Wealth Inequality: Causes and Cures

… only a carefully designed mechanism for redistribution can compensate for the natural tendency of wealth to flow from the poor to the rich in a market economy.

Is Inequality Inevitable?

The “natural tendency” mentioned here ^ is the hypothetical outcome of a random process. It is remarkable that, in a market economy, although there are structural barriers to randomness, the end result of wealth distribution seems to follow a “power law” in which very few people end up with most of the wealth.

Only by conscious action can a society choose to counteract this tendency for wealth to be concentrated within a small minority of people. One solution, as mentioned, is to have a method to redistribute wealth before it becomes too lopsided. Another approach is to rely less on a market economy. Examples abound of failures of command economies, but there are success stories as well. Native American cultures may have much to teach us in that regard.

I became aware of power laws † (although I don’t recall that term being used) back in the early 1970s, when I was doing research on security prices.

Stevens’s power law is … named after psychophysicist Stanley Smith Stevens (1906–1973). Although the idea of a power law had been suggested by 19th-century researchers, Stevens is credited with reviving the law and publishing a body of psychophysical data to support it in 1957.


It seems that the concepts of power laws have been known and studied for centuries, but that term came into common use only in the past few decades, often in connection with what has come to be called the science of complexity. But I digress…

In my research in the security markets, in the early days of the Efficient Market Hypothesis, I became aware of differing attempts to explain and describe movements in stock prices. In my industry (security analysis), it came to be generally accepted among us academic-types (quants, short for quantitative analysts), that stock prices followed a random walk, and changes in stock prices could be modeled with formulae from physics, including Brownian motion and heat-diffusion (the latter being the basis of the highly influential Black-Scholes option-pricing model).

The emphasis then shifted to analysis of risk, rather than the prediction of stock prices. In an efficient marker, higher risk was compensated by higher rates of return, but it also carried with it a higher chance of losing money. The idea was to balance the risk and return in such a way as to maximize return while minimizing the risk of loss. One of my early tasks was to provide investment advice to small-business pension funds, operating within a complex regulatory environment. In those early days of ERISA, I worked for an insurance company, so that, in addition to IRS regulations, we had to be aware of constraints placed on us by insurance regulators. Within that framework, I was able to model the best “asset mix” of stocks, bonds, and cash.

During this work, I became aware of Gibrat’s Law of Proportionate Effect (published in 1931), that proposed (among other things) that the growth rate of firms in an economy was independent of their size. This formulation, along with numerous more recent variations, have provided insight into the power laws that seem to regulate the distribution of a wide variety of phenomena. Such things as the size of cities, and the number of species in a given land area, seem to follow this pattern, which is best described in statistical terms as a lognormal distribution.

Which brings us back to wealth inequality. The Affine Wealth Model (AWM) is one variation on this general theme, that growth (or dispersion) in a random process will create a power-law distribution of outcomes. To oversimplify (and perhaps to somewhat mischaracterize), and without getting into particulars, this Model purports to show/predict the effect of a market economy on the wealth accumulation of its participants. Its premise seems to be that, when goods are exchanged, the exchange does not always happen at fair value, so that for (at least) some transactions, there are winners and there are losers. Even if no skill is involved (meaning that the win/loss is a random outcome), the result is a lognormal distribution of wealth.

Carried to its logical conclusion, such a model, in its raw form, would suggest a concentration of wealth far in excess of what is observed, so a modification is introduced to allow for some arbitrary redistribution of wealth (which may come by way of taxation or other means). All of this (and more) is explained quite well in the Scientific American article quoted at the beginning of this post.

All of this is well and good (and interesting), but I have a couple of major quibbles (which I guess is oxymoronic). One is that the AWM describes the outcome of the process, but is not based on the actual process. The real world is much more complex than a bunch of random transactions. There are issues of skill, cheating, privilege, unfair regulations, and so on, to name a few. The other objection I have is that wealth is not completely measured by the sum total of one’s physical, tradeable possessions.

Wealth, in a broader sense, includes many intangibles, such as a sense of well-being and community; access to cultural and recreational activities; feelings of self-worth and accomplishment; and much more. The richest person may very well be the one whose every need is met. Needs spring not from a market economy, but from within a person, and cannot be measured in dollar terms. True wealth is, in many ways, the absence of desire.

So, where does that leave us in trying to find a cure for the inequality of wealth, that, at its extreme, seems to violate our sense of fair play? Just as the problem is complex (life is not a 3-parameter process), the solution(s) will also be complex. Clearly, the “free” market is not to be trusted. But we already know that, which is why there really is no “free” market, but one that is constrained on all sides by laws and regulations designed to mitigate its worst offenses. Economists have long pointed out the distorting effect of “externalities” — those costs to society that are not priced into the market economy. We could benefit from more efforts to bring those costs into the market system, such as a carbon tax, as one example. Beyond that, we probably need more ideas on how to work outside the market model. We already have many successful examples, such as our system of public parks, and private land trusts. We need more such common actions that benefit all members of society; not just those with the money to buy access.

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