All of this distorts the implications of income statistics that treat annual salaries and multi-year capital gains as if they were the same. Talk of how much of a country’s income is received by the top ten percent, or top one percent, proceeds as if this is a given set of people when, because of the high turnover in high income brackets, there can be thousands of people in the “top 400” during just one decade. When incomes received by thousands of people are reported statistically as if these were incomes received by hundreds of people, that is a severalfold exaggeration of income disparities.
SIDEBAR: CAPITAL GAINS AND INEQUALITY
A hypothetical example may illustrate how income statistics can exaggerate inequality when they make no distinction between (1) people who receive annual salaries in a given year and (2) people who receive capital gains in that same year, representing income earned over a previous span of years.
If, for example, there are 10 people who are in a high income bracket, each earning $500,000 a year, while there are also 10 people in a lower income bracket, each earning $50,000 a year, it might seem as if there is a ten-to-one difference in income between people in these two brackets. But, if only one of the ten people in the higher bracket is earning $500,000 every year in a decade, while the others are there for just one year each in that decade—the year in which their accrued capital gains are turned into cash income—then given the very high rate of turnover in very high income brackets, the situation is very different from what it would be if there were the same ten people in the higher bracket every year of the decade.
If most of the people in the higher income bracket have a one-year spike in income from capital gains, after which they return to some lower level of income, which may still be above the national average—say, an individual income of $100,000 a year—then, over the course of a decade, the income disparity between people is substantially less than the income disparity between income brackets.
In this hypothetical example, where there are nine people initially in the higher income bracket, earning $500,000 each in the first year covered, and $100,000 in each of the subsequent nine years of the decade, that adds up to a total of $1.4 million each during that decade, which in turn adds up to $12.6 million for all nine people collectively. The tenth member of the top bracket, who is in that bracket every year of the decade, receiving $500,000 a year in all ten years, has a total income of $5 million. For all these particular ten people put together, that adds up to $17.6 million received collectively in a decade by the ten people initially in the higher bracket.
Meanwhile, among the ten people in the lower income bracket, receiving $50,000 a year each initially and throughout the decade, that adds up to $500,000 each in a decade, for a total income of $5 million as a group. With the ten people initially in the higher bracket earning a total of $17.6 million during that same decade, and the ten people initially in the lower bracket earning a total of $5 million during that decade, the disparity in income between people is less than four-to-one, while the disparity in income between their respective income brackets is ten-to-one.
That is because nine of the ten people in the higher bracket are replaced each year by someone else having a one-year spike in income from capital gains, for an income of $500,000 in this example. Counting all 91 people who are in the higher income bracket at some point during the decade, their average annual incomes are less than three times that of people in the lower bracket.23
Although this exercise assumes, for the sake of simplicity, that people in the lower income bracket have constant incomes throughout the decade, data from the real world show the incomes of people initially in lower income brackets to usually be rising over time more sharply than the incomes of people initially in higher brackets.24 This would make the disparity in incomes between people in these two brackets even less than that in this example.
A hypothetical example cannot pretend to be an exact replica of the real world. The point is merely to illustrate how, under some approximation of these conditions, the disparities between income brackets can be much greater than the disparities between actual flesh-and-blood human beings.
Racial and Ethnic Disparities
In trying to determine the reasons for economic and social disparities between blacks and whites, some observers attribute these differences primarily to policies and practices by people outside the black community, while other observers attribute these same differences to internal differences in behavior between black and white Americans.
In seeking to resolve this issue, sociologist William Julius Wilson relied heavily on statistics from opinion surveys. These surveys, according to Professor Wilson, show that “nearly all ghetto residents, whether employed or not, support the norms of the work ethic.”25 In one survey, “fewer than 3 percent of the black respondents from ghetto poverty census tracts denied the importance of plain hard work for getting ahead in society, and 66 percent expressed the view that it is very important.”26
After admitting that “surveys are not the best way to get at underlying attitudes and values,”27 Professor Wilson nevertheless presents—as a refutation of “media perceptions of ‘underclass’ values and attitudes” in inner-city ghettos—the fact that “residents in inner-city ghetto neighborhoods actually verbally endorse, rather than undermine, the basic American values concerning individual initiative.”28
Despite William Julius Wilson’s reliance on opinion surveys to refute claims that ghetto residents have different cultural values from those of the American population as a whole, there is no necessary correlation between what people say and what they do. A survey of low-income people by Columbia University researchers showed that 59 percent regarded buying goods on credit as a bad idea. Nevertheless “most of the families do use credit when buying major durables.”29
The difference between survey results and demonstrable realities was also pointed out by the author of Hillbilly Elegy: “In a recent Gallup poll, Southerners and Midwesterners reported the highest rates of church attendance in the country. Yet actual church attendance is much lower in the South.”30 He also found another survey, indicating that working-class whites worked more hours than college-educated whites to be “demonstrably false.”31 Those who did the survey “called around and asked people what they thought. The only thing that report proves is that many folks talk about working more than they actually work.”32
If someone with no pretensions of being an academic scholar could see the tenuous relationship between survey results and the realities of life, it is hard to understand why surveys were relied on by Professor Wilson for deciding such a crucial issue as the internal or external sources of racial differences in socioeconomic outcomes.
Economists tend to rely on “revealed preference” rather than verbal statements. That is, what people do reveals what their values are, better than what they say. Even when people give honest answers, expressing what they sincerely believe, some people’s conception of hard work, for example, need not coincide with other people’s conception, even when both use the same words.
When black students in affluent Shaker Heights spent less time on their school work than their white classmates did, and spent more time watching television,33 that was their revealed preference. Nor are black and white Americans the only groups with different revealed preferences. In Australia, for example, Chinese students have spent more than twice as much time on their homework as white students did.34
How surprised should we be that Asian students in general tend to do better academically than white students in general, in predominantly white societies such as Australia, Britain or the United States? The same pattern can be seen among whole nations, as such Asian countries as Japan, Korea and Singapore likewise show patterns of hard work by their students and academic results on international tests that place these countries well above most Western nations.35
Statistics compiled from what people say may be worse than useless, if they lead to a belief that those numbers convey
a reality that can be relied on for serious decision-making about social policies.
Incidentally, the high correlation between the amount of work that different groups put into their education and the quality of their outcomes does not bode well for theories of genetic determinism. When we find some race whose lazy students get educational results superior to the results of hard-working students in other races, this would be evidence supporting that hypothesis, but such evidence does not seem to be available.
Minimum Wages and Unemployment
One of the important areas in which survey research has done major damage has been in trying to resolve differences of opinion as to the effect of minimum wage laws on unemployment. Advocates of minimum wage laws argue that such laws raise the income of the poor, while critics argue that these laws cause more of the poor to be unemployed, because low-income workers tend to be workers with few skills and/or little work experience, so that employers find them worth employing only at low wage rates. Despite an abundance of detailed statistics on unemployment, this controversy has raged for generations.
Part of the problem is that, as we have seen in other contexts, most of what are called “the poor” are not permanent residents in low-income brackets, any more than other people are permanent residents in other income brackets. Most of the people being paid the minimum wage rate are young workers, and of course they do not remain young over the years. So, when people say, as Senator Ted Kennedy once said, “Minimum wage workers have waited almost 10 long years for an increase,”36 they are not talking about a given set of human beings, but about a statistical category containing an ever-changing mix of people.
Because young people are usually, almost by definition, less experienced as workers, their value to a prospective employer tends to be less than the value of more experienced workers in the same line of work. Some young people may acquire valuable work skills through education, but education also takes time, and people grow older with the passage of that time.
Often what younger, inexperienced workers acquire from an entry-level job is primarily the habit of showing up every day and on time, and the habit of following instructions and getting along with others. But, simple as such things may seem, the absence of these prerequisites can negate whatever other good qualities a young worker may have.
After having acquired work experience in some simple, entry-level job, most young beginners go on to other jobs where work experience of some sort may be a prerequisite for getting hired.
High rates of employee turnover, sometimes exceeding 100 percent per year, are common in many entry-level jobs in retail businesses or fast-food restaurants.37 These jobs are stepping stones to other jobs with other employers, though some observers falsely call entry-level jobs “dead-end jobs.”
If workers in fact stayed on permanently in such jobs, which usually have no automatic promotions ladder, those workers would in fact be in dead-end jobs. But, when the average tenure of supermarket employees has been found to be 97 days, that is clearly not the case.38
Like most things in a market economy, inexperienced and unskilled workers are more in demand at a lower price than at a higher price. Minimum wage laws, based on what third parties would like to see them paid, rather than being based on productivity, can price unskilled workers out of a job.
This traditional economic analysis has been challenged by advocates of minimum wage laws, and survey research data has been a major part of that challenge.
Back in 1945, Professor Richard A. Lester of Princeton University sent out questionnaires to employers, asking how they would respond to higher labor costs. Their responses, which were not along the lines of traditional economic analysis, convinced Professor Lester that the traditional economic analysis was either incorrect or not applicable to minimum wage laws.39
However, what traditional economic analysis seeks to do is predict economic outcomes, not predict how people will answer questionnaires. Moreover, outcomes are not just the fruition of beliefs or intentions, as we have seen in discussions of the costs of discrimination.
Decades after Professor Lester’s challenge to traditional economic analysis, other economists, also at Princeton, again challenged traditional economic analysis on the basis of survey research, though this time by surveying the same employers before and after a minimum wage increase, and asking each time how many employees they had. The answers convinced the Princeton economists that the minimum wage increase had not reduced employment. They and their supporters therefore declared the traditional analysis to be a “myth.”40
Devastating criticisms of the Princeton economists’ conclusions were made by other economists, who challenged both the accuracy of their statistics and the logic of their conclusions.41 But, even if the Princeton economists’ statistics were accurate, that would still not address the key weakness of survey research in general—which is that you can only survey survivors. And what may be true of survivors need not be true of others in the same circumstances who did not survive in those particular circumstances.
An extreme hypothetical example may illustrate the point that is applicable in less extreme situations. If you wished to determine whether playing Russian roulette was dangerous, and did so through survey research, you might send out questionnaires to all individuals known to have played Russian roulette, asking them for information as to their outcomes.
After the questionnaires were returned and the answers tabulated, the conclusion from these statistics might well be that no one was harmed at all, judging by the answers on the questionnaires that were returned. Not all questionnaires would have been returned, but that is not uncommon in survey research. Basing your conclusions on the statistical data from this research, you might well conclude that you had disproved the “myth” that playing Russian roulette was dangerous. This is the kind of result you can get when you can only survey survivors.*
In the case of minimum wage studies, if all the firms in an industry were identical, then any reduced employment resulting from the imposition of a minimum wage, or the raising of an existing minimum wage rate, would appear as a reduction of employment in all the firms. But, in the more usual case, where some firms in a given industry are quite profitable, others are less profitable and still others are struggling to survive, unemployment resulting from a minimum wage can push some struggling firms out of the industry—and reduce the number of their replacements, now that labor costs are higher and profits more problematical.
The only firms that can be surveyed for their employment data, both before and after the minimum wage was imposed or raised, are the firms that were there in both time periods—that is, the survivors. If there has been a net decrease in the number of firms, the employment in these surviving firms need not have gone down at all, regardless of a decline in employment in the industry as a whole. The firms surveyed are like the people who survived playing Russian roulette, which may well be a majority in both cases, though not an indicative majority.
Empirically, a study of the effect of minimum wages on employment in restaurants in the San Francisco Bay Area found that the principal effect was through some restaurants going out of business—and reducing the number of new firms entering to replace them. Those restaurants going out of business were primarily restaurants rated lower in quality. Employment in five-star restaurants was unaffected.42
In Seattle as well, the response to a higher local minimum wage rate increase was that a number of restaurants simply closed down.43 A study published by the National Bureau of Economic Research measured employment by hours of work, as well as by the number of workers employed, and concluded that “the minimum wage ordinance lowered low-wage employees’ earnings by an average of $125 per month in 2016.”44 Thus a theoretical increase in income from a higher minimum wage became, in the real world, a significant decrease in income.
Another problem with trying to determine the effect of a minimum wage law on unemployment is that the proportion of the work force directly affected b
y a minimum wage is often small. Therefore unemployment among that fraction of the work force can be swamped by fluctuations in the unemployment rate among the larger number of other employees around them.
This is less of a problem in situations where most of the employees are earning a wage low enough to be directly affected by a minimum wage law. But five-star restaurants were unlikely to be having inexperienced teenagers delivering food to their customers’ tables, even if restaurants like McDonald’s or Burger King often have teenagers delivering food over the counter to their customers.
Alternative ways of assessing the effect of a minimum wage on unemployment would include gathering data restricted to just the kinds of inexperienced and unskilled workers directly affected, such as teenagers. We have already seen, in Chapter 2, how minimum wage laws affect both teenage unemployment in general and racial disparities in teenage unemployment rates as well.
Yet another way of assessing the effect of minimum wage laws on unemployment would be to gather unemployment data on places and times where there have been no minimum wage laws at all, so that these unemployment rates could be compared to unemployment rates in places and times where there have been minimum wages laws—especially where these have been comparable societies or, ideally, the very same society in the same era, with and without a minimum wage law.
By focusing on teenagers in general, or black teenagers in particular, it is possible to see the effects of minimum wage laws more clearly and precisely, since these are workers on whom such laws have their greatest impact, because these are a population most lacking in education, job skills and experience, and therefore earning especially low wage rates. Moreover, there are extensive statistics on what happened to these populations in the labor markets from the late 1940s to the present.
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