Friday, March 14, 2008

Constructing gender difference with stats and graphs

I've been reading on the Learning as Enabling blog about the UN's release of data records and I was curious to see what's out there. Lots of stats, and I am by no means a figures person. But I was still curious, more about the idea that visualizations of social science data may provide new insights into social processes (also check this blog post).

Out of the UN data, I decided to pull out the table on the minimum legal age for marriage, which provides data for both women and men, including data on possible exceptions or further ammendments to the legal age (which I've totally ignored in creating the graph below). My computer skills are low, so the only thing I could experiment with was an excel graph:

After I looked at the graph, I thought about what were the things one could pull out of it: like the fact that women seem to be deemed as more 'able' to marry legally from an earlier age than men. But how to interpret this against the 'common wisdom' that women mature earlier than men? Is the data in the graph yet another proof of this? Is it a consequence?

It's interesting to see that in most countries of the world, the legal age for marriage is 18 for both women and men. But what does the graph say about the singular case where the marriage can happen at 12? (For your curiosity, this country is Equatorial Guinea) Does that mean most people get married there a lot at 12? Is it a paradise for perverts and pedophiles who can go and just legally marry a very young person? Or maybe the category of 'pedophiles' is differently defined there? Why 18 is so big for legal marriage age, but 19 is not? How come in the Central African Republic, men can legally marry at 22, but women at 18?

Of course, many problems derive from the choice of the graph as well (yes, I am not skilled with visualizations, nor with computers). But there's something so compelling about 'seeing' things, 'seeing' the differences, 'seeing' the aggregations, which I find quite scary. I am not sure we have the literacy necessary for understanding and dealing with such visualizations. I am not sure if my graph above doesn't actually construct gender as a woman/man binary, and the categories themselves as something given, with intrinsic features. I am also not sure the explanation lies in the graph - but rather in the eye of the beholder.

It's great that we can do so many cool things with statistical data; but we should not forget its limitations. And we should not forget that even if the visualization is created through some complex software, it is still a matter of the researcher's position and assumptions which frame the data gathering process, the selection of categories and ultimately the interpretation proposed.

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