For the third year running, I’ve compiled publicly-available information on newly-hired tenure-track assistant professors of ecology and allied fields (e.g., fish & wildlife) at N. American colleges and universities. This week (and probably into next week) I’ll be running a series of posts summarizing the data. I’ve already updated several past posts with the new year’s worth of data.
For those of you who are new to this little exercise, here’s why and how I do it. Think of this as the Introduction and Methods; the Results and Discussion start tomorrow.
Why I do this
Three years ago, I was curious about gender diversity in current faculty hiring in ecology. (Well, I was–and remain–curious about diversity in current ecology faculty hiring on many dimensions. But unfortunately it’s hard to compile data on many of those dimensions.) Departments, and the colleges and universities comprised of them, are institutional wholes that are greater than the sum of their parts. Those institutional wholes are best able to teach and inspire the full range of students who come through their doors, and best able to pursue new knowledge, if they’re comprised of diverse, complementary mixtures of people.
The most oft-cited data on faculty gender diversity concern the diversity of current faculty. Which mostly tells you about how things were a decade or more ago, because that’s when most current faculty were hired. Without wanting to downplay the ongoing legacy of past inequitable hiring, I personally don’t find data on the diversity of current faculty very helpful as a spur for action or as a guide to what actions to take (your mileage may vary, of course). We can’t go back in time and make the past more equitable. All we can do is do better in the future. So, how gender diverse is faculty hiring right now?
Being an ecologist, I wanted an ecology-specific answer to that question. Publicly-available data on diversity in academia are highly aggregated across fields, which obscures a lot of heterogeneity among fields. The point is not to give ecology a pat on the back for hiring more women recently than, say, computer science or physics. The point is that different fields have different problems with diversity and equity, suggesting a need for different solutions. So if I wanted an answer about gender diversity in faculty hiring that was specific to ecology, as opposed to (say) biology or all of STEM, I was going to have to compile the data myself.
In the first year, I just compiled data on gender diversity of newly-hired TT ecology faculty. But I gradually realized that, without too much additional effort, I could compile data on many other attributes of newly-hired ecology faculty and the institutions that hired them. Some of these data would aid interpretation of the gender diversity data. And some would be useful for faculty job seekers and those who advise them. The faculty job market in ecology, as in most academic fields, is both highly competitive and highly opaque. There are more people who would like TT faculty positions in ecology than there are TT faculty positions. And faculty job applicants necessarily remain ignorant of most of the information that informs search committee hiring decisions. That combination of competitiveness and opacity is a fertile breeding ground for speculation, rumors, misunderstandings, and anxiety. Social media helps in some ways, by allowing past and present faculty job seekers to share their own experiences. But social media hurts in other ways. You can’t really build up an accurate overall picture of the ecology faculty job market via social media (or ecoevojobs.net comment threads). The sample of personal experiences that get shared publicly is too small and too statistically biased. And cognitive biases have too large an effect on how we interpret what others share. Don’t misunderstand, I think it’s fine for people to share their experiences on social media or ecoevojobs.net. There are many things you can learn from those individual experiences that you can’t learn from data, and data don’t undermine the value of anybody’s experiences. But by the same token, there are many things that you can learn from data but not from individual experiences.
None of which is to criticize any faculty job seekers for holding inaccurate impressions of any aspect of the faculty job market, or for being anxious or stressed about the job market. I sympathize with anyone who finds being on the faculty job market stressful–I’ve been there and I remember what it was like for me. I would never tell anyone who is unhappy or anxious about the ecology faculty job market that actually they should be happy about it! (And if you aren’t stressed or anxious about it, I’d never tell you you should be.) But to the extent that faculty job seekers’ anxieties are based on misunderstandings or unrepresentative anecdotes, hopefully a big unbiased sample of data can relieve some of those anxieties.
How I do it
Here’s how I compiled this year’s data. It’s the same as in the past, except that each year I’ve compiled data on more variables.
I started with the 2017-18 ecoevojobs.net list of tenure track job ads, as of late May 2018 (near the end of the 2017-18 job season). I first culled any job that seemed unlikely to be filled by an ecologist (e.g., evolution, genomics, cell biology, anatomy & physiology). I also culled any senior-level job that couldn’t be filled by an assistant professor (e.g., ads for department chairs). That left me with over 250 jobs in ecology and allied fields (e.g., fish & wildlife, rangelands, evolutionary ecology, microbial ecology, the ecological sides of forestry and soils, ecohydrology, behavioral ecology, etc. I also retained positions in biology, zoology, botany, organismal biology, vertebrate biology, entomology, etc.). I checked to see who, if anyone, was hired into those jobs by looking at department websites, emailing friends at some of the hiring institutions, and by googling public information. For instance, I searched on “[name of institution] new faculty 2018”, without the quotes. That search is good for finding the press releases some institutions put out in early fall, listing all their newly-hired faculty. I performed these searches and website checks repeatedly, up through mid-Sept. 2018.
I supplemented those approaches with various other methods that are less work for me, and that sometimes identify people hired into jobs not listed on ecoevojobs.net (ecoevojobs.net isn’t quite comprehensive). I posted several times on Dynamic Ecology asking people to email me information (thank you to the many folks who did!). In late Aug. 2018 I posted on Ecolog-L asking people to email me information. And I did various Twitter searches (e.g., “starting professor ecology”, without the quotes); those searches turn up newly-hired ecology profs tweeting their good news. I didn’t bother to email department chairs this year; I did that last year but only got a few responses. Finally, I used serendipity: if I stumbled across someone who seems like they might be a newly-hired ecologist, I checked and if they were, I added them to the database. I included any spousal hires I was told about.
This year I ID’d 161 newly-hired TT asst. profs in ecology and allied fields at N. American colleges and universities. That’s a bit lower than in the past, probably because I stopped my search a bit earlier in the fall this year. But it’s still a substantial majority of all new hires. Because I’m sampling the bulk of a finite population, and doing so in an unbiased or close-to-unbiased way, my estimates of the features of that population are unbiased or only slightly biased and have narrow confidence intervals.
(As an aside, I can tell you that using a variety of methods to ID new hires is essential. In particular, if I just did the easy thing and asked around on this blog, Ecolog-L, and Twitter, my sample size would be both much smaller and more statistically biased. For instance, the positions I ID’d by asking around online were more likely to have been filled by women than positions I ID’d in other ways.)
For every position I was able to ID, I recorded if it was filled by an ecologist at the asst. professor level, filled in some other way (e.g., by a non-ecologist, or at the associate level), or not filled. Who to count as an “ecologist” (as opposed to, say, an evolutionary biologist or systematist or microbiologist or etc.) is a judgment call. But the vast majority of cases are clear-cut, and the results don’t change appreciably if you either throw out all the borderline cases or count all the borderline cases.
I recorded the new hire’s gender (man or woman, as judged from name, photographs on department and lab websites, and pronouns in social media profiles if available. A few new hires also volunteered their gender, unasked for, in emails to me.) Using a gender binary, and judging gender from publicly-available names and photographs, obviously isn’t ideal. But based on research that I’ve read, the error rate should be low. And there doesn’t seem to be any other way for me to do it. For instance, emailing new hires to ask their gender identification would be really intrusive and inappropriate. If you can suggest a better approach, I would love to hear from you either in the comments or via email (email@example.com).
I also compiled various easily-collected pieces of information about the new hire and the hiring institution. I looked at publicly-available information (e.g., cv’s, ResearchGate, LinkedIn, department websites) to determine where new hires got their bachelor’s degrees and PhDs, when they got their PhDs, their positions at the time they were hired (e.g., postdoc, assistant professor, visiting assistant professor, etc.), their Google Scholar h-indices, number of papers in Science/Nature/PNAS (counting first- and co-authored papers separately, and not counting letters to the editor, news & views pieces, etc.), and if they’d ever worked or studied at the hiring institution. I can’t always identify all bits of information for every new hire, which sometimes introduces mild sampling biases. For instance, people without Google Scholar profiles tend to be less active researchers, and would have below-average h-indices if they did have Google Scholar profiles. So my data slightly overestimate the mean h-index of newly-hired ecology faculty. I also looked up some additional information for some subsets of the new hires (for example).
For a subset of new hires, I also tried to identify their PhD supervisors and looked up various crude indicators of how “famous” those supervisors are (their current rank, # of Nature/Science papers, Google Scholar h index, membership or not in the US National Academy of Sciences).
Finally, I looked up the hiring institution’s “basic” 2015 Carnegie classification (R1, R2, R3, M1, M2, M3, BC, or TC [tribal colleges]), lumping together all non-tribal bachelor’s institutions into the “BC” category. I classified Canadian institutions as their US equivalents when the equivalency was clear (e.g., Toronto is an R1-equivalent), otherwise I didn’t classify them. A couple of US institutions lacked Carnegie classifications.
No, I don’t know how long all this took me. Many hours, but I wouldn’t venture to guess how many. I don’t track my time. Most of the hours were hours I would otherwise have spent procrastinating on real work in some other fashion, or hours when I would’ve been doing something else that didn’t require much thought (compiling these data is mentally undemanding). Bottom line: compiling these data is a voluntarily-imposed task. I’m happy to do it, and I’m glad many readers appreciate it, but I’m not looking for a cookie by doing it.
This is probably going to be my last year compiling these data. The patterns in the data are mostly quite clear at this point, and don’t change much from year to year. So compiling more data wouldn’t add much information. And next year I’m going to be writing my NSERC grant in the summer and early fall, so I won’t have as much time for this data compilation.
In five years or so I plan to return to my list of newly-hired ecology asst. profs and see if they’re still in academia, if they’re tenured, etc.