Ruedin, Didier, and Eva Van Belle. 2022. “The Extent of Résumé Whitening.” Sociological Research Online. https://doi.org/10.1177/13607804221094625
I’m a big fan of 2-page CV, but in the most recent template I have received from a funder, they also ask to list reviewing activities. On the one hand, I appreciate that they try to acknowledge reviewing activities, on the other hand, I wonder what selection criteria would be appropriate — listing everything would fill most of the 2 pages (and still not tell us much about the quality of the reviews); only listing activities for “prestigious” work kind of defeats the point of trying to acknowledge the less glorious parts of what we do.
Our publication on hiring discrimination against Blacks in Switzerland is now properly published at JEMS. Using a correspondence test, we find the unfortunately usual pattern of discrimination in Switzerland, too.Continue reading “Out now: Hiring Discrimination on the Basis of Skin Colour”
I’m currently screening candidates for a fixed-term position and thought I would share some views “from the other side”, so to speak.
It’s not an easy task, especially if you do your best to provide a fair and equitable selection. One thing that really struck me this time was how strongly the advert resonated in some disciplines and not in others. The advert was for a “Post-Doctoral Researcher”, with a clear preference for “economics; sociology, or political sciences”. That’s simply because for this particular position — in a specific project — that’s the skills we need; and it worked, we have received mostly applications demonstrating excellent quantitative skills. Another generic observation concerns LinkedIn. It’s the first time I’ve also advertised on LinkedIn, and this enticed a fair number of applicants to press the “apply” button, even though the advert asked for applications by e-mail. One thing I noticed compared to the applications by e-mail is that the share of speculative applications was noticeably larger: applications without any reasonable fit. Some of them obviously clicked through the screening questions, because the CV did not always back up the skills. On the other hand, LinkedIn also makes it almost too easy to reject applicants.
The hardest cases are always those truly excellent candidates that just don’t match, but impress otherwise. Let’s be clear here, there are positions that are open, where you set your own research agenda, and there are jobs in projects where the general direction and research design are given.
Reasons we rejected you
Here’s a list of reasons why we have not continued with your application into the second round (in no particular order):
- very poor English in the cover letter; I know not everyone has had the same opportunities to learn English, grew up with a similar native language or was given the necessary resources, but if your cover letter fails to demonstrate good language skills, we cannot count on your writing those articles in English
- you did not demonstrate any of the skills we asked for; these are the skills you need to carry out the job, so a motivation to learn advanced quantitative methods is not going to be sufficient, certainly not when you’re up against more than 100 other candidates
- you really want to work on a (widely) different topic; in this case, it’s a job for a specific project, so no matter how great your “project” is, it’s not useful. I know you need a job, but when you tell us that you really want to work on a different topic, you’re not going to be happy in this job.
- your best quantitative skills are QCA or Atlas.ti (I’m not making this up!), or you’ve done economic theory up to now
- you provided a list of keywords from my webpage, but there is no coherent statement. Yes, you got my attention for 1/4 of a second, but not more than that
- your e-mail bounces when I sent the acknowledgement mail. OK, technically you have not been rejected, but how should we communicate?
Some bad signals we ignored
Let’s be clear here, with over 100 applications, we could be very picky and work only with the applications that immediately impress us. But we also know that we might have missed something in the first round. Here are some things you might want to avoid next time:
- you’re not following the instructions (i.e. send a single PDF by e-mail in this case, like sending 7 PDF and Word documents, or sending me an updated CV twice); this doesn’t really signal attention to detail
- your PDF application is poorly organized, like putting the job market paper first, not the cover letter (sure, we love scrolling through documents… .~)
- your personal website doesn’t work — when did you update it last time?
- you use a script font for the cover letter — it’s just very hard to read, OK?
- you can’t spell STSTA [sic.]
Stuff we appreciated in the first round
It’s not magic really, but there are things you did do to aid the screening process, thank you:
- you have the skills asked for, and you clearly show this
- you demonstrate in the cover letter that you have read the advert beyond the word “postdoctoral researcher”, like mentioning how your previous work relates to the project we advertised. I know you’re probably writing many applications, but it does make a difference for jobs with clear requirements.
- that one applicant using Julia
- your material and especially your CV is nicely organized
- your cover letter is short and relevant; it’s not just your CV in prose, and probably you don’t want to lead with your teaching statement when applying for a pure research position.
Now on to the second round …
My colleagues are sometimes surprised to learn that I teach statistics using SPSS and R/Rstudio in parallel. (Part of this is due to a misconception that R is hard to learn, ignoring that there are more difficult problems like proper model specifications and interpretation of results.) In my opinion, there are many benefits in doing so; here’s an unordered (and incomplete) list:
– introduction to a statistics package that remains available after they leave university and have access to the SPSS site licence (between jobs, moving to another university, out of academia)
– exposure to a different paradigm, making the shift to other software like Stata or SAS appear less threatening
– understanding that it doesn’t matter what package we use for basic statistics (we could even do it by hand)
– that line on the CV
– overcoming limitations in SPSS (ever tried to plot an interaction effect the way we want them?)
– ensuring that those who want to progress to more advanced (contemporary) methods actually can (being “future ready”)
– encourage a mindset that we are in control of the analyses, not the software package
At the same time, I acknowledge that many students have been exposed to SPSS before and feel more at ease when they can see the menu bar. (And the day the university gets rid of that site licence, PSPP will do nicely to work in parallel with R/Rstudio).