VITERBO FACULTY SALARY COMPARISON

EXECUTIVE SUMMARY

This report compares the average salaries of Viterbo University faculty with the average salaries of faculty at other US institutions of higher education. Institutions are grouped by a variety of relevant characteristics, and the faculty are grouped by academic rank. Comparisons are made across years from 2016 - 2021.

The primary objective of this report is to inform ongoing conversations about Viterbo faculty salary equity by summarizing and providing easy access to average faculty salary data.

Notable findings:

• In comparison with our Peer Group, Viterbo faculty average salaries fell below the median at every rank, every year.
• Viterbo’s faculty body shrank by 14% from 2016 - 2021, versus 4% for our Peer Group.
• The average Viterbo Assistant salary decreased by -1.09%. The average Peer Group Assistant salary increased by 7.34%.
• The average Viterbo Professor salary was $11367 lower than the Peer Group Professor average, per year.
• In 2021, the average Viterbo Professor ($69959) earned less than the average University of Wisconsin - La Crosse Assistant ($71393), less than the average Winona State University Assistant ($72158), and less than the average Western Technical College Instructor ($73129).

OVERVIEW

Faculty at Viterbo University have repeatedly raised concerns about perceived low salaries relative to faculty at other colleges and universities. These concerns permeate discussions of faculty morale and work/life balance, and have resulted in a variety of task forces, committees, fora, and consultancies over the past 8-10 years. The most recent and ongoing exploration of compensation equity remains in progress by Casagrande Consulting, with results expected towards the end of the 2022-2023 academic year.

Some of these efforts have resulted in limited and targeted salary increases, but the perception of relatively low faculty compensation persists. To inform this perception, this report tabulates and visualizes publicly-available salary data reported by institutions of postsecondary education.

Specifically, this report summarizes Viterbo University faculty salaries as reported to the Integrated Postsecondary Education Data System (IPEDS), and compares these salaries with those reported for faculty at other institutions of higher education. Comparisons are made across time and by various groupings of institutions.

Data are included from academic years 2016-2017 (“2016”) through 2021-2022 (“2021”), which is the most recent year for which full data are available. Refer to the Appendix for details regarding the data characteristics, data limitations, data sources, variables, renaming and recoding, and filtering.

COMPARISON GROUPS

Several groups of institutions were established for the purposes of salary comparison. In the following sections you will find plots, tables, and analyses using data from each of these groups:

• Group A: BROAD-SPECTRUM (n = 3185)
  • All public and private (non-profit) 4+ year and 2-4 year institutions.
• Group B: 4+ YEAR (n = 2408)
  • All public and private (non-profit) 4+ year institutions.
• Group C: CARNEGIE (n = 464)
  • All public and private (non-profit) 4+ year institutions.
  • With the same Carnegie designation: “Master’s Colleges & Universities: Larger Programs”.
• Group D: 100-MILE RADIUS (n = 16)
  • All public and private (non-profit) 4+ year institutions.
  • Within a 100-mile radius of La Crosse, WI.
• Group E: CARNEGIE-100 (n = 6)
  • All public and private (non-profit) 4+ year institutions.
  • With the same Carnegie designation: “Master’s Colleges & Universities: Larger Programs”.
  • Within a 100-mile radius of La Crosse, WI.
  • Note: In 2021, the Carnegie designation changed for the University of Wisconsin - La Crosse and Saint Mary’s University of Minnesota; both were reclassified as “Doctoral/Professional Universities”. As such, the sample size for this comparison group reduced to n = 4 in 2021.
• Group F: WISCONSIN (n = 47)
  • All public and private (non-profit) 4+ year institutions.
  • Within the state of Wisconsin.
• Group G: WAICU (n = 22)
  • All institutions that are part of the Wisconsin Association of Independent Colleges and Universities.
  • Except the Medical College of Wisconsin.
• Group H: PEER (n = 20)
  • All institutions identified as “Peer Institutions” through an internal process, considered similar to Viterbo on key characteristics.
  • The current list was generated in 2018.

PEER GROUP COMPARISON

Viterbo University maintains a list of peer institutions for use when a task requires evaluating one or more business-related metrics in comparison with similar institutions. The list is generated based on about 12 variables including undergraduate enrollment, tuition, student-faculty ratio, etc. The current list was generated in 2018 and includes 20 colleges and universities, all of which are private and offer Baccalaureate and Master’s degrees. Some, like Viterbo, also offer Doctoral degrees.

In this section you will find data comparing Viterbo with the Peer comparison group, which is also known as Group H in this document; see the comparison groups section for more detail.

Specifically, I plotted the faculty body size for Viterbo and the average faculty body size for the Peer Group. I also created a table that summarizes average salaries for Viterbo and the Peer Group. Data in the plots and table are split by rank across years. The table also includes values representing the absolute and relative changes in salary between 2016 and 2021.

VITERBO and PEER (GROUP H) AVERAGE SALARIES								2016 vs. 2021
INSTITUTION(S)	RANK	2016	2017	2018	2019	2020	2021	CHANGE	PERCENT
Viterbo	Instructor	42653	42804	41526	41972	45130	46378	3725	8.73
Peer Group	Instructor	48858	50755	52207	52512	51549	56156	7298	14.94
Viterbo	Assistant	54962	54742	55843	56682	55900	54364	-598	-1.09
Peer Group	Assistant	56428	57390	58834	59076	59657	60570	4142	7.34
Viterbo	Associate	60099	61251	60331	61421	62783	61842	1743	2.90
Peer Group	Associate	65592	66246	68484	68741	67872	67486	1894	2.89
Viterbo	Professor	69895	69727	69566	70400	69529	69959	64	0.09
Peer Group	Professor	79940	81177	80500	82440	82262	80957	1017	1.27

SALARIES ACROSS TIME

Beyond the Peer Group, it is informative to compare faculty salaries at Viterbo with additional comparison groups. Plots in this section depict average salaries by rank across years, from 2016-2021, for each comparison group.

Boxes show the 75th %ile (upper line), 50th %ile (median; middle line), and 25th %ile (lower line.) Each gray line represents an institution, and the red line represents Viterbo University.

Note that the y-axis scale differs across conditions to simultaneously capture the entire range of data while enhancing the size of the boxplot. Remain mindful of scale differences during interpretation.

Click on the tabs to cycle through the comparison groups.

GROUP A

GROUP B

GROUP C

GROUP D

GROUP E

GROUP F

GROUP G

GROUP H

SALARIES IN 2021

Plots in this section visualize the average salary data for each comparison group in a different format than above, and only for 2021. These are the most recent data available.

Each raincloud plot features a density distribution on top, with a boxplot and individual data points below. The value in bold text is the median, and the dashed line identifies the position of - - - Viterbo University - - -.

Note that the x-axis scale differs across conditions to simultaneously capture the entire range of data while enhancing the size of the plot. Remain mindful of scale differences during interpretation.

Data tables are also provided. You can sort these by clicking on the column of interest; by default, this is NAME. Click once for ascending sort, twice for descending.

Sort by multiple columns by clicking on the column of primary interest, holding shift, and then clicking one or more subsequent columns of interest. For example, to sort by RANK and then by SALARY AVERAGE: click on RANK, hold shift, and click on SALARY AVERAGE. Note that if you click SALARY AVERAGE a second time (while still holding shift), you can change the order from ascending to descending.

Use the buttons to copy the data (for pasting somewhere else), to save a CSV or Excel file, or to print the table. The print option is useful if you want to save the table as a PDF. Sort the table as desired prior to copying, saving, or printing.

Use the Search box to filter the data by one or more specific observation(s) of interest (e.g., the name of an institution, or a specific academic rank).

Click on the tabs to cycle through the comparison groups.

GROUP A

GROUP B

GROUP C

GROUP D

GROUP E

GROUP F

GROUP G

GROUP H

PERCENTILE RANKS IN 2021

The following table summarizes the position of Viterbo University average salaries relative to those of each comparison group, by academic rank, in 2021.

Note that Viterbo University was added to Group H for this table to allow the generation of percentile ranks. This increased the group size to 21. All of the other groups inherently include Viterbo.

GROUP	ACADEMIC RANK	VITERBO POSITION	GROUP SIZE	%ile
GROUP A	Instructor	618	2176	28
GROUP A	Assistant	635	2240	28
GROUP A	Associate	716	2263	32
GROUP A	Professor	739	2342	32
GROUP B	Instructor	459	1534	30
GROUP B	Assistant	499	1879	27
GROUP B	Associate	544	1891	29
GROUP B	Professor	572	1960	29
GROUP C	Instructor	42	214	19
GROUP C	Assistant	36	294	12
GROUP C	Associate	46	297	15
GROUP C	Professor	41	299	13
GROUP D	Instructor	2	10	11
GROUP D	Assistant	6	16	33
GROUP D	Associate	5	16	27
GROUP D	Professor	6	15	36
GROUP E	Instructor	1	1	NaN
GROUP E	Assistant	2	4	33
GROUP E	Associate	2	4	33
GROUP E	Professor	2	4	33
GROUP F	Instructor	7	29	21
GROUP F	Assistant	8	39	18
GROUP F	Associate	9	40	21
GROUP F	Professor	10	38	24
GROUP G	Instructor	7	16	40
GROUP G	Assistant	7	22	29
GROUP G	Associate	7	22	29
GROUP G	Professor	8	21	35
GROUP H	Instructor	4	16	20
GROUP H	Assistant	6	21	25
GROUP H	Associate	8	21	35
GROUP H	Professor	5	21	20

CONCLUSIONS

Data provided to IPEDS support the perception of relatively low faculty salaries: Viterbo University faculty salaries consistently fall below the mean and median, for every academic rank, every year, for every comparison group¹.

These data in and of themselves do not provide evidence of inequitable compensation. The data are also not particularly useful to gauge the appropriateness of an individual faculty member’s salary; much more information is required to inform such perspectives. Hopefully, the report in progress by Casagrande Consulting will provide the robust picture necessary to thoroughly evaluate Viterbo faculty salaries at both institutional and individual levels.

However, it remains likely that Viterbo faculty salaries will remain relatively low for the foreseeable future. And, after all, some institutions will necessarily provide faculty salaries below the mean and median; that’s the nature of statistics. It is also possible that the comprehensive analysis will reveal that overall faculty compensation is comparable to peers once non-salary financial benefits are taken into consideration (e.g., health and retirement plans).

Regardless, opportunities certainly exist to tighten salary gaps, such that even if Viterbo salaries remain relatively low, the absolute differences won’t be so stark. Alleviating these differences requires substantial one-time bumps in base salary, and/or increasing salaries on a yearly basis at higher rates than our peers.

One significant concern with large salary gaps is that experienced faculty with disciplinary expertise and transferable skills may feel enhanced motivation to seek alternative employment opportunities. A related concern is that new faculty may regard Viterbo as a stepping-stone rather than an organization at which to establish a lengthy and fulfilling career. Non-financial benefits mitigate these concerns somewhat, but not completely.

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I hope that the data and analysis provided in this report will serve to inform decisions about faculty salaries at Viterbo. Please do not hesitate to contact me with questions, comments, thoughts, or concerns.

CONTACT INFORMATION

David J. Bauer, Ph.D.
Professor of Psychology
Viterbo University
djbauer@viterbo.edu

APPENDIX

DATA CHARACTERISTICS

• Personnel include full-time, non-medical, instructional staff.
  • These personnel have instruction as their primary job responsibility, but additional responsibilities may include research and/or public service.
  • Administrator and other non-instructional staff salaries are not included. These data are available from IPEDS but not incorporated into this report.
• Each institution reports the total number of faculty and the total salary outlay for each academic rank.
  • Total salary outlays have been standardized to a 9-month contract.
  • The average salary by rank is calculated by dividing the total outlay by the number of faculty.

DATA LIMITATIONS

• The analyses are limited to the salary data provided by institutions to IPEDS.
  • Data for individual faculty are not available. I do not have access to individual salaries, discipline/department, teaching loads, number of course preps, class sizes, number of advisees, committee responsibilities, accreditation requirements, research expectations, etc.
  • I also do not have compensation data for perks and benefits such as health insurance coverage, retirement plan options, tuition remission availability, etc.
  • Salaries are not weighted by any cost of living indices.
  • Salaries are not adjusted for inflation.

DATA SOURCES

All of the data files necessary to complete this report are publicly available from the IPEDS Data Center. Many additional files, containing many additional variables of potential interest, exist at the same location.

The files are primarily organized by year. For each year of interest, download the following files:

• “Directory information”. This file contains basic information for every institution (name, location, Carnegie classification, etc.).
  • Example (2020-2021): hd2020.csv (.zip, 1.0 MB).
  • Also download the associated Dictionary file: hd2020.xlsx (.zip, 217 kB).
• “Number and salary outlays for full-time nonmedical instructional staff, by gender, and academic rank: Academic year ####-##”. This file contains the salary data. When available, use the revised data file, which will be included in the zipped folder.
  • Example (2020-2021): sal2020_is_rv.csv (.zip, 2.0 MB).
  • Also download the associated Dictionary file: sal2020_is.xlsx (.zip, 57 kB).

VARIABLES

The following variables exist in one or both of the two data files indicated above, for each year. I combined these into a single data set for each year by selecting the variables of interest in each file and joining them based on the identification key.

• UNITID = Unique identification number (data key)
• INSTM = Institution name
• CITY = City
• STABBR - State abbreviation
• ZIP = ZIP code
• SECTOR = Institution categorization that combines ICLEVEL with CONTROL.
• ICLEVEL = Institution categorization based on program year length (4-year, 2-year, <2-year).
• CONTROL = Institution categorization based on operating officials / funding sources (Public, Private non-for-profit, Private for-profit).
• C15BASIC = Basic Carnegie classification using criteria updated in 2015.
• C18BASIC = Basic Carnegie classification using criteria updated in 2018.
• C21BASIC = Basic Carnegie classification using criteria updated in 2021.
• ARANK = Academic rank (Professor, Associate professor, etc.).
• SATOTLT = Number of full-time, non-medical, instructional staff.
• SAEQ9OT = Total salary outlays of full-time, non-medical, instructional staff equated to a 9-month contract.
• SAEQ9AT = Average salary for full-time, non-medical, instructional staff equated to a 9-month contract.

Then I created a YEAR variable and joined all of the years together to create a single data set. The resulting data set includes 112065 observations of 16 variables from 8156 institutions, covering 6 years (from 2016 to 2021).

RENAMING AND RECODING

I renamed and recoded several variables for easier reading and interpretation. Specifically:

• INSTNM = “NAME”
• STABBR = “STATE”
• ICLEVEL = “LEVEL”
  • 1 = “4+ years”
  • 2 = “2-4 years”
  • 3 = “0-2 years”
• CONTROL
  • 1 = “Public”
  • 2 = “Private”
  • 3 = “For_Profit”
• C15BASIC = “CARNEGIE_15”
• C18BASIC = “CARNEGIE_18”
• C21BASIC = “CARNEGIE_21”
• ARANK = “RANK”
  • 1 = “Professor”
  • 2 = “Associate”
  • 3 = “Assistant”
  • 4 = “Instructor”
• SATOTLT = “COUNT”
• SAEQ9OT = “SALARY_TOTAL”
• SAEQ9AT = “SALARY_AVERAGE”

I also created the variable CARNEGIE, which basically combined CARNEGIE_15 and CARNEGIE_18 and CARNEGIE_21. Each institution has one of these classifications, depending on year; combining them into a single variable facilitated analyses.

Finally, I converted all ZIP codes to five digits (some were nine), which made it easier to form the 100-mile comparison group.

FILTERING

Several categorical variables include levels that aren’t relevant for comparison purposes; for example, there are 7 levels of Academic rank (ARANK): Professor, Associate professor, Assistant professor, Instructor, Lecturer, No academic rank, and All instructional staff total. Only the first four of these are relevant, so I filtered the data to only include those observations.

I also filtered observations based on the following criteria:

• ICLEVEL: include “4+ years” and “2-4 years” institutions. I removed “0-2 years” institutions.
• CONTROL: include “Public” and “Private” institutions. I removed “For_Profit” institutions.

Finally, I removed any observations that did not contain complete data.

This initial filtering process leaves a data set of 3185 institutions. This still includes a broad array of institutions that aren’t really appropriate for comparison purposes, but it’s worth beginning with a birds-eye view of where Viterbo faculty salaries stand in higher education.

Subsequent filtering was conducted to subset the data to form the desired comparison groups; details are provided in that section.

Footnotes

1. With a few exceptions specific to the WAICU comparison group (Group G): the average Viterbo Assistant salary equaled or slightly surpassed the median each year from 2016 - 2020, and the average Viterbo Associate slightly surpassed the median in 2017.