Friday, December 25, 2009

The Origins of the Bell Curve -- The Real Secret of the Super Rich Part 2

This is Part 2 of The Real Secret of the Super Rich. In Part 1 I demonstrated how the super rich are outside any (Gaussian) bell curve fit of the measured wealth distribution of the rest of us. At the end I gave a one word hint -- nonlinearity -- as to the real secret, how the super wealthy dwell outside the bell curve, as if they were a different sort of being than the rest of us. Finally, after much delay, I shall add flesh that that explanation, by explaining the linear case, where the bell curve comes from and why statisticians expect to find them for diverse phenomena. Then, we'll have a look at where the assumptions underlying the bell curve break down when it comes to income and wealth.

Bell curves arise when we add up multiple independent random variables. Instead of working through a hairy mathematical proof, let's do some simple experiments to demonstrate the idea. Consider the following simple game: Flip four quarters at the same time; add up the value of all those which come up heads; that's your score. Multiply by $100,000 and that's your annual family income. Think of this as a very primitive competitor to Milton Bradley's Life game.

Now consider many people playing this game. If we record their scores we get a wealth distribution. You can generate such a wealth distribution at home. Just get a sheet of 1/4 inch graph paper and write the possible values ($.0, $.25, $.50, $.75, $1.00) in a row. Then flip the quarters again and again. After each flip fill in a square above the number corresponding to the score. With around 100 flips you should see something like this:

Probability distribution for four quarters

By the way, I cheated. I didn't actually flip physical coins. I wrote a C# program to flip virtual coins using a random number generator and then plot the results. I had a good reason: the next experiments are more complicated and involve more flips. A physical flipping session would take too much time. (As it was, writing the C# code took some time, since I am a C++ programmer by day. I used this exercise as an excuse to play with C#, and that's one reason why it took me so long to get this post up.)

Your results -- if you did your homework -- should similar but not identical to mine. I can say this with confidence even though I don't have your quarters. Yes, you could have a distribution where most of the flips are $.00 or $1.00, but this is very unlikely. There is only one way to score $.00: TTTT. There is only one way to score $1.00: HHHH. Meanwhile, there are four ways to score $.25: HTTT, THTT, TTHT, and TTTH. Similarly, there are four ways to score $.75: THHH, HTHH, HHTH, and HHHT. Finally, there are six ways to score $.50: HHTT, HTHT, HTTH, THHT, THTH, and TTHH.

This is a general principle. With multiple independent random variables you get more possible flips/rolls. And as we increase the number of independent random variables, we get a smoother bell shaped curve. For example, let's replace the four quarters with 10 dimes:

10 Dime Distribution

Or replace the four quarters with 20 nickels:

20 Nickel Distribution

Note that I went up to 1000 rolls in order to get a good statistical sampling. With the computer doing the rolling for me, why not? Diligent readers can perform these experiments with graph paper and watch the distribution evolve.

No rule says that all the random variables have to be the same size, nor do they all have to be binary. We could replace one quarter with 5 nickels, two quarters with 5 dimes and one quarter (approximately) with two 12 sided dice. The result:

5 Nickels 5 Dimes and 2 Twelve Sided Dice Distribution

Note how the hump narrows as we use smaller coin denominations. To map these experiments with the national income distribution, we'll need to scale by standard deviation and shift to match median values. Our four quarter experiment so rescaled gives us:

4 quarter probability distribution scaled to US income distribution

Our 20 nickel experiment gives us:

20 Nickels Scaled to National Income Distribution

By now many of you are thinking, "So what? What do all these coin flips and dice rolls have to do with the super rich?" The answer: let's consider each of the original quarters as a factor which leads to income. Let one quarter represent intelligence. Let another represent health. Let the third represent ambition and the fourth represent startup capital; that is, family wealth or connections which come from growing up in the right neighborhood. Our first experiment crudely models these four factors as "you either have it or you don't." The 20 nickel experiment can be thought of as replacing each quarter by 5 nickels, giving us a rough bell distribution for each factor. We could get smoother yet by replacing each quarter with 25 pennies, as below:

100 Pennies Scaled to National Income Distribution

But even this 100 penny experiment gives us a mere $425,000/year maximum income when scaled to the census figures -- and no one got that much even after 100,000 rolls! With 100,000 rolls the highest income was $215,000/year and I only rolled that once (which scales to 1160 people having that income). Where are the Bill Gateses and Sam Waltons? We could stretch our distribution further by using more coins/dice. Income is a function of more than four factors. For example Bill Gates is intelligent, from a well-off family, very interested in computers (vs. yoga, philosophy, or politics), very interested in business and making money (unlike his early competitors), had access to computer time back when it was very expensive (thus getting in on the ground floor of an industry), and perhaps a bit lucky. Yet even with more factors the factors still don't add up to give us Bill Gates income.

But should we be adding? Does computer skill = intelligence plus interest plus access to computers? Maybe we should be multiplying some of these factors! We'll look at some experiments with multiplied random variables in the next post, and then meditate on the consequences.

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