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.

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.

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 –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.

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 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.

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.

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.

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.

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.

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.

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.

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.