Experimental & Behavioral Economics

Lecture 9: Discrimination in the Labor Market

Dorothea Kübler, Roel van Veldhuizen

Summer term 2015

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Roel van Veldhuizen

12.7.1986, Bennekom (NL)

University Degree

Experimental Economist

WZB Berlin

Arjen Robben

23.1.1984, Bedum (NL)

No University Degree

Professional Footballer

FC Bayern Munich

Question: why do I not play for FC Bayern as well? • Because of Preferences or Ability? (Sorting, last week’s topic) • Or because of discrimination?

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Roel van Veldhuizen

12.7.1986, Bennekom (NL)

University Degree

Experimental Economist

WZB Berlin

Arjen Robben

23.1.1984, Bedum (NL)

No University Degree

Professional Footballer

FC Bayern Munich Question: why do I not play for FC Bayern as well? • Because of Preferences or Ability? YES • Or because of discrimination? NO, Robben better than me.

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Roel van Veldhuizen

12.7.1986, Bennekom (NL)

Postdoc economist, WZB

PhD in 2013

Dorothea Kübler

10.1.1966, Tübingen (DE)

Professor, TU & WZB

15 years of experience Question: who will get the new Professorship in Exp. Economics at Friedrich Wilhelm Universität Rheine? • Assume both apply, but Dorothea gets the job:

• A possible example of discrimination (gender, age). • But probably not, Dorothea just better candidate.

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Roel van Veldhuizen

12.7.1986, Bennekom (NL)

Postdoc economist, WZB

PhD in 2013, similar cv

Fake Dorothea Kübler

10.1.1977, Tübingen (DE)

Postdoc economist, WZB

PhD in 2013, similar cv Question: who will get the Junior Professorship at Friedrich Wilhelm Universität Rheine? • Assume both apply and have equal ability/cv, yet I get the job:

• A possible example of discrimination (gender, age). • Not ability, because that’s the same (though difficult to prove).

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Types of Discrimination

‐ Discrimination: making a (e.g., hiring) decision based on an individual‘s membership of a group/class (e.g., race, gender, nationality), rather than individual merit.

‐ Traditionally, economists have differentiated between two types of discrimination.

‐ Taste-based discrimination: employers have a preference against a certain (type of) worker.

‐ Male committees hiring men because they really prefer working with other men.

‐ Not hiring a catholic/Dutch/female person because of a dislike for their type. ‐ Could be conscious, but also unconscious.

‐ Statistical discrimination: employers believe certain

subgroups more productive than others ‐ Not hiring a catholic/Dutch/female person because of a (possibly wrong)

belief that members of this group, ceteris paribus, are worse candidates.

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Types of Discrimination

‐ Let‘s write this up slightly more formally.

‐ Suppose an employer wants to hire a single worker i. The firm‘s utility for choosing a given worker:

‐ 𝑈𝑖 = 𝐸 𝑥𝑖 − 𝑤𝑖 − 𝐷𝑖 ‐ 𝐸 𝑥𝑖 is the expected productivity of the worker. ‐ 𝑤𝑖 is the worker‘s wage. ‐ 𝐷𝑖 is a taste parameter reflecting how much an employer likes a certain

worker.

‐ Taste-based discrimination: employers have a preference against a certain (type of) worker (𝐷𝑖 < 0).

‐ For example, gender discrimination based on 𝐷𝑚 = 0 for men and 𝐷𝑓 < 0 for women.

‐ Statistical discrimination: employers believe certain subgroups more productive than others

‐ 𝐸 𝑥𝑖|𝑍, 𝐺𝑖 > 𝐸 𝑥𝑖|𝑍, 𝐺𝑗 ‐ Even conditional on the same observables (Z, e.g., grades, experience),

employers might believe a member of group i is more productive than a member of group j.

‐ For example, gender discrimination based on 𝐸 𝑥𝑖|𝑍, 𝐺𝑚 > 𝐸 𝑥𝑖|𝑍, 𝐺𝑓

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Examples of Statistical Discrimination

‐ Reduced prices for students, elderly ‐ (I.e., increased prices for non-students).

‐ Higher prices for insurance for risky groups

‐ Elderly for life/medical insurance. ‐ More expensive car insurance for (young) men.

‐ Racial profiling

‐ In the US, criminal offenders are more likely to be from a minority background.

‐ Labor market discrimination: ‐ Requiring job applicants to have certain qualifications

(university degree). (Legal) ‐ Recruiting Uruguayan, not Indian, football youngster. ‐ (Not) hiring minorities or women because of what this

implies (on average) for ability. (Illegal)

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Statistical Discrimination

‐ Statistical discrimination can be utility-maximizing ‐ Given imperfect knowledge of employee‘s productivity xi. ‐ Given a positive correlation between group membership

and productivity.

‐ For example, assume Rheine has to choose between me and fake Dorothea. ‐ Similar characteristics (grades, experience, etc.). Id est,

𝑍𝑚 = 𝑍𝑤 = 𝑍. ‐ Suppose that the committee thinks that men are better

economists (unrealistic!), that is, 𝐸 𝑥𝑖|𝐺𝑖 = 𝑀 >𝐸 𝑥𝑖|𝐺𝑖 = 𝐹

‐ In that case, 𝐸 𝑥𝑖|𝑍, 𝐺𝑖 = 𝑀 > 𝐸 𝑥𝑖|𝑍, 𝐺𝑖 = 𝐹 as well, and therefore Rheine Univ. should hire the man (me).

‐ Utility-maximizing for the employer (Rheine) ‐ Bad news for good women such as fake Dorothea.

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Statistical Discrimination (2)

‐ Now assume a slightly different scenario: ‐ Rheine still has to decide between me and fake Dorothea. ‐ Fake Dorothea now has better grades than me, such that

𝑍𝑚 < 𝑍𝑓. ‐ This implies 𝐸 𝑥𝑖|𝑍𝑚 , 𝐺𝑖 =? < 𝐸 𝑥𝑖|𝑍𝑓 , 𝐺𝑖 =? ‐ Nevertheless, it might still be that 𝐸 𝑥𝑖|𝑍𝑓 , 𝐺𝑖 = 𝑀 >

𝐸 𝑥𝑖|𝑍𝑓 , 𝐺𝑖 = 𝐹 . ‐ Intuition: when differences in grades are small it may be

outweighed by the (perceived) superior average productivity of men.

‐ Utility-maximizing for Rheine university ‐ Even worse news for good women such as fake

Dorothea. ‐ Even women who are superior might now lose out against

men

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Statistical Discrimination (3)

‐ Statistical discrimination may, of course, also be based on false beliefs ‐ Employers overestimate the correlation between group

membership and productivity (which might not even exist).

‐ For example, pass on a woman with superior grades because one thinks men are more productive, even when there‘s no evidence that this is the case.

‐ With false beliefs, statistical discrimination is no longer utility maximizing for the employer.

‐ And is still harmful for the affected group (e.g., women).

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Long Run Effects

‐ In the long run, statistical discrimination may solve itself. ‐ Suppose Rheine university incorrectly believes that men

are more productive than women. ‐ Then, the productivity of the women who are actually

hired will, on average, be higher than the productivity of men university may change its beliefs.

‐ In the long run, statistical discrimination can also

create a negative spiral. ‐ Assume that Rheine university believe that women, on

average, are worse than men. ‐ This results in statistical discrimination against women. ‐ This may in turn discourage women from investing in their

PhD education. ‐ This would then decrease the productivity of the average

woman... ‐ Which would lead reinforce the bank‘s belief that women

are worse than men.

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Preventing Discrimination

‐ Discrimination (of both types) can be fought using ‚affirmative action‘ ‐ E.g., force employers to discriminate against men (or

majority), in favor of women (or minority). ‐ E.g., by creating gender quota, lowering standards for

women, giving ‚preference‘ to women. ‐ Controversial, laws differ across countries.

‐ Another option is to force employers to be ‚blind‘ to

the gender/ethnicity of job applicants. ‐ But might be difficult to achieve in practice.

‐ Whether these types of methods are welfare-

improving may depend: ‐ Likely when discrimination taste-based, or statistical based

on inaccurate beliefs. ‐ Less clear when statistical with accurate beliefs. Depends

on long-run effects, social welfare function.

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Why Experiments on Discrimination

- Difficult to separate discrimination from other effects/mechanisms, such as:

- Actual ability differences.

- Sorting preferences of different subgroups.

- For example, the fact that women are less likely to be CEOs could be due to discrimination, but also due to sorting preferences.

- Experiments can rule out alternative mechanisms and ensure causality by design:

- Create environments in which discrimination can be studied while keeping all other aspects constant.