Faculty Pay Equity Study
Prepared for Morgan State University
Patrina Clark, President, Pivotal Practices Consulting, LLC
August 1, 2014
Project Number 14/PRO-2008-S
(E-Maryland Solicitation MDR 1331011071)
Table of Contents
Executive Summary....................................................................................................................................... 1
Introduction .................................................................................................................................................. 4
Background ................................................................................................................................................... 4
Study Methodology....................................................................................................................................... 5
Policy Analysis ............................................................................................................................................... 6
Internal Policies and Practices Survey ...................................................................................................... 6
Internal Policies and Practices Documentation Review ........................................................................... 8
Summary of Results .................................................................................................................................. 9
Statistical Analysis ......................................................................................................................................... 9
Comparison of MSU Faculty Gender Counts and Salaries to National Averages ..................................... 9
Comparison of Salary Averages ................................................................................................................ 9
Composition of the Faculty Pool ............................................................................................................. 11
Data Analysis and Methodology ............................................................................................................. 12
Assumptions and Data Limitations ..................................................................................................... 13
Demographic Data Analysis ................................................................................................................ 13
Univariate, Bivariate, and Multivariate Analyses................................................................................ 14
Tenure Status ...................................................................................................................................... 14
Average Years of Service ..................................................................................................................... 15
Tenure and Average Years of Service ................................................................................................. 15
Faculty by College (Discipline) ............................................................................................................ 15
Regression Analysis - Overview........................................................................................................... 17
Regression Analysis – Results ............................................................................................................. 18
Transactional Data Analysis ................................................................................................................ 22
Summary of Results ................................................................................................................................ 24
Recommendations .................................................................................................................................. 26
Salary Compression ................................................................................................................................. 27
Appendix A: References ........................................................................................................................... 1
Appendix B: Faculty and Staff Recommendations for Process Improvement ......................................... 1
Appendix C: Regression Analysis Summary Output Tables...................................................................... 1
GENDER ..................................................................................................................................................... 1
GENDER AND TENURE............................................................................................................................... 3
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GENDER, TENURE, YEARS OF SERVICE ...................................................................................................... 5
GENDER, TENURE, YEARS OF SERVICE, SCHOOL ....................................................................................... 7
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List of Tables:
Table 1: Internal Policies and Practices Survey Summary ............................................................................ 7
Table 2: Education Level Doctorate, National Public Institutions Comparison ............................................ 9
Table 3: Education Level Master’s, National Public Institutions Comparison ............................................ 10
Table 4: Education Level Doctorate, Regional Public Institution Salary Comparison ................................ 10
Table 5: Education Level Master’s, Regional Public Institution Salary Comparison .................................. 10
Table 6: Average Salaries, Standard Deviation, and Expected Salaries - By Rank ..................................... 13
Table 7: Salaries, by Rank and Gender, Outside Expected Range.............................................................. 14
Table 8: Number and Percentage of Tenured and Non-Tenured Faculty, by Gender ................................ 15
Table 9: Average Years of Service, by Rank and Gender............................................................................ 15
Table 10: Tenure and Average Years of Service, by Rank and Gender ...................................................... 15
Table 11: MSU Faculty by College, Rank, and Gender ............................................................................... 16
Table 12: Average Annual Salary by College (Discipline), Rank, and Gender ............................................. 17
Table 13: MSU Enrollment by School/College, 2006 ................................................................................. 17
Table 14: Regression Results - Gender....................................................................................................... 19
Table 15: Regression Results - Gender and Tenure ................................................................................... 20
Table 16: Regression Results - Gender, Tenure, and Years of Service ....................................................... 21
Table 17: Regression Results - Gender, Tenure, Years of Service, and School ........................................... 22
Table 18: Number and Average Salaries of New Hires, by Rank and Gender............................................. 23
Table 19: Number of Promotions by Gender and Effective Date (Year).................................................... 23
Table 20: Adjustments by Type, Number, and Average % Increase .......................................................... 24
List of Figures:
Figure 1: Post-Secondary Faculty by Gender, National Totals ................................................................... 11
Figure 2: Faculty by Gender (2014), MSU .................................................................................................. 12
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Executive Summary
Introduction
Morgan State University (MSU) contracted with Pivotal Practices Consulting LLC (PPC), a
Maryland-based management consulting firm, to conduct a faculty pay equity study for its
tenured and tenure-track (Assistant Professor, Associate Professor, and Professor) faculty to:

Determine whether there are gender-based salary inequities.

Make recommendations to resolve any such inequities.
Methodology and Analysis
PPC developed a study project plan consistent with widely accepted practices for conducting
academic pay studies for higher education faculty.1 PPC conducted quantitative and qualitative
data analysis that included the following elements:

Policy and Procedures Review

Internal Survey

Statistical Analysis
The study’s methodology was applied through the following analyses:

Internal Policies and Practices Survey

Internal Policies and Practices Documentation Review

General Statistical Analysis

Regression Analysis
Key Findings
The following are some of the key findings of the study:

There is no evidence of gender-based salary inequities. Further, PPC found no
meaningful relationships between tenure, years of service, and salary.

While beyond the scope of the study, PPC did find disparities in salaries attributable to
factors other than gender among MSU faculty, most notably School/College affiliation
and academic discipline.

In comparing MSU faculty salaries with salary averages at the national level:
1
Resources included Paychecks: A Guide to Conducting Salary-Equity Studies for Higher Education Faculty Second
Edition whose principal author is Lois Haignere.
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o Average salaries for MSU female faculty at the Assistant Professor level were
significantly higher than the national average.
o The salary gaps between male and female faculty at the Associate Professor and
Professor levels were smaller for MSU faculty than for faculty at the national level.

In comparing MSU faculty salaries with average salaries at the regional level2:
o For faculty with a master’s degree, the MSU faculty average salaries were generally
comparable to their regional counterparts.
o For faculty with a doctorate degree, MSU faculty average salaries were generally less
at the Associate Professor and Professor ranks.

Generally, the Board of Regents’ Policies and Procedures on Appointment, Promotion,
and Tenure is followed to some degree. However, there are differences in how the
guidance is applied and the processes employed for salary setting.
Key Recommendations
Following are the key recommendations in support of the above findings:
2

Make this report available to all MSU tenured and tenure track faculty, as well as those
with a role in the compensation process.

Conduct further analysis to determine the relationship between salary, school, college,
and discipline.

Conduct facilitated discussions with faculty to identify issues that are contributing to
perceptions of gender-based salary inequity and develop strategies to address these
issues.

Update, align, and make broadly internally (e.g., posted to MSU’s internal website)
available the Board of Regents’ Policies and Procedures on Appointment, Promotion and
Tenure and the Faculty Handbook to create a comprehensive resource on MSU talent
management.

Design a compensation process to compare faculty salaries to appropriate external
sources on a fixed basis to ensure competitiveness and equity in salary determinations.

Provide annual briefings on compensation policies and procedures, to include relevant
and appropriate updates on any compensation initiatives to foster open communication.

Charter a faculty compensation working group to address ongoing salary issues and
facilitate transparency and open communication.
Gender data was not available at the regional level.
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Questions
Please direct any questions regarding this study to:
Patrina M. Clark, President, Pivotal Practices Consulting LLC
6301 Ivy Lane, Suite 108
Greenbelt, Maryland 20770
(301) 220-3179 office
[email protected]
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Introduction
Morgan State University (MSU), a State-supported university located in a residential area of
Baltimore, Maryland, identified a need for assistance from a qualified firm to analyze the salaries
among its tenured and tenure track faculty. MSU contracted under Project Number 14/PRO2008-S (E-Maryland Solicitation MDR 1331011071) with Pivotal Practices Consulting LLC
(PPC), a Maryland-based management consulting firm, to:


Conduct a study to determine whether there are gender-based salary inequities.
Make recommendations to resolve any such inequities.
Background
MSU’s faculty members deliver a comprehensive set of undergraduate academic programs and a
selective set of master’s and doctoral programs to approximately 8,000 students, approximately
70% of whom are Maryland residents.
MSU has a total of 43 academic departments listed in their departmental directory. These 43
departments offer:

45 programs leading to the bachelor’s degree,

30 programs leading to the master’s degree, and

15 programs leading to the doctorate, including the online Community College
Leadership Program.
As a testament to the desirability of MSU’s academic programs, the University has seen a 25%
increase in enrollment over the last decade.
To remain competitive for the most talented staff available and sustain quality academic
programs, MSU recognizes the importance of ensuring pay parity among similarly situated
faculty members and acknowledged the following:

Salary inequities among similarly situated professionals are disruptive to quality
education programs and cause discord within productivity, departments, faculty
turnover, and productivity.

There is a meaningful distinction between salary differences (based on variances in
productivity, contributions to the University, and economic climate) and salary
inequities (based on unjustified factors, such as qualifications, assigned responsibilities,
and market forces).

While no system can perfectly determine to the individual level, the appropriate salary,
certain inequities are obvious and extreme as to be readily identifiable and to demand
attention and remedy.
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MSU further acknowledged that identifying internal salary inequities can be difficult because of
the large number of variables that can influence pay determinations. Additional potential factors
that increase the challenges of identifying these inequities include the absence of:

Documentation for setting base salaries,

Guidance for awarding incremental salary adjustments,

Guidance for awarding discretionary salary increases,

Clearly defined pay categories for departments and occupational groups (i.e., position
titles).
Further, inconsistent application of any established guidance can also be a complicating factor.
This study endeavored, to the maximum extent possible, to identify true gender-based inequities
based on appropriate statistical analyses.
Study Methodology
PPC developed a study project plan consistent with widely accepted practices for conducting
academic pay studies for higher education faculty identifying critical milestones, collaboration
and input points to ensure timely delivery of the study.
PPC participated in the following meetings associated with the study:

Work initiation meeting on April 8, 2014, with Mrs. Armada Grant, Director of Human
Resources, and Mr. Hambisa Belina, MSU Procurement.

Meeting with Dr. David Wilson, President, and Mrs. Armada Grant on April 18, 2014.

Provided a project overview and status update and addressed questions during the MSU
Deans’ Meeting on May 12, 2014.
PPC conducted quantitative and qualitative data analysis that included the following elements:

Policy and Procedures Analysis
o An internal survey regarding use of internal pay-setting and compensation policies
and practices to determine how such practices are applied and if the application of
said practices is a contributing factor to gender inequity in salaries for professors,
assistant professors and associate professors.
o A review of MSU’s current written compensation and pay-setting practices to
determine how such practices are applied and if the application of documentation is,
in any way, a contributing factor to gender inequity in salaries for professors,
assistant professors and associate professors.
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
Statistical Analysis
o A general statistical analysis of educational compensation to establish the overall
context of MSU compensation, policies and pay-setting practices.
o The development of a comprehensive regression analysis to identify variables that
may be impacting gender-based salary decisions.
Recommendations were developed based on the results of the application of the study’s
methodology.
Policy Analysis
Internal Policies and Practices Survey
An internal survey was conducted regarding the application of compensation policies by college
deans and other salary determination participants and stakeholders. The survey was designed to
identify any particular gaps or trends that may influence gender based salary decisions. The
survey was viewed as an opportunity to identify compensation best practices and processes
within MSU, and to assess the faculty’s awareness of MSU’s requirements for salary
determinations and merit increases.
The survey was sent to 33 participants, and all 33 participants at least partially completed the
survey. Of the 33 respondents, 6 respondents provided only their names and titles and the other
27 answered some or all of the questions, resulting in a 100% response rate and a 82%
substantive response rate.
In summary, PPC did not observe any policy or procedure that resulted in gender-based pay
setting or merit pay decision. However, it is noteworthy that several survey respondents
identified a lack of replicable written process documentation, which makes it impossible to
establish a clear, consistent process that can actually be assessed. In terms of best practices, more
than one respondent identified that the process of securing the Dean’s recommendation for
submission to the Provost worked well. Finally, there were several recommendations for
improving the process, to include establishing a fair, equitable and transparent process that
allows for some flexibility.
Table 1 on the following page provides an overview of the survey and summarizes the responses
and findings. Appendix B includes the narrative comments for process improvement
recommendations without individual attribution.
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Table 1: Internal Policies and Practices Survey Summary
Survey Question
1. Please provide your name:
2. What is the title of your
current position?
3. Please describe how you use
the "Board of Regents' Policies
and Procedures on Appointment,
Promotion, and Tenure" and the
"Faculty Handbook" in pay
setting determinations.
4. Please describe the process
for setting salary for a new
faculty member, to include any
peer and/or upper level reviews.
5. Is this process documented in
writing?
Purpose
Self Evident
Self Evident
To determine consistency
in application and
knowledge of processes to
be used in pay setting.
At a variety of levels, respondents indicated
awareness that these two documents drive MSU
pay-setting processes was evident.
To determine consistency
in application and
knowledge of processes to
be used in pay setting for
new faculty.
To assess the existence of
written process guidance
for the pay setting decisionmaking process.
All respondents indicated a general understanding
of the requirements, with process variations
among schools.
6. Is the written process
available to all faculty within the
Department and/or School?
To assess the degree of
transparency regarding the
pay setting process.
7. Please describe any required
review process before a merit
salary increase is
approved/finalized. If no review
process exists, please enter
"None."
8. What aspects of the pay
setting process work particularly
well?
To determine consistency
in application of processes
to be used in requests for
merit salary increases.
9. What recommendations do
you have for improving the pay
setting process?
Response/ Finding
Provide an opportunity to
MSU faculty and staff to
identify internal best
practices
Provide an opportunity to
MSU faculty and staff to
provide process
improvement
recommendations based on
process experience
The process for setting salary of new faculty
members is not consistently documented. Nine (9)
respondents indicated the process was
documented in writing and fourteen (14) indicated
it was not.
Where written process documentation exists, it is
generally made available. Of the nine (9)
respondents indicating the process is documented
in writing in Question 5, seven (7) responded that
it is available to faculty within the Department.
Interestingly, two (2) of the faculty members who
responded that the process is not documented in
writing responded that it is available to faculty.
Almost all respondents indicated the annual report
is a key element of the process. Some schools
described a fairly robust process for merit
increases, with almost all requiring multiple
levels of review.
Responses to this item were somewhat sparse,
with some indicating a clear need for more robust
process rigor beginning with a documented
policy.
Responses were more robust than those for
Question 8. Responses indicated consensus in the
need for a fair, equitable and transparent process
that allows for appropriate, supportable
flexibilities.
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Internal Policies and Practices Documentation Review
As part of the analysis regarding MSU’s pay practices, a review of the University’s current
written compensation and pay-setting practices was conducted to determine how such practices
are applied and if the application of the documentation is a contributing factor to gender inequity
in salaries for professors, assistant professors and associate professors.
The following information was requested:

Compensation policies, memorandum, negotiated agreements, processes and any other
documentation used for salary determinations. This includes any information MSU may
have on classification or positional salary decisions, as well as information on longevity
increases, if this salary approach is used by the University.

Pay grade matrix for position categories that includes the complete salary ranges (i.e.,
minimum salary and maximum salary) for each position category.

Job analysis for each position category that describes the required knowledge, skills and
abilities.

Pay setting decision matrix for newly hired faculty members.

A title comparability matrix showing how position title variations align with the position
categories of #3 above.

A description of the bases for discretionary salary increases (e.g., attainment of a terminal
degree, publication in nationally recognized trade journal, etc.).

Any compensation or pay-related studies conducted by or on behalf of MSU.

Copies (may be redacted of personal information) of any compensation complaints or
appeals filed under negotiated agreements.
While not all requested documentation was received, sufficient information regarding the
University’s compensation and pay practices was provided. Analysis focused on the Board of
Regents' Policies and Procedures on Appointment, Promotion, and Tenure and the MSU Faculty
Handbook. The analysis indicated the following:

Board of Regents’ Policies and Procedures on Appointment, Promotion and Tenure:
A review of this document did not find any obvious policies or procedures that would
result in gender-based pay decisions. All documentation presented is neutral and meritbased.

MSU Faculty Handbook: A review of this document did not find any obvious policies
or procedures that would result in gender-based pay decisions. All documentation
presented is neutral and merit-based.
In summary, the documentation review did not identify any policy applications or procedures
that would result in a gender-based salary or merit pay decision. There may be some process
discrepancies between these two documents, which should be addressed to ensure consistency.
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Summary of Results
The policy analysis and internal policy survey did not reveal any specific trends, gaps or issues
that would indicate the MSU compensation and pay-setting policies and practices contribute,
either positively or negatively, to gender-based salary decisions.
Statistical Analysis
Comparison of MSU Faculty Gender Counts and Salaries to National Averages
According to the U.S. Department of Education, in 2010-2011 there were 2,870 Title IV postsecondary four-year colleges.3 The Title IV status encompasses those institutions that have
written agreements with the Secretary of Education to participate in Title IV federal student
financial assistance programs. Within the state of Maryland, this includes a total of 62
institutions, 30 of which are public.4
Comparison of Salary Averages
To establish a baseline for the analysis, current average MSU salaries were compared to the
national averages for public institutions, differentiating for educational level. The following two
tables provide comparisons of the national and MSU average faculty salaries and include
differences in dollars and percentages. Specifically:


Table 2 provides a comparison of faculty with doctorate degrees.
Table 3 provides a comparison of faculty with master’s degrees.
Table 2: Education Level Doctorate, National Public Institutions Comparison5
Rank
Avg. National Faculty Salaries
Female
Male
Avg. MSU Faculty Salaries
Female
Male
Difference
Female
Male
Assistant Professor
$
69,936.00 $
76,219.00 $
80,398.69 $
76,380.45 $
10,462.69
14.96% $
161.45
0.21%
Associate Professor
$
80,500.00 $
86,825.00 $
80,470.88 $
85,052.72 $
(29.13)
-0.04% $
(1,772.28)
-2.04%
Professor
$
113,456.00 $
126,469.00 $
107,754.00 $
101,497.09 $
(5,702.00)
-5.03% $
(24,971.91)
-19.75%
For faculty with doctorate degrees, the average salaries for MSU faculty as compared to their
national counterparts are:


Less than the national averages at the Associate Professor and Professor ranks; and
More than the national average at the Assistant Professor rank.
3
SOURCE: U.S. Department of Education, National Center for Education Statistics. (2013). Digest of Education
Statistics, 2012 (NCES 2014-015), Chapter 2.
4
SOURCE: National Center for Education Statistics (NCES), data from the Integrated Postsecondary Education Data
System (IPEDS), 2009 through 2011.
5
SOURCE: American Association of University Professors, 2013-2014, found at: www.higheredjobs.com.
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Gender variance is most pronounced at the Assistant Professor level, where MSU females’
average salary is 14.96% higher than the national average, while MSU males’ average salary is
less than 1% higher.
Table 3: Education Level Master’s, National Public Institutions Comparison
Avg. National Faculty Salaries
Rank
Female
Male
Avg. MSU Faculty Salaries
Female
Difference
Male
Female
Male
Assistant Professor
$
59,873.00 $
62,345.00 $
62,985.33 $
62,028.17 $
3,112.33
5.20% $
(316.83)
Associate Professor
$
69,696.00 $
72,693.00 $
69,730.50 $
72,605.33 $
34.50
0.05% $
(87.67)
Professor
$
86,263.00 $
90,392.00
n/a
n/a
n/a
n/a
n/a
-0.51%
-0.12%
n/a
For faculty with master’s degrees, the average salaries for MSU faculty as compared to their
national counterparts are generally equal. Again, MSU female Assistant Professors earned more
(5.2%) than their national counterparts. Data is not provided for the Professor rank in Table 2
because MSU faculty at the professor rank was reported to have doctorate degrees.
From a regional perspective, Tables 4 and 5 provide a comparison of faculty salaries as
compared to public institutions within the South Atlantic, which comprises the states of
Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, Puerto Rico, South
Carolina, Virgin Islands, Virginia, and West Virginia. These comparisons are gender neutral and
show the differences in dollars and by percentage.
Table 4: Education Level Doctorate, Regional Public Institution Salary Comparison6
Assistant Professor
Avg. Faculty
Avg. MSU
Salaries - South
Faculty
Atlantic
Salaries
$
77,960.00 $
78,847.79 $
Associate Professor
$
88,717.00 $
83,476.17 $
(5,240.83)
-6.3%
Professor
$
133,039.00 $
103,111.77 $
(29,927.23)
-29.0%
Rank
Difference
887.79
1.1%
For faculty with doctorate degrees, average salaries for MSU faculty are slightly above regional
counterparts at the Assistant Professor rank. At the Associate Professor and Professor ranks,
however, salaries are below regional counterparts (6.3% less at the Associate Professor rank and
29% less at the Professor rank).
Table 5: Education Level Master’s, Regional Public Institution Salary Comparison
Avg. Faculty
Salaries - South
Atlantic
Rank
Avg. MSU
Faculty
Salaries
Difference
Assistant Professor
$
61,507.00 $
62,219.60 $
712.60
1.1%
Associate Professor
$
70,407.00 $
71,455.40 $
1,048.40
1.5%
Professor
$
90,202.00
n/a
n/a
n/a
6
SOURCE: American Association of University Professors, 2013-2014, found at:
http://www.higheredjobs.com/documents/salary/region_category_rank_14.pdf
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For MSU faculty with master’s degrees, average salaries were comparable to those of their
regional counterparts. Assistant Professors earned 1.1% more and Associate Professors earned
1.5% more than their regional counterparts. Again, there is no MSU faculty at the Professor rank
holding master’s degrees.
Composition of the Faculty Pool
In reviewing average salaries, consideration must also be given to the percentages of males and
females in the faculty pool. The National Center for Education Statistics (NCES) is the primary
federal entity for collecting and analyzing data related to education in the United States and other
nations. NCES fulfills a Congressional mandate to collect, collate, analyze, and report complete
statistics on the condition of American education; conduct and publish reports; and review and
report on education activities internationally.
Figure 1 provides a comparison of faculty by gender, as a percentage, at the national level in
2011 as reported in NCES’s fall digest.
Figure 1: Post-Secondary Faculty by Gender, National Totals7
Comparatively, Figure 2 reflects faculty by gender, as a percentage, at MSU based on the faculty
data provided on April 17 and 18, 2014 (see next page).
7
SOURCE: NCES, Digest of Education Statistics, Full-time instructional faculty in degree-granting institutions, by
race/ethnicity, sex, and academic rank: Fall 2007, fall 2009, and fall 2011.
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Figure 2: Faculty by Gender (2014), MSU
In comparing the national and MSU data, the percentages for MSU were higher than the national
percentages for males in Professor (75% MSU versus 70.88% nationally) and Associate
Professor (62.73% MSU versus 57.82% nationally) faculty positions.
The only faculty position at MSU for which females constitute a higher percentage than the
national average is the Assistant Professor (51.9% MSU versus 49.34% nationally). The overall
gender representation for all faculty positions nationally as compared to MSU was relatively
consistent with the total percentage of females being only slightly higher at MSU (40.72% MSU
versus 39.98% nationally).
Data Analysis and Methodology
Data analysis was conducted to determine, among other things, the impact gender had on salary.
Additionally, the analysis took into account other data considered relevant to salary
determination, including tenure status, years of service, and the school/discipline in which the
faculty was associated.
The analysis conducted was based on demographic and transactional (e.g., salary increases for
cost-of-living adjustments, promotions, and merit) data. The transactional data covered salary
adjustments for the period August 2012 through April 2014. Because of differences in certain
fields in the data provided, data was merged using the VLOOKUP function in Microsoft® Excel.
Specifically, this was done using the faculty ID numbers to locate information in other data
sources. Additionally, one anomaly, namely a faculty member with the rank title of “Professor”
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whose salary ($344,597) was significantly above the average salary for professors, was removed
so as not to influence the analysis results.
As an initial analysis, data was reviewed to look at the overall number of employees by gender
and rank (Assistant and Associate Professor and Professor), including average salaries, average
years of service, and tenure status. Relationships among the variables to look for indications of
gender bias were also reviewed. Following this analysis, regression analysis was performed
based on overall faculty within the three ranks in addition to separate analyses for each of the
faculty rankings. Subsequent to the regression, an analysis was conducted that examined
transactional data, and in particular salary increase rates, promotions, and new hires.
Assumptions and Data Limitations
In conducting this analysis, the following assumptions were made:
1) Because all faculty within the three ranks hold either masters or doctorate degrees, and
based on the principle of parsimony8, education levels were not considered in the
regression analysis.
2) The data provided did not contain fields related to promotion rationale and was limited to
two years (2012 and 2013). There were, however, a limited number of promotions (6 in
2012 and 17 in 2013), which limits the ability to conduct statistical analysis.
3) While some new hire data was provided, additional factors, such as previous years of
experience, was not available. Therefore, the impact of these factors was not addressed
in this study.
Demographic Data Analysis
Before conducting regression analysis, it is often beneficial to conduct a high-level analysis to
identify any discrepancies and better focus the regression analysis. Common elements of this
high-level analysis include ratios and averages.
Toward that end, we established average salaries by rank (Assistant Professor, Associate
Professor, and Professor) as well as minimum and maximum salaries, standard deviations, and
expected minimum and maximum salaries given the calculated standard deviations. Table 6
below provides the outcome of this analysis.
Table 6: Average Salaries, Standard Deviation, and Expected Salaries - By Rank
Average
Salary
Assistant Professor $ 75,611.86 $
Associate Professor $ 84,206.00 $
Professor
$ 103,111.77 $
Rank Title
Min
Max
42,024.00 $
55,370.00 $
68,089.00 $
133,900.00 $
132,452.00 $
157,580.00 $
Expected
Range-Min
22,349.75 $ 53,262.11
20,688.38 $ 63,517.62
21,304.26 $ 81,807.51
S.D.
Expected RangeMax
$
97,961.60
$ 104,894.38
$ 124,416.04
8
Parsimony refers to the concept of economy; in regression analysis, it means that additional independent
variables should not be added to the analysis when their impact adds minimal value to the outcome.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 13
Based on the calculated expected values, the data was further analyzed to determine the number
of faculty, by rank and gender, whose salaries fell outside the expected range. Table 6 presents
those results.
Table 7: Salaries, by Rank and Gender, Outside Expected Range
Total #
<>Expected
Assistant Professor
17
Associate Professor
39
Professor
9
65
Rank Title
Rank Title
Assistant Professor
Associate Professor
Professor
# <Expected
Range
4
18
6
28
Female - <Expected
#
%
3
75.0%
6
33.3%
1
16.7%
10
35.7%
# >Expected
Range
13
21
3
37
All Faculty
#
%
76
35.2%
109
50.5%
31
14.4%
216
100.0%
Male - <Expected
#
%
1
25.0%
12
66.7%
5
83.3%
18
64.3%
Male
#
35
69
23
127
Female - >Expected
#
%
9
69.2%
7
33.3%
1
33.3%
17
45.9%
Female
%
46.1%
63.3%
74.2%
#
41
40
8
89
%
53.9%
36.7%
25.8%
Male - >Expected
#
%
4
30.8%
14
66.7%
2
66.7%
20
54.1%
As the data in Table 7 shows, 22.4% of all faculty at the Assistant Professor rank have salaries
that fall outside (either above or below) the expected range. This percentage increases to 35.8%
for Associate Professors, and 58.1% for Professors. In terms of gender, 29.3% of all female
Assistant Professors have salaries outside the range (as compares to 14.3% of males), while
32.5% of female Associate Professors (compared to 37.7% of their male counterparts), and
25.0% of female Professors (compared to 30.4% of their male counterparts) have salaries that
fall outside the expected ranges.
Univariate, Bivariate, and Multivariate Analyses
Other than salary, data on other aspects of the faculty including years of service, tenure, and
affiliated schools (disciplines) were also provided. This data was reviewed to identify any
potential relationships among the various demographic factors and, more specifically, as relates
to gender.
Tenure Status
Table 8 reflects the relationship of tenured to non-tenured faculty by both rank and gender.
Specifically, there are a total of 131 tenured faculty within the Assistant, Associate, and
Professor ranks. Of these 131 faculty, 46 (35.1%) are female. Additionally regarding gender,
the majority of both female (73.9%) and male (65.9%) tenured faculty are at the Associate
Professor rank.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 14
Table 8: Number and Percentage of Tenured and Non-Tenured Faculty, by Gender
Non-Tenured On Track
Rank
Female
Tenured
Male
Female
Male
#
%
#
%
#
%
#
%
Assistant Professor
37
86.0%
27
65.9%
4
8.7%
8
9.4%
Associate Professor
6
14.0%
13
31.7%
34
73.9%
56
65.9%
Professor
Totals:
0
0.0%
1
2.4%
8
17.4%
21
24.7%
43
100.0%
41
100.0%
46
100.0%
85
100.0%
Average Years of Service
Table 9 provides the average years of service by both rank and gender. At the Assistant and
Associate Professor ranks, females have fewer average years of service than their male
counterparts. At the Professor level, however, the average years of service for females is slightly
higher than for male Professors.
Table 9: Average Years of Service, by Rank and Gender
Rank
Average Years of Service
Female
Male
Assistant Professor
5.56
10.31
Associate Professor
9.50
11.96
Professor
19.25
17.96
Tenure and Average Years of Service
Non-tenured females at all faculty ranks averaged lower years of service than their male
counterparts. From a tenure perspective, female Professors averaged slightly higher average
years of service than their male counterparts. Table 10 provides a comparison by rank and
gender, of average years of service at both the tenured and non-tenured levels.
Table 10: Tenure and Average Years of Service, by Rank and Gender
Rank
Avg. Years of Service - Non-Tenured
Average Years of Service - Tenured
Female
Male
Female
Male
Assistant Professor
3.24
5.11
27.00
27.88
Associate Professor
1.67
7.15
10.88
13.07
Professor
0.00
1.00
19.25
18.43
Faculty by College (Discipline)
In reviewing the school/discipline in which faculty is associated with average salaries (Tables 11
and 12), female faculty are most concentrated in Business and Liberal Arts. From a salary
perspective, average salaries for females were also higher than their male counterparts in those
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 15
disciplines at all levels with the exception of Professor (the average female salary was less in the
Business discipline).
In the Engineering discipline, female faculty averaged lower salaries than their male counterparts
at all rank levels. Alternatively, in both the Health and Liberal disciplines, female faculty
averaged higher salaries than their male counterparts at all rank levels.
Table 11: MSU Faculty by College, Rank, and Gender
Faculty by College
Rank/School
Assistant Professor
Female
Male
#
%
#
%
41
100%
35
100%
Architecture
2
4.9%
6
17.1%
Business
11
26.8%
5
14.3%
Education
3
7.3%
1
2.9%
Engineering
2
4.9%
6
17.1%
Health
4
9.8%
1
2.9%
Liberal
13
31.7%
6
17.1%
SCMNS
4
9.8%
6
17.1%
Social Work
Associate Professor
2
4.9%
4
11.4%
40
100%
69
100%
Architecture
1
2.5%
2
2.9%
Business
7
17.5%
20
29.0%
Education
7
17.5%
4
5.8%
Engineering
3
7.5%
4
5.8%
Health
2
5.0%
1
1.4%
Liberal
16
40.0%
15
21.7%
SCMNS
2
5.0%
21
30.4%
2
5.0%
2
2.9%
Professor
Social Work
8
100%
23
100%
Business
2
25.0%
7
30.4%
Engineering
1
12.5%
5
21.7%
Health
1
12.5%
1
4.3%
Liberal
3
37.5%
5
21.7%
SCMNS
1
12.5%
5
21.7%
89
100%
127
100%
Total:
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 16
Table 12: Average Annual Salary by College (Discipline), Rank, and Gender
AVERAGE ANNUAL SALARY
College
Assistant Professor
Female
Assoc. Professor
Male
Female
Professor
Male
Female
Male
Architecture
$
76,588.50 $
66,956.83 $
60,060.00 $
76,353.00 $
-
$
Business
$
114,804.91 $
96,524.40 $
119,501.43 $
108,063.85 $
112,839.50 $
Education
$
67,364.67 $
61,105.00 $
74,418.00 $
85,438.00 $
-
Engineering
$
80,316.50 $
81,155.17 $
83,882.33 $
87,310.50 $
97,401.00 $
99,726.20
Health
$
72,951.00 $
70,045.00 $
85,598.50 $
76,622.00 $
119,647.00 $
91,610.00
Liberal
$
60,055.92 $
59,163.33 $
71,391.94 $
68,846.47 $
107,099.00 $
83,754.40
SCMNS
$
68,088.00 $
63,225.33 $
68,372.00 $
79,023.71 $
98,008.00 $
96,859.00
Social Work
$
63,690.00 $
64,676.00 $
79,316.50 $
63,336.00 $
-
$
$
120,160.71
-
-
Finally, in comparing student enrollment (2006 figures, depicted in Table 13 below9) to faculty
data, while there is a greater number of faculty, both male and female, in those
schools/disciplines in which there is higher enrollment, the impact of enrollment on average
salaries is not consistent. For example, in the Business discipline, female Assistant Professors
had an average salary of $114,804.91 as compared to an average salary of $60,055.92 for their
female counterparts in the Liberal discipline. (Note: 2006 figures are the most recent available
for this analysis – see footnote for sourcing.)
Table 13: MSU Enrollment by School/College, 2006
2006 Enrollment
School
Female
Male
Total
% Total
Business
840
695
1,535
25.2%
Education
418
186
604
9.9%
Engineering
213
522
735
12.1%
Health
70
20
90
1.5%
Liberal
1,184
673
1,857
30.5%
SCMNS
438
337
775
12.7%
Architecture
114
144
258
4.2%
Social Work
203
41
244
4.0%
3,480
2,618
6,098
Total:
100.0%
Regression Analysis - Overview
Regression analysis is used to study the relationship among variables; specifically, the impact of
independent, or explanatory, variables on a single dependent, or response, variable. In the case
of this study, the dependent variable is salary. Independent variables consisted of those factors
9
Information from
http://www.morgan.edu/administration/planning_and_information_technology/planning_instituti
onal_research_and_assessment/institutional_research/enrollment_data.html
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 17
considered most likely to have impact on faculty salaries, namely years of service, tenure status,
and the school in which the faculty member is affiliated. Regression explains the fit of the line
through the independent variables; the closer the fit, the greater the degree of explanation (i.e.,
how much of the dependent variable can be explained by the independent variable(s)).
While the output of regression analysis includes several statistics, those most frequently analyzed
(and reviewed for this study) are as follows:

R-Square: the “coefficient of determination,” which is always between 0 and 1, and
explains the percentage of variation of the response or independent variable(s) explained
by the regression line.

Adjusted R-Square: the R-Square value, adjusted for testing multiple variables.

Standard Error (of Estimate): indicates the typical error likely to be made using the
fitted value based on the regression line.

Coefficient: the numerical value of the constant (in this study, the constant is the
expected salary) and independent variables (either positive or negative numbers).

T-Value: ratio of the estimated coefficient to its standard error, indicating the number of
standard errors the regression coefficient is from 0. T-values are used for hypothesis
testing to determine if the regression coefficient belongs in the regression equation.
Where the t-value is <1, the Adjusted R-Square value will increase.

P-Value: the probability a value will be beyond the t-value.
Parsimony is a term that means explaining the most with the least. For example, if two
independent variables yield an R-Square value of 0.980 while the use of 10 independent
variables yield an R-Square value of 0.981, then the parsimony principle indicates that only the
two variables should be used.
Regression Analysis – Results
The analysis for the MSU salary study was conducted in a tiered manner, first looking at faculty
as a whole, followed by the three groups of Assistant Professors, Associate Professors, and
Professors. This tiering of the data was done based on the varied results from the univariate,
bivariate, and multivariate analyses conducted above. Additionally, the regression was done
initially as simple regression based on the relationship between gender and salary. Subsequently,
and based on the initial results, other factors were added to examine what relationship, if any,
existed between those additional factors and MSU faculty salaries.
In terms of overall findings, it was evident from the demographic data analysis that there were
wide disparities in salary data among the various faculty ranks and genders. Likewise, the
resulting regression analysis showed very high P-values in many instances, indicating that most
of the variables should be excluded. However, continuing the regression by adding other
independent variables ultimately increased adjusted R-squared values to more than 50% in most
instances. Although 50% is well below the threshold of what is normally considered explanative
(typically, adjusted R-squared values should be at the 85% level or higher), the results indicated
that the factors analyzed explained approximately half of the differences in salaries across the
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 18
organization. While the resulting formulas are of very little value in predicting salaries, they are
indicative that faculty salary structure is widely variable, and gender alone provides no
explanation as to salary variances.
As an initial regression, gender was analyzed (Table 13). In all but one instance (Associate
Professor rank), however, the adjusted R-square value was negative and, in general, the adjusted
R-square values were less than 2% for all ranks. This is indicative that gender was NOT
predictive of salary for any of the ranks. Based on these results, the resulting formula for a
female’s salary is as follows:

Assistant Professor: $71,299 + $7,994 = $79,293 (error margin of $5,094)

Associate Professor: $84,907 - $2,957 = $ 81,950 (error margin of $4,068)

Professor: $101,587 + $6,167 = $107,754 (error margin of $9,025)
In these formulas, the base salary, that ranges from $71,299 for Assistant Professors to $101,587
for Professors, is adjusted in a positive direction at both the Assistant Professor and Professor
ranks but is adjusted downwards at the Associate Professor rank. The error margins indicate the
variation for each rank, e.g., female Assistant Professor salaries range from $74,199 to 84,387
based on the resulting formula.
Table 14: Regression Results - Gender
Gender
Regression Results
R-Square
Adjusted R-Square
StErr of Estimate
Constant
GENDER
Std Error
t-Stat
P-Value
Rank
Assistant Associate
All
Professor Professor
0.000959665 0.0322058 0.0049616
-0.003708748 0.0191275 -0.0044256
23067.1366 22134.969 20414.311
Coefficients
84491.17323 71299.429 84907.059
-1445.768734 7993.7666 -2957.3588
3188.773698 5094.0096 4067.824
-0.453393333 1.5692484 -0.7270125
0.650724937 0.1208565 0.4688209
Professor
0.0164037
-0.0187248
21859.707
101586.77
6167.2273
9025.0294
0.6833471
0.5000036
Subsequently, the regression independent variables were expanded to include both gender and
tenure. Results are provided in Table 15.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 19
Table 15: Regression Results - Gender and Tenure
Gender and Tenure
Regression Results
R-Square
Adjusted R-Square
StErr of Estimate
Constant
GENDER
Std Error
t-Stat
P-Value
TENURE
Std Error
t-Stat
P-Value
Rank
Assistant Associate
All
Professor Professor
0.029946984 0.0492233 0.0179135
0.020838505 0.0231747 -0.0007929
22783.31927 22089.257 20377.362
Coefficients
79068.74951 73144.772 79998.862
-210.760662 6936.0695 -3207.2306
3187.354724 5167.0187 4066.0097
-0.066124006 1.3423736 -0.7887907
0.947341121 0.1836347 0.4320117
8101.738965 -8073.3788 6068.3157
3211.309413 7062.9377 5156.8091
2.522877096 -1.1430624 1.1767579
0.01237039 0.2567476 0.241953
Professor
0.2143681
0.1561731
19894.937
136573
9665.85
8322.6494
1.161391
0.2556478
-38484.85
14754.48
-2.6083501
0.0146471
Notable from this analysis is that the rank of Professor results in an adjusted R-square value of
15.6%, indicating that the factors of gender and tenure explain 15.6% of a Professor’s salary.
While tenure is still of very little value in predicting salary, it does have some significance at the
Professor level; however, the relationship to salary is negative. Without considering the
applicable error margins, for a female with tenure, the resulting salary prediction formulas are as
follows:

Assistant Professor: $73,145 + $6,936 – $8,073 = $72,008

Associate Professor: $79,999 - $3,207 +$6,068 =$82,860

Professor: $136,573 + $9,666 - $38,485 = $107,754
Years of Service was added as a variable for the third regression analysis. Consistent with the
findings for gender and tenure, this variable had minimal impact on salary. For Assistant
Professor and Professor ranks, it actually decreased the R-squared values. Table 16 provides the
overall results.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 20
Table 16: Regression Results - Gender, Tenure, and Years of Service
Gender, Tenure, Years of Service
Regression Results
R-Square
Adjusted R-Square
StErr of Estimate
Constant
GENDER
Std Error
t-Stat
P-Value
TENURE
Std Error
t-Stat
P-Value
YRS_SRVC
Std Error
t-Stat
P-Value
Rank
Assistant Associate
All
Professor Professor
0.036188675 0.0530346 0.0438073
0.022549835 0.0135777 0.0162248
22763.40078 22197.501 20203.368
Coefficients
80260.66903 74070.329 82376.42
-720.4492726 6618.8586 -4274.5954
3214.13953 5225.6693 4081.1561
-0.224149968 1.266605 -1.0473982
0.822856484 0.2093788 0.2973441
10180.27458 -3814.9951 8733.7587
3666.235459 10627.939 5353.7958
2.776765075 -0.3589591 1.6313208
0.005981948 0.7206762 0.1058473
-205.1590405 -184.10406 -376.4021
175.0925312 342.00235 224.29012
-1.171717829 -0.5383123 -1.678193
0.242625508 0.5920218 0.0963121
Professor
0.2392196
0.1514372
19950.689
129903.77
9563.2464
8346.7144
1.1457498
0.2623353
-41588.608
15174.288
-2.7407289
0.010936
513.01791
556.67189
0.9215804
0.3652204
The faculty member’s school/college was added as a variable in the final regression, the results
of which are shown in Table 17. This variable increased the adjusted R-square value for both
Assistant and Associate Professor to 66.7% and 56.2%, respectively; meaning that 66.7% of
Assistant Professor salaries and 56.2% of Associate Professor salaries can be explained by the
variables of gender, tenure, years of service, and school. At the Professor level, however, the
adjusted R-square value decreased to 8.3%. Overall, coefficients for the various
schools/disciplines resulted in both positive and negative values, ranging from a premium of
$29,701 for faculty in the School of Business to a reduction of -$13,622 for faculty in the School
of Social Work. Using the base salary (Constant) of $79,047, faculty in the School of Business,
notwithstanding the factors of gender, tenure, and years of service, would earn $108,748
($79,047 + $29,701), while faculty in the School of Social Work would earn $65,425 ($79,047 $13,622).
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 21
Table 17: Regression Results - Gender, Tenure, Years of Service, and School
Gender, Tenure, Service, and School
Regression Results
R-Square
Adjusted R-Square
StErr of Estimate
Constant
GENDER
Std Error
t-Stat
P-Value
TENURE
Std Error
t-Stat
P-Value
YRS_SRVC
Std Error
t-Stat
P-Value
SCHOOL:Business
SCHOOL:Education
SCHOOL:Engineering
SCHOOL:Health
SCHOOL:Liberal
SCHOOL:SCMNS
SCHOOL:Social Work
SCHOOL:Architecture
Rank
Assistant Associate
All
Professor Professor
0.544491001 0.724947 0.6120955
0.517393001 0.6672466 0.561796
15914.04614 12590.842 13324.297
Coefficients
79047.2931 63607.792 81568.593
1335.604653 3686.9178 1403.3726
2375.128864
3306.94 2917.4124
0.562329343 1.1149031 0.4810333
0.574506019 0.2689972 0.6315761
7565.144291 -3386.1046 2219.7401
2628.277449 6218.2101 3851.9997
2.87836594 -0.5445465 0.5762566
0.00442108 0.5879288 0.5657765
-242.3606523 -266.96234 -302.51164
124.4700262 203.70871 154.92468
-1.947140687 -1.3105102 -1.9526368
0.052883829 0.1946352 0.0537451
29701.16798 45753.666 29891.02
-6338.021698 5011.8776 -4330.3208
7605.733934 19275.273 5427.9408
0 6987.1083
0
-12240.2418 -3507.8042 -10204.931
-3482.398162 4013.5621 -1174.0881
-13621.55531
0 -10037.485
-10693.84014 5536.005 -10914.031
Professor
0.4423183
0.083056
18569.352
132313.69
11189.779
8034.8923
1.3926483
0.1776424
-36927.419
14677.878
-2.5158555
0.0196732
357.48776
546.04037
0.6546911
0.5194534
9249.2053
0
-10025.265
0
-15723.076
-6933.419
0
0
Transactional Data Analysis
Subsequent to the analysis of demographic data, an analysis of the transactional data was
undertaken to look for other trends or relationships evident between salaries and gender (female)
and to determine if additional regression analyses was merited. Although there were
significantly more data provided for transactional events, it encompassed only a three-year span
(from 2012 to 2014).
Table 18 depicts new hires during 2012 and 2013 by rank, gender, and average salary. Females
constituted more than 58% of all new hires, with 83% hired at the Assistant Professor level and
17% at the Associate Professor level. Males constituted approximately 42% of new hires, with
71% hired at the Assistant Professor level and 24% at the Associate Professor level. An equal
number of males and females were hired at the Associate Professor level, and the average salary
for males was less than 1% higher than their female counterparts at this level.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 22
Table 18: Number and Average Salaries of New Hires, by Rank and Gender
New Hires
Rank
Female
#
Assistant Professor
Associate Professor
Professor
Total/Average:
Male
Avg. Salary
20
4
0
$
$
$
24
$
#
83,830.90
78,715.75
81,273.33
Avg. Salary
12
4
1
$
$
$
69,503.75
78,874.25
115,566.00
17
$
87,981.33
In terms of starting salaries, female Assistant Professor new hires earned on average 20.61%
more than their male counterparts. At the Associate Professor level, however, females earned
slightly less (0.20%) than their male counterparts.
Table 19 below provides a breakout of these promotions by gender. As a second analysis, and
from a promotions perspective, while there were relatively few promotions in 2012 and 2013 (6
and 17 respectively), females accounted for more promotions at the Assistant Professor and
Associate Professor levels in 2012 and 2013. Of the total seven promotions from Associate
Professor to Professor in 2013, two (22.2%) were for female faculty and five (62.5%) were for
male faculty. The only promotion from Associate Professor to Professor in 2012 was for a
female faculty member.
In reviewing average salary increases due to promotion as a percentage of the average salaries
for each level, the average increase at the Associate Professor level for female faculty was 1.4%
and for male faculty was 2.2%. At the Professor level, the average salary increase associated
with a promotion was 2.6% for female faculty and 2.5% for male faculty. This does not vary
from a noteworthy perspective from either national or regional comparison.
Table 19: Number of Promotions by Gender and Effective Date (Year)
2012
Rank
2013
Female
Male
Female
Male
#
%
#
%
#
%
#
%
Assistant Professor
2
50.0%
1
50.0%
1
11.1%
0
0.0%
Associate Professor
1
25.0%
1
50.0%
6
66.7%
3
37.5%
Professor
1
25.0%
0
0.0%
2
22.2%
5
62.5%
4
100.0%
2
100.0%
9
100.0%
8
100.0%
As a third and final analysis, other salary adjustments were reviewed. These adjustments
included variables other than promotion, i.e., cost-of-living adjustments (COLAs), merit raises,
and pay increases. In general, fewer female faculty were granted increases than male faculty.
Specifically, female faculty accounted for 196, or 42.2%, of all COLA increases, while males
accounted for 269, or 57.9%. In terms of merit increases, females accounted for 75, or 41%,
while males accounted for 108, or 59%. In reviewing average increases, female faculty received
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 23
slightly higher COLA adjustments at the Assistant Professor level by a full percentage point
(3.4% female versus 2.4% male), but COLA increases at the Associate Professor level were
more than a full percentage point less (2.4% female versus 3.6% male). At the Professor level,
female faculty received greater salary increases for COLAs (5.1% female versus 2.4% male), as
well as merit increases (3.4% female versus 3.0% male). Table 20 provides further detail on
these results.
Table 20: Adjustments by Type, Number, and Average % Increase
ADJUSTMENT TYPE - NUMBER AND AVERAGE %
COLA
Rank
Merit Raise
Female
#
Avg. %
Avg. %
Male
Avg. %
#
Avg. %
#
Avg. %
74
2.4%
33
2.5%
25
2.8%
-
-
-
-
Associate Professor
82
2.4%
146
3.6%
34
3.0%
62
2.9%
-
-
-
-
Professor
20
5.1%
49
2.4%
8
3.4%
21
3.0%
2.9%
-
-
75
#
Female
3.4%
269
#
Pay Increase
Male
94
196
#
Female
Assistant Professor
Totals:
Avg. %
Male
108
1
1
Summary of Results
This study was undertaken specifically to examine gender disparities in the area of faculty
salaries, specifically at the levels of Assistant Professor, Associate Professor, and Professor.
Based on a review and analysis of the data, the following provides a summary of results:

While there are significant disparities in salaries among MSU faculty, there is no evidence
that salary disparities are based on gender bias. In performing regression analysis based only
on salary and gender, the adjusted R-square was both negative and less than 0.5%, which
indicates no relationship between gender and salary exists.

In comparing MSU faculty with averages at the national level, while female faculty at the
Assistant Professor level average salaries significantly higher than the national average, this
gap does not continue at the Associate Professor and Professor ranks. However, the salary
disparity between male and female faculty at the higher levels, and in particular the Professor
level, is less than the gap at the national average level.

At a regional level, while data specifically on gender is not available, MSU faculty salaries
were generally comparable to their regional counterparts for those with master’s degrees.
For faculty holding doctorate degrees, however, average salaries were less at both the
Associate Professor and Professor ranks.

While the regression analysis did not show any meaningful relationships among gender,
tenure, years of service, and salary, it did reveal there may be some significance in the
school/college/discipline a faculty member is associated with and salary. The relationship,
however, is negative for certain schools, i.e., liberal arts. In comparing student enrollment
with faculty ranks, the area of liberal arts represented the highest number of students enrolled
yet the average faculty salaries, both male and female, were less than average salaries, and in
most cases significantly so. This suggests that salary differences may be more influenced by
market forces, i.e., the supply of faculty in various disciplines, than by other demographic
factors.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 24

Reviewing other data, including new hire and promotions data, revealed some interesting
relationships among gender and salaries. While female faculty receives significantly higher
salaries at the Assistant Professor level, this gap is eliminated at the Associate Professor and
Professor levels. Female faculty accounted for more promotions in 2013 (9 female
promotions versus 8 male promotions), although average salary increases due to promotions
were slightly less for females at the Associate Professor level (1.4% females versus 2.2%
males) and slightly higher at the Professor level (2.6% females versus 2.5% males).
Since the regression analysis performed reflected that the school/college/discipline in which
faculty is associated has some significance on salary, areas of further analysis may include:

Comparison of average faculty salaries by degree (master’s, doctorate) and discipline
area (e.g., business, liberal arts) to estimates of actual and future employability of those
job markets, including predicted expansions, contractions, and changes in overall skill
sets and knowledge areas.

Comparison of academic disciplines (e.g., science, engineering) in which females have
historically been under-represented with current faculty.

Comparison of academic disciplines (e.g., health, liberal arts), in which females have
historically been the dominant gender with current faculty.
There is some evidence provided in literature reviews, both anecdotal and peer-reviewed, that
suggest gender biases exist in certain occupations, which relate to educational disciplines that
have traditionally been male dominated.10 Additionally, certain female-dominated occupations
have been historically devalued, resulting in lower overall salaries. Additional analysis in this
area may reveal useful information on economic and market forces and their influence on gender
salaries.
10
See: Mangan, K. Despite Efforts to Close Gender Gaps, Some Disciplines Remain Lopsided. The Chronicle of
Higher Education. October 29, 2012. Found at: https://chronicle.com/article/In-Terms-of-Gender/135304/ and
The Simple Truth About Gender Pay Gap. American Association of University Women (AAUW), March 10, 2014.
Found at: http://www.aauw.org/research/the-simple-truth-about-the-gender-pay-gap/
Bellas, ML. Disciplinary Differences in Faculty Salaries: Does Gender Play a Role? The Journal of Higher Education,
Vol. 68, No 3.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 25
Recommendations
As indicated in the summary of results, the study did not identify any MSU policies or practices
that would influence gender-based salary decisions.
a)
b)
c)
d)
e)
Engage faculty in a facilitated discussion to surface specific points of contention
regarding gender-based pay perceptions and identify solutions to address these
perceptions. Small focus groups will allow MSU to constructively engage faculty in
identify issues for further exploration and appropriate solutions.
Conduct briefings on this report at appropriate staff briefings to leverage the findings
regarding gender-based salary determinations.
Design a process to compare faculty salaries to appropriate external sources on a fixed
basis to ensure competitiveness and equity in salary determinations.
Establish a committee to update and align the MSU Board of Regents’ Policies and
Procedures on Appointment, Promotion and Tenure and the MSU Faculty Handbook to
create a “one-stop” information source for decision-makers and faculty members
regarding pay, performance, and other resource management issues.
Provide annual updates on current MSU compensation policies and procedures to all
faculty and staff who are involved in the compensation process.
Since the performed regression analysis reflected that the school/college/discipline in which
faculty is associated has some significance on salary, areas of further analysis may include and
are recommended:

Comparison of average faculty salaries by degree (masters, doctorate) and discipline area
(e.g., business, liberal arts) to estimates of actual and future employability of those job
markets, including predicted expansions, contractions, and changes in overall skill sets
and knowledge areas.

Comparison of academic disciplines (e.g., science, engineering) in which females have
historically been under-represented with current faculty.

Comparison of academic disciplines (e.g., health, liberal arts) in which females have
historically been the dominant gender with current faculty.
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 26
Salary Compression
While a review of salary compression is beyond the scope of this study, MSU has expressed an
interest in understanding whether salary compression exists and its potential impact on MSU
faculty salaries. The following information on salary compression is provided at the request of
the University.
Salary compression occurs when there is only a small difference in pay between employees
regardless of their skills or experience. It is the result of the market rate for a given job outpacing
the increases historically given by the organization to high-tenure employees. Therefore,
newcomers can only be recruited by offering them as much or more than senior professionals. 11
According to an article in Inside Higher Ed12, it is difficult to tell how widespread a problem
salary compression is for two primary reasons:


Pay for existing faculty tends to increase over time at the rate of inflation.
Pay for new faculty is usually negotiated based on the market rate.
While salary compression can be a faculty morale issue that should be addressed, market realities
seem to create and sustain a certain amount of compression.
When addressing salary compression, an institution must thoughtfully identify solutions that can
be implemented within the existing culture and any budgetary constraints.
11
Retrieved online August 1, 2014 from http://definitions.uslegal.com/p/pay-compression/.
See: Flaherty, C. Decompressing Salaries. Insider Higher Ed. February 11, 2013. Found at
http://www.insidehighered.com/news/2013/02/11/university-tries-deal-salary-compression-among-facultymembers.
12
MSU Gender-Focused Salary Analysis, Pivotal Practices Consulting LLC | Page 27
Appendix A: References
U.S. Department of Education, National Center for Education Statistics. (2013). Digest of
Education Statistics, 2012 (NCES 2014-015), Chapter 2. Found at:
http://nces.ed.gov/fastfacts/display.asp?id=84
National Center for Education Statistics (NCES), data from the Integrated Postsecondary
Education Data System (IPEDS), 2009 through 2011. Found at:
http://nces.ed.gov/programs/stateprofiles/sresult.asp?mode=short&s1=24
Wooten, B. H. Gender Differences in Occupational Occupations. US Department of Census,
Bureau of Labor Statistics (BLS), Monthly Labor Review. April 1997. Found at:
http://stats.bls.gov/OPUB/MLR/1997/04/art2full.pdf
NCES, Digest of Education Statistics, Full-time instructional faculty in degree-granting
institutions, by race/ethnicity, sex, and academic rank: Fall 2007, Fall 2009, and Fall 2011.
Found at: http://nces.ed.gov/programs/digest/d12/tables/dt12_291.asp
MSU 2006 Enrollment by School/College, found at:
http://www.morgan.edu/administration/planning_and_information_technology/planning_instituti
onal_research_and_assessment/institutional_research/enrollment_data.html
Mangan, K. Despite Efforts to Close Gender Gaps, Some Disciplines Remain Lopsided. The
Chronicle of Higher Education. October 29, 2012. Found at: https://chronicle.com/article/InTerms-of-Gender/135304/
The Simple Truth About Gender Pay Gap. American Association of University Women
(AAUW), March 10, 2014. Found at: http://www.aauw.org/research/the-simple-truth-about-thegender-pay-gap/
Bellas, ML. Disciplinary Differences in Faculty Salaries: Does Gender Play a Role? The
Journal of Higher Education, Vol. 68, No 3.
Page | A-1
Appendix B: Faculty and Staff Recommendations for Process Improvement
Q9. What recommendations do you have for improving the pay setting process?

The process should be reviewed more often to keep pay current and competitive.

In a market-driven environment, new faculty salaries are higher than more senior faculty who have
served the University for many years. Periodic reviews are needed for salary increases and equity.
Reviews for salary equity should consider faculty contributions in various areas.

Require chairs to more consistently link their grading of their faculty member's performance with a
merit pay recommendation so that there is not a wide variance between the merit pay
recommendations for faculty members with the same (or nearly the same) performance ratings.

Accept the practice used by the School of Business. Other disciplines may have similar databases,
which need to be used justifiably.

The dean should be able to set the salary offer and not the Provost who is far removed from the
discipline or uses websites like "salary.com" as his basis. Most of the salaries in that website are not
geared towards academic positions.

Establishing a process for salary setting within the University that allows for variation across
disciplines/schools.

Providing sufficient time for faculty to complete annual reports and Chairs to make evaluations for
use in the merit process.

I would like to become more familiar with the salary settings across disciplines.

Establishing a pay range for each faculty rank based on data that is pertinent to the discipline;
accepting the recommendation of the Dean who is more knowledgeable about salaries that those who
make the decision.

Should be based on fairness and equity.

Develop a way to "catch up" salaries of existing personnel following a period of salary freezes.

There needs to be something other than merit, which can bring all well performing employee salaries
up to competitive levels. In our area, faculty salaries are typically 20% below the national norm. Also
the admin salaries need to be increased to expect courteous good quality output. These salaries should
also be determined by the direct supervisor. Better coordination of the availability of the information
used to determine pay increases (FAR data in a usable form).

There does need to be a published salary range that is based on rank, years in service, and broad
disciplinary engagement. The disparity in salary ranges in the College has very little logic, especially
given the disparity in rank and experience. I think that we need to have a salary range based on rank
that is university wide first, and then disciplinary focused second. For example, all full professors
should have a range of $85,000 - $110,000 (a guestimate), and then consider disciplines. The salary
should take into account publications/creativity, grant writing and fund raising, disciplinary
Page |B-1
participation and service, major administrative service, and community service and engagement.
There should be a published standard for each rank.

It would be useful if the response to the recommendations made by the Deans and Chairs were
relay[ed] back to them.

No comment.

I do not know what the pay setting process is. More transparency in the process is surely desirable.

Gender should not have an impact on salary.

It should involve the faculty and the chairperson and the Dean. Once the recommendation is done, it
should not be changed in the Office of the Provost. In the most recent merit increase, an individual
who was given a zero increase at the departmental level for lack of productivity in research and
teaching effectiveness ended up with a 5% increase when the final merit increases were announced.
This shouldn't have happened that way. Thanks.

White women at Morgan State University appear targeted for the lowest salaries. Within my school,
the lowest paid individuals if compared relative to the criteria of 1) length of service; 2) prior
experience in field: 3) quality of educational preparation; 4) distinctions such as service as Senior
Fulbright Scholars (for example) are the lowest paid. Then next lowest paid cohort is the white male
registered architect, because the university appears to only give "lip service", or false assertions of
parity between professional degrees, professional registration, and academic terminal degrees such as
the Ph.D. The university administration does not have the knowledge that architecture is not the same
as engineering in terms of final degrees, and other factors. My recommendation is that for
professional schools and fields, the university should look at national standards of pay setting for
professionals within universities. Next, existing and dramatic discrepancies between individual
salaries should be resolved. White women are the most dramatically underpaid, based on my
knowledge of salaries at my school.

Give the form earlier in the process.

None.

Base salary scales on rank and similar other to state institutions.

Criteria should be applied and shared among administration and possibly include the faculty as
needed. Transparency at all stages would be helpful.
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page B-2
Appendix C: Regression Analysis Summary Output Tables
GENDER
ALL
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.030978455
R Square
0.000959665
Adjusted R Square
-0.00370875
Standard Error
23067.1366
Observations
21
6
ANOV
A
df
SS
MS
F
Significance F
Regression
1 109379928.1 109379928.1 0.205565514 0.650724937
Residual
21 1.13868E+11 532092790.8
4
Tota
21
1.13977E+11
l
5
Intercept
GENDER
Coefficients Standard Error
t Stat
P-value
Lower 95% Upper 95%
84491.17323 2046.877381 41.27808241 7.1457E-104 80456.55018 88525.79628
-1445.76873 3188.773698 -0.45339333 0.650724937 -7731.19647
4839.659
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$ 83,045.40
$ 84,491.17
-1.7111%
ASSISTANT PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.179459866
R Square
0.032205844
Adjusted R Square
0.019127544
Standard Error
22134.96875
Observations
76
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
SS
MS
F
Significance F
1 1206538628 1206538628 2.462540629 0.120856482
74 36256806261 489956841.4
75 37463344889
Coefficients Standard Error
t Stat
P-value
Lower 95% Upper 95%
71299.42857 3741.492603 19.05641308 2.90625E-30 63844.34182 78754.51532
7993.766551 5094.009595 1.569248428 0.120856482 -2156.26913 18143.80223
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$ 79,293.20
$ 71,299.43
11.2%
Page |C-1
ASSOCIATE PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.070438299
R Square
0.004961554
Adjusted R Square
-0.0044256
Standard Error
20414.31131
Observations
108
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
SS
MS
F
Significance F
1 220268904.6 220268904.6 0.528547138 0.468820891
106 44174875244 416744106.1
107 44395144149
Coefficients Standard Error
t Stat
P-value
Lower 95% Upper 95%
84907.05882 2475.598876 34.29758336 3.34166E-59 79998.94327 89815.17438
-2957.35882 4067.824043 -0.72701247 0.468820891 -11022.2156 5107.497986
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$ 81,949.70
$ 84,907.06
-3.5%
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$107,754.00
$101,586.77
6.1%
PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.128076885
R Square
0.016403689
Adjusted R Square
-0.01872475
Standard Error
21859.70677
Observations
30
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
SS
MS
F
Significance F
1 223136861.1 223136861.1 0.466963199
0.5000036
28 13379709842 477846780.1
29 13602846703
Coefficients Standard Error
t Stat
P-value
Lower 95% Upper 95%
101586.7727 4660.505143 21.79737381 4.19578E-19 92040.16071 111133.3847
6167.227273 9025.029401 0.683347056
0.5000036 -12319.7074 24654.16195
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-2
GENDER AND TENURE
ALL
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.173051969
R Square
0.029946984
Adjusted R Square
0.020838505
Standard Error
22783.31927
Observations
216
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
2
213
215
SS
3413274497
1.10564E+11
1.13977E+11
MS
1706637248
519079636.9
Coefficients Standard Error
t Stat
79068.74951 2950.718302 26.79644121
-210.760662 3187.354724 -0.066124006
8101.738965 3211.309413 2.522877096
F
Significance F
3.287813906 0.039238969
P-value
Lower 95%
Upper 95%
3.71443E-70 73252.40016 84885.09886
0.947341121 -6493.559184
6072.03786
0.01237039 1771.721825 14431.75611
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
86,959.73
87,170.49
-0.2%
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
72,007.46
65,071.39
10.7%
ASSISTANT PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.221863328
R Square
0.049223336
Adjusted R Square
0.02317466
Standard Error
22089.25669
Observations
76
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
2
73
75
SS
1844070821
35619274069
37463344889
MS
922035410.4
487935261.2
Coefficients Standard Error
t Stat
73144.7723 4067.830972
17.9812713
6936.069535 5167.018688 1.342373611
-8073.378812 7062.937656 -1.143062449
F
Significance F
1.889667511 0.158440334
P-value
Lower 95%
Upper 95%
1.56337E-28 65037.59686 81251.94773
0.183634698 -3361.783858 17233.92293
0.256747577 -22149.79326 6003.035637
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-3
ASSOCIATE PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.133841284
R Square
0.017913489
Adjusted R Square
-0.00079292
Standard Error
20377.3619
Observations
108
ANOVA
df
SS
795271943.6
43599872205
44395144149
MS
397635971.8
415236878.1
F
Significance F
0.957612372 0.387134305
Coefficients Standard Error
79998.86231 4848.013652
-3207.230646 4066.009661
6068.31569 5156.809099
t Stat
16.50136903
-0.78879071
1.17675787
P-value
Lower 95%
Upper 95%
6.25537E-31 70386.14679 89611.57783
0.432011732 -11269.37671 4854.915414
0.24195296 -4156.684155 16293.31554
Regression
Residual
Total
Intercept
GENDER
TENURE
2
105
107
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
82,859.95
86,067.18
-3.7%
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
107,754.00
98,088.15
9.9%
PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.462999018
R Square
0.214368091
Adjusted R Square
0.156173135
Standard Error
19894.9374
Observations
30
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
2
27
29
SS
2916016278
10686830425
13602846703
MS
1458008139
395808534.2
Coefficients Standard Error
t Stat
136573 14067.84515 9.708167709
9665.85 8322.649427 1.161390983
-38484.85 14754.48047 -2.608350059
F
Significance F
3.683619763 0.038499762
P-value
Lower 95%
Upper 95%
2.67006E-10
107708.166
165437.834
0.255647795 -7410.816073 26742.51607
0.014647073 -68758.54328 -8211.156724
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-4
GENDER, TENURE, YEARS OF SERVICE
ALL
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.190233211
R Square
0.036188675
Adjusted R Square
0.022549835
Standard Error
22763.40078
Observations
216
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SS
MS
F
Significance F
3 4124685153 1374895051 2.653354387 0.049601093
212 1.09853E+11 518172415.1
215 1.13977E+11
Coefficients Standard Error
80260.66903 3118.701768
-720.4492726 3214.13953
10180.27458 3666.235459
-205.1590405 175.0925312
t Stat
P-value
Lower 95%
25.73528186 3.74332E-67 74113.03105
-0.224149968 0.822856484 -7056.215794
2.776765075 0.005981948 2953.32887
-1.171717829 0.242625508 -550.3044167
Upper 95%
86408.30701
5615.317249
17407.22029
139.9863357
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
89,515.34
90,235.78
-0.8%
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
76,690.09
70,071.23
9.4%
ASSISTANT PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.230292445
R Square
0.05303461
Adjusted R Square
0.013577719
Standard Error
22197.5008
Observations
76
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SS
MS
F
Significance F
3 1986853897 662284632.3 1.344115277 0.266865364
72 35476490992 492729041.6
75 37463344889
Coefficients Standard Error
74070.32938 4434.641668
6618.858601 5225.66925
-3814.99515 10627.93925
-184.1040639 342.0023484
t Stat
P-value
Lower 95%
Upper 95%
16.70266392 1.7946E-26 65230.03278 82910.62597
1.266604962 0.209378815 -3798.322661 17036.03986
-0.358959066 0.720676224 -25001.40483 17371.41453
-0.53831228 0.592021804 -865.8732852 497.6651574
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-5
ASSOCIATE PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.209301923
R Square
0.043807295
Adjusted R Square
0.016224813
Standard Error
20203.36819
Observations
108
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SS
MS
F
Significance F
3 1944831169 648277056.4 1.588228899 0.19670351
104 42450312980 408176086.3
107 44395144149
Coefficients Standard Error
82376.42049 5011.060303
-4274.595419 4081.156052
8733.758733 5353.795849
-376.4021044 224.2901213
t Stat
P-value
Lower 95%
Upper 95%
16.43892021 1.10549E-30 72439.29986 92313.54112
-1.04739818 0.297344084 -12367.68102 3818.490183
1.631320838 0.105847304 -1883.019345 19350.53681
-1.678192968 0.096312066 -821.1778318 68.37362305
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
86,459.18
90,733.78
-4.7%
Expected Salary-Female:
Expected Salary-Male:
Variance (%)
$
$
98,391.42
88,828.18
10.8%
PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.489100789
R Square
0.239219582
Adjusted R Square
0.151437226
Standard Error
19950.68864
Observations
30
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SS
MS
F
Significance F
3 3254067299 1084689100 2.725144241 0.064625636
26 10348779404 398029977.1
29 13602846703
Coefficients Standard Error
129903.7672 15855.1353
9563.246419 8346.714403
-41588.60833 15174.28751
513.0179065 556.6718871
t Stat
P-value
Lower 95%
8.193166743 1.12759E-08 97313.06986
1.145749807 0.26233531 -7593.670754
-2.740728901 0.010935964 -72779.80303
0.921580411 0.365220435 -631.2375452
Upper 95%
162494.4646
26720.16359
-10397.41364
1657.273358
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-6
GENDER, TENURE, YEARS OF SERVICE, SCHOOL
ALL
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.73789633
R Square
0.544491
Adjusted R Square
0.517393
Standard Error
15914.0461
Observations
216
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SCHOOL:Business
SCHOOL:Education
SCHOOL:Engineering
SCHOOL:Health
SCHOOL:Liberal
SCHOOL:SCMNS
SCHOOL:Social Work
SCHOOL:Architecture
SS
MS
F
Significance F
11 6.206E+10 5641779995 24.5045994 9.54596E-32
205 5.1918E+10 253256864
216 1.1398E+11
CoefficientsStandard Error
79047.2931 5502.11228
1335.60465 2375.12886
7565.14429 2628.27745
-242.36065 124.470026
29701.168 5565.07085
-6338.0217 6502.29576
7605.73393 6213.5434
0
0
-12240.242 5486.92445
-3482.3982 5796.07018
-13621.555 7176.4659
-10693.84 7061.41841
t Stat
14.3667176
0.56232934
2.87836594
-1.9471407
5.33706915
-0.974736
1.22405743
65535
-2.230802
-0.6008206
-1.8980868
-1.514404
P-value
7.6852E-33
0.57450602
0.00442108
0.05288383
2.4931E-07
0.33084002
0.22233548
#NUM!
#NUM!
0.54862321
0.05908962
0.13146369
Lower 95%
68199.30927
-3347.207726
2383.223275
-487.7661915
18729.05481
-19157.97077
-4644.909966
0
-23058.28124
-14909.95041
-27770.70059
-24616.15739
Upper 95% Lower 95.0% Upper 95.0%
89895.2769 68199.3093 89895.2769
6018.41703 -3347.2077 6018.41703
12747.0653 2383.22327 12747.0653
3.04488686 -487.76619 3.04488686
40673.2811 18729.0548 40673.2811
6481.92737 -19157.971 6481.92737
19856.3778
-4644.91 19856.3778
0
0
0
-1422.2024 -23058.281 -1422.2024
7945.15409 -14909.95 7945.15409
527.589969 -27770.701 527.589969
3228.47711 -24616.157 3228.47711
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-7
ASSISTANT PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.85143821
R Square
0.72494702
Adjusted R Square
0.66724656
Standard Error
12590.842
Observations
76
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SCHOOL:Business
SCHOOL:Education
SCHOOL:Engineering
SCHOOL:Health
SCHOOL:Liberal
SCHOOL:SCMNS
SCHOOL:Social Work
SCHOOL:Architecture
SS
MS
11 2.7159E+10 2468994574
65 1.0304E+10 158529301
76 3.7463E+10
CoefficientsStandard Error
63607.7917 5282.52662
3686.91784 3306.94004
-3386.1046 6218.21009
-266.96234 203.708708
45753.6659 6256.09876
5011.87761 8669.36718
19275.2733 6876.76101
6987.10834 7812.49919
-3507.8042 6133.70317
4013.56207 6660.43018
0
0
5536.005 6806.30161
t Stat
12.0411682
1.11490314
-0.5445465
-1.3105102
7.31345007
0.57811343
2.80295814
0.89434996
-0.5718901
0.60259802
65535
0.81336463
F
Significance F
17.131811 3.90152E-15
P-value
3.3085E-18
0.2689972
0.58792883
0.19463516
4.8351E-10
0.56518512
0.00666697
0.37443441
0.56936898
0.54887277
#NUM!
#NUM!
Lower 95%
53057.8575
-2917.497469
-15804.72772
-673.7967203
33259.37393
-12302.04423
5541.433173
-8615.529948
-15757.65532
-9288.235529
0
-8057.117961
Upper 95% Lower 95.0% Upper 95.0%
74157.7258 53057.8575 74157.7258
10291.3331 -2917.4975 10291.3331
9032.51845 -15804.728 9032.51845
139.872044 -673.79672 139.872044
58247.9579 33259.3739 58247.9579
22325.7994 -12302.044 22325.7994
33009.1134 5541.43317 33009.1134
22589.7466 -8615.5299 22589.7466
8742.04693 -15757.655 8742.04693
17315.3597 -9288.2355 17315.3597
0
0
0
19129.128 -8057.118 19129.128
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-8
ASSOCIATE PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.7823653
R Square
0.61209546
Adjusted R Square
0.56179603
Standard Error
13324.2968
Observations
108
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SCHOOL:Business
SCHOOL:Education
SCHOOL:Engineering
SCHOOL:Health
SCHOOL:Liberal
SCHOOL:SCMNS
SCHOOL:Social Work
SCHOOL:Architecture
SS
MS
F
Significance F
11 2.7174E+10 2470369662 15.3061524 9.5344E-17
97 1.7221E+10 177536885
108 4.4395E+10
CoefficientsStandard Error
81568.5929 8689.79209
1403.37264 2917.41243
2219.74011 3851.99974
-302.51164 154.92468
29891.0201 8232.46224
-4330.3208 8918.26623
5427.94083 9378.95754
0
0
-10204.931 8133.17504
-1174.0881 8443.13494
-10037.485 10234.4216
-10914.031 10922.5615
t Stat
9.38671399
0.48103334
0.57625656
-1.9526368
3.63087242
-0.4855563
0.57873605
65535
-1.2547291
-0.1390583
-0.9807575
-0.999219
P-value
2.8312E-15
0.6315761
0.56577651
0.05374512
0.0004534
0.62837671
0.56410871
#NUM!
#NUM!
0.88969237
0.32915222
0.32017479
Lower 95%
64321.76108
-4386.883447
-5425.413085
-609.9942415
13551.86139
-22030.61055
-13186.69366
0
-26347.03256
-17931.3738
-30349.97924
-32592.29197
Upper 95% Lower 95.0% Upper 95.0%
98815.4248 64321.7611 98815.4248
7193.62873 -4386.8834 7193.62873
9864.89331 -5425.4131 9864.89331
4.97096693 -609.99424 4.97096693
46230.1788 13551.8614 46230.1788
13369.969 -22030.611 13369.969
24042.5753 -13186.694 24042.5753
0
0
0
5937.16958 -26347.033 5937.16958
15583.1975 -17931.374 15583.1975
10275.0084 -30349.979 10275.0084
10764.2303 -32592.292 10764.2303
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-9
PROFESSOR
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.66507018
R Square
0.44231835
Adjusted R Square
0.08305601
Standard Error
18569.3516
Observations
30
ANOVA
df
Regression
Residual
Total
Intercept
GENDER
TENURE
YRS_SVC
SCHOOL:Business
SCHOOL:Education
SCHOOL:Engineering
SCHOOL:Health
SCHOOL:Liberal
SCHOOL:SCMNS
SCHOOL:Social Work
SCHOOL:Architecture
SS
11 6016788693
22 7586058009
33 1.3603E+10
CoefficientsStandard Error
132313.689 19740.2074
11189.7788 8034.89225
-36927.419 14677.8779
357.487759 546.04037
9249.2053 15422.6367
0
0
-10025.265 15756.0829
0
0
-15723.076 15349.2568
-6933.419 15742.9691
0
0
0
0
MS
F
Significance F
546980790 2.49271852 0.041332107
344820819
t Stat
6.70275072
1.39264827
-2.5158555
0.65469108
0.59971622
65535
-0.636279
65535
-1.0243542
-0.4404137
65535
65535
P-value
9.7477E-07
0.17764237
0.01967318
0.51945344
0.55482247
#NUM!
#NUM!
#NUM!
#NUM!
0.66393448
#NUM!
#NUM!
Lower 95%
91375.00479
-5473.567834
-67367.47512
-774.9306579
-22735.38552
0
-42701.38149
0
-47555.48686
-39582.33857
0
0
Upper 95% Lower 95.0% Upper 95.0%
173252.374 91375.0048 173252.374
27853.1255 -5473.5678 27853.1255
-6487.3638 -67367.475 -6487.3638
1489.90618 -774.93066 1489.90618
41233.7961 -22735.386 41233.7961
0
0
0
22650.8506 -42701.381 22650.8506
0
0
0
16109.3339 -47555.487 16109.3339
25715.5006 -39582.339 25715.5006
0
0
0
0
0
0
MSU Gender Salary Analysis, Pivotal Practices Consulting LLC | Page C-10