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

by Daniel Badger

  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, y
ou 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.