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Using Internal Market Ratios to Detect Gender Differences in Faculty Salaries

Using Internal Market Ratios to Detect Gender Differences in Faculty Salaries. Chunmei Yao, Ed. D SUNY College at Oneonta. Introduction. Literature Review Conceptual Framework Methods Results & Model Comparison Conclusions Recommendations. Literature Review.

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Using Internal Market Ratios to Detect Gender Differences in Faculty Salaries

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  1. Using Internal Market Ratios to Detect Gender Differences in Faculty Salaries Chunmei Yao, Ed. D SUNY College at Oneonta

  2. Introduction • Literature Review • Conceptual Framework • Methods • Results & Model Comparison • Conclusions • Recommendations

  3. Literature Review Recommended Reading Materials: • AAUP Publication • Haignere, L. (2002). Paychecks: A guide to conducting salary-equity studies for higher education faculty (2nd ed.). Washington, DC: American Association of University Professors. • AIR Publications • Mclaughlin, G. W. & Howard, R. D. (2003). Faculty salary analyses. In W. E. Knight (Ed.), The Primer for Institutional Research (No.14), (pp. 48-73). Tallahassee, FL: Association of Institutional Research. • Toutkoushian, R. K. (Ed.). (Fall, 2002). Conducting salary-equity studies: Alternative approaches to research. New Direction for Institutional Research, No. 115. San Francisco: Jossey-Bass. • Toutkoushian, R. K. (Ed.). (Spring, 2003). Unsolved issues in conducting salary-equity studies: Alternative approaches to research. New Direction for Institutional Research, No. 117. San Francisco: Jossey-Bass. • The Author’s Publication • Yao, C. (2012). Using market factors to detect gender differences in faculty salaries. Paper presented in 2012 AIR Annual Forum. LA: New Orleans.

  4. Salary Studies • Comparability: mission vs. salary rewarding structure • Equity: gender, race/ethnicity • Compression: newly hired v. senior • Competitiveness: comparing with peers/national benchmarks The purpose is to monitor the salary rewarding policies and structure for reinforcement of the institution’s mission. McLaughlin & Howard (2003). Faculty salary analyses. In W. E. Knight (Ed.), The Primer for Institutional Research (No.14), (pp. 48-73). Tallahassee, FL: Association of Institutional Research.

  5. Conceptual Framework The conceptual framework was modified based on McLaughlin & Howard’s model (2003).

  6. Internal & External Markets in Higher Ed • Internal labor market • Price and allocate based on teaching, research, and service • Key disciplines • Stable employment • Promotion hierarchies • External LaborMarket emphasizes on price and allocate faculty based on economic competition. • The internal and external markets would cause instability/imbalance in salary rewarding system at an institution. Breneman, D. W. & Youn, T. I. K. (1988). Academic labor markets and careers. Philadelphia, PA: The Falmer Press.

  7. What We Have Found in Salary Studies… Data Source: the Annual Report on the Economic Status of the Profession in Academe(1980-2010) published by the AAUP. • The observed differences cannotbe totally explained by variances, such as individual characteristics, professional maturity, and productivities/merit. • At larger, the observed differences are considered the effects of market factors, not a result of gender discrimination. • National trend analysis • % of Salary Changeacrossdisciplines (1980-2010) (Reference Groups: Asst. Prof & English Discipline) • Salary Differencesbetween Male and Female (All Rank) Accordingly, it is predicted that salary differences across disciplines may continue to affect gender differences in faculty salaries.

  8. Regression Models Dummy Model • Pros • Allow the regression to assign an appropriate value for each discipline based on faculty salaries paid in that discipline • Reflect the unique history of the academic programs • Cons • Produce a large numbers of degrees of freedom and limit statistical power • Cause attention if • A department has less five faculty or uneven distributed by gender • Complicated to explain the statistical results Haignere, L. (2002). Paychecks: A guide to conducting salary-equity studies for higher education faculty (2nd ed.). Washington, DC: American Association of University Professors.

  9. Regression Model Cont. Market Model • Use external market ratios to replace the categorical discipline variables • Assumption: the external labor market is related to the internal labor market at the position of entry level at a particular institution. Market Ratio: • The average salary for a specific discipline (numerator) divided by the average salary of all disciplines combined (denominator). • Formula: Luna (2007). Using a market ratio factor in faculty salary equity studies.

  10. Regression Model Cont. A market ratio measures the relative strength of salaries between a particular discipline and disciplines as a whole. • Ranges: • Below 0.95 -- Lower • 0.95 – 1.05 -- Normal Range • Above 1.05 -- Higher • Pros • Simple, effective and efficient • Cons • Tainted variable that may mask gender bias in pay • May reflect different salary rewarding structures • Internal Market Ratios vs. External Market Ratios Luna, A. L. (Spring, 2007). Using a market ratio factor in faculty salary equity studies.

  11. Methods • Population/Sample • 248 full-time faculty • 13.7% full professors and distinguished professors • 32.7% associate professors • 43.9% assistant professors • 9.7% lecturers • Gender • Male: 60.5% • Female: 39.5% • Minority:18.1%

  12. Variables & Regression Models • Dependent Variable • 9-10 month base salaries in 2010 • Independent Variables • Individual characteristics • Gender (Male = 0) • Race/Ethnicity (White = 0) • Highest degree earned (Doctor = 0) • Professional Maturity • Years of service • Performance/Merit • Current rank (Assistant Professor = 0) • Disciplines • Three Regression Models • k-1 DummyModel • Internal Market Model • External Market Model

  13. Research Questions • Which model would have the best fit (in terms of R2 and adjusted R2 , and F-ratio) • Which model would be best to appropriately explain gender differences in pay (unstandardized coefficients, t-test)? • Which type of market ratios would largely contribute to faculty salaries (standard errors, t-test, partial correlation)?

  14. Limitations of the Study • Omission of variables related to measuring faculty performances (e.g., publications) in teaching and research would affect the strength of explanation. • Due to the limited numbers of faculty, three disciplines were removed. Faculty in these disciplines were grouped with other related disciplines

  15. Before Running Regression • Curvilinearity issue for time related variables • Years of service /Years in current rank • Quadratic term (not sig.) • Tainted variables • Initial rank / Current rank • Whether gender differs in assigning current ranks • Categorical analysis (multinomial regression) • Asst. to Asso., odds ratio = 1.95 • Asso. To Full, odds ratio = 1.41 Allen, 1984; Haignere, 2002; Scott, 1977.

  16. Results • Dummy Model • Internal Market Model • External Market Model • Check lists (multicollinearity): • Correlation coefficients between predictor variables (r < .80) • VIFs (Variance inflation factors): 1< VIF < 10 • Tolerance (1/VIF) > .02 • Condition index > 30

  17. Regression Model Comparison • Regression model • R2 and adjusted R2 • F- ratios • Gender variable • Unstandardized coefficients (B) & t-values • Luna’s analysis results • Market ratios • Std. errors • t-values • Partial correlation • Negative residuals

  18. Conclusion • Conclusion 1 This study supports the premise that a single, continuous variable can be used to replace categorical discipline variables to explain variances in faculty salaries at a small-size public institution.

  19. Conclusion • Conclusion 2 This study demonstrates that the internal market ratio may serve as the best indicator to represent disciplinary differences in testing gender differences in faculty salaries because it truly reflects the local institution’s salary rewarding structure and practice.

  20. Conclusion • Conclusion 3 The external market approach should be used with caution compared to using the internal market model when conducting salary analysis at medium and small size institutions. • Unstandardized coefficient for females • Yao (2012) • Luna (2007)

  21. Recommendations • Whether or not using gender in regression model • Yes: Regression line is against the average salary of Males (Blue Line) • No: Regression line is against the average salary of Males and Females(Red Line) • Affect all faculty members falling between the blue and red lines • Males paid less Paid more • Females paid less Paid more

  22. Recommendations • Salary remedy • Multiple regression analysis is group-level analysis and aims to detect systemic bias, the results should not directly apply to the individual level. • If the unstandardized coefficient for female faculty is negative, We should give all females the same amount of salary remedy, including those superstars. • Scattergram of residual distribution (Before v. After) Haignere, 2002; Gary, 1990.

  23. Questions?

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