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 ReviewRecommended 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.

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. 48-73). Tallahassee, FL: Association of Institutional Research.

Conceptual Framework

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

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 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.

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 (1980-2010) (Reference Groups: Asst. Prof & English Discipline)

Salary Differences between Male and Female (All Rank)

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 (1980-2010) published by the AAUP.

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.

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.

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.

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%

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

Research Questions

1. Which model would have the best fit (in terms of R2 and adjusted R2 , 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)?

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

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.

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

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

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.

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.

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)

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

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.

Questions?