wage discrimination: mbas powell chapter in moe book. reviews theories of discrimination arising...
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Wage Discrimination: MBAs
• Powell chapter in Moe book.• Reviews theories of
discrimination arising from prejudice: – employers – fellow employees– customers
• Recent examples in medicine:– Female patients prefer female
OB/GYNs but male patients prefer male urologists.
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Chapter Focus
• Statistical Discrimination:– Discrimination in absence of
prejudice.– Employers use actual average
labor market attachment differences by sex as a signal of what to expect from individual workers.
– Causes gender gap even for women who never leave LF to raise kids.
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Regression• Regression model to test for
discrimination: – Multivariate regression: wage as
dependent variable (on left hand side) with FEMALE as an exogenous (right hand side) variable.
– With actual hourly wage as dependent variable, coefficient on FEMALE is average $ wage difference from being female, holding constant other relevant factors.
• See Table 11.2.– See FEMALE coefficient as # other
controls.– Statistical significance: effect we estimate
with data is a true difference, not one arising just from our particular sample.
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MBA Study by Montgomery and Powell
• Unique data for study: – GMAT Registrant Survey– Longitudinal survey of 4285 GMAT
test-completers. – Surveyed 3 times from 1991 to 1994.
• Focus on test-completers helps to statistical problems results more reliable.
• Authors improve even more by separating sample into two groups:– Those who completed MBA;– Those who did not complete MBA;– Use statistical correction for this
selection.
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Focus of Study• Focus on statistical discrimination:
– Look at coefficient on FEMALE.
• Note: model has natural logarithm of wage as dependent variable so coefficient on FEMALE is %wage difference by sex.
• See Table 11.3– Very good list of control variables– See two sets of results.– See t-statistics (big is good).– See difference in FEMALE coefficient: – Conclusion:
• Employers use MBA degree as a positive signal that helps to lessen the negative signal of being female.
• Supports idea of statistical discrimination.
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Empl. discrimination, economists and the law
• Hirsch Chapter in Moe book• Economists and lawyers view
discrimination in different ways.– They differ in questions asked and
approach.– Economists:
• ”Are women systematically paid less than men with equal qualifications?”
• Primary concern is data.• Role of economists: provide empirical evidence
for lawyers so primary concern is data and approach uses regression.
– Lawyers: • “Were this individual’s civil rights violated by
this employer?” • Primary concern is identifying specific laws
violated, interpreting existing laws, and establishing evidence.
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Continue with Hirsch
• Centerpiece of federal employment discrimination law: – Title VII of the Civil Rights Act of
1964: Prohibits employment discrimination by employers, unions, and employment agencies on the basis of race, color, religion, sex, or national origin.
– Title VII established the EEOC:• Equal Employment Opportunities
Commission
• Have been many extensions.
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• When is discrimination permitted?– When group membership is
essential for job performance:• BFOQ: bona fide occupational
qualification
• BFOQ often point of controversy.
– When mandated by affirmative action plan.
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1st type of case filed under Title VII
• Disparate treatment– Intentional discrimination– Showing proof of motive is critical to legal
case. – Often easier to understand. – Worries employers more due to potential
for punitive damages (in addition to compensatory damages).
– Systemic disparate treatment: affects groups rather than individuals.
– Famous case: EEOC v. Sears, Roebuck and Co. –
• Employed many women but very few in sales commission jobs.
• Sears won (Judges rejected regression evidence)• Sears claimed women did not want to work in
sales.
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2nd type of case filed under Title VII
• Disparate impact– Showing proof of motive not necessary– Most often occurs at point of hire.– Problem occurs when hiring standard not
really correlated with job performance.– Here regression is key.
• Griggs v. Duke Power Co:– Prior to Title VII: hired AA only in one
department by explicit policy.– After Title VII: imposed new hiring standards
(HS diploma and tests)• 58% whites and 6% AA passed test.• Disparate impact clear but how to show hiring
standard not appropriate?• Whites hired prior to new standard did NOT meet
standard and performed well.• Duke Power lost.
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Role of Regression in These Legal Cases
• Regression can be used to fight both types of discrimination or defend against those charges– Ends up with legal fight: sample
size; what controls included, etc.– Courts have ruled on many points
relating to regression models, such as even if don’t include ALL relevant variables, results cannot be ignored.
– Also used in reverse discrimination cases.