What happens to your data after you submit it. End-to-end view of the anonymous sharing flow — from validation and anonymization to publication in the open EU dataset.
All submissions are immediately stripped of personally identifiable information and aggregated to protect contributor privacy.
Data is cross-referenced with regional economic data and industry benchmarks to ensure accuracy and filter outliers.
Advanced statistical models calculate median salaries, pay gaps, and confidence scores for every data point.
Contributors submit salary data through our secure form. No email, name, or company-specific identifiers are collected. IP addresses are immediately hashed and discarded.
Privacy First: We never ask for personal information that could identify you or your employer.
Each submission is cross-referenced with regional economic data from Eurostat, national statistics offices, and industry reports. Our algorithm flags outliers and inconsistencies for manual review.
Every data point receives a confidence score (0-100%) based on sample size, data recency, and validation checks. Low-confidence data is clearly marked in search results.
90-100%
High Confidence
70-89%
Medium Confidence
Below 70%
Low Confidence
Validated data is aggregated into statistical summaries. Individual submissions are never published—only median values, percentiles, and pay gap metrics are shown.
Minimum threshold: We only publish salary data when we have at least 10 submissions for a given job title, industry, and country combination.
Gender Pay Gap (%) =
(Median Male Salary - Median Female Salary) ÷ Median Male Salary × 100
Adjusted for role, seniority, industry, and location
Median Male Salary: €65,000
Median Female Salary: €58,500
Calculation: (65,000 - 58,500) ÷ 65,000 × 100
= 10% Pay Gap
Our pay gap calculations are only as accurate as the data we receive. Factors we cannot fully control:
We always display confidence scores and sample sizes alongside pay gap metrics to provide context.
Fixed annual compensation before bonuses, stock options, or benefits. Excludes variable pay components.
Base salary + annual bonuses + equity/stock grants + benefits (estimated cash value).
The middle value when all salaries are sorted from lowest to highest. More robust than average against outliers.
Statistical reliability rating (0-100%) based on sample size, data recency, and validation checks.
Pay difference after controlling for role, experience, industry, and location. Isolates gender-based disparity.
Raw pay difference between genders without controlling for other variables. Reflects systemic inequalities.
Your privacy is our top priority. We operate under strict GDPR compliance and never collect personally identifiable information. All data is anonymized, encrypted, and stored securely in EU-based servers.
Every submission strengthens our database and brings us closer to pay equity. Share your salary anonymously and join the movement.
Contribute Your Data Now