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 Read online



  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

  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 ha
ve, 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 bet
ter 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.