But when your big data is corrupted by big silences, the truths you get are half-truths, at best. And often, for women, they aren’t true at all.

A trove of research across multiple domains comprehensively documenting the “data gap” when it comes to women, and the harm that causes. Was nice to both learn about research that backed up my existing intuition – e.g. use of “generic masculine” isn’t neutral at all, discrepancy in performance reviews – and then also plenty of new research – e.g. snow-clearing streets “pedestrian-first” (a need that is often hidden in transport studies) reduces overall injury rates, the impact of various quota systems in political representation.

A big takeaway for me was the importance of sex-disaggregating data.

A more hidden data gap comes courtesy of the way transport agencies around the world present their data. On the whole, all travel for paid work is grouped together into one single category, but care work is subdivided into smaller categories, some of which, like ‘shopping’, aren’t distinguished from leisure. This is failing to sex-disaggregate by proxy.

Cover image for Invisible Women