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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

Purpose of this Report

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.

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 equity.

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.

1 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.

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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

2 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.

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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.

Results

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 Salary

Standard Dollar

difference Percent

difference Cohen's d Deviation

(SD)

Assistant Professor $74,596 $22,518 $3,299 4.4% 0.15 Associate 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<0.001

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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

Men Women Dollar

Difference Percent

Difference

Percent difference at

UM-TC, only1

Assistant Professor $30,560 $28,270 $2,290 *** 8.1% 8.0% Associate Professor $35,130 $33,140 $2,010 *** 6.1% 6.2%

Professor $46,800 $43,640 $3,160 ** 7.2% 7.9% **p<0.05; *** p< 0.01 1Bold-faced numbers in the rightmost column reflect analyses for UM-TC campus faculty only (Goodman et al., 1989)

Regression Models

To more fully understand the salary gap, three sets of regression analyses are presented. The first examines sex differences in salary using the regression model developed by Dr. Striebel in the Rajender case. The second examines sex differences in salary controlling for year of hire and

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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 models.

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.

3 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).

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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 Difference

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

*p<0.05 *** p<0.001

Historical comparison: Striebel Model 1986 data

Table 4 presents the results of the analyses conducted by Dr. Striebel using the same models as those described above. Across the four models, the percentage differences in male and female salaries in 1986 are remarkably similar to those obtained using the 2007 data presented above. For example, using 1986 data, female faculty made 4.1% less than male faculty, even when equated in terms of all six factors (highest academic degree, time from highest academic degree, length of University employment, percentage of women in the discipline, academic rank, and time in current academic rank). The difference in the 2007 data was 3.6%.

4 Striebel’s model is very similar to the model recommended by Haignere (2002) in the AAUP guide for conducting salary equity studies with the exceptions that it 1) does not include race and 2) uses % women in discipline rather than Classification of Instructional Program (CIP) codes to control for discipline differences.

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Table 4. Results of Striebel Model (1986 data)

Model Dollar difference Percent Difference Variables in model

A $3,680** 10.3% -Highest academic degree -Time from highest academic degree -Length of UMN employment

B $2,780** 7.8% -Model A variables -Market factors that correlate with sex

C $2,080** 5.8% -Model A variables -Academic rank -Time in current academic rank

D $1,450** 4.1%

-Model A variables -Market factors that correlate with sex -Academic rank -Time in academic rank

** p<0.01. Cohen’s d cannot be computed because standard deviations were not provided in Striebel’s report

Results of Regression Analyses Using UC-Irvine Model

The UC-Irvine regression model estimates average sex differences in salary controlling for year of hire and year of degree. These two variables are included as objective indicators of experience. Thus, these analyses determine whether there are differences between male and female salaries when men and women are equated in terms of experience levels.

When controlling for hire year and year of degree, women still earn significantly less than men (Table 5; see Appendix C for full regression output). Using this model, overall, the average female faculty member earns roughly $11,000 dollars less than the average male faculty member, when they are equated in terms of experience (hire year and year of degree). When faculty rank is also included in the model, the average sex difference decreases to $7,120.

Examining sex differences within each rank allows greater insight into sex disparities. At all ranks, female faculty earn less than male faculty when controlling for hire year and year of degree. All differences except those at the Assistant Professor level are statistically significant. Similar to the previous analyses, differences between male and female faculty salaries become progressively greater with increasing rank. In fact, the differences between male and female faculty salaries are greater within each rank when hire year and year of degree are controlled than when these factors are not accounted for in the model.

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Table 5: Sex Differences in Salary Using UC-Irvine Model (controlling for hire year and degree year)

Faculty group Mean Salary Standard Deviation (SD)

Dollar difference Percent difference

Cohen's d

All $100,184 $ 37,714 $11,342*** 11.3% 0.30 All (with rank in model)

$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<0.001

Institutional Research (UM-OIR) Regression Model

Dr. Goldfine (UM-OIR) also developed a regression model to examine salary differentials, controlling for various factors. We will refer to this as the UM-OIR model. The UM-OIR model included 16 to 18 predictors depending on the model: sex, rank (2 variables, full faculty model only), number of years since earning highest degree, number of years of external experience (hire year – degree year), an indicator for Regents Professor, an indicator for whether the faculty member was on payroll prior to minimum faculty rank appointment, number of internal promotions, minority status, and dummy variables for nine academic colleges and departments that were deemed as having salaries outside the typical range (i.e., CSOM, Law, Public Health, Child Development, American Indian Studies, Economics, Psychology, Statistics, Theater). See Appendix D for the regression output for the UM-OIR model. Using the UM-OIR model, female faculty members earn significantly less than male faculty members overall and at each rank. As a percentage of average salary, women earn between 4.4% and 7.7% less than men, controlling for 18 factors, including disciplinary differences in salary.

Table 6: Sex Differences in Salary Using IR model (with 16-18 control variables) Faculty group Mean Salary Standard

Deviation (SD) Dollar difference

Percent Difference

Cohen’s d

All $100,184 $37,714 $6,113*** 6.0% 0.16 Assistant $ 74,596 $ 22,518 $3,451** 4.6% 0.15 Associate $ 83,460 $ 21,883 $3,713** 4.4% 0.17 Full $120,841 $ 38,922 $9,283*** 7.7% 0.24 **p<0.01 *** p<0.001

Model Comparison and Summary of Findings

This study applied increasingly complex analytical models to the 2007 University of Minnesota faculty salary database to examine sex disparities in salaries. Not surprisingly, the differing models produced varying estimates of salary differences between male and female faculty overall and at each rank. However, it is critical to emphasize the consistency of results found

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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 impacted

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 following:

Recommendations

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.

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d. Make equity statistics and progress public.

e. Continue to evaluate the factors that are correlated with sex-based salary differences.

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 university.

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References

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, MI, USA.

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Appendix A

Variable Striebel Study (1986 data) Current study (2007 data) Population included Full time faculty and academic

staff from TC, Duluth, and all coordinate campuses Academic Professionals, Administrators and Regents Professors

Tenured and tenure-track faculty on TC campus. Regents Professors and administrators excluded.

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

100% FTE faculty

Degree Degrees were classified as bachelors, masters and higher than masters. The higher than masters group included all doctorates and professional degrees (JD, MD, DDS). It was measured from the earliest degree held in the group of highest level

Degree was The current dataset does not include any VetMed, Dentistry, or Medical faculty and was only coded for Masters (variable: MA), Doctorate (variable: PHD), and Law (variable: LAW)

Market factors by sex

All employees were grouped by discipline. These groups were identical to academic departments withhich smaller departments grouped together to obtain 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 variabl.e

Academic departments were grouped to obtain discipline groups of ten or more. The percent female by department was calculated from a dataset (not People Soft system).

Time in rank Measured from the earliest entry data into either academic rank or administration rank

Calculated by taking the year of the HR snapshot (2007) and subtracting 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

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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 rank

Model B – Model A, plus Percent female by discipline

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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

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Appendix C

UC-Irvine Regression Output

Regression Analysis Coefficient Table: All faculty

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Regression Analysis Coefficient Table: Assistant Professors

Regression Analysis Coefficient Table: Associate Professors

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Regression Analysis Coefficient Table: Full Professors

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Appendix D

UM-OIR Models

Regression Analysis Coefficient Table: All faculty

Adjusted R2 = .63

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Regression Analysis Coefficient Table: Assistant Professors

Adjusted R2 = .78

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Regression Analysis Coefficient Table: Associate Professors

Adjusted R2 = .56

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Regression Analysis Coefficient Table: Full Professors

Adjusted R2 = .47

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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 Relations 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 Cooperative 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