Discrimination and Disparities Read online

  But such statistics, so damaging to the prevailing preconception that intergroup differences in outcomes showed racial discrimination, in the sense of Discrimination II, were almost never mentioned in most of the mass media.

  The omitted statistics would have undermined the prevailing preconception that white lenders were discriminating against black applicants. However, that preconception at least seemed plausible, even if it failed to stand up under closer scrutiny. But the idea that white lenders would also be discriminating against white applicants, and in favor of Asian applicants, lacked even plausibility. What was equally implausible was that black-owned banks were discriminating against black applicants. But in fact black-owned banks turned down black applicants for home mortgage loans at a higher rate than did white-owned banks.4

  Household Income Statistics

  It is, unfortunately, not uncommon to omit statistics that are discordant with prevailing preconceptions. This has become a common practice in politics, in the media and even in much of academia. Such errors of omission are not confined to mortgage loan issues, but are also common in many discussions of income statistics.

  Household income data, for example, are often used to indicate the magnitude of economic disparities in a society. But to say that the top 20 percent of households have X times as much income as the bottom 20 percent of households exaggerates the disparity between flesh-and-blood human beings, which can be quite different from disparities between income brackets. That is because, despite equal numbers of households in each 20 percent, there are far more people in the top 20 percent of households.

  Census data from 2002 showed that there were 40 million people in the bottom 20 percent of households and 69 million people in the top 20 percent of households.5 Such facts are usually omitted in statistics about disparities in incomes.

  No doubt people in the top quintile average higher incomes than people in the bottom quintile. But the fact that there were also 29 million more people in this top quintile exaggerates the disparity in incomes among people. Later data for 2015 from the U.S. Bureau of Labor Statistics indicated that there were now over 36 million more people in the top quintile than in the bottom quintile.6 Moreover, the number of people earning income was four times as great in the top quintile as in the bottom quintile.7 That is yet another of the errors of omission, when the truth would undermine a prevailing preconception.

  There are not only different numbers of people per household at different income levels, there are also different numbers of people per household from one ethnic group to another, and different numbers of people per household from one time period to another. Omitting those differences when drawing conclusions can distort the meaning or implications of those statistics.

  As the Bureau of the Census pointed out, more than half a century ago, the number of households has been increasing faster than the number of people.8 In short, American households tend to contain fewer people per household over time—a trend continuing into the twenty-first century.9 There are not only smaller families in later times, more individuals are financially able to live in their own individual households, rather than live with relatives or roommates, or live as individual roomers or in boarding houses, as average incomes rise from generation to generation.

  When income per person is rising over the same span of years when the average number of persons per household is declining, that can lead to statistics indicating that the average household income is falling, even if all individual incomes are rising.

  For example, if per capita income rises by 25 percent over some span of years, during which the average number of persons per household declines from 6 persons to 4 persons, then four people in the later period have as much income as five people had in the earlier period. But that is still less income than six people had in the earlier period, so average household income falls, statistically, even if income per person has risen by 25 percent.

  Household income statistics can be misleading in other ways. If two low-income people are sharing an apartment, in order to make the cost of rent less burdensome to each, and if either or both has an increase in salary, that can lead to one tenant moving out to live alone in another apartment—and that, in turn, can lead to a fall in average household income.

  If, for example, each of the two tenants has an income of $20,000 a year, and later both reach an income of $30,000 a year, leading to each living in a separate apartment afterwards, that will mean a fall in household income for these individuals from $40,000 a year to $30,000 a year. There will now be two low-income households instead of one, and each household will be poorer than the one they replaced. Again, a rise in individual income can be reflected statistically as a fall in household income.

  Since most income is paid to individuals, rather than to households, and “individual” always means one person while “household” can mean any changeable number of persons, why would household income statistics be used so often instead of individual income statistics?

  Clearly, omitting individual income statistics, and using household income statistics instead, is less useful to someone seeking the truth about economic differences among human beings. But household income statistics can be very useful for someone promoting political or ideological crusades, based on statistics that exaggerate income disparities among people.

  Time and Turnover

  Another factor often omitted, or distorted, in discussions of income disparities is the time dimension. People in the bottom 20 percent are often spoken of as “the poor” and, if the income in that quintile has not changed much over some span of years, it may be said that the income of “the poor” has stagnated. But the great majority of people initially in the bottom quintile do not stay there permanently.

  Most of the people in that bottom quintile initially are likely to be gone in later years, precisely because their incomes have not stagnated—and our concern is for the fate of flesh-and-blood human beings, not the fate of abstract statistical categories.

  A University of Michigan study that followed a given set of working Americans from 1975 to 1991 found that 95 percent of the people initially in the bottom 20 percent were no longer there at the end of that period. Moreover, 29 percent of those initially in the bottom quintile rose all the way to the top quintile, while only 5 percent still remained in the bottom 20 percent.10

  Since 5 percent of 20 percent is one percent, only one percent of the total population sampled constituted “the poor” throughout the years studied. Statements about how the income of “the poor” fared during those years would apply only to that one percent of the people.

  Similar distortions of reality occur when the time dimension is ignored in discussing people in the upper income brackets, who are often also spoken of as if they were an enduring class of people, rather than transients in those brackets, just like “the poor” in lower brackets. Thus a New York Times essay in 2017 referred to “This favored fifth at the top of the income distribution” as having collected “since 1979” a far greater amount of income than others.11

  Considering how much turnover there was among people in different quintiles from 1975 to 1991, the implicit assumption that there were the same people in the top quintile over the even longer period from 1979 to 2017 is a staggering assumption. But of course the very idea of turnover was omitted.

  Another of the relatively few statistical studies that followed a given set of Americans over a span of years found a reality very different from what is usually portrayed in the media, in politics, or in academia: “At some point between the ages of 25 and 60, over three-quarters of the population will find themselves in the top 20 percent of the income distribution.”12 For most Americans in other quintiles to envy or resent those in the top quintile would mean envying or resenting themselves, as they will be in later years.

  Calling people in particular income brackets “the poor” or “the rich” implicitly assumes that they are enduring residents in those brackets, when in fact most Amer
icans do not stay in the same income quintile from one decade to the next.13

  The turnover rate among people in the highest income brackets is even greater than that of the population in general. Fewer than half the people in the much-discussed “top one percent” in income in 1996 were still there in 2005. People initially in the top one hundredth of one percent had an even faster turnover, and those with the 400 highest incomes in the country turned over fastest of all.14

  Crime Statistics and Arrest Statistics

  Some of the most gross distortions of reality through errors of omission have involved quite simple omissions. No one needs to be an expert on the complexities of statistics in order to see through many statistical fallacies, including those based on simple omissions. But it does require stopping to think about the numbers, instead of being swept along by a combination of statistics and rhetoric.

  Statistics cited in support of claims that the police target blacks for arrests usually go no further than showing that the proportion of black people arrested greatly exceeds the roughly 13 percent of the American population who are black.

  If anyone were to use similar reasoning to claim that National Basketball Association (NBA) referees were racially biased, because the proportion of fouls that referees call against black players in the NBA greatly exceeds 13 percent, anyone familiar with the NBA would immediately see the fallacy—because the proportion of black players in the NBA greatly exceeds the proportion of blacks in the American population.

  Moreover, since blacks are especially over-represented among the star players in the NBA, the actual playing time of black players on the floor would be even more disproportionately higher, and it is the players on the floor who get cited for fouls more so than secondary players sitting on the bench.

  What would be relevant to testing the hypothesis that blacks are disproportionately targeted for arrest by the police, or disproportionately convicted and sentenced by courts, would be objective data on the proportions of particular violations of the law committed by blacks, compared to the proportions of blacks arrested, convicted and sentenced for those particular violations.

  Such objective data are not always easy to come by, since data reflecting actions by the police would hardly be considered valid as a test of whether the actions of the police were warranted. However, there are some particular statistics that are both relevant and independent of the actions of the police.

  The most reliable and objective crime statistics are statistics on homicides, since a dead body can hardly be ignored, regardless of the race of the victim. For as long as homicide statistics have been kept in the United States, the proportion of homicide victims who are black has been some multiple of the proportion of blacks in the population. Moreover, the vast majority of those homicide victims whose killers have been found were killed by other blacks, just as most white homicide victims were killed by other whites.

  Since the homicide rate among blacks is some multiple of the homicide rate among whites, it is hardly surprising that the arrest rate of blacks for homicide is also some multiple of the rate of homicide arrests among whites. It has nothing to do with the proportion of blacks in the general population, and everything to do with the proportion of blacks among people who commit a particular crime.

  Another violation of the law that can be tested and quantified, independently of the police, is driving in excess of highway speed limits. A study by independent researchers of nearly 40,000 drivers on the New Jersey Turnpike, using high-speed cameras and a radar gun, showed a higher proportion of black drivers than of white drivers who were speeding, especially at the higher speeds.15

  This study, comparing the proportion of blacks stopped by state troopers for speeding with the proportion of blacks actually speeding, was not nearly as accepted, or even mentioned, either by the media or by politicians, as other studies comparing the number of blacks stopped by state troopers for speeding and other violations with the proportion of blacks in the population.16

  Yet again, specific facts have been defeated by the implicit presumption that groups tend to be similar in what they do, so that large differences in outcomes are treated as surprising, if not sinister. But demographic differences alone are enough to lead to group differences in speeding violations, even aside from other social or cultural differences.

  Younger people are more prone to speeding, and groups with a younger median age have a higher proportion of their population in age brackets where speeding is more common. When different groups differ in median age by a decade, or in some cases by two decades or more,17 there was never any reason to expect different groups to have the same proportion of their respective populations speeding, or to have the same outcomes in any number of other activities that are more common in some age brackets than in others.

  The omission of data on the proportion of blacks—or any other racial group—engaged in a given violation of law, as distinguished from the proportion of blacks or others in the population at large, is sufficient to let racial profiling charges prevail politically, despite their inconsistency with either logic or evidence.

  Some professional statisticians have refused to get involved in “racial profiling” issues. As a professor of criminology in North Carolina explained: “Good statisticians were throwing up their hands and saying, ‘This is one battle you’ll never win. I don’t want to be called a racist.’”18

  Among the other consequences is that many law enforcement officials also see this as a politically unwinnable battle, and simply back off from vigorous law enforcement, the results of which could ruin their careers and their lives. The net result of the police backing off is often a rise in crime,19 of which law-abiding residents in black communities are the principal victims.

  Some people may think that they are being kind to blacks by going along with unsubstantiated claims of “racial profiling” by the police. But, as distinguished black scholar Sterling A. Brown said, long ago: “Kindness can kill as well as cruelty, and it can never take the place of genuine respect.”20

  ERRORS OF COMMISSION

  Statistical errors of commission include lumping together data on things that are fundamentally different, such as salaries and capital gains, producing numbers that are simply called “income.”

  Other errors of commission include discussing statistical brackets as if they represented a given set of flesh-and-blood human beings called “the rich,” “the poor” or “the top one percent,” for example. Errors of commission also include using survey research to resolve factual issues that the inherent limitations of survey research make it unable to resolve.

  Capital Gains

  While annual income statistics for individuals avoid some of the problems of household income statistics, both of these sets of statistics count as income (1) annual salaries earned in a given year and (2) income from capital gains accrued over some previous span of years, and then turned into cash income during a given year. Treating the incomes earned by some individuals over various numbers of years as being the same as incomes earned by other individuals in just one year is like failing to distinguish apples from oranges.

  Capital gains take many forms from many very different kinds of transactions. These transactions range from sales of stocks and bonds that may have been bought years earlier to sales of a home or business that has increased in value over the years.

  If a farm was purchased for $100,000 and then, 20 years later—after the farmer has built barns and fences, and made other improvements to the land and the structures on it—the farm is then sold for $300,000, that sale will result in a net increase of the owner’s income by $200,000 in the particular year when the farm is sold. Statistically, that $200,000 that was earned over a period of 20 years will be recorded the same as a $200,000 salary earned by someone else in just one year.

  Looking back, that farmer has in reality earned an average of $10,000 a year for 20 years as increases in the value of the farm, through the investment of time, work and m
oney on the farm. Looking forward, the farmer cannot expect to earn another $200,000 the following year, as someone with a $200,000 annual salary can.

  Capital gains in general are recorded in income statistics as being the same as an annual salary, when clearly they are not. Nor is there some easy formula available to render salaries and capital gains comparable, because capital gains by different individuals accrue for differing numbers of years before being turned into cash income in a given year.

  If capital gains were equally present at all income levels—say, 10 percent of all incomes being capital gains—then the disparities in income statistics might not be affected much. But, in reality, low annual incomes are far more likely to be salaries and very high annual incomes are far more likely to be capital gains. While people making twenty thousand dollars a year are probably getting that from a salary, people making twenty million dollars a year are more likely to be making such a sum of money from capital gains of one sort or another.

  The exceptionally high rates of turnover of people at very high income levels reinforce this conclusion. Internal Revenue Service data show that half the people who earned over a million dollars a year, at some time during the years from 1999 and 2007, did so just once in those nine years.21

  This does not imply that all the others in that bracket made a million dollars every year. Another study, also based on tax data, showed that, among Americans with the 400 highest incomes in the country, fewer than 13 percent were in that very high bracket more than twice during the years from 1992 to 2000.22 The highest incomes are usually very transient incomes, reinforcing the conclusion that these are transient capital gains rather than enduring salaries.