The Rich and Poor Rise and Fall Together


Alan Reynolds writes The Truth About the One Percent in The National Review Online


The table shown here — which uses Piketty and Saez’s data — shows the top 1 percent’s average real income fell by 16.3 percent from 2007 to 2012, and ended up 6.4 percent lower than it was back in 2000:

Average Real Income of the Top 1 Percent (2012 dollars)
2000 $1,350,006
2001 1,063,706
2002 933,878
2003 964,989
2004 1,143,104
2005 1,323,935
2006 1,414,985
2007 1,510,932
2008 1,213,199
2009 961,785
2010 1,076,379
2011 1,056,640
2012 1,264,065

What about the “other 99 percent,” whose income supposedly rose by only 0.4 percent from 2009 to 2012? Piketty and Saez compare real incomes at different income levels without including Social Security, unemployment and disability benefits, food stamps, Medicaid, etc. Government transfers totaled $2.3 trillion in 2012, up 24.6 percent in real terms from 2007 and up 68 percent since 2000. Because Piketty and Saez estimate only pre-tax, pre-transfer income, they also ignore $149 billion in Treasury checks to lower-income families from refundable tax credits. They’ll also ignore huge Obamacare subsidies next year.

Once transfers and taxes are properly taken into account, my own research for the Cato Institute shows no clear trend toward greater inequality after 1989, aside from the tech-stock boom of 1998–2000. Instead of any predictable trend, data on income shares are dominated by cyclical variations in which rich and poor rise or fall together: When the top 1 percent’s share rises, the poverty rate falls, and when the top 1 percent’s share falls, the poverty rate rises.

There are numerous conceptual and measurement problems with attempting to judge the relative living standards of the rich, middle-class, and poor by relying on income reported on individual tax returns (ignoring, for a start, income that’s unreported or reported on corporate returns).

Saez himself has hinted that the seemingly strong surge in top-percentile incomes in 2012, for example, was largely a matter of strategic tax timing — reporting bonuses and capital gains in 2012 to avoid higher tax rates in 2013. The same thing happened in late 1992, when professionals and executives arranged to cash in bonuses and stock options in December rather than in January 1993, when income-tax rates went up. It also happened in 1986, when investors rushed to cash in capital gains before the capital-gains tax went up, briefly inflating reported real income of the top 1 percent by 34.6 percent in a single year.

Because reported capital gains and bonuses were similarly shifted forward from 2013 to 2012, we can expect a sizable drop in the top 1 percent’s reported income when the 2013 estimates come out a year from now. The befuddled media will doubtless figure out some way to depict that drop as an increase.


Alan Reynolds has done an extensive job challenging the income disparity data in his book Income and Wealth.  You can find excerpts in the search feature in this blog.  In short the data is manipulated depending on what is included in the wealth distribution data, what is measured (household income vs individual income), and what periods are selected.  For example much of the growth in income in the 1980′s had much to do with changes in the tax law that caused Subchapter C corps to convert to Subchapter S status.  Income data that used to show up on corporate returns now showed up on personal returns via K-1 reports. Similarly wealth that resided in tangible assets during the inflationary 1980′s returned to reportable financial statements in the 1980s as inflation was tamed.

Distorted Income Gap

“This means that many of these families have no one working and earning a salary at all. (It’s hard to have a very impressive income if you don’t work.) By contrast, the average high income family has on average two people working—usually a husband and a wife. So it should not be too surprising that the rich have more income, since for every hour worked in a low income family, the members of a high income family work four hours.

Here’s another statistic that distorts the income gap. The average poor household consists on average of about one and a half people. This means that a lot of low income households consist of a single person living alone. By contrast, the average high income household has about three people living under the same roof. An income of say, $15,000 a year presents much more a situation of acute financial distress for a family of four than it does for a single person living alone.

So when we adjust for the number of people in the household and the number of workers in the household, we find that at least two-thirds of the income gap ‟magically” disappears.”

Excerpt From: Moore, Stephen. “Who’s the Fairest of Them All?.” Encounter Books, 2012. iBooks.
This material may be protected by copyright.

Check out this book on the iBookstore: https://itunes.apple.com/us/book/whos-the-fairest-of-them-all/id549259072?mt=11


Some of the gap is also explained by hours worked, the value of benefits, government transfers and after tax effects.  A better determination of household wealth is spending.  Disposable income automatically filters income distortions from the wealth picture. And even this does not recognize the impact that technological progress that has put consumable items in the hands of the poor that were once out of reach from all classes but the wealthiest.

Much of the reporting  pertaining to important public policy is very biased.  The media has done a very poor job on analyzing such data on income distribution, health care statistics, and gun crime.  The best source of careful analysis on income distribution data in Income and Wealth by Alan Reynolds.  Use the search feature of the blog to find other postings on the subject.

A Piece of the Truth

The Grumpy Economist writes How to Lie With Statistics,  4/20/12.

John Cochrane takes to task the methods and conclusions of Emmanuel Saez and Thomas Piketty, that are commonly used to justify higher taxes on the wealthy.  Saez and Piketty were also targets of Alan Reynolds in his book, Income and Wealth.  Both Cochrane and Reynolds analyze carefully the data that is used to perpetuate the myth that the discrepancy in income an wealth has grown as large as many redistributionists contend.

Both Cochrane’s blog post and Reynold’s book deal with data and analysis that can quickly get a bit complicated.  It takes some effort to understand the true picture underneath the reams of data.  As we often find in such situations a piece of the truth can be more misleading than all of a lie.

I seem to keep returning to the prospect of bad policy originating from bad data.  Too many people wish to believe there is a problem and will quickly jump on any study that confirms their suspicions.  Few in the media seem qualified to critique complex studies.   Economics bloggers like Mr. Cochrane do a great service performing the analysis that the main media are either unable or unwilling to perform.

A Part of the Truth Can Be More Misleading than All of a Lie

From The Atlantic by Derek Thomson, The Most Important Graphs from 2011, 12/21/11

This graph shows how the richest 1% have take a larger share of the economy in the last 35 years:

… or does it?

This graph does consider the effect after taxes and transfer payments, but…

  1. Why does it begin in 1979? The Reagan tax reform caused the income discrepancy to widen for two reasons. First the control of inflation caused a transfer of assets from tangible to investment.  During the 1970′s many investors moved assets into tangible assets like real estate and gold to benefit from inflation. Tangible assets were often not reported. When they moved their assets to security investments that were recorded it appeared to be a growth in investment income when in fact it was really a transfer from one asset to another. Secondly Reagan changed the tax code in 1987 reducing tax rates and encouraging subchapter C corporations to convert to sub -S corporations.  Unlike a C-corp which filed taxes as a a corporate entity, a sub -S reported its income on PERSONAL tax returns.  This shift in assets from corporate to personal returns also inflated the growth in the wealth of the upper income.  The effects of these two changes was short lived. It could be that most of the growth in the wealth of the upper income all occurred in the 1980′s as a result of these two non recurring events.
  2. Why does it end in 2007?  Is it a coincidence that this picture ended just before the economic collapse that had a much larger impact of the wealthy?  There was a dramatic drop in the wealth of the upper quintile in the last few years.    How different would this graph have looked if it began in 1990 and ended in 2010?  It can be easy to achieve the outcome you desire by selecting the beginning and ending periods to accentuate the picture you wish to paint.
  3. This chart shows income as a percent of the total. But this does not mean that the actual dollars in income of the lower quintiles did not also grow.  It is possible that the upper quintile achieved a larger share of a larger pie, and that all groups showed an increase in income.
  4. Measurements such as this do not include improvements in living standards at all income levels.
  5. Measurements such as this do not consider what drives these results. A better measurement may be the difference in income per hour worked.  Other wise we will be comparing the income of one who works with one who does not. Or we may be comparing the income of one who works 70 hours a week with one who works 30 hours a week.

The point is to be very skeptical of the statistics used to push political agendas.  A part of the truth can be more misleading than all of a lie, especially in this debate. For more information on how the statistics can be intentionally misleading read Income and Wealth by Alan Reynolds.  You can search several postings from this book at the search function in the upper right hand portion of this blog.

The Superwealthy and Income Statistics

I have posted several excerpts from Alan Reynold’s book Income and Wealth in this blog.  You can find them by putting his name in the ‘search the archives’ window in the upper right.  Income and Wealth was written years ago and focused on how misleading and erroneous much of the information we have about income and wealth distribution in his country has been.

When the data takes into account individuals instead of households, the mobility among the categories, hours worked, government transfer payments, tax effects, and consumption we find a very different picture from the one the redistributionists want you to believe. ( It may be more accurate to say the picture THEY want to believe.)

Rick Moran writes in American Thinker 7 reasons why Obama is wrong about income inequality October 29,2011.


And why did the top 1 percent do particularly well? One potential explanation from CBO: “The compensation of ‘superstars’ (such as actors, athletes, and musicians) may be especially sensitive to technological changes. Unique characteristics of that labor market mean that technical innovations, such as cheap mass media, have made it possible for entertainers to reach much wider audiences. That increased exposure, in turn, has led to a manyfold increase in income for such people.” The CBO also mentioned “changes in the governance and structure of executive compensation, increases in firms’ size and complexity, and the increasing scale of financial-sector activities” as possibilities.

HKO comment:

Alan Reynolds pointed out several statistical distortions in the numbers as well and Moran highlights one of them.  Because of the superstar syndrome where a success such as Mark Zuckerberg at Facebook can earn billions while still in his twenties, the uppermost bracket becomes distorted. In other words the variance within the upper brackets is far more pronounced than in the lower brackets. Statistically this widens the difference between the average income and the median (the  midpoint of the number of earners).  This is logical since there is no real limit for the highest income but the lowest can not earn less than zero.

Policies that focus on such outliers are hazardous when applied to the whole group.  Tax policies that would have little effect on the super wealthy would create severe job killing disincentive for the other upper quintile working wealthy.