If you want to learn more, our paper describing the research is available here: drew-johnston.com/files/cross_...
(with bonus maps here: drew-johnston.com/files/cross_...)
and all the data is available to download at HDX: data.humdata.org/dataset/cros...

You can read the full article here: economist.com/internationa...
Many thanks to the team behind the research (
Mike Bailey, @ayushkumar.bsky.social, Theresa Kuchler, and Johannes Stroebel) and the team at the Economist (@ainsliejstone.bsky.social ) for making this all possible!

On the flip side, they find that labor force participation gaps are a better predictor of the rate of more marginal (top 200) friendships in a place.

We'd love to see you use the data in your own research.
You can download it at a variety of granularities here: data.humdata.org/dataset/cros...
and find the paper here: doi.org/10.1257/pand...
or on my website: drew-johnston.com/files/cross_...

Our paper describes the construction of the data, but we think there are still a ton of stories to tell from people who have more local context than we do. For instance, I'd like to know more about the differences we see here!

Stay tuned!

We use self-reported gender from individuals' profiles.

Would love to see something like this, but it would take a bunch of data work to pull off. Perhaps it's possible in the future if there's enough interest!

In the data release, we provide information about how common cross-gender ties are among the closest friendships relative to among less-close friendships; it might be possible to use this to say something about cross-country patterns in within-family friendships relative to more distant ones.

Great questions! I think the first few "closest" friendships are likely to be partners or family, though this is just an educated guess based on the fact that the closest few friendships display weaker gender homophily in most countries (see Figure 1 in the paper).

My post is for the top 200 friends. And yes, it's very likely due to the different number over which the statistics are calculated! Check out the appendix to the paper, which has many maps plotted separately by N top friends: drew-johnston.com/files/cross_...

Thank you so much for your interest! Glad to hear that people find this interesting--one small note though, 1 here actually indicates no bias, not 0.5! I've copied the formula for the values here. You can see more details in the paper: drew-johnston.com/files/cross_...

This project sprung out of a collaboration with Mike Bailey, Theresa Kuchler, @ayushkumar.bsky.social, and Johannes Stroebel and would not have been possible without them!

We think our data is an appealing way to measure attitudes on gender, particularly in countries not often surveyed.
If you're interested in working with this data, download it here: data.humdata.org/dataset/cros...
The paper (out soon in AEA P+P) is available here: drew-johnston.com/files/cross_...

Across countries, the Cross-Gender Friending Ratio is strongly predictive of gender differences in labor force participation.
Within countries, we also find a strong correlations with gender attitudes in the World Values Survey, such as opinions about women's suitability for political office.

We measure gender differences using the Cross-Gender Friending Ratio, the ratio of female friends in men's networks to the share of female friends in women's networks in a given place.
Men almost always have a lower share of female friends than women do, but the degree varies across countries: