Experimental & Behavioral Economics .Experimental & Behavioral Economics Lecture 9: Discrimination

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  • 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 alternati

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