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Comment by riazrizvi | Hacker News Reader

This is a well constructed empty argument because it glosses over the central concern, ‘employee performance’. Without defining that we have no idea what the graph represents.

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For analyses like this it just doesn't matter. Pick a metric and measure it over your workforce. Across the universe of salient metrics of interest you won't see a gaussian across your workforce.

In a previous job I modelled this and concluded that due to measurement error and year-over-yead enrichment, Welchian rank-and-yank results in firing people at random.

All of Jack Welch’s management tactics should be considered suspect now.

His performance at GE was 100% fueled by financial leveraging that blew up in 2009, basically killing the company. Nobody should be taking management lessons from this guy.

> Nobody should be taking management lessons from this guy.

Rank and yank is simply about lowering labor costs, once the business has achieved a significant moat and no longer needs to focus solely on growing revenues. A negotiating tool for the labor buyer, due to the continuous threat of termination.

Stack ranking will tell you when something isn't working, but the solution isn't always to fire, but rather use that data to fix things in a more general solution.

I found that team composition and role assignment matters a lot, at least if you hire people who are at least above a certain bar. Match a brilliant non-assertive coder with someone who is outgoing and good at getting along and at least decent coder, and the results from the two outperform generally either of them individually.

You can bring out the best of your employees or you can set them up against each other. This either brings everyone up or brings everyone down.

Wholeheartedly agree with you on team composition mattering a ton, but how often do you have such an abundance of engineers and tasks that you can match them up the right way?

I think if you get to know your engineers, you can figure out the right pairings to bring out the best. But this requires intimate knowledge and probably subjective based on how good the manager is at managing coders. So I guess from up high, stack ranking-based firing is easier.

But I think it is also cheaper to make great teams rather than just doing brutal firings all the time. But it may be a micro-optimization?

So you're saying that if you don't think about construct validity and just pick any given metric that can spit out a comparable number across all your different positions and teams, that these metrics have weird distributions? Hmm, I wonder why.

I think it's more charitable to interpret their statement as "for all metrics" rather than "run this experiment once and arbitrarily just chose a single metric". Their statement is a lot more actionable because as much as we've tried to over decades finding an accurate metric to represents performance seems to be an impossible task.

A researcher friend at a previous job once mentioned that in grad school he and several other students were assisting a professor on an experiment and each grad student was given a specific molecule to evaluate in depth for fitness for a need (I forget what at this point) and one of the students had a molecule that was a good fit while the others did not - that student was credited on a major research paper and had an instant advantage in seeking employment as a researcher while the other students did not. That friend of mine was an excellent science communicator and so fell into a hybrid role of being a highly technical salesperson but tell me - what metrics of this scenario would best evaluate the researchers' relative performance? The outcome has a clear cut answer but that was entirely luck based (in a perfect world) - a lot of highly technical fields can have very smart people be stuck on very hard low margin problems while other people luck into a low difficulty problem solution that earns a company millions.

Most of the world is ruled by luck. Where you are born, who your parents, how rich they are, who you know, whether or not someone “better” than you applies for the same position, etc. etc.

Ignoring luck or trying to control for it would be a mistake.

Ignoring luck is a requirement - conditions born from luck may be worth consideration but past luck is not a predictor of future luck.

I'd clarify - trying to ignore someone's education because it's a result of their citizenship or the wealth of their family is going to be endlessly frustrating... but if your metrics can't exclude luck and happenstance during the execution of the task then they're not worth much of anything.

You can literally get a 1:1 model of capitalism by modeling luck alone. To ignore it is to pretend that you have some kind of control of luck.

Oh, the answer to that is apparent enough, but frustratingly circular:

Performance is "visibly doing the things that the company rewards during the performance review process".

Theoretically, each role at a company should have a set of articulated accomplishments that are expected. (This is sadly often not the case.)

But you're right that the subjective nature of "performance", and the lack of a clear numerical scale, are a difficulty of the entire process!

You could replace "employee performance" with "value to the company" and the same argument would hold. Performance is difficult to measure, but we get a good estimate of value to the company any time someone receives a competing offer and drags their manager to the negotiating table.

The amount of money the manager is willing to match is the perceived value to the company. This is how the company actually behaves (we know for sure whether they match the offer or not) and that behavior implies a value to the company, regardless of what anyone says in performance review season.

> The amount of money the manager is willing to match is the perceived value to the company.

This assumes the manager is irrelevant here. But we all know that different managers (or non-managers) can communicate value differently for the same employee. So this metric can't be solely measuring the value of the employee.

You are talking about value as some intrinsic quality. I'm talking about value as a belief that is subjectively assigned, and that we can infer from actions. We can all agree on the actions, and we can agree on the possible beliefs that an action can imply.

The action to not match an offer implies that the company believes the employee adds less value than their new offer. If the company believed the employee was adding more value than their new offer, they would match the offer to keep the employee.

A company isn't a single rational agent. It's made up of people performing different functions. But behaving irrationally is a categorically bad thing for the company to do, and the leadership has a fiduciary duty to prevent the company from acting irrationally or otherwise not in its own self interest.

The manager may matter here, but the leadership is supposed to be creating a management structure such that the company acts rationally to make progress towards set goals.

The article does briefly caution about measuring difficulties. But given that the main conclusion is an argument against stack-ranking-and-firing, the question of "what is performance" passes forward to whatever metric the stack-ranking manager was going to use when they were planning to fire the "bottom" 10% of their payroll.

I'm not sure this is the argument the author is making, but you could claim that the rest of the argument is true for any (or most) reasonable measure of employee performance that a company actually cares about.

You could claim anything, but is there hard quantitative data to support such a claim? Or are we just guessing?

The author presents some data in the article. Also, the absence of hard quantitative data doesn't necessarily make it a complete guess. (At least not any more than starting with the assumption of a Gaussian distribution.)

On a meta note, you're right to note that unclear terms undermine our collective reasoning, despite a proper chain of propositions.

I've found Term Logic[1] to be useful for figuring out why certain discussions confuse me. I've also used to avoid unnecessary arguments by seeing if the participants are starting with clear concepts (signaled by terms).

[1] https://en.wikipedia.org/wiki/Term_logic#Basics also this explainer https://adoroergosum.blogspot.com/2015/05/the-three-acts-of-...

It also assumes that "productivity" is something that is meaningful at all at the level of individuals, not teams or larger. IMHO, it is not.

Yes, is performance Pareto, or perception of performance Pareto?

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