Women's Faculty Cabinet Salary Equity Study: Findings and Recommendations May 24, 2010

Women's Faculty Cabinet
            Salary Equity Study: Findings and Recommendations
                                             May 24, 2010
                                           Executive Summary
People are the most valuable asset of an educational institution, and salary is a key way in which
institutions demonstrate the value they place on individual employees. Salary equity is important
at the University of Minnesota not only for reasons of fairness, or because it improves morale,
protects the University’s investment in human capital, and has a positive impact on recruiting,
retention and productivity, but also because it is the law. The most recent salary studies and
adjustments at the University of Minnesota were completed 20 years ago using data from 1986.
A follow-up salary study is long overdue.
The Women’s Faculty Cabinet initiated this study in 2007. 1 Our findings show that salary
inequities exist between men and women at all ranks, and that differences increase with
increasing rank. Furthermore, the differences between male and female salaries have remained
virtually unchanged since 1986 except at the Assistant Professor level where there has been
improvement. We recommend a vigorous and sustained commitment on the part of the
University of Minnesota administration to document and reduce salary inequities. As a first step,
we recommend immediate action to remedy salary inequities through a combination of across-
the-board salary adjustments for all female faculty, and targeted individual adjustments.

                                          Purpose of this Report
The purpose of this study is to assess the current state of salary equity at the University of
Minnesota. In 1973, Shyamala Rajender charged the University of Minnesota with sex
discrimination. Her charge later became a class action lawsuit affecting 1,300 female faculty
members and academic professionals at the University of Minnesota. The Rajender Consent
Decree led to a variety of affirmative action goals and in 1989 a salary settlement was reached
under which all women covered by the decree received permanent increases to their base salaries
(Spector, 1990). This salary study is being completed, approximately 20 years after this salary
equity settlement, to assess where the University of Minnesota currently stands in terms of salary

                                         Structure of the Report
We will first present differences in male and female salaries and compare sex differences today
to those at the time of the Rajender settlement. Our results show that the size of the difference
between male and female salaries at the Assistant Professor level are smaller now than at the
time of the Rajender settlement. However, at the Associate and Full Professor levels the
differences are as large or larger than in 1989.

  The opinions and recommendations expressed in this report are those of the Women’s Faculty Cabinet and do not
necessarily represent the opinions of the Administration, the Office of Institutional Research, or any other
individual, body, or organization.

There clearly are multiple factors that may contribute to these salary differentials. For example,
if women in general have less seniority than men, and if seniority is related to salary, part of the
sex difference may be explained by differences in seniority. Thus, in addition to presenting
average differences in salary between men and women, we also report the results of three sets of
regression analyses in which we control for various factors that might affect the size of the sex
differences in salary. Specifically, we first report the results of analyses of the 2007 salary data
using models developed by Dr. Charlotte Striebel to conduct statistical analyses for the Petitioner
in the Rajender case, and compare our results to findings obtained by Dr. Striebel in her analyses
of salary data from 1986. These analyses allow us to assess whether the percentage sex
differences in salary have changed over time (from 1986 to 2007) using the same regression
model. However, because Striebel did not conduct analyses by rank, and because the size of the
sex difference in salary does differ by rank, we next present the results of two sets of regression
models that analyze sex differences in salary for the three ranks independently (Assistant,
Associate, Full). Specifically, we present the results of the regression model employed by the
University of California-Irvine in their salary equity studies and then present the results of a
model developed by Dr. Leonard Goldfine from the University of Minnesota Office of
Institutional Research (UM-OIR) that controls for several additional variables. We conclude with
a brief summary of our analyses along with recommendations for the University of Minnesota to
address inequities.
                                                     The Data
The Women's Faculty Cabinet (WFC) first requested salary data for the University of Minnesota
in 2007 and the data were ready for analysis in January 2010. The long data-gathering process,
completed under the supervision of Vice Provost Arlene Carney, ensured that the data were
comprehensive and accurate. The WFC has worked closely with Dr. Goldfine since mid-January
to analyze the salary data collected; however, this report reflects the independent assessment of
the analyses by the WFC. The analyses are based on 2007 salaries for tenured or tenure-track
faculty with 100% FTE appointments. 2

                                 Statistical Significance and Effect Sizes
Statistical significance addresses whether a result (e.g., a difference between male and female
salaries) is likely to have happened by chance alone. The significance level is a measure of the
probability of getting the same result due to chance alone if male and female salaries do not
differ. The lower the significance level, the less likely it is that the results occurred by chance.
Typically, the level for statistical significance is set at the 1% (0.01) or 5% (0.05) level. In this

  The data came from a 2007 Human Resources (HR) snapshot file and are therefore based on projected salaries for
fall contracts rather than actual end-of-year salaries. P & A staff, administrators with faculty appointments, and
faculty on phased retirement were excluded, as were faculty from the Academic Health Center (because of concerns
regarding the differing salary structure for faculty in the AHC). Salary data were used for faculty from the following
colleges: Architecture/Landscape Architecture; Biological Sciences; Education; Liberal Arts; Food, Agricultural,
and Natural Resource Sciences; Carlson School of Management; Public Affairs; Institute of Technology; and Law.

report, if a sex difference in salaries is significant at the 5% level (or less), the hypothesis that the
difference occurred due to chance alone is rejected.
It is important to note, however, that a result that is not statistically significant may still be
important (Haignere, 2002). Statistical significance is based on drawing inferences from a
sample to a population. If a random sample of faculty were taken and sex differences in salary
were found, then statistical significance would be used to estimate whether this difference was
due to chance or if the same result would occur if a second sample were selected. However,
because the entire population is used in this study, any difference found in that population is an
actual salary difference between men and women (Haignere, 2002). Therefore, although we
report statistical significance as a matter of convention, the computed sex differences in salaries
reflect the true average differences for the population of faculty examined.
Whether a difference is statistically significant also depends on the size of the sample. If a
sample is large enough, a very small difference may be statistically significant, but perhaps not
practically significant. Thus, it also is important to include a measure of the size of an effect.
Cohen's d is one such measure and represents the size of a difference in terms of standard
deviation units. For example, if an exam had a mean score of 100 and a standard deviation of 10,
and men and women differed by 5 points on the exam, they would differ by 1/2 of a standard
deviation and Cohen's d would equal 0.50. As a rough guideline, a Cohen's d of 0.20 can be
considered small, 0.50 can be considered medium, and 0.80 can be considered large. Cohen's d
values are reported to help interpret the practical significance of the computed sex differences.


Average Differences in Male and Female Salaries in 2007

Male faculty earn more than female faculty overall, and at all ranks (Assistant, Associate, and
Full Professor) at the University of Minnesota (Table 1). Differences at the Associate and Full
Professor levels are statistically significant. The percentage difference between male and female
salaries increases with rank: female faculty fall further behind their male counterparts with
increasing rank.

Table 1. Average Differences in Male and Female Salaries in 2007 at the UMN

                 Mean           Deviation        Dollar           Percent
                 Salary           (SD)         difference        difference          Cohen's d
 Professor         $74,596         $22,518 $3,299                          4.4%              0.15
 Professor         $83,460         $21,883 $5,332**                        6.4%              0.24

 Full          $120,841            $38,922 $9,586***                       7.9%              0.25
** p< 0.01 *** p
Average Differences in Male and Female Salaries in 1986

In the Petitioner’s Statistical report, Striebel (1989) reports average salaries for multiple
employee groups. Table 2, below, summarizes average salaries by rank for faculty with doctoral
degrees only, which provides the greatest similarity with the 2007 data set available for the
current study. As in 2007, in 1986, women earned significantly less than men at all ranks.
Comparing the two datasets, the size of the percentage difference between male and female
salaries at the Assistant Professor level is now smaller (4.4 now vs. 8.1% then), but the
differences at the Associate (6.4% now vs. 6.1% then) and Full Professor (7.9% now vs. 7.2%
then) levels remain virtually unchanged. Thus, although differences between male and female
salaries at the Assistant Professor rank have been reduced by approximately 50% over the past
20 years, the differences in salary between male and female faculty at higher ranks have
persisted, and are effectively the same as those that warranted adjustments 20 years ago.
Striebel’s data set differs from ours in that she included faculty from the University of Minnesota
- Duluth in her sample. Therefore, an additional analysis was done using data from another
report prepared in the context of the Rajender case (Goodman, Hoenack, & Rasmussen, 1989).
From this report, we calculated salaries for tenured and tenure-track faculty on the Twin Cities
campus only. The percentage differences are included in Table 2 in bold, after the percentages
from the Striebel report. Considering University of Minnesota-Twin Cities (UM-TC) faculty
only does not alter the conclusion: percent differences between male and female salaries today
are virtually the same as in 1986 when considering either UM-TC faculty alone or the combined-
campus population.

Table 2. Average Differences in Male and Female Salaries in 1986 at the UMN for Faculty with
Doctoral Degrees

                    UM Twin Cities and Duluth Campuses, combined                    UM-TC

                                                Dollar          Percent         difference at
                 Men           Women          Difference       Difference       UM-TC, only1
Professor         $30,560        $28,270       $2,290 ***              8.1%                8.0%
Professor         $35,130        $33,140       $2,010 ***              6.1%                6.2%

 Professor     $46,800       $43,640       $3,160 **          7.2%                 7.9%
year of degree, which we refer to as the UC-Irvine Model. The third uses a more complex model
developed by Dr. Goldfine (UM-OIR). Prior to presenting the results of these analyses, we
provide a brief discussion of regression analyses and their interpretation. 3
A multivariate regression analysis is used to assess whether women are paid less than their male
colleagues who have comparable attributes (Haignere, 2002). Regression analysis examines the
differences between groups by creating a line that "best fits" the relationship between salary and
the independent variables, or attributes, in the model (e.g., year of hire, year of degree, and an
indicator for female sex). For a given set of attributes, points below the line represent individuals
whose salaries are lower than what is predicted by the attributes used in the regression analysis
(Haignere, 2002). If, when all of the positive and negative (squared) distances from the line and
each observed salary are added together, there is a lower total for female faculty than for male
faculty, the regression analysis assigns a negative coefficient to the female variable. The negative
coefficient indicates the average difference between male and female salaries after taking into
account salary differences explained by other attributes in the model (e.g., year of hire and year
of degree).

Results of Regression Analyses Using the Striebel Model (2007 data)
We first present analyses using the regression models developed by Dr. Striebel in her analyses
of salary data from 1986 to provide a comparison of the size of male and female salary
differentials 20 years later. Dr. Striebel included four different regression models in her study.
These models control for a variety of factors that may explain sex differences in salary.
Appendix A describes the differences between the 2007 and 1986 analyses in terms of the
samples and variable definitions and Appendix B contains for the full regression output for these
All four models show significant differences between male and female salaries, after various
factors are controlled (see Table 3). The first model (model A) examines sex differences in salary
controlling for highest academic degree, time from highest academic degree, and length of
University employment. After controlling for these variables, the difference in average salary for
men and women in 2007 is $11,116, an 11.1% difference.

The second model (Model B) includes all of the independent variables from Model A plus the
percentage of women in the faculty member's discipline at the U of MN. This variable is meant
to take into account market factors, such as outside wage offers, that vary by discipline.
Controlling for these four factors, the difference in average salary between men and women is
$6,263 (6.3%). The coefficient for the market factor variable (percentage of female faculty in a
discipline) is -$29,022. This means that moving from a discipline with 0% females to 100%
females is predicted to decrease salaries by $29,022. In other words, a percentage point increase
in female faculty in a department is predicted to decrease salary by $290.

  An almost limitless number of regression models can be run that control for various factors that may explain sex
disparities in salary. We do not report the results of analyses conducted that identify female faculty who earn less
than predicted based on regression models using male data (outlier analyses) or analyses that examine sex
differences at the college level, based on recommendations from an AAUP-published guide to conducting salary
equity studies (Haignere, 2002).

Model C includes all of the independent variables in Model A plus the variables academic rank
and time in current academic rank. The difference in average salary for men and women using
this regression model is $6,765 (6.8% difference). Thus, female faculty earn 6.8% than male
faculty, even when equated in terms of highest academic degree, time since highest degree,
length of UMN employment, rank, and time in academic rank.
The fourth model, Model D, includes all six independent variables. Correcting for all of these
variables, female faculty are paid on average $3,574 (3.6%) less than male faculty. 4

Table 3: Results of Striebel Model (2007 data)

Model Dollar difference                Percent               Cohen's d       Variables in model
A         $11,116***                   11.1%                 0.29            -Highest academic degree
                                                                             -Time from highest academic degree
                                                                             -Length of UMN employment
B         $6,263***                    6.3%                  0.17            -Model A variables
                                                                             -% female in discipline
C         $6,765***                    6.8%                  0.20            -Model A variables
                                                                             -Academic rank
                                                                             -Time in current academic rank
D         $3,574*                      3.6%                  0.09            -Model A variables
                                                                             - % female in discipline
                                                                             -Academic rank
                                                                             -Time in academic rank
Table 4. Results of Striebel Model (1986 data)
Model       Dollar difference       Percent Difference        Variables in model
                                                             -Highest academic degree
A          $3,680**                 10.3%                    -Time from highest academic degree
                                                             -Length of UMN employment
                                                             -Model A variables
B          $2,780**                 7.8%
                                                             -Market factors that correlate with sex
                                                             -Model A variables
C          $2,080**                 5.8%                     -Academic rank
                                                             -Time in current academic rank
                                                       -Model A variables
                                                       -Market factors that correlate with sex
D           $1,450**            4.1%
                                                       -Academic rank
                                                       -Time in academic rank
** p
Table 5: Sex Differences in Salary Using UC-Irvine Model (controlling for hire year and degree

Faculty group       Mean Salary Standard            Dollar difference Percent          Cohen's d
                                Deviation (SD)                        difference
All                 $100,184    $ 37,714            $11,342***        11.3%           0.30
All (with rank in   $100,184    $37,714             $ 7,120***         7.0%           0.19
Assistant         $ 74,596       $ 22,518           $ 3,408              4.6%         0.15
Associate         $ 83,460       $ 21,883           $ 5,487**            6.6%         0.25
Full              $120,841       $ 38,922           $10,653***           8.8%         0.27
** p< 0.01 *** p
among these models. Specifically, these models provide strong and consistent statistical support
for the following key conclusions:
       1. Women faculty at the University of Minnesota earn significantly less than male
          faculty, even when incorporating specific variables that may account for sex-
          based differences in salary into the analysis.
       2. Salary discrepancies between male and female faculty increase with rank.
       3. The differences in salary between male and female faculty at higher ranks are
          virtually unchanged from those documented at the University of Minnesota 20
          years ago.
Some may argue that salary differences will disappear over time as the younger groups, with less
difference in salary, proceed through rank. Unfortunately, this has not been true at the University
of Minnesota. Our results are consistent with an MIT (1999) study showing that salary
differentials tend to persist over time in the absence of efforts to address disparities.
Over time these differences accumulate to non-trivial differences in compensation. Considering
differences in salary by rank reported here, a newly hired female Assistant Professor will accrue
nearly $200,000 less in wage compensation relative to a man over a 25-year career, assuming six
years each at the Assistant and Associate ranks. Female retirement benefits will be similarly
The WFC is deeply concerned about the ongoing salary disparities between male and female
faculty at the University of Minnesota. While recognizing the complex factors that go into
determining salary for individual faculty members, the persistent sex-based differentiation in
salary sends a strong message to female faculty at the University of Minnesota. It is absolutely
critical that the University of Minnesota develop strategies for understanding more fully the
factors that generate the salary differential and for reducing and eliminating differences in salary
between male and female faculty members. To achieve these goals, we recommend the

   1. Make salary adjustments to address current salary inequities. Haignere (2002)
      recommends across-the-board adjustments, corrected for number of years at the
      institution. Remedies that are distributed equally to all those in the affected group can be
      applied easily, efficiently, and promptly, and without prolonged attention to the issue.
   2. Make a vigorous and ongoing commitment to tracking salary equity across the University
      of Minnesota.
           a. Conduct a comprehensive salary equity study every 3 to 5 years.
           b. Conduct a systematic review of procedures currently used at the U of MN to
              evaluate merit and equity and to translate merit and equity ratings into salaries.
           c. Require Deans, Department Heads, and Chairs to report sex equity statistics
              annually to the Provost.

d. Make equity statistics and progress public.
           e. Continue to evaluate the factors that are correlated with sex-based salary
   3. Use equity status as a criterion in allotting lines, space, and money.

Why is action necessary now?
1. Equity matters. First and foremost, action is necessary because equity matters. Beyond basic
principles of fairness, discrimination based upon race or sex is illegal in the United States. As a
publicly-funded institution, systematic differences in salaries paid to male and female faculty are
a legitimate concern. Furthermore, full investment in and support of all faculty is fundamental to
achieving the aspiration of top-tier status among United States research universities. Salary
parity between male and female faculty is a critical metric to consider when evaluating the status
of women within the institution. Moreover, increased academic morale, reduced staff turnover, a
sense of inclusiveness, and an enhanced public image are all benefits of equity policies
(Haignere, 2002).
 2. Productivity can be impacted by inequitable reward structures. Salary equity is a key
issue to faculty. Although faculty may not enter academic life for salary, salary and salary equity
are viewed by faculty as a reflection of respect (Wenzel & Hollenshead, 1998). Salary is viewed
by faculty as legitimization and recognition of their work and worth to their institutions. Thus,
relative salary and raises can have a significant effect on a faculty member’s attitudes and
performance (Hearn, 1999).
3. Faculty retention can be enhanced by an equitable reward structures. Women faculty
who are underpaid are far more likely to seek alternative employment (Blackaby, Booth &
Frank, 2005). Failure to address salary equity is a failure to protect significant past investments
in faculty recruiting and development.
4. Recruitment may be enhanced by equitable reward structures. Equity in current salaries
and new offers may have the additional benefit of helping to achieve numerical parity of male
and female faculty. For example, the 2009 WFC report comparing the proportion of women
faculty at the University of Minnesota to peer institutions revealed that the Institute of
Technology (IT) has fewer female faculty members than do its peers. Analyses performed in
conjunction with this report (data not shown) indicate that 40% of female Assistant Professors
and 44% of female Associate Professors in IT are paid one standard error or more less than their
male counterparts, when they are equated in terms of year of hire and year of degree. The
substantial salary differential between male and female faculty in IT may be a major disincentive
to prospective female faculty candidates to accept an offer at the University of Minnesota, and
may be a major barrier to increasing the proportion of female faculty in IT.
5. Leadership. Exercising institutional leadership regarding pay inequity may help to foster
institutional loyalty and heighten moral. Moreover, the U of MN can serve as a leader among Big
Ten Universities as well as peer institutions by proactively and aggressively addressing pay
inequity. Such leadership would prove advantageous to the overall external reputation of the

Blackaby, D., Booth, A. L. and Frank, J. (2002). Outside offers and the gender pay gap:
Empirical evidence from the UK. (CEPR Discussion Papers 3549). C.E.P.R. Discussion Papers.
Goodman, R., Hoenack, S., and Rasmussen, M. (1989). Statistical analysis of salaries for
tenured and tenure track faculty at the Twin Cities and Duluth campuses of the University of
Minnesota. Office of Management Planning and Information Services: University of Minnesota.

Haignere, L. (2002). Paychecks: A guide to conducting salary-equity studies for higher
education faculty. (2nd ed.). Washington, DC: American Association of University Professors.

Hearn, J. (1999). Pay and performance in the university: An examination of faculty salaries. The
Review of Higher Education, 22(4), 391-410.

Massachusetts Institute of Technology. (1999). A study on the status of women faculty in science
at MIT. How a committee on women faculty came to be established by the Dean of the School of
Science, what the Committee and the Dean learned and accomplished, and recommendations for
the future. Boston, MA: Author.

Spector, J. (1990). The Minnesota Plan II: A project to improve the university environment for
women faculty, administrators, and academic professional staff. Women's Studies Quarterly,
18(1/2), 189 – 206.

Striebel, C. (1989). Petitioners’ statistical report. Differences in salary between men and women
on the faculty and academic staff at the University of Minnesota. Unpublished manuscript.
Wenzel, S. A. and Hollenshead, C. (1998) Former Women Faculty: Reasons for Leaving One
Research University. Center for the Education of Women, University of Michigan, Ann Arbor,

Appendix A

Variable            Striebel Study (1986 data)              Current study (2007 data)
Population included Full time faculty and academic          Tenured and tenure-track faculty on
                    staff from TC, Duluth, and all          TC campus. Regents Professors and
                    coordinate campuses Academic            administrators excluded.
                    Professionals, Administrators and
                    Regents Professors

Full time faculty    Possibly defined full-time as 67%      100% FTE faculty
                     FTE, but it is unclear

Degree               Degrees were classified as             Degree was The current dataset does
                     bachelors, masters and higher than     not include any VetMed, Dentistry,
                     masters. The higher than masters       or Medical faculty and was only
                     group included all doctorates and      coded for Masters (variable: MA),
                     professional degrees (JD, MD,          Doctorate (variable: PHD), and Law
                     DDS). It was measured from the         (variable: LAW)
                     earliest degree held in the group of
                     highest level
Market factors by    All employees were grouped by          Academic departments were
sex                  discipline. These groups were          grouped to obtain discipline groups
                     identical to academic departments      of ten or more. The percent female
                     withhich smaller departments           by department was calculated from a
                     grouped together to obtain             dataset (not People Soft system).
                     discipline groups of ten or more.
                     The percent of women in the
                     discipline was computed and a
                     percent value was assigned to
                     every member of the discipline as
                     their value for this market
Time in rank         Measured from the earliest entry       Calculated by taking the year of the
                     data into either academic rank or      HR snapshot (2007) and subtracting
                     administration rank                    the year they started in their fall
                                                            2007 rank. An approximate
                                                            calculation to get the start date in
                                                            rank using tenure data was used; ,
                                                            thus the factor may be off by one or
                                                            so year for those hired in at associate
                                                            rank who but have had a
                                                            probationary period before getting
                                                            confirming tenure

Appendix B

                  Full regression output for Striebel model (2007 salary data)

Model A - Highest Academic Degree, Time from highest academic degree, Time since hired at

Model B – Model A, plus Percent female by discipline

Model C – Model A, plus Academic rank, Time in current academic rank

Model D – Model A, plus Percent female by discipline, Academic rank, Time in academic rank

Appendix C

                                 UC-Irvine Regression Output

Regression Analysis Coefficient Table: All faculty

Regression Analysis Coefficient Table: Assistant Professors

Regression Analysis Coefficient Table: Associate Professors

Regression Analysis Coefficient Table: Full Professors

Appendix D

                                       UM-OIR Models

Regression Analysis Coefficient Table: All faculty

       Adjusted R2 = .63

Regression Analysis Coefficient Table: Assistant Professors

       Adjusted R2 = .78

Regression Analysis Coefficient Table: Associate Professors

       Adjusted R2 = .56

Regression Analysis Coefficient Table: Full Professors

       Adjusted R2 = .47

Women’s Faculty Cabinet

Patricia Frazier, Ph.D., Distinguished McKnight University Professor, Psychology

Linda Kinkel, Ph.D., Professor, Plant Pathology

Colleen Flaherty Manchester, Ph.D., Assistant Professor, Human Resources and Industrial

Helga Leitner, Ph.D., Professor, Geography

Caroline Hayes, Ph.D., Professor, Mechanical Engineering

Alice Larson, Ph.D., Professor, Pharmacology and Neuroscience

Nancy Raymond, M.D., Professor, Psychiatry

Keya Ganguly, Ph.D., Professor, Cultural Studies and Comparative Literature

Lisa Channer, Ph.D., Assistant Professor, Theater Arts and Dance

Peg Lonnquist, Ph.D., Director, Women’s Center

Rhonda Franklin, Ph.D., Associate Professor, Electrical and Computer Engineering

Michele Goodwin, J.D., Everett Fraser Professor of Law, Law School

Linda Halcon, Ph.D., Associate Professor and Chair, Integrative, Global and Public Health

Roberta M. Humphreys, Ph.D., Professor, Astronomy

Janet Schottel, Ph.D., Professor, Biochemistry, Molecular Biology and Biophysics

Raya Hegeman-Davis, Ph.D. Candidate, Educational Policy and Administration, Graduate
Research Assistant

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