Social equity & data
Gender Data Gap
Definition
The gender data gap is the persistent imbalance in data collection, analysis, and interpretation whereby data is sourced primarily from men, so that the solutions, designs, and decisions built on that data reflect male bodies, preferences, and life patterns. Popularised by Caroline Criado Perez's "Invisible Women", it spans medical research, product design, workplace policy, and official statistics.
References
definition and causes
popularisation and examples
Overview
What it means
Where women are missing from the data — crash-test dummies modelled on male bodies, time-use surveys ignoring unpaid care, clinical trials under-recruiting women — systems fail women by design rather than by intent. The gap is both a measurement problem (sex-disaggregated data not collected) and an analytical one (data collected but not disaggregated or used).
How it is used
The concept drives sex-disaggregation requirements in SDG monitoring, gender-responsive budgeting, and standards such as UN Women's data programmes; organisations audit datasets and AI systems for gender bias.
Why it matters
What is not measured is not managed: closing gender gaps in health, safety, and economic opportunity starts with making women visible in the data.