.143)
Husband’s Matrix Reasoning			−0.007 (0.134)			0.003 (0.151)
Constant	−0.395 (0.373)	0.435 (0.740)	0.908 (0.947)	−0.244 (0.422)	0.591 (0.834)	1.15 (1.070)
R2	0.020	0.066	0.138	0.020	0.073	0.146
F	2.226	2.193	2.073	2.300	2.447	2.227
Observations	224	224	211	224	224	211

Notes:

^*p<0.1

^**p<0.05

^***p<0.01.

OLS regressions with dependent variable: wife-husband difference in normalized financial sophistication score.

Data sources: CogEcon and CogUSA.

The first column of Table 4 estimates the equation with no covariates, while the second includes the usual education and health controls. Column (3) adds measures of fluid intelligence (Number Series and Matrix Reasoning), processing speed (Visual Matching), and working memory (Auditory Memory) for both husband and wife, as well the wealth decile. Including these cognition variables increases the magnitude and precision of the estimated coefficient on the time to widowhood. A one-year reduction in the expected time to widowhood is associated with a statistically significant 0.04 standard deviation increase in the wife-husband difference in normalized financial sophistication, which is about 11 percent of the mean difference. This result arises even when controlling for four different cognitive measures that decline markedly with age.

Several of the questions in the financial sophistication battery measure concepts that may not be relevant to members of households who do not participate in the stock market. I construct a second financial literacy score excluding stock-related questions and standardize them over all respondents. Columns (4) through (6) of Table 4 repeat the analysis with this smaller set of more basic literacy questions. The coefficient on the time to widowhood is much larger in magnitude; in the specification with full cognition controls in the last column, this coefficient increases 15 percent to −0.047 from −0.043 in column (1).

This suggests that women make the greatest gains on basic forms of literacy. The Number Series score is a strong predictor of financial knowledge, and since this is a measure of fluid intelligence, having a higher Number Series score can be interpreted as lowering the woman’s marginal cost of acquiring knowledge. A one standard deviation increase in the wife’s Number Series score is associated with a 0.37 standard deviation increase in her financial sophistication relative to her husband. Coefficients on the control variables are generally as expected: the lower the education and health levels of the husbands, the greater the woman’s financial knowledge. Likewise, the greater are a woman’s levels of health or education, the greater her financial knowledge, and these effects are smaller in magnitude than those of her husband’s levels.

Even after including controls for working memory, processing speed, and an additional measure of fluid intelligence, all of which decline strongly with age and therefore help control for the husband’s cognitive decline, I still find a statistically significant effect of time to widowhood on financial literacy. The magnitude of the effect is large; if all women acquired financial literacy at the estimated 0.04 standard deviation per year, almost 80 percent of women in the sample would fully catch up with their husband’s current level of financial literacy before the expected onset of widowhood. The coefficients on the expected duration of widowhood are positive — the longer the length of widowhood, the more financial literacy the woman has relative to her husband — but are not statistically significant. These results are consistent with the fact that the model predicts the effect of the marginal year closer widowhood should be much larger than the marginal year during widowhood.

D. Robustness Checks and Extensions

1 Falsification Tests

As a robustness check, I run false regressions of equation (5) by replacing the difference in financial sophistication scores on the left hand side with differences in cognition scores. Table 5 reports the results for the six cognition scores detailed in Section IV.C. Because the left hand side variables are all wife-husband differences in standardized scores, the coefficients are directly comparable to each other. The columns are ordered from most highly correlated to least correlated to financial literacy. All of the falsification regressions have coefficients on the time to widowhood that are smaller in magnitude than the same coefficient in the financial literacy regression; all but two are statistically indistinguishable from zero. The measure with largest negative coefficient on time to widowhood, Matrix Reasoning, is not highly correlated with financial literacy; furthermore, the full regressions in Table 4 control for this measure of fluid intelligence. This suggests that the estimated effect of time to widowhood on financial literacy is not a spurious relationship solely attributable to the cognitive decline of men.²⁷

Table 5.

Falsification Tests: Regressions Using Cognition Outcomes

	Financial Literacy	Calculation	Number Series	Matrix Reasoning	Verbal Analogies	Auditory Memory	Visual Matching
Mean dependent variable	−0.367	−0.316	−0.192	0.027	−0.198	0.251	0.383
Expected time to widowhood	−0.041** (0.019)	−0.001 (0.018)	−0.009 (0.017)	−0.036** (0.015)	−0.019 (0.017)	−0.031* (0.018)	−0.017 (0.018)
Expected duration of widowhood	0.051 (0.039)	0.043 (0.037)	0.006 (0.034)	0.044 (0.031)	0.018 (0.036)	0.004 (0.036)	0.063* (0.037)
Woman’s education	0.06 (0.041)	0.144*** (0.040)	0.187*** (0.036)	0.094*** (0.033)	0.144*** (0.038)	0.047 (0.039)	0.068* (0.039)
Husband’s education	−0.107*** (0.038)	−0.147*** (0.037)	−0.123*** (0.034)	−0.067** (0.031)	−0.069* (0.035)	−0.083** (0.035)	−0.085** (0.036)
Woman’s health	0.058 (0.079)	0.192** (0.076)	0.039 (0.070)	0.178*** (0.063)	−0.029 (0.073)	0.116 (0.074)	0.147* (0.075)
Husband’s health	−0.08 (0.075)	−0.026 (0.071)	0.072 (0.066)	0.012 (0.059)	0.069 (0.069)	−0.072 (0.070)	0.042 (0.070)
Wealth decile	−0.017 (0.028)	−0.049* (0.027)	−0.045* (0.025)	−0.039* (0.023)	−0.034 (0.026)	−0.018 (0.026)	−0.056** (0.027)
Constant	0.435 (0.740)	−1.104 (0.710)	−1.145* (0.651)	−0.858 (0.591)	−1.154* (0.682)	1.138 (0.692)	−0.288 (0.707)
R2	0.066	0.136	0.134	0.093	0.074	0.069	0.077
F	2.193	4.832	4.726	3.146	2.401	2.227	2.493
Observations	224	223	222	223	219	219	217

Notes:

^*p<0.1

^**p<0.05

^***p<0.01.

OLS regressions with dependent variables: wife - husband difference in normalized cognition scores as specified in the column labels. These falsification tests reproduce the specification in column (2) of Table 4. The cognition variables are detailed in Section IV.C. Because the cognition scores are normalized, the coefficients are directly comparable with those estimated using the wife-husband difference in financial literacy in column (1).

Data sources: CogEcon and CogUSA.

2 Longitudinal Analysis Using Financial Knowledge Proxies on the HRS

For additional evidence that the effects I find in the CogEcon sample are not attributable to the cognitive decline of husbands, I turn to panel data in the Health and Retirement Study (HRS) and analyze proxies for financial literacy. This longitudinal dataset allows me to track outcomes for a given individual over time and therefore preclude the need for an external baseline, albeit without the rich set of cognition measures or the direct measure of financial literacy from the main analyses. Respondents’ beliefs about stock market returns, as well as the extent to which they follow the market, complement financial literacy as measures of general knowledge because they have direct bearing on financial planning, stock market participation, and investment behavior. First, I use the question, “How closely do you follow the stock market?” coded as one if the respondent answers “very closely” or “somewhat” and zero otherwise. In addition, the HRS asks about respondents’ probabilistic expectations of future stock market performance: “By next year at this time, what is the percent chance that mutual fund shares invested in blue chip stocks like those in the Dow Jones Industrial Average will be worth more than they are today?” Answering this question, instead of refusing or stating “don’t know,” could be a rough proxy for financial literacy, or at least comfort with or willingness to answer economic questions. I construct a variable that takes the value of one if the respondent provides an answer to this question, and zero if she refuses or does not know. Third, I compare responses to the Dow Jones expectations question to actual realizations of the Dow Jones Industrial Average (DJIA) 12 months after the interview date. If a respondent answered at least 50 percent and the DJIA indeed rose, or if she answered at most 50 percent and the DJIA fell, the variable is coded as one, and zero otherwise.

These variables are available starting in 2002, and I estimate a set of regressions with individual-level fixed effects using data on married women through 2010. Individual-level fixed effects regressions allow for an analysis of within-woman changes in financial literacy proxies, and are therefore free of confounders from time-invariant individual-level factors like birth cohort or education of either spouse. This strategy also precludes the need for an external baseline for the outcome, like husband’s financial literacy used in the main analysis.

These fixed-effects regressions control for time-varying measures of the wealth decile, husbands’ and wives’ self-reported health (identical to the CogEcon measure) and 27-point composite cognition scores, and year dummies.²⁸ Results, as well as means of the dependent variables, are reported in the first three columns of Table 6. The negative and precisely measured coefficients for all three regressions on the expected time to widowhood provide longitudinal evidence that the closer a woman is to widowhood, the higher her measures of financial sophistication.^{29, 30}

Table 6.

Panel Regressions of Financial Knowledge Proxies: Health and Retirement Study

	(1) Follow Stock Market	(2) Answer Stock Expectations	(3) Stock Expectations Realized	(4) Follow Stock Market	(5) Answer Stock Expectations	(6) Stock Expectations Realized
Mean dependent variable	0.246	0.844	0.645	0.246	0.844	0.645
Expected time to widowhood	−0.018** (0.008)	−0.022** (0.009)	−0.030** (0.014)	−0.021** (0.008)	−0.023** (0.010)	−0.028* (0.015)
Husband’s age>60 X Exp. time to widowhood				−0.020*** (0.005)	−0.007 (0.006)	0.021** (0.009)
Expected duration of widowhood	0.013 (0.015)	−0.019 (0.018)	0.000 (0.027)	0.034** (0.016)	−0.015 (0.019)	−0.022 (0.030)
Husband’s age>60 X Exp. duration of widowhood				0.006 (0.005)	0.008 (0.006)	−0.007 (0.009)
Husband’s age>60				0.222*** (0.085)	−0.030 (0.096)	−0.211 (0.147)
R2	0.308	0.013	0.012	0.309	0.013	0.013
F	522.878	8.291	7.691	412.636	6.816	6.437
Observations	17764	11490	11490	17764	11490	11490
Women	4815	4447	4447	4815	4447	4447

Notes:

^*p<0.1

^**p<0.05

^***p<0.01.

Individual-level fixed effects regressions on panel data with dependent variables specified in the column labels. Control variables: wealth decile, wives’ and husbands’ health, 27-point cognition scores, and year dummies.

Data source: Health and Retirement Study.

Given that cognitive decline accelerates with age, and the effects found in the main cross-sectional analysis could still be partially driven by cognitive decline of men not captured by my control variables, columns (4) through (6) report estimates that interact each widowhood variable with an indicator that the husband is over 60. For following the stock market and answering the DJIA expectations question, the effect of time to widowhood accelerates or continues after the husband is 60. For the accuracy of the expectations question, the effect of time to widowhood is more modest when the husband is over 60, but continues to be negative. These results are consistent with the idea that women increase financial literacy as they approach widowhood, whether their husbands are relatively young or old.³¹

3 Probabilistic Survival Measures

As a third robustness check, I repeat the analysis of the CogEcon financial literacy outcome using widowhood probabilities computed from ten-year probabilistic survival measures. Instead of using life table widowhood measures that mask individual variation in actual survival expectations, here I use individual-specific survival measures drawn from subjective probabilities elicited on the survey and objective probabilities predicted using individual characteristics. The results, detailed in the online appendix, are qualitatively similar to the main analysis, again showing a negative effect of widowhood probabilities on women’s financial literacy.

4 Heterogeneity in Effects

In this section, I explore possible heterogeneity in the CogEcon sample by repeating the estimation on particular subsamples of the data that face varying incentives to acquire financial literacy as they approach widowhood. First, the analysis above assumes that the husband has been the CFO of the family. However, women who are already the financial specialist in the family do not have face the same incentives of widowhood to acquire additional financial literacy. Columns (1) and (2) of Table 7 report estimates of equation (5) on the subsample where the woman reports that she is the CFO, and she is not the CFO, respectively.³² For women who are not currently CFOs, time to widowhood has an even larger and more precisely measured effect on women’s financial literacy, whereas where if she is the CFO, the effect is positive (albeit imprecisely measured). These results are consistent with the idea that when husbands manage finances in the division of labor, women have larger incentives to gain financial literacy relative to women who are already CFOs.

Table 7.

Heterogeneity in Effect of Time to Widowhood on Financial Literacy

	(1)	(2)	(3)	(4)	(5)	(6)
	Woman is CFO?		Top 5 wealth decile?		Couple owns stock?
	Yes	No	Yes	No	Yes	No
Mean dependent variable	0.245	−0.496	−0.37	−0.368	−0.389	−0.327
Expected time to widowhood	0.048 (0.081)	−0.059*** (0.022)	−0.068** (0.028)	−0.003 (0.036)	−0.047* (0.024)	−0.046 (0.043)
Expected duration of widowhood	0.062 (0.140)	0.069 (0.043)	0.095* (0.056)	−0.004 (0.072)	0.067 (0.049)	0.033 (0.082)
R2	0.536	0.178	0.201	0.207	0.255	0.194
F	1.539	2.294	1.996	1.043	2.924	0.821
Observations	36	175	135	76	144	67

Notes:

^*p<0.1

^**p<0.05

^***p<0.01.

OLS regressions with dependent variable: wife-husband difference in normalized financial sophistication score. The specifications are identical to Table 4 column (3), estimated for subsamples of the data. Control variables: wealth decile, and wives’ and husbands’ health, education, Number Series, Visual Matching, Working Memory, and Matrix Reasoning.

Data sources: CogEcon and CogUSA.

Next, I split the sample by wealth; couples in the upper five deciles of wealth are included in column (3), and those with wealth in the lower five deciles are in column (4).³³ Those with higher wealth are more responsive to time to widowhood than those with less wealth, consistent with the idea women with more assets to manage may have more incentive to learn than those with less wealth. Similarly, the final two columns of Table 7 reports results for couples with and without stock holdings, respectively. Here, the two coefficients on time to widowhood are similar, though the effect is precisely measured only for the stock-owning subsample.

VI. Conclusion

Empirical studies on financial literacy have generally shown that women have less financial knowledge than men (Fonseca et al. 2012; Lusardi and Mitchell 2008; Kotlikoff and Bernheim 2001). One possible explanation for this gender gap is that it reflects the household division of labor. Unequal life expectancies of household members imply that a division of labor that emerges when the couple forms will eventually change when the longer-living spouse takes over the responsibilities held by the shorter-living spouse. Household financial management is a task that is frequently the responsibility of the husband, who generally has a shorter life expectancy than the wife. Because the benefits of financial knowledge for women are not realized until she is a widow, the theoretical model predicts that a woman has an incentive to delay the acquisition of financial knowledge until later in life. Conversely, because knowledge cannot be acquired instantaneously, she also has an incentive to begin her acquisition of financial knowledge well before widowhood so that she will be equipped with the knowledge needed to manage her wealth when her husband dies.

Using matched data on wives and husbands, I show that women do indeed increase their financial knowledge on a number of dimensions as their husbands age. Women acquire financial literacy at a rate of 0.04 standard deviations per year, assuming the cognitive decline of men is captured by the rich set of controls for cognition that typically decline with age. At this rate, about 80 percent of the women in the sample would catch up with their husbands in financial literacy before the expected onset of widowhood. In addition, women have increased self-rated financial skills and follow the stock market more closely as widowhood becomes more imminent. Because cohort effects related to age at first marriage and divorce probabilities work against my finding a result, my estimates understate the actual effects. Furthermore, I find statistically significant effects of the time to widowhood in spite of the measurement error associated with using population-average life table calculations.

However, I do not find a statistically significant effect of the expected length of widowhood on women’s financial knowledge. This may not be surprising given that the model predicts a much larger effect of time to widowhood than the length of widowhood. Assuming a discount factor of 0.97 and no depreciation, the effect of time to widowhood is predicted to be on average 50 percent larger than the duration’s effect, with a larger gap if human capital depreciates. Furthermore, while the model does not specify the functional form of the returns to financial knowledge, the financial decisions faced by widows may be less complex than the planning decisions made earlier in the life cycle. If this is the case, then the marginal returns to financial knowledge may decline sharply after a certain threshold.

Women may aim to reach a level of financial knowledge at widowhood sufficient to manage their decumulation, but not necessarily so much as to make complex investment decisions. The financial literacy outcome uses the husband’s literacy as a baseline in order to identify effects from a synthetic cohort formed by a cross-section. My results show that older women do indeed plan for the future by strategically investing in financial knowledge as widowhood becomes more imminent. The results support the idea that the poor economic outcomes associated with widowhood may reflect insufficient preparation due to an unexpectedly early onset of widowhood. In addition, poor outcomes may also reflect low levels of husbands’ financial knowledge; in this case, merely catching up with their husbands (as many women would, according to my estimates) may not fully equip women with the tools needed to manage their finances alone.

The model generates implications for other aspects of the life cycle. First, the time profile of financial literacy acquisition should change at widowhood. Second, we should expect differences in the financial literacy profiles and even financial outcomes of women who go through anticipated versus unanticipated widowhood. While this paper shows that women generally do respond rationally to the prospect of widowhood, those whose husbands die unexpectedly may need to increase their financial human capital investments to “catch up” with their planned profile, or may suffer from the poorer economic outcomes associated with widowhood more generally. Lastly, annuitization might be a substitute for acquiring the sophistication required to actively manage ones’ finances in retirement and widowhood. The analysis can be applied not only to financial literacy but also to any other task specialized in by a spouse. In addition, the model can also be generalized to other questions related to the length of time a person can depend on a spouse to continue specializing. Korniotis and Kumar (2011) find that older investors exhibit greater investment knowledge, but that these effects are offset by the adverse effects of cognitive aging which further incentivizes early planning for women who may want to prepare not only for widowhood but also for the cognitive decline of their husbands. Future work will specifically consider the effects of cognitive decline. Since the model shows declining incentives to invest after widowhood, it sheds some light on the stylized fact that widows have very low levels of financial knowledge. Furthermore, financial literacy profiles should differ between women whose timing of widowhood is anticipated and those who become widows unexpectedly early, due to an accident, for example. The model can also be applied more generally to the expected duration of the union rather than the expected timing of widowhood, so the same implications can be drawn for women facing varying probabilities of divorce. One extension not yet considered is the availability of an outside option for dealing with the shorter-living spouse’s tasks. Instead of learning to manage her own wealth, she can have a third person, whether an adult child or a financial planner, manage her finances on her behalf. Indeed, the third-person option may be one reason why women react less strongly to a longer expected duration of widowhood.