Understanding the Gender Pay Gap Analysis

What This Study Is About

We use data from the Labour Force Survey (2024-2025) (a national survey about the workforce) to understand how big the pay gap is, and which factors impact different groups of women.

Key Findings

The Main Question

Do women earn less than men, and if so, which factors impact this gap?

Understanding the Pay Gap

The gender pay gap is calculated as: Pay Gap = (Men's Average Pay - Women's Average Pay) ÷ Men's Average Pay × 100

For example, if men earn £20 per hour on average and women earn £18 per hour, the pay gap is: (£20 - £18) ÷ £20 × 100 = 10%

How Equal Pay Day is Calculated

Equal Pay Day represents the last date when women are effectively paid equally. After this date, they work for free for the rest of the year due to the gender pay gap. Here's how we calculate it using the official methodology:

This means that if you have an 8.8% pay gap, November 28 is your Equal Pay Day - the last day you earn what your male counterpart earns. From November 29 onwards, you effectively work 32 days for free.

Our Data and Methods

Who We Studied

• Full-time and Part-time workers: People working at least a few hours per week
• Working age: Adults aged 16-65
• Recent data: 2024-2025 surveys
• Sample size: Several thousand UK workers

What We Measured

• Hourly pay: How much people earn per hour (adjusted for inflation using the Consumer Prices Index including owner occupiers' housing costs - CPIH)
• Gender gaps: The percentage difference between men's and women's average pay

Because we use the logarithm of income in our analysis, the distribution becomes much less skewed, meaning the mean of log pay is close to the typical (median-like) pay and is not distorted by extremely high earners.

Key Variables Explained

1. Gender

• What it measures: Whether someone is male or female
• Why it matters: This is our main focus - we want to understand if and why gender affects pay

2. Ethnicity

Note: Ethnicity analysis will be added in a future version of the calculator to provide more comprehensive intersectional analysis.

3. Household Type

This tells us about someone's current living situation:

• No children: Adults living without dependent children (under 18) in their home
  • This includes: Childless couples, single people, and "empty nesters" whose children have grown up
• With children: Adults with dependent children living in their home
  • This includes: Parents with children under 18, regardless of marital status

Why this matters: Having children at home creates time constraints and care responsibilities that can affect career progression and pay.

4. Industry

We use detailed industry sectors based on the official UK Standard Industrial Classification (SIC 2007), including:

• A - Agriculture, Forestry and Fishing: Farming, fishing, forestry work
• B - Mining and Quarrying: Coal, oil/gas extraction, mining
• C - Manufacturing: Making physical products (cars, food, textiles, chemicals, etc.)
• D - Electricity, Gas, Steam and Air Conditioning Supply: Energy utilities
• E - Water Supply, Sewerage, Waste Management: Water companies, waste disposal
• F - Construction: Building houses, offices, roads, infrastructure
• G - Wholesale and Retail Trade: Shops, supermarkets, wholesale distributors
• H - Transportation and Storage: Railways, airlines, logistics, postal services
• I - Accommodation and Food Service Activities: Hotels, restaurants, pubs, catering
• J - Information and Communication: Telecommunications, media, IT services
• K - Financial and Insurance Activities: Banks, insurance companies, financial services
• L - Real Estate Activities: Property sales, rentals, estate agents
• M - Professional, Scientific and Technical Activities: Legal services, accountancy, consulting, research
• N - Administrative and Support Service Activities: Business support, office administration, security
• O - Public Administration and Defence: Government services, civil service, military
• P - Education: Schools, universities, training providers
• Q - Human Health and Social Work Activities: NHS, private healthcare, social care
• R - Arts, Entertainment and Recreation: Museums, sports, entertainment venues
• S - Other Service Activities: Personal services, repair services
• T - Activities of Households as Employers: Domestic workers, private household staff

Why this matters: Some industries pay better than others, and men and women are often concentrated in different sectors. For example, women are more likely to work in education and healthcare, while men are more common in manufacturing and construction.

5. Social Class (Occupation Level)

We use the National Statistics Socio-economic Classification (NS-SEC) system:

• 1. Professional & Managerial Class: Higher professional and managerial occupations (doctors, lawyers, engineers, university lecturers, senior managers, finance directors)
• 2. Intermediate Class: Clerical, sales, service, and lower supervisory/technical occupations (office administrators, teaching assistants, police officers, nurses, technicians, customer service supervisors)
• 3. Working Class: Skilled, semi-skilled, and unskilled manual and routine occupations (carpenters, electricians, drivers, retail assistants, cleaners, warehouse operatives)

Why this matters: Higher-level occupations typically offer better pay and career progression opportunities, but women may face different barriers to reaching senior positions.

6. Region

We group the UK into main regions to account for geographic pay differences:

• London: Greater London area (typically highest pay due to higher living costs)
• South: South East and South West England
• East: East of England and East Midlands
• Midlands: West Midlands region
• North: North East, North West, Yorkshire and the Humber
• Wales: All Welsh regions
• Scotland: All Scottish regions
• Northern Ireland: All Northern Ireland regions

7. Employment Duration

How long someone has been in their current job:

• 1-2 years: New to current job
• 2-5 years: Established in current role
• 5-10 years: Experienced in current position
• 10+ years: Long-term employee
• Unknown: Duration not specified

Why this matters: Longer tenure usually leads to higher pay through promotions and experience. However, career interruptions (more common for women due to childcare) can affect progression.

8. Working Hours and Age

We also control for weekly working hours (both linear and squared terms to capture non-linear effects) and age (as a proxy for general work experience and career stage). These allow us to compare like-with-like when examining gender differences.

Our Statistical Model Explained

The Mathematical Approach

We use an OLS regression analysis to estimate how much each factor affects someone's hourly pay.

The Comprehensive Model Equation

What This Means in Plain English

• Base Pay: This is what our reference person would earn: a 45-year-old White man with no children, working 36 hours per week in public administration (O - Public administration and defence), with intermediate social class and 2-5 years experience, living in London, surveyed in 2025 (our reference point).
• Gender Effect: The basic difference in pay between men and women with identical characteristics.
• Gender × Every Factor: How each characteristic affects women differently than men:
  • Gender×Ethnicity: Whether ethnicity affects women's pay differently than men's
  • Gender×Household: Whether having children affects women's pay differently than men's
  • Gender×Hours: Whether working longer hours benefits women differently than men
  • Gender×Industry: Whether certain industries have different pay gaps
  • Gender×Age: Whether career progression differs by gender
  • Gender×Region: Whether location affects men and women differently
  • Gender×Experience: Whether years of experience are rewarded equally
  • Gender×Social Class: Whether occupational level affects gender gaps

Why We Use Interactions

This comprehensive approach recognizes that gender intersects with every aspect of working life. Rather than assuming factors affect men and women equally, our model reveals where and how these differences occur.

Limitations and Caveats

What This Study Can't Tell Us

1. Causation: We show associations, not definitive proof that gender causes pay differences
2. Individual experiences: These are average patterns - individual experiences vary greatly
3. Unmeasured factors: We can't account for every possible difference between workers
4. Strange results for rare combinations: When some categories or combinations are very rare in the data, the model may produce counterintuitive or unusual predictions. These models work best for common combinations that exist in the real world.

Annual Salary

Why we ask for your annual salary: Our model is based on hourly pay and estimates percentage differences between men and women after accounting for factors such as age, hours, industry, experience, region, and ethnicity. Because the model works entirely with hourly earnings, it does not use your annual salary as an input. Instead, we ask for your yearly salary only to convert the percentage gap into an illustrative monetary amount—so if the model estimates a 10% gap and you earn £65,000 per year, we show an example shortfall of £6,500. This step is simply to make the percentage more concrete; your entered salary does not influence the model's estimate of your gender pay gap.

Important Notes About Our Household Measure

• "No children" includes women whose children have grown up
• We're measuring current family responsibilities, not lifetime fertility
• The "motherhood penalty" we find reflects current caregiving constraints

Conclusion

The gender pay gap is a complex issue with multiple causes. By breaking it down into measurable components, we can better understand where inequality comes from and how to address it. This research provides evidence-based insights to inform discussions about workplace fairness and gender equality in modern Britain.