Archive for the ‘Sociology’ Category

Income Inequality: Cross sectional and Trends

October 5, 2013

Although there are many ways of measuring inequality of income and wealth, this time I’m only going to use one measure:  The Gini Index (or Gini Coefficient), to show income inequality.

The Gini index has a value from zero to one, but is usually shown as a percent from zero percent to 100 percent.  At zero, there is perfect equality and everyone in a population has the same income.  At 100 percent, only one person has all the income and nobody else has any income at all. Wikipedia has a very good explanation of it here

One has to be careful in interpreting the Gini Index, though, because it is a measure of dispersion not a measure of absolute well-being.  For example, there could be two countries, one very rich and one very poor.  Yet both of them may have a low Gini Coefficient.  Thus if everybody is equally poor, they will have a low Gini, and if everybody is equally rich, they, too, will have a low Gini.

If you want to look for the well-being of an entire population, you have to use other measures alongside the Gini, like, for example, the general health of the population, or the mortality rate, or food insecurity, or pandemics, or whether or not they are having civil wars, or the level of corruption in both government and business.

The Gini is also used to measure the dispersion of wealth, but I’m not going to delve into that here.

So, let’s begin.  The first two graphs show the twenty lowest Gini countries and the twenty highest Gini countries.  Data are from the World Bank and the U.N. Statistics Division.  The year being measured is 2011. (click the image to get a larger picture)

Twenty lowest Gini countries-2011

Twenty highest Gini countries-2011

The very lowest Gini Index was Iceland, and it is a relatively rich country.

The very highest Gini was Seychelles.  I know you may not have heard of this country, but it is a bunch of scattered islands off the east coast of Africa, not far from Madagascar.  If you want to know more about it, Here is the Wikipedia entry for it

Seychelles is somewhat of a mystery, since they have a high human development index, yet, according to Wikipedia,

It has the highest Human Development Index in Africa and the highest income inequality in the world, as measured by the Gini index. Seychelles is a member of the African Union.

Among those countries with a low Gini, there are the usual suspects: Sweden, Norway, Finland, and so on.  But looking at the top graph, you can see that Afghanistan is also in this group.  The others are relatively rich, but Afghanistan is very poor.

So you can see why there is a warning above about being careful in using the Gini as a measure of well-being.

Now, let’s turn to trends.  Since I am an American, my first impulse was to look at the U.S., as I assume that anybody in any country would look at theirs first.

But, really, the unemotional approach would be to look at the slopes of the curves and see which ones have an upward trend and which ones have a downward trend, and what the magnitude of those slopes were.

But I succumbed and here is what the long-run trend looks like in the U.S.

Gini index-US-1960 to 2011You can see that since 1960, the slope is upward, indicating an increasing rate of inequality.  But notice that the rate of upward change is slowing down, indicating some kind of limiting factor or factors.  I have no idea what those factors are, but if you do, please post them in the comments.

There are a number of ways to select trends in other countries.  One way is to bracket those within one or two standard deviations from the world mean (average).  Another way might be to include only OECD countries, and yet another could be to only highlight the extremes.  Another way might be to use the U.S. as a benchmark case and select those countries who are “nearest neighbors” and bracket anything that varies by a certain percentage from the U.S.

There is a technical problem with selecting trend lines based on the mean for all countries for all years in the dataset.  The reason is that before 1986 there were less than 30 countries in the sample universe.  A better starting year is 1997 with a population of 103 countries in the dataset, but the tradeoff is shorter trend lines.

So, given these limitations, here is the world average trend from 1997 to 2011.

(click on the graph to enlarge it)

World average Gini index-1997 to 2011You can see that worldwide, the distance between the rich and poor is declining.  Yet there are some countries where the slope is upward, while the bulk of them are downward.

I started with the idea of comparing all OECD countries, but that would make an unreadable graph, since there are 34 members.  So I decided to break the data into “chunks” to simplify things.

The most obvious candidates are the U.S. neighbors, Canada and Mexico, since they are members of NAFTA and are contiguous.  So here they are.

Gini index-Canada Mexico and US-1997 to 2011It’s difficult on this graph to see the slope in Mexico, but it is declining, even though it has the greatest gap between the rich and poor among the three.

Toward the end of the period, the U.S. also shows a small drop after 2009, but it is relatively insignificant.  Canada, on the other hand, is far below it’s neighbors, and the gap between the rich and poor is slowly declining.

Examining the extremes:

From the bar charts above, I’m going to pick the trends from four of the lowest Gini coefficients and four of the greatest, and show two trend charts.  Some of the data are short trends, so I’ll have to start with a target year and show them, even though some of the countries may have no data in the early years.  For the lowest index there is Iceland, Sweden, Norway and Finland, and for the greatest are Seychelles, South Africa, Comoros and Namibia.  The target beginning year is 1999.

Four lowest Gini index trends-1999 to 2011

Four greatest Gini indices

The first thing you notice is that the lowest Gini index countries are tracking together and are on average only three to four percentage points apart from lowest to highest.

The second thing is that the high Gini countries are converging, and are nearly the same at the end of the period.  From 2006 onward, the group is trending downward, indicating a trend toward less dispersion, even though the Gini is quite high compared to the world average, which has a beginning point of about 42 and an ending point of about 39.


As you can see, this subject could go on for quite some time, given a dataset of up to 191 countries.

This post has been simple descriptive statistics, and only a sketch at that, but it does give some insight into global trends.

The really interesting part will be when the correlates are introduced and analyzed.  But I am a long way from that stage, and it will take quite awhile and a lot of grunt work to put the pieces of this puzzle together.


Individual and Corporate Tax Rates in the U.S.

August 15, 2011

On a discussion group I subscribe to, there has been much discussion of economic subjects, and that motivated me to look into historical patterns in the United States.

What follows are two sets of graphs:  One showing individual income tax rates in the lowest and highest brackets from 1913 to 2008, and the other showing corporate tax rates in the period from 1942 to 2009.  These were the longest series I could find at the moment, so this is the result.

As usual, clicking on the graph will bring up a larger,more readable image.

Individual Tax rates, 1913 to 2008

Corporate Tax Rates, 1942 to 2009

Significant Points:

For individual income tax rates, the lowest and highest in 1913 were at 1% and 7% respectively.  by 1944, the lowest bracket was 23% and the highest bracket was 94%.

After that, the top tax bracket remained at a range between 94% and 70% until 1980, after which it dropped steadily, reaching 35% in 2009.

For the low bracket, it was 1% in 1913, reaching a high of 22.2% in 1952 and thereafter dropping to 10% in 2008.

For Corporate taxes, the lowest bracket was 25% in 1942 while the highest bracket was 40%.  By 1952, the lowest bracket had reached 30% and thereafter steadily dropped to 15% by 2009.  During that same time period, the highest bracket began with a 40% rate in 1942, reaching a high of 53% in 1968, and thereafter began a drop until by 2009 it was 35%, with a low of 34% in 1988.

I am currently exploring correlations with these tax rates and other economic variables, and the only one I have looked at thus far is the unemployment rate, which has no significant correlation along the trend lines. (16% with the low income bracket and 0.00% with the high income bracket.

I haven’t done any correlations with the corporate rates yet, but suspect similar findings.

An interesting set of variables to explore would be with infrastructure spending and with defense spending, but those datasets are not complete yet.

More to come.

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

January 3, 2009

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.


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.


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.


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

The Staff of Life: Wheat Trends in the U.S.

November 10, 2008

UPDATE:  November 09, 2009

I’ve just updated the wheat production data recently published by the U.S. Department of Agriculture.  See the new graphs below.



Whenever I look at a nation-state or a geographic cultural cluster, the first thing I think is, “How do they take care of their basic needs?”  You could do a thought experiment that included a question like, “If this unit of analysis suddenly found itself cut off from the rest of the world, would they be able to survive on their own natural resources?”

Let’s examine the “unit of analysis” thing a little.  In this particular piece, the unit of analysis is the United States as a whole.  But sometimes, the nation-state is not the best one.  Borders are always changing all over the world.  Cultures, on the other hand, tend to persist over longer time periods.  The up-side of nation-states is that they keep lots of detailed statistics.  Cultural groups, however — even though they persist longer than nation-states, often have boundaries that cross national borders, and to get any kind of useful data, you have to send an anthropologist out into the field every time you want a datapoint.  Not practical.  So, you go with what you got.

The other day, I was trying to get trend data on wheat production in the former USSR countries, and fortunately the Russian Republic archived some of the old records from each of the newly (relatively) formed nations.  The data are pretty spotty, though, and I don’t have a Russian specialist ready at hand who knows where all the dusty records are kept.  I still haven’t found the dusty old records at Statistics Canada, but I probably have a better shot at getting trend data from them than anybody other than the U.S.

So, that leaves me with a very nice dataset from the U.S. Bureau of the Census and the U.S. Department of Agriculture.  Most of what follows runs from 1866 to 2008 (the 2008 data points are estimates made by the respective agencies).  There is one series that starts in 1919 and runs to 2008, and that is on the acres planted.  For some reason, this element was either not collected or not reported until 1919.  I don’t know why, because you would think that it would be important to know how much was planted and then how much was actually harvested.  From that you could infer possible causes, like hail storms or dust storms wiping out a planting before it could be harvested.  You will see a stark example of the dust bowl in the first graph.

OK, so here we go…

This first graph runs from 1919 to 2009, and it is the acres harvested as a percent of the acres planted.  It’s quite dramatic to see the results of crops wiped out and never harvested because they were literally buried in dust before they came to maturity.

Wheat harvested as pct of acres planted

This next graph shows the yield per acre of all types of wheat for the period 1866 through 2009

Wheat yield bushels per acre

Beginning in about 1936, the trend line starts to go exponential (at least in the short term).  You might surmise that the sharp jump just after that was the beginning of World War II, and you would probably be right.  You might also surmise that it was the result of farm subsidies from the New Deal, the introduction of large sowing and reaping machines, the introduction of fertilizers and pesticides, cheap fuel, etc., and you might very well be right on all counts.

Now, let’s ask the fundamental question:  For each person in the U.S., how many acres were harvested during this same time period.  So, the two graphs that follow control for the population by putting them on a per capita basis.

First, the number of acres harvested per capita:

Wheat acres harvested percapita

Oh, oh, something is strange here, no?  The yield goes up for every acre but the acres per person goes down.  Let’s see what happens when we ask the next obvious question:  If the apparent efficiency is so high, then the yield per person must go up, no?

Here’s what happened:

Wheat yield bushels percapita

The curve looks for all the world like a very nice sine wave, except for that nasty dip during the depression and dust bowl.  The straight line that runs through the series is the overall average through the entire series.

Keep in mind that none of this takes into account imports and exports.  If I were to attempt to do that, I would very much like to have an economist explain the subtleties, because as a mere sociologist, I haven’t a clue about the mysteries of balance of trade dynamics.

I almost put a regression line through these series, but then thought better of it.  This is the pure raw data and some simple ratios for per-unit production (by unit acre, and per capita).

UPDATE:  November 09, 2009.  Well I finally did succumb to the temptation and put regression lines through them.

Please leave comments in the comments section (just click on “comments” and it will take you to the input page).  I really would like to have your opinion of these trends.

Employees by Class Size, U.S., Part 1, the Basics

October 29, 2008

It is often said that most jobs are created by small business.  I usually take this as propaganda by the latest political candidate, but I decided to look into, you know, actual statistics.  First, I found data from the U.S. Statistical Abstract, and it wasn’t detailed enough for me, so I went ‘ahunting at the County Business Patterns reports, one of the auxiliary report series the BOC puts out in coordination with many other federal agencies.

NOTE:  You can get a larger, more readable view of these graphs by double-clicking on them.

This is the first in a short series, because the detail would be just too much for one post.  So, here are two graphs: one showing the number of employees in each of five size classes the reports present, and the second showing a trend line comparing the different class sizes.

Notice that most of the employees in the U.S. work in small businesses.  In fact, between 71% and 74% of all employees in the U.S. work in establishments smaller than 500 persons.

The next question was, how has this ranking changed over time?  The answer is, well, not much.  Looking at the graph below, you can see that the greatest number of employees have worked in establishments of between 20 and 99 persons in the period from 1980 to 2004.

Coming soon, some other graphs I did on the percentage distribution of these statistics, and a comparison of the percentage of persons with the percentage of payrolls in each size class.  It will probably surprise you as it did me. And it raised more questions that it provided answers.

More to follow…

Farm Cooperatives in the U.S.: A Snapshot Through Time

October 24, 2008

Just recently there was a lively discussion of cooperatives on one of the discussion groups I subscribe to, so I thought I would trace at least one of them:  Farm Cooperatives.  I had to narrow it down because, much to my surprise, there is a wealth of information out there.  In this particular case I went to the USDA web site and they have an entire section on cooperatives.  Furthermore, they have statistics with long term trends, some beginning with 1913.

The next task was to narrow down the data from “Farm Cooperatives” in general to just one out of all the kinds of farm cooperatives they have data for.  In this very narrow category, I assembled data for Farm Cooperatives specializing in marketing, farm supplies and services, a very narrow category indeed, since there are ones for rice and corn and machinery and everything else imaginable in the world of farms.

So, here is a graph of the number of these particular cooperatives from nearly the beginning of the 20th century until 2002.

So, the next question was, if there was such a precipituous decline in the number of coops in this category, what was happening to the membership during this period.  Not surprisingly, it was in decline as well, but with a different envelope.  Here is that graph.

Finally, one might ask, what was the average membership for each of these coop organizations?  One would think that it, too, might be declining, but noo, it was going the other way around.  Here is the graph for that.

So, we might conclude that there was a consolidation and concentration going on.  But that is only part of the story.  Stay tuned for the tangled web, and perhaps a few hints that the coop movement may be on the move again.

Interlude 2: More Old Building Designs

October 19, 2008

So, I am a Project Gutenberg addict. There is a goldmine of lost practices and designs for structures in there. This one is called Wordward’s Graperies and Horticultural Buildings and has some beautiful building designs of “Graperies” that would make fine Little House designs in their own right.

Here are a few pictures from the ebook:

This one is a front view of what, in the book, is a quite long structure intended to grow grapes indoors in the Hudson river valley, where they were not successful at growing European grapes because of mildew problems.

The one below is an open-ended structure that appears to be a kind of promenade with fountains.

This one looks to be a full-scale greenhouse.  I like the idea of the long windows along the side walls.

This one appears to be built on a foundation that barely reaches above grade, but I don’t see why it wouldn’t make a livable structure. With the gutters at the edge, they could act as part of a rainwater collection system, in my estimation.

Finally, there is this one, with the greenhouse/gazebo structure built right onto the house.

I hope you enjoy these pictures as much as I do.  If you feel so inclined, go read the book.  This sometimes feels like rediscovering the wheel instead of reinventing it.

Interlude: Houses from the past

October 17, 2008

So, I was browsing around at Project Gutenberg for books on how people thought of houses in the past, and ran across this ebook. Rural Architecture. which has the extended title, “Being a complete description of Farm Houses, Cottages and Outbuildings.”  The book is about 380 pages long with another 12 or so pages advertising a series of publications by this publishing house.

I was particularly taken by some of the line drawings, some of which follow.  In between each segment on a particular type of house there are rather detailed plans and notations, and I was reminded of Yogi Berra’s comment that “you can see a lot by just observing.”

This is one example of an old cottage from the book. A couple more of them are below.
Old Two-Story Cottage line drawing
Above is a two-story cottage.  Notice the extension on the back.  This seemed to be a popular design in the mid 1800’s.

Here is another:

another example of a cottage design around 1850
It seems to me that there is a striking resemblance to modern “Little Houses.” So what is old becomes new again.

Social Trends 2: The World Future Society

March 1, 2007

For about a decade, I was a member of the World Future Society, a group that does research, analysis and forecasting of future trends. I eventually decided that they were a bit too stodgy for me, and dropped my membership. I still drop in on them from time to time though, because their demographics-related forecasts are usually pretty close to reality. Some of their forecasts, however, are a bit silly in my estimation.

One of the ways to get a quick overview of WFS’s forecasts is to look at their Top Ten Forecasts newsletter.

Here are a few of them:

  • Many Generation Y Americans will spend significant if not all of their adult lives overseas.
  • 75% of the U.S. population will live on the coasts by 2025.
  • Workers will increasingly choose more time over more money.
  • A rise in disabled Americans will strain public transportation systems.

Go take a look. Some of the forecasts will surprise you.

Not mentioned in the top ten, but in my estimation an important set of trends are the following:

  • There will be a resurgence in the “Corner Grocery Store” as the Boomers age and the disabled vets become a significant part of our U.S. population.
  • Small electric “golf cart-like” vehicles will become a significant part of street-level traffic. Look here and here (warning, slow loading), and here.
  • Grocery shopping will be done online, and the big box stores will become distribution centers rather than shopping destinations. Large trucks will unload into the big boxes on one side, stocking will be automated, and small delivery vehicles will exit the other side. This is not a new idea. When the potential of the web was first realized, a couple of companies attempted to do this, thinking that if you can delivery pizzas, why not all groceries? In that first attempt, most of the companies went belly up, but one of the oldest, Peapod lives on.

So, what kinds of forecast do you think will be viable, both in the near term, and in the long term? Don’t limit your prognostications to the U.S. Zoom out and look worldwide.

Social Trends 1: Tiny Houses

February 28, 2007

Many years ago, I became somewhat obsessed with building my own house. I read many detailed books on house design and even some complex architectural design books. I’m glad I never became an architect. Calculating beam loads and tensions is hard.

So, the other day PerryA ran across a news article on tiny houses, the latest trend in personal housing. She passed it on to me and I was hooked. I had to follow this trail. These things are so damned cute. But they are designed to be efficient and functional and real people actually live in them. Take a look here.

This trend, and it appears to be one, is significant, especially if you couple it with the growing interconnectedness of the Internet, and the availability of small CAD/CAM hardware and software, and the developing infrastructure of delivery systems like UPS and FedEx.

I once worked in a community college, and the head of the CAD/CAM department gave me a tour of the facility. He showed me students working on design in one classroom, and other students taking the design files and putting them into Computer Aided Manufacturing (CAM) machines, where the designs were rendered into real objects by high speed cutters in housings, not much larger than a household refrigerator, from billets of aluminum, brass, steel, plastic, and even wood.

He told me that many of his graduates had started their own businesses (usually in idyllic rural settings), building small parts that were sent to assembly plants where they were combined with other small parts – made by other people like them – into large consumer-level machines, like printers, cars, even house parts.

Although a bit dated, The Cluetrain Manifesto gets it mostly right. The interconnectedness of the Net changes everything, from manufacturing, to delivery to lifestyles.

Another variable driving this is the aging of the boomers. They no longer have childern in the home and so don’t need large houses. They are easy to maintain and keep clean, and they are easy to get around in for those with age-related disabilities.

An advantage of small structures is that they are modular, so one structure could be the living room, another the kitchen, yet another the bathroom, and yet another (or more) could be small workshops or studios or offices.

I am sure there are many other implications connected to this one trend, and they may be obvious to you (but not to me). Sometimes asking obvious questions is a revolutionary act.

  • Will this lead to more “ruralification?”
  • What will happen to the transportation infrastructure?
  • What will happen to the communications infrastructure?
  • Will tiny houses lead to isolation and alienation, or will they lead to more conviviality and sociality?
  • How is this related to the larger social change characterized by decentralization?

I am interested in your take on this. Is this a fluke, or is it a real trend? What are the socioeconomic implications of a “distributed society?”

So, what are you thinking today? Do you know of other social trends?