Note: An updated version of this graph is here.
Income inequality in the United States has been rising since the 1970s. What is the most effective way to succinctly convey this fact?
Here is my choice (a pdf version is available here):
The chart shows average inflation-adjusted incomes of the poorest 20%, middle 60%, and top 1% of households since the 1970s. The incomes include government transfers and subtract taxes. For the bulk of American households, incomes have increased moderately or minimally. For those at the top, by contrast, they have soared.
Why This Chart?
Here are what I think should be the principal considerations. Some are obvious, others perhaps not.
1. Tell the substantive story clearly. The graph does a good job of conveying the two key aspects of the rise in income inequality over the past generation. One is the dramatic increase in incomes for households at the very top. In 1979 household income among those in the top 1% averaged $325,000 (in 2005 dollars). By 2005 that had increased to nearly $1.1 million.
The other is stagnation at the bottom and modest growth in the middle. Among the poorest 20% of households, average income was $14,500 in 1979 and $15,500 in 2005. Among the middle 60% of households, average income rose from $42,000 to $51,000.
2. Use the best available data. There are various sources of income data, including the annual Current Population Survey (CPS), the decennial census, IRS income tax records, and others. The data used in this chart are from the Congressional Budget Office (CBO), which has merged tax records with the Current Population Survey. They’re available here. Tax records are incomplete, because many low-income citizens do not file tax returns. But they have the advantage of providing relatively good data on those with high incomes. The CPS data are from interviews of 50,000 or so households. They are more representative of the population. But for various reasons the CPS data are not as good for those at the top of the income distribution. Also, the CPS data are for pretax income. The CBO data arguably combine the best features of these two sources.
The main disadvantage of the CBO data is that they begin in 1979. It’s thus not possible to see the contrast with earlier periods. I say more about this below.
3. Show incomes, rather than a summary inequality measure. Common inequality measures include the Gini coefficient, percentile ratios (e.g., P90/P50 and P50/P10), and income shares (e.g., the income share of the top 1%). They are quite useful. Nice examples from the Economic Policy Institute’s The State of Working America are here, here, and here. But they have two drawbacks. One applies to the Gini index, which is the most commonly-used inequality statistic. It doesn’t identify where in the income distribution the rise in inequality has occurred. For example, suppose the Gini rises over time. Is that because those at the top have pulled farther away from everyone else? Because those at the bottom have fallen behind? Because of a widening spread in the middle? All three? Something else?
To address this problem analysts often turn to percentile ratio or income share measures. These, however, fail to provide information about trends in actual incomes. Suppose, for example, that the 90/50 ratio increases over time. Is that because the incomes of those at the top have risen faster than the incomes of those in the middle? Because incomes at the top have risen while those in the middle have been stagnant? Because both have decreased but those at the top have done so less rapidly? Something else?
Showing trends in actual incomes — adjusted for inflation, of course — overcomes these problems. A potential drawback of doing so is that it may not be obvious from the raw income data whether or not inequality has increased, or by how much. If the magnitude of the rise in inequality is small, it may be preferable to use an inequality measure. For the United States over the past generation, however, the increase in inequality is easy to spot from data on incomes.
4. Show income levels, rather than growth rates. A common and helpful inequality graph is a bar chart showing rates of growth of real incomes during different periods for households at various points in the income distribution. See here for an example. This gives a good sense of the magnitude of the change in inequality, but it doesn’t convey anything about the magnitude of the level of inequality.
5. Show the full trend, rather than snippets or period averages. A frequent choice is to show the level of inequality in selected years, or averaged over groups of years (e.g., business cycles). That’s fine in many instances, but when there is relative stability within periods it is usually preferable to show all data points.
A Helpful Supplement
The chief limitation of the above graph is that it doesn’t fully convey what has happened at the bottom of the distribution since the 1970s. It is clear from the chart that incomes for those in the top 1% have jumped dramatically and that incomes for much of the bottom half of the distribution have been stagnant. But the latter aspect is not highlighted to the degree it could be.
Here is a second chart that helps to flesh out this point:
This chart shows trends in real incomes for families at the 20th, 40th, 60th, 80th, and 95th percentiles of the income distribution. In order to go back to the 1940s we have to accept two data limitations: the income data, from the CPS, are pretax; and the units are families rather than households, so adults living alone are not included. These data are available here.
Between the late 1940s and the mid-1970s incomes increased at roughly the same pace throughout the distribution; they doubled for each group. Since the 1970s the story has been quite different. At the 95th percentile, incomes have continued to rise. At the upper-middle levels (the 80th and 60th percentiles), they’ve increased at a moderate pace. In the bottom half of the distribution (the 40th and 20th percentiles), they’ve been fairly stagnant.
This chart makes it clearer that a defining feature of rising inequality in the United States is the stagnation of incomes in the lower half of the distribution. Even at the 95th percentile, where incomes have increased appreciably since the 1970s, the rate of growth did not accelerate relative to earlier years; the average growth rate of family income at the 95th percentile was 1.5% per year between 1979 and 2005, compared to 2.4% per year between 1947 and 1979.
If you’ve seen an inequality graph that is as good or better, please let me know.
Looks to me like your first graph is designed to game a political argument.
Suppose everybody’s earnings rise by 10%. Your graph would show a noticeable rise in the highest line, and no noticeable increase in the bottom lines.
In other words, I don’t think its a good idea to try to combine information about the magnitude of inequality with information about the magnitude of _changes_ in inequality in a single graph.
I like the second graph though.
Minor quibble–I’d love to see the first graph, but with a logarithmic scale. I suppose that would only appeal to nerdier folk, however.
For something like income, logarithmic scales and comparison of percentage increases are somewhat misleading, because the basis of comparison is a single household. A household earning $10m is not 1000x larger than a household earning $10k. They don’t need to eat 1000x more food, they don’t need to buy 1000 tickets to see a movie, they don’t have to pay 1000x more for a root canal, etc.
What 1000x income can get you is “better lifestyle”, and maybe cost of lifestyle improvement follows a power law, but I’m skeptical of that. It’s not clear to me how a 1000% raise could improve the life of someone earning $10m, but it’s pretty clear to me how a 1000% raise could improve the life of someone earning $10k.
You have similar graphics for France here
Click to access presentation_C.landais.pdf
A graph showing no change when everyone’s income increases by 10 percent would be no less gaming a political argument. Equity measures often have either scale invariance (your suggestion, Gini-coefficient) or translation invariance (the first graph above). You may feel that scale invariance is most natural, but don’t expect that everyone will agree. Least of all the people on those bottom lines…
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So, what about median, rather than average (mean) incomes? Would this also display the disparity better?
This post is the best overall delineation I have seen of what is I think is weakly called (but I am a struggling American worker, not an academic) “inequality”; which I prefer to call the “great wage depression”. For what I think the above numbers should mean in social policy outlook, see below:
Being barely above poverty, barely meeting minimum needs is not a satisfactory political or social goal — not if we are talking about a quarter or a third of families it is not! A satisfactory life is a satisfactory social goal.
A materially-satisfactory (that’s what we are talking today: economics) life is about Disney vacations and big screen TVs and nice vehicles — it is if we are talking about a quarter to half of the country — which it is when the American median (50 percentile) wage has grown only 10% while average income has grown 70% over the last 35 years (average income up 70% with a lot more family members working). A satisfactory life is even about keeping up with the Joneses — it is when between a quarter to three-quarters of the country has not kept up even closely with overall economic growth.
Eliminating poverty is nevertheless a satisfactory social goal when a quarter of the workforce is earning less than the federal minimum wage of 1968 (double the average income later!). It is when that quarter cannot afford replacement teeth and their kids join gangs to make $10/hr and children have children because they cannot have anything else. Nevertheless, the genesis of much social pathology (not talking psychological pathology) is stated in the opening proposition in this paragraph: unavailability of a living wage even if willing to do the most strenuous work.
The materially unsatisfied in America are for the most part not left back because they are undereducated or pathological or because America is undercapitalized — neither are they materially-satisfaction poor because they are short on some kind head start in life. Most are short on life’s goodies because American labor is short on sheer bargaining power in the free labor market. All this is why I worry more about pay than the pain of poverty as America’s core social and economic concern. In any case it may be easier to kill both these two birds with one stone than one at a time.
Super-simply, how I think American labor got this low: is by allowing its pioneer, taking care of oneself by one’s own efforts to get in the way of the only way of taking care of itself in the age of mass production; namely, collective bargaining (I think it that psychologically simple and very ironically true).
Collective-collective bargaining (a.k.a. sector-wide labor agreements) as practiced around the world from Germany to Norway, from Argentina to Indonesia) could be the simple easy way for American labor to get its bargaining mojo back — the most powerful labor stance and mandated by law yet. French Canada uses the simplest version of sector-wide (non-union firms must adhere to conditions worked out by unionized firms) in an economy almost identical to ours — should be simplest to adopt.
I do think about pain more than pay when thinking about taxing high incomes: how much will the highest earners really suffer? Not enough for me to worry about.
I’d like to see one more line added: the numbers for the top one tenth of one percent. I suspect it would be illuminating, and would also require you to change the vertical axis scaling.
Excellent response Denis, and Joel, I agree the top of the top line should be added as well. Also, middle 60, bottom 20, top 1… there are 19% missing from this graph?
I’d be interested in also seeing the WORST graphs. Like a news item I saw today talking about “average incomes” in the U.S. increasing. Of course the average goes up when the top one percent earns a million more dollars this year than last, even when last month 60,000 people lost their jobs.
Here’s another graph I’d like to see: What if you adjusted that graph for what the inflation rate WOULD be if corporations didn’t lower costs by outsourcing and using sweatshop labor? I’m very interested in finding out what the inflation rate would be if businesses continued to sell their products expecting the same profit margins without using underpaid labor.
And to the other Alex, I’d just like to point out (and someone else was making the same point) that a 10% income increase for a rich person and a poor person mean completely different things. For a poor person, it’s not enough, and for a rich person, it’s an incredible luxury.
Most US data is published in terms of households. There is an income-increasing effect there because of the rise in double-income households over the timespan you plot, and I think that as a result your graph may even underestimate the increase in inequality.
I don’t know what the “real” thing to compare is, but two incomes come with additional expenses (childcare, transport etc) so doubling a nominal household income by adding an extra earner does not double the disposable income.
Caveat: not an economist, may use terms incorrectly.
I wasn’t suggesting that the graph is inherently flawed – only that it is flawed for the purpose which it is used here, which is, to quote the post above,
“Income inequality in the United States has been rising since the 1970s. What is the most effective way to succinctly convey this fact?”
If you want to show that income inequality in the United States is rising, you should not be using a graph which, in the case when income inequality is unchanged, will have the top line shooting up while the bottom ones remain in place. That is, if your method would appear to show change when no change has taken place, you are gaming the argument.
As others have said, a log scale for the y-axis is more appropriate.
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At the end of 2004, I drew an animated picture of the distribution of income, the distribution of the Bush tax cuts, and the distribution of after-tax income, in this little video:
Supply-siders will tell us that we need to embrace or at least tolerate this inequality for the sake of economic growth. All boats rise, and all that.
So how’s that theory working out?
I got curious, and overlaid real-GDP-per-capita growth. Here:
If the top 1% were redistributed to the rest of us, how much would the rest of us benefit. For that matter if the top 10% were redistributed to the rest of us what would the benefit be. Keep in mind there would still be a top 1% and a top 10% when the redistribution was done.
What is the total amount of tax contribution to the total tax pool by the top 1%, 5%, or 10% ???
On how much the rest would benefit, see this post: https://lanekenworthy.net/2008/01/22/size-of-the-pie-distribution-of-the-pie/
On tax contribution, see this one: https://lanekenworthy.net/2008/01/14/taxes-at-the-top/
Both of your graphs, while nice, are less than perfect because they would look this way even if income inequality hadn’t been rising.
(a) Take logs. Problem: average readers hate logs.
(b) Show trends in the ratios (90/10 etc.)
(c) Show growth rates. I think the EPI graphs you link to fail not because they show growth rates, but rather because they show only 2 points in time, not every year.
I really like Figure 2 from this CBO report: http://www.cbo.gov/ftpdocs/81xx/doc8113/05-16-Low-Income.pdf
Alex and Roger:
As I suggest in the post’s text, showing change or rates of change is fine, but there’s no need to do so here. Average income for the top 1% more than triples over this period; average income for the bottom 80% increases only slightly. A quick glance at the numbers on the vertical axis makes this clear.
And by showing levels of income, the viewer can see how much inequality there is, not simply how much it has increased.
I agree on the CBO data, they are the best of an imperfect “both worlds” of the CPS and IRS SOI.
However, there is an interesting set of studies by economists who compare consumption inequality with income inequality and then note how Credit (weighted by GDP) has steadily risen with the increase in inequality and argue that credit has “smoothed” the rise in income inequality.
But this leads to a question of “debt inequality” that needs more study. Sociologists should step up to the plate here.
In the 1990s most of the rise in inequality is due to a surge at the top. The Census Gini indices barely budge during the 1990s and in the Census Income reports the C.I.’s indicate no change. But if you look at the IRS data, the story is different. Here is a Gini series I generated from IRS adjusted gross income:
The story is much different than the one told by the Census Gini for families or households which is pretty flat during this time.
The well-known studies by Piketty and Saez of the Top 10% (variably divided) and adjusted for capital gains can be downloaded here…I especially enjoyed their series on the average pay of Top 100 CEOs (culled from Forbes) which mirrors their estimates from the IRS data.
I have found a pretty good historical cause of the rise in income inequality outlined in a book by two French economists and cited by David Harvey in his (2005) book “A Brief History of Neoliberalism”.
Disclaimer: I use Marxist paradigms.
Basically around 1967-68 you have a capital accumulation crisis. You can get data on the rate of change in non-residential fixed capital from U.S. BEA. Indeed, sometime around that time this measure bottoms out (looking at its rate of change). During this time you also have continuous rioting in American cities (from 1965 through after the assassination of King) plus an anti-war movement. The upper class in the U.S. in the late 1960s was panicking as it was faced with a “social breakdown” and a capital accumulation crisis.
In the 1970s, the capitalist class starts mobilizing (see e.g., Akard 1992 in ASR). During the early 1970s neo-liberal economic theorizing starts circulating through elite circles and free-market ideology is spread throughout the U.S. The first neo-liberal experiment is carried out in Chile. 1973 coup, overthrow of Allende and you have Milton Friedman visiting the country.
Nonetheless, full-fledged neoliberalism and the first “shock therapy” get implemented in the US in ’79 when Volcker raises interest rates to stimulate a recession in the U.S. Reagan is elected in 1980 and starts a full assault on the accumulated gains of the civil rights and labor movements.
A new means of “getting rich” is implemented where labor standard and unions are fought against and “accumulation by dispossession” becomes a new means of getting rich.
Several billionaires are “created” in Mexico when nationalized industries are opened up to the free market.
An amazing look at “shock therapy” is provided in Naomi Klein’s (2007) book “The Shock Doctrine” where she draws parallels between neo-liberalism and CIA interrogation techniques (parallels that are not completely independent of each other). She cites passages from Milton Friedman where he basically says that after a disaster is the best time to implement neo-liberal economic policies. She illustrates this with examples from the 2004 Tsunami and Hurricane Katrina.
This is turning into a really long comment, my apologies.
Piketty and Saez
The question of whether lower pay equality leads to higher economic growth seems to forget that the big creator in economic growth is advancing technology — more precisely, maturing technologies (plural).
If a more equal distribution of output is sought by means other than high pay — by over-regulation which makes employers hesitate to hire because they cannot fire or because they will have go on paying after they fire; or by requiring benefit levels that raise labor’s price too high — then that may cause unemployment. But even that is not the same thing as lowering output per worker (sometimes over-regulation generates higher output per worker as a way to avoid hiring). I am thinking mostly of Europe here.
On the other hand, if workers wish a higher price for their labor at the cost of fewer jobs that is not necessarily an irrational choice — fewer better paying jobs conceivably result in the same overall amount of pay for less work. Thinking of big jumps in the American minimum wage here, too (not that I am saying that even a $500/wk minimum has to cause any job loss — probably some shifting of demand). In any case this is labor’s choice in a democratic society.
Much unemployment in Europe can undoubtedly be blamed on the automatic — and high — dole, in combination with over-regulation. All of which, once again, is not the same thing as lower productivity growth which is the only thing that can really devastate a national economy. As long as technology is kept up with, the GDP can always catch up — come to think: technology can always be caught up too.
It all adds up to: the sky ain’t going to fall because of more fair slicing of the economic pie. Didn’t that go out with Ricardo or somebody?
I’ve ran into many of the issues that you mention in trying to graph the data in the most accurate and informative way. The two graphs that I came up with are at the following URL:
My first graph is like your first graph except that the top income that I graph is the top 5%, not the top 1% like you. This makes the lower incomes more visible as the vertical scale goes up to just $300K. My second graph shows cumulative percent growth in income and I think is a bit more informative than the chart you reference in point 4. In any case, I think that both graphs taken together give a good indication of what’s been going on with quintile household income since 1967.
The problem of the CBO approach is the mixture of data and open low end of distribution. When one uses time consistent personal income data from the Census Bureau obtained in CPS economic inequality has been decerasing since 1947, as defined by teh Gini coefficient. This decrease in (personal income) Gini comes from the fact that the portion of people with income has been increasing. If to exclude non-incomers, what, of course, is an illigal operation for income inequality consideration, Gini has been increasing since 1947, but constant from approximately 1980, when the portion of people with income reached its ceiling.
Some figures demonstrating Gini are in
I still do not understand how constant Gini for personal incomes can be transformed into a growing Gini for households. It should be some trick with exclusion of non-incomers. The CPS, year-by-year, show very consistent portion of total income in the top income bin. http://www.census.gov/prod/www/abs/income.html
Watch out for Census household income figures over time. I would assume (no expert here) that they are arrived at using the same Top Code that Census family quintile income figures are warped out of shape with.
All family income above $1 million dollars a family is excluded from the Census family figures by the simple expedient of making the highest income category a check-box: “above $1 million dollars.”
The Top Code may hide as much as half the income of the top fifth of families. Repeat; if you adjust for the Top Code you may double the income of the top-quintile families in the Census survey.
I tried (in my amateur way) to adjust for the Top Code by comparing per capita income growth since 1967 (not Top Coded) to overall family income growth reported by the Census.
Per capita income doubled between 1967 and 2005 according to the Census. I assume, therefore that the sum of mean quintile family income in 2005 should be roughly double the sum of parallel numbers in 1967. Any shortfall should be what the Top Code is hiding (I added a small amount to the 1967 top quintile number for the Top Code then). The short fall was equal to 100% of the top quintile mean income reported by the Census: effective doubling it.
Not sure of the exact relation between per capita and family income growth but it should be ball park.
The Top Code was instituted at a time of more equal distribution of pay when the idea was to keep a few billionaires from from warping the general picture of family welfare reported by the Census. Now of course it is the Top Code itself that is warping the picture of what is really happening in our powerless labor economy. Just one more example of misreporting — like the 50 year outdated poverty formula of three times the price of an emergency diet (only dried beans please; no canned).
http://www.bls.gov/opub/mlr/1995/08/art5abs.htm…Ryscavage on top code changes
I believe it is the title of an article, but “no matter which way you cut it”, income inequality in the U.S. has increased
Social scientists spend a lot of time showing different techniques for assessing income inequality, but it is quite clear that generally speaking, income inequality in the U.S. has increased (despite what the Heritage foundation says :-)
As a sociologist, there is an interesting story here. A highly organized capitalist class mobilized in the early 1970s to recoup what it rightfully saw as its share of the income and wealth pie.
All the “most prominent” explanations are designed to deflect attention from this:
It was technology
It was demographics
It was globalization
Now it has somewhat moved to
de-unionization and class struggle…
but the role of a highly organized upper class slowly instituting neo-liberal reforms to enable itself to accumulate vast shares of income and wealth still escapes the careful sociological investigator, but I am optimistic…
(I know of one sociologist who had to cancel her book on neoliberalism and the Reagan tax cut initiatives b/c they decided to close particular archives in the Regan library)
First, great blog.
Second, I suggested the CBO figure because it is an example in which your graph would not emphasize (and in fact, might hide) the finding that earnings of households with children have growing faster in the bottom quintile than among any other quintile. Even in your graph, it is very hard to tell that income did not rise at the bottom of the distribution. for example, what would it look like if you (counterfactually) made the incomes of the bottom 20% triple over the period? My guess is that it would not look very different.
I think it is very hard to convey both the level and trend in inequality in a single graph when the level of inequality is large.
Another interesting thread would be the opposite. What is “best inequality graph” that is (a) factually accurate, yet (b) “cheats” in that is exaggerates the truth through a slight of hand.
My favorite is Figure 1 from a policy brief by David Ellwood, available at:
Click to access issue2.pdf
Can you figure out how he cheats?
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This graph is seriously flawed if it looks only at household income without consider changes in the size of households over time.
Stagnant household income for the bottom 20% is less troubling if the size of those households has been shrinking over time so that there is more per capita income for the members of those households.
Without information on the change in size of households, the graph has very little to teach us.
No, the CBO’s numbers don’t adjust for household size. But that doesn’t actually change the story. Paul Ryscavage has a good discussion of this in his book “Income Inequality in America.”
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This is also a good chart, showing the income inequalities, in my opinion.
Also, it’s my opinion that the reason the gap is widening is the loss of unions and inflationary monetary policies.
Inflation is deceptive, most people don’t understand it. In Economics in One Lesson by Henry Hazlitt, he points out that inflation is ‘the opium of the masses’ that is meant to ‘deceive labor’ by reducing their real wages, but deceiving the workers because they see the same amount (or more) dollars in their paycheck. he also states that inflation doesn’t work anymore (to deceive labor) because Unions ‘hire economists and lawyers, and ensure workers get cost of living raises’ (Note this book was first written in the 1940s).
That doesn’t happen anymore. Without Unions, workers have no leverage, and no hope of understanding the system. People feel, just like I did about a year ago before researching this. that prices ‘just go up’ for natural reasons.
Who can beat inflation? Those that put the lowest percentage of their income into consumables, in my opinion. If most of my salary goes to paying for gas, bread, and baby formula, but Bill Gates can put most of his income into assets that appreciate in value, stocks, bonds, etc. then his total wealth will rise with inflation, while mine will always shrink.
I’m just a blue collar guy trying to figure all of this out though, I might be wrong. It just seems that by always inflating the currency, those who can invest their money are in the best position, while those of us spending most of our paycheck just to live, are screwed. Until recently the one thing that the blue collar worker could count on to make him some money was his home – but you can’t count on that anymore either.
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This graph works for me and makes me sick to my stomach…
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Here is my theory about why this happens: http://www.economics-ejournal.org/economics/journalarticles/2007-13
This is a worthy cause, to try to communicate a key concept to people.
I wonder whether it might work to show, as stacked bars, how the average $1million gets distributed? In early years, there’d be many people, with not too much dissimilarity of size. As time progressed, there’d be fewer people sharing each $1MM, but as a guess, the one at the top would be taking an increasingly large slice of it, and those at the bottom would be scrunched together.
And if we’re gonna talk about how policy matters, how can one avoid using after-tax data?
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I DONT HAVE A ECONOMICS PROBLEM AND NEITHER DO MILLIONS OF PEOPLE. I’M NOT RICH OR WELL OFF, BUT COMFORTABLE. YOUR ECONOMICS PROBLEM IS ONLY IN STOCK AND YOU EITHER DEPEND ON IT OR WORK FOR A UNION COMPANY THAT DABBLES IN IT AND WHEN THEY LOOSE, YOU GET LAID OFF. I DONT HAVE THE PROBLEM AND ALL CITIES, COUNTIES IN AT LEAST A 100 MILE AREA FROM ME IS WORKING AS USUAL. HAVE A NICE DAY. MIKE
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In a German article “The Crisis as a Challenge to the Left”, there was a graph showing the top 1% now has 55% of the world’s assets. Back in 1968-1970, it was around 15-17%. Is this right? Have you seen any graphs that show this explosion? Thanks for your help.
Click to access kontrovers_01_09.pdf
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Looking at these figures and given the recent acceptance of quanitiative easing my curiosity has been piqued once more about the ratio of debt based to debt free money supply in the economy. My inquiry has been: Does this have an effect on income inequality on a macro level? Gold standard ending in 1971 plus bank deregulation in the 80s has meant ratio of debt free money has shrunk from 20% to 3% – that is until quantiative easing (not sure if this is a short term form of debt free money – i.e. will govt treat this ultimately as debt or not?) Are these figures simply a correlation? It would need some pretty dedicated and extensive research from top econimists to demonstrate this I think.
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How is this a bad thing? This is an indication of opportunity.
Get thee to the top 1%.
I’d like to see a graph that somehow indicates the number of workers per household in each income level. The assumption seems to be that the problem is pay inequality, and at the top of the chart it may well be. But the continuing divergence of P60 from P40, for example, may be esacerbated by the number of earners in each family.
The disparity in the unemployment rates for White wives and Black men seems to me an important datum in understanding not only the distribution of incomes below P95 but the distribution of opportunity as perceived from the bottom.
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I believe the link I provide above to the analysis of Emmanuel Saez is also excellent. Thank you for your work. I make it a point to educate my friends that are open to this.
I believe the link I provide above to the analysis of Emmanuel Saez is also excellent. Thank you for your work. I make it a point to educate my friends that are open to this.
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Do you have graphs of after tax income?
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I would like to see this graph in terms of total income per year earned by each of those percentiles. Maybe that can’t be adjusted for inflation, I don’t know. But, I would like to see if the top 1% are really taking down a measurable chunk of the country’s income.
I don’t see why the top 20% minus that top 1% are excluded from this graph, since all groups are given equal ink in this picture.
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I don’t know if anyone commented on this, but these graphs are meaningless. If individual X started his 20s in the lowest quintile and over the course of 45 years rose to the third or fourth quintile upward, typical for hard working, sober, responsible adult, his income is not still that of the lowest quintile over those 45 years. Wage levels are meaningless – what matters is how quickly people move between them, especially the poor. A lot of those people supposedly getting wealthier and wealthier in the top quintile were in the bottom quintile only ten years ago.
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