"As for the immigrants, they are the ones to whom it can be accounted a merit to be Americans. For they have had to take trouble for their citizenship, whereas it has cost the majority nothing at all to be born in the land of civic freedom." Albert Einstein [1].
In the first post I discussed basic definitions surrounding racism. In the second post in this series, I looked at some of the most common forms of racism. Beyond blatant racism, it is racist to:
- Prejudge someone based on race.
- Use the behavior of an individual (or individuals) to judge others in a race.
- Exaggerate our fears of the threats of others.
- To claim that having a friend of a certain race is a defense while being prejudiced against others of that race.
- Select anecdotes to prove a racist point.
I will continue to look at the various forms in which racism expresses itself.
Various Forms of Racism, Part Two.
- Statistics and racism.
- Selective statistics.
- False statistics.
- Exaggerating the meaning of statistics.
- Sins of omission.
- Reverse discrimination.
Why do we fear those whom we fear? I know someone who is afraid of bears. This person has never encountered a bear outside of a zoo and has never lived in an area where bears are prevalent. Other than those who wrestle bears for a living, the rest of us are thousands of times more likely to be killed by a human than a bear. So, why aren't we afraid of humans? Oh, that's right, we are afraid of humans, and that's what I'm writing about.
Statistics and Racism.
Statistics don't lie: people lie. People can lie with words and they can lie with statistics. At their best, statistical processes act like a sieve to wash out the sand and save the gold nuggets.
However, statistics are often wielded like they are pre-packaged and indisputable truths. A few basic tests will allow you to flag statistics that are probably lies. These same tests can be sorted into means by which statistics are misused by racists.
Other websites point out how false conclusions can be packed into statistics. For example, they look at how the average is misleading and how linking two findings together doesn't mean they are related. I'll talk a little bit about these at the end of this section, under the section of good statistical hygiene. However, I find that racists tend to misuse statistics in a much more basic way: they select out statistics that prove their point and ignore the context or statistics that disagree; they exaggerate what the statistics say; and, they just out-and-out invent statistics.
Selective Statistics, or: Statistical Anecdotes.
I ended my previous post by describing racism by anecdote. Similar to this is using selective statistics. This is one of the most common forms of lying with statistics. It is easy to select a single bit of information that misleads or which even contradicts the overall picture.
I have written a fair amount on the decrease in violent crime that has taken place in the United States since its peak in the early 1990s. Overall, the violent crime rate has dropped by approximately 50% with murders down by a similar number.
A popular game among fear-mongers is demonstrating the violent crime rate is going up by grabbing a small bite of the data.
Here are the figures for murder rates in Alabama by year, per 100,000 population:
- 2014, 5.7
- 2015, 7.2
Now, this represents an alarming 26.3% increase. However, if we look at these years in context, we have:
- 2012, 7.1
- 2013, 7.2
- 2014, 5.7
- 2015, 7.2
It is bad that the numbers rose from 2014, however, the bigger picture says that 2014 was unusually low and that the murder rate has stayed steady. Individual numbers that jump around are called blips. Rule one: Do not pay attention to blips. Rule two: whenever the focus of a claim becomes strangely narrow, the person is probably trying to distract you from the bigger picture.
While the murder rate in Alabama could possibly be the stuff of racist comments (Alabama is 33% minority) or demonstrate anti-Southern sentiments, let's look at an example more directly applied to racism.
Trump not only chose anecdotes to support his statement that illegal aliens brought crime, he provided statistics. "Thousands of Americans have been killed by illegal immigrants."
As detailed in this article, Trump gave no time frame and provided examples which included a case of a legal alien who injured but did not kill someone in 1990. Illegal aliens make up about 3.5% of the national population. With approximately 16,000 homicides per year, if this population murdered at an equal rate, this would be about 500 murders per year. Given enough years, the number can total into the thousands.
The states bordering Mexico (California, Arizona, Texas and New Mexico) have all had dramatic decreases in violent crime in murder in the past 25 years, outperforming other areas of the country, some of which have shown increases. California has had the second largest improvement of any state. [detailed here]
It is very common these days to say "urban crime is rising" by pointing to an individual city where crime has gone up. In this case, there is two dimensions to the lie: a slice of time and a slice of geography.
False Statistics.
Those promoting an agenda often simply resort to invented statistics. For Donald Trump, inner city and African-American appear interchangeably (even though blacks in metropolitan areas mostly live in the suburbs).
"But I want to do things that haven't been done, including fixing and making our inner cities better for the African-American citizens that are so great, and for the Latinos, Hispanics, and I look forward to doing it." Donald Trump, October 9, 2016.
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| Murders, 1960-2014, U.S., FBI Uniform Crime Report [source] |
Beyond the lie that inner city crime has reached record levels (it has dropped to its lowest numbers in fifty years), Trump has presented invented numbers to state that black crime is rampant. He passed along this graphic that claims that 81% of whites are murdered by blacks, a retweet from WhiteGenocide. This is a wholly invented statistic, a lie and extreme racism. Blacks are the perpetrators in 15% of homicides where the victim is white in the 62% of cases where the perpetrator is found. (About 9% overall.)
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| A Trump Tweet. |
This story which ran on the last day of 2015 from Breitbart has both phony statistics and statistics out of context. It points out a 54% homicide increase in Washington, D.C. in 2015 over 2014. This is using a statistical anecdote to contradict a long term trend. The homicide rate in the period 2012 to 2014 homicide rate was the lowest since the 1960s and through December 30th, have dropped approximately 16% for 2016 (162 in 2014, 136 this year). [Numbers here]
The same article goes on to mention a 20% increase in homicides in New York City. The actual number was 5.7%. (It is down 3.8% through 12/25 this year, with totals 333, 352 and 330 (so far) 2014, 2015 and 2016, respectively). [numbers here]
Nevertheless, there will always be a city to pick on where crime has gone up.
How do you defend against the false statistic sort of racism? Not easily. These statistics are often "hit and run," they appear in the middle of a piece, sometimes without supporting background info, sometimes with a phony source (Trump's tweet about homicide rates: there is no Crime Statistics Bureau - San Francisco).
If a person or website puts out these sorts of statistics several times, it is not a coincidence. They have an agenda. Avoid going there.
Statistics without hygiene.
For a statistic to be honest it should compare scrubbed apples to scrubbed apples. The amount of yearly deficit (the annual amount by which governmental spending exceeds income) must be adjusted for inflation. Dollars in 1975 are 22% of dollars in 2016. In terms of financial health, the numbers should be adjusted by the Gross Domestic Product (GDP).
In 1985 there were 89 murders in the city of Phoenix. In 2015 there were 113. This represents a 27.0% increase. The population in 1985 was 890,746. In 2015, it was 1,563,025, a 75.5% increase. Consequently, the murder rate fell by 27.7%. [Uniform Crime Report, FBI, derived from their historical table for Phoenix]
Not adjusting for this is a lie. Expect a minimum amount of hygiene with the statistics. Otherwise, someone is selling you a lie.
Exaggerating the meaning of statistics.
As mentioned in a previous post, the exaggeration of fear leads to prejudice. Even when a statistic does elucidate a fact accurately, it is necessary to place that fact in relation to others.
This story at US News & World Report discusses some of the actual differences in race crimes. Do African-Americans have a higher crime rate than whites in America? Yes. And from an honest starting point accounting for such factors as poverty, crime rates can be discussed.
And, when it's all said and done, they still don't support racism; they don't support danger from some random individual of another race (fear of others). In America, 41.7% of murders are performed by family and those you know, 45.4% of unknown relation, and 12.8% by strangers. Not a mysterious stranger on which we project our fears. (3.3 times more likely to be murdered by someone you know than by a stranger [Source].
Sins of Omission.
That car that was stolen in Washington, DC which I spoke about in the previous post. After it was found I got a call to pick it up at an Anacostia tow-yard. When I got there, there was another individual waiting to pick up his car, a young black man. We were not far different in age, he was dressed a little better than me. Before claiming his car he was asked for his driver's license for ID. He (foolishly) didn't bring it and was told he'd have to return. That was reasonable. I wasn't asked for an ID.
I tell this story because for me, it was an instance in which discrimination was both subtle and vivid.
Reverse Discrimination.
What is reverse discrimination? I started this process by going to the Merriam-Webster Dictionary online.
Reverse discrimination: discrimination against whites or males (as in employment or education).
(In England, the term reverse discrimination refers to discriminating in favor of a minority rather than against a set of individuals.)
After having twice being rejected for admission at the University of California Davis Medical School, Allan Bakke sued the school citing the fact that school had set aside 16 openings (out of 100) to minorities and that this practice discriminated against him for being white. In 1978, the Supreme Court sided with him, saying that although schools could use race as a consideration in admission, they could not create specific numbers (quotas or set-asides). (California was one-third minority by population in 1980. Being a state-run medical virtually all admissions would be in-state. Sixteen out of one-hundred admissions set aside still underrepresented the state demographics.)
At that time, I was a pre-med undergraduate student. An alumnus, a former pre-med student, came visiting our campus on an official recruiting visit promoting the medical school where he'd been spent his first year. He declared that by considering his school you didn't have to worry about reverse discrimination: only one student out of two hundred was a minority. I raised my hand and asked how did they manage to keep it down to a single student without forward discrimination? I was told the school was rural and minorities prefer urban environments. (Insulting on several levels and ludicrous: there is no medical school applicant who is going to turn down acceptance solely because a school is rural.)
These were my introductions to the concept of reverse discrimination and affirmative action. From early on, it seemed to me to be a numbers game. Bakke applied for admissions where there were 100 spots. Sixteen were set aside for minorities. That meant he definitely didn't score in the top 84. Let's make an ungenerous assumption (ungenerous against the minority applicants): only half of the sixteen would have been accepted by the standards of the top 84. That leaves Bakke among or below the bottom eight (possibly being below the bottom eight because there is little reason to believe that he had exactly the 101st best application in both times he applied). In other words, he didn't have that impressive an application.
There are deeper issues here. Why is minority used as a near synonym of disadvantaged? Yes, there are other forms of being disadvantaged which are not adequately addressed. Yes, there are good numbers of minorities who come from a background of financial well-being. Yes, there are a good number of white people who come from poverty. None of these change the fact: being a minority is a disadvantage.
Forward discrimination exists and is a potent force. More on this below, but an example here. There have been many studies that have shown discrimination against minorities. Typical of these is a résumé sent out with an "African-American sounding" name (e.g. Jamal and Lakisha) or a "white sounding" name (e.g. Greg and Emily). The qualifications are identical, the names are switched. The same application got 50% more requests for interviews for the "white-sounding" names.
In summary, I believe reverse discrimination exists. However, overall, it is a small arrow pointing in the opposite direction of the huge arrow that is discrimination.
Reverse Discrimination as Racism Against the Majority.
Reverse discrimination is also used to describe racism directed from a minority against the majority. Of course, such racism exists. Minorities are not saints who are immune from fear or hatred. Is there less racism from minorities? One argument is that being a minority "sensitizes" an individual to prejudice. Einstein, whom I have quoted at the beginning of these posts, mentioned this. He described how, being Jewish, having just escaped from Nazi Germany, he felt the plight of the African-American. A second argument is that if one is a minority in an environment where most of the population is majority, then there is less fear of the other. Many "others" are among the people you know. A third argument is that the media for a long time has provided many idealized examples of whites. They are Brad Pitt, Bruce Willis, Chris Evans, stars who sometimes work with a black sidekick. Yes, more recently there are positive role models for blacks in media. [Completely by coincidence and because I have a nine-year-old son and didn't personally choose the station, on the television while writing this are several really painful black stereotypes being played for laughs].
In contrast to reverse discrimination, "forward" discrimination has the force of a majority. Forward discrimination also shapes public attitude and public policy in the way that a majority can. The numbers behind this are explored in this post..
In summary, this section has looked at how statistics can be used selectively to promote racist statements, looked at racism by omission, and the matters of reverse discrimination in terms of affirmative action and in racism directed toward the majority.
The notion of reverse discrimination as a numbers game is further explored in the coming post.
In the final section I hope to put this together to answer the questions: Is what I did racist? Is there a way to reduce prejudging on my part?
[1] Albert Einstein as cited in Einstein on Race and Racism. Fred Jerome and Roger Taylor, Rutgers University Press.
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Martin Hill Ortiz is the author of Never Kill A Friend, Ransom Note Press.
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| Never Kill A Friend, Ransom Note Press |
Never Kill A Friend is available for purchase in hard cover format and as an ebook.
The story follows Shelley Krieg, an African-American detective for the Washington DC Metro PD as she tries to undo a wrong which sent an innocent teenager to prison.
Hard cover: Amazon US
Kindle: Amazon US
Hard cover: Amazon UK
Kindle: Amazon UK
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