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Gender Equity Salary Studies: The Good, the Bad, and the Ugly

Gender Equity Salary Studies: The Good, the Bad, and the Ugly. Presentation April 3, 2008 University of Illinois at Chicago Carol Livingstone livngstn@uiuc.edu. Gender Equity Studies: The Bad, the Better, and the Ugly. Why should salaries be equitable?. Fairness – the “right thing to do”.

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Gender Equity Salary Studies: The Good, the Bad, and the Ugly

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  1. Gender Equity Salary Studies: The Good, the Bad, and the Ugly Presentation April 3, 2008 University of Illinois at Chicago Carol Livingstone livngstn@uiuc.edu

  2. Gender Equity Studies: The Bad, the Better, and the Ugly

  3. Why should salaries be equitable? • Fairness – the “right thing to do” • Retention of best faculty • It’s the law

  4. What are our goals in studying salary equity? • To identify and correct any systematic bias • To identify and correct any individual salary errors • To emphasize the institutional commitment to gender equity

  5. Some BAD Ways to Study Gender Equity • Anecdotal evidence • Simple campus-wide averages

  6. Simpson’s Paradox (The Fallacy of the Averages) The average salary of female faculty members at one institution is 64% of the average male's salary. Does this institution discriminate against women?

  7. Suppose the institution has just two colleges, Engineering and Social Work Fact: Engineers are paid more than Social Workers. Fact: Engineering is predominantly a male field, and Social Work is predominately female.

  8. 94,000 60,500 64% Averages are misleading

  9. A BETTER Way to Look at Gender Equity Multiple regression analysis Dependent variable = constant + independent variable 1 * coefficient 1 + independent variable 2 * coefficient 2 + independent variable 3 * coefficient 3 + …

  10. Using Multiple regression to look for systematic discrimination Include gender or race/ethnicity as an independent variable. A coefficient statistically different from zero implies a correlation between gender and salary.

  11. Using Multiple regression to look for individual discrimination • Exclude gender and race/ethnic code from independent variables. • Find the regression equation. • For each person, see what salary the regression equation predicts.

  12. Assumptions of Multivariate Regression • Factors are independent • Each factor is linearly related to dependent variable • Variables can be measured accurately • Populations are sufficiently large • All relevant factors are included

  13. Urbana’s History of Gender Equity Studies • Chancellor commissioned first one in early 90’s. Took a year to complete. • Found some systematic bias, individual bias based on gender • Resulted in many salary corrections • Repeated many times since then; results vary

  14. BOT Gender Equity Report • All three campuses were asked to submit a gender equity report in June, 2000 • Included a regression analysis of salaries, retention and promotion studies, comparisons with national benchmarks

  15. Urbana Gender Equity Studies Nine studies since 1990’s (hmmm, 8 ½) http://www.dmi.uiuc.edu/reg

  16. Urbana Process • Tenure-system faculty only • On-going salary, no lump sums • Much manual data collection/fixing • Periodic revisions, especially with input from CSW

  17. Urbana Independent Variables • Rank • Department • Years from degree • Having a Ph.D. • Administrator flag • Hired in as assistant professor • Gender • Race/ethnic group • Years to reach associate professor • Years to reach full professor

  18. Urbana Regressions • All faculty combined • Assistant Professors • Associate Professors • New Assistant Professors • Others - appendix

  19. Regression Evaluation R2 – usually about 0.6-0.9 Model significant at the 0.0001 level

  20. Significance of Gender term & Regressions (2004) Regression Gender effect R2

  21. Coefficients from 2004 Dept factor ranged from –$30,000 to $66,000

  22. Actual Salaries as % of Predicted (2006)

  23. Other regressions run • Using peer salaries instead of department dummy factor • Using log(salary) instead of salary as dependent variable • Added terms interacting gender with other variables: significant but small interactions found with years to reach full professor & number of other departments

  24. Publication/Follow-up • Report, general statistics, outcomes reported to Provost, Deans and posted on web • Deans & business managers get list of faculty with actual and predicted salaries • Deans must fix or justify salaries 7% or more below prediction

  25. The Ugly • Claiming to have a precise answer • Taking individual predictions as truth • Selecting one regression (e.g. all faculty) result over another • Confusing correlation with causality

  26. The Ugly Data wars! Adversarial attitudes from administration or faculty are counterproductive.

  27. Beyond Salary Equity: Hiring • Who is in the pool? • Who applies? • Who is on the hiring committee? • Who is a finalist? • Who gets an offer? • What salary is offered? • Who actually accepts?

  28. Beyond Salary Equity: Retention • Promotions • Teaching & advising workload • Committee assignments • Salary increases, esp. matches • Administrative appointments • Sabbaticals • Awards/Chairs • Climate

  29. Beyond Salary Equity: Policy Analysis Some data gathering is helpful, but don’t get bogged down in data. Spend your time thinking about processes, policies, and decision points

  30. Questions??

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