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

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Presentation on theme: "Using Internal Market Ratios to Detect Gender Differences in Faculty Salaries Chunmei Yao, Ed. D SUNY College at Oneonta."— Presentation transcript:

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 (2 nd 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 ). 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 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 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 1. Comparability: mission vs. salary rewarding structure 2. Equity: gender, race/ethnicity 3. Compression: newly hired v. senior 4. 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 ). 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 Internal labor market Price and allocate based on teaching, research, and service  Key disciplines  Stable employment  Promotion hierarchies  External Labor Market 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 … The observed differences cannot be 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 Change across disciplines ( ) % of Salary Change (Reference Groups: Asst. Prof & English Discipline) Salary Differences between Male and Female (All Rank) Salary Differences Accordingly, it is predicted that salary differences across disciplines may continue to affect gender differences in faculty salaries. Data Source: the Annual Report on the Economic Status of the Profession in Academe ( ) published by the AAUP.

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 (2 nd 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 Lower 0.95 – Normal Range Above 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 Dummy Model Internal Market Model External Market Model

13 Research Questions 1. Which model would have the best fit (in terms of R 2 and adjusted R 2, and F-ratio) 2. Which model would be best to appropriately explain gender differences in pay (unstandardized coefficients, t-test)? 3. 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 Curvilinearity  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)(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  R 2 and adjusted R 2  F- ratios Gender variable  Unstandardized coefficients (B) & t-values  Luna’s analysis results 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)(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.superstars.  Scattergram of residual distribution (Before v. After)BeforeAfter Haignere, 2002; Gary, 1990.

23 Questions?


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