Faculty
Networks and Inequality in Hiring Practices in Universities
I came across this
paper which is a detailed study of hiring networks and
discrimination practices in hiring of faculty among a large number of
universities in the US. They also provided the data
for other researchers to download and play.
How I constructed the graphs
What I did was to graph all hiring data as a weighted directed graph.
If university hired someone from
university , the weight of the
directed edge is increased
by 1. The final graphs are huge. So, I only pictured those edges whose
weight are above certain value and removed “All others” (which I am
guessing, indicates Universities not from Northern American
continent).
Computer Science
(with threshold value 1.5)
Image
File
The graph is very much like a tree. Almost a pyramid, one can say.
You should probably play with several threshold values to see what Iam
about to observe:
- There is a tight clique between MIT, Stanford, Berkeley and Carnegie
Mellon. They hire from each other a lot.
- There is a strong network between Canadian universities, but this is
expected as by Canadian Law, Canadian universities must show a
preference to Canadian citizens and permanent residents. There are two
groups: Universities in the West (British Columbia, Calgary and
Saskatchewan) and the East (Toronto, Waterloo, Ottowa, York, Queens,
McMaster). University of Alberta, even though it is in the West, has
strong connections with Toronto and Waterloo. Simon Fraser too exhibits
a similar behavior with Toronto. University of British Columbia, on the
other hand, is more open to hiring from the US and East Canadian
Universities.
History (with threshold value
2.0)
Image
File
The network is a lot more connected and complicated than the network
for computer science, but the pyramid-like structure is still
present.
Business (with threshold
value 1.2)
Image
File
The network is as not as connected compared to history or computer
science, but the pyramid-like structure is again present.
The lisp code
Here is the code. If you are going to run the code, here are the
instructions:
- Put your data files in a directory called “data” and the following
code in “src”. Rename the files
you downloaded as “faculty-inequality-cs-vertices.csv”,
“faculty-inequality-cs-edges.csv” for Computer Science. You should
replace “cs” with “bus” and “hist” for the other files.
- I prepared the code to be run on the command line with SBCL.You could hard code which files you
are going to process. The part with “*posix-argv*” works only with SBCL.
Make the appropriate changes in the main section.
- If you don’t want “All others” (foreign universities, I assume) as I
did, grep out those.
- The code prints out the “.dot” file to be processed with graphviz, which you should do
separately. So, pipe the output into a “.dot” file and run one of the
graph generating functions in the graphviz package.
;; from the plain data files creates an assoc list to be fed into
;; `dot-write`
(defun weighted-edges (edges vertices attribute)
(let (res y)
(dolist (x edges res)
(setf y (mapcar (lambda (i) (elt (assoc i vertices) attribute)) x))
(if (assoc y res :test #'equal)
(incf (cdr (assoc y res :test #'equal)))
(push (cons y 1) res)))))
;; process the vertices file
(defun process-vertices (filename)
(mapcar
(lambda (x) (mapcar (lambda (i j) (funcall j i))
x
(list
#'read-from-string
#'read-from-string
(lambda (i) (if (string= i ".") 0 (read-from-string i)))
(lambda (i) (if (string= i ".") 0 (read-from-string i)))
(lambda (i) i)
(lambda (i) i))))
(read-file filename)))
;; process the edge file
(defun process-edges (filename)
(mapcar
(lambda (x) (mapcar (lambda (i j) (funcall j i))
(list (car x) (cadr x))
(list #'read-from-string #'read-from-string)))
(read-file filename)))
;; reads data file into an assoc list
(defun read-file (name)
(let (res)
(with-open-file (in name :direction :input)
(read-line in)
(do ((line (read-line in nil) (read-line in nil)))
((null line) res)
(push (cl-ppcre:split #\Tab line) res)))))
;; The graph is weighted and represented as a assoc list where items
;; are of the form '((a b) . n)' where a is the source b is the target
;; and n is the count. I also pass a threshold value. If the weight is
;; below a certain value, it will not show up in the graph as the
;; graph is very large.
(defun dot-write (graph threshold)
(let* ((num (mapcar #'cdr graph))
(a (reduce #'min num))
(b (reduce #'max num)))
(format t "digraph transition {~% node[shape=\"rectangle\"];~%")
(dolist (edge graph)
(let ((weight (* 10 (/ (+ (cdr edge) (- a)) (- b a)))))
(if (> weight threshold)
(format t
" \"~a\" -> \"~a\" [penwidth = ~2,1f];~%"
(string-trim '(#\Space) (caar edge))
(string-trim '(#\Space) (cadar edge))
weight))))
(format t "}~%")))
;; the main part of the code
(let* ((base (concatenate
'string
"data/faculty-inequality-"
(elt *posix-argv* 1)))
(vertices (process-vertices (concatenate 'string base "-vertices.csv")))
(edges (process-edges (concatenate 'string base "-edges.csv"))))
(dot-write (weighted-edges edges vertices (read-from-string (elt *posix-argv* 2)))
(read-from-string (elt *posix-argv* 3))))
How about mathematics?
AMS has the necessary data
but doesn’t disclose it for public use. I would very much like to see
the network.
How is this useful?
If you are about to go into PhD, screw the US News Rankings. Get the
hiring network to determine your chances of being hired, and if there is
any, by whom.
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