I had no plans to write this post. That is because when I got the National Science Foundation data on archaeology grants it did not come with gender or sex of PIs. However, yesterday when I was looking at the top PIs, in terms of number of grants and amounts, for NSF grants to archaeology projects I noticed a significant disparity in the number of men and women receiving grants. Women barely appear in the top grant receivers.
So last night, I took a sample of years and looked at the number of male and female PIs receiving NSF grants for archaeology projects. I looked at the years 1985, 1990, 1995, 2000, 2005, 2010, and 2013. The results were:
|Count Men||Count Women||Men Total Amount||Women Total Amount|
The numbers are adjusted for inflation to 2013 dollars. The numbers are slightly misleading because PIs can also be the faculty advisers for PhD dissertation grants. These grants are put in by students and so it should be their genders/sexes we look at. Here are the number of those grants that are from students, again amount adjusted for inflation:
|Count Student Men||Count Student Women||Men Student Amount||Women Student Amount|
This brings the PI, non-dissertation grants to the following numbers:
|Count Men||Count Women||Men Amount||Women Amount|
Removing PhD students only slight changes the numbers. What the data shows is that there are significantly fewer women leading archaeology projects than there are men. This does not mean that the NSF is discriminating against women. In fact, women have roughly the same percentage of accepted grants as men. The problem is that fewer women apply for NSF grants. Here is the breakdown for all NSF grant applications for the last few years:
75% of the applications put into the NSF are from men. The reason women are not getting as many NSF grants as men in archaeology is because fewer women are applying. Fewer women are applying because well there are fewer women in tenure track positions at universities, the main recipients of NSF grants. Until archaeology fixes its gender problems we are going to see this sort of distribution over and over again.
My slightly insensitive methods
Because the data I had did not come with the sex or gender information I had to base my assumptions on names. Of course I went beyond placing people by the name of John in the male category. I went and looked up their profiles online or their digital trails. I then used descriptions of them e.g. ‘he did this dig’, ‘she excavated this’, etc. To assign sex to the PIs. However, this is me assigning them sexes and genders and not them self identifying. This meant I have overlooked transgender and transsexual or any of the categorizes of third genders. So I humbly apologize if I have miss characterized anyone.
Surprisingly there was only one or two people I could not find an online profile for. So I am very confident that this data is accurate in terms of finding the right people and characterizing them into at least one of two categories.
There were 706 grants for the years in question