Income Distribution in the U.S., 1967 to 2007

Everyone in every sector of this society has beat this horse to death, so I may as well take a stab at it too.

Seriously, though, any complete inventory of the basic elements in any society has to include some indicator of how resources are distributed.  And since we push around symbols instead of actual, you know, goods, that is our measure of well-being.  In my opinion, it’s not the best measure, but it is a measure.

What follows is a set of graphs, one comparing the beginning and ending points of a series that runs from 1967 to 2007, and then six others showing the trend in the time-frame.

Conventionally, comparisons are made between segments of the population divided into quintiles, and then the proportion of aggregate income in each of these quintiles is computed.

The idea is this:  Suppose that 20% of the population is considered and they make 20% of the income.  If you were to compute the percent of the population in each “bucket” and develop a cumulative distribution, then if the income were totally equally distributed you would see 40% of the population getting 40% of the income, 60% of the population getting 60% of the income and so on, until 100% of the population had 100% of the income.

Under this condition, if you were to plot the percent of the population against the percent of the income, you would see a straight line.  Of course, income is never equally distributed in any society that we know of, so the real curve sags from this straight line.  The curve under the straight line is called a Lorenz curve, after the guy who developed it, Max O. Lorenz.  Here is a Wikipedia article on it

There is a very rich body of work on this subject, so I won’t go into it here.  I’ll just show the pictures and give some links at the end if you want to explore.

OK, so here is the first picture.  It shows the percent of aggregate income in each quintile of the population for the two end-points of the time series, 1967 and 2007.

us-income-distribution-comparison-1967-and-20072

Income Quintiles, 1967 and 2007

Notice how all quintiles have lost shares in aggregate income while the top quintile has gained in the period.  This is the reason there is so much debate over income inequality these days.

So, with that overview, let’s take a tour of each quintile and see what the trend has been for each of them over these last forty years.

Here is the trend for the lowest quintile.  What else is there to say; they started out with little and ended up with less.  Here is the graph.

Lowest fifth

Lowest fifth

Next, we have the second quintile.  Like the lowest, they have been on a consistent decline.

Second fifth

Second fifth

Next is the third quintile.  This group has been known in the past as blue collar workers, but of late it is fashionable to call them middle class.

Third fifth

Third fifth

Next we have the fourth quintile.  Traditionally these have been called the middle class, and they are the group that grew the fastest after World War II.  Notice the steep decline that began about 1982.

us-income-didtribution-trend-fourth-fifth2

Fourth Fifth

Next, we have the top fifth quintile.  This group has been labeled the upper middle class in past decades.

Highest fifth

Highest fifth

Ah, Hah!  They are the ones who have been getting all the dough at the expense of all the other quintiles.

But wait, there’s one more.  These are the people we have thought of as the upper class in past decades.  They are the top five percent of the income group.  In other words, five percent of the population are getting the most dough.

us-income-distribution-trend-top-five-percent2

Top Five Percent

An interesting little blip at the 2007 end of the trend line shows a decline in their share of aggregate income.  Since all of this adds to 100%, then if their share goes down, someone else’ must be going up.  Who are they?

Looking back at the graphs, it is the third and fourth quintiles who have gained at the expense of the top-tier groups.  Why is this so?  If I were an economist, I could probably answer that, but I’m not.  And besides, I never trust a blip unless it persists for more than three successive data points.

In summary, I have spent very little time on this subject because it is covered so well by so many people that you can find good analysis all over the place.

A good place to start is the U.S. Bureau of Census American Community Survey.  Here is a link to several tables and graphs that can get you started.   Bureau of Census Income Statistics Page

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