> Performance management, as practiced in many large corporations in 2024, is an outdated technology that is in need of an update

Author made a couple of fundamental mistakes: the first is they assume employees are (or should be) paid according to how much they "individually" earned the company. Employers strive to pay employees the minimum they can bear, on employer's terms. Those terms are information asymmetry and a Gaussian distribution. Fairness is the last thing one should expect from employers, but being honest about this is not good for morale, so instead, they rely on keeping employees uninformed, while the employers collude to gather everyone's remuneration history via the Work Number.

The second mistake they made is assume that companies would prioritize being lean and trimming the mediocre & bottom 5%. There are other considerations, combined productivity is more important than having individual superstars working on the shiniest features. How much revenue do you think a janitor or café staffer generates? Close to zero. The same goes for engineering. Someone has to do the unglamorous staff, or you end up with a dysfunctional company, with amazing talent (on paper).

Edit: there's an infamous graph that shows when aggregate worker productivity and average income. The two tracked closely, rising in tandem until the 1970s, where they got decoupled. With income becoming much flatter, and productivity continuing to rise. That's how the world has been for the past 50 years on the macro and the micro

  Economists will teach you something called the Marginal Productivity Theory of Wages, the idea being that the amount of money that a company is willing to spend on an employee is essentially the value that the company expects to get out of their work. This strikes me as mostly true, most of the time

  The marginal productivity theory of wages states that under perfect competition, workers of the same skill and efficiency will earn a wage equal to the value of their marginal product. The marginal product is the additional output from employing one more worker while keeping other factors constant. However, the theory has limitations as it assumes perfect competition, homogeneous labor, and other unrealistic conditions. In reality, competition is imperfect, labor is not perfectly mobile, and other factors like capital and management efficiency affect productivity.

"IQ is Gaussian" – it was pointed out somewhere, and only then became obvious to me, that IQ is not Gaussian. The distribution is manufactured.

If you have 1000 possible IQ questions, you can ask a bunch of people those questions, and then pick out 100 questions that form a Gaussian distribution. This is how IQ tests are created.

This is not unreasonable... if you picked out 100 super easy questions you wouldn't get much information, everyone would be in the "knows quite a lot" category. But you could try to create a uniform distribution, for instance, and still have a test that is usefully sensitive. But if you worry about the accuracy of the test then a Gaussian distribution is kind of convenient... there's this expectation that 50th percentile is not that different than 55th percentile, and people mostly care about that 5% difference only with 90th vs 95th. (But I don't think people care much about the difference between 10th percentile and 5th... which might imply an actual Pareto distribution, though I think it probably reflects more on societal attention)

Anyway, kind of an aside, but also similar to what the article itself is talking about

I would feel better if this was derived from empirical data rather than just rhetoric. This seems super testable, no? There is probably a ton of data already in different industries with regards to productivity.

Even if human talent have a Pareto distribution (which is not clear), the people employed by a company are a selected sub-set of that population, which would likely have a different distribution depending on how they are selected and the task at hand.

I think that any of these simplified distributions are likely not generalizable across companies and industries (e.g. productivity of AWS or Google employees are likely not distributed like employees of MacDonalds or Wal*Mart because of the difference in hiring procedures and the nature of the tasks.)

Get hard data within the companies and industry you are in and then you can make some arguments. Otherwise, I feel it is too easy to just be talking up a sand castle that has no solid footing.

One of the things I loved about working at Netflix was that the base assumption was that everyone was a top performer. If you weren't a top performer, you were given a severance check.

The analogy we used was a sports team. Pro sports teams have really good players and great players. Some people are superstars, but unless you're at least really really good you're not on the team.

Performance and compensation were completely separate, which was also nice. Performance evals were 360 peer reviews, and compensation was determined mostly by HR based on what it was costing to bring in new hires, and then bumping everyone up to that level.

So at least at Netflix 10 years ago, performance wasn't really distributed at all. Everyone was top 10% industrywide.

Cool data/idea, and anecdotally lines up with my experiance at BigCos from a coworker perspective.

But in my experiance employee perf evals are more political than data based.

At the end of the day a lot of mgmt at BigCo, esp these days, wants that 10% quota for firing as a weapon/soft layoff and the "data" is a fig leaf to make that happen. More generously it's considered a forcing function for managers to actually find underperformers in their orgs, even if they don't exist. Either way it's not really based on anything other than their own confirmation bias.

IME the scrutiny of perf evaluation is basically tied to the trajectory of the company and labor market conditions. Even companies with harder perf expectations during the good times of ~2021 relaxed their requirements.

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.

It's worth hammering on this point as much as possible hoping a few people listen, but there is at least one other important point about employee performance. If you're allocating bonuses, a single year's performance is probably a good way to do that, assuming you can accurately measure it. When you're talking retention and promotion, though, you're making a prediction of future performance, possibly at a variety of different jobs. That is even harder to do and more poorly reflected in the last year's results. You have some analogies to sports performance in this article, and you see this kind of thing all the time there. Guy does great in a single year, gets a huge, possibly long-term contract, then tanks. On the other hand, one of the better dynasties of the past decade was accomplished by the Golden State Warriors in the US NBA thanks to underpaying one of the all-time great players in NBA history because he suffered a series of ankle injuries early in his career and scared off other suitors. Single-year performance isn't necessarily reflective of a person's true mean abilities, and their place in the Pareto distribution won't be the same at all levels of advancement and responsiblity, either.

The problem, from a company's perspective, is you probably need to retain everyone at least five years, and actually give them a wide variety of assignments in that time, to really get any usable data about their long-term prospects.

This is very unconvincing. The author already admits one reason why:

> But there are low-performing employees at large corporations; we’ve all seen them. My perspective is that they’re hiring errors. Yes, hiring errors should be addressed, but it’s not clear that there’s an obvious specific percentage of the workforce that is the result of hiring errors.

I think it is clear that we expect a certain percentage of hiring "errors". And that they are not binary but rather a continuum. And that there are lots of other factors like employees who were great when they were hired but stopped caring and are "coasting" or just burnt out, who got promoted or transferred when they shouldn't have been and are bad at their new level/role, and so forth.

The Pareto distribution isn't particularly relevant here, because a hiring process isn't trying to get a whole slice of the overall labor market with clear cutoffs. For any position, it's trying to maximize the performance it can get at a given salary, and we have no reason to expect the errors it makes in under- and over-estimating performance to be anything but relatively symmetric.

So a Gaussian distribution is a far more reasonable assumption than a slice of the Pareto distribution, when you look at the multiplicity of factors involved.

This article: "Wouldn't it be cool if when you measure employee performance, it turned out to fit a Pareto distribution better than a Gaussian?"

Would that be cool? We could posit the implications of all sorts of improbabilities. But I feel more strongly about how cool it would be that P = NP.

All this aside, being laid off sucks - being pushed out, even when you're a high performer, sucks even more. The truth is that "data science" does not help you process grief the way reading Dostoevsky does, so maybe getting an A in your liberal arts education is valuable even when you are working as a software developer.

As employees, our expectations for performance management come from the system of giving grades in school.

What's interesting is that school grades often doesn't follow a normal distribution, especially for easier classes. I suspect that getting an "A" was possible for 95%+ of students in my gym class and only 5-10% of the students in my organic chemistry class.

In the same way, some jobs are much easier to do well than others.

So we should expect that virtually all administrative positions will have "exceptional" performance, which is to say that they were successful at doing all of the tasks they were asked to do. But for people who's responsibility-set is more consequential, even slightly-above average performance could be 10x more meaningful to the company.

This concept is not new - see [0].

There's ample research that Welchian stack ranking, and assuming a Gaussian distribution of employee performance, is not well-founded. Even its original pioneers (General Electric) have abandoned the practice (see [1]).

Not sure why there are so many commenters here defending the Gaussian model. Most researchers at this point agree that a pareto distribution is more realistic.

[0]: https://hbr.org/2022/01/we-need-to-let-go-of-the-bell-curve

[1]: https://qz.com/428813/ge-performance-review-strategy-shift

To me the biggest insight here is that no matter what data science you're trying to do on a group of employees, the people you already have decided should be fired or promoted from that group are outliers and should be removed from the sample.

There are certainly times that you would want them included, but those can be classified under "budgeting," not gaining insight on a workforce.

Going through some performance reviews as a manager, I always try to push back a bit against the bell curve. It kinda reminds me of the "stack ranking.". There are also some factors to be considered:

If you are in a hiring freeze or not promoting, most of the curve should shift right, assuming you are hiring great people. They will probably perform better quarter after quarter. Some might counter-argue that if everyone performs better, this should be the "new expectation," but I disagree: the market sets expectations.

If you have someone at a senior level with expectations of staff, for example, they won't be in the company for long. I hired many great engineers who later said they only looked for a new job because they were never promoted despite being overperformers.

A lot of focus on employee performance, but relatively little on management performance. I always wonder how a once great company can slowly decline into irrelevance. Take yahoo for example, it could only be due to management failure over several decades right? How can companies optimize for management performance?

> a helpful order of magnitude estimate is that the hiring process all told costs the company approximately a year’s salary

It feels weird to gloss over this since transaction costs this high have a huge impact on how the system should be designed.

Unless you are measuring the output of people on simple assembly lines, it is very difficult to define "performance".

In a properly functioning team, people perform different, discrete roles which are probably not entirely understood by other team members or management.

1) performance reviews are never aligned with employee value, because companies are strongly invested to take excess production from employees and transfer it to management, secondarily shareholders

2) the are also not aligned with the replacement cost of employees because the religion of management is that labor is effortlessly replaceable and low value

3) employee retention is not aligned with corporate performance in Machiavellian middle management, it is aligned with manager promotion for things like loyalty and maintaining fiefdom power, budgetary size, headcount, etc

4) there are no absolute or ever directly derived metrics in software development that have ever worked, to say nothing of other positions

Those are off the top of my head.

I had assumed stack ranking was specifically designed to force managers to fire low performers, without relying on their individual judgment. Since nobody likes to fire, this overcomes the inertia, and since relying on personal judgment exposed you to legal risk and principle agent problems, a simple rule was substituted. The author’s proposal to go back to managerial discretion would of course be incompatible with that intention.

I do wonder whether those implementing stack ranking are really that committed to a particular statistical model of employee productivity, or if they’re trying to solve a human and legal problem with an algorithm.

X + Y + [XY] = 8

X = the individual's contribution

Y = the contribution of the system they work within

[XY] = the interaction of the individual with the system

8 represents some measure of productivity, e.g., rate of errors, millions of dollars in profit, whatever you're measuring

The person who can solve for X is competent to rate people on their performance.

What to do instead of (destructively) rating people?

Build better systems for doing the work, make their work easier, give them psychological safety and job security so they can relax and enjoy their work and share better methods with each other.

(All paraphrased from W. Edwards Deming.)

Competition within organizations is for amateurs.

> Hey, wait – is employee performance Gaussian distributed?

Well, the Gaussian distribution gives positive probability to any interval of the real line, including the whole real line (probability 1), so, strictly speaking, no.

But maybe the issue is a distribution with a bell curve or even with just a unique maximum and falling off monotonically from that maximum.

Well, then, in my college teaching, still no: Instead, commonly, roughly, there were three kinds of students: (1) understood the material at least reasonably well, (2) understood some of the material a little, and (3) should have just dropped the course but from me got by with a gentleman C. So, the distribution had a peak for each of (1) -- (3), three peaks, no Gaussian!

Approximate Gaussian is guaranteed, under meager assumptions, from the central limit theorem (CLT) of averaging random variables, the easiest case, independent, identically distributed (IID), and, more depending on how advanced the CLT proof is. A proof due to Lindeberg-Feller long was, maybe still is, regarded as the most powerful CLT.

Apparently ~100 years ago, especially in education, the CLT was commonly regarded as standard, true, without question, maybe some law of nature. Maybe some of the people measuring IQ, SAT scores, etc. also thought this about the Gaussian.

For me, I, in mathematical and applied probability, care first about finite expectation, conditional independence, independence, several convergence results (e.g., the martingale convergence theorem), then IID, and hardly at all, Gaussian.

The author looks at "observables" of performance without considering whether there might be confounders such as those discussed in great nuance here https://onlinelibrary.wiley.com/doi/full/10.1111/joes.12328 .

He cites similar work by William Shockley who taught both electrical engineering and scientific racism at Stanford https://en.wikipedia.org/wiki/William_Shockley (no swipe at the author, just pointing at the biased motiviations of some of the researchers foundational to the idea of "high performers").

In general, when you see pareto structures or power laws, you should think of compound or cascade effects, which in human structures generally means some form of social mediation. Affinity for a desireable skill might be gaussian, but the selection process means that the people who _get_ to do that skill might become pareto shaped because if you aren't much better than the next guy, you wouldn't stably stay at the top. Similar logic can hold for other expressions.

In general, I wish more people would read https://blackwells.co.uk/bookshop/product/Causality-by-Judea... or at least the more accessible https://mixtape.scunning.com/ before starting to conjecture from data about social systems - the math will tell you what you can and cannot speculate on.

(fun exercise: draw the causal models of IQ in https://dagitty.net/ and ponder the results)

Setting aside the issue of defining a function for 'employee performance', this glosses over the invisible interactions. An employee in a dysfunctional organization will perform worse than if they were in a well functioning one because they don't have to waste time dealing with people and processes that are a hindrance.

> For what it’s worth, human height is also Gaussian, and that’s correlated with workplace success.

Height is generally not considered to be Gaussian and this is exactly the kind of statistics mistake the author seems to be accusing employers of. Adult height is somewhere between Gaussian and bimodal.

Employee performance MEASUREMENT appears to be Gaussian distributed. To my first simple, and let's be real probably somewhat wrong, approximation there are roughly 3 things that go into it.

1. There is a certain skill in communicating all the important things you've done, we shall lump likability + politicking into this one for convenience.

2. There is a premium that is placed on shiny new features and saving the day heroics. A lot less priority is placed on refactoring and solving the problems before they require heroics.

3. Finally there are individual's technical and self-management skills. I.E. it's important to work on important things and be good at it.

> How much revenue do you think a janitor or café staffer generates? Close to zero. The same goes for engineering. Someone has to do the unglamorous staff, or you end up with a dysfunctional company, with amazing talent (on paper).

If the company would be dysfunctional without that janitor or software engineer, and not bring in as much revenue as a result, it sounds like the model that attributes close to zero revenue to them is already dysfunctional. If the company can't function without the janitor, then a significant portion of the revenue of the company should be attributed to them.

This is a subtle aspect of intelligence measurement that not many people think about.

To go from an IQ of 100 to 130 might require an increase in brainpower of x, and from 130 to 170 might require 3x for example, and from 170-171 might be 9x compared to 100.

We have to have a relative scale and contrive a Gaussian from the scores because we don’t have an absolute measure of intelligence.

It would be a monumental achievement if computer science ever advances to the point where we have a mathematical way of determining the minimum absolute intelligence required to solve a given problem.

IQ scores have proven highly correlated to educational achievement, occupational attainment, career advancement, lifetime earnings, brain volume, cortical thickness, health, longevity, and more.

To the point of being accurate predictors of these things even when controlling for things like socioeconomic background.

It's used because it works as a measuring tool, how the tests are constructed is largely irrelevant to the question of if the outcome of the test is an accurate predictor of things we care about.

If you think you have a better measuring tool you should propose it and win several awards and accolades. No one has found one yet in spite of many smart people trying for decades.

Correct. IQ isn't an effective measurement of intelligence as is typically stated. It is (at best) a measurement of learning disabilities.

I didn't know that about how IQ tests are formed. Would that mean that there could be some sliver of the population that could score in the top %'s on the 1000 question test but due to the selection of questions, scored average on the final IQ exam? If so, that'll be my excuse next time I have to take an IQ exam. I just got the wrong distribution.

Sum of N independent similarly distributed variables (questions), will tend to be normally distributed, that the central limit theorem, no need to manufacture anything.

> and then pick out 100 questions that form a Gaussian distribution. This is how IQ tests are created.

You missed an extremely important final step. People's scores on those 100 questions still aren't going to form a Gaussion distribution. You have to rank-order everyone's scores, then you assign the final IQ scores based on each person's ranking, not their raw score.

It's worse, because every test is obviously bounded, and it's absurd to not expect some noise to be there.

Join those two, and the test only becomes reasonable near the middle. But the middle is exactly where the pick of questions makes the most difference.

All said, this means that IQ is kinda useful for sociological studies with large samples. But if you use it you are adding error, it's not reasonable to expect that error not to correlate with whatever you are looking at (since nobody understands it well), and it's not reasonable to expect the results to be stable. And it's really useless to make decisions based on small sample sizes.

That’s not how IQ tests are made as can be found by reading how they’re actually made via Google scholar. And it would be spectacularly hard to do what you describe.

How they’re actually made is a batch of questions thought to take some form of reasoning are curated, then ALL of those questions are used in the test. It is an empirical fact the percentages of decent sized groups of people will score a bell curve, in exactly the same way humans do on hard calc exams, on hard writing items, on chess problems, and across a bewildering amount of mental tasks, none of which are preselected and fidgeted with to fake a Gaussian.

A simple example: see how many simple arithmetic problems people can do in fixed time. What do you find? Gaussian. No need to fiddle with removing pesky problem. Do reading. Do repeat this sequence for length. Just about any single class of questions has the same bell curve output in human mental ability. The curve may bend based on some inherent difficulty, say addition versus calculus, but there will be a bell curve.

Now take plenty of types of questions to address various wobbles in people’s knowledge, upbringing, culture, etc, giving a host of bell curves per category (and those also correlated by individual). Then the sum of gaussians is gausdian. All IQ tests do is shift the mean score to be called 100 (normalized) and the std dev to match a preset amount of people so such tests can be compared over time.

And the empirical evidence is these curves do strongly correlate over time, so scaling a test to align with this underlying g factor is well founded.

This latter fact, that score on one form of intelligence seems to transfer well to others, forms the basis of modern intelligence research on the g factor. IQ tests correlate well with this g factor. And across all sorts of things the results are bell curves.

For anyone wanting to hear all this and a ton more, Lex Fridman has an excellent interview with a state of the art intelligence researcher at https://www.youtube.com/watch?v=hppbxV9C63g. The researcher goes into great depth on what researchers do know, how they know it, what they don’t know, and what has been proven wrong. This is all there.

People may find that manufactured or "oh IQ is just made up and there is no measure of intelligence". But I find beauty in the way that IQ tests create and reconfigure a distribution across a multi-dimensional vector or dimensional space. It figures out what we need in the general case, and allows us to use and reason with it, without ever having to do the grunt work or arguably impossible task of finding out an actual measure of intelligence or some way to untangle the way a brain works.

Yes, this has always bothered me. IQ doesn't easily correspond to any measurable real-world quality.

For example, if we would postulate that height is gaussian, we could measure people's heights and just ordering them we could create a gaussian distribution. Then we could verify the hypothesis of height being gaussian by mapping the probability distribution function's parameter to a linear value (cm) and find that these approaches line up experimentally.

We could do the same thing with any comparable quantity and make an order of them and try to map them to a gaussian distribution, but we would have no knowledge if what we were making actually corresponded to a linear quantity.

This is a serious issue, as basically making any claim like 'group A scores 5 points higher than group B' is automatically, mathematically invalid.

I think your comment about an easy test having everyone in the “knows a lot” category hints that the reverse (a hard test) would be Pareto distributed.

To me it says that our system is built on a reasonable but untested assumption (performance is a gaussian) and by replacing it with an equally reasonable assumption (performance is a pareto), suddenly our system looks stupid. It isn't really offering a solution but a new perspective

I thought that Bonus Content #1 and the references down the article were reasonably convincing. It would be great if large companies disclosed such details but it is unlikely.

> I would feel better if this was derived from empirical data rather than just rhetoric.

This exact statement applies to the practice of Gaussian performance ranking. It is pure corporate politics, it isn't founded in sound statistics.

The present author at least provides multiple sources of statistical evidence for their beliefs, if you read the footnotes.

The problem is that intellectual productivity is generally not possible to measure directly, so you instead end up with indirect measurements that assume a Gaussian distribution.

IQ is famously Gaussian distributed... mainly because it's defined that way, not because human "intelligence" (good luck defining that) is Gaussian.

If you look at board game Elo ratings (poor test for intelligence but we'll ignore that), they do not follow a Gaussian distribution, even though Elo assumes a Gaussian distribution for game outcomes (but not the population). So that's good evidence that aptitude/skill in intellectual subjects isn't Gaussian (but it's also not Pareto iirc).

Agree with you - although, rhetorically speaking, I have come across many instances which the author refers to "of low performers are 3x as common as high performers." This is unfortunate as I always think do your best, and as Tyler Cowen states - Average is Over. So agree it would have been way better to use empirical data to back up this claim especially.

> I think that any of these simplified distributions are likely not generalizable across companies and industries

It’s going to be multivariate statistics with dependent variables. The quality of non developers at company affects the quality of developers they can retain, and the quality of the developers you have affects the quality of developers you can recruit and improve. Almost all the people I’d want to work with again left my last employer before I did.

You can take on more and more work yourself but it causes everyone around you to disengage. At some point you have to realize it’s more fruitful, emotionally and mathematically, to make coworkers produce one more unit of forward progress a month than to do it to yourself. Because it’s 2% for the team one way and 5-10% the other.

> There is probably a ton of data already in different industries with regards to productivity.

Uh. Not really. Our industry is notoriously bad at measuring productivity.

And the bigger problem is that when we try to measure it - "performance review" - we like grading on a gaussian curve. We'll never know if that's correct because we put our thumb on the scale.

An even bigger problem is that productivity is strongly influenced by completely non-technical factors. How enthusiastic are folks about what they are doing[1], how much variety do their tasks have [2], what are their peers like, etc. (Of course, that whole field of study has issues rooted in the inability to measure precisely as well)

Ultimately, it's a squishy judgment applied by humans.

[1] https://www.semanticscholar.org/paper/What-Predicts-Software...

[2] https://research.google/pubs/what-predicts-software-develope...

It's really difficult for me to believe that they really got 10% top performers. For one, knowing the cut-throat nature of employment there, I would expect only a minority of developers would be willing to try working there, despite the awesome rewards.

Another reason I really don't trust that to be true is that I've never seen a good way to measure who is a top performer and who is not. I don't think there's one, people are good in different things, even within the same job... for one assignment, Joe may be the best, but for another, Mary is the winner (but again, to measure this reliably and objectively is nearly impossible IMHO for anything related to knowledge work - and I've read lots of research in this area!).

Finally, just as a cheap shot at Netflix, sorry I can't resist as a customer: they absolutely suck at the most basic stuff in their business, which is to produce good content in the first place, and very importantly, NOT FREAKING CANCEL the best content! I won't even mention how horrible their latest big live stream was... oh well, I just did :D.

In summary, Netflix told all their employees that they are so amazing at their job, they are the top 10% of the whole world, they are like NFL athletes. If they don't perform to top tier levels, they'll be shown the door.

Here's a thought experiment: pretend that Netflix is lying and that their employees are not actually made up of the top 10% of talent industrywide. Let's for this thought experiment assume the realit is that they have slightly above average talent because Netflix pays slightly above industry average.

But now they've convinced those employees that they're not just slightly above average, they are like elite NFL players. And that means they have to work like elite NFL players. Netflix convinces their employees to work XX% harder with longer hours than the rest of the industry because they think they are elite.

"Only amazing pro athlete geniuses can work here" is way more motivating than "You have to work yourself to death with extra hours to make quota or you're fired!" because it's a manipulation of the ego.

I think this thought experiment is closer to reality than Netflix or their kool-aid-drunk employees will admit, and that Netflix's "pro athlete" culture is worker-harming psychological manipulation.

How are 'top performers' and 'low performers' being defined in this context?

In my experience, these labels in corporate environments often correlate more with social dynamics and political acumen than actual work output. People who are less socially connected or don't engage in office politics may find themselves labeled as 'low performers' regardless of their actual contributions, while those who excel at workplace networking might be deemed 'top performers'.

The interview process of these kind of companies also often falls into a problematic pattern where interviewers pose esoteric questions they've recently researched or that happen to align with their narrow specialization from years in the same role. This turns technical interviews into more of a game of matching specific knowledge rather than evaluating problem-solving abilities, broader engineering competence or any notion of 'performance'.

Let's be honest: how many people can truly separate personal feelings from performance evaluation? Even with structured review processes in place, would most evaluators give high marks to someone they personally dislike, even if that person consistently delivers excellent work?

A bit offtopic, but I've been curious about this.

Could you please describe how the unlimited vacation policy worked? How did people feel about it and whether they were anxious regarding using it (afraid that it will reflect on them badly when they take "too much" time off)?

Logically no-one else will try this - because if every company competed for the top talent, wages would rise to consume all profit.

I think this is probably how labour and capital should compete - I expect we need to equalise tax treatment so that becomes more possible

> Performance and compensation were completely separate, which was also nice.

Huh? How is that nice? Does performance and compensation not correlate in your ideal world, or am I misunderstanding it?

How can 360 peer performance reviews ever work? The incentives are against a fair evaluation: the reviewers have the incentive to overly criticize others so that they can stand out more.

I'm not saying that everyone on a 360 review process does that. But the incentive is there and it's working against fair reviews.

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.

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 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.

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?

Literally this. I’ve been banging on about this my entire career, not that corporate leaders tend to listen to the riff-raff. Especially in tech companies, they tend to only evaluate promotions and raises based on the past half-year of work, rather than a repeated pattern of successes across a diverse array of tasks and backgrounds over a significant period of time (years); even then, you only get the promotion if you’re on the right team, doing the right work, at the right time, and for the right leader. This leads to otherwise stellar performers going elsewhere, because the janitors, maintainers, and firefighters in an organization never get properly rewarded, respected, or recognized by leaders. Said leaders pass this off as “bad performers”, failing to realize the importance of superb talent working on less-than-stellar projects that keep the company running efficiently.

The only people who benefit from performance reviews are shareholders whose price pops when layoffs happen, and those who game the system for their own political ends. Top talent never really thrives in these, because they’re too busy doing actually meaningful and important work.

> On the other hand, one of the better dynasties of the past decade was accomplished by the Golden State Warriors in the US NBA thanks to underpaying one of the all-time great players in NBA history because he suffered a series of ankle injuries early in his career and scared off other suitors.

In case people want to read more about this:

https://www.essentiallysports.com/nba-active-basketball-news...

Interestingly enough, sports salaries are Pareto-distributed, which says something about how valuable (as assessed by the marketplace) each player is

https://marginalrevolution.com/marginalrevolution/2024/08/go...

One thing where this analogy stops to work, is that more so than in school your performance in a company can be highly dependent on how well and/or timely others do their job. Your managers performance metric may or may not catch that. E.g. imagine you are assigned a project where you have to interact a lot with department X and now department X is running at/over capacity, so you are performing worse, because their part isn't done in time and each back and forth takes half a week. Now you spend half your time not being productive with no fault of your own and the others are 110% productive while setting the whole shop on fire. Based on that metric they should fire you and hire more people for department X, when in fact they should probably just hire more for them (or reorganize the department).

Another example where this analogy stops working is that in school the students usually get the same/comparable assignments, that is somewhat the point of those. As the goto hard-problem-person at my current workplace I am pretty sure that it is absolutely impossible to compare my work to the work of my collegue who just deals with the bread and butter problems, it isn't even the same sport. How would you even start doing a productivity comparison here, especially if you understand 0 about the problem space

Having a shifted mean doesn't mean they aren't a normal distribution. Not saying they are necessarily, but the anecdote you are providing isn't convincing.

Would “doing all of the tasks they were asked to do” really be “exceptional”? What could be exceptional about that? I would think it would be “meets expectations” at most.