Sunday, November 6, 2011

The 99%ers: Part 2


To quote Mark Twain: “Liars, damned liars, and statistics.” I used to study the Survey of Consumer Finance every three years (which is how often reliable data on the “wealth” distribution in the US are available). In between, I'd create estimates based on National Income Accounts data on personal income, supplementing it with Summary of Income data from income tax filings. It was great sport. The intent was to see if differences in savings rates in an age cohort explained differences in wealth distribution as the cohort aged. Unfortunately the data were too aggregated to find anything conclusive.

What I learned was to just ignore media reports on the distribution of income and especially wealth. One can't even rely on the media to know the difference between income and wealth. Heaven help them if one asked them the difference between income and cash flow. Plus, they’ll go to any source for a story regardless of whether the data is at all reliable or even well defined.

Any analysis of the issue, even by the best academics, should be taken with a big grain of salt. If one plays my game of analyzing the data, two things should stand out. The data is highly questionable, and it doesn’t support any broad conclusions. Minor differences in analytical technique, those pesky assumptions, really change the impression one gets.

If one researches “distribution of wealth in America,” the result will be lots of opinion pieces with different data and definitions. There are few actual data sources where the sources, measurement errors, or even the data are well defined. For example, an asset may be owned by one person, managed by another, while yet a third is the beneficiary of the assets value. Some often unspoken assumption has to be made in order to attribute the wealth to someone.

There are related issues arising from unfunded or underfunded assets represented by promises/entitlements. Viewed from the recipients’ perspectives, the assets have one value. Viewed from the perspective of economy (as capital stock), the assets have a different value. Which is right?

Often assumptions about issues like these are never spelled out. One has to suspect many of the “analyses” are designed with political objectives in mind, not in order to investigate the phenomena.

It is particularly interesting that in order to make data meaningful, analysts usually drop outliers from a sample. However, the 99%er crowd is largely focused on quirks created by a few outliers in a highly questionable data set. Even the Survey of Consumer Finances, which intentionally includes an over-sampling of the upper tail of the distribution, doesn’t try to pretend to know what the 99%ers purport to know.

One of my favorite mental games is to pick a few dozen stocks where ownership is concentrated in the hands of the members of the Forbes 400 wealthiest people. One then tries to guess how much the wealth distribution at the 1% level would change if some of their stocks underperformed or over performed relative to GDP. The 99%ers are reacting to supposed changes in society caused by such ephemeral issues as how a very limited set of stocks are preforming.

Now, you might ask: Why play that silly game? Well, taken at market value, Buffet, Gates, etc. look rich. When one focuses on the 1%, if they tried to sell their stock, mark-to-market would hardly be what they would get. They still are rich under mark-to-cash assumptions, but the gap between them and someone more liquid would shrink appreciably (i.e., the wealth distribution would narrow). That’s one illustration of the types of assumptions hidden in wealth distribution data.

Another more dramatic example is illustrated by one of the recent bankruptcies by a real estate tycoon. (The major owner of Simon properties, I think). Under mark-to-market he was in that 1%. Alas, when Simon had to liquidate a big portion of its real estate, conversion to cash bankrupted the company. Oops!

That’s why, although often described as a permanent plutocracy, the Forbes 400, the richest of the rich, is actually quite unstable. One study found that only 27% of America's top 400 have made the list more than once since 1994. Some of today's wealthy (very rich) become tomorrow's fallen kings. It’s very similar among the 1%. A small slice of a distribution is seldom made up of a stable population.

It may be nice to think that once the evil 1% is vanquished all will be right with the world. An evil villain is so convenient. But, like the poor, the rich will always be with us; they just won’t be the same people.

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