Longitudinal Data on Gender Wage Gap

The gender pay gap continues to be a contentious topic in public discourse. Before proceeding, let's clarify what we mean by "gender pay gap". Gender gap is a reference to the uncontrolled gender pay gap measures the median salary for all women versus all men, regardless of job type or worker seniority. According to PayScale's 2025 Gender Pay Gap Report, women earned 83 cents for every dollar earned by men when looking at this uncontrolled metric. However, the controlled gender pay gap, which compares men and women in similar positions with comparable qualifications, shows women earning 99 cents for every dollar earned by men.

This distinction is crucial. The difference between these two metrics suggests that the majority of the pay discrepancy stems not from direct pay discrimination for identical work, but from differences in career choices, industry representation, and hierarchical placement within organizations.

Actual causal factors

The standard narrative often presents the gender pay gap as primarily resulting from discrimination.

1. Occupational sorting: Women are overrepresented in fields like education, healthcare support, and social work, while men dominate higher-paying sectors like technology, finance, and engineering. This horizontal segregation accounts for a significant portion of the aggregate gap.

2. Work schedule flexibility: Research indicates that women often prioritize flexibility in working arrangements over salary potential, especially after having children. Jobs offering greater flexibility frequently come with lower compensation.

3. Career interruptions: Women are more likely to take career breaks for family responsibilities, with mothers experiencing what researchers call the "motherhood penalty" - a substantial reduction in earnings after having children.

4. Negotiation differences: Studies suggest gender differences in salary negotiation approaches, with women negotiating less frequently and less aggressively than male counterparts.

5. Discrimination: While the above factors explain much of the gap, research does indicate that unconscious bias and discrimination continue to play a role, particularly in promotion decisions and performance evaluations.

Overcoming Confirmation Bias

As rationalists, we must be particularly careful to avoid motivated reasoning when discussing such politically charged topics. Those identifying as progressives may be motivated to attribute the gap primarily to discrimination, while those with conservative leanings might emphasize individual choice and biological differences.

To combat this tendency, let's examine evidence that challenges both narratives:

• Countries with greater gender equality paradoxically show larger gender differences in career choices, not smaller ones - the so-called "gender-equality paradox". This suggests that when given more freedom of choice, innate preferences may play a larger role than previously assumed.

• However, field experiments using identical resumes with only the gender changed reveal persistent biases in hiring and evaluation, indicating discrimination remains a significant factor.

Expected Value Calculations and Policy Implications

If we wish to reduce the gender pay gap, we should prioritize interventions with the highest expected value. Based on the evidence, these might include:

1. Transparency in compensation: Pay transparency policies have been shown to reduce gender pay disparities by making inequities visible and actionable. The expected value calculation here suggests high returns for relatively low implementation costs.

2. Flexible work policies: Organizations that have implemented robust flexible work arrangements while maintaining promotion opportunities report reduced gender pay gaps over time. The counterfactual world where high-paying careers are compatible with family responsibilities would likely show a significantly smaller gap.

3. Early education interventions: Programs exposing young girls to traditionally male-dominated fields like STEM have shown promising results in shifting educational and career choices. The long-term expected value of such programs could be substantial, as occupational sorting occurs early.

The Map Is Not The Territory

We must remember that statistical aggregates like the gender pay gap represent maps of reality, not the territory itself. Individual experiences vary widely, and many women earn more than the median man. The territory contains multitudes of individual circumstances that aggregate statistics necessarily obscure.

Conclusion: Embracing Complexity

The rationalist approach to the gender pay gap rejects simplistic narratives in favor of nuanced understanding. The gap results from a complex interplay of choice, constraint, market forces, social norms, and discrimination.

By disentangling these factors and maintaining epistemic humility, we can design more effective interventions while respecting individual agency. Progress requires us to recognize both the role of structural barriers and the importance of personal preferences in shaping economic outcomes.

For those seeking to deepen their understanding of this topic, I recommend exploring the work of Claudia Goldin, whose research on the gender pay gap earned her the 2023 Nobel Prize in Economics.

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Note: This article represents an attempt to approach the gender pay gap through rationalist principles of clear thinking and evidence-based reasoning. True rationality requires constant updating of beliefs in light of new evidence, and I encourage readers to seek out diverse perspectives on this complex issue.

The gender wage gap is a measure of the disparity in earnings between men and women in the workforce. It is typically calculated as the ratio of women's median earnings to men's median earnings, expressed as a percentage. For example, if women earn 82 cents for every dollar earned by men, the wage gap is 18%¹. However, this figure varies widely depending on factors such as occupation, education, race, and geography.

Key Terms:

• Unadjusted (Raw) Gap: Reflects overall median earnings differences without accounting for variables like education or experience.
• Adjusted Gap: Controls for measurable factors (e.g., hours worked, job type), isolating discrimination or systemic bias.
• Lifetime Earnings Gap: Estimates cumulative losses over a woman’s career due to wage disparities. In the U.S., women lose approximately $407,760 over 40 years compared to men².

1.2 Historical Evolution of the Wage Gap

The wage gap has roots in centuries of gendered labor division. Pre-industrial economies confined women to unpaid domestic roles, while men dominated paid work. The Industrial Revolution introduced factory jobs for women, but wages were often a fraction of men’s. For example, in 19th-century Britain, female textile workers earned 30–50% less than male counterparts³.

The 20th century saw incremental progress: - World War II: Women temporarily filled roles in manufacturing and engineering, but post-war policies (e.g., the "Family Wage" concept) pushed them back into domestic roles.
- 1963 Equal Pay Act: U.S. legislation prohibited sex-based wage discrimination, but enforcement gaps persisted. By 1980, women earned 60% of men’s wages⁴.
- 21st-Century Stagnation: Progress slowed post-2000. The U.S. gap narrowed by just 4% between 2000 and 2023⁵, while countries like South Korea saw widening gaps due to cultural backlash⁶.

1.3 Global Snapshot (2023)

Country	Unadjusted Gap	Adjusted Gap	Key Driver
Iceland	10%	4%	Equal pay certification laws
Japan	24%	15%	Cultural norms & part-time work
Rwanda	12%	8%	Post-conflict gender quotas
United States	18%	6%	Occupational segregation
Germany	14%	9%	Parental leave policies

Source: [World Economic Forum Global Gender Gap Report 2023][WEF2023]

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2. Methodologies in Wage Gap Analysis

2.1 Cross-Sectional vs. Longitudinal Studies

Cross-Sectional Studies

These studies analyze data from a single point in time, offering a "snapshot" of wage disparities. For example, the U.S. Census Bureau’s Current Population Survey (CPS) annually compares median earnings by gender. While useful for tracking annual trends, cross-sectional data cannot explain why gaps persist over time.

Limitations:
- Fails to account for career interruptions (e.g., maternity leave).
- Overlooks wage growth trajectories.

Longitudinal Studies

Longitudinal research tracks the same individuals over decades, revealing how life events (e.g., childbirth, promotions) impact earnings. Landmark studies include:
- U.S. National Longitudinal Survey of Youth (NLSY): Found that women’s wages plateau after age 30, while men’s continue rising⁷.
- Swedish Registry Data: Linked parenthood to a 20% drop in women’s earnings over 10 years⁸.

2.2 Regression Analysis and Control Variables

Economists use regression models to isolate factors contributing to the wage gap. Common control variables include:
- Education: Women now outpace men in college attainment but remain underrepresented in high-paying fields like engineering.
- Experience: Women’s careers are often interrupted by caregiving, reducing cumulative experience.
- Occupation: Nurses (female-dominated) earn 30% less than software developers (male-dominated)⁹.

Oaxaca-Blinder Decomposition:
This statistical technique separates the wage gap into "explained" (e.g., education) and "unexplained" (e.g., discrimination) components. A 2022 study found that 38% of the U.S. gap remains unexplained after controls¹⁰.

2.3 Challenges in Data Collection

1. Underreporting: High-income earners and gig workers are often excluded from surveys.
2. Non-Wage Benefits: Health insurance, stock options, and pensions are rarely measured.
3. Intersectionality: Most datasets lack granularity to analyze overlapping identities (e.g., Black transgender women).

Example: The U.S. Bureau of Labor Statistics (BLS) excludes self-employed workers, omitting 15 million Americans—disproportionately women of color¹¹.

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3. The Role of Longitudinal Studies

3.1 U.S. Panel Study of Income Dynamics (PSID)

Launched in 1968, the PSID follows 18,000 individuals across generations. Key findings include:
- Motherhood Penalty: Women’s earnings drop 30% after their first child, while men’s rise 6%¹².
- Racial Disparities: Black women face a "double gap," earning 63% of white men’s wages due to compounded discrimination¹³.

Policy Impact: PSID data informed the 1993 Family and Medical Leave Act (FMLA), though unpaid leave limits its efficacy for low-income families.

3.2 Sweden’s Lifelong Labor Market Surveys

Sweden’s registry-based system tracks citizens from birth to death, offering unparalleled insights into policy impacts:
- Gender-Neutral Parental Leave: Reserved "daddy months" increased men’s leave-taking from 12% to 30%, reducing the motherhood penalty by 14%¹⁴.
- Childcare Subsidies: Universal preschool access boosted maternal employment by 22%¹⁵.

3.3 Australia’s HILDA Survey

The Household, Income, and Labour Dynamics in Australia (HILDA) survey, ongoing since 2001, reveals:
- Flexibility Stigma: Men who take parental leave face 8% slower wage growth¹⁶.
- Sectoral Gaps: Women in female-dominated sectors (e.g., education) experience slower promotions despite higher qualifications.

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[Continue to Section 4: Structural Drivers of the Wage Gap]