You're missing a big piece of the puzzle! The main reason some companies fire the bottom X% of performers every year is to motivate everyone to work hard (if not frantically) to try to stay out of firable bucket. The system you are measuring is affected by the performance management tactics you use, by design.
I have a gut sense that software work is likely different than other forms of work. The value of a top performer can really have a non-linear effect on a team, they can just do things others wont do as well, and this effect is compounding over time.
Really the same for certain leadership roles. They wield decision making over dozens or hundreds. Thus even a few percentage better can have an outsized return since the opportunity cost for taking mediocre action is often so high.
This is not an argument for a gaussian curve, rather it is an argument for some kind of truncated exponential or such, I am not exactly sure. I think my main point, is that i would expect the curves to really be different for different roles
I dont think anyone who has managed larger teams or even a small/medium business thinks the value of employees to the business is normally distributed. Price's Law also has important relevance (50% of the work is done by the square root of the total people who participate in the work). Curious if you had additional thoughts on this point. I would think more time should be spent identifying and keeping Price+Pareto talent
Very interesting article (linked to from Byrne Hobart's newsletter); I subscribed post haste and hope to see more posts from you in future. Incidentally, re log-normal vs. power-law, for the Patreon "creator economy" service the data show that both the (monthly) earnings per project and the number of patrons per project appear to follow a log-normal distribution. See for example https://rpubs.com/frankhecker/993611 and https://rpubs.com/frankhecker/994383 (Patreon is a more extreme example of "pay for performance" than any corporate environment, with a Gini coefficient of 0.84.)
I wanted to agree with this because we loved Pareto distributions but we used to say 80% of the work was done by 20% of the team. I think though you're kind of saying 80% of the team is essentially one level of productivity/contribution and then 20% are either hiring mistakes or promotion mistakes.
There's no way around three aspects of performance management:
• At some "n" (~100) there is a normal distribution of contribution. It might cluster around 3 or 5 groups but it is. Just as in freshman calculus at MIT there is a cluster of scores this way even though everyone got 1600 on their SATs and 4.0 GPA at admission. The biggest failure of the implementation of this distribution is too small a set of people (lazy management that avoids the process of finding groups) OR mixing job functions and/or expectations to make the group. The latter is not comparing similar levels of experience or combining job functions that make it impossible to compare output.
• There is a budget for dollars/stock and it is fixed which means no matter what you have to create some distribution and enforce it. You can always just give everyone a gold star and randomly assign amounts of money but that is always perceived as political. You can give everyone the same amount of money but that is a different kind of politics. If you presume too much similarity in people then the 20% doing all the work won't get meaningful rewards.
• Part of a finite budget is that you can't promote everyone all the time or said another way you can't promote every individual through the entire system. There's not enough salary budget or bonus budget. I like what you say about a short term performance "error". As with giving everyone a gold star you can also just separate out titles/levels/rank from compensation and then everyone will be a VP like in a bank but people will find other ways to know who is senior.
I was at GE from '94-'02. I imposed the Welch Rule on my team.
A few issues come to mind:
First, no one knows what the marginal productivity of someone is unless the job is extraordinarily routinized. It doesn't work if workers call on different customers, have different products with different customers in different markets or if the returns are a result of team efforts and on and on.
Second, Competition for employees by competitors drives the greatest change in individuals' compensation, not promotion. But changing jobs is almost entirely driven by the ever changing needs of business, e.g. AI engineers in the bottom 10% are doing pretty well right now.
Finally, performance of a single employee can change enormously just by moving to a different job (and boss) in the same business)
> On the other hand, looking at the Pareto percentile plot, the bottom 10% aren’t really all that different from the folks in the next 10%. As a matter of fact, there’s not an obvious place to draw a line to identify the “lowest end” employees to expunge. ~65% of employees are performing below the expectations that are associated with the salary midpoint (the green dashed line)!
> Summarizing:
> There is no intrinsic bottom 10% that needs to be expunged annually. Let managers identify any hiring errors if they think their team can do better, but don’t set a target for this number based on faulty statistical notions.
That's funny. The summary I would have put together from this is that managers should target cutting the bottom 65%
You're missing a big piece of the puzzle! The main reason some companies fire the bottom X% of performers every year is to motivate everyone to work hard (if not frantically) to try to stay out of firable bucket. The system you are measuring is affected by the performance management tactics you use, by design.
I have a gut sense that software work is likely different than other forms of work. The value of a top performer can really have a non-linear effect on a team, they can just do things others wont do as well, and this effect is compounding over time.
Really the same for certain leadership roles. They wield decision making over dozens or hundreds. Thus even a few percentage better can have an outsized return since the opportunity cost for taking mediocre action is often so high.
This is not an argument for a gaussian curve, rather it is an argument for some kind of truncated exponential or such, I am not exactly sure. I think my main point, is that i would expect the curves to really be different for different roles
I dont think anyone who has managed larger teams or even a small/medium business thinks the value of employees to the business is normally distributed. Price's Law also has important relevance (50% of the work is done by the square root of the total people who participate in the work). Curious if you had additional thoughts on this point. I would think more time should be spent identifying and keeping Price+Pareto talent
Very interesting article (linked to from Byrne Hobart's newsletter); I subscribed post haste and hope to see more posts from you in future. Incidentally, re log-normal vs. power-law, for the Patreon "creator economy" service the data show that both the (monthly) earnings per project and the number of patrons per project appear to follow a log-normal distribution. See for example https://rpubs.com/frankhecker/993611 and https://rpubs.com/frankhecker/994383 (Patreon is a more extreme example of "pay for performance" than any corporate environment, with a Gini coefficient of 0.84.)
I wanted to agree with this because we loved Pareto distributions but we used to say 80% of the work was done by 20% of the team. I think though you're kind of saying 80% of the team is essentially one level of productivity/contribution and then 20% are either hiring mistakes or promotion mistakes.
There's no way around three aspects of performance management:
• At some "n" (~100) there is a normal distribution of contribution. It might cluster around 3 or 5 groups but it is. Just as in freshman calculus at MIT there is a cluster of scores this way even though everyone got 1600 on their SATs and 4.0 GPA at admission. The biggest failure of the implementation of this distribution is too small a set of people (lazy management that avoids the process of finding groups) OR mixing job functions and/or expectations to make the group. The latter is not comparing similar levels of experience or combining job functions that make it impossible to compare output.
• There is a budget for dollars/stock and it is fixed which means no matter what you have to create some distribution and enforce it. You can always just give everyone a gold star and randomly assign amounts of money but that is always perceived as political. You can give everyone the same amount of money but that is a different kind of politics. If you presume too much similarity in people then the 20% doing all the work won't get meaningful rewards.
• Part of a finite budget is that you can't promote everyone all the time or said another way you can't promote every individual through the entire system. There's not enough salary budget or bonus budget. I like what you say about a short term performance "error". As with giving everyone a gold star you can also just separate out titles/levels/rank from compensation and then everyone will be a VP like in a bank but people will find other ways to know who is senior.
I was at GE from '94-'02. I imposed the Welch Rule on my team.
A few issues come to mind:
First, no one knows what the marginal productivity of someone is unless the job is extraordinarily routinized. It doesn't work if workers call on different customers, have different products with different customers in different markets or if the returns are a result of team efforts and on and on.
Second, Competition for employees by competitors drives the greatest change in individuals' compensation, not promotion. But changing jobs is almost entirely driven by the ever changing needs of business, e.g. AI engineers in the bottom 10% are doing pretty well right now.
Finally, performance of a single employee can change enormously just by moving to a different job (and boss) in the same business)
Though this conflates outcomes with performance
> On the other hand, looking at the Pareto percentile plot, the bottom 10% aren’t really all that different from the folks in the next 10%. As a matter of fact, there’s not an obvious place to draw a line to identify the “lowest end” employees to expunge. ~65% of employees are performing below the expectations that are associated with the salary midpoint (the green dashed line)!
> Summarizing:
> There is no intrinsic bottom 10% that needs to be expunged annually. Let managers identify any hiring errors if they think their team can do better, but don’t set a target for this number based on faulty statistical notions.
That's funny. The summary I would have put together from this is that managers should target cutting the bottom 65%