﻿ The Grasshopper and the Ant or the Beautiful and the Damned? Why We Have What We Have and How Government Should Take What It Needs for Charity Published by Daniel Badger, Jr. at Smashwords Copyright 2012 Daniel Badger, Jr CONTENTS Introduction Calendar Lottery Gender Lottery Genes Lottery Parents Lottery Education Lottery Job Lottery Why We Have What We Have How Government Should Take What it Needs for Charity The Great Chain of Earning Notes Data and Methodology Sources About the Author Introduction I work hard for what I have. I will share it with who I want to. Government cannot force me to be charitable. -- Glenn Beck: Seventh Principle to Live By I'd rather be lucky than good. -- Lefty Gomez Fortuna, noun, Latin: luck, fate, prosperity, possessions What do Ayn Rand, Rand Paul, Paul Ryan, Ron Paul and L. Ron Hubbard have in common? They believe that self-reliance and self-actualization--Glenn Beck's “hard work”-determine the path we travel in life. There is no place in their theology for, “There but for the grace of God go I.” I disagree. In my experience, self-reliance and hard work account for less than half of how well we do and what we have. The rest is accidental. So when I read Beck's “Seventh Principle to Live By,” my first thought was that his parents must have read him Aesop's Fable of the Grasshopper and the Ant once too often. My second thought was, “Does this mean that if have what I have by accident, then government can force me to be charitable?” I believe the answer is “yes,” and I believe this provides a strong argument for the fairness of sharply progressive income taxation. Since we don't “deserve” what we have by accident, what we have by accident is fair game for taxation to fund whatever safety net we decide we want. So our question is, how much of what we have comes our way by accident? Part One is a discussion of six “lotteries,” whose outcomes play an important role in determining our lifetime earnings. At the moment of conception, we draw tickets in four “birth lotteries”: calendar, gender, genes and parents. Later, we draw tickets in the education and job lotteries that screen, sort, and select us for whatever earnings path we follow to retirement. In discussing the lotteries, I make the “weak” claim that they matter “a lot” in explaining lifetime earnings. Most people intuitively accept that a lot does indeed depend on chance, so Part One shouldn't be particularly controversial. In Part Two, however (Why We Have What We Have), I make a stronger claim: the lotteries explain at least half of what we earn in our lifetimes, and half or less is explained by Beck's “hard work.” To prove this, I ask you to look at the numbers. In “Why We Have What We Have,” I do my best to present a readable summary of the findings of research on the question of how much of Americans' lifetime earnings can be “explained” or “predicted” (in the statistical sense) by the birth accidents. If I have correctly understood this literature, the answer is, at least half is explained by gender, genes and parents. More precisely, half or more of an individual's lifetime earnings, relative to the average American's lifetime earnings, is the result of drawing better or worse tickets than the average American in the gender, genes and parent lotteries. “Why We Have What We Have” is hard work. (Glenn Beck will be pleased with you if you get through it.) There is no rigorous way to discuss what “explains” lifetime earnings without a basic understanding of probability and statistics-the difference between a mean and a median, between variance and standard deviation, between correlation and R2, between a normal distribution and a log-normal one, and the meaning of coefficients in a multiple regression analysis. That's about it. You don't need to understand any of this to understand the conclusions of “Why We Have What We Have”, but you do if you want to understand how I reach these conclusions, as explained in Data and Methodology. The following graph summarizes the conclusions. “Rich boys” represents American men over age 25 whose parents' income was higher than the median (roughly 25% of the population). “Poor girls” represents American women whose parents' income was less than the median (another 25%). “Everyone else” is everyone else. Statistically, a randomly selected “poor girl” has an 18% chance of earning above the population median during her lifetime. A randomly-selected “rich boy” has a 75% chance of earning above the median during his lifetime. A poor girl can do better than a rich boy if she fights hard enough, but only 8% of poor girls will succeed in earning more than the “rich boy” median. Some-especially if their name includes a Ron, a Rand or a Paul somewhere--will object that we are not statistics, that every individual is unique, and can achieve whatever he or she sets out to achieve by dint of hard work and self-reliance. To which I reply: “yes and no.” In our youth, and as individuals, we face possibilities. But over the course of our working lives, and as members of a population, we face probabilities. Every young person can and should try to beat these odds, and government should do whatever it can to change them. But the odds are what they are. Part Three of the essay (How Government Should Take What it Needs for Charity) examines the implications of this for fair income taxation. It begins with the question whether we deserve credit (or blame) for things that happen to us by accident—in particular, the accidents of when, to whom and with what we are born. My answer: we deserve credit or blame depending on what we do with the tickets we draw, but we can take no credit, and deserve no blame, for the tickets themselves. It follows that we deserve some but not all of what we are worth in the labor market. We deserve whatever value we add as a result of our hard work, but we don't deserve the value that derives from the endowments we were born with, or the investments our parents make in our human capital. You may ask, “How can you possibly distinguish between the market value of my genetic endowments, my parents’ investments in me, and my own hard work?” My answer: I can’t do this for a given individual, but the statistical evidence presented in “Why We Have What We Have” allows us to do this for groups—in particular the two groups who lie above and below any given percentile of the earnings distribution. This evidence shows that at least half of what the upper group has, relative to the lower group, is the result of drawing better tickets than the lower group in the Six Lotteries. We have already answered this. “Why We Have What We Have” shows that the value of what was present at the creation is at least half of the total. If we deserve less than half of what we have, I then ask: if we live in a society that decides to provide its members with a social safety net, what is the fairest way to fund it? My answer: 50% of all income above the median income is fair game, because income above the median represents the fruits of being luckier than average, and no one deserves to be luckier than average. Though I am a card-carrying liberal, a sound rationale for progressive taxation has always eluded me. If we benefit from government in proportion to what it makes it possible for us to have, this only justifies proportional taxation, and even Steve Forbes is in favor of this. But why is it fair for rates rise as we earn more? Does government give us progressively more-as opposed to proportionally more-- as we earn more? I don't think so. But things are different when we come to consider how to fund the safety net. For this, “the user pays” applies only up to a point. Payroll taxes paid by Boomers' during our working lives cover the cost of our Medicare and Social Security benefits only up to a point. Beyond that point, someone else must pay, and pay quite a lot, absent drastic changes in Medicare and Social Security. And then there are the users of Medicaid, food stamps and a variety of other safety net programs, who do not and cannot pay anything. The conventional view on meeting the funding requirements of the safety net is, “We can't afford this.” To which I say, “What do you mean 'we,' kemosabe?” If “we” means those who earn the median income or less, this is correct. They can't afford it, and shouldn't be asked to contribute. But if “we” means those of us who earn more than the median, and if half of what we earn above the median has come our way by accident, then I say we can afford it. I conclude that a 50% tax on the fruits of good fortune, to the extent this is needed to fund the safety net, passes the fairness test with flying colors. I call it the “Fortune Tax.” The figure below shows the average (not marginal) tax rates that would result under a Fortune Tax that “cuts in” at the 65th percentile of Americans’ earnings, when combined with a flat 10% “user tax” on all income. The Fortune Tax funds the safety net, while the user tax funds government's “non-charitable” activities. The figure shows average rates for all Americans reporting Adjusted Gross Income to the IRS in 2009, from the 25th to the 100th percentile. (Americans below the 25th percentile reported no income.) The figure compares the Fortune Tax with current rates (which include the “Bush Tax cuts”) and with rates that prevailed during America's “Age of Affluence” from 1953-1973. The Fortune Tax rates are steeply progressive compared to current rates, but not when compared to Age of Affluence rates. Would such a steeply progressive tax kill jobs? Well, consider this: during the twenty years of the Age of Affluence, the top marginal rate was never lower than 70%, and was at times as high as 91%, yet real median family income rose at an annual average rate of 2.8%. By contrast, during the twenty years from 1991 to 2011, when the top rate was never higher than 39.6%, real median family income rose at an annual average rate of 0.3%. Why would anyone expect the Fortune Tax to kill jobs? The reason why sharply progressive rates don't kill jobs should be obvious. HL Hunt put his finger on it when he said, "Money is just a way of keeping score." High marginal tax rates didn't discourage entrepreneurs during the Age of Affluence--and won't do so in the Age of Austerity-- because all entrepreneurs face the same rates. If your main objective is getting a higher score than the other guy, it makes no difference whether the marginal rate is 90% or 20%, because it's the same for you and the other guy (unless he has a cleverer accountant). And, of course, psychology experiments show that people would rather earn $75,000 in a world where no one else earns more than $50,000 than earn $100,000 in a world where many are earning $150,000. In sum, we have at least half of what we have (or lack) by accident--mainly the accident of our birth. No one deserves a better gender, better genes or better parents than anyone else. If those who draw better than average tickets in the birth lotteries work hard to make the most of their birth accidents, they deserve the wealth created by their hard work, but not the wealth created by their birth lottery tickets. The data show that at least half of our lifetime earnings is explained by the birth lottery tickets. Therefore, if society decides to fund a safety net for its least fortunate members, it is entirely fair to do so with a tax of 50% on as much of all income above the median income as necessary.For those with earnings above the tax’s “cut-in” point this 50% represents the fruits of drawing better tickets than the group falling below the cut-in point. So here is my amended version of Glenn Beck's Seventh Principle to Live By: “I work hard for what I have. But if I have much of what I have by accident, government has every right to force me to be charitable.” Calendar Lottery For unto everyone that hath shall be given, and he shall have in abundance. But from him that hath not shall be taken away even that which he hath. -- Matthew 25:29 What do we have? Figure 1 is one way of showing it.[1] Each cone represents the average earnings in 2011 for 1/12 of Americans aged 25 and over. Now let's do a hypothetical (if you don't do hypotheticals, you shouldn't be reading this.) Imagine a parallel universe, in which earnings depend only on the month in which you were born. If it was September, you earned $8,600; if it was March, $58,865. The lucky 8.3% born in January earned $205,000. Figure 1 This universe is not entirely fanciful. In Outliers: The Story of Success, Malcolm Gladwell's insightful examination of hidden, chance factors that influence career success and failure, we are presented with the anomaly of the birth-months of Canadians who play in the National Hockey League. It turns out that the Canadian players' birth months do not fall equally in all months of the year. The NHL Canadians are more likely to have January birth dates than any other month. The second-most frequent month is February, and the third is March. The least frequent are October, November and December. Statistically, Canadians in the NHL are twice as likely to have been born in the first three months of the year than in the last three months. This has been consistently true for many years. The accepted explanation lies in the way Canadian youth hockey is organized. Youth leagues are formed for each birth year, with a December 31 cutoff date. Players in the eight-year-old league turn eight between January 1 and December 31. Players born in January are eleven months older than those born in December, and at age eight, eleven months makes a lot of difference in size and strength. On average, therefore, the January kids perform significantly better than the December kids. A similar phenomenon is found in major-league baseball, except that the cut-off date for most little-league play is July 31 rather than December 31. Gladwell reports that among the Americans playing in the major leagues in 2005, 60% more were born in August than in July. He further reports that at the tryouts for the Czech national soccer team, the “coaches might as well have told everyone born after mid-summer that they should pack their bags and go home.” Nor is the “cut-off” effect found only in sports. It also plays a role in educational performance, where kids born in the last month before the class cutoff date consistently out-perform kids born in the first month. [2] You might think that this birth-month accident is only an initial disadvantage, and that its significance diminishes with time, as ability, hard work, self-discipline and “fire in the belly” erase the initial, accidental disadvantage. But if this were true, we would not see the birth-month statistics that we do fifteen years later among those kids who make it to the NHL. Nor would people born in January be less likely to go to college than people born in December. But they are. [3] Here's Gladwell's explanation, which he calls the “Matthew Effect” with reference to the book of Matthew at the head of this chapter. This being Canada, the most hockey-crazed country on earth, coaches start to select players for the traveling “rep” squad--the all-star teams--at the age of nine or ten, and of course they are more likely to view as talented the bigger and more coordinated players, who have had the benefit of critical extra months of maturity. And what happens when a player gets chose for the rep squad? He gets better coaching, and his teammates are better, and he plays fifty or seventy-five games a season instead of twenty-five games a season like those left behind in the “house” league, and he practices twice as much as, or even three times more than, he would have otherwise. In the beginning, his advantage isn't so much that he is inherently better but only that he is a little older. But by the age of thirteen or fourteen, with the benefit of better coaching and all that extra practice under his belt, he really is better, so he's the one more likely to make it to the Major Junior A league, and from there into the big leagues. This passage from Outliers lays out succinctly why the consequences of birth-month accidents persist rather than fade away: “Hockey and soccer are just games, of course, involving a select few. But these exact same biases also show up in areas of much more consequence, like education. Parents with a child born at the end of the calendar year often think about holding their child back before the start of kindergarten: it's hard for a five-year-old to keep up with a child born many months earlier. But most parents, one suspects, think that whatever disadvantage a younger child faces in kindergarten eventually goes away. But it doesn't. It's just like hockey. The small initial advantage that the child born in the early part of the year has over the child born at the end of the year persists. It locks children into patterns of achievement and underachievement, encouragement and discouragement, that stretch on and on for years and years.” Another illustration from Outliers showing how the accident of birth-timing can drive success focuses on the Silicon Valley pioneers: Bill Gates, Paul Allen, Steve Ballmer, Eric Schmidt, Steve Jobs, Bill Joy, Scott McNealy, Vinod Khosla and Andy Cechtolsheim, all of whom were born between 1953 and 1956. It's true that fluoride was first introduced into America's municipal water supplies at around this time, but this probably does not explain the extraordinary success of this three birth-year cohort at the dawn of America's IT industry. Gladwell's explains this as the result of a timing accident--the advent of computer time-sharing on college campuses. If you were born before 1953, you would have entered college before 1970-71. Before 1970-71, if you wanted to learn to write computer code, you did it by spending the afternoon at the computer center punching your code into a stack of cards (“do not fold, bend or mutilate”) which you would hand in at the desk before leaving the center. You came back the next day to see if your code produced the expected result. But once computer time-sharing was introduced in 1970-71, you could test as much code in one all-night session as you could test in a year using the old punch-card system. As a result of this timing accident, those entering the colleges with advanced computing facilities between 1971 and 1974 got a huge leg up --a "first-mover" advantage--on everyone who arrived before or after they did for careers in the industries of science and commerce that code-writing created. And, like the young Canadian hockey player born in January, the advantage from this initial, accidental leg up did not diminish, but was compounded over time. It's not hard to find other examples where being accidentally in the right place at the right time has advantaged certain groups seeking to climb to the top of the lifetime earnings ladder. Outliers gives a portrait of the cohort of Wall Street M&A lawyers who set up firms in the late 1960s, just in time for the M&A boom that saw the annual action rise from around $125 million in 1975 to almost $2.5 billion in 1989. Chrystia Freeland's recent New Yorker profile of Leon Cooperman, a “Bronx-born, sixty-nine year old billionaire,” who spent twenty-five years picking stocks at Goldman Sachs before starting a hedge-fund, helps fill in this picture. Quoth Cooperman: “While I have been richly rewarded by a life of hard work (and a great deal of luck), I was not to the manor born.” Freeland adds, Cooperman differs from many of his fellow super-rich in one important regard. He understands that he isn't just smart and hardworking but that he has also been lucky. “I joined the right firm in the right industry,' he said. “I started an investment partnership at the right time.” In the fall of 1963, he enrolled in dental school at the University of Pennsylvania, but within the first week he began to have doubts, and dropped out soon afterward. 'My father, may he ret in peace, was going to work saying, “My son, the dentist,”' Cooperman said. 'It was a total embarrassment among his friends.' Cooperman went on to make a series of fortunate choices. Chief among those was entering the financial markets, after graduating from Columbia Business School in 1967. In the sixties, Wall Street wasn't yet the obvious destination for the smart and ambitious, but it was on the verge of becoming the most lucrative industry in America. Cooperman became an analyst at Goldman Sachs, at the time a scrappy partnership that had nearly failed during the Great Depression. In 1976, Cooperman was named a partner. He went on to found Goldman's asset-management business, but, after twenty-five years at the firm, he decided to start his own hedge fund. Between 1991 when Cooperman founded Omega Advisors, and the 2008 financial crisis was the best time in history to make a fortune in finance. Cooperman's partners who stayed behind at Goldman Sachs are hardly paupers…but the real windfalls on Wall Street have been made by the financiers who founded their own investment firms in the period that Cooperman did. Professional sports offers another example of how timing matters. In Figure 2, we see average major league baseball salaries growing at 19% annually between 1975 and 2010. [4] The average salary in the NFL rose at 12.5% per year from 1975 through 2001. The average salary for NBA players rose at 14% annually from 1970 to 2009. The median American worker's wages rose by 4.4% during this period, and the annual average inflation rate was 4.2%. Figure 2 Today's CEO's are even happier about their timing than today's professional athletes, as shown in Figure 3. Total CEO compensation (cash, shares and options) for the top 50 firms in the USA, measured in constant 2000 dollars increased by 27% from 1975 to 2003, and this is in real terms, so with inflation the annual increase was 31%! [5] Figure 3 I haven't found good figures on trends in compensation in banking and finance, but I am certain that when I do, they will show even sharper increases relative to the common man. We will look at some reasons why this might have happened in the “Job Lottery,” but here are some reasons that do not explain the differential salary growth: in the case of CEOs and bankers, it was not because they were a lot smarter or worked a lot harder in 2010 than in 1980. In the case of hitters, it was not because they had higher batting averages in 2010 than 1980, nor did pitchers have lower ERAs. Running backs did not rush more yards, quarterbacks did not throw for more yards, and point guards did not score more points or capture more rebounds. No, as we will see in “Job Lottery,” something else changed between 1980 and 2010, and the “hard work” of bankers, CEOs and athletes had nothing to do with it. Derek Jeter, Eli Manning, Michael Jordan, Jack Welsh and Jamie Dimon were in the right place at the right time. Mickey Mantle, Joe Namath, Bob Cousy, Reginald Jones and David Rockefeller were there at the wrong time--victims, as it were, of a bad timing accident. Of course, the dynamics which perpetuate the accidental advantages of good timing apply equally to the accidental disadvantages of bad timing. As a consequence of the Great Recession, the lifetime earnings prospects for students graduating from college during the ten years after 2009 will be significantly lower, on average, than those graduating in the ten years before 2009. Many of the post-2009 graduates who have managed to find jobs found them at lower salaries than the pre-2009 graduates, and may not catch up with the salary progression of the pre-2009 entrants for a long time, if ever. Here's another timing accident, which, in synergy with accident of who your parents are, has important consequences for lifetime earnings. We shall see in the Parent Lottery that between 1980 and 2010, the gap between the resources (time and money) that higher-income parents devoted to preparing their children for life widened substantially compared to the resources devoted by lower-income parents. Although it was bad luck to be born poor in 1980, it was much worse luck in 2010. And vice-versa. Notwithstanding all of this, we are still a very long way from the situation depicted in Figure 1, where the birth accident explains all. Many other things explain lifetime earnings, in addition to these timing accidents. Some are also accidental, and will be explored in the upcoming “lotteries.” But many are intentional, including Glen Beck's “hard work,” and such things as ambition, self-discipline, determination, greed, cunning, and ruthlessness. Notice, however, that although “hard work” is clearly intentional, often the opportunity to work hard comes by accident. We have already seen an example of this: ten-year-old Canadian hockey players who are tapped for the “rep squad” get to practice three times as much as those who are not tapped, and this “hard work” differential gets wider in each succeeding year. As undergraduates, the cohort of Silicon Valley pioneers born in 1953-56 got to spend one-thousand times as many hours sharpening their code-writing skills as anyone born before 1953. After graduation (or, in many cases, after dropping out of college) these guys used their acquired coding skills to set up businesses in which they could spend even more time writing code. In the Genes Lottery, we will look at the accident of talent, and again we will see the Matthew Effect at work: the more talent you are born with, the better your chances of getting to work hard to make the most of your talent. Whether it is a violin, a tennis racket, or an algorithm for trading currency futures, if you are born with more than the average aptitude for using your body or your mind in a particular pursuit, chances are you will be given the opportunity to work hard so that you can do it even better. It would not be an exaggeration to say it this way: the luckier you are, the harder you get to work. The converse is also true. Many of us "make our luck" by being persistent. In which case, it can also be said that the harder you work, the luckier you get to be. But no amount of hard work can improve your luck in the four birth lotteries (calendar, gender, genes and parents). Gender Lottery Unto the woman he said, I will greatly multiply thy sorrow and thy conception; in sorrow thou shalt bring forth children; and thy desire shall be to thy husband, and he shall rule over thee. --Genesis 3:16 At the moment of conception, each of us draws a ticket in the gender lottery. Each ticket bears either a zero or a one. If you draw a one, you will have a seven-in-ten chance of earning more than the average person during your lifetime. If you draw a zero, you will have a seven-in ten chance of earning less than the average person. The “one” in this lottery is the male gender. The zero is female. The consequences are illustrated in Figure 4.1, which shows estimated lifetime earnings for men and women in each of eight classes of education. [6] The average earnings of all women are 55% of the average earnings of all men. Figure 4.1 What explains this huge earnings gap? One obvious explanation is that on average, men work for pay more than women. More men than women work full-time (65% vs 45%), fewer work part-time (22% vs 30%) and fewer don't work at all (15% vs 25%). Figure 4.2, however, shows that something else is going on, because this figure shows earnings only for men and women who work full-time for forty years. It excludes part-time workers and those who don't work at all. Across all education classes, the average lifetime earnings of full-time working women is 65-70% of the average for full-time working men. Figure 4.2 What explains this persistent earnings difference between men and women who work full-time for forty years? Why do men, on average, have better-paying jobs than women? Is it because: 1) Men are better than women, on average, at doing the higher-paying jobs? 2) Men are better than women, on average, at getting the higher-paying jobs? 3) Managers are less likely to promote women to these jobs because women are more likely to quit to raise children? or 4) Women are less likely to choose to do these jobs because they are more likely to have other priorities? or 5) Women face endemic discrimination in the form of unequal-pay-for-equal-work and glass ceilings? It must be true that there are fewer “alpha” women -- a concept that incorporates self-assurance, self-assertion, risk-seeking, and aggression -- than alpha men. Whether this alpha-differential is “wired in” at conception, or learned along the way, it supports explanations 1-2 above. Alpha traits are an advantage in performing many or most of our economy's highest-paying jobs. Alpha is also likely to be an advantage in the competition to get those jobs, whether or not those who get them actually perform better. Managers' promotion decisions are surely influenced by expectations that women are more likely to quit or ask for reduced hours in order to raise children. Such expectations surely handicap all women--including those who have no intention of quitting or seeking reduced hours--in the competition with men for the highest-paying jobs. Are women who work full-time less likely to choose the highest-paying jobs? Women who have or want to have children are not so likely to choose the jobs that are hardest to reconcile with the demands of child-rearing. In her painful essay “Why Women Still Can't Have it All,” Anne-Marie Slaughter explains why she found it so much harder to be Assistant Secretary of State for Policy Planning -- to the point where she gave up the job sooner than she or the Secretary of State would have wished -- than to be the Dean of Princeton's Woodrow Wilson School of Public and International Affairs. The explanation: as dean, Slaughter had control over her schedule. As Assistant Secretary of State she did not. With two sons at home, Slaughter couldn't afford to lose this control. Figure 5 illustrates the problem from a different angle, and helps explain why the female/male differential in Figure 4.2 is widest among those with professional degrees (65%) than among the other education classes, where the average differential is 70%. [7] Figure 5 compares the rate at which wages for men and women with college degrees or higher rise between the ages of 22 and 67. Women's wages are around 80% of men's at age 22. They rise at the same rate for both sexes for ten years, at which point women's wages level off, while men's wages keep rising. By the time they are 48, women's wages are only 63% of men's wages. The male/female wage growth gap begins to open up at around age 30, when the majority of women start having children. Within another ten years, women's wage growth has stopped altogether, implying that they are no longer rising within whatever career path they have chosen. Figure 5 The alpha deficit, unequal-pay-for equal work, glass ceilings, and promotion discrimination based on expectations that women may leave are undeniably important in explaining the gender earnings gap. These have nothing to do with the choices made by any one woman. However, managers' discrimination against women in promotions clearly has something to do with choices made by women-not as individuals, but collectively. Women are far more likely than men to leave jobs in mid-career in order to raise children. Furthermore, among women who continue to work full-time (Figure 4.2), child-rearing responsibilities provide a strong motivation to choose jobs that place fewer demands on their time, and accordingly pay less. Some may argue that, to the extent that the male/female earnings gap is the result of women's choices in favor of child-rearing, this gap is the consequence of intentional rather than accidental factors. I disagree. Child-rearing is a role that has been assigned almost exclusively to women by biology and by society. Men can have a demanding career and eat it, too. Women cannot. The gap between these two positions is the consequence of the birth accident of gender. The different choices that men can make, and women are forced to make, derive from this accident. These choices are therefore fundamentally accidental, and only secondarily intentional. _ Genes Lottery Scarecrow: The sum of the square roots of any two sides of an isosceles triangle is equal to the square root of the remaining side. Oh joy! Rapture! I got a brain! How can I ever thank you enough? Wizard of Oz: You can't. Another lottery in which we draw tickets at the moment of conception is the genes lottery. Our genetic endowment plays a lead role in determining what we do, and how well we do it, during our lifetime. Studies show modest correlations between income and measures of looks and height, somewhat stronger correlations with health, and a fairly significant correlation with intelligence (IQ). Were it possible to measure talent the way we measure intelligence (call it “Talent Quotient”), no doubt we would find a fairly substantial correlation also between income and talent. For each of these, our genes determine what we have to work with. There is nothing we can do to make ourselves taller, and only so much we can do to make ourselves better-looking. We can do nothing to boost innate athletic or artistic talent, but there is a lot we can do to make the most of whatever we are given. In this following passage from Outliers, Malcolm Gladwell quotes the neurologist Daniel Levitin: The emerging picture…is that ten thousand hours of practice is required to achieve the level of mastery associated with being a world-class expert-in anything. In study after study, of composers, basketball players, fiction writers, ice skaters, concert pianists, chess players, master criminals and what have you, this number comes up again and again. Of course, this doesn't address why some people get more out their practice sessions than others do. But no one has yet found a case in which true world-class expertise was accomplished in less time. It seems that it takes the brain this long to assimilate all that it needs to know to achieve true mastery. But remember this: if we are not born with any exceptional talents, we generally don't get the opportunity to spend 10,000 hours practicing. Talented youngsters--whether in music or sports--pass through a succession of competitions to get into the schools or leagues, or to be accepted by the coaches or teachers, that will allow them to move up to the next stage. Success at each stage affords you the opportunity to spend another 1000 hours or so practicing for the next competition. And if you fail at any stage, it's really hard to get back on the ladder by going off to practice on your own. Hard work is necessary for success, but hardly sufficient. To become a star, you need to draw top tickets in the genes lottery. Which brings us to intelligence. The existence of a single, innate “general intelligence” trait was first posited in 1904 by Charles Spearman, the psychologist and statistician, and is referred to today in the field of psychometry as “Spearman's g.” Psychologists are by no means in agreement there is such a thing as a g--the opposing school asserts that people have many different kinds of intelligence that are quite independent of each other. Nor do psychologists agree on the degree to which the trait (or traits) measured by intelligence tests is acquired or inherited. Charles Murray, author of the controversial book The Bell Curve, is a strong believer that Spearman's g exists, that it can be measured accurately with IQ tests, and that it is entirely inherited. Murray's work is controversial in public policy circles as well as within academe. This is because he and other members of the “hereditary g” school have made their theory the central plank of the platform from which they proclaim the futility of policies and programs-- early childhood education, and children's health and nutrition programs, for example-- that aim to reduce “inequality of outcomes” by eliminating “inequality of opportunity." Murray and his camp say it is wrong to believe that improving early childhood environments can improve life outcomes that are driven by innate factors. The way they see it, it is simply not possible to improve upon whatever cognitive ability you were dealt at birth. [8] Figure 6 is taken from a paper by Murray arguing this point of view. Murray followed a cohort of 12,686 subjects born between 1957 and 1964. Their IQ was measured in 1989 using the Armed Forces Qualification Test. Their incomes were measured over the period 1978-1993. For the youngest members of the cohort, the income measurement period was from age 14 to 29. For the oldest members, the measurement period was from age 21 to 36. “Very dull” and “very bright” refer to the bottom and top 10% of the AFQT test score distribution, while “dull” and “bright” refer to those between the 11th and 25th percentiles on the low side and between the 76th and 89th on the high side. “Normal” includes 50% of the total -- those falling between the 26th and 75th percentiles. Figure 6 Figure 6 conceals as much as it reveals. It shows that the median income after 15 years for the individuals with the highest AFQT score was around five times greater than the median income for the individuals with the lowest AFQT score. But it tells us nothing about the variation around the median of the individuals within each group. As a result, Figure 6 cannot tell us whether IQ is strong or weak predictor of income. Another section in Murray's paper, however, helps with this question. In a multiple regression analysis using IQ and parents' income as the two independent variables, Murray found that IQ explains (predicts) around 12% of the subjects' income, while parents' income explains 4%. As we shall see in Why We Have What We Have, other scholars come to very different conclusions as to the relative importance of IQ and parents' income as predictors of children's income. However, for our purposes, it doesn't matter whether the more important ticket is drawn in the Genes Lottery or in the Parents Lottery. We choose neither our genes nor our parents. Both are birth accidents. Parents Lottery Oh your daddy's rich, and your mammy's good lookin' So hush little baby, don't you cry. -- DuBose Heyward, Porgy and Bess The last of the birth lotteries is the one in which we draw tickets for who our parents are. These are by far the most important of the birth lottery tickets. Not only do they come with genes lottery tickets stapled to them, but they bring with them a host of other advantages or disadvantages that are equally if not more important to our prospects than genes. The correlation between the socio-economic status of parents and that of their children is much-studied by sociologists and economists. In the Genes Lottery, we noted Charles' Murrary's work in this field, and his conclusion that IQ “explains” 12% of income, while parents' socio-economic status explains 4%. More recently, however, a collection of papers published under the title Unequal Chances: Family Background and Economic Success, reaches rather different conclusions. In Table I.2 of the Introduction, authors Bowles, Gintis and Groves report findings that only 25% of the correlation between parents' and children's income is the result of genetic factors, and 75% is due to “environmental” and “wealth” factors.” What are these environmental and wealth factors that account for three-quarters of the correlation? Let's look at three "channels" through which parents might transmit their economic status to their children. First is the parent-to-child transmission of character traits --persistence, self-discipline, self-confidence, ambition, gregariousness, risk-taking, and their opposites--that influence how well their children will do. The research indicates that some trait-transmission is genetic, but much is the result of parent behavior. "Monkey see, monkey do" applies also to homo sapiens, especially in relations between parents and children. Second, parents invest in their children's "human capital"-to the extent they are able, and to the extent they are willing. They may invest both time and money. Recent study of the time variable shows a strong relationship between parents' level of education and the time they spend with their children. The “Patticake and Goodnight Moon” gap (Figure 7) shows that parents with a college degree or more spend 50% more time on early childhood development activities than parents with a high school degree or less. [9] For older children, parents' time investment is equally if not more important. Helping with homework, talking through problems-whether the world's or the child's-is time spent developing skills and outlook that is critical preparation for adult life. And the research shows a widening gap between the amounts of time that more and less-educated parents spend on this (“Time With Mom and Dad Gap”). Figure 7 In addition to time, parents also invest money in their children's human capital. This includes lessons in an array of non-academic pursuits, private tutors, test-prep and, of course, private school education. Figure 8 (“Enrichment Dollar Gap”) shows that parents in the top income quartile now spend almost eight times as much on enrichment activities (not including private school education) for their children as do parents in the bottom income quartile. [10] What I cannot show here, because I have not found suitable supporting data, is a figure comparing what parents in the upper and lower income brackets spend on private schools and colleges. I am certain that the “private education gap” figure would show much more dramatic spreads between expenditures by high and low income families than the gaps shown in Figure 7. Figure 8 Then there's sports. As we shall see, sports achievement can be quite important in boosting your chances in the two remaining lotteries: Education, and Jobs. The research shows a widening gap between the high school sports achievements of children of richer parents compared to children of poorer parents. Here are a couple of rather extreme examples of this phenomenon: When I grew up in Greenwich Connecticut in the 1950's, there were a couple of squash courts at the tennis club of which our family was a member. I fooled around some before going away to prep school, where I made the team because few other classmates had ever played the game. At Yale I made the team because few other classmates had ever played he game. By the time I graduated, I probably had played for around 1000 hours. Things have moved on in Greenwich since my day. Résumé planning and management is now a major league sport for Greenwich families hoping to find a slot for their child in the Ivy League. Squash has become a feature item here, because a good 13-year-old squash player has a leg up in getting into squash-playing prep schools like Andover, Exeter or Deerfield, from where they have a leg up at getting into the Ivies, most of which care a lot about their squash teams. So what is a Greenwich mom doing these days to manage the squash component of her daughter's résumé? Two things. First, her husband (who played varsity squash at college) pays to have a squash court erected behind one of the garages. Next, a personal trainer is engaged to ensure that from the age of eight, the child works tirelessly to nail her rail shots, sharpen her reverse-corner, and perfect her boast. She plays with her dad an hour every morning before he goes to work and she goes to school, and she plays an hour with her mom every evening before dinner. By the time she is thirteen and applying to Deerfield, she has racked up 5,000 hours in the court, and is ranked #3 in the country. Dads on the other coast pay Steve Clarkson $3000 to assess their 8-year-old boys for admission to Clarkson's quarterback academy in Pasadena, California. If Junior is admitted, dad will pay Clarkson many more thousands annually for five years' of intensive QB coaching. The payoff comes when Junior is thirteen, and USC offers him a scholarship, good for the day he graduates from high school. These are extreme examples of how parents' money helps their children do well in sports. But the correlation between parents' income and children's sports achievements reaches down through all levels of the income pyramid. For example, children's participation in high school sports bears a strong relationship with parents' income, In 2004, you were twice as likely to play sports during your senior year in high school if your parents were in the top income quartile than if they are in the bottom quartile. You were 2.5 times as likely to be a team captain, as shown in Figure 9. Figure 9 Finally, there is the direct route by which we transmit our "economic status" to our children-we buy them homes, we invest in their businesses, we give them gifts, we set up trusts for their benefit, and we leave our assets to them when we die. I have found no useful data on any of this, so I am unable to say whether these transfers are quantitatively significant, or to compare their importance to that of the other transmission vectors discussed here. My guess, however, is that they are quite important, and increasingly so. These, then, are the channels through which economic position can be transmitted from one generation to the next. And it is no surprise that, as we shall see in (“Why We Have What We Have”), there is a very strong correlation between parents' and children's income. The important point is that, from children's standpoint, all of this transmission is accidental. We don't choose our parents, so we can neither be blamed, nor claim credit, for the tickets we draw in this lottery, or the value these tickets have in the marketplace. And if we can't claim credit for these tickets, we can't claim that we deserve what these tickets turn out to be worth in the market place. Education Lottery In the education lottery, schools and colleges screen and select for aptitude and achievement. The more selective schools look for this not only in the academic subjects, but in other areas as well. How well we do in this lottery depends largely on tickets we drew in the birth lotteries--key being IQ, talent, motivation, self- discipline and social skills learned from our parents, and our parents' ability to pay for private secondary and college education. Hard work is also important, although, as we have seen, good genes and well-off parents can create opportunities to work hard, and bad genes or poverty can thwart those opportunities. Other chance factors are also at play in the education lottery. Whether we draw a good, bad or indifferent teacher can make a difference in how much we learn, and how much we like to learn. The lottery in which we draw teachers is not a highly-repeatable one that would allow good and bad luck to even out. Most of us will have no more than eight teachers by the time we start high school. Teachers can also matter in another way. One of my wiser but more delinquent college classmates described the process by which he finally received his BA degree as “a successful series of negotiations.” That was in 1967. My impression is that negotiating with teachers to improve one's grades has become an even more important determinant of GPA since then. How successful you are depends on your negotiating skills, but also on the teacher's willingness to negotiate. As for standardized tests, I have to wonder if scores don't depend more than a little on chance. One of the goals of test designers is “repeatability”--a person taking a test more than once should get nearly the same score each time. The only standardized test I remember taking twice was the law school admission test (LSAT). The first time I scored 625 out of 800 (only good enough for a tier 2 school), and the second time I scored 750. My suspicion is that test designers have found it difficult to come up with highly “repeatable” tests, and that this explains why schools these days allow students to take the tests as often as they like, and submit only their best score. But how many students take these tests more than once or twice? Finally, pity the poor admissions director who, after accepting the 5% of applicants who are clearly keepers and rejecting the 10% who clearly are not, is left with five times as many applicants as he has places in the class. On measures of aptitude and achievement, they all look pretty much alike. What to do? It's a lottery. So at the end of the day, how much difference does hard work make in the education lottery? Can a person of average intelligence, with no special talents, make it to the top by dint of hard work? Of course she can, but the odds are not in her favor. Remember, this system selects for aptitude and achievement. The higher she rises, the smarter and more talented are the people around her, and they, too, work hard. How much difference does the education lottery make to lifetime earnings? In Figure 4.2, we saw an apparently strong relationship between levels of education and lifetime earnings. But we must not be fooled by averages. If we want to know whether levels of education “explain” earnings, we need to look at the variation of each individual's around the median or average for his or her education class. We do this in Figure 10. [11] Figure 10 Figure 10 suggests that educational attainment is indeed strongly correlated with how much we earn. A randomly selected person who doesn't go beyond high school has an 18% chance of earning more than the population median of $1.1 million during his lifetime. The randomly selected person with a graduate degree has an 82% chance of earning more than the population median. We should not confuse correlation with causation. In part, college graduates earn more than K-12 graduates because the jobs that require a college degree pay more than the jobs you can get with a K-12 degree. However, the reason why they pay more is because they require higher levels of intelligence and motivation, which is what college selects for. In this respect, in the education lottery can be seen as a screening system used by employers to select for the final lottery-the Jobs Lottery. Job Lottery You can get it if you really want. --Jimmy Cliff I didn't become Head Jailer because I like head jailing. I didn't become Assistant Tormenter because I like assistant tormenting. --Wilfred Shadbolt, The Yeoman of the Guard After we have been screened, sorted and selected (or rejected) in the education lottery, we present ourselves to the job lottery, the last we will look at. Unlike the birth lotteries, in which tickets are drawn only once, or the education lottery, in which they are drawn only a few times, in the jobs lottery we draw tickets continuously throughout our working lives. Success in the education lottery is no guarantee of success in the job lottery, nor is failure in the one a guarantee of failure in the other. This is apparent from the size of the “tails” of the distributions shown in Figure 10. Bill Gates and Steve Jobs are the poster boys for the idea that you can drop out of college and still succeed. Peter Theil has established a foundation whose objective is to persuade college students with talent to drop out and jump-start their careers without wasting their time and talent on completing that “successful series of negotiations.” Notice, however, that among the many fabled stories of drop-out success, it's hard to find anyone who was of average intelligence, had no particular talents, was raised by ordinary, middle class parents, and achieved extraordinary success by hard work alone.. These stories almost always involve people who drew top tickets in two or more the four birth lotteries. Let's broaden the discussion to the entrepreneurial class as a whole. This includes a much larger number of people, most with less distinguished intellects than Gates, Jobs or Thiel's chosen few, but all with sufficient drive and determination to start a business. Here, if anywhere, is where “hard work” should explain why those with relatively less education end up out in the right-hand tails in Figure 10. Persistence obviously matters. People who fall down, pick themselves up and try again, over and over, have a higher probability of succeeding than those who slink away after the first failure. The persistent ones “make their own luck.” They try their idea, or variations thereupon repeatedly until they succeed. Figure 11 illustrates the importance for entrepreneurs of picking yourself up, dusting yourself off, and trying again. [12] In 1992, approximately 600,000 businesses were started (and around the same number were shut down). Of these, 25% had failed after one year, 36% after two years, 50% after five years, and 71% after ten years. Starting a single new business is clearly not a route to lifetime prosperity. Starting serial new businesses may be. Here more than anywhere, you learn from your mistakes. You must be able to “try, try and try, till you succeed at last.” Figure 11 The problem here is that in order to start a business, you need capital. The first time you cold-call VC's, angel investors or your local bank, a good idea may be all that you need. But if you have tried and failed once, twice or thrice, the sell gets harder and harder. Each time you fail, it gets harder to raise money from people you don't know. So you turn to people you do know--family and friends. And now we are back into the territory where the success we may attribute to entrepreneurial persistence should perhaps also be attributed to the accident of having family and friends with money. More generally, there is this question: is success or failure in small business “explained” by gender, genes, and parents to the same extent that economic success or failure generally is explained by these variables? How important is the entrepreneur's hard work compared to the tickets she draws in the birth lotteries? Hard work is not the only variable that explains the variation around the median lifetime earnings for each education class in Figure 10. Many other, serendipitous factors are at play. Take the process by which we get first jobs. Wilfred Shadbolt may have become Head Jailer and Assistant Tormentor because that was the job to which his class and education suited him in Tudor England. These days, while class and education do play an important role in the “first-job” selection process, luck also matters. Michael Lewis, the author of Liar's Poker and Moneyball, gives this account of how he got his first job, with a BA in art history from Princeton and an MA from the London School of Economics, and then wrote his first book. This is an excerpt from Lewis's commencement address to Princeton's class of 2012, which he titled, “Don't Eat Fortune's Cookie.” One night I was invited to a dinner, where I sat next to the wife of a big shot at a giant Wall Street investment bank, called Salomon Brothers. She more or less forced her husband to give me a job. I knew next to nothing about Salomon Brothers. But Salomon Brothers happened to be where Wall Street was being reinvented-into the place we have all come to know and love. When I got there I was assigned, almost arbitrarily, to the very best job in which to observe the growing madness: they turned me into the house expert on derivatives. A year and a half later Salomon Brothers was handing me a check for hundreds of thousands of dollars to give advice about derivatives to professional investors. Now I had something to write about: Salomon Brothers. Wall Street had become so unhinged that it was paying recent Princeton graduates who knew nothing about money small fortunes to pretend to be experts about money. I'd stumbled into my next senior thesis. The book I wrote was called "Liar's Poker." It sold a million copies. I was 28 years old. I had a career, a little fame, a small fortune and a new life narrative. All of a sudden people were telling me I was born to be a writer. This was absurd. Even I could see there was another, truer narrative, with luck as its theme. What were the odds of being seated at that dinner next to that Salomon Brothers lady? Of landing inside the best Wall Street firm from which to write the story of an age? Of landing in the seat with the best view of the business? Of having parents who didn't disinherit me but instead sighed and said "do it if you must?" Of having had that sense of must kindled inside me by a professor of art history at Princeton? Of having been let into Princeton in the first place? Lewis is, of course, a talented guy, so it's unlikely he would have led a life of quiet obscurity if he had been seated elsewhere at the dinner table that night. Nevertheless, his story illustrates the role of chance in deciding how we get our start. Many or us will recognize our own version of Lewis' dinner-table moment in this story. Here are some other connections between the earlier lotteries discussed earlier and the job-selection lottery. If you played varsity lacrosse at the University of Maryland, you will find that the U of M lacrosse alumni on Wall Street are delighted to open doors for you. This is true of most every college sport and most every firm in business or finance. And how do we become varsity athletes? Of course, we work hard, but that doesn't help if we are born with no talent for sports. And as we saw in Figure 9 (Parent Lottery), even with talent, if our parents are poor, we are less like to play sports in high school, regardless of talent. Is there not also an element of chance in finding that the company or industry you chose for your first job was on its way up or down? Did the Silicon Valley pioneers foresee that the new technology they were fooling around with in college would spawn a group of industries with a combined market capitalization of $6 trillion by the time they reached 45? Could the people who joined Enron in 1990, when it was a second-tier gas pipeline company with a market cap of $2.6 billion, have foreseen that by 2000, it would have a market cap of £60 billion, and would have received Fortune's “Best Company to Work For” award for four straight years? Could the people who joined Enron in 2000 have foreseen that before the end of 2001, Enron would disappear? Could the people who entered law school in 2005 have foreseen that at the time they graduated three years later, firms would be contracting rapidly and getting rid of lawyers as fast as possible? Did Michael Lewis foresee that derivatives were about to revolutionize finance when he took the job at Salomon Brothers? The accident of being in the right or wrong place at the right or wrong time at the beginning of one's career--or any time along the way--has as much to do with how well we do as hard work or judicious career planning. Serendipitous forces play out along the road as our careers progress or fail to do so. The quality of the people who manage or mentor us varies enormously. We can be assigned to work in a division or on a deal that turns out to be career elevator up, or a side trip to nowhere. Of course it's true that with persistence, agility and opportunism (not to mention cunning and ruthlessness), we can overcome setbacks from drawing bad tickets in the job lottery. But starting a new job or getting a new boss is not something that happens to people several times every year. If it were, then this would be a lottery in which bad and good luck will tend to even out. But that is not the case. My final observation on the job lottery is to caution against the fallacy that the best-performing people get the highest-paying jobs. What is generally true is that the highest-paying jobs are those in which people are the most productive, where productivity is measured as the dollar value of output divided by hours of time at work. However, a person's productivity is a function not only of his own effort, talent and intellect, but also of the other “factors of production” that his job puts at his disposal. The best face you can put on the ten-fold increase (1000%) increase in CEO pay relative to the average worker between 1980 and 2005 (Figure 3) is that an hour of CEO time in 2005 added ten times more value than it did in 1980, by virtue of the additional factors of production--mainly in the form of capital investment--that American companies have put at the disposal of their CEO's. It certainly did not do so because the average American CEO was 10 times smarter or worked 10 times harder in 2005 than in 1980. Nowhere is this clearer than in the finance industry, where the highest-paying jobs are to be found. The reason for this is not that the hardest-working, most talented and most intelligent people end up in finance. The reason is that people who work in finance are, generally speaking, in the business of making bets with other people's money. Financial capital, not human capital, is the primary “factor of production” in this industry, and accounts for most of the value added by an hour of a finance worker's labor. In 1972, the ratio of capital employed to people employed in the top 50 US securities firms was $124,000. In 1987, it was $203,000, and in 2004 it was $1,789,000. (These figures are in constant 2004 dollars. [13] So, if the average worker in finance in 2004 was paid fourteen times as much in real terms as the average finance worker in 1972, was this because he worked fourteen times harder, or was fourteen times smarter? Of course not. It was because he had fourteen times as much of other people's money to make bets with. Sure, high earners work more hours than low earners, but not 1000, 100, or even 10 times more hours. Yet their salaries are 1000 times higher. There is a limited number of these highly productive jobs in which you get the chance to work long hours for high earnings. Few of the people who get these jobs create the high levels of productivity their jobs exhibit. In most cases, that was done progressively over time by their predecessors. For this reason, these prize jobs are the object of an intense competition, in which the contestants fight to win using their smarts, education, social skills, and job experience. This is, however, a lottery within a lottery, in which a large number of equally-qualified people struggle over a much smaller number of winning positions. If I were to include a seventh lottery here, I would call it “Personal Life.” This is the lottery in which we connect with other people as friends or as partners. Clearly chance plays a large role here. Consider how serendipitous were the circumstances under which you met your best friend or partner. Consider also that, although most of us marry someone we are convinced is Mr. or Ms Right, in hindsight we usually realize that we had no clue whether this would turn out to be true. Finally, consider how much difference the right or wrong choice of partner makes to how well we function in the world. This lottery may therefore be as important as any of the others in determining how well we do in life. Why We Have What We Have In discussing the lotteries, I have stated repeatedly that these accidents “contribute significantly,” “strongly predict,” “are very important in explaining,” and so forth. Such phrases beg this question: what is the relative strength of the accidental and the intentional in determining what we have? Is it 90/10, 50/50, or 10/90? If we can't quantify the answer, then the bottom line of the six lotteries would be “so what?” and I wouldn't have bothered to write this. But if we can answer “Why do we have what we have” quantitatively, this will provide firm ground on which to discuss how government should take what it needs for charity, which is the goal of the exercise. The lotteries discussion has suggested a number of ways in which chance affects lifetime earnings prospects: gender, IQ, looks, smarts, parents, health, and unpredictable, unintentional, but life-changing things that may happen at school, on the job, or in our personal lives. I will argue that two of these variables--gender and parents--explain (in the statistical sense) at least half of the observed variation in the population's lifetime earnings. We begin with gender. In our discussion of the Education Lottery, we saw how, in order to understand the importance of educational attainment in explaining earnings, it is not enough to look at average values (as in Figure 4.1 and Figure 4.2). We also had to look at the variation of each individual's earnings around the median for his education class (as in Figure 10). The same is true for gender. Figure 4.2 tells us that for people who work full-time for forty years, within any education class, the average woman will earn only 75% as much as the average man. But there is no such thing as an average woman or man. Figure 12 fills in this picture by showing the variation of individual men and women's earnings around the gender medians. [14] The median value of lifetime earnings for men is $1.4 million, while for women the median is $750,000. Figure 12 Now that we know where these distributions lie, we can predict the likelihood that a woman selected at random will earn more than the “average person” (the population median), or more than a man selected at random. It turns out that the random woman has 40% chance of earning more than the population median, and a 30% chance of earning more than the random man. The gender variable clearly has some "explanatory power" in predicting what you or I can expect to earn during our lifetime. Statisticians measure explanatory power with something they call the "coefficient of determination," usually referred to as “R2”. For the relationship between gender and earnings, the correlation is 0.22. [15] Since, in statistics, “explanation” is the square of correlation, this gives a value for R2 of 0.10, meaning that gender “explains” around 5% of the observed variation in the lifetime earnings of individuals above or below the population median of $1.1 million. So what explains the other 95% of the variation in the earnings of individuals? Naturally, it is a combination of things. There are intentional factors, which include, in addition to hard work, such things as ambition, persistence, self-discipline, risk-taking, aggression, cunning, deception, and ruthlessness. In the academic literature that I have looked at, I found hardly any studies that included these "intentional" variables as predictors of economic outcomes. This is not because intention is not important--it obviously is--but because it is hard to measure in a way that can be coded into a data set and included as an independent variable in multiple regression analysis. There are, however, other variables that have an effect on earnings and can be measured, allowing calculation of their R2. One of these is parents' “socio-economic status” (education and income), which turns out to be a powerful “predictor” of children's income, much more powerful than gender. Quite a lot of research has been done on the “inter-generational transmission of socio-economic status.” Sorting out the relative importance of the genetic and the environmental factors that transmit economic status from one generation to the next is important to the public policy debate because, if it's mostly all genetic, as Charles Murray argues, there's not much that public policy can do to reduce economic inequality. For our purposes, however, it doesn't matter whether genes or environment predominate, because both forms of transmission are entirely accident from the child's point of view. The child chooses neither her genes nor her parents. The best research summary I could find is a collection of papers published under the title “Unequal Chances: Family Background and Economic Success”. The key paper is “The Apple Falls Even Closer to the Tree Than We Thought,” in which Bhashkar Mazumder reports that when incomes are measured over sixteen years, the “intergenerational income elasticity” reaches 0.6. Under certain assumptions, this would mean that the correlation is also 0.62, in which case parents' incomes would “explain” 36% of children's incomes (explanation being the square of correlation). [16] Another paper in Unequal Chances, authored by Thomas Hertz, is titled “Rags, Riches and Race: The Intergenerational Economic Mobility of Black and White Families in the United States.” Among other things, Hertz focuses on the problem of data quality. The measured correlation between parents' and children's income gets stronger as the time period over which incomes are measured gets longer. This is not surprising, since the effect of transitory factors-which create “noise” in the data-diminishes as longer periods are considered. Hertz reports that when incomes are measured are over seven years (as opposed to sixteen), the correlation drops to 0.45. We can only speculate on how high the correlation would be if incomes were measured over thirty or forty years. Unfortunately, the challenge of obtaining data of sufficient quantity and quality over such a long period for both generations is too great. What does this correlation tell us about causality? If there is a correlation between A and B, three things are possible. A could cause B, B could cause A, or C could cause both A and B. It's obvious that children's incomes don't cause their parents' incomes. But the ways in which parents' incomes might “cause” their children's incomes are not entirely clear. In Parents Lottery we delineated the primary “vectors of transmission” by which this might come about. For example, race and parents' education “explain” a significant part of the relationship between parents' earnings and children's earnings. In the language of multiple regression analysis, when race and parents' education are introduced directly into the equation as independent (explanatory) variables, along with parents' income, the coefficient for parents' income drops significantly. The same might also be true if we introduced parents' IQ, parents' health, or the traits in parents' personalities that make them good or bad performers at work. The direct transmission of these underlying parental variables to their children, rather than the things that parents' income itself can buy, may explain most of the “inter-generational transmission of economic status.” Since these parent variables also explain why parents' incomes are what they are, we will observe a strong correlation between parents' income and children's income. For our purposes, however, it doesn't matter whether the relationship between parents' income and children's income is direct, or derived from a direct relationship between parents' race, IQ, education, health, or personality and their children's income. All these underlying explanatory parental variables are accidents that happen to children in the birth lotteries. The question of inter-generational transmission of economic status is closely related to the question of intergenerational mobility. Table 5.10 in the Thomas Hertz paper presents a “transition matrix” showing the probability that children born to parents in a given income bracket will end up in the same or a different bracket. The matrix is thus a convenient way to summarize the degree of “intergenerational income mobility” in our society. Table 1 is a simplified version of Hertz's transition matrix. Table 1 Table 1 tells us that if your parents' earnings were in the bottom 20% of the population, there is 48% chance that you will end up there, too, and only a 4% chance you will end up in the top 20%. If your parents were born in the top 20%, there is a 39% chance that you will end up there, and a 6% chance you will end up at the bottom. The more detailed matrix in the Hertz study shows that the child of parents earning less than the median has a 34% chance of earning more than the median. In a perfectly mobile society, the transition matrix would look like Table 2. There is no correlation at all between your income and your parents’ income, and your chance of ending up in a given bracket bears no relation to your parents’ bracket Table 2 In a perfectly immobile society, the matrix would look like Table 3. Your income and your parents' income are perfectly correlated, and no-one escapes his parents' bracket. Table 3 Hertz's transition matrix, we can construct Figure 13, showing the distribution of lifetime earnings for people whose parents' earnings were below the median (“poor children”), and the distribution for people whose parents' earnings were above the median (“rich children”). [17] However, since Hertz's matrix is based on data measuring parents' and children's incomes over only 11 years on average, and since, as mentioned, the measured correlation gets stronger as incomes are measured over longer periods, Hertz's matrix understates the true degree of inter-generational immobility, and the “rich children” and “poor children” distributions in Figure 13 would not overlap as much as they do if Hertz's data had measured incomes over longer periods. Figure 13 Now, if gender ”explains” around 10% of our lifetime earnings and parents' income explains at least 38%, does that means that these two variables together explain around 43%? The answer is yes, but only if there is no meaningful correlation between gender and parents' income. If there were a correlation, it would not be appropriate to add the two R2 because this would be double-counting. If we were talking about India or China, where “gender-planning” is widely practiced, it would not be unreasonable to expect a degree of correlation between parents' income and children's gender. However, since gender-planning is not prevalent in the USA, we may safely add the R2 of the two variables without fear of double-counting. We can therefore conclude that at least 43% of our earnings are determined by our gender and our parents' income. Remember also that gender and parents' income are only two of the accidents we identified in the Six Lotteries that influence our lifetime earnings. Only a portion of our IQ is inherited from our parents (estimates indicate it is around 50%), and the rest is an accident having nothing to do with parents. The same is true for health, looks, athletic or artistic talent-much of what we have is genetic, and much is not. The part that is not is an accident that has nothing to do with parents. Then there are the accidents that happen in the school and college admissions lotteries, the jobs lottery, and the partner selection lotteries. The outcomes are all to some degree independent of the parents accident. Therefore, if gender and parents' income alone account for 43% of what we have, it is not a stretch to conclude that overall, more than half of what we have, we have as the composite result of chance factors at play in the Six Lotteries. Figure 14 shows the result when we combine the distributions for gender and parents' income. [18] The “poor girls” distribution shows the earnings of women whose parents' income was below the median, and the “rich boys” distribution shows men whose parents' income were above the median. The distributions for “rich girls” and “poor boys” are very nearly the same, so they have been combined into a single distribution with twice as many people in it. A “poor girl” taken at random has 18% chance of earning more than the population median. A “rich boy” has a 75% chance of earning more than the population median. A “poor girl” has an 8% chance of earning more than a “rich boy.” Figure 14 I don't know about you, but I am an individual, not a statistic. I am with Paul Ryan, Rand Paul, Ayn Rand and Jimmy Cliff. I can get it if I really want, regardless of what your studies say. Of course each of us is an individual. And as individuals we face possibilities. But as members of a population, and when the subject is earnings over a lifetime, we face probabilities. When I ask you to stipulate that we have half of what we have by accident, as I will do in How Government Should Take What it Needs for Charity, I am talking about the lifetime earnings of the population of all Americans. Individuals' earnings vary widely, and many people find their way into the “tails” of these distributions. Notwithstanding this, the tickets we draw in the Six Lotteries do indeed “explain” or “predict” more than half of where we end up. How Government Should Take What it Needs for Charity The “Six Lotteries” and “Why We Have What We Have” should have convinced you that at least half of what we have we have (or lack) is accidental. I hope you will also agree that we can't take credit for the portion we have by accident, nor be blamed for the part we lack. To me, these conclusions have important implications for fair tax policy in a society that decides to fund a social safety net. The question here is not whether it is legitimate for government to engage in such “charitable” activities as Social Security, Medicare, Medicaid, food stamps, early childhood education, subsidized college loans, disability and unemployment insurance, public housing or public television. [19] The question is this: if a society decides to provide these things to its members, what is a fair way to pay for them? Let's go back to the parallel universe of Figure 1, where what we have depends only on the month in which we were born. The line in Figure 15 shows Americans' Adjusted Gross Income as reported to the IRS in 2009. [20] In our parallel universe, the bottom 25% were born in October, November or December and the top 25% were bon in January, February or March. Figure 15 Of course, we might find ourselves in the next parallel universe over, in which earnings depend only on our IQ (Charles Murray's universe); or in the universe parallel to that one, in which earnings depend only on parents' income. Depending on which universe you prefer, you can label the quartiles of figure 15 by calendar month, as I have done; you could label them “very dull,” “dull,” “bright” “very bright,” as would Charles Murray; or you could label them “very poor parents,” “poor parents,” “rich parents,” “very rich parents.” Now if, in any of these universes, society wants to provide a social safety net, what is an equitable to pay for it? Is it a flat tax at the same rate on all earnings? Or is it a tax that takes first from those who have the most-a “top-down” tax, as shown in Figure 16? To me it depends on whether people deserve what they have. If they don't, then those who have the most have no cause to complain if a top-down tax takes what it needs from them first, before taxing those who have less. I don’t see any grounds on which those born in January, February and March could object that a “top-down” tax is unfair, and that part of their tax burden should be shifted to people born in earlier months of the year. Figure 16 So do people deserve to have what they have in these parallel universes? There should be no disagreement in the case of the calendar month universe: no-one deserves the month of her birth, so no-one deserves what she has as a consequence of that accident. What about Charles Murray's universe? Some might argue that, since the market rewards intelligence, people deserve what their intelligence is worth in the marketplace. But why do we deserve our intelligence? Remember that in Murray's universe, we can't grow our IQ at the gym. It's is a number printed on a ticket we draw at birth. If we don't deserve our IQ, there's no way we deserve what it's worth in the marketplace. The same arguments apply to the market value of innate talent. Of course, how well we perform depends not only on whatever talent we were born with, but also on how hard we work to make the most of it. We can take credit for the “value added” by hard work, but we can take no credit for the market value of what we were born with. Finally, there is the universe in which the quartiles of Figure 15 are labeled “very poor parents,” “poor parents,” “rich parents,” “very rich parents.” None of us deserves his parents, or his parents' income, and so none of us deserves what he has as a result. As we saw in “Why We Have What We Have,” the underlying factors that explain the correlation between parents' income and that of their children are complex. But in a parallel universe where there is a perfect correlation, it doesn't matter what these are. Children who end up in the fourth quartile can take no credit for anything they have. Let's now leap back from these parallel universes--in which 100% of what we have results from things for which we can take no credit--to the universe we actually live in. Here, as we have stipulated, gender, parents' income, and other accidents account for at least half of what we have (or lack), while intentional factors” account for the other half. The situation in our universe is illustrated in Figure 17. Figure 17 The upper area above the median (“fortune”) represents the fruits of drawing better lottery tickets than the average American, while the lower area (“ant”) represents area represents the fruits of working harder than the average American. The upper area below the median (“misfortune”) represents the blight of drawing worse than average tickets, while the lower area (“grasshopper”) represents the blight of being lazier than average. So what is the fair way to fund the social safety net? It is to take as much of the “fortune” area in Figure 17 as is needed. We would do this with a “Fortune Tax” of 50% on all income above the median income, or whatever level above the median satisfies the funding requirement. For example, if a tax that “cuts in” at the 65th percentile ($25,500) satisfies the funding requirement, then the Fortune Tax would be zero for everyone earning less than $25,500. For someone earning $35,000, the tax would be 0.5 x ($35,000-$25,500) = $4,750, which is an average tax rate of 13.6%. Are you saying it's fair that I should pay the same tax as someone who drew better tickets than me in your lotteries, didn't work as hard as me, and earned the same as me? No. That is not fair, but I see no practical way to devise a tax that would be any fairer, primarily because there's no way to say, for a given individual, what part of what she has is a consequence of the lotteries, and what part is a consequence of how hard she worked. Notice also that the Fortune Tax is no less fair in this respect than the current income tax regime, since both extract the same tax from everyone who earns the same amount (ignoring, of course, the differences arising from the rules for credits and deductions).  However, while the Fortune Tax is no fairer than the current regime as between individuals, I am arguing that it is indeed fairer as between groups--specifically, the group of all people above the "cut-in" percentile, and the group of all below that percentile. We found in "Why We Have What We Have" that the upper group draws better lottery tickets on average than the lower group. And we found that at least half the earnings of the upper group in excess of the earnings at the cut-in percentile are attributable to their better tickets (the "fortune" area in Figure 17). Under the current rules, the upper group keeps a substantial portion of their accidental earnings, while the lower group gives up a substantial portion of their earnings. This is despite the fact that everyone in the lower group earns less than the lowest earner in the upper group, and despite the fact that half of the lower group’s earnings shortfall relative to the lowest earner in the upper group ("misfortune" in Figure 17) is attributable to the worse tickets drawn by the lower group.  On this basis, therefore I find the Fortune Tax to be fairer than current rules for income taxation.  I would combine the Fortune Tax to fund the safety neet with a “user tax”—paid at the same rate by all-- to fund the “non-charitable” functions of government: national defense, the judiciary, infrastructure, R&D, education, regulation of commerce, debt service. That’s because everyone benefits from these government services more or less in proportion to their consumption or income. (This “user tax” could be levied either on consumption or on income.) When combined with a 10% “user tax” on all income, the Fortune Tax would result in a sharply progressive schedule of effective rates, as shown in Figure 18. [21] This figure shows the average (not marginal) tax rates when the 50% rate is applied to all income above the 65th percentile. This figure also shows average rates under current tax law, and the 1969 rates. The 1969 rates are fairly representative of the rates that were in effect throughout America's “Age of Affluence” that began in 1953, and ended in 1973 when an OPEC oil embargo quadrupled permanently the world oil price, and triggered America's first “Energy Crisis.” Figure 18 The total amount of tax revenue that would have been raised in 2009 under each of the three Figure 18 rate schedules is: [22] ο Current rates: $1.6 trillion ο Fortune Tax rates: $2.1 trillion ο Age of Affluence rates: $2.3 trillion If the objective is to raise revenue, then the Good Luck rates are a clear winner--$540 billion per year more than current rates. But if that's not enough to satisfy the requirements for social safety-net funding, we can lower the percentile above which the Good Luck Tax applies. If we brought it all the way down to the median income level, the Good Luck tax would raise $2.5 trillion per year. Wouldn't the Fortune Tax rates kill jobs? Not according to Figure 18. The top marginal rate during the Age of Affluence was never lower than 70%, and was at times as high as 91%. Real median family income rose at an annual rate of 2.8% during that period. By contrast, during the twenty years from 1991 to 2011, when the top rate was never higher than 39.6%, real median family income rose at an annual average rate of 0.3%. The Great Chain of Earning In the middle ages, court intellectuals developed the doctrine of “the Great Chain of Being.” This held that all living things have an ordained place in a hierarchy. God sat at the top, and the king sat immediately below God. Below the king came the nobility, then the clergy, the landed gentry, merchants, and so on. At the bottom of the Chain were the peasants and serfs. Since everyone's place in the Chain was ordained by God, an attempt to improve one's position without the consent of one's superiors--for example, to depose the king--was both blasphemous and unnatural. This doctrine served the king's interests well. In fact, it served the interests of all but serfs and peasants. Although the doctrine prevented you from aspiring to rise to a higher station than the one to which you were born, it also prevented anyone below you from aspiring to rise to (or above) your level. In the middle of the 17th century, deconstructionists, in what was to be the first ray of the Enlightenment, realized, “OMG, the Chain was not invented by God, but by the men whose interests it serves!” These days, many are true believers in what might be called the Great Chain of Earning. This doctrine--developed by high-earners to justify the positions they have attained (or hope to attain) in the wealth pyramid--holds that an individual's quantum of hard work and talent establishes his right and proper position in the pyramid. Believers claim that this doctrine follows from a proper understanding of market economics in a meritocratic society. And they believe that government intervention to fiddle with positions in the pyramid -- for example, progressive taxation to fund social safety net spending -- is both blasphemous and unnatural. However, while it is true that we have evolved from monarchy and aristocracy to democracy and meritocracy, it is hardly the case that the birth accident no longer matters. In the middle ages, people were chained to their places by gender and family: if your father was a baron, you would be a baron if you were a boy, but not if you were a girl. If your mother was a seamstress, you would be unlikely to fall to kitchen maid, or to rise to shop girl. A “transition matrix” for medieval society would have looked very much like Table 3. The research summarized in Why We Have What We Have suggests that today, we have barely half-emerged from the medieval system, since gender and parents' income still “explain” half of children's income. Our transition matrix now looks like Table 1, which is more or less half way between the medieval matrix of Table 3, and the fully mobile matrix of Table 2. The “transmission vectors” linking the status of parents to the status of their children have also changed. When I entered Yale in 1964, the majority of the freshman class was drawn from a few private schools, and legacy candidates for admission were almost never rejected. By the time I graduated, Kingman Brewster and his admissions director Inslee Clark had reformed the admissions policy so that aptitude and achievement were vastly more important than the school from which you were applying or who you parents were. (I am full of admiration for Brewster and Clark's courage in carrying out this transformation. Hell hath no vitriol like the vitriol of an “Old Blue” when you tell him something at Yale is going to change.) Notice, however, that a system enshrining aptitude and achievement doesn't make it irrelevant who your parents are. To the extent that genes determine intelligence and talent, we have our parents to thank or blame. We can mostly thank or blame our parents for the quanta of self-esteem and self-discipline with which we enter adult life (unless you believe that the child of alcoholics is every bit as capable of learning self-discipline as the child of professional musicians). Finally, we must thank or blame parents for how much or how little they sacrificed in order to give us the best chances in life. So, yes, we live an age of meritocracy. But it turns out that at least half of our merit is the result of birth accidents. And the corollary is this: since we don't deserve half of our merit, we don't deserve whatever it is worth in the marketplace. How can you say that I don't deserve what I am worth in the marketplace? Market economic theory holds that the market rewards people in proportion to the value added by their labor. And if my labor adds more value than yours because I am more clever, more talented, or work harder than you, why do I not deserve to have more than you? Think of it this way. Do you deserve to be cleverer or more talented than me, or to be born to better parents? I don't think so. I am not envious or resentful; I just don't believe that any of us deserves anything that comes our way entirely by accident. Hard work, of course, is another matter. If I had been born to any other parents, or with any other genes, I wouldn't be me, I would be someone else. Are you saying I am lucky not to be someone I am not? Yes, and you cannot gainsay my assertion by invoking the “anthropic principle.” I make my assertion in the context of a discussion about fair taxation, where the question is, “Why me rather than the other guy?” My answer is, “because you're luckier than the other guy.” This is not at all the same thing as saying that you might have been the other guy. There are only two people who can fairly claim that what we have came our way mainly from hard work. These are our parents, and the hard work is theirs, not ours. They may not have worked too hard to furnish us with genes, but they deserve the credit or the blame for much of the rest of what we have become by the time we leave the nest. The corollary is that parents may fairly complain that a Fortune Tax applied to their children is unfair, because it is to some extent a tax on wealth created by the parents' hard work. The Fortune tax is, in this respect, akin to the estate and gift tax. But the Fortune Tax is arguably more benign than estate or gift taxation, because it does not apply until after children reaped the benefit of their parents' largesse to reach whatever station it allows them to achieve. Only then does the state reach in to extract a portion of the “value added” by their parents' hard work. In fact, the Fortune Tax points the way to a useful reform of trust, estate and gift taxation. But that is a subject for another time. It comes down to this: the word “fortune” means both “wealth” and “luck.” The same was true for the Latin word fortuna which, to the Romans, meant “luck,” “fate,” “chance,” “prosperity,” “goods,” “possessions,” or “property.” The understanding that wealth and luck are two sides of the same coin is ancient. Our forebears understood what Glenn Beck apparently does not: people are unlikely to have (or lack) one without having (or lacking) the other. The apostles of the Great Chain of Earning may deny that the link between the two is very strong, but in this they are wrong. ### Notes [1] Source: see Data and Methodology [2] Gladwell cites a study showing that the oldest 4th graders score between 4 and 12 percentiles than the youngest. [3] The study referred to in note 2 found that among undergraduates at American four-year colleges, 88% as many students are born in December as in January. [4] Source: Baseball Almanac [5] Source: “CEO Compensation” (2010), Frydman and Jenter [6] Source: see Data and Methodology [7] Source: Payscale website [8] There is evidence, however, that we can improve performance on IQ tests by working out regularly at a mental gym. For example, regular practice at “n-back” memorization drills has been shown to boost IQ scores by statistically significant amounts. [9] Source: recent research at Harvard’s Kennedy School of Government [10] Source: Duncan, Greg and Murnane [11] Source: see Data and Methodology [12] Source: “The Illusions of Entrepreneurship”, p. 99 [13] Source: Kaplan and Rauh [14] Source: see Data and Methodology [15] This calculation is explained in Data and Methodology [16] The condition for the income elasticity and the correlation coefficient to be the same is that the standard deviation of the distribution of parents’ income must be the same as the standard deviation of the distribution of children’s income. In an age of increasing income inequality, this is probably not true—the standard deviation for children is probably getting wider than that for parents. On the other hand, if increasing inequality is accompanied by increasing “rigidity,” that is, if the power of the transmission vectors is increasing, this will tend to increase the parent-child correlation over time. Lastly, as mentioned, the correlation would be higher if earnings were measured for more than 16 years [17] Source: see Data and Methodology [18] Source: see Data and Methodology [19] If you don’t believe that PBS is an important part of the social safety net, then read this op-ed by Charles Blow. [20] Source: see Data and Methodology [21] Source: see Data and Methodology [22] These calculations assume that the rates apply to 2009 Adjusted Gross Income with no tax credits. This is of course a counter-factual assumption. Data and Methodology Calendar Lottery Figure 1: The source data for Figure 1 are found in the table “PINC-03. Educational Attainment--People 25 Years Old and Over, by Total Money Earnings in 2011, Work Experience in 2011, Age, Race, Hispanic Origin, and Sex” in U.S. Census Bureau, Current Population Survey, 2012 Annual Social and Economic Supplement. The sample is 163,000 people, with 25% falling into each of four age groups: 25-34, 35-44, 44-54, and 55 and over. Since no income amounts are reported for the 7.3% of the sample with incomes above $100,000, the average 2009 Adjusted Gross Income for individuals with AGI above $100,000 was used ($205,000). Gender Lottery Figure 4.1 and Figure 4.2: These figures are based on the study “Education and Synthetic Work-Life Earnings Estimates” published by the US Census Bureau. Source data for this study are from the Census Bureau's Multiyear American Community Survey (ACS). “ESWLE” used data for 2006, 2007 and 2008. The Census Bureau collects the ACS data each year by interviewing approximately 2 million households. The ESWLE methodology sorts individuals between the ages of 25 to 65 into eight age groups of five years each, nine education level groups, five ethnic groups, and two gender groups. The average “synthetic work-life earnings” for each education/ethnic/gender group (9x5x2=90 groups in all) is then calculated as the sum of the earnings for each of the eight age classes in that group. For example, the lifetime earnings for females with a K-12 education are estimated by summing the average annual earnings for K-12 black females between age 25 and 29, age 30 and 34, age 35 and 39, and so forth. This sum is then multiplied by five, since there are five years in each age group. The result is the estimated earnings over forty years for a K-12 female Table 2-C in ESWLE (“Median Synthetic Work-Life Earnings by Education, Race/Ethnicity, and Gender: All Persons”) shows the estimated median 40-year earnings for each of the 45 groups of males and the 45 groups of females. For Figure 4.1, these groups were ranked from lowest to highest median group earnings. Using this distribution, the weighted average of the median earnings for each 10% of males and females is calculated. These values are plotted in Figure 1. It should be noted that this method does not yield a representation of the full distribution of earnings, because it is based on the median values for each of the 90 groups reported in ESWLE, and so obscures the underlying variation within each of those 90 groups. The source data for Figure 4.2 are found in Table 2-A in “Education and Synthetic Work-Life Earnings Estimates,” which shows median earnings for full-time workers rather than for all people as in Table 2.C. The decile values for males and females in Figure 4.2 are developed in the same way as those in Figure 4.1. Education Lottery Figure 10: This is derived from the interquartile ranges of for lifetime earnings presented in Table 1A of “The College Payoff: Education, Occupations, Lifetime Earnings.” The authors estimated lifetime earning using the same source data and a similar methodology to that described for Figures 4.1. and 4.2. Since earnings are assumed to be log-normally distributed, the natural logs of the interquartile ranges were used to calculate standard deviations for each education class. These were used to calculate the distributions reproduced as Figure 10. Why We Have What We Have Figure 12: The source data for this figure are the same as described above for Figure 1. From these data, the mean and standard deviation for each gender was computed, and these were used to compute the values in Figure 12, assuming that earnings are log-normally distributed. The correlation of 0.22 between gender and earnings was calculated from the same source data as Figure 12, except that those reporting no income were included in this calculation. When one variable can take on only one of two values (gender in this case), and the other can take on a range of values (earnings), the appropriate statistical algorithm for calculating the correlation between the two variables is the “point biserial” correlation coefficient. The algorithm for this is the difference between the mean for males and the mean for females, divided by the population standard deviation, multiplied by the square root of the product of the male fraction times the female fraction. The values for mean male earnings, mean female earnings and standard deviation for the full sample can be calculated from the source data described for Figure 1. Figure 13: The source data for this figure are taken from Table 5.10 in Hertz (2005). These data were used to calculate the probability that a child born to parents in the lower five deciles (“poor children”) will have lifetime earnings in each of the ten children's earnings deciles. The same calculation is performed for the child born to parents in the upper five deciles (“rich children”). These probability distributions are then transformed into earnings distributions shown in Figure 13 using the earnings values at each decile reported in the source data described above for Figure 12. Figure 14: Figure 14 combines the source data and calculations described for Figure 12 and Figure 13. The median for “rich boys” in Figure 14 was calculated as the median value for “rich children” from Figure 13 times the ratio of the median value for men (Figure 12) to median value for the entire population (calculated as described above in the discussion of Figure 12). The standard deviation for “rich boys” was calculated as the median for rich boys times the average of the ratios of the standard deviation to the mean for rich children (calculated as described in the discussion of Figure 13) and for men (calculated as described in the discussion of Figure 12.) Calculations of probabilities that a “poor girl's” or “rich boy's” earnings will exceed a specified value are performed using the natural logs of the earnings distributions described for Figure 13. For example, the log of the median annual income for “poor girls” is 9.24, and the log of the “poor girls” standard deviation of 1.02. The log of the median income for “rich boys” is 10.69. The difference between the log of the “rich boy” and “poor girl” medians is 10.69-9.24 = 1.45, which is 1.42 “poor girl” standard deviations. From the probability density function of the normal distribution, we find that 1.42 standard deviations corresponds to a probability density of 92.2%, which means there is a 7.8% probability that a “poor girl's” earnings will exceed those of a “rich boy.” How Government Should Take What it Needs for Charity Figure 15: The source data for Figure 15 are the same as described for Figure 1. Figure 15 plots the values for each income bracket from lowest to highest (Y axis) against the percentage of the sample falling within each income bracket (X axis). Figure 18: The source data for Figure 18 is Adjusted Gross Income for 2009 as reported in the IRS publication “Statistics of Income Division, July 2011, Table 1.1, Selected Income and Tax Items, by Size and Accumulated Size of Adjusted Gross Income, Tax Year 2009.” The line “current rates” in Figure 18 is derived by applying the marginal tax rates for 2012 to this 2009 AGI data to calculate the average tax rate for each AGI bracket. “Fortune Tax rates” are the average rates that result when all 2009 AGI is taxed at the lesser of 10% of AGI or 50% of the amount by which AGI exceeds the 65th percentile amount of $38,750. “Age of Affluence” average rates are calculated using the marginal tax rates for 1970, with tax brackets adjusted for inflation. Sources Bedard and Dhuey, 2006. “The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects,” Quarterly Journal of Economics 121 no. 4 Carnevale, Anthony P., Stephen J. Rose, and Ban Cheah, “The College Payoff: Education, Occupations, Lifetime Earnings.” Georgetown University Center on Education and the Workforce, http://www9.georgetown.edu/grad/gppi/hpi/cew/pdfs/collegepayoff-complete.pdf Duncan, Greg and Murnane, 2011. Whither Opportunity? Rising Inequality, Schools, and Children's Life Chances. Freeland, Chrystia: “Super-Rich Irony.” The New Yorker, October 8, 2012. Gladwell, Malcolm: “Outliers: The Story of Success.” Back Bay Books, Little Brown, 2008 Hertz, Tom, 2005. “Rags, Riches and Race: The Intergenerational Economic Mobility of Black and White Families in the United States,” in Unequal Chances: Family Background and Economic Success, edited by Bowles, Gintis and Groves, Russell Sage Foundation, New York, Princeton University Press Julian, Tiffany A. and Robert A. Kominski, 2011. “Education and Synthetic Work-Life Earnings Estimates.” American Community Survey Reports, ACS-14. U.S. Census Bureau, Washington, DC. http://www.census.gov/prod/2011pubs/acs-14.pdf Kaplan and Rauh, 2007. “Wall Street and Main Street: What Contributes to the Rise in the Highest Incomes?” National Bureau of Economic Research Levitin, Daniel J., This is Your Brain on Music: The Science of Human Obsession (New York: Dutton, 2006), p.197 Lewis, Michael: “Don't Eat Fortune's Cookie,” Princeton University's 2012 Baccalaureate Remarks. Mazumder, Bhashkar, 2005.“The Apple Falls Even Closer to the Tree than We Thought: New and Revised Estimates of the Intergenerational Inheritance of Earnings” in Unequal Chances: Family Background and Economic Success, edited by Bowles, Gintis and Groves, Russell Sage Foundation, New York, Princeton University Press Murray, Charles, 1998. “Income Inequality and IQ.” The AEI Press, Washington DC Putnam, Robert D., 2012. “Requiem for the American Dream? Unequal Opportunity in America.” Slides presented at Aspen Ideas Festival. The Saguaro Seminar, Kennedy School of Government, Harvard University. Schmidt, Frank L.,2009. “Select on Intelligence,” in Handbook of Principles of Organizational Behavior¸edited by Edwin A. Locke, 2nd edition. John Wiley and Sons. Slaughter, Anne-Marie. “Why Women Still Can't Have It All.” The Atlantic, July/August, 2012. Shane, L. Scott, 2008. The Illusions of Entrepreneurship: the Costly Myths that Entrepreneurs, Investors and Policymakers Live By. Yale University Press About the Author Daniel Badger drew top tickets in the birth lotteries in 1946. He did even better in the education lottery, attending Greenwich Country Day School, Phillips Academy Andover, Yale University, Cambridge University, and Harvard's Kennedy School of Government--a total of fifteen years of private education. His tickets in the job lottery were also pretty good, although two employers ran aground. He worked at the U.S. Regulatory Commission, the U.S. Department of Energy, and the International Energy Agency, a couple of energy consulting firms, Enron, and Babcock & Brown. He currently works in London for a financial advisory firm specializing in renewable energy. Readers' comments are welcome, and can be emailed to Mr. Badger at badgerd@gmail.com.