1. Overview and Summary Findings of the Gender Equity Salary Study
May 2nd, 2018

2. Historical Faculty Salary Equity Analysis at UB
The University of Buffalo has continually monitored their internal salary structure for evidence of inequitable treatment, particularly among gender groups of faculty members.
2009 & 2011 – it was persistent in both studies that average received salaries were comparable between male and female faculty members after taking into account work-related factors.
2017 – the university again undertook an internal salary equity study to address possible presence of inequities associated with gender. A unique shared-governance approach was adopted in which both faculty and professional staff undertook the study together. Prior studies were undertaken only by professional staff without faculty input.
GESS 2018

3. Committee Charge
The Gender Equity Salary Study (GESS) Committee was jointly appointed by the University Provost and the Chair of the Faculty Senate to assess whether there is statistically significant inequity in ladder faculty salaries by gender at the University at Buffalo.
Gender equity in salaries is a reasonable expectation of everyone at the University at Buffalo.
Factors that should be considered in order to assess salary equity should be factors that may be expected to impact salary differences.
GESS 2018

4. GESS Committee Co-Chairs
Craig Abbey – Associate Vice President and Director of Institutional Analysis
Glenna Bett – Chair of the Faculty Senate Committee on Equity and Inclusion, Associate Professor, Obstetrics and Gynecology, Jacobs School of Medicine and Biomedical Sciences
Peter Elkin – Chair of the Faculty Senate Committee on Budget Priorities, Professor and Chair, Biomedical Informatics, Professor of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences
Sharon Nolan-Weiss – Director, Office of Equity, Diversity and Inclusion, Title IX and ADA Coordinator
GESS 2018

5. GESS Committee Members
Sharmista Bagchi-Sen – Professor, Geography
Rajan Batta, – Associate Dean for Faculty Affairs, Human Resources & Diversity, School of Engineering and Applied Sciences
Lucinda Finley – Professor, Law
Brenda Moore – Associate Professor, Sociology
Neel Rao – Assistant Professor, Economics
Gregory Wilding – Professor, Chair, Biostatistics
GESS 2018

6. Specific Aims The focus of the internal equity study was twofold:
Determine if there is a statistically significant difference in the pay of tenured and tenure-track faculty (ladder) by gender when controlling for academic rank, time in rank, rank at hire and department affiliation.
Determine if there is a statistically significant difference in the pay of tenured and tenure-track faculty (ladder) by gender when controlling for academic rank, time in rank, rank at hire and market factor discipline when comparing the salaries to a national database of academic salaries by discipline.
GESS 2018

7. Statistical Methods
A Hierarchical Linear Model and Regression analysis were the statistical procedures used in this study and are most often used by researchers in the field to measure unexplained wage differences for gender.
Regression analysis provides information about the average percent difference in salaries between male and female faculty members and whether this change is significantly different from a zero percent (0%) difference.
A 5% significance level was used for this study. This is considered a standard significance level for this type of study.
GESS 2018

8. Statistical Models Model 1 – Departmental Salary Comparison
This model tests for gender differences in ladder faculty pay conditional on
Current academic rank
Initial rank
Time in rank
Department affiliation
Model 2 – Discipline Salary Comparison
Discipline-specific salary market factor
GESS 2018

9. Stakeholder Input October 19th, 2017 was held on South Campus
It was important to the Committee to provide the UB Community with an opportunity to review the proposed methodology, ask questions, and provide suggestions and feedback.
The method was presented to the Faculty Senate Executive committee then the full Faculty Senate where feedback was obtained.
Faculty were invited to attend either of two Town Hall meetings where the GESS study was discussed.
October 19th, 2017 was held on South Campus
October 23rd, 2017 was held on North Campus
Faculty were also invited to provide feedback by .
A Frequently Asked Questions (FAQ) has been created to address commonly asked questions pertaining to UB’s Gender Equity Salary Study.
GESS 2018

10. Dataset – Study Group Inclusion Criteria
All ladder faculty including full professors, associate professors and assistant professors
Primary unqualified appointments with FTE of 1.0
State faculty title and annualized base state salary only
Exclusion Criteria
Faculty with qualified title (e.g. clinical, research, visiting)
All Geographic Full-Time (GFT) faculty
All Educational Opportunity Center (EOC) faculty
All librarians
Tenured faculty serving in an administrative capacity
GESS 2018

11. Data Collection
University at Buffalo - Human Resources Data Collection
All human resources data was collected from Payroll 17 of the academic year.
Market Factor Data Collection
The Oklahoma State University Salary Study by Discipline, a national sample of average faculty salaries by discipline, was used to assign corresponding market factors based on discipline and rank.
GESS 2018

12. Faculty Demographics Gender and Rank Distributions Rank Female Male
Total
Assistant 94
165
259
Associate
144
214
358
Professor
105
320
425
343
699
1042
Ethnicity/Race Distributions
American Indian/Alaskan Native 5
Asian
238
Black or African American 23
Hispanic 34
Multiple Races 8
White
734
Total
1042
GESS 2018

13. Unadjusted Gender Pay Gap Comparison
Gender Pay Gap is the difference in the average man's and average woman's remuneration without accounting for other differences.
Group
Male
Female
Gap ($)
Gap (%)
Overall
All Public Doctoral
$102,331
$81,174
-$21,157
-21%
University at Buffalo
$122,774
$105,144
-$17,630
-14%
Full
Professors
$133,468
$119,761
-$13,707
-10%
$151,809
$139,037
-$12,772
-8%
Associate
$91,354
$84,997
-$6,357
-7%
$103,259
$94,700
-$8,559
Assistant
$80,858
$73,741
-$7,117
-9%
$91,774
$83,283
-$8,491
Source: All Public Doctoral from Academe March/April 2015, UB from human resource data.

14. Faculty Salary Distribution

15. Faculty Salaries Before Regression Analysis 25th Percentile, Median, 75th Percentile
Assistant Professor
Associate Professor
Professor
25th Percentile
Median
75th Percentile
Female
$66,865
$72,792
$92,825
$80,046
$87,558
$104,561
$111,716
$127,430
$155,972
Male
$71,398
$91,270
$94,911
$83,536
$94,926
$116,943
$120,065
$142,743
$177,185
GESS 2018

16. Contribution of Variables to Base Differences in Salaries
Variables Considered
Remaining Difference in Salaries by Gender
Female – Male Salaries
Difference Amount Accounted For
Gender
- $17,630
Departments
- $8,393
$9,237
Current Rank
- $3,498
$4,895
Rank at Hire
- $1,407
$2,091
Time in Rank
+ $119
$1,526
Underrepresented Minority Status
+ $60
- $59

17. Contribution of Variables to Total Explained Variance
R-squared is statistical measure of how close the data fits the model. It can be interpreted as the percentage of explained variance. In general, the higher the R-squared value the better the model fits the data. But the R-square will generally increase as additional predictors are added to the model even by chance, so an adjusted R-squared value is generally preferred. The adjusted R-square value is adjusted for the number of predictors in the model. It will increase only if new predictors improve the model fit.

18. Gender + Component R-squared
Base Salary
Ln Base Salary
Model
Variables
Adjusted R2
Gender
0.036
0.042
Gender +
Market Factor
0.596
0.694
Departments
0.444
0.452
Current Rank
0.388
0.448
Rank at Hire
0.323
0.314
Time in Rank
0.173
0.201
URM Status
0.037
0.043

19. Overall Results – Market Factor Model
Base Salary
Unstandardized Coefficients
p-value
Ln Base Salary
Gender
-1,951.04
0.273
-0.012
0.322
Market Factor
0.816
<0.001
0.857
Is Professor 2016
-5,436.58
0.069
-0.071
0.001
Is Associate 2016
0.762
-0.009
0.555
Rank at Hire Professor
36,016.84
0.217
Rank at Hire Associate
4,031.68
0.074
0.040
0.010
Time in Rank
452.37
0.004
URM Status
0.976
-0.005
0.866
Gender x URM Status
2,798.04
0.654
0.028
0.518
Adjusted R2
0.670
0.745 N
1042

20. Unstandardized Coefficients
Overall Results – Department Model
Model
Base Salary
Unstandardized Coefficients
p-value
Ln Base Salary
Gender
60.56
0.970
-0.001
0.933
Departmental Effects
85 Departments
Is Professor 2016
44,023.72
<0.001
0.404
Is Associate 2016
12,956.68
0.146
Rank at Hire Professor
30,641.73
0.192
Rank at Hire Associate
5,858.36
0.005
0.056
Time in Rank
594.91
URM Status
6,553.35
0.081
0.049
Gender x URM Status
441.21
0.934
0.001
0.969
Adjusted R2
0.770
0.828 N
1042

21. Model Fit Results – Market Factor Model
Base Salary
Ln Base Salary
Model
Variables
Adjusted R2
Δ Adjusted R2 1
Gender
0.036
- -
0.410 2
0.595
0.559
0.693
0.652
Market Factor 3
0.600
0.005
0.694
0.001
Current Rank 4
0.664
0.064
0.735
0.041
Rank at Hire 5
0.670
0.006
0.745
0.010
Time in Rank 6
<0.001
URM Status

22. Model Fit Results – Department Model
Base Salary
Ln Base Salary
Model
Variables
Adjusted R2
Δ Adjusted R2 1
Gender
0.036
- -
0.041 2
0.394
0.358
0.403
0.362
Department 3
0.715
0.321
0.782
0.379
Current Rank 4
0.759
0.044
0.814
0.032
Rank at Hire 5
0.769
0.010
0.827
0.013
Time in Rank 6
0.771
0.002
0.828
0.001
URM Status

23. Outlier Analysis
In order to assess the impact of faculty salaries considered outliers at each end of the salary range, a residual analysis was done.
In each model, a total of 17 faculty members were identified as outliers with 7 faculty members considered outliers within both regression models.
Each model was re-run excluding the outliers and the results did not change. There was no statistically significant effect for gender.
GESS 2018

24. Discussion
After controlling for the effects of current academic rank, time in current rank, rank at hire, departmental affiliations, or discipline market factors, the unexplained wage gap between male and female faculty members varied between 0.1% and 1.3% and was not statistically significantly different (p=0.933 departmental model and p= salary market factor model).
Likewise, there was no evidence of a systemic pay bias against faculty in underrepresented race/ethnicity categories relative to non-underrepresented faculty.
Work-related factors such as rank, experience, and discipline have an effect on pay and were statistically significant.
GESS 2018

25. Limitations
Study did not analyze salaries of non-tenure track, EOC, GFT and librarian faculty
Study does not address differences in promotion and tenure rates between men and women
Study did not consider research productivity and professional achievement other than academic rank
Study considered only state base salary, therefore excluding compensation from Also Receives, the Research Foundation, UB Foundation or other sources.
The study did not address any differences in rank at hire between men and women
Individual cases of gender bias may not show up in population-based statistics
GESS 2018

26. How UB Addresses Individual Cases
UB’s Office of Equity, Diversity and Inclusion will address individual reports of salary inequity under UB’s Discrimination and Harassment Policy.
Contact info: (716) (phone), ( )
In addition to gender and race/national origin, EDI will investigate salary disparities on any basis covered by law (ex. age, disability, sexual orientation)
Process
Confidential meeting with EDI representative
Data review analyzing the salaries of similarly-situated colleagues
Follow-up meeting with reporting party to review the data
If the reporting party decides to move forward, EDI will meet with the chair/dean for an explanation of any salary differentials
Resolution or an explanation of why a resolution is not supported by the evidence.

27. Questions?