4 ms·

You are unlikely to successfully hire better developers after getting rid of 7% of your workforce. Better developers will stay away.

throwawaymaths 4y ago
Depends on how bad the people you laid off are. Look at it this way, if 20% of programmers are bad, and you lay off the bottom 10%, and rehire let's say at a disadvantage (50% bad), you now have a 75% bad workforce instead of an 80% bad workforce. This assumes you can identify bad workers (for some metric of bad which may or may not be ethical or sensible)
- wintogreen74 4y ago
  it's way easier to tell if someone is bad after you hire them vs. before though.
  - throwawaymaths 4y ago
    Stats still work out, try it yourself with whatever numbers you care to use. I'm not saying I defend layoffs I'm just pointing out the underlying logic of why a company might want to do them (I think it's missing important factors, but what you claim is not one of them)
- ithkuil 4y ago
  This also assumes that every company lays off people and thus new hires don't select companies based in whether they recently laid off people
  - throwawaymaths 4y ago
    "rehire at a disadvantage"
- PuppyTailWags 4y ago
  This also assumes that PagerDuty's hiring process is so shitty that 1 in 5 of their hires suck. In that case there's no guarantee they'll hire better talent, lol
  - Phlarp 4y ago
    On the one hand PagerDuty's hiring process is shitty, but on the other hand the rest of the industry's process for identifying which heads to reduce is worse?
  - ivalm 4y ago
    Start with distribution for employee Good: P(good)=p Bad: P(bad)=(1-p) Have a bad employee detector P(detected as bad|bad)=q1 P(detected as good|bad)=1-q1 P(detected as good|good)=q2 P(detected as bad|good)=1-q2 Fraction of employees detected as good: pq2+(1-p)(1-q1) Fraction of employees detected as bad (and therefore fired) p(1-q2)+(1-p)q1 Suppose you pay a discount on ability to hire new good employees as d (so probability to hire a new good employee is dp instead of p) So you benefit as long as p(1-q2)/((1-p)q1)<dp/(1-dp) Or (1-q2)/q1 < d (1-p)/(1-dp) Plugging in p=.8, d=.5, q1=q2=q (knowing good and bad is equally hard) (1-q)/q < .167 Break even is then q = 1/1.167=.857 So if you start with 80/20 distribution and after a while improve your discrimination to 86/14 then firing benefits you even if you are hiring from a 40/60 pool.
- Phlarp 4y ago
  If you subscribe to this logic then doesn't it follow that you're trying to rehire only 50% "bad" programmers from a pool that is (again by your definition) newly comprised of individuals a previous company recently identified as 100% "bad"? This not only assumes that you can identify bad workers, but that you can also identify some percent of "good workers that other companies identified as bad workers". Shameless profit taking by executives and shareholders seems a lot more likely. Google and Microsoft providing PR cover for smaller companies to do it under the radar.
  - throwawaymaths 4y ago
    What's bad for one company is not necessarily bad for another company. For example I would think very hard before hiring any senior from FAANG (layoffs or otherwise) at a startup, especially in the pre-100 employees stage. Might even be worse during the rapid growth phase.

throwawaymaths 4y ago

Depends on how bad the people you laid off are. Look at it this way, if 20% of programmers are bad, and you lay off the bottom 10%, and rehire let's say at a disadvantage (50% bad), you now have a 75% bad workforce instead of an 80% bad workforce. This assumes you can identify bad workers (for some metric of bad which may or may not be ethical or sensible)

wintogreen74 4y ago
it's way easier to tell if someone is bad after you hire them vs. before though.
- throwawaymaths 4y ago
  Stats still work out, try it yourself with whatever numbers you care to use. I'm not saying I defend layoffs I'm just pointing out the underlying logic of why a company might want to do them (I think it's missing important factors, but what you claim is not one of them)
ithkuil 4y ago
This also assumes that every company lays off people and thus new hires don't select companies based in whether they recently laid off people
- throwawaymaths 4y ago
  "rehire at a disadvantage"
PuppyTailWags 4y ago
This also assumes that PagerDuty's hiring process is so shitty that 1 in 5 of their hires suck. In that case there's no guarantee they'll hire better talent, lol
- Phlarp 4y ago
  On the one hand PagerDuty's hiring process is shitty, but on the other hand the rest of the industry's process for identifying which heads to reduce is worse?
- ivalm 4y ago
  Start with distribution for employee Good: P(good)=p Bad: P(bad)=(1-p) Have a bad employee detector P(detected as bad|bad)=q1 P(detected as good|bad)=1-q1 P(detected as good|good)=q2 P(detected as bad|good)=1-q2 Fraction of employees detected as good: pq2+(1-p)(1-q1) Fraction of employees detected as bad (and therefore fired) p(1-q2)+(1-p)q1 Suppose you pay a discount on ability to hire new good employees as d (so probability to hire a new good employee is dp instead of p) So you benefit as long as p(1-q2)/((1-p)q1)<dp/(1-dp) Or (1-q2)/q1 < d (1-p)/(1-dp) Plugging in p=.8, d=.5, q1=q2=q (knowing good and bad is equally hard) (1-q)/q < .167 Break even is then q = 1/1.167=.857 So if you start with 80/20 distribution and after a while improve your discrimination to 86/14 then firing benefits you even if you are hiring from a 40/60 pool.
Phlarp 4y ago
If you subscribe to this logic then doesn't it follow that you're trying to rehire only 50% "bad" programmers from a pool that is (again by your definition) newly comprised of individuals a previous company recently identified as 100% "bad"? This not only assumes that you can identify bad workers, but that you can also identify some percent of "good workers that other companies identified as bad workers". Shameless profit taking by executives and shareholders seems a lot more likely. Google and Microsoft providing PR cover for smaller companies to do it under the radar.
- throwawaymaths 4y ago
  What's bad for one company is not necessarily bad for another company. For example I would think very hard before hiring any senior from FAANG (layoffs or otherwise) at a startup, especially in the pre-100 employees stage. Might even be worse during the rapid growth phase.