• Home
  • About
  • Contact
  • Research
  • Teaching
  • Blog
  • Placement data

Soaked feet, bone-dry cuffs

Mind & psychology. A little bit of epistemology. Plenty of other stuff. Lots of swearing. Some jokes, mostly self-deprecating. Everything's coming up Milhouse.

When does a PhD go stale? A crack at figuring it out...

5/18/2021

 
A friend on the Facebook prompted to me to think about "expiration dates" on PhD's. It's common to hear that a PhD can go stale, i.e. after a certain number of years a candidate is no longer as appealing as they once were to prospective employers. But what are some of the numbers?

The PhilJobs appointment dataset has both (i) the year of hiring and (ii) the year the appointee earned their PhD. We can use that to start getting some clarity on when staleness sets in.

Using the data I downloaded from PhilJobs at the end of April, here's a set of counts:
Picture

1/3 of junior placements posted to PhilJobs get a job the same year they finish their PhD. And, as you can see, things drop off quickly after that. Less than 1% of posters list a job after 8 years post-PhD. There are two hypotheses possible here, which are not mutually exclusive:
(1) very few people get a job after 8 years post-PhD
(2) some people change jobs and don't post their new jobs
(3) as people get further out from their PhD, they are less likely to post a job
EDIT 9:43 am PDT:
(4) X people drop out of the market after N years (thanks to Marcus Arvan for this suggestion)

If I had to put money on it, I'd bet (1) does more explaining of the phenomenon than (2) or (3). In the case of (2), it's difficult to reliably track individuals across positions in PhilJobs, so it's hard to be certain. I'm not sure how we'd rule out (3). I'd love to hear readers' thoughts, though. 

Here's a thought about (4). We have two explanations for the drop off: PhD's get stale or people drop out of the market. Clearly, these aren't independent of one another: stale PhD's are more likely to drop out of the market and folks who drop out of the market thereby have stale PhD's. My hunch is that these hypotheses would be really hard to tease apart. What would we need? One step towards answering it would be the number of people applying for jobs 1 year out, 2 years out, etc. We want to know (e.g.) the number of 8-years-out job posters out of the number of 8-years-out applicants.

Here's another informative plot: the differences between successive years in job postings. That is, the drop in ratio of postings from 0 years from PhD to 1 year from PhD, from 1 year from PhD to 2 years from PhD, etc. (If you were to measure the gaps between the tops of the bars in the previous plot, you'd get this one.) 
Picture
What does this tell us? Between 0 years from PhD and 1 year from PhD, there's a 16% drop in postings. That's to say, one year seems to matter a lot. From years 1-2, 2-3, and 3-4, things level off. There's not a big change in differences there. I think the way to interpret this is that there's not a big difference in ratios of job postings if you're between 1 and 4 years out from the PhD. 4-7 years out from the PhD is another drop. After that, it doesn't seem to much matter whether you're 8 or 28 years out from the PhD. 

So there you have it. I think this gives us some idea of what it means to say a PhD is stale. The takeaways are:
1. there are levels of staleness: 8+ years is the most stale, 4-7 years is 2nd most stale, 1-3 years is the least stale, 0 years is baby fresh.
2. decay in job placements is exponential. The costs of not placing are greatest earliest on. After a while, the costs level out, but that's largely because jobs aren't being won.

Thanks for Greta Turnbull, Tim Weidel, and Maria Howard for making sure I didn't ramble incoherently.

Onlooker
11/2/2021 02:45:10 am

I haven't seen anyone address this possibility, so just to throw it out there: in order to extrapolate from this to an individual's chances, don't we have to take into consideration the fact that the pool is also decreasing from year to year?

Example: if you start with 1000 people in 2016, 30% of these, so 300, get a job in 2016. You then only have 700 left in this cohort without a job. In 2017, 200 (20% of initial cohort) get a job. This is still 28% of the remaining pool (of 700). In 2018, 150 get a job out of the remaining 500: this is ~15% of the original cohort (1000 people), but it remains 30% of the pool of people remaining (500 people). In 2019, 100 get a job (10% of original cohort), but there are only 350 people left who have never gotten a job, so this is still 29% of the people left.

I'm just eyeballing the ratios on your chart, and I'm not sure whether you're also counting people who already have jobs who get second jobs, but, if the above assumptions are correct, one's individual chances are actually remaining the same at around 25-30% per year, since one's competition (from the initial cohort at least) are also taking themselves out of the running as one progresses through the years.


Comments are closed.

    Author

    I do mind and epistemology and have an irrational interest in data analysis and agent-based modeling. 

    Archives

    December 2021
    October 2021
    May 2021
    April 2021
    November 2020
    March 2020
    January 2020
    December 2019
    September 2019
    August 2019

    Categories

    All
    But They're Not So Big
    Data
    Empty Promises
    Epistemology
    Introductions
    Music
    Philoso Twitter
    Philoso-twitter
    Professional Navel Gazing
    Professional Navel-gazing
    Psychology
    Rhetorical?

    RSS Feed

Powered by Create your own unique website with customizable templates.