Income inequality has been an issue of considerable contention among intellectuals as of late.
Unfortunately, there are few areas where the relevant statistics
are fraught with so many shortcomings. Thus, people who are
intellectually honest may unintentionally mislead because the
numbers they use do not show what they think such numbers do. For
example, a blog post by the American Prospect’s Ezra
Klein shows why statistics on income inequality can make inequality
look worse than it really is:
I was thinking through Robert Reich’s proposal to
mandate that countries who want to trade with us set a minimum wage
of half their median wage, and I ended up digging into some
median wage statistics domestically. For those fuzzy on
the terms here, median means, essentially, in the middle. If I make
$6, and Matt makes $7, and Tom Friedman makes $150, the median wage
is $7. The mean is the average, so in this example, it would be
$54.33. If we outsource Tom’s job to a bright Bangladeshi making
$1, my wage is now the median, and the mean is $4.66.
America’s mean wage in 2005 was $35,448.93. That’s the number
you generally hear quoted. Its median, however, was $23,962.20. And
if you want another example of rising inequality, in 1990, the
median was 71% of the mean. In 2005, it was 67%. Indeed, over the
same time period, the mean wage increased by 75%. The median only
increased by 65%.
The mean and median numbers Klein uses suggest that the incomes at
the top are growing much faster than those at the bottom. However,
data on wages are some of the most misleading as this excellent
article by Tim Worstall shows.
The fact is our economy has expanded over the last two-plus
decades so that people who previously would have a hard time
entering the labor force now do so with much less difficulty. As a
result, more housewives are able to get jobs (see table 584 of the
2007 statistical abstract (pdf)). Also finding access to such
jobs are immigrants. This nation has seen more legal immigration in
the last 25 years than at any time since the beginning of the last
century (see table 5 of the 2007 abstract (pdf)). However, such jobs are often low wage
because those getting them usually either want part-time work or
are entering the labor force with few skills.
This increase in low-wage workers results in downward pressure
on means and medians. Yet the numbers Klein points to have gone up.
That can only happen if those already in the workforce are seeing
their wages rise. When those two phenomena converge, it’s also
likely the difference between the mean and median will, as Klein
noted, increase, making inequality appear as though it has
risen.
Consider a hypothetical example. Suppose that you have three
workers, John, Sally and Matt. John makes $11 per hour, Sally makes
$9, and Matt makes $7. For inequality, let’s use both a comparison
of the highest and lowest paid workers and the difference between
the median and mean. On the first measure, Matt makes about 64
percent of what John does. On the second, the median wage is $9,
while the mean (27 divided by 3) is also $9, which means that the
median is 100 percent of the mean.
Now let’s assume that a number of years go by, and two new
workers, Tony and Lisa, enter the workforce for the first time,
Tony being hired at $7 per hour while Lisa is hired at $5.
Meanwhile, the three other workers have received a number of
raises, so that both John and Sally make $15 per hour and Matt
makes $10.
Who under this situation is any worse off? John, Sally and Matt
now make more money than they did a few years ago. Tony and Lisa
have gone from having no income to having income, and they are on
their way to earning valuable job experience which will benefit
them later on. Indeed, everyone is better off.
Yet look at what has happened to our measures of inequality. A
comparison of the lowest to highest paid worker shows that Lisa
makes only 33 percent of what John does. The difference between the
mean and median has also worsened. The median is now $10, while the
mean (52 divided by 5) is $10.40. The median has dropped to 96
percent of the mean.
However, it is hard to argue with a straight face that
inequality has really increased — after all, the real gap
between John and Lisa has declined, since Lisa went from earning $0
per hour to earning $5. Unfortunately, actual government wage
statistics don’t count the folks who are not yet in the workforce,
leaving us with an inaccurate picture of inequality.
That one has to very careful with income statistics lest they be
misleading is not the only lesson to be derived here. More
important, as Diana Furchtgott-Roth recently suggested, is that income inequality isn’t very
important. Rather, our focus should be on whether entry-level jobs
remain open and abundant for those at the bottom of the income
scale and whether, over time, people are able to move up the income
ladder.
David Hogberg is a senior analyst at the National Center for
Public Policy Research. He also hosts his own website, Hog
Haven.
