### Logical maximum pay

I like creating simple algorithms to solve complex social issues. My 28th, 29th and 30th Amendments, FED tax schedule, college tuition, inequality tax and dividend maximums, among others, are examples.

One of the bizarre social numbers out there is CEO pay (or corporate executive pay). Generally, these numbers are incomprehensible.  Some examples (BI):

 Name Company Salary Steve Wynn Wynn Resorts \$28.2 million Leonard Schleifer Regeneron Pharmaceuticals \$28.3 million Ginni Rometty IBM \$32.3 million Jeff Bewkes Time Warner Inc. \$32.6 million Brian Roberts Comcast Corp. \$33 million Robert Kotick Activision Blizzard Inc. \$33.1 million David Zaslav Discovery Communications \$37.2 million Bob Iger Walt Disney Co. \$41 million Les Moonves CBS Corp \$68.6 million Tom Rutledge Charter Communications \$98 million

What is reasonable? Certainly not \$100 million a year! Some say that executive pay is necessarily high as it needs to attract the best (the best sociopaths…) who are willing to take the heat and dish out the sometimes oppressive company actions that keep a corporation healthy.

Yeah, right!

But as I asked, what is reasonable? What is a logical maximum salary? What simple algorithm could we create to deduce this? How about this. I’ll admit that someone might be:

• twice as smart as me
• twice as skilled as me
• twice as educated as me
• twice as experienced as me
• twice as industrious as me and
• twice as lucky as me.

(Twice being 100% better. “Me” being the average Joe.)

That’s 2 x 2 x 2 x 2 x 2 x 2 = 64 times “better” than me.

If the median household salary is \$59k (US Census Bureau 2016) then:
64 x \$59k = \$3,776,000

That is the maximum logical pay anyone could possibly be paid based on the reasonable comparison of people’s abilities. \$3.7M is a pretty hefty paycheck in my book. Plenty, I’m sure, on which to live a lavish life.

But there are 482 CEO’s of the S&P 500 paid more than this number.
(cite: https://aflcio.org/paywatch/highest-paid-ceos)

The highest, Sundar Pichai of Google fame, gets \$100M. That means that he’s effectively 1694 times “better” than me.

Boy, that sure is one-hell-of-a-lot better! I’m sure he’s worth it.

## 7 thoughts on “Logical maximum pay”

1. PDF of original research article: https://arxiv.org/pdf/1802.07068

[quote of TechnologyReview article:]

The distribution of wealth follows a well-known pattern sometimes called an 80:20 rule: 80 percent of the wealth is owned by 20 percent of the people. Indeed, a report last year concluded that just eight men had a total wealth equivalent to that of the world’s poorest 3.8 billion people.

This seems to occur in all societies at all scales. It is a well-studied pattern called a power law that crops up in a wide range of social phenomena. But the distribution of wealth is among the most controversial because of the issues it raises about fairness and merit. Why should so few people have so much wealth?

The conventional answer is that we live in a meritocracy in which people are rewarded for their talent, intelligence, effort, and so on. Over time, many people think, this translates into the wealth distribution that we observe, although a healthy dose of luck can play a role.

But there is a problem with this idea: while wealth distribution follows a power law, the distribution of human skills generally follows a normal distribution that is symmetric about an average value. For example, intelligence, as measured by IQ tests, follows this pattern. Average IQ is 100, but nobody has an IQ of 1,000 or 10,000.

The same is true of effort, as measured by hours worked. Some people work more hours than average and some work less, but nobody works a billion times more hours than anybody else.

And yet when it comes to the rewards for this work, some people do have billions of times more wealth than other people. What’s more, numerous studies have shown that the wealthiest people are generally not the most talented by other measures.

What factors, then, determine how individuals become wealthy? Could it be that chance plays a bigger role than anybody expected? And how can these factors, whatever they are, be exploited to make the world a better and fairer place?

We finally get an answer thanks to the work of Alessandro Pluchino at the University of Catania in Italy and a couple of colleagues. These guys have created a computer model of human talent and the way people use it to exploit opportunities in life. The model allows the team to study the role of chance in this process.

The results are something of an eye-opener. Their simulations accurately reproduce the wealth distribution in the real world. But the wealthiest individuals are not the most talented (although they must have a certain level of talent). They are the luckiest. And this has significant implications for the way societies can optimize the returns they get for investments in everything from business to science.

Pluchino and co’s model is straightforward. It consists of N people, each with a certain level of talent (skill, intelligence, ability, and so on). This talent is distributed normally around some average level, with some standard deviation. So some people are more talented than average and some are less so, but nobody is orders of magnitude more talented than anybody else.

This is the same kind of distribution seen for various human skills, or even characteristics like height or weight. Some people are taller or smaller than average, but nobody is the size of an ant or a skyscraper. Indeed, we are all quite similar.

The computer model charts each individual through a working life of 40 years. During this time, the individuals experience lucky events that they can exploit to increase their wealth if they are talented enough.

However, they also experience unlucky events that reduce their wealth. These events occur at random.

At the end of the 40 years, Pluchino and co rank the individuals by wealth and study the characteristics of the most successful. They also calculate the wealth distribution. They then repeat the simulation many times to check the robustness of the outcome.

When the team rank individuals by wealth, the distribution is exactly like that seen in real-world societies. “The ‘80-20’ rule is respected, since 80 percent of the population owns only 20 percent of the total capital, while the remaining 20 percent owns 80 percent of the same capital,” report Pluchino and co.

That may not be surprising or unfair if the wealthiest 20 percent turn out to be the most talented. But that isn’t what happens. The wealthiest individuals are typically not the most talented or anywhere near it. “The maximum success never coincides with the maximum talent, and vice-versa,” say the researchers.

So if not talent, what other factor causes this skewed wealth distribution? “Our simulation clearly shows that such a factor is just pure luck,” say Pluchino and co.

The team shows this by ranking individuals according to the number of lucky and unlucky events they experience throughout their 40-year careers. “It is evident that the most successful individuals are also the luckiest ones,” they say. “And the less successful individuals are also the unluckiest ones.”

That has significant implications for society. What is the most effective strategy for exploiting the role luck plays in success?

Pluchino and co study this from the point of view of science research funding, an issue clearly close to their hearts. Funding agencies the world over are interested in maximizing their return on investment in the scientific world. Indeed, the European Research Council recently invested \$1.7 million in a program to study serendipity—the role of luck in scientific discovery—and how it can be exploited to improve funding outcomes.

It turns out that Pluchino and co are well set to answer this question. They use their model to explore different kinds of funding models to see which produce the best returns when luck is taken into account.

The team studied three models, in which research funding is distributed equally to all scientists; distributed randomly to a subset of scientists; or given preferentially to those who have been most successful in the past. Which of these is the best strategy?

The strategy that delivers the best returns, it turns out, is to divide the funding equally among all researchers. And the second- and third-best strategies involve distributing it at random to 10 or 20 percent of scientists.

In these cases, the researchers are best able to take advantage of the serendipitous discoveries they make from time to time. In hindsight, it is obvious that the fact a scientist has made an important chance discovery in the past does not mean he or she is more likely to make one in the future.

A similar approach could also be applied to investment in other kinds of enterprises, such as small or large businesses, tech startups, education that increases talent, or even the creation of random lucky events.

Clearly, more work is needed here. What are we waiting for?

Ref: arxiv.org/abs/1802.07068 : Talent vs. Luck: The Role of Randomness in Success and Failure

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1. I created this post to establish a “reasonable” number from which to compare reality. I would not expect any of this information to actually affect the world. But it’s interesting to understand “value” regarding compensation.
The 64x seems reasonable. But this is neither here nor there, just a thought exercise; establishing a logical baseline for comparison.
RE: I’d take anything more than my pathetic “Joe” salary!

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1. I’ve had people ask me why I don’t earn 3xs my income. A few times I started that direction but my heart and lazy streak just aren’t in it.
I think for 31 million a year I would be tempted to work a month and retire!

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1. Yes, I’ve read and commented on Steve’s posts. He and I are probably very similar — a bit pedagogical, and authoritative sounding — which I attribute to age. Once over fifty, I found myself becoming as the instructor to my private world, whether my people need instruction or not. I have to constantly remind myself to be humble; daily humiliation on the various blogs I post on tends to help. (And this writing endeavor I’ve set upon has cut me down a dozen notches or so.) Still, it’s a challenge.

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