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Is wealth created?  This is a question that, in some fashion or another, seems to occupy a lot of economic and political discussions.  Much of the rhetoric associated with taxes and income re-distribution is based on the idea that if Wall Street gains wealth then it does so at the expense of Main Street.  But is this really true?  And what is wealth?  Is it money?  Is it fancy cars and big houses?  Is it the ‘good life’?  The questions abound. I’ll try to answer the related questions ‘what is wealth’ and ‘is wealth created’ by asking a different, and admittedly more provocative, question.

But, before I do, I want to be clear that I am no fan of Wall Street.  Near the top of my list of reforms I would like to see enacted is the severing or curtailing of the special status enjoyed by Wall Street, and the cozy relationship it has with Big Government.  This relationship, which starts with Big Government having the power to pick and choose winners and losers based solely on the judgement of a small group of people, can and often does lead to cronyism, whereby Wall Street’s winners reward government’s pickers in lots of different ways.  I’ll explore these rewards in future posts, but for now I just want to emphasize that I am not talking about wealth in terms of any ‘Wolves of Wall Street’ notion.

In order to address the question of wealth, I would like to pose a question that is a slight variation of a question I first heard posed by Prof. Timothy Taylor in his course Economics.  My version of the question is simple to state:

Take a few minutes to reflect on it before you read on.

There is, of course, no correct answer, but to help frame the question let’s compare the lifestyles of the two groups in both 1914 and today.  To make things concrete, assume that we will compare lifestyles by three measures: money, possessions, and intangibles, where into the latter category go all the miscellaneous things that affect quality of life but which are hard to quantify.  Now let’s talk about what life was like 100 years ago.

Consider first a typical member, let’s call him Everyman, of the 99% in the year 1914.  How much money would Everyman be likely to command?  According to Mary Braswell, the average national annual income in 1914 was $577.  To get an idea of how much variation existed across the country, consider wages resulting from urban and rural employment scenarios.  First assume that Everyman was employed in New York City.  It was likely that his typical job would have been in construction as a bricklayer, carpenter, painter, etc.  His work week would have been 44 hours long with an hourly rate ranging from $0.50 to $0.75 an hour, resulting in a gross income of about $1500/year. In contrast, if Everyman worked on a farm, his wages would be a lot less (somewhere at about $300/year) as, presumably, were his costs of living.  A new car cost about $500, which is about 1 year’s worth of income.  So, Everyman was unlikely to buy one without financing it or saving for years to do so. The cost of an average house was about $3500 or about 7 years of his income, again representing a substantial financial investment.  Everyman would have only modest spare time, little access to higher education, the fine arts, and ready entertainment, and he would be unlikely to travel often or far.  Correspondence between him and friends and family would be mostly by letter with days of delay between exchanges.

Next let’s look at what it would be like to be in the 1%.  A typical representative of this group might be someone like George Eastman, a man who made hundreds of millions of dollars from his inventions and entrepreneurial efforts.  Located in Rochester, New York, the Eastman house is a huge mansion with large rooms, a conservatory, opulent chandeliers, regal staircases, lots of bathrooms, and more. The original house being considered too small, Eastman actually paid for it to be split and pulled apart and rebuilt with an insertion that provided even more room.  Outside of the 35,000-square-foot house, well-manicured gardens populated the 8-and-1/2 acres of land.  Eastman had servants, money, and resources to spend much of his time however he wanted, journeying often to Africa on safari.  As a captain of industry, he could obtain the finest things available in his time – cars, entertainment, fine clothes, luxurious trappings, etc.

Certainly each of us would want to be in the 1% rather than in the 99% in 1914, but would any of us be willing to move from the 99% in 2014 to become Eastman?  Consider all the things that we have as commonplace that Eastman, despite his millions, didn’t possess.

We have access to much better health-care than Eastman did.  Vaccines for numerous diseases, such as polio, mumps, rubella, chickenpox, and measles, all found their birth in the 100 years between 1914 and 2014.  Smallpox and rinderpest have been eradicated.  Influenza no longer poses the huge threat it once did when it wiped out an estimated 50 to 100 million people in 1918.

The technology we take for granted wasn’t even dreamed of by Eastman.  His trips to Africa took days of travel whereas we can jet to there in less than a day.  Cooking food in short order in a microwave oven provides us with a convenience no number of servants could have given him. Turning on the television for immediate entertainment, adjusting the central heating or air-conditioning for comfort, surfing on the internet for information, settling in to play a video game, the list goes on and on of the luxuries the ‘poorest’ of us has that Eastman didn’t.

In the final analysis, the original questions of ‘what is wealth’ and ‘is wealth created’ can be answered as follows.  Wealth is not rightly measured in terms of money or possessions.  These things are only tools – means to an end.  The real measure of wealth is the amount of time we each have to better ourselves, to explore our existence, and to expand our personal horizons.  In this regard, how could any of us argue that wealth hasn’t been created.  After all, the population in the country has more than tripled from 1914 to present but the vast majority of us have access to more time and opportunities to be ourselves than any other people had in the past.  We live with conveniences that even the highest of the 1%-ers of days gone by never enjoyed.  I can’t speak for you, but I can say that, given the choice, I would rather stay in the 99% now, with all that that entails, than to have lived as Eastman did in 1914.  I can also voice an appreciation for what he and countless other entrepreneurs like him did to bring the wealth that we enjoy today to each of us.

Some much of the narrative that is offered on the economy is built on statistics.  And as often quoted there are lies, damn lies, and statistics.  One particularly annoying set of statistics rests on combining individual statistics by joining together (aggregating) statistics to tell a story that they don’t tell on their own.  This is at the heart of Simpson’s Paradox.

To illustrate the paradox consider a two demographic groups labeled ‘A’ and ‘B’.  Each is trying for a position at a large corporation ‘U’ with many divisions or departments.  Suppose that the hiring percentage for each group at the company is:

Can we conclude that the company discriminates against group ‘B’ in favor of group ‘A’?  At first glance, one may be inclined to say that ‘U’ clearly favors ‘A’ over ‘B’ and maybe has violated equal opportunity laws and is being unethical and unfair.

But suppose that we actually drill down to examine the hiring by division and that, for simplicity, ‘U’ is made of two divisions ‘S’ and ‘H’.  Also suppose that, upon request, the hiring percentages for the two divisions are:

and

At this point, we may be tempted to say that the company ‘U’ has cooked the books.  But a simple table shows that the statistics presented above can be understood very easily.  Again for simplicity, assume that 10 members of ‘A’ and 10 of ‘B’ apply for jobs but that 8 members of ‘A’ apply to ‘S’ and 2 to ‘H’ while the reverse is true for group ‘B’.

Note that by combining the statistics for ‘S’ and ‘H’ into one whole under ‘U’ the combine statistic tells a much different story than is told by tracking the two divisions separately.

The situation becomes more interesting when salary is factored into the analysis.  Suppose that each member of ‘A’ is paid on average $100K for his position in ‘S’ and that each member of ‘B’ is paid on average $125K for his position in ‘S’ and $60K for his position in ‘H’.  Members of ‘B’ seem to be doing quite well.  But when the statistics are combined into one roll-up, one would conclude that ‘B’s are paid only 92 cents for every dollar that an ‘A’ makes.

Okay, one may be willing to concede that the combined statistic doesn’t tell the whole story but one may object that there is still unfairness in the system.  After all only 4 members of ‘B’ have been employed whereas 5 of ‘A’ have been.  This objection can also be addressed by considering the simple modification of the results shown above.

Now ‘B’ clearly has the upper hand in employment not just at the division level but at the corporate one as well.  But if the same average salaries are used ($100K and $125K for ‘A’ and ‘B’ in ‘S’ and $60K for ‘B’ in ‘H’) and then all the statistics are combined into on measure, the story told is that members of ‘A’ are paid on average more than those in ‘B’.  In fact the margin between the average pay of ‘A’ and that of ‘B’ is now larger, even though more members of ‘B’ are now employed.

This is the heart of Simpson’s paradox.  What is not being accounted for is the reasons for why members of ‘B’ preferentially apply for employment in the lower paying jobs in division ‘H’ rather than for the higher paying jobs in ‘S’.

By now it should be clear that this situation has real world applications.  The most famous example of this type of situation that has worked its way through the courts is the case of the Berkeley gender bias case.

Other examples are the oft-quoted statistic that women make 77 cents for every dollar a man makes.  This statistic can be quite true and yet be quite misleading.  The common interpretation that women are being widely discriminated against is not supported by that statistic.  There are surely pockets of discrimination out there but more likely explanations are that women preferentially enter different fields (or that they interrupt their working years for various reasons, such as raising a family, which being a personal choice and one which I wish I could have pursued, is not addressed here).

If society really wants women to make on average the same as men, then steps should be made to address why so few women, comparatively speaking, enter high-paying STEM jobs.  This is where our focus should be and not on trying to fix what is mostly an imaginary problem caused by Mr Simpson and his paradox.