Income Disparity

Income disparity.

Wikipedia describes it like this:

Income inequality in the United States of America is the extent to which income, most commonly measured by household or individual, is distributed in an uneven manner.

Pretty fair I think.  It hits what I think are the important aspects of the topic:

  1. Income
  2. How measured
  3. Distributed
  4. Uneven

I think that most reasonable people wanna help out the folks who need the help.  Further, I think that most reasonable people wouldn’t personally help those folks, who-while down on their luck, aren’t down due to luck.

Anyway, very often when solutions are discussed, or when examples of success are presented, I am faced with the argument that the Income Disparity, the Income Inequality of America is very very poor.  So poor, perhaps, that we rank near, tied for or dead, last.  A common tool to measure the disparity in incomes is the GINI Coefficient.  Or the GINI Index.

The Gini coefficient is a measure of statistical dispersion developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper “Variability and Mutability”

The Gini coefficient is a measure of the inequality of a distribution, a value of 0 expressing total equality and a value of 1 maximal inequality. It has found application in the study of inequalities in disciplines as diverse as sociology, economics, health science, ecology, chemistry, engineering and agriculture.

It is commonly used as a measure of inequality of income or wealth.  Worldwide, Gini coefficients for income range from approximately 0.23 (Sweden) to 0.70 (Namibia) although not every country has been assessed.

Most uses of the GINI Coefficient that I have ever heard of deal with Income Disparity.  Though from reading wiki, it seems that the GINI Coefficient is simply a tool to measure dispersion.  So, it’s nice to learn that the GINI is simply a statistical tool that has been applied to measure income disparity.

As I am generally ignorant of many things the measuring of income between the people of a nation, I think it’s important to learn more.  As I enter into this investigations, I’m struck by two aspects of the inquiry:

  1. Does it matter?
  2. What is being measured?

The second first.  Because the GINI can be used to measure seemingly anything at all;  fish in a body of water, water in a body of land or pine cones in a body of grass.  It’s important to know what the subject of the measurement is.  And I think that most discussions surrounding the GINI are clear on what they are measuring.

For example, we are having a discussion concerning taxation on my post concerning Denmark and the United States.  One of my friends  points out that:

The US pre-tax and transfer GINI index is at .46, while Sweden is at .43, and Denmark and Norway are at .42. That means pre-tax they are slightly more even in income distribution, but not much. German has a bigger pre-tax gap between the rich and the poor than the US at .51.

After tax the US GINI index moves to .38 — a modest improvement. But it is the most income disparity of the entire industrialized world. Taxes and transfers move the wealth distribution from .46 to .38.

After taxes and transfers Denmark is at .23.

Clearly the GINI is being used to measure two different things.  Income pre-tax and then income post-tax.  Which is valid as long as the measurements are clearly labelled.  And again, I think in most cases they are honestly so represented.

Now the first.  Does it matter?

This is trickier.  Does the fact that the richest among us make more than the poorest among us matter?  Perhaps.  It sounds like there is a body of evidence that suggests it does matter AND that when that disparity is high, society suffers.  I don’t know, I haven’t looked at it.  First blush, I think my take is that I don’t care as long as I have a reasonable shot at getting pretty close to the top.  And reasonable can mean many things.  When I buy a lottery ticket I have as reasonable a shot as anyone else.  I certainly would resent the rich having a better shot at winning numbers than me JUST because they were rich.

So, where are we.

I wanna look at Income Disparity.  Perhaps as it’s measured by the GINI.  And I wanna know, at the end, several things.  The first of which is: DOES IT MATTER?

And if it does, which of the following matters the most:

  1. Straight income.  The MONEY paid from employer to employee.
  2. Total compensation.  The total compensation from employer to employee.
  3. This measurement BEFORE taxes.
  4. This measurement AFTER taxes.
  5. Finally, this measurement AFTER social entitlement programs.

Let’s see how this goes.


1 comment
  1. My blog today links to a New York times graphic:

    This is my grip — almost all economic growth has gone to a small percentage of the wealthiest in the last 32 years, very different from the 30 years previous (1949 to 1979). Let those numbers sink in.

    Eisenhower was President during the highest tax rates, and he argued against tax cuts because we were paying down the WWII debt. Then under Nixon and Ford we put in place programs that reached the lowest level of disparity (1976, Ford’s last year). Arguing for higher taxes on the wealthiest to pay off debt, along with cuts in spending, is not an especially liberal or leftist argument. Republicans like Eisenhower, Nixon and Ford went down that path. Wanting economic growth to be shared with both workers and employers is also not radical — I don’t think markets work well and capitalism can function with the kind of distortions and debt growth we’ve seen. The wealthiest have not made jobs with their wealth, but built bubble economies – the dotcom bubble followed by the real estate bubble. That’s because it seems that at a certain point excess wealth to the wealthiest becomes less productive than having it spent by workers on goods and services, creating demand. Instead in seeking quick profits it builds bubbles, goes overseas, or perhaps increases consumption — but often of foreign made goods.

    I believe that the current crisis and all the stats built around it shows that when there is too much de-regulation, the market gets distorted by those with the most power and information — they are able to rig the game. Income disparities are important because they are a symptom and result of that rigging. End the rigging and let the market work — that requires a stronger state. The lesson of the current crisis is that markets are not magic, and unregulated can destroy themselves.

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