#MyGenderGap – my history of inequality in numbers

 

By Jonas Waldenström

Last week Nature published a news story on the gender gap in science. That is, the ever-growing gap between women and men as their careers develop. Typically, women are in majority in undergraduate studies, are more or less equally represented at the PhD level, but plummet at the postdoc, tenure track, and tenure levels. As a consequence the number of female professors is low at most universities. As an example, in the comparatively gender friendly Sweden only roughly 20% of professors are women. Clearly something is rotten in the state of research.

We all know this, it is nothing new. However, knowing it is wrong at a systematic level is not the same as being aware of your own part in replicating it. That’s where change is needed. Alex Bond, an ecologist and blogger in Canada, did this. He took the larger question down to the personal level and asked people to calculate their own gender gaps and tweet it on Twitter under the hash tag #MyGenderGap. This is a great initiative and I suspect he will write an informed and eloquent blog post about it soon (as he usually does – great blog, please visit it). I calculated mine too, and tweeted it. But as twitter-tweets are limited to 140 characters I thought that I should devote some space here in the blog to present my own data in a bit more depth. And it doesn’t look very good…

First of all, my own supervising career mimics the field as a whole. I have supervised 12 undergraduate students at honor’s and MSc levels, 9 women and 3 men. I supervise 4 PhD students, plus one that graduated last year, in total 3 women and 2 men. I have three postdocs in the lab, 1 woman and 2 men. On top of that I have acted as co-supervisor for an additional 9 PhD-students, 4 woman and 5 men. Over all not too bad, but the trend with a stepwise reduction in women with each step up the academic ladder is obvious.

But it is when you look at the publications that things become really clear. I went through the 102 peer-reviewed publications that I have authored/co-authored and for each publication I counted the number of men and women co-authors, and whether the female scientist was my own PhD student and if the leading PI was woman or man. The table looks like this:

Year	Men in pub	Women in pub	Women PhD students	Ratio F/M	# publications
2000	1				1
2001	2				2
2002	12	6		0.50	4
2003	12	5		0.42	5
2004	19	5		0.26	7
2005	25	8		0.32	8
2006	22	1		0.05	5
2007	98	14	2	0.14	13
2008	45	17	3	0.38	9
2009	35	10	6	0.29	7
2010	44	23	10	0.52	10
2011	45	13	3	0.29	10
2012	56	16	3	0.29	11
2013	47	21	11	0.45	10
	463	139	38	0.30	102

Out of 602 co-author across 102 publications (note that the same co-author often is found on the author line-up of several articles), only in 139 occasions the co-author was woman, and in many cases my own PhD student. This gives a gender gap statistic (the simple division of #F/#M) of 0.30. A totally equal proportion would be 1.0. Thus, my stats are not very flattering. And as regards the long-term trend the line is depressingly flat. Why? One reason that I can see is that I have established a core of collaborators that I tend to publish with. Most of them are men. Most of them are in turn PIs in their labs, thus it reinforces the system.

My Gender Gap in publications across time. I took out the data from 2000 and 2001, since only few publications were published.

What could be done about it? That’s a great question; one which I don’t have a good answer to. In my mind I haven’t chosen collaborators because of their gender, but of common research interests. But seeing your own stats staring you in the face is the first step in a thought-process that may lead to actual change.

We should remember that the university system is strange. It has a high intrinsic growth rate [the cohorts upon cohorts of PhD-students that start each year], but a very stable carrying capacity [the old folks sits on their position for life and most departments don’t grow]. Thus, if you have succeeded in science and is a PI, the highest likelihood is that (on average) only one of your students you teach during your career will make it in academia. Which gender will that person have?

Now go compute your own gender gap.

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