Income Disparity by the Numbers

© Phil Wendt 2011


Like most people, whether you’re in the bottom “99%” or the top “1%” of income earners, you’ve heard much lately about the issue of income disparity.  I was curious if all this acrimony is based on something real or imagined.  Is there an actual structural demarcation in income levels that has suddenly manifested itself here? There’s a lot of economic misinformation out there masquerading as absolute truth from various partisan think tanks and the daily talking heads on TV.  I was really looking for a way of elucidating the nature of this disparity and possibly quantifying it in order to more fully understand the extent to which it may segregate our society.  As a retired scientist I don’t believe that I have any unique insights into this issue, but I still have good research and number crunching skills and a heightened desire to separate fact from fiction regarding this contentious issue.   I also have a somewhat selfish motivation for pursuing this issue.  Now that my wife and I are retired and living on more of a ‘fixed’ income, developing a greater understanding of this issue may also help us to better plan our own financial future.

This income disparity is not a new or even recent event.  In fact, it’s not an event at all, but a process that has been steadily ongoing, virtually unchecked, for the last forty years and more.”


The Data

My first stop, and pretty much my last stop as it turns out, was the U.S. Census Bureau.  What a treasure trove of data, and more importantly, data that is unbiased and unattached to any partisan organization.  Demographers, economists and others may quibble with how the Census Bureau gathers its data, but as far as I know no one has ever made an adequate case that the data is politically biased.  So, the information I present here is gathered entirely from the U.S. Census Bureau and can be accessed on-line through their website.  My contribution was to gather and array the data in a form that is more illustrative than the hundreds of rows and columns of tabular data in the Census Bureau’s database.  Also, where ranges of data are provided, these ranges represent the total range of data available through the Census Bureau, with no truncating on my part.  I presented what I found.

My intent here is not to make a political statement, although I acknowledge that the subject itself is politically charged given today’s climate.  As a social issue I believe that this subject touches millions of lives in one way or another.  This is not an in-depth economic analysis, but merely an attempt to understand and quantify basic income-related trends.  I also wanted to use fairly basic, easily accessible and unbiased data to hopefully bring clarity to a complex and often misrepresented subject.  Anyone can access this data, and hopefully my cursory analysis here may stimulate others to dig even deeper into this often polarizing issue.

Historical View of Income Disparity

Figure 1. Gini Index of Income Dispersion 1947-2010

First let’s provide some quantification to the income disparity question itself.  Figure  1 shows the Gini Index of Income Dispersion data from 1947 through 2010.  Yes, an actual quantitative measure of income disparity (or dispersion) actually exists, and has for quite some time.   This index was first developed in 1912 by the Italian statistician Corrado Gini and has been used by economists, scientists (including ecologists) and others to measure disparities across various groups and populations.  Ecologists use similar computational indices to measure diversity within an ecosystem, that is, how evenly the individuals in an ecosystem are distributed among the species present.  In the case of income disparity (or dispersion) the Gini Index measures how evenly the wealth of a nation is distributed amongst its people. The index has a theoretical range from 0 to 1, where a value of 1 would mean that one person has all the accumulated wealth, and a value of zero means that the wealth is distributed evenly among its population. 

The Gini Index graph (see Figure 1) shows that at least through the mid to late 60s the index fluctuated somewhat but remained more or less flat or in a slight decline until the late 60s.  Beginning in about 1968 the index increased steadily from year to year, indicating that the income distribution in America has become increasingly more concentrated among a smaller and smaller minority of the country’s wealthiest people.  This trend, according to the Gini Index, has been occurring steadily over the last 40+ years.   The amazing thing to me is that, given how accessible this information is through the Census Bureau, we haven’t heard much about it before now. 

If you’re interested in how the US compares with other countries, you can check the Wikipedia discussion of the Gini Index (I’m sure there are many other sources as well).  Historically, developed European nations and Canada tend to have Gini indices between 0.24 and 0.36, while the United States’ and Mexico’s Gini indices are both above 0.43, indicating that the United States and Mexico have greater income inequality than that of either Europe or Canada.  One interesting trend shown in the Wikipedia graph of the Gini Index of other countries is now that China has moved into a more capitalistic economic model, their Gini Index has now risen to the level of the US, after decades of much lower GI values.  Be careful what you wish for. 

 I believe that the main value of the Gini Index in this discussion is in observing the long-term trend in the US, which has been increasing steadily over the last 40 years. The Gini Index data for the US most definitely shows that there is a long-term trend of increasing income disparity in this country, where a greater share of the aggregate wealth is accumulating within the top income ranks.  Now let’s take a look at other ways of identifying just where this disparity is occurring within our society.

Wealth Distribution

Figure 2 Adjusted Median Income by Each Quintile and the Top 5 Percent

Figure  2 shows the long-term median income trend across income brackets in the US, using data from 1967 through 2010, and adjusted for 2010 dollars (which basically takes into account inflation, and is a better indicator of purchasing power over the decades observed).  The adjustment for inflation was done by the Census Bureau.  This graph is most telling in terms of showing where this income disparity is occurring.  You can see that the incomes of the lower four quintiles (the lower 0-20%, 20-40%, 40-60% and 60-80%) of households has remained fairly flat over the last 43 years, while the incomes of the upper 20% of all households have risen considerably, especially the top 5% who have seen their incomes rise significantly during this period.  This figure also shows that over the last decade, even the upper 20% and 5% income brackets seemed to level off a bit.  In any event it looks like “Trickle-Down Economics” doesn’t really trickle down very far, certainly not below the upper 20% household income level.  

Figure 3. Percent Increase in Adjusted Median Income between 1967 and 2010

Figure 3 shows the relative increase of each of the 20% (quintile) segments of household earnings between 1967 and 2010.  The lowest quintile of wage earners increased their purchasing power by almost 21% over this 43 year period, and the second quintile only increased by about 13.2%.  The third quintile (the 40-60% range of household incomes) increased by about 22.1% over the same period.  The wage earners in the 60-80% quintile fared somewhat better with an increase of almost 40%.  The top 20% of wage earners had a nearly 67% increase in their real purchasing power over this 43 year period. The top 5% of wage earners weighed in with a 79.3% increase over the same time period.  Unfortunately the Census Bureau does not give data for the top 1% (or at least I didn’t come across such data in their database). 

So it’s quite clear from the data so far that the wealth of this country is being more concentrated over time into fewer and fewer people at the top.  In addition, those upper end (top 20% and the top 5%) wage earners are not only earning more, their wealth is expanding at a faster rate than those in lower-income brackets.    

Winners and Losers

Figure 4. Percent Share of Income 1967-2010

Now let’s take a look at another aspect of income disparity, namely an individual’s share of the wealth and how this has changed over time.  Figure 4 shows the share of aggregate income by each fifth (quintile) income bracket of the population and the top 5% income bracket.  What this figure shows is that unless you’re in the top 20% (and especially the top 5%) income bracket, you’ve seen your share of the pie actually decrease

Figure 5. Percent Change in Share of Income 1967-2010

steadily over the last four decades.  Figure 5 shows the percent change in share of aggregate income between 1967 and 2010 for each of those 20% segments of household incomes.  The lowest, second lowest and third lowest 20% income brackets have seen their share decrease by 22.2%, 27.3% and 18.5% respectively between 1967 and 2010.  Conversely, the top 20% have seen their share increase by 13.2% and the top 5% gained by 19.2% over the same time period.  Essentially the old adage seems to hold true that “…the rich get richer, and the poor get poorer.”  And now we know by how much. 

Concluding Remarks

The preceding data makes it pretty clear that there is a quantifiable income disparity in this country and it can be characterized by the following trends:

  1. The wealth of this country is being more concentrated over time into fewer and fewer people at the top.

  2.  Upper end (top 20% and the top 5%) wage earners are not only earning more, their wealth is expanding at a faster rate than those in lower-income brackets.

  3. The bottom 80% of wage earners have seen their share of aggregate wealth steadily decline over the past forty years, while the upper 20% has steadily increased their share of this country’s aggregate wealth.

  4. This income disparity is not a new or even recent event.  In fact, it’s not an event at all, but a process that has been steadily ongoing, virtually unchecked, for the last forty years and more.

I’m not in a position to look at causative factors here, as this is well beyond my expertise and beyond the limits of what these data can provide.  But I am intrigued by the fact that, for the long-term income data shown here, the trends observed in these graphs are fairly steady and uniform over time, especially in the lowest four income brackets.  With the exception of the first twenty or so years of the Gini Index the trend line trajectories of each indicator, whether rising or falling, are fairly linear with no major peaks and valleys.  One might expect to be able to look at such long-term income data and be able to observe the effects of changes in political party control or major shifts in economic policy.  This doesn’t seem to be the case here when looking at these data, at least not to me. 

Such a relatively unwavering trajectory as seen in these graphs would seem to suggest that the ability to significantly alter the vector of these trends is beyond the power of either the White House or Congress for that matter.  With all the tinkering and political dynamics occurring over the last forty years, one would think that some evidence of the effect of all this tinkering would have somehow manifested itself in these income trends.  Perhaps intrinsic market forces overwhelm any attempts to control the direction of these trends by purely political means.  This is well beyond my pay grade to figure out.   Along these lines, however, it might be worth studying history to see what if anything may have happened in or around 1968 which is when the Gini Index seemed to take off and continue its steady increase for the next 40+ years. 

I am not an economist, but when I look at the long-term trends shown in median income (Figure 2) and aggregate share of income (Figure 4), I would suggest that there are two distinct functions or causative agents at work here;  one controlling the upper 20% (and 5%) and another distinctly different function controlling the lower 80%.  Perhaps the upper 20%, whose wealth has been expanding continually is driven more by market forces, and the lower 80% is controlled more by labor related economic policies.  Obviously this is a simplistic view of a very complex set of issues, but one can’t help but try to make some sense of what’s actually happening here.   

With all the rhetoric today concerning the 1% vs. the 99%, perhaps it may be more appropriate to take a broader look at the upper 20% vs. the lower 80%, because that’s where the real point of economic demarcation seems to lie.  Perhaps we have reached some sort of tipping point of income disparity, which has prompted such movements as “Occupy Wall Street”.  It also seems quite clear that an increasingly robust wealthy class appears to do little or nothing to either enhance the economic condition of the lower wage earners, or to shelter them from national economic downturns.  I think this data clearly shows the limits of the “Trickle Down” theory. What we need to be asking ourselves is not why this disparity seems so insidious now, but how and why has it been growing unchallenged for the last 40 years or more, and is there a way to bring this disparity back into some kind of balance. 

Further Reading:  If you found this essay intriguing, then you may wish to read my follow-up essays entitled; “Income Disparity by the Numbers: Volume II – The One Percent.”   ,  “Income Disparity by the Numbers: Volume III – How We Got Here.” and “Income Disparity by the Numbers: Volume IV – The 2012 Election: Wealth vs. Jobs“.

13 Responses to Income Disparity by the Numbers

  1. Chris Wendt says:

    I read this over several days and slept on it one night after having completed reading it. Personally, I believe in quantifying statements, especially those which take on the nature of a mantra, as the 99%-1% concept of the OWS movement. (Recently here, on Long Island, a blogger stated that businesses pay the largest share of property taxes, in the context of comparing business owners to homeowners as to which should have a greater say in determining a change in state law that would benefit business but was controversial for families; upon quantifying that statement, I determined from County and State data that homeowners pay about twice the gross amount of property taxes that businesses pay, in aggregate.) So the first take-away from your analysis is the sense that the major divide in wealth share does not occur at the ninety-ninth percentile, but at about the fifth quintile, or top twenty percent of households realizing significantly more wealth share than the other eighty percent.

    I attribute this to the following reasons:

    1. The population of households is not static for the purpose of these analyses. The names, numbers, locations, demographics and psycho-graphics change fluidly and continuously among the population. In all likelihood, many people in the “top 5 percent” in the latter years of the data had migrated from any or through all of the lower quintiles during the 40-year span studied.

    2. Other indices need to be evaluated to provide context for the term “wealth” as used in the picture provided of its accumulation. Chief among those would be the poverty index. The poverty index could also be used as a proxy to compare arbitrary quitiles based upon multiples of that index, such as 2X, 3X, and 4X the poverty level, quantifying numbers of households below or above each “poverty” level over the years. In other words, at some point “wealth” becomes less meaningful than poverty or the lack of poverty, for any household having overcome or escaped poverty, and having migrated up the wealth accumulation chart to a safe level, then on to progressively safer levels, and finally into their own “comfort zone”.

    3. I recognize that the data presented have been adjusted for inflation. However, historical advances in productivity and efficiency, automation, and radically new technologies, products and services which occurred during the 40-year study period served to collectively change the wealth-measurement paradigm, and not in any linear way. One might be tempted to think of inflation adjusting as “devaluation”, but that concept requires a linear way of application: over any segment of the 40-year period, old dollars were worth X% more than newer dollars, with the value of ‘X’ (inflation or CPI) varying according to annual changes in economic conditions over the span of the segment or the entire study period. But the wealth-measurement paradigm was not impacted linearly by economic conditions (those conditions themselves being results of applying wealth-measurement protocols) but rather by random and seemingly unrelated quanta resulting from the effects upon modern life from such things as medical advances, ubiquitous computing, vastly expanded communicating ability, networking over networks of all types, exponentially increasing bandwidth (in terms of both raw band width and the universality of access to it), war, terrorism, natural disasters, economic ‘crashes’, and, self- and group-awareness and realization of such things as the apparent “disparity in wealth accumulation”. These things happened here, there, bigger, worse, less, suddenly, over time, shockingly or by careful design.

    These events and factors rocked the wealth-measurement paradigm a little here, a lot someplace else, sometimes up, sometimes down, sideways to the left and then to the right.

    4. But before drawing any conclusion from the third point, the fourth reason needs to be considered: the realization and fulfillment of the global economy. We have successfully tied together practically the entire sphere of the Earth, matching by severely strained equations the mega-wealthy markets of the U.S. and Euro Zone with the production (and service) assets of “low cost” production (and service) nations (BRICK), or more accurately, continents (Asia and South America). The major conclusion upon which I would rest my case flows from the realities of the global economy: (I know the data are not available in any compatible format, but…) overlay the US wealth distribution data for the past 40 years with the same data from the rest of the world, and you will find the top 4 quintiles of American households are actually all in the top 5% of the planet. The bottom quintile of American households would, of course, be spread among widely varying echelons of wealth-share around the world.

  2. Phil Wendt says:

    Thanks Chris, I absolutely agree about where the income disparity exists. I believe that these data clearly show a line of economic demarcation at the top 20% quintile, as I indicate in my analysis.

    I also agree that during the period spanning this data set that there certainly was migration between quintiles. However, I would suggest that the Gini Index curve shows that wealth is being concentrated into fewer people over time. This seems to suggest that perhaps the source of this concentration in wealth is more likely caused by market forces within the top income tiers, rather than from migration from lower income thresholds. I can only guess at this, and I’m trying to avoid too much speculating here, but my sense is there probably has been a greater migration from higher to lower quintiles, as suggested by an overall continually shrinking of wealth share in the lower 80% over the period of record.

    I think looking at poverty data may have some merit, but after a cursory look at poverty index levels from the Census Bureau between the late 50’s to now, it looks like a far more complex issue. But the general trend looked like more of a “U” shaped curve, with poverty ultimately rising during the last several decades. I would have to spend considerable time looking at this data as poverty measurement schemes seem to have changed often over the years. I’m also a little doubtful about how much this info will add to the discussion considering the fact that the trend lines for the lower 20% was about the same for the 40-80% quintiles. Talking about the top or the bottom 1% or 5% isn’t going to bring much clarity to the larger problem affecting the lower 80% as whole. I think the poverty issue is well beyond my ability to clarify at this moment in time.

    Finally, I agree that “adjusting” the income data to present day values covers a multitude of sins. However it does (Like most data transformations) bring order to time series data and provides a stable platform against which to make relative observations through time. Those same variables you discuss also are present for the unadjusted data. It’s merely a way of bringing order to time-series data and thus not falsely attributing variability to factors such as inflation.

    In sum, I’m greatly encouraged and appreciative that you took the time to do such a thoughtful analysis of my essay. It got you thinking, which was a primary goal of presenting this analysis. I tried to present an unbiased overview of the data without wandering too much into causation. But as I said in my original essay, it’s almost impossible to not engage in some degree of speculation about causes given the nature of what the data shows.

    Thanks again for your effort.


  3. Ian says:

    Its nice to see someone actually doing math instead of just trying to cheat numbers to prove their opinion.

    I appreciated reading this

    • Phil Wendt says:

      Thanks very much for the comment Ian, it’s greatly appreciated. I knew there had to be some real “factual” data out there, and I learned quite a bit researching all this. If you have the time, and/or the inclination, I hope you read my subsequent two follow-up essays on this issue. You can access them from my main page at

      Thanks again for your interest.

      Phil Wendt

      • Chris Wendt says:

        Bernie Sanders seems to have made this topic a big plank in his campaign platform.

        Is America more likely to elect a(n old, male) Socialist over a woman, in order to address the wealth disparity issue? Or is the income disparity issue really more of a women’s issue, which Clinton could more effectively champion, you know, for her own gender?

        Or, do income/wealth disparity pale as an issues, in comparison to the illegal immigration issue, which Trump has put front-and-center before us?

        -Chris Wendt

        • My sense of Bernie is that he has a low ceiling, which he may already have reached. The media is as much responsible for his high poling numbers as he is because they keep fueling the idea of a Bernie-surge by limiting their poling only to Iowa and New Hampshire. If they started poling in South Carolina his low ceiling would be revealed, but the media would lose a big David and Goliath story. Be that as it may, I think the Income disparity “issue” is being politicized so much that I fear that the full story will be lost forever due to the bumper sticker mentality of a political campaign. It isn’t just a women’s issue; in fact the pay inequity that exists between men and women pales at the decades of stagnant growth in overall income between the low/middle class and the uber rich. But Hillary needs to hang on to the women’s vote so that’s her take on it. But I will say that she has been much more vocal about the broader income disparity issue than most, with the exception of our good friend Bernie. I also don’t see the immigration issue competing with the income disparity issue and a wise candidate would realize that they are inextricably tied together. But the candidate’s handlers would probably think that’s just too complicated for our tiny little brains to grasp. And, it wouldn’t fit nicely on a bumper sticker… and, we have a loooong way to go.

          But, good thought-provoking questions, as usual.

  4. Klaus Volpert says:

    Hi Phil,
    very nice analysis. Say, where did you get the Census data from 1947 onward? My colleague Bob Jantzen and I wrote an article modeling the Lorenz curve of the income distribution in the US
    (to appear in the Mathematical Monthly in December)
    but could only find census data from 1967 onward. It would be great if you could send me the exact URL where you found those data.
    I also liked your question regarding the 80% point. Would you say that the 80% is the point where income growth exactly kept up with GDP growth? (with higher percentiles growing faster and lower percentiles growing slower than the GDP?)

    • Phil Wendt says:

      Klaus, thank you very much for your interest in my essay on Income Disparity. The Gini Index data you mentioned can be found at the US Census Bureau at the following Link: Once at this link, select Table F4 at the very bottom of the list (Gini Ratios for Families, by Race and Hispanic Origin of Householder [XLS])

      As far as the 80% break point relative to GDP growth, I have not looked at that particular question, as it was beyond the scope of my interest (and my pay grade) at the time. I’m a retired environmental scientist and not an economist, but I became interested in this issue of income disparity after all the uproar over the “1 Percent” and “Occupy WS” issue. Obviously it got more complicated the more I looked into it, but it was a good learning experience for me and helped me form my own opinions about income disparity, the 1 % and more.

      I hope you find this information useful, and again, thank you for your interest in my work on this issue, and thank you for the link to your article on income distribution.


  5. Pingback: Sir Issac Newton Explains Income Inequality

    • Phil Wendt says:

      I appreciate you pinging me that you used data from my essays on income disparity in your current article. Unfortunately I think your article totally misses the point of the income disparity paradigm. Your conclusion in the final paragraph is absolutely disconnected from the “information” above it. In addition, you cannot have a serious discussion about income disparity/inequality and only discuss median income levels. Income disparity doesn’t exist as a statistical artifact confined within the median income ranges. It relates to the difference between the lower or even the median income levels and the upper income ranges, which you totally ignore. The Gini Index data, which you reference from my essays, should be looked at in a little more detail, as it tells a great deal of the story. This index is a measure of how uneven the income distribution is in this country…across all income levels, and not just within the median income levels. I suggest you reread my essays, and then perhaps you can present a more informed and balanced discussion of this issue. Somehow I don’t think Newton would be impressed with your take on this either.

  6. Pingback: Russia, Sixteen Tons, and Inequality | Frankly Curious

  7. Randall Briggs says:

    Interesting analysis, and nice number-crunching.

    A couple of observations.

    1. The term “trickle-down economics” is not a neutral one, any more than “tax-and-spend” economics would be. “Trickle-down” is a pejorative used by opponents of supply-side theory. Nobody advocating supply-side economics has asserted that riches for the wealthy will “trickle down” to the poor. The closest comment to that that I know of was JFK’s comment–in arguing for a tax cut–that “a rising tide lifts all boats.”

    2. As for causation, perhaps looking at the impact of LBJ’s “Great Society/War on Poverty” programs might be a good place to start. By 1967, as LBJ’s programs were coming fully online, the poverty rate that had been declining since the late 1940s leveled off, and has stayed essentially level–bumping between 11% and 14%–ever since. That’s about the same time that the Gini index spiked, then within two years began its long climb to the present day.

    • Thank you Randall for taking the time to read and comment on my essay on income disparity. Your point regarding the pejorative nature of the term “Trickle-down economics” is well taken. I can only say that historically the term trickle-down economics and supply-side economics have been inexorably linked, rightly or wrongly. My intent in linking them was more to remind readers who may be unsure which economic policy was in fact described as trickle-down economics by its critics, again rightly or wrongly.

      As for the Gini index issue, I’m not sure I see the connection with the war on poverty, in that a rising index indicates that wealth is being concentrated into fewer and fewer people at the top. Perhaps you’re suggesting that the war on poverty backfired and ended up transferring what little “wealth” was contained within the poor to the wealthiest few??

      I actually tried to look at “poverty” as an issue to delve into more deeply, and found that it would take much more work than I was able to put into it to do it justice. The term poverty alone is somewhat loaded as the various indices and metrics describing it have changed over the decades. This naturally makes it difficult to really discuss it without having some way to adjust the poverty statistics to reflect these changes. This would be considerably more than just adjusting for inflation. Perhaps this is why I also tend to look somewhat askance at discussions about long-term poverty levels.

      Again, thank you for your interest. I would also suggest that you consider reading my subsequent essays on income disparity ( as I delve more deeply into potential causative factors. Some you may agree with and some, perhaps not.