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Gender Wealth Gap: Families Headed by Women Have Lower Wealth

KEY TAKEAWAYS

  • Families with female survey respondents had lower median family wealth than those with male respondents.
  • These results reflect associations between wealth and gender rather than causal links. Yet the results are telling. Even after accounting for a variety of factors, the gender wealth gap remained significant.
  • The “raw” gender wealth gap was largest between men and women who had never been married. In this group, women had 34 cents per $1 of men’s wealth. After controlling for various factors (age, children, race and ethnicity, education, income, homeownership, inheritance, employment, and financial risk taking), this gap was significantly reduced—71 cents per $1—yet remains potentially consequential for current and future generations.
Asian Woman Working at Home

By

Ana Hernández Kent , Lowell R. Ricketts
Tuesday, January 12, 2021

Wealth inequality is greater than income inequality, yet relatively little is known about the genderThe Survey of Consumer Finances has the reporter code the respondent’s sex (binary: male or female) without asking the respondent. If necessary, the question is asked. While sex and gender are not the same construct, in this article we refer to a “gender” wealth gap as opposed to a “sex” wealth gap for simplicity and to be consistent with both academic literature and common vernacular. wealth gap—the female-to-male ratio of median wealth—compared with the gender income gap. Household wealth, or net worth, is the difference between total assets (e.g., financial assets and tangible assets like durable goods, such as vehicles) and debts.We utilized median regressions and defined net worth as total assets minus total liabilities. Researchers’ decisions regarding methodology and variable measurement can have important effects on results. See Emmons, William R.; Kent, Ana H.; and Ricketts, Lowell R. “Just How Severe is America’s Racial Wealth Gap?” Federal Reserve Bank of St. Louis Open Vault blog, Oct. 9, 2019.

In this article, we explore the gender wealth gap using the Survey of Consumer Finances (SCF), a triennial survey of wealth data in the United States. Our findings show that the gender wealth gap remained statistically significant, even after accounting for marital status and other characteristics.

Challenges in Gauging the Gap

A major challenge in studying the gender wealth gap is that most surveys collect wealth data at the family level. Assigning specific assets either to the male or to the female in coupled U.S. families is difficult because family finances are often co-mingled. Consequently, documenting the gender wealth gap requires simplifying assumptions.

One approach used to circumvent this barrier is to focus on the net worth differences between singles (particularly individuals who have never been married).In our research, families are considered single if the sole economically dominant individual has never been married, or if the person is divorced, widowed or separated. Families are considered coupled if they are living with a partner and both individuals are financially interdependent (i.e., part of the same primary economic unit) or if they are married. Individuals who are living together but not financially interdependent are not considered coupled. Each family further consists of all other individuals (e.g., minor children, dependent adult children) in the household who are financially interdependent with the individual or couple. See the SCF codebook for further detail. However, doing so removes the majority of the population and results in a sample that is not broadly representative; for example, in 2019, only 32.4% of Americans (15 years and older) had never been married.Annual Social and Economic Supplement, Table MS-1; marital status of the population age 15 and over.

An Alternative Approach

Instead of focusing on only singles, we assigned family-level wealth to the gender of the most financially knowledgeable individual, as they are likely to be more involved in the family’s finances and decision-making.This method of using the gender of the best financial reporter in the family when studying the gender wealth gap has been used by other researchers, notably. Ruel, Erin; and Hauser, Robert M. “Explaining the Gender Wealth Gap.” Demography, August 2013, Vol, 50, No. 4, pp. 1155-76. In the SCF, a family can consist of one or more individuals. The respondent is the most financially knowledgeable member of the family, and in a little over half of heterosexual couples, this individual is identified as male.

Exploring the gender wealth gap among currently coupled families and previously married individuals (in addition to never-married individuals) yields a more complete and nuanced understanding of gender wealth gap dynamics for the majority of the population.

An observation of less wealth among coupled families with a female respondent is linked to the female by assumption; however, it is important to note that the deficit is ultimately borne by the entire family. Inversely, more wealth observed among families with a male respondent cannot be attributed solely to the male.The respondent role is not randomly assigned, so the gender wealth gap identified here may be associated with the characteristics of the respondent (e.g., financial numeracy, occupation) that are correlated with the assignment of respondent status rather than the gender of the respondent per se. It is therefore difficult to isolate the effects of gender and the determinants of respondent status on the one hand, and the wealth outcomes of families. Therefore, since a family’s wealth is shared within couples, our analysis doesn’t directly link the wealth gap to gender. Rather, it measures the respondent gender wealth gap. Notably, we are unable to form causal conclusions from our data, and our results should be thought of as descriptive.

We explored the relationship between family wealth and the following characteristics: demographics (the gender, age, race and ethnicity, and education of the respondent), family structure (marital status and presence of children under 18), financial (family income, homeownership status, and received inheritance), work status (full-time, part-time, retired or not working), and family financial risk-taking preference. We used data from all 11 available survey waves, providing observations for 52,892 families.In the married and partnered groups, only heterosexual couples were included.

First, we begin with a general overview of the gender wealth gap. We later provide results that are grouped into four categories: married; partnered; divorced, separated or widowed; and never married. A “raw” and an “adjusted” gender wealth gap are presented for each group, which captures how wealth varies controlling for the aforementioned variables. The regression analysis used here is a statistical method that helps tell us to what degree gender affects wealth when the effects of the other variables are taken into account.We use median regressions with the dependent variable, net worth, transformed by the inverse-hyperbolic sine. Wealth as discussed in this article therefore refers to predicted wealth from our models. Survey year binaries are included in every model. For an application of this transformation with SCF data, see Emmons, William R.; Kent, Ana H.; and Ricketts, Lowell R. “Is College Still Worth It? The New Calculus of Falling Returns,” Federal Reserve Bank of St. Louis Review, Fourth Quarter 2019, pp. 297-329.

Overall Respondent Gender Wealth Gap

We found a raw gender wealth gap in which the median wealth of female respondents was just 55 cents for every dollar of male respondent wealth. In other words, female respondents suffered a 45% penalty in median family wealth compared with male respondents.

Adjusting for other individual and family characteristics (as shown in the figure below) decreased this gap by 36 percentage points, but the median among female respondents was still significant: Their median wealth was 9% less than the median family wealth among male respondents, or 91 cents per $1.By “decreased this gap” we mean to say that some of the variation in wealth originally attributed to gender in the “raw” model can instead be explained by other individual and family characteristics. Models build upon each other such that they include all variables to the left of each column in the figure below. For example, the education bar represents the model in which gender, age, marital status, children, race and ethnicity, and education are used to predict net worth.

Figure 1

Gender Wealth Penalty Difference between Female Respondent Wealth and Male Respondent Wealth

SOURCES: Survey of Consumer Finances and authors’ calculations.
NOTES: This bar chart shows how far below (in percentage terms) female respondents’ predicted median family wealth was compared with male respondents’ wealth. The effects of adding a number of variables in nested models, from left to right, are also shown, as are 95% confidence intervals in black.

Gender Wealth Gap: Largest Among Never-Married

Never-married families experienced the largest gender wealth gap for both the raw model and the adjusted model accounting for all other characteristics. The raw gender wealth gap among never-married respondents was 34 cents of female median wealth per $1 of male wealth, or a penalty of 66%. Including the full set of individual and family characteristics (age, children, race and ethnicity, education, income, homeownership, inheritance, employment, and financial risk taking) reduced the gap to 71 cents per $1.

For the other groups (divorced, separated or widowed; partnered; and married groups), raw gender wealth gaps were 85, 72 and 63 cents per $1, respectively. After accounting for the full set of individual and family characteristics, these gaps were reduced to 86, 89 and 96 cents per $1, respectively, but the results remained statistically significant, as shown in the figure below.

Figure 2

Gender Wealth Penalty by Marital Status Difference between Female Respondent Wealth and Male Respondent Wealth

SOURCES: Federal Reserve Board’s Survey of Consumer Finances and authors’ calculations.
NOTES: These bar charts show how far below (in percentage terms) female respondents’ predicted median family wealth compared with male respondents’ wealth, grouped by marital status. The effects of adding a number of variables in nested models, from left to right, are also shown, as are 95% confidence intervals in black. Children are minor children living with the respondent. Income and inheritance are based on logarithmic values.

Across all marital status groups, age, income and homeownership had the strongest associations with wealth; however, the various characteristics had different effects on reducing the respondent gender wealth gap. For example, controlling for education slightly widened the gender wealth gap among the never-married group, but education reduced the gap in the other three. Conversely, including the presence of minor children and race and ethnicity had relatively larger effects in the never-married group, but smaller or negligible effects in the married group.We would ideally run separate models for each racial and ethnic group given their vastly different wealth distributions. However, the sample is rapidly winnowed with the disaggregation by family structure. Given how critical this structure is for the analysis, we made the difficult decision to not partition by race and ethnicity in this paper.

An Important Gap with Potential Multigenerational Consequences

We found a large wealth gap in which families headed by female respondents had 55 cents of median wealth per $1 of male respondents’ wealth. This gap was reduced, though still statistically significant, after the full set of observable characteristics were included in our models.

The remaining gap may reflect some combination of additional unobserved factors.For example, families with female respondents may have different comfort levels in making investment decisions in part because of different credit and financial literacy scores. They may have differential access to and enrollment in retirement accounts and pension entitlements and exhibit different financial reporting practices and gendered expectations. More broadly, they may also experience restrictive social norms and prejudices against females. The links between gender, marital status and other characteristics (e.g., homeownership, income) are complex and merit further exploration; therefore, it is important to understand that these results are only part of the story.

Notwithstanding, we found a gender wealth gap in each marital status group and expanded upon seminal research that focused only on singles. By definition, family wealth is shared between both individuals in partnered and married couples. That we still found a significant gap related to the gender of the survey respondent in couples points to the broad consequences of a respondent gender wealth gap for both males and females.

Overall, a persistent respondent gender wealth gap suggests that families with a female respondent may be less resilient to unexpected shocks (e.g., income loss), less prepared for retirement and less able to help children achieve upward economic mobility than families with a male respondent. These consequences are important, as most singles (64%) were female and nearly half of couples had female respondents.

Importantly, after accounting for all variables, the largest gender wealth gap was among never-married families. Given the significant and rising share of children growing up in never-married families, this gender wealth gap could be increasingly consequential for the next generation of Americans.

Endnotes

  1. The Survey of Consumer Finances has the reporter code the respondent’s sex (binary: male or female) without asking the respondent. If necessary, the question is asked. While sex and gender are not the same construct, in this article we refer to a “gender” wealth gap as opposed to a “sex” wealth gap for simplicity and to be consistent with both academic literature and common vernacular.
  2. We utilized median regressions and defined net worth as total assets minus total liabilities. Researchers’ decisions regarding methodology and variable measurement can have important effects on results. See Emmons, William R.; Kent, Ana H.; and Ricketts, Lowell R. “Just How Severe is America’s Racial Wealth Gap?” Federal Reserve Bank of St. Louis Open Vault blog, Oct. 9, 2019.
  3. In our research, families are considered single if the sole economically dominant individual has never been married, or if the person is divorced, widowed or separated. Families are considered coupled if they are living with a partner and both individuals are financially interdependent (i.e., part of the same primary economic unit) or if they are married. Individuals who are living together but not financially interdependent are not considered coupled. Each family further consists of all other individuals (e.g., minor children, dependent adult children) in the household who are financially interdependent with the individual or couple. See the SCF codebook for further detail.
  4. Annual Social and Economic Supplement, Table MS-1; marital status of the population age 15 and over.
  5. This method of using the gender of the best financial reporter in the family when studying the gender wealth gap has been used by other researchers, notably. Ruel, Erin; and Hauser, Robert M. “Explaining the Gender Wealth Gap.” Demography, August 2013, Vol, 50, No. 4, pp. 1155-76.
  6. The respondent role is not randomly assigned, so the gender wealth gap identified here may be associated with the characteristics of the respondent (e.g., financial numeracy, occupation) that are correlated with the assignment of respondent status rather than the gender of the respondent per se. It is therefore difficult to isolate the effects of gender and the determinants of respondent status on the one hand, and the wealth outcomes of families.
  7. In the married and partnered groups, only heterosexual couples were included.
  8. We use median regressions with the dependent variable, net worth, transformed by the inverse-hyperbolic sine. Wealth as discussed in this article therefore refers to predicted wealth from our models. Survey year binaries are included in every model. For an application of this transformation with SCF data, see Emmons, William R.; Kent, Ana H.; and Ricketts, Lowell R. “Is College Still Worth It? The New Calculus of Falling Returns,” Federal Reserve Bank of St. Louis Review, Fourth Quarter 2019, pp. 297-329.
  9. By “decreased this gap” we mean to say that some of the variation in wealth originally attributed to gender in the “raw” model can instead be explained by other individual and family characteristics.
  10. We would ideally run separate models for each racial and ethnic group given their vastly different wealth distributions. However, the sample is rapidly winnowed with the disaggregation by family structure. Given how critical this structure is for the analysis, we made the difficult decision to not partition by race and ethnicity in this paper.
  11. For example, families with female respondents may have different comfort levels in making investment decisions in part because of different credit and financial literacy scores. They may have differential access to and enrollment in retirement accounts and pension entitlements and exhibit different financial reporting practices and gendered expectations. More broadly, they may also experience restrictive social norms and prejudices against females.
ABOUT THE AUTHORS
Ana Hernández Kent 

Ana Hernández Kent is the senior researcher for the Institute for Economic Equity at the Federal Reserve Bank of St. Louis. Her research interests include economic disparities and opportunity, class and racial biases, and the relationship between psychological factors and the household balance sheet. Read more about Ana’s research.

Lowell R. Ricketts 

Lowell R. Ricketts is the data scientist for the Institute for Economic Equity at the Federal Reserve Bank of St. Louis. His research has covered topics including the racial wealth divide, growth in consumer debt, and the uneven financial returns on college educations. Read more about Lowell’s research.