about this areaThe Theory area contains articles and essays that address the various models used to describe Human Capital Sustainability (HCS).
These articles and essays are contributed by members of the HCS editorial team. To respond to the ideas presented in an article, click the Respond link at the bottom of that article. |
getting back to normalPaul Kordis, September 2008 The current state of our economy and our culture is not normal—even if other people tell us that it is, and even if we have been tricked into believing that it is. We need to get back to normal. OK, so what exactly is normal? Most Americans that we have interviewed say that "normal" looks like the 1950s and 1960s, even if they had yet to be born at that time in history. To them, the normality of that time implies an economy in compression rather than recession, where prosperity is being spread around far more broadly and evenly. It means that human rights and civil rights are being held up for inspection, even if they need some work. Normal means that educational standards are far more demanding and that more and more people are gaining access to higher quality education. It means that a family can live on the income of one person. It means that there is more of an emphasis on the other things that give life meaning than on overwork and over-consumption. It means that stress is lower and leisure is more available. It means that the environment is healthier, even if it still needs more attention. Normal means that most kids can play unsupervised without fear. It means that the media is far more focused on providing real information than on inexpensive sensationalism, spin, and a content-free broadcast. It means that most business leaders value integrity, their communities, their customers, and their employees as much as they do profit and shareholder equity. And it means that a company president who makes many hundreds of times more than the person on the shop floor will likely have to hang his or her head in shame and be shunned and sanctioned by the community. It may not bear mentioning, because it is most likely intuitively obvious, but these people are not advocating a return to the limitations and dysfunctions of the 1950s and 1960s. Rather, they would love to see a future that embodied so many of the positive aspects of our fairly recent history that seem to have vanished without being replaced by something better. To move forward toward what they believe to be more normal circumstances would be radically different from what they experience today. In their minds, life would greatly improve if we made a radical return to normal as we move forward toward a brighter future. normal in natureScience, on the other hand, takes the position that "normal" looks like a bell-shaped curve in most instances. While forcing a bell-shaped distribution when the number of things one is observing is small is a non-productive and inaccurate idea—such as forcing a bell-curve distribution of grades among a small number of students—the bell-shaped curve remains quite accurate and descriptive when looking at a large number of phenomena in nature.
For example, the height of trees in a forest will form a bell-shaped curve when the distribution of their individual height is put on a graph. The same is true of the height or weight of people in a room, the number of times your buddies call you in a day, the size of dogs, people’s performance in the hundred-yard dash, or the average amount of rainfall in a given region—or almost any other event in the natural world. From a scientific viewpoint the bell-shaped curve describes a vast number of natural events and can be used to predict whether or not something is behaving “normally.” To be exact, a standard normal curve (bell-curve) is one in which the mean, median, and mode of a distribution occupy the same point, and the standard deviation of the distribution equals one. If you observe a series of events and their distribution deviates significantly from a standard normal curve, you could generally conclude that what you are observing is not random, and that something of significance is causing the phenomenon to deviate from normal. In some cases, it can also mean that what one is observing “ain’t right.” And this is what we hypothesize is the case with our economy, as well as with other aspects of our political, environmental, and social systems. things are not normalBased on anecdotal evidence, people seem to sense that things in America aren’t normal. Casual observation would also indicate that critical American trends, ones that we can observe with a degree of scientific rigor, are turning out to be abnormal as well. For example, there seems to be a very real disconnection between what we tout as the American Dream—the path to success in America—and the distribution of wealth and income. On one hand, we define the American Dream as the realization of a better life for our children than we had for ourselves. But currently, economic mobility in America does not support this desire. Most economic movement for individuals is between deciles, and on average Americans improve their economic standing by only ten percent over the course of a lifetime. Currently the data indicates that many, if not most, of our children will have a harder time succeeding and making ends meet than we did. In addition, we have generally defined the path to success in America as follows: If you want to succeed, all you need to do is to work hard, be good, get smart, be creative and innovative, and show initiative, perseverance, and diligence. Luck is rarely mentioned in the equation, and the important factors of social capital—who you know and who your parents are—are barely mentioned if they are mentioned at all. On the other hand, the distribution of wealth and income is highly skewed and clearly abnormal (although some claim that this is normal, because it is how distributions of wealth and income generally turn out). This skewing of the wealth and income distributions are a strong indication that the playing field is not level and that what we say leads to success is, in fact, erroneous. While working hard, being good, getting smart, etc. may be necessary, they are not sufficient, and in many cases they are irrelevant if luck and social capital are present in abundance. While highly publicized as self-made rags-to-riches heroes, the rare Horatio Alger must still rely on social capital—someone on the inside who will give them the right opportunities and introduce them to the right people—to succeed. measuring wealth and income distributionThe Survey of Consumer Finances, the Joint Tax Committee, the Federal Reserve Board, the United States Census, the Congressional Budget Office (and various other reputable sources) use different parameters and sample populations in many different ways to estimate income and wealth distribution data. Be that as it may, the most ubiquitous model used in the economic literature to analyze and describe inequality is called the Gini Ratio, named after Corrado Gini (1884 -1965), a man with four honorary degrees in separate disciplines and a person who was primarily concerned with measures of variability. In 1921, Gini published the basic concepts for the graphical measure that was to later bear his name. This ratio is illustrated here:
The horizontal axis (X) in this figure is the cumulative percentage of the population ranging from 0 to 100 percent. The vertical axis (Y) is the cumulative percentage of wealth or income, depending on which is being measured, ranging from 0 to 100 percent. The green Equality Line describes X = Y, which is to say that each person on this line has exactly the same amount of money or wealth as any other. The orange Lorentz Curve line describes the distribution of money if the market is, hypothetically, allowed to operate completely freely. This curve most closely matches the real distribution of wealth and income in the United States, although the curvature of the wealth curve is more acute that the curvature of the income curve. It is generally accepted under the free market hypothesis that the distribution of the lowest incomes and the middle range of incomes approximate respectively a normal to lognormal distribution, whereas the highest incomes are best approximated by a Pareto distribution. This means that the Lorenz Curve for real incomes (and wealth) accelerates slowly at the bottom end, moderately in the middle, and rapidly towards the top end—which also experiences the most volatility compared to the other parts of the curve. To determine the Gini Ratio, divide the area between the Equality Line and the Lorentz Curve by the entire area under the Equality Line. The Gini Ratio is A / A+ B as shown here:
history of the GiniBefore 1970 the Gini had been in decline, giving evidence to the great post-WWII compression, a term denoting the movement of many people into the middle class from both the bottom and the top of the economic distribution. Since the mid-1970s, however, the trend in income and wealth inequality has become increasingly positive, i.e. income and wealth inequalities have become more pronounced. In 1970 the Gini hovered around 0.35 for family income; in 2002 it was near 0.44 and currently is hovering around 0.47. More importantly, recent data shows that since 1998, the Gini for wealth in the U.S. has exceeded 0.8—which appears to be a borderline for the maximum inequality a modern society can sustain without experiencing some sort of breakdown. Historically many economists have written about income disparity in the United States. However, there appears to be growing concern since the late 1970s. In 1977, the Congressional Budget Office (CBO) began recording data on after-tax income that included various forms of income that standard Census data miss, such as capital gains and income from the Earned Income Tax Credit. After-tax income has increased dramatically since 1977 for the highest one percent in the income distribution of the American population, but it had risen only modestly for those in the middle and declined for those in the bottom fifth. The CBO data show that after-tax income is more concentrated among the richest one percent of the population, and also among the most affluent 20 percent of the population, than it has been at any other time since 1977. Census data on before-tax income, which goes back to 1947, indicate that income is also more concentrated than it has been at any other time during the last fifty years. Large gains in wealth have accrued to the richest 20 percent, and especially to the richest 1 percent, with the concurrent increase in income concentration. But the overall wealth holdings of most families have fallen since 1989. Indebtedness has continued to rise, and stock and pension accounts have begun to replace bank deposits, financial securities, and investment real estate as a percentage of total household wealth. In fact, over the past few years, average savings in America has gone into the negative. In addition the concentration of stock ownership is extremely skewed in favor of the wealthy, as the distribution of financial assets such as stocks and bonds is even more asymmetrical than that of overall wealth. Only a few years ago the richest 1 percent of Americans owned 53 percent of the stocks, the next 4 percent owned 25 percent, the next 5 percent owned 10 percent, the next 40 percent owned 11 percent, and the poorest 50 percent owned less than 1 percent. issues with the metricsThere is an interesting, and arguably significant, disparity between the metrics used to describe the economic data and the message that they convey. The mean of a distribution is its real average. That is, the value of all points on the distribution are added together and then divided by the number of points. The median, however, is the place on a distribution where half of the points fall on one side and half on the other. The mean is the average and the median is the midpoint. In a normal distribution these two parameters are the same. But the distribution of wealth and income in America is anything but normal. Unfortunately, in the kind of distributions portrayed by wealth and income in the United States the mean is far higher than the median. But the median is most often published as the “average.” This tends to hide the inequality in wealth and income distribution. The mean, or real average is a much more accurate indicator. In the 1970s a rapid separation of mean and median wage began to manifest. And while the mean is arguably a much better indicator of economic disparity, in most government analysis the median is emphasized while the mean is often omitted. The disparity between the median and the mean seems especially evident when analyzing that of income vs. that of wealth. For example, in 1998 median income in America was $33,400 while mean income was $52,300. But in the same year median net worth was $60,700 while mean net worth was $270,300. Data from the Consumer Finance Survey showed that in 2001 there was a 323% gap between the median and the mean for family wealth even after stock ownership had been adjusted for devaluation, with median family wealth equaling $80,700 and mean family wealth equaling $341,300. concentration of wealthThe following graph illustrates illustrates the concentration of wealth in the U.S. by plotting the cumulative percentage of wealth against the cumulative population:
If the distribution of wealth were graphed according to absolute percentages for each quintile it would look like this:
The beginning of the red arrow in both graphs dips below zero. This indicates that those in the bottom quintile have negative wealth, on average. Hopefully these graphs illustrate that while the upper fifth of the population in America may make an impressive percentage of total income, it controls an intimidating percentage of the wealth, with nearly half of its wealth concentrated in the top one percent. Between the 1970s and the 1990s broad-based income growth was highly unusual. For example, between 1979 and 2000 the real income of households in the lowest 20 percentile grew 6.4 percent, while those in the top 20 percent grew 70 percent, and those in the top 1 percent grew 184 percent. In contrast, in the 1950s and 1960s real incomes nearly doubled for each income level. Also, between 1947 and 1973 family income and productivity both grew by 104 percent, but in the mid-1970s the parallel growth between the two decoupled. From 1973 to 2002 median family income grew at about one-third the rate of productivity, leading to an income inequality marked by those at the top of the income scale grabbing the lion’s share of the larger economic pie that resulted from the increase in productivity. We have experienced far greater income inequality since the 1970s, and the concentration of income among the top 1 percent of households is as great in 2005 as it was in the period just before the Great Depression—which was the worst period of income disparity in the twentieth century. Recent regressive changes in federal taxation will push income inequality further. For those in the top 1 percent the 2001 to 2003 tax cuts provided around $67,000 in additional savings, for the middle class the cuts provided just under $600 and for the lowest 20 percent the savings was around $61. This has redistributed after-tax income from the bottom 99 percent to the top 1 percent. a more equitable distributionWonnacott and Wonnacott (1979) suggest that the most likely income distribution falls between one produced by an unfettered marketplace and one produced by complete equality. They note that free markets will, even when perfectly competitive, efficiently produce in a way that maximizes total income. This efficiency, however, only maximizes income and production; it does not divide income in an equitable way. Income is typically maximized for those who have the income to pay for it. Complete income equality, on the other hand, seems hardly attainable. They point out that considerations of equity rarely lead to complete equality, because, strangely, this also leads to inequalities. For example, should little babies or very old or infirm people be required to work the same way that a healthy adult does? These authors suggest that the best course is a compromise between absolute equality and the free market. One example of this compromise is the notion that the race, if one may call it that, should be fair. Nobody should begin the race handicapped because they are a minority or the child of parents who are not influential. Everyone should have the right to an equal start. two different economiesBradbury and Katz (2002) illustrate this point by asking the reader to imagine two different economies. They suggest that both of these economies are experiencing increasing inequality, but differ in how inequality manifests over time for individual families. In the first economy the broad income inequality is merely a factor of chance. Some people win the lottery, others get sick; some farmers have good weather, others bad. In any case, random events determine income and this income varies from year to year. In an economy such as this mobility is likely to be very high and variations in luck will, over the long run, provide families with roughly equal lifetime incomes. The second economy also has wide income inequality; however, rather than being relegated to the caprice of chance, the differences in incomes are permanent. There is no economic mobility. This may be due to social discrimination; or the society might enforce a class system; or talent, motivation to work hard, and-or access to a high-quality education or a high paying job persist only within certain families. But in this economy, regardless of the specific cause, the inequality will increase over time, providing families at the top with both large and growing advantages over those at the bottom. Bradbury and Katz suggest that the increasing inequality experienced in the United States since the early 1970s has not been accompanied by increasing mobility and that lifetime incomes will grow increasing unequal. They note that some consider a growing gap between the top and bottom acceptable so long as everyone’s income is rising in real terms. But they also argue that people often judge their well-being by comparison with others, making relative improvement more important than absolute improvement beyond a basic level. the 80/20 ruleVilfredo Pareto (1848 – 1923) was the first to formally introduce the observation that the wealth and income of many nations approximated an 80-20 distribution. That is, 20 percent of the people typically have 80 percent of the wealth or income. Since then, his observation has been used to establish a standard regarding accurate expectations. In other words, if Pareto observed that wealth everywhere was distributed in an 80-20 fashion, then that is what is normal and that is what people should expect. However, if one were to ask most people their idea of what normally distributed wealth should be they would likely state that some people would be poor and some rich, but most would be in the middle. As stated before, this is consistent with what statisticians and other scientists observe in nature and in natural systems. For example, people’s height approximates a bell curve; some are very short, some are very tall, but most are somewhere in the middle. Over time this observation of natural systems came to be represented in the standard normal curve, which is used to compare results obtained in scientific research against what would happen “normally” in most situations (Mandel, 1964; Patel & Read, 2006) . the extremesAlso, as noted earlier, the Gini is configured to represent absolute equality in income or wealth, extreme socialism, as a diagonal line bisecting the graph. Absolute inequality, extreme capitalism, is depicted by a 90 degree angle to the right of the graph. And finally the current state is depicted as a Lorenz curve lying between extreme socialism and extreme capitalism:
In the resources I have researched, it is strongly implied, if not outright stated, that the normal economic state lies along some iteration of the Lorenz curve situated between extreme socialism and extreme capitalism. This, I believe, is a fallacious assumption. getting normalAlthough I have not found an instance of the standard normal curve being applied to the Gini, I believe that the cumulative standard normal curve so applied is the most accurate measure of normalcy. This is illustrated here:
The cumulative standard normal curve would appear as the green arrow in the above graph. The cumulative aspect of the standard normal curve is used as the other parameters on the Gini are cumulative as well. If this application is correct, and I currently have no information that would contradict it, it radically alters what should be considered normal in this context. In addition, because wealth is so concentrated to the right of this graph it may well be that the act of changing the Lorenz curve at the upper end to match the cumulative standard normal curve would cause most, if not the rest entirely, of the Lorenz curve to approximate the cumulative standard normal curve. I realize that in this observation I depart from conventional economic wisdom. But to me the notion seems appropriate and convincing. On the Lorenz curve as it is applied to the Gini there is a disconcerting disparity between the median and the mean. Axiomatically, however, the cumulative standard normal curve would have normal variability and a single locale for the median and the mean, rather than the disparity seen in the Lorenz curve, as illustrated in the next figure. In other words, it would more truly define average.
As noted earlier, socialism and capitalism have mirror-like failings in the creation of wealth and its equitable distribution. But rather than socialism, capitalism, or some chimera in between; the cumulative standard normal curve could define a new “normalism,” or that benchmark for which Americans should aspire. Unfortunately, according to many, economics has performed rather poorly in its overall ability to predict America’s economic behavior, and often builds its foundations with assumptions that are patently incorrect (Keen, 2001; Omerod, 1997, 2000, 2006) or is used merely to add credibility to the prevailing political or economic ideology. Therefore, when imperfections in the economy are found they are often used to offer a patina of respectability, if not an outright excuse, for economic inequality rather than to inspire a prescription for change (Champekowne, 1953; Kuznets, 1979; Landes, 1999; Loury, 1981; Malthus, 1986; Pareto, 1991) . Perhaps, though, the real purpose of economic study is to clarify how America should behave in order to have a vital and long-lasting democratic economy, one that provides a level playing field for everyone. the real playing fieldSo often Americans are handed the party line—told in seminars, on TV, in books, and by their churches that if they will only persevere, work hard, get smart, and behave themselves that they will have as much chance of prospering as anyone else who has triumphed in the American economy. But the mere fact that America’s economic distribution is described by a Lorenz curve begs these propositions to reveal themselves for what they truly are – illusions fashioned to keep people motivated and distracted while maintaining the status quo (Hartmann, 2002; Hartmann & Miller, 2006; Hedges, 2007; Martin, 2005; Phillips, 2006; Schor, 1993; Warren & Tyagi, 2003) . The field on which the American people play out their lives is, apparently, far more tilted toward those who already have the advantage. For better or for worse, real success is all too often determined by one’s social capital (Baker, 2000; Cross & Parker, 2004; Laird, 2006; Lin, 2001) , giving credence to the popular idea that it is not what one knows, but who one knows that makes all the difference. Being a moral person, working hard, persevering, and having intelligence may be necessary for economic success, but, as noted before, it is very often not sufficient. And in some cases, it may not even be necessary (Kelley, 2004; Minutaglio, 2001; Phillips, 2002, 2004) . the bottom lineAn equitable distribution would be a normal one, and a normal distribution is most likely achieved through a level playing field where true personal ability, motivation, and luck are the only factors that determine whether or not one succeeds. referencesBaker, W. (2000). Achieving Success Through Social Capital: Tapping Hidden Resources in your Personal and Business Networks. San Francisco: Jossey-Bass Inc. Bradbury, K., & Katz, J. (2002). Are lifetime incomes growing more unequal?: Looking at new evidence on family income mobility. Regional Review, Q4, 3 - 5. Champekowne, D. (1953). A model of income distribution. Economic Journal, 63, 318 - 351. Cross, R., & Parker, a. (2004). The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations. Boston: Harvard Business School Publishing. Hartmann, T. (2002). Unequal Protection: The Rise of Corporate Dominance and the Theft of Human Rights. Emmaus, Pennsylvania: Rodale Press. Hartmann, T., & Miller, M. C. (2006). Screwed: The Undeclared War Against the Middle Class - and What We Can Do About It. San Francisco: Berrett-Koehler Publishers, Inc. Hedges, C. (2007). American Fascists: The Christian Right and the War on Amerca. New York: The Free Press. Keen, S. (2001). Debunking Economics: The Naked Emperor of the Social Sciences. New York: St. Martin's Press, Inc. Kelley, K. (2004). The Family: The Real Story of the Bush Dynasty. New York: Doubleday. Kuznets, S. (1979). Growth, Population, and Income Distribution: Selected Essays. New York: Norton. Laird, P. W. (2006). Pull: Networking and Success Since Benjamin Franklin. Cambridge: Harvard University Press. Landes, D. S. (1999). The Wealth and Poverty of Nations: Why Some are So Rich and Some So Poor. New York: W. W. Norton & Company. Lin, N. (2001). Social Capital: A Theory of Social Structire and Action. Cambridge: Cambridge University Press. Loury, G. (1981). Intergenerational transfers and the distribution of earnings. Econometrica, 49, 843 - 867. Malthus, T. (1986). Principles of Political Economy (2nd ed.). New York: A. M. Kelley. Mandel, J. (1964). The Statistical Analysis of Experimental Data. New York: Interscience Publishers. Martin, G. T. (2005). Millennium Dawn: The Philosophy of Planetary Crisis and Human Liberation. Sun City, Arizona: Institute for Economic Democracy Press. Minutaglio, B. (2001). First Son: George W. Bush and the Bush Family Dynasty. New York: Three Rivers Press. Omerod, P. (1997). The Death of Economics. New York: John Wiley & Sons, Inc. Omerod, P. (2000). Butterfly Economics: A New General Theory of Social and Economic Behavior. New York: Basic Books. Omerod, P. (2006). Why Most Things Gail: Evolution, Extinction and Economics. New York: Pantheon. Pareto, V. (1991). The Rise and Fall of Elites: An Application of Theoretical Sociology. Piscataway, New Jersey: Transaction Publishers. Patel, J. K., & Read, C. B. (2006). Handbook of the Normal Distribution (2nd ed.). New York: Marcel Dekker, Inc. Phillips, K. (2002). Wealth and Democracy: A Political History of the American Rich. New York: Broadway Books. Phillips, K. (2004). American Dynasty: Aristocracy, Fortune, and the Politics of Deceit in the House of Bush. New York: Viking. Phillips, K. (2006). American Theocracy: The Peril and Politics of Radical Religion, Oil, and Borrowed Money in the 21st Century. New York: Viking. Schor, J. B. (1993). The Overworked American: The Unexpected Decline of Leisure. New York: BasicBooks. Warren, E., & Tyagi, A. W. (2003). The Two-Income Trap: Why Middle-Class Mothers and Fathers are Going Broke. New York: Basic Books. Wonnacott, P., & Wonnacott, R. (1979). Economics. New York: McGraw-Hill Book Company. Respond to this topic. Next topic: False Social Norms Marketing |
founderPaul L. Kordis, PhD advisorsJames H. Banning, PhD Gary Geroy, PhD Ed Goodman, P.E., MSCE Bruce Hall, PhD M.L. Johnson, EdD, PhD David T. Moran, PhD Beverly Title, PhD
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