Promoters of the income inequality meme—Occupy Wall Street, the Obama White House, liberal think tanks, “progressive” bloggers—typically point to data showing how “rich” households and families have been getting even richer vs. those in the middle class. But yet there’s no evidence of any significant change in income inequality among individuals as this gobsmacking chart from the must-follow Political Calculations blog shows:
An explanation from PC:
The chart [above] shows what we find for each grouping of Americans according to their Gini Coefficient, where a value of 0 indicates perfect equality (everyone has the same income) and a value of 1 indicates perfect inequality (one person has all the income, while everyone else has none). … But here’s the thing. We have already confirmed that there has been absolutely no meaningful change in the inequality of individual income earners in the years from 1994 through 2010. If income inequality in the United States was really driven by economic factors, this is where we would see it, because paychecks (or dividend checks, or checks for capital gains, etc.) are made out to individuals, not to families and not to households.
It would seem then that the real complaint of such people isn’t about rising income inequality, but rather, how people choose to group themselves together into their families and households.
This is another version of The Big Sort, the supposed red state/blue-state trend of like-minded people clustering with each other.
The reason that the income inequality levels recorded for families and households are lower than those for individuals are because most families and households may have one high income earner, who is balanced out by individuals within the families or households who have low or no incomes. But, if people with very high income earning potential join together to form families and households, and increasingly do so over time, perhaps because such people might have things in common that make forming themselves into families and households an attractive proposition, then income inequality among families and households will increase.
The same holds true for the opposite end of the income earning spectrum. If people with really low income earning potential join together to form families and households, or perhaps if they choose to split apart, and increasingly do so over time, then the resulting low income family and household will also make income inequality among families and households rise, even though there has been no real change in the amount of actual income inequality among individuals.
So what we have here, as always in America it seems, is culture trumping economics (though the data don’t take into account how different income groups have different inflation rates, another equalizer). AEI’s Charles Murray has a new book coming out that will expand on how values and culture influence inequality. In any event, it is hard to see what of any of this has to do with forcing more equality through higher taxes and more government spending—or class warfare politics.
Unfortunately, the left doesn’t care about facts and the public responds to emotions and envy. I will link to this from my Old Jarhead blog.
Robert A. Hall
Author: The Coming Collapse of the American Republic
(All royalties go to a charity to help wounded veterans)
For a free PDF of my book, write tartanmarine(at)gmail.com
Impressive research that casts a completely different light on the subject.
Our take is that there is almost certainly an opportunity to help the less skilled part of the labor force in the United States, but the only way to tackle is is through education. Our 4 suggestions are here: http://bit.ly/vfu68c
So your argument is that income/wealth inequality is not a problem because it has not increased since 1994? Even if that’s true, it is not relevant to the concerns that Obama and OWS have articulated. In order of importance, here are the points that people are correctly making: A) U.S.A. is too unequal; B) U.S.A. has become more unequal over the last 50+ years; C) U.S.A. is far less equal than most other rich countries.
You do not contest A, B, or C. Instead, you quibble over the level of CHANGE (rather than the level of inequality) over the last 15 years. The truth is that inequality was already outrageous 15 years ago, so even if it stayed the same or went down slightly, outrage is warranted.
Your initial error is to assume that there SHOULD be no difference in income and that, accordingly, a difference indicates that something is “outrageously” wrong. But incomes differ because of differences in intelligence, education, perseverance, ambition, interests, jobs (due to difficulty or danger or level of expertise or training required, etc.). A nation with a wide range of incomes is, more than anything, a reflection of how differences in income can depend on many different factors, many of which are under the control of the individual.
The US is a lot less unequal than most countries. What’s the income inequality in the PRC or India or South Africa or Qatar or Brazil? It’s far more stark there than here and in any of those places it’s far more difficult for an individual, born into a family of a lower income level, to rise to higher income levels than it is in the US.
To implement a program whereby all the differences noted above are eliminated or for which compensation is made to adjust for those differences in order to eliminate or to minimize differences in monetary outcome would require a degree of social control and individual compulsion so complete as to make East Germany or slavery in the American South look like paradise by contrast.
Wow.. after multiple credible sources just DESTROYED this guy’s earlier articles on this subject (including the very researchers whose data he was intentionally and manipulatively misinterpreting), he just doubles, triples, and quadruples down on the stupid.
Why is Mr. Pethokoukis relying on misinterpreted five year old studies and such well-known indicators as the “GIGI Coefficient” (huh?) instead of simply using the CBO’s plain-as-day average income data? Could it be because he’s trying ever-so-desperately to cherry-pick data that he can utterly misuse to fit some pre-determined ideological bent?
John Chait called this guy out BEFORE he started double-downing on the misleading stats.
http://nymag.com/daily/intel/2011/10/life_imitates_annie_hall_econo.html
But the reality is that Mr. Pethokoukis has found a way to get clicks… he’s a classic click-wh*re, who’ll keep repeating the most ridiculous junk once he finds out that it’s getting views.
Listen people… bottom line is that income inequality has increased massively in the last 30 years. Now go and debate about whether that’s good, bad, natural, unnatural, whatever… but just ignore anybody who claims that the facts are not what they are. The facts on income inequality now vs. 70, 50, or 30 years ago are blatant, simple, and obvious.
Any time spent arguing with this dolt is time wasted that should be spent deciding whether income inequality is a problem or not and/or if something should be done about it….
But maybe that’s the point.
@ Crafty Bernardo — you might consider educating yourself a little before you post a comment. The Gini (not GIGI) index is one of the standard measures used to determine income inequality.
As the CBO report you mention says: “The Gini index is a widely used measure of income inequality….The index provides a useful summary metric, characterizing the entire income distribution with a single number.”
Also worth noting is the fact that the index is derived from the “plain-as-day average income data” you say we must all stick to.
So the fact that it isn’t showing an increase for individuals is important, regardless of what the conventional wisdom says.
Yes, it’s useful to measure income inequality from nation to nation… not from different groups within a nation… And, like I said elsewhere, that’s why it’s useful to Mr. Pethokoukis… because it’s intentionally misleading in the context of our current debate on income inequality in that it is virtually useless at measuring inequality within the US between the very, very top of the spectrum (basically the 1%) versus large swaths of the rest.
It is amazing how Peth keeps peddling this bs no matter how many times it produces a click. The downside of this for the AEI of course is that it just totally discredits one of their front men amongst what I’ll loosely call the Bloggariat by simply exposing his commentary for what it is. Obviously the likes of JohnM are impervious but ultimately the drip drip of this sort of stuff makes the AEI look smaller.
From the World Bank: “A disadvantage of both the Gini coefficients and the Theil indices is that they vary when the distribution varies, no matter if the change occurs at the top or at the bottom or in the middle (any transfer of income between two individuals has an impact on the indices, irrespective of whether it takes place among the rich, among the poor or between the rich and the poor). If a society is most concerned about the share of income of the people at the bottom, a better indicator may be a direct measure, such as the share of income that goes to the poorest 10 or 20 percent. Such a measure would not vary, for example, with changes in tax rates resulting in less disposable income for the top 20 percent at the advantage of the middle class rather than the poor.”
Sooooo, essentially the World Bank is saying that the Gini Coefficient is flawed in that it is not a good measure of movement of income from groups in different areas of the income spectrom.
That it’s useful when comparing income disparity from country to country, but not income disparity within groups within a country.
Which is exactly what makes it useful to Mr. Pethokoukis… What a shocker.
(Oh.. and even by the GINI Coefficient, America’s inequality has grown considerably since 1980.. which is what most people are talking about… and it’s why Mr. Pethokoukis cherry-picked 1994 as his starting point).
(Oh2… and the GINI Coefficient is also flawed in that it does not take into account differences in wealth.. just income via actual monetary transactions (i.e. checks cashed), which, of course, misses yet ANOTHER aspect of inequality that would not have worked well for Mr. Pethokoukis’ cause).
At the end of the day, this guy’s just a horrible hack that is doing what horrible hacks do. Cherry-picking information to make a factually-ridiculous, but emotionally-instigating point that gets eyeballs, but breeds ignorance.
This might matter if the CBO used another measure.
Gini is a really non-intuitive way of looking at income data.
Here’s how Mr. Pethokoukis does “research” for an article.
“OK. Today I’m writing an article about how Income Inequality is a myth. Off to the internets to find easily manipulatable factoids to support my position!”
A real journalist doesn’t have the article written with an end result in mind, and then finds “facts” to back into his position.
A real journalist finds facts, compiles them in illuminating ways and then writes about what they say so that the average joe doesn’t have to do the mind-bending weeks of work necessary to understand the facts without going into it with an end result in mind.
A good journalist does this with enough regularity that people eventually can trust what his analysis uncovers.
A good journalist also recants utter lies or errors in his work when they are subsequently pointed out (see numerous articles over the last 24 hourse noting that even the people who’s work Mr. Pethokoukis is citing are pointing out how wrong he is).
I’d hardly call Census Bureau data “easily manipulable factoids”.
Another interesting twist on the inequality debate: PBS Newshour recently had an interview with an economist who looks not at financial assets to determine inequality, but also at the value of people’s expected government benefits – Social Security and Medicaid. Turns out distribution of income flattens dramatically when you include that.
I’d definitely call Census Bureau data “easily manipulatable factoids”. I never said the raw data was incorrect. Or that the providers of the data were wrong.
I said Mr. Pethokoukis here was cherry-picking data that could be easily misapplied in an effort to mislead.
Unlike people who comment on other people’s blogs.
Now let’s have an example of a “real journalist”.
I haven’t read all of the comments but I’m sure someone has already pointed out that the math is not applied well. The statement about Gini at 1 and 0 is correct but it’s not what the authors must think it implies for values in between. An example: enter
150,140,130,120,110,100,90,80,70,5 at this online Gini calc http://www.had2know.com/academics/gini-coefficient-calculator.html
Is income inequality causing concrete problems?
Who determines the point at which inequality becomes a problem?
On some views, it absolutely is causing concrete problems; for example, see Bill Mitchell on The origins of the economic crisis.
The way I would put it (I am not an economist): People with low incomes spend nearly all of their income; people with high incomes invest, seeking returns. Returns ultimately come from selling things at a profit. Debt complicates the picture, but it always works out: profits from selling goods and services in the “real” economy are the origin of net real (i.e., adjusting for inflation and ignoring the false, paper-only “wealth” seen in bubbles) returns. If the amount of money in the return-seeking sector (mostly from wealthy folks) is too great compared to the amount of money flowing in consumer markets (mostly from ordinary and poor folks), the latter can no longer support the former. There are not enough good investments, because there are not enough sales, because there is not enough money (among those who will spend it) to buy enough to support generating the expected profits. The result is an over-large and overly speculative financial sector, unsustainable household debt and inadequate consumer demand — leading to unemployment, which only exacerbates the trend… exactly the downward-spiraling situation we have at present.
An important point in this view is that it’s not specifically how wealthy some people are that is a problem, but the balance between the total amount of wealth in the “return-seeking” sector versus the amount of money flowing in the consumer sector.
And those are the questions that we should be debating… but Mr. Pethokoukis would rather not have that debate, so he tries to muddy the water before you get there, by claiming inequality does not exist at all.
Here’s what this is like…
Imagine you and another person are traveling across the country by car, and all the sudden large amounts of steam start pouring out from under the hood… the other person either doesn’t want to pay for this problem or deal with it at all, so instead of engaging you in a rational converstion of whether this is a big problem or a small problem, expensive or cheap, needs lots of work or not… he just says “what steam?” while large billows of steam pour from the car and pass right over his head.
That’s what Mr. Pethokoukis is doing here.
After reading all the comments it is abundantly clear that the original post has been discredited to the point of the post losing all significance.
From the Wikipedia article on the Gini Coefficient: (Also note it was developed in 1912)
“Disadvantages of Gini coefficient as a measure of inequality
The limitations of Gini largely lie in its relative nature: It loses information about absolute national and personal incomes. Countries may have identical Gini coefficients, but differ greatly in wealth. Basic necessities may be equal (available to all) in a rich country, while in the poor country, even basic necessities are unequally available.
In addition, Gini does not address causes: income equality may reflect differences in opportunity, or capability. For example, some countries may have a social class structure that presents barriers to upward mobility; some people may have more skills than others.
By measuring inequality in income, the Gini ignores the differential efficiency of use of household income. By ignoring wealth (except as it contributes to income) the Gini can create the appearance of inequality when the people compared are at different stages in their life. Wealthy countries (e.g. Sweden) can appear more equal, yet have high Gini coefficients for wealth (for instance 77% of the share value owned by households is held by just 5% of Swedish shareholding households).[12][dead link] These factors are not assessed in income-based Gini.
Gini has some mathematical limitations as well. For instance, different sets of people cannot be averaged to obtain the Gini coefficient of all the people in the sets: if a Gini coefficient were to be calculated for each person it would always be zero. For a large, economically diverse country, a much higher coefficient will be calculated for the country as a whole than will be calculated for each of its regions. (The coefficient is usually applied to measurable nominal income rather than local purchasing power, tending to increase the calculated coefficient across larger areas.)
As is the case for any single measure of a distribution, economies with similar incomes and Gini coefficients can still have very different income distributions. This results from differing shapes of the Lorenz curve. For example, consider a society where half of individuals had no income and the other half shared all the income equally (i.e. whose Lorenz curve is linear from (0,0) to (0.5,0) and then linear to (1,1)). As is easily calculated, this society has Gini coefficient 0.5 — the same as that of a society in which 75% of people equally shared 25% of income while the remaining 25% equally shared 75% (i.e. whose Lorenz curve is linear from (0,0) to (0.75,0.25) and then linear to (1,1)).
Too often only the Gini coefficient is quoted without describing the proportions of the quantiles used for measurement. As with other inequality coefficients, the Gini coefficient is influenced by the granularity of the measurements. For example, five 20% quantiles (low granularity) will usually yield a lower Gini coefficient than twenty 5% quantiles (high granularity) taken from the same distribution. This is an often encountered problem with measurements.
Care should be taken in using the Gini coefficient as a measure of egalitarianism, as it is properly a measure of income dispersion. For example, if two equally egalitarian countries pursue different immigration policies, the country accepting a higher proportion of low-income or impoverished migrants will be assessed as less equal (gain a higher Gini coefficient).
Expanding on the importance of life-span measures, the Gini coefficient as a point-estimate of equality at a certain time, ignores life-span changes in income. Typically, increases in the proportion of young or old members of a society will drive apparent changes in equality, simply because people generally have lower incomes and wealth when they are young than when they are old. Because of this, factors such as age distribution within a population and mobility within income classes can create the appearance of differential equality when none exist taking into account demographic effects. Thus a given economy may have a higher Gini coefficient at any one point in time compared to another, while the Gini coefficient calculated over individuals’ lifetime income is actually lower than the apparently more equal (at a given point in time) economy’s.[13] Essentially, what matters is not just inequality in any particular year, but the composition of the distribution over time.”
But the Gini index has in fact been trending upwards in the US since the late 70s. Even in this post-1994 data, the coefficient is clearly trending upward for families and households.
It doesn’t seem possible to compose all these charts together and get the rising Gini index that the nation as a whole displays. Why not?
It seems we have just about the right amount of income inequality. If it’s trending upward just a little from where it was that’s a good thing because it would signal trouble in the culture as well as the economy if it were trending downward. Income inequality is a good thing that inspires people to work and invest in their own futures. They see that others just like them have made it and are better off. That is a wonderful and tangible example for them, giving them hope for the future that if they work and save and make good decisions they can live well.
This is the essence of American Exceptionalism over dismal places like the European Union. As Abraham Lincoln said in 1836, in America “A man labors for others this year and next year will labor for himself; in the year after that he will hire others to labor for him.”
God bless America.
Hahaha, is the title meant to imply that every other chart he’s seen supports the non-myth of income inequality? Pethokoukis, you are utterly useless… except for maybe making anagrams. You can spell “Joke”, “Misspoke”, and still have enough letters to spell “Utah”
Quit your day job man.
Whether income inequality is rising, falling, or staying the same is completely irrelevant, morally and practically. Everything depends on the reason for the numbers.
Provided the differences, if any, are generated by voluntary trade there is no moral reason it can’t be 10:1, a 1000:1, or any other number. If the reason is that some are (dis)advantaged by government interfering with voluntary trade and property use, a difference of 1.000001:1 would be an outrage.
One thing we might want to do is to understand what may be happening that could result in the observed data. It’s possible, at least, that distinctions among the 3 Gini coefficient trends shown in Pethokoukis’ article actually are telling us something.
To illustrate, consider a society consisting of 2 couples, with each individual making $100/yr. The individual, household and family Gini coefficients all show perfect equality.
But now couple 1 divorces, with each individual setting up a separate household. Each individual in the society still makes $100/yr. The individual Gini coefficient still shows perfect equality but the family and household coefficients do not.
Could rising divorce or separation rates and later family formation rates have something to do with the observed data? Maybe. Other phenomena might contribute too, including rising immigration, more globalization and so on. The point, though, is to try to understand the data before deciding what, if anything, should be done about it.
Garbage in, garbage out.
The Gini coefficients, quoted in this post, were computed by binning the US population into income ranges, and treating the lower-end of the income range as the income for everyone in that bin.
For most of the dataset, the top bin was “$100,000 and over” (changed, in the most recent years to “$250,000 and over”).
Since the shocking shift in the income distribution has been concentrated in the share garnered by the top 1% (those earning $340,000 or more), their gains are completely invisible if we do the calculation pretending that they earn only $100,000 (or $250,000).
A generous interpretation of this post is that the author is honest, but innumerate.
A less generous interpretation …
physguy:
Please scroll down and read the excellent critique of the Gini coefficient (11/2/11, 10:10 AM).
Then offer your own, superior metric to quantify the opportunistic liberal complaints about supposed unfairness in America.
You can’t complain about a problem but refuse to measure it.
My criticism isn’t of the use of Ginni Coefficients, per se. My criticism is of choice of dataset used to compute those Ginni coefficients.
In the Census Bureau dataset, everyone earning $100,000 or more is treated as if they each earned only $100,000. If you use that dataset, then the Ginni coefficients that you calculate are totally insensitive</strong (ie, do not change at all, in response) to an increasing concentration of share of national income in a subgroup which earns (a lot) more than $100,000.
Since that (the concentration of income in the top 1% of earners) is the subject of discussion, the Ginni coefficients computed from the Census Bureau dataset are completely irrelevant. They never gave an accurate picture of the level of income inequality, and they are incapable of detecting a change in the level of income inequality.
The CBO, Piketty and Saez and many others have done a lot of work measuring precisely that. If you’re interested, you are welcome to read they reports on their findings.
This blog post, claiming the contrary, based on a completely bogus analysis of an inadequate dataset, is not the place to find enlightenment.
Actually, Political Calculations is simply innumerate. Since 2005, he’s been pushing a historical measure of the public Debt that double-adjusts for the size of the population: Debt/GDP per person. The result is simply idiotic, akin to measuring fuel efficiency as miles per gallon squared as I explain in great detail on my blog:
http://jerseyretort.blogspot.com/2011/11/idiotic-political-calculations.html
The bottom line is that conservatives need to either stay 100 miles away from this guy, despite the pretty charts, and avoid linking or citing. If one of his chart seems really interesting then recreate it yourself from scratch.
-Mercy
Thanks physguy, that totally answered my question about why these series don’t match the overall nationwide one.