Gender and Retirement Readiness Gap Analysis

A comprehensive data analytics project examining retirement preparedness disparities and their business implications

Roles

Data Analyst
Business Intelligence Analyst
Data Visualization Specialist

Tools Used

Federal Reserve SCF Data
PostgreSQL
DBeaver
Tableau Public

Timeline

Started: 7/28/2025

Completion: 7/31/2025

Duration: 4 days (part-time)

Status

Complete

Skills Showcased

SQL Analytics
Data Visualization
Statistical Analysis
Business Intelligence
Gap Analysis
Demographic Research

BUSINESS CONTEXT & PROBLEM

Why This Analysis Matters

Female-headed households represent 24% of American families but face systematic barriers to retirement security that compound over decades. While wage gap research is well-documented, the investment behavior disparities that create exponentially larger wealth gaps have been underexplored, representing a critical blind spot for financial services companies.

Key Business Questions Addressed

Questions

How do retirement readiness metrics differ between genders, and what actionable insights can financial services companies use to address these gaps?
At what life stages do wealth-building gaps emerge, and what behavioral patterns drive compound disadvantages over time?

Target Audience

Financial services executives, product development teams, and policy makers seeking data-driven strategies to address retirement readiness disparities and capture underserved market opportunities.

Data Sources

Primary Dataset: Federal Reserve Survey of Consumer Finances (SCF) 2022

Sample Size: 22,975 households (nationally representative)

Data Quality: Gold standard for U.S. household financial data, collected every 3 years

Coverage: Comprehensive financial information including income, assets, debt, and investment behavior

Data Limitations & Acknowledgments: This analysis is limited to male-headed and female-headed households as defined by the Federal Reserve SCF survey. The dataset does not include non-binary, transgender, or other gender identities, representing a limitation in understanding retirement readiness across all gender identities. Future research with more inclusive data collection would provide valuable insights for all individuals.

METHODOLOGY

Data Processing & Analysis Framework

22,975 household records imported into PostgreSQL database with proper data type optimization for large financial values (BIGINT for assets exceeding standard integer limits)
Multi-dimensional demographic segmentation examining retirement preparedness across gender, age groups, and investment behavior patterns

Retierment Readiness-The Gender Gap Crisis Dashboard pg 1.png

Dashboard Page 1, the Executive Summary -

This shows the key metrics cards and main retirement gap chart

Statistical Approach & Visualization Strategy

Gap analysis with compound effect modeling, behavioral pattern identification across lifecycle stages, and market opportunity quantification
Four-page interactive dashboard progression from executive summary to detailed behavioral analysis

KEY FINDINGS & INSIGHTS

Uncovering the Investment Behavior Crisis Behind a $Trillion Market Gap

Major Finding #1:

The 6X Retirement Savings Crisis

Male-headed households average $742K vs female-headed households' $118K in retirement savings, representing a 6-times disparity that worsens with age rather than improving.

Major Finding #2

Critical 35-49 Investment Cliff

Female-headed households experience a devastating drop in stock ownership from 10% to 8% during prime wealth-building years, while male-headed households increase from 27% to 29% during the same period

Major Finding #3:

Double Gap: Participation AND Investment Depth
Even among households that DO own stocks, female stock owners average $785K in retirement accounts compared to male stock owners' $2.3M, revealing systematic under-investment beyond participation rates.

Major Finding #4

Years of Income Saved Reveals Systemic Under-Preparedness

Female-headed households at retirement (65+) have saved only 1.2 years of current income compared to male-headed households' 4.0 years, both far below the recommended 10-12 years.

Major Finding #5

Income Disparity Compounds Investment Gaps

Ultra-wealthy outliers drive averages, with 65+ male-headed households averaging $3.98M annual income vs female-headed households' $496K, demonstrating massive income inequality at high wealth levels.

Major Finding #5

Market Opportunity: 24% of Households Systematically Underserved

5,490 female-headed households in dataset represent a trillion-dollar market opportunity for targeted financial products addressing specific barriers during critical wealth-building windows.

BUSINESS IMPACT & RECOMMENDATIONS

Strategic Market Opportunity: Capturing the Underserved 24% Through Targeted Intervention

Strategic Market Opportunity

Target Market Size

24% of households (5,490 in dataset)

Revenue Potential

Multi-billion dollar opportunity in underserved demographic

Competitive Advantage

First-mover advantage in addressing systematic exclusion from wealth-building products

Implementation Priorities

Priority 1:

Emergency Intervention
(Ages 35-49)

Deploy targeted products addressing the participation cliff during peak wealth-building years, with potential for 20-30 years of compound growth impact.

Priority 2:

Investment Depth Enhancement

Develop tools and education to help existing female investors (8-16% who participate) increase contribution amounts and close the 3X investment gap.

Priority 3:

Market Expansion Strategy

Create accessible investment platforms and financial literacy programs to convert the 84-92% non-participating majority into wealth-building participants.

Priority 4:

Lifecycle Product Ecosystem

Design age-specific solutions addressing unique barriers at each life stage, from early career through retirement.

Business Recommendations & Strategic Opportunities.png

Dashboard Page 4, Business Recommendations:

Showing the strategic priorities and implementation framework

TECHNICAL IMPLEMENTATION

Data Engineering & Analysis Process

Database Architecture & Data Import

Engineered PostgreSQL database with optimized data types for large financial values, resolving integer overflow issues for billion-dollar asset values through strategic BIGINT implementation and custom DDL generation.

Advanced SQL Analytics

Developed complex multi-dimensional queries combining demographic segmentation, statistical aggregations, and lifecycle analysis across 22,975 records, including calculated fields for savings ratios and compound growth modeling.

Data Visualization Innovation

Created interactive four-page Tableau dashboard with executive summary cards, dual-axis charts for participation vs. investment depth comparison, and professional color schemes optimized for colorblind accessibility.

Statistical Analysis Framework

Implemented gap analysis methodology with compound effect modeling, behavioral pattern identification across age cohorts, and market opportunity quantification using real-world financial data distributions.

Interactive Tools & Future Analysis

PROJECT ASSETS & NEXT STEPS

View Live Analysis

[Interactive Dashboard]

[Retirement Readiness Guide for Female-Headed Households]

[Executive Summary]

Future Enhancement Opportunities

Longitudinal Analysis: Multi-year SCF data for trend identification and policy impact measurement
Geographic Segmentation: State-level analysis for regional product development strategies
Predictive Modeling: Machine learning for retirement readiness risk scoring and intervention targeting

Supporting Documentation

[Complete Project Repository]

[Methodology & Project Log]

Business Applications

Financial Services: Product development for underserved demographics and market expansion strategies
Policy Development: Evidence-based retirement security initiatives and intervention program design
Academic Research: Framework for additional demographic gap analyses in financial behavior studies

Resume

Tableau Public

GitHub

Stack Overflow

Privacy Policy (coming soon)

Rachel Beeson

Gender and Retirement Readiness Gap Analysis

Roles

Tools Used

Timeline

Status