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Wage Discrimination: MBAs

Wage Discrimination: MBAs

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Wage Discrimination: MBAs

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  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.