Discrimination (11): Types of Discrimination

After this post about the reasons why discrimination is wrong, I thought it might be useful to add something about the differences between some types of discrimination and in the process link to some older and forgotten posts.

The most common type or at least the most commonly referenced type of discrimination is explicit discrimination: “whites only” signs, apartheid, miscegenation laws etc. When this type starts to become more and more unacceptable, hidden discrimination often takes over. Someone who wants to discriminate no longer does so explicitly – because that’s illegal or socially frowned upon – but instead adopts a policy or a law that doesn’t reference the target group, that remains facially neutral and that, when applied, accomplishes the intended discrimination. The most famous examples are the Jim Crow era literacy tests for voters. These tests effectively excluded blacks from the franchise although blacks weren’t explicitly targeted. Decades of educational discrimination made sure that very few blacks passed the tests.

Both these forms of discrimination are intentional, but some cases of discrimination are unintentional. People may not intentionally aim to impose disadvantages on other groups, but the structures of society, as they have been influenced by decades of previous – intentional – discrimination, make it very hard to avoid the imposition of systematic harm on some groups. The enduring effects of slavery in the U.S. are an example. Some would argue that this isn’t discrimination at all, since discrimination is typically defined on the basis of intentions and not purely in terms of consequences or outcomes.

Then there’s unconscious discrimination (see also here). People often have unconscious motives for their actions. And finally there’s statistical discrimination (see also here), which is discrimination that doesn’t arise from prejudice or bias, but occurs when people use aggregate group characteristics, such as group averages, to evaluate individual personal characteristics (for example, employers avoiding to hire African Americans because it’s statistically more likely for an African Americans to be an ex-convict).

More posts in this series are here.

The Causes of Human Rights Violations (23): Unconscious Bias

No matter how egalitarian, unbiased and unprejudiced we claim to be and believe to be, underneath it all many of us are quite different.

If you ask people whether men and women should be paid the same for doing the same work, everyone says yes. But if you ask volunteers how much a storekeeper who runs a hardware store ought to earn and how much a storekeeper who sells antique china ought to earn, you will see that the work of the storekeeper whom volunteers unconsciously believe to be a man is valued more highly than the work of the storekeeper whom volunteers unconsciously assume is a woman. If you ask physicians whether all patients should be treated equally regardless of race, everyone says yes. But if you ask doctors how they will treat patients with chest pains who are named Michael Smith and Tyrone Smith, the doctors tend to be less aggressive in treating the patient with the black-sounding name. Such disparities in treatment are not predicted by the conscious attitudes that doctors profess, but by their unconscious attitudes—their hidden brains. (source)

And even if most of our actions are guided by our conscious beliefs, some will be caused by unconscious prejudice, in which case we’ll have identified a cause of discrimination, a cause that will be very hard to correct.

Gender Discrimination (23): Reverse Gender Discrimination in Criminal Justice

Using data obtained from the United States Sentencing Commission’s records, we examine whether there exists any gender-based bias in criminal sentencing decisions. … Our results indicate that women receive more lenient sentences even after controlling for circumstances such as the severity of the offense and past criminal history. …

Studies of federal prison sentences consistently find unexplained racial and gender disparities in the length of sentence and in the probability of receiving jail time and departures from the Sentencing Guidelines. These disparities disfavor blacks, Hispanics, and men. A problem with interpreting these studies is that the source of the disparities remains unidentified. The gravest concern is that sentencing disparities are the result of prejudice, but other explanations have not been ruled out. For example, wealth and quality of legal counsel are poorly controlled for and are undoubtedly correlated with race. …

The findings regarding gender in the case of serious offenses are quite striking: the greater the proportion of female judges in a district, the lower the gender disparity for that district. I interpret this as evidence of a paternalistic bias among male judges that favors women. (source)

Discrimination (6): Should People Be Liable For Unconscious Discrimination?

First of all, it’s evident that people often have unconscious motives for their actions. For example, parents “wishing the best” for their children can act out of frustration about their own past failures. So it’s likely that some acts of discrimination are based on similar “deep” motives. Some of us who genuinely believe that we are colorblind may still avoid black neighborhoods at night, cross a lonely street when a tall black male comes our way, or favor a CV sent in by someone with a “‘Caucasian” name. Tests have shown that people are more biased than they admit to themselves.

So we may be violating anti-discrimination laws without “really” and consciously wanting to. You could say that in such cases we shouldn’t be prosecuted for breaking the law, because there is no intent on our part. Discrimination takes place but no one really wants it to take place. True, normally there’s an intent requirement when deciding liability: if you drive your car and you hit someone who crosses the road where he or she shouldn’t do so, you’re not criminally liable. You killed a person but didn’t intend to. In some cases, the lack of intent diminishes rather than removes liability: if you’re in a fight with someone and the other person dies because of your actions, you won’t be charged with homicide but with the lesser crime of manslaughter if you didn’t intend to murder.

As the example of manslaughter already makes clear, intent isn’t always necessary for liability. Hence, lack of intent can’t be the reason not to make unconscious discrimination a crime.

Anyway, intent or the absence of it is often very difficult to prove. In the case of homicide/manslaughter, you can use witness accounts or physical evidence, you can reconstruct the crime and try to figure out if the killing was planned or intended, or you can interrogate the perpetrator, and even then it’s rarely easy. Things seem to be much more difficult still in cases of unconscious discrimination. Looking for intent is basically trying to look inside people’s minds, which isn’t obvious, and when people fool their own minds it’s becomes even harder.

If we accept that unconscious discrimination should be a crime in certain cases, and perhaps equivalent to conscious discrimination, then the problem is how to prove that it took place. In the case of conscious discrimination, you can often rely on the utterances of the person(s) who discriminate. That’s evidently impossible in the case of unconscious discrimination. Perhaps you can’t prove it in individual cases – if one black person’s CV is rejected, it’s probably impossible to say it’s because of implicit or unconscious racism. However, if a company rejects a large number of such CVs, and correcting for other factors such as education or skill level doesn’t remove bias in the distribution, then you may perhaps have evidence of discrimination (that’s a technique that’s useful in cases of conscious discrimination as well, by the way). So you would need to rely on statistical analysis, something that usually isn’t done in the determination of criminal liability. It’s not because x % of all killings are manslaughter that everyone charged with a killing has x % change of “getting away” with manslaughter. The decision to sentence someone for the crime of murder or manslaughter is always made on an individual basis and not a statistical one, although past conduct of the suspect can sometimes come into play.

An additional difficulty: if we accept that laws aren’t only meant to punish but also to prevent and deter, it seems that the latter goal is futile in the case of unconscious discrimination. People who are not aware that they engage in discriminatory activities will hardly be persuaded by laws telling them to stop doing so.

I’m personally not yet ready to take a firm position on these issues. For more information on this topic, take a look at this interesting paper.

Discrimination (5): Statistical Discrimination

Definition

Here’s the New Palgrave Dictionary of Economics definition of statistical discrimination:

Statistical discrimination is a theory of inequality between demographic groups based on stereotypes that do not arise from prejudice or racial and gender bias. When rational, information-seeking decision makers use aggregate group characteristics, such as group averages, to evaluate individual personal characteristics, individuals belonging to different groups may be treated differently even if they share identical observable characteristics in every other aspect. … group-level statistics, such as group averages, are used as a proxy for the individual variables. (source)

Statistical discrimination, also called rational prejudice (or rational racism), occurs when some stereotypes are considered to be statistically “accurate”. Accurate here in the sense of statistical generalization or average behavior (or characteristics).

Some examples

Young drivers are on average more likely to be involved in traffic accidents. Hence, auto insurers impose higher auto insurance premiums on young people. This insurance discrimination is called statistical discrimination because it’s based on some level of accuracy regarding the stereotype, and not on plain prejudice of or disgust for young people.

Regarding life insurance premiums, the customer’s likelihood of dying is one of the most relevant variables affecting the insurers profitability. Since gender is highly correlated with life expectancy, it is therefore optimal for the company to adopt a policy setting higher premiums for men, even for those men who may be healthier and less risk prone than the average woman. (source)

Employers often use the average performance of an applicant’s group to determine whether to hire him or her.

You don’t hire a guy with a Mohawk as a receptionist at a law firm – even if he promises to get a hair cut. Why not? Because on average, … guys with Mohawks have trouble with authority. (source)

Similarly, people with facial tattoos are unlikely to be hired as CEO’s, perhaps because they signal an anarchic, counter-cultural, contrarian, nonconformist, bohemian and “wandering” attitude.

Why?

The reason people engage in statistical discrimination is the high cost of making case-by-case judgments and the relative accuracy of group averages. Given that it’s not a product of bigotry, irrational prejudice, hatred and dislike, is it correct to call it discrimination? It is in the sense that people judge other people simply on the basis of group membership, not individual merit, as in “regular”, “irrational” discrimination or prejudice. Yet it’s not discrimination because people engaging in statistical discrimination often don’t do so because of their dislike or hatred of the discriminated group but because of economic self-interest and risk avoidance, as is shown by the examples above. The stereotypes are not baseless as most stereotypes are (“Blacks are lazy”, “Jews are money hungry”, “women are only productive at home” etc.).

This discrimination is “statistical” because contrary to normal prejudice it acknowledges that a relatively large number of members of a group do not conform to the stereotype. Just as it isn’t irrational to claim that it’s statistically accurate that republicans in general are pro-life while at the same time knowing that many aren’t. “Irrational” prejudice doesn’t have so much tolerance for exceptions. “People see others as average members of their groups until proven otherwise”. (source)

Is it acceptable?

So we know what it is and why people do it, but is it morally and legally acceptable? I think it depends on the type.

While such discrimination is legal in some cases (e.g., insurance markets), it is illegal and/or controversial in others (e.g., racial profiling). (source)

The difference between acceptable and unacceptable statistical discrimination hinges on several facts, I think:

1. How accurate are the group generalizations? You can forgive profit-seeking insurers for imposing higher auto insurance premiums on young people. However, wage gaps between genders resulting from theories about lower productivity of women (said to result from higher female investment in child rearing and a focus on “caring jobs” as opposed to jobs that tend to pay well) are less acceptable because the generalization is more dubious and the category of “women” too broad. Generalizations may be more accurate when they are not about behavior but about more “technical” and less controversial things such as life expectancy.

2. What is the “weight of history”? Statistical discrimination of blacks or women is by definition less acceptable than statistical discrimination of young people, given the history (maybe that’s a stereotype about discrimination…). Differentiated treatment, even if it isn’t really discrimination, can still cause harm given a certain history. The harm can be feelings of insecurity regarding a possible repetition of the past, feelings of continued attacks on self-esteem etc.

3. What are the consequences of statistical discrimination? Paying a slightly higher insurance premium is a lesser harm than being pulled over by the police a few times a week because of your skin color, even if your skin color is a statistically “accurate” prediction of the probability that you engage in crime in your neighborhood.

4. To what extent is statistical discrimination self-fulfilling? To some extent it clearly is.

People think teen-age males are criminally inclined (and they are), this angers the teen-age males, leading them to commit more crimes. (source).

If everyone assumes that boys are not good at domestic work, the benefit of learning domestic work is zero because no one will hire you as a domestic worker. In some areas, this self-fulfilling process is obviously harmful, and hence statistical discrimination is harmful as well.

It is possible that, because employers are less likely to hire women in jobs that require labour market attachment, then women are more likely to be involved in child-rearing than men, and are less prone to acquire the skills that are necessary to seek and perform well in those jobs, confirming the asymmetric belief that employers hold regarding labour market attachment. (source)

This phenomenon is called the stereotype threat. On the other hand, there can be self-reversing prophesies at work as well. A-typical members of a group, who of course suffer from the prejudice in the same way as typical members, may incite the latter to change their behavior. E.g. responsible young drivers who pay too much insurance because of the typical behavior of their fellow group members, may pressure the latter to change their ways. In this way, statistical discrimination may work to undermine itself. The same result will occur because a non-typical member of a group may get relatively more exposure. A woman who’s relatively good at soccer – compared to other women – but just as good as an average male soccer player, may get more attention than the latter. This excess of attention may undermine the stereotype on female soccer players.