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Full-time working men spend an average of 15 hours doing housework per week, in addition to their 40 hours in paid labour. When weighed together, full-time working women spend 6. Averaged across the year, this means a additional hours or two weeks of hour days of work. Women shoulder the time-intensive and routine tasks such as cooking, laundry and dishes. By contrast, men are more likely to do the episodic chores such as mowing the lawn or changing the light bulbs.

Data from the United States show a large and lasting gender gap. Women do more housework than men even when they are more educated, work full-time and are more egalitarian. In fact, some studies show women spend more time in housework even when their husbands earn less money or stay at home. The jury is out on whether this claim is reliable but housework studies consistently confirm the symbolic gendered value of housework as a way to demonstrate femininity and masculinity in domestic partnerships. Even Swedish women spend more time in housework than Swedish men, indicating that our Nordic sisters, supported by a system of equality, cannot get a fair shake on housework.


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Emerging research is investigating housework allocations among same-sex partners for whom gender could be reduced or amplified. The results show same-sex partners are more likely to share housework than opposite-sex partners. This suggests the cultural scripts associated with heterosexuality, marriage and family severely disadvantage women by holding them accountable for a larger share of the domestic labour.

Women consistently spend more time in housework and, as a result, less time in employment. Recent estimates show Australian women account for two-thirds of the domestic load, while Australian men account for two-thirds of the paid work. Income is consistently tied to power within relationships. So lower-earning women are less able to get their husbands to equally share in the domestic work. One response to housework inequality could be to monetise domestic work and pay someone to complete it. This approach is currently being applied in Sweden where the government subsidises families for their outsourced domestic work.

Through tax breaks, Swedish families are encouraged to hire maid services to help with the domestic load. The Swedish government is betting the policy benefits will be two-fold. First, by encouraging women to more actively engage in the labour market. Second, to reduce the hiring of domestic labour off the black market, raising the wages, status and protection for women working in these domestic jobs. State governments could play a role in implementing these services through tax incentives or direct services.

This, in turn, could help protect the workers in these positions who are often disproportionately poor and of immigrant status. A second response could be to stop penalising women for dirty homes. Finally, bringing men into the cleaning process is essential. This means expecting men to be equal housework sharers and not helpers. Cleaning the house is a skill men can learn one toilet bowl at a time. And this is the key to reducing gender inequality in housework. Read the other instalments in the Changing Families series here. Being Well Together — Manchester, Manchester.

Walter Carroll Lunchtime Concerts: 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.

317 Employing equal number of men and women

These factors put women at higher risk than men of having financial problems e. Unmarried, particularly divorced, women near retirement age have substantially lower wealth levels than married couples and unmarried men, and the difference is only partially explained by lower levels of permanent earnings and labor force attachment Levine, Michell, and Phillips ; Zissimopoulos, Karney, and Rauer 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.

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Lusardi and Mitchell document that financial illiteracy is even more prevalent among women than men. Chen and Volpe find similar gender differences at younger ages. Understanding how and why men and women have different levels of financial literacy is crucial to developing policies aimed at reducing the gender gap and improving the saving and investing decisions of women.

Changing demographic trends and changes in the types of financial decisions being made further increase the importance of understanding what accounts for the low levels of financial knowledge and literacy among women. Higher rates of divorce and lower remarriage rates have increased over time the percent of women who approach retirement age unmarried.

Moreover, individuals are offered a large number of financial products i. 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. 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 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.

How equal are chores in your marriage — and does it matter?

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. Finally, greater financial decision making responsibility within couples is correlated with higher financial literacy for men, but not women.

The ALP consists of over 2, respondents aged 18 and over who are interviewed periodically over the Internet. 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.

Since researchers have fielded over modules in the ALP and published papers using these data on a wide variety of topics e. We designed a module survey MS73 that was administered in June 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 and fielded in March 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 and Hung et al. The index is based on answers to 23 questions on basic financial concepts, investing, life insurance and annuities, and includes the item scale used by Lusardi and Mitchell The index also includes six additional items measuring knowledge of stocks, bonds and mutual funds and four items measuring knowledge about life insurance and annuities based on different questionnaires e. Table 1 lists the variables used to construct the index. 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. 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. Of these, 1, 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. Combining these two groups gives us 1, respondents females and males reporting information on financial decision making within the household.

Note that in most cases only one member of the couple is an ALP respondent, who reports information e. Our sample includes 1, 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. 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. 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. Financial literacy index is standardized. Summary statistics limited to those with non-missing financial literacy. Table 2 compares characteristics of men and women, and finds that more women in our sample belong to minority ethnic groups than men.

How much do men and women do?

Fewer women are currently married or cohabiting, and more women are divorced, widowed or never married, and they remain unmarried longer than men. 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 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. Standard errors in parentheses. Dependent variable is standardized financial literacy index.

We also control for being separated but do not report due to very small sample size. Interactions in 3 were jointly significant for all groups: When we focus on the combined regression specification i. 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: 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. 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. These findings are consistent with selection out of marriage: 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 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.

Some findings emerge from the model with gender interactions with all covariates. The effects of age, race and income on financial literacy are not statistically different between men and women. However, men benefit more from education than women; indeed, there is no discernible gain to women in terms of financial literacy from graduating high school or attending some college compared with dropping out of high school.

Only college-educated women are more financially literate than women without a high school degree, whereas any education increase is associated with higher financial literacy for men. Turning to marital status, married women are significantly more financially literate than unmarried women, which is not the case for men. Indeed, married women are financially more literate than married men.

Divorcees are no less financially literate than never-married individuals, nor is there a significant difference between the financial literacy of divorced men and women. Similar to what we saw in the specification without interactions, years since divorce are associated with increased financial literacy for both men and women.


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Table 3B presents the results of a Blinder—Oaxaca decomposition of the gender gap into variation due to endowments, coefficients and their interaction Blinder ; Oaxaca Note that we estimated the following conditional expectation function CEF using ordinary least squares regression:. Then we can decompose the gender gap as follows:. The first term captures how much of the gender gap is due to differences in characteristics among men and women e. 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. The decomposition suggests that the great majority of the gender gap is due to differences in coefficients rather than differences in characteristics between men and women. For whatever reason, men and women have very different production processes for financial literacy.

The interaction effect is statistically significant and has the opposite sign, suggesting that the endowment and coefficient effects together account for more than the total effect. 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: 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 decisions and study the correlation between decision making and financial literacy. A finding of a positive correlation, however, does not indicate a causal mechanism: We asked married and cohabiting respondents who in their household is responsible for the following activities: Table 4 presents self-reported division of labor for coupled men and women separately. Data are weighted and include those with missing financial literacy index.