### Stack Ranking

# Why stack-ranking is always a case of ‘the house always wins':

Disclaimer:

1. It’s partly a defendant’s argument, and I am biased towards my client(i.e the employee).

2.I’ve little experience managing a group of people and don’t claim to know all the challenges involved.

3.My research/reading has been restricted to supporting ideas/theories/assumptions only. Not the thorough, covering all other bases(and unbiased) kind of literature survey.(**wink** stack ranking vs performance/vitality curve distinction)

At first look it looks like a wonderful meritocratic setup. It uses relative comparison with peers(not unlike pagerank algorithm/ eigen morality. . On the face of it is a very brilliant idea or a good idea that works well, when measuring quantities that haven’t been quantified before enough. In fact, if I were trying to do science on measuring performance, it’s a reasonably sane and tried approach. However, there are problems with using it.

I understand why it makes it easier to make decisions(especially big organizations), you get a single number that’s guaranteed to fall within some expected values(in the probability theory sense) and forcing a curve simply makes it easier to fit a fixed amount for bonuses and incentives. However, here’s the challenge how do you know your employees’/managers’/directors’ performance falls into a bell curve*? Maybe your company’s hiring practices always get bad performing employees or average performing employees or high performing employees(all three in comparison to the general population)?. In which case, aren’t you alienating a high performing employee, because his peers did better(perhaps in revenue)? The catch is revenue has more factors influencing than the performance of your employees.

Here’s a quote from here. :

“You have to have an objective when you do stuff like this. At GE there was only one objective, and that was to force honesty. That’s all it ever was—to force an honest discussion between your manager and you. And there’s nothing that quite forces that more than employees knowing that they expect to know how that manager ranks them, and then asking that manager, ‘Tell me where I rank and tell me why.’”

See anything wrong in that argument? Try replacing ‘honesty’ with ‘dishonesty’ and the argument still is logically consistent and sounds right. Guess why, because there’s an underlying assumption, stack-ranking raises honesty(or honest communication). While I agree, it’s a good way to force managers to give feedback(especially negative) to their employees, am not convinced it’s good or encourages honesty. I get that people(and managers) are more likely to avoid giving negative feedback and they are also subject to confirmation bias. . All of which can create bloated inefficient departments/teams. Here’s the catch, when you force something like this you’re eventually pushing the lowest ranks to people who are bad negotiators(with their managers) and therefore don’t push back when given negative feedback. Over half a decade or so you get a whole company of employees, who are all very good negotiators(no correlation positive or negative with performance).

In the end that defense sounds way too much like someone (who’s a reformer) and is stuck in the values/virtues node aka holy priest(I know, I’ve been guilty of it so often and probably right now). Enough of debate-level arguments, here’s an attempt at discussion of why it becomes something bad.

In theory, it can encourage managers to be honest to give negative feedback to their hires/employees, but in practice, it comes down to compromises/favours/future promises traded between the employee and the manager. You’re forcing the manager to make compromise/favour/future promise to one employee to pay the other. Even then, if it is still one number and some subjective reasoning between manager and employee it has some hopes of being a measure**. Now that post doesn’t make it clear why it’s a bad idea to use a normal curve on measuring performance, but it’s basic necessity before we can talk about using/finalizing measures of a hitherto unquantified phenomenon. For that we need to understand where does this vitality curve concept comes from.

Here goes the google scholar search result showing up nothing.I’ve been trying to find what research went into the whole stack ranking idea. A google search shows up Vitality curve. Ok where could Jack Welch have picked up this insane idea of vitality curve? The closest I can find is

## Central Limit Theorem in Statistics.

The basic premise of this theorem is that if we take enough number of samples of random variable of unknown distribution, the average of the samples will form a normal standard distribution.

This is not the strongest form of the theorem, but is the basic one the rest of the theorems are based on.

Now let’s look at what this means. When you’re examining a measurable quantity, whose distribution is unknown, you can essentially take samples(enough no. of times and enough size) and average it to form a normal distribution, if there’s enough samples and sample sizes.

Why/How is this useful?

Well it becomes useful when you want to compare two random variables and see if they have anything in correlation or common causal factors.

Especially, when you have figured out ways to manipulate/control one of the variables, we can simply design experiments that measure both of these variables, plot the difference of their averages(of the samples) and see how much it varies from the standard normal curve. This can give us whether they are positively or negatively correlated or simply unrelated. This is how experimental sciences work. Ofcourse, it’s not perfect, but it’s the best we have.**

Now, let’s get back to the original topic, if your organization/manager is implementing a stack ranking and if they refer to central limit theorem(you’re in luck, I haven’t heard any manager relate both of these, or name any of these.) you can question where does their idea of normality comes from. There’ll be cases, where your manager will tell you, your performance was average/below-average/above-average with respect to the rest of the team/organization’s. You get to question, how did they arrive at the normal curve’s values( most likely answer would be past year’s performance).

But here’s the catch, if they understand the experimentation process, the challenge then is to prove/question the current curve has seen enough samples. I don’t think it’s possible in most organizations/most roles. Of course, in very well established industries, with very specifically defined roles, it makes sense and is possible, but I’m not sure it applies well in the modern business environment.

Now the bigger your organization, more likely your performance is rated among different aspects/vectors/areas, which essentially multiplies the number of variables, and actually complicates the problem(requiring more samples to normalize).

What are the basic premises of the “Central Limit Theorem”?

Well, for one that you are comparing two distributions of random variables. (aka random distributions).

* — A quick read based on the blog here suggests not all companies use standard normal distribution, but normal/gaussian distribution with different spreads. facebook seems to have a narrower spread than amazon( which makes me think of the differences in corporate culture and what this model entails for it, but that’s more thinking and perhaps another blog post, about nash equilibrium competition vs co-operation.Hunch/Guess: more competition than co-operation at facebook and vice-versa at amazon.). It’s not clear what google uses.

** — Scientists, don’t get angry with this. I know there are more nuances that go into statistical inferences, but think this is core value/process, and can be explained simply. Besides, am not a real scientist, just a guy who went out of the academics.

P.S: To put a cynical quip (paraphrasing i think Douglas Adams), The universe is either mildly malevolent or neutral(i.e: definitely not benevolent), the modern workplace is definitely malevolent(either mildly or more).