Gender and poverty in the United States: Evidence from the Survey of Consumer Finances

Overview

This study investigates gender-based differences in poverty determinants among single-headed households in the United States, utilizing data from the 2022 Survey of Consumer Finances. The analysis focuses on identifying how various socioeconomic and demographic factors differentially influence poverty risk for female-headed versus male-headed households. The research employs logistic regression combined with decomposition methods to examine whether gender itself contributes to poverty or whether gender disparities arise from differential responses to specific determinants. The study sample comprises 1,383 single-headed households, with 833 female-headed and 550 male-headed households represented. By examining factors such as employment status, education level, health status, age, and presence of dependent children, the research aims to elucidate the mechanisms through which gender influences poverty outcomes and inform targeted policy interventions.

The investigation addresses persistent gender disparities in poverty rates, with particular attention to the structural and contextual factors that may amplify poverty risk differently for men and women. Female-headed households face distinct challenges, including higher rates of dependent children and differences in labor market participation patterns. The decomposition approach allows for parsing whether observed poverty differences stem from variations in characteristics between male and female-headed households or from differential returns to these characteristics. This analytical strategy provides insight into whether gender-neutral poverty reduction strategies are adequate or whether gender-specific interventions are warranted to effectively reduce poverty disparities.

Methods and approach

The study analyzes data from the 2022 Survey of Consumer Finances, examining 1,383 single-headed households stratified by gender of the household head: 833 female-headed and 550 male-headed households. The analytical approach employs logistic regression to model the probability of living below the federal poverty threshold as a function of multiple independent variables. Key predictor variables include employment status (working for an employer, self-employment), educational attainment, age, health status, income uncertainty, presence of dependent children, and net worth. The federal poverty threshold serves as the binary outcome variable, classifying households as either below or above the poverty line.

Decomposition methods are applied to the logistic regression results to partition observed gender differences in poverty rates into distinct components. This approach distinguishes between the coefficient effect, which captures differences in how independent variables influence poverty risk across gender groups, and the constant effect, which would indicate an unexplained gender effect independent of measured characteristics. Statistical significance of these components is assessed to determine whether gender disparities result from differential vulnerability to specific risk factors or from gender itself as an independent determinant. The decomposition analysis specifically examines whether employment, education, health status, and other factors exert different protective or risk effects for female-headed versus male-headed households.

Key Findings

The logistic regression analysis identified several factors significantly associated with poverty risk across both male and female-headed households. Working for an employer, self-employment, higher education level, and older age were negatively associated with poverty, reducing the likelihood of falling below the federal poverty threshold. Conversely, fair health status and income uncertainty were positively associated with increased poverty risk. Descriptive statistics revealed that female-headed households were substantially more likely to have dependent children present (38.3 percent compared to 12.7 percent for male-headed households) and reported poorer overall health status. Male-headed households exhibited higher average net worth than their female counterparts.

The decomposition analysis yielded a statistically significant coefficient effect (p < 0.0001), indicating that the impact of independent variables on poverty probability differs meaningfully by gender. Employment status demonstrated a marginally significant differential effect (p = 0.068), with working for an employer providing a smaller protective effect against poverty for women compared to men. This suggests that equivalent employment does not confer equal poverty protection across gender groups. Critically, no statistically significant constant effect was detected in the decomposition, indicating that gender differences in poverty do not arise from gender per se but rather from differential responses to specific determinants. The absence of a constant effect suggests that observed gender disparities in poverty rates result from how various risk and protective factors operate differently for male versus female-headed households rather than from an independent gender penalty.

Implications

The findings demonstrate that gender-neutral poverty reduction strategies may be insufficient to address persistent disparities in poverty rates between male and female-headed households. The significant coefficient effect coupled with the absence of a constant effect indicates that policy interventions must account for differential vulnerabilities and returns to protective factors across gender groups. The marginally significant differential employment effect suggests that simply increasing employment rates among female household heads may not achieve parity in poverty outcomes if structural barriers diminish the poverty-reducing benefits of employment for women. The substantially higher prevalence of dependent children in female-headed households (38.3 percent versus 12.7 percent) points to childcare responsibilities as a potential mechanism through which employment translates differently into economic security across gender groups.

These results support the implementation of targeted interventions designed to address gender-specific pathways into poverty. Affordable childcare support emerges as a particularly relevant policy lever, given its potential to enhance labor market participation and improve the poverty-reducing returns to employment for female household heads with dependent children. The differential health status findings suggest that health-related interventions may also yield gender-differentiated poverty reduction benefits. More broadly, the research underscores the need for poverty reduction frameworks that recognize how ostensibly universal factors such as employment, education, and health operate within gendered contexts that modify their protective effects. Policy development should incorporate explicit consideration of how interventions interact with gender-specific constraints and opportunities rather than assuming equal responses to universal programs.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.