The resulting harmed mental health of sexual minorities in turn likely feeds statistical discrimination against them. Gender-based statistical discrimination constitutes one of the few illustrations of a productivity gap across groups that does not stem from a self-fulfilling process of the type described above.
As such, policymakers can focus on the most straightforward way to dampen statistical discrimination: eliminate its source.
In essentially every country, social norms entail that women, due to their biological characteristics, devote more time to childrearing, while men devote more time to working in the labor market. This pervasive norm is only partly counteracted by a general trend toward gender equality. Even in Scandinavian countries, which are the most advanced on this issue, gender norms remain fairly traditional: survey respondents in these countries support the view that women should work full-time before having children and after children have left home, but should work only part-time or not at all when children live at home.
In this setting, women likely suffer from statistical discrimination when it comes to high-responsibility jobs, especially when they are of childbearing age. Because women typically bear the brunt of childrearing, recruiters associate them with a lower expected productivity at positions that often entail sacrificing at least some components of family life. To counter gender-based statistical discrimination, a more radical shift toward egalitarian gender norms is necessary.
Such a cultural change can be achieved by better balancing the time that men and women spend at home with children, through the reform of parental leave policies. Yet the literature points to the necessity of implementing these policies in stages rather than in one step, or else the reform becomes counterproductive. This lesson is well illustrated by the Swedish case [8] , [9]. In particular, when a reform sharply impacts whether fathers take up any parental leave, unintended effects emerge provided the length of the compulsory leave is substantial.
Rather than inducing less traditional views on gender roles, the reform is detrimental to women: they compensate for the decreased paid parental leave with additional unpaid leave, leading to a lower total income for the household. This latter effect is concentrated among couples where the mother had relatively low labor income and, hence, where traditional views of gender roles were likely dominant.
In instances where statistical discrimination results from a self-fulfilling process, its eradication is trickier. Affirmative action, either through quotas or hiring subsidies, incentivizes recruiters to hire individuals from discriminated groups that are the most productive [3]. However, as already stressed, affirmative action is known to generate perverse effects: because the fair share of people from the minority group is often unknown, there is always the suspicion that the quota or hiring subsidy would be excessively advantageous to minority groups.
There are three joint ways to mitigate the resentment among non-discriminated groups induced by affirmative action policies. First, these policies should favor hiring subsidies over quotas since, by offering more flexibility, hiring subsidies better take into account that the unknown proportion that discriminated groups would represent were they not discriminated against varies greatly from one firm to the next.
Put differently, hiring subsidies do not penalize a firm where this proportion is lower than the quota would be, which is an important step toward stressing the fairness of affirmative action policies and gathering support for them. Second, the level of the subsidies should be computed based on i the extent of hiring discrimination against various groups, as measured by correspondence studies, and ii the sensitivity of labor demand with respect to labor costs.
In other words, these subsidies must be set at a level that ensures closing the average hiring gap across groups ceteris paribus , no more, no less, which is also an important prerequisite to getting the general public on board. Third, hiring subsidies should be accompanied by the development of employment intermediaries specialized in certifying the cognitive and non-cognitive skills of the individuals the subsidies target. Obviously, affirmative action policies also require that the information used as a criterion for the hiring subsidy e.
Yet this opinion does not seem to be held by the general public. Taste-based discrimination is a widespread cognitive bias: people tend to be more hostile toward out-group members, even when they do not expect these individuals to represent any real threat to them. But statistical discrimination, which is supposed to be more rational, is not devoid of cognitive bias either. The stereotypes on which statistical discrimination relies do not always represent real differences between groups [10].
First, stereotypes tend to amplify supposed differences. This implies that stereotypes are especially inaccurate when groups are similar. Second, they are context dependent, to the extent that the assessment of a given target group depends on the group to which it is compared.
Third, stereotypes distort reactions to information: stereotypical thinking implies overreaction to information that generates or confirms a stereotype, and underreaction to information that contradicts it although stereotypes can change if enough contrary information is received.
Finally, on top of cognitive biases, an additional source of unfair treatment across groups at the hiring stage lies in attention-based discrimination. The models of taste-based and statistical discrimination implicitly assume that individuals are fully attentive to available information. However, as long as acquiring information is costly, it should be rational for decision makers to optimize how much information to acquire based on expected net benefits. In this setting, the use of big data for human resources HR , i.
HR analytics, might represent the next frontier to limiting the expression of cognitive biases and attention-based discrimination in recruitment and career management. More precisely, relying on machine learning trained on historical HR data should improve the probability that individuals from discriminated groups will be invited to and will pass job interviews as well as be promoted, for two main reasons. Second, and more importantly, machine learning does not indulge attention-based discrimination: it takes all variables into account, and therefore does not underweight positive signals from minority applicants.
These shortcomings of anonymous job applications might explain why no government has passed and enforced laws that mandate them thus far. Put differently, algorithms benefit candidates who would otherwise have been discriminated against, such as individuals who lack job referrals, those without prior experience, or those with atypical credentials.
Impact evaluation of the use of job-testing recruitment technologies further confirms that firms that rely less on human judgment when making hiring decisions end up with better hires [12].
However, the potential for algorithms to limit discrimination is not fully harnessed when they are trained on historical human data, since, by definition, these data reflect discriminatory practices. In particular, observations on individuals belonging to discriminated groups might be too scarce for the algorithms to derive proper inferences about them.
And the performance that the algorithm assigns to minority employees might be underestimated. Evidence notably shows that managers biased against ethnic minorities avoid contact with them, leading these minorities to exert less effort [13]. Whatever the approach historical or forward-looking , a large number of observations is needed to train the algorithm, meaning that HR analytics cannot easily be internally developed in firms that publish only a few job openings and host only a few employees.
It is therefore critical that governments and social partners think about ways to provide small firms with access to relevant data sets i. Groups previously discriminated against may remain excluded long after discrimination ends, due to habitual behaviors. In other words, anti-discrimination policies must be supplemented by interventions that counter the mental models that discrimination has set up since these models may be powerful enough to outlive the elimination of discrimination.
For instance, to counter self-stereotyping, interventions that frame the idea of intelligence as a malleable trait that grows in response to hard work rather than as a fixed trait have proven to help socially excluded groups improve their performance. Anti-discrimination policies that rely on a punitive approach are necessary but not sufficient to combat discrimination. However, much more research is needed to measure the impact of prejudice-reducing approaches and its persistence.
It is also critical to minimize the perverse effects associated with policies that seek to counter statistical discrimination. Is this legal? Title VII of the Civil Rights Act of protects individuals against employment discrimination on the basis of national origin as well as race, color, religion, and sex.
Under this Act, discrimination on the basis of pregnancy, childbirth, or related medical conditions constitutes unlawful sex pregnancy discrimination. I am pregnant, and my doctor has placed me on restrictions. Is my supervisor required to adhere to these restrictions? If an employee is temporarily unable to perform her job due to pregnancy, the agency must treat her the same way as any other temporarily disabled employee. For example, if an employee with a broken hand received modified tasks or alternative assignments, the same must be done for a pregnant employee.
I am pregnant, and I am thinking about taking three months off after my baby is born. Is my supervisor required to approve my leave request? An employer may not have a rule that prohibits an employee from returning to work for a predetermined length of time after childbirth. For instance, an employer may not require an employee to return to work 4 weeks after childbirth.
Sexual harassment is unwanted and unwelcome advances of a sexual nature. It could be a touch, written note, joke, picture, etc. It can be intentional or unintentional. The first type is Quid Pro Quo. This means that a person in a position of power over another offers to trade a tangible employment action or benefit such as promotion for a sexual favor. The second type is hostile work environment. In this instance, the environment is created by obvious sexually oriented activity by employees and supervisors.
Sexual harassment is rarely found as the result of a single incident or event. The victim as well as the harasser may be a woman or a man. The victim does not have to be of the opposite sex. I have first-hand knowledge of a co-worker who is being harassed by his supervisor.
He is afraid and embarrassed to come forward and report the harassment. Since I am in the immediate work area, can I report the harassment? Yes, the victim does not have to be the person harassed, but could be anyone affected by the offensive conduct. A co-worker constantly tells lewd jokes in my presence.
Her behavior is offensive, but I am afraid to speak up in fear of not being perceived as a team player. Any advice? Inform the individual that her conduct is unwelcome and must stop. If her behavior continues, inform the supervisor or the OEEO. My supervisor often asks me to lunch, but I decline his offers. Is this a form of sexual harassment?
The conduct as described is not sufficient to constitute sexual harassment; it must be of a sexual nature. I am aware that Title VII of the Civil Rights Act of prohibits discrimination based on race, color, religion, sex including gender identity, sexual orientation, and pregnancy , and national origin. What is the difference between race discrimination and color discrimination?
Race discrimination occurs when employees are treated differently than other employees because of unalterable characteristics, such as physical features attributed to their race.
For example, this Act prohibits discrimination against an Asian individual because of physical characteristics such as facial features or height. Color discrimination occurs when persons are treated differently than others because of their skin pigmentation. Color discrimination can occur within the same ethnic group.
So does that mean that individuals of the same race can discriminate against another because of different skin pigmentation? Ideally, anti-harassment language will be wrapped into a non-discrimination policy. An employer's non-discrimination policy, or equal employment opportunity policy, typically covers conditions of employment including hiring, promotions, termination and compensation. Employers should include "gender identity" and "sexual orientation" as protected classes, in addition to other federally-protected classes, in non-discrimination policies.
The policy is generally available in employee handbooks and included in a business' "Code of Conduct" but should also be incorporated as part of job announcements, on the employer's website and as part of career or diversity-related materials.
Thousands of businesses, including the vast majority of Fortune corporations, already prohibit discrimination based on sexual orientation and gender identity. Specifically referencing "sexual orientation" and "gender identity" in anti-harassment policies sends a clear message that all employees will be respected and able to work free of any kind of harassment, and that no form of harassment or offensive conduct directed at individuals based on sexual orientation or gender identity, in addition to other classes protected by law, will be tolerated.
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