Gender Inequality in Education: Datasets and Sources

Returning to the subject of education and gender, this post is a bit more about using the data available to you to make informed decisions when running ICT4D projects. For some, these will be painfully apparent; for others, perhaps a little less so, so I am essentially writing this post for the latter group. Experts and data-savvy types, avert your eyes.

It is probably best to frame this around a particular question or conjecture, so I am going to assume that one of the potential pathways for greater employability for women in some countries and in particular greater employability in "innovative" fields is research. That essentially a good barometer for greater inclusion is the percentage of women participating in research oriented fields. This could include women in independent, private, NGO or government run research centres or think tanks, and the more humble variety where I ply my trade some of the time, higher education. The former, almost without exception worldwide, pays better than the latter. But that is besides the point (or is it?). Our real focus here is on exploring the pathways of research that I am not even sure are real. I mean I know research is real, but I am not sure it is a legitimate or accurate barometer for any sort of greater inclusion overall.

So the hypothesis I am running with here is that there have some gains in women's participation in higher education worldwide and that some of those gains might translate to greater participation in research oriented sectors. Some parts of the conjecture are supported in evidence. There has indeed been an uptick in both sexes in higher education.

Good on you, world. But clearly the problem here is that this data isn't disaggregated. We don't know how this breaks down according to gender. OECD has a good datastore for this sort of thing. Good on you, Canada.

You will still have to dig to get anything granular, but this larger case can save you time outright by rejecting your hypothesis outright. If you prefer the more granular data (but without all these pretty iFrame embed options), navigate to the full data set at UISStat. If you want to jump straight to the indicators, they are listed here. I am going to cheat a bit here in the interests of time and just tell you that I have the data to make the leap from [overall increase in higher education enrolment for women] to [overall increase in higher education graduation for women] to [overall increase in women in research fields-public] and to a lesser extent [overall increase in women in research fields-private]. Ultimately, we land at the following conclusion, which UIS has succinctly presented:

Only about 29% of the world’s researchers are women. Latin American and the Caribbean has the highest share of female researchers at 45%. In contrast, the share falls to 23% in Asia. But there are some exceptions at the country level. Women researchers outnumber men in: Argentina, Armenia, Azerbaijan, Bolivia, Georgia, Kazakhstan, Latvia, Lithuania, Myanmar, New Zealand, Paraguay, Thailand, Trinidad and Tobago, Tunisia and Venezuela.

But just looking at the data generates more investigation. I can see (try the slide scale) that many developed nations have stalled or slowed a bit in their capacity to get women into advanced tertiary education, most likely a prerequisite for participating in research as employment (from 1998-2015, the UK's share of female PhDs rose from 34 to 47%, a very modest increase in comparison to other developing nations). So trends are forming even within the data. 

UIS's Women in Science resource assists with our investigation here, documenting the lag that exists here in the sciences and providing a good regional and country by country summary.

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UNESCO - Women In Science Interactive

Ultimately though, this is about data you have at your disposal to investigate however you want. For professionals, it should inform your practice early and often, from the design straight through to the evaluation stages. For students or those wanting to transition into development, choose a dataset, tear through it, ask questions and test your hypotheses. You will get sharper each time and learn to live more and more comfortably with the data. 

And did I answer the hypothesis I advanced at the beginning of this post? Not at all, but I know I can if I dug far enough. The data is there to get you started.