1. Statistical Discrimination 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.

2. 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. In table of results –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.

3. 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.

4. Focus of Study Focus on statistical discrimination: –Look at coefficient on FEMALE. Note: model has lnwage 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: In MBA sample, females do not earn less than males. In test-taking sample (but no MBA), women do earn less than men

5. Results cont. –Conclusion: Employers use MBA degree as a positive signal that helps to lessen the negative signal of being female. Supports idea of statistical discrimination.