a detailed examination of the executive/administrative...
TRANSCRIPT
OIS: 10/19/2009 Page 1 of 7
A Detailed Examination of the Executive/Administrative/Managerial Staffing Levels within the
Context of Other Institutional Characteristics.
A Report Prepared by the University of Vermont Office of Institutional Studies1 Introduction: For institutional benchmarking purposes, this study examines the size of UVM’s Executive/Administrative/Managerial staff 2 (hereafter “administrators”) in relation to other public and private Doctoral/Research – Extensive and Intensive institutions. Two hundred and sixty universities form the foundation of the study group (source IPEDS), and from which, three additional subsets of peers and competitors are then extracted for further analyses:
• Public Institutions only (n=168) • Research Universities - High Activity, UVM’s 2005 Carnegie Classification (n=103). • UVM’s list of public Peers and Aspirants (n=21 including UVM).
The implicit assumption of this undertaking is that the size of the administrative staff grows in tandem with other measurements such as the total student headcount, total number of faculty and other staff, etc. In reality, organizational structures are complex and idiosyncratic, sometimes to the exclusion of systematic expectations. Given the multifaceted nature of these data, the administrative staffing level will be scrutinized from three different perspectives to render a more complete overview:
1. Examining the ratio of administrators to faculty members. 2. Modeling the growth in administrative staffing over a 6-year period. 3. Modeling the number of administrators using other institutional characteristics.
Summary of Findings:
• The simplest measurement of the relationship between the numbers of administrators to faculty members is a ratio of the two. UVM’s ratio is consistently in the bottom 25% of each of the four groups examined.
• The rate of UVM administrative growth from fall 2003 to fall 2007 is slightly lower than
the average rates of growth in 3 of the 4 groups and slightly higher in one group, meaning there is no significant departure from the norm.
1 This report has been authored by Fred A. Curran, Director, Institutional Studies, and Nguyen K. Bach, Senior Institutional Studies Analyst. The authors acknowledge with gratitude assistance received from Ronald G. Ehrenberg, the Irving M. Ives Professor of Industrial and Labor Relations and Economics at Cornell University and Director of the Cornell Higher Education Research Institute. Professor Ehrenberg provided a critique of an earlier draft as well as suggestions for additional analysis. They also wish to thank the University of Vermont Faculty Senate and its Committee on Financial and Physical Planning for reviewing the next-to-last draft and for raising a few points for clarification that the authors hope are addressed in this final draft. Any shortcomings that remain in the study are of course the authors' alone. 2 “Executive/Administrative/Managerial staff “will be referenced as “senior administrators”. The group is comprised of both (a) officials whose job titles contain the term “president” or “provost”, including associate and assistant vice presidents and provosts as well as vice provosts and (b) other individuals who have been identified as executive, administrative or managerial staff in the Integrated Postsecondary Education Data System (IPEDS) Human Resources Survey.
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• When the number of administrators is modeled by a more complex regression equation, the predicted levels for UVM administrative staffing are higher than the actual numbers by 46 to 214 persons.
• On the basis of these three analyses, it is concluded that the number of University of
Vermont administrators is below what could be expected when compared to the norm at equivalent public and private universities.
Information about the datasets: All data for the three analyses included in this report are for the fall 2003 (FY 2004), fall 2005 (FY 2006), and fall 2007 (FY2008). With one exception (#10 – Institution has a foundation) the data were taken from the Integrated Postsecondary Education Data System’s on-line Peer Analysis System (IPEDS PAS) for the 2003-04, 2005-06, 2007-08, and 2008-09 IPEDS Survey cycles. The data elements from IPEDS PAS taken for use in these analyses were:
1. IPEDS Unit ID 2. Institution Name 3. State 4. Institution control (Public; Private, not-for-profit; Private, for Profit) 5. 2000 Carnegie Classification 6. 2005 Carnegie Classification 7. Does the institution offer a medical degree? (Yes/No) 8. Is the institutional a land-grant institution? (Yes/No) 9. Is the institution part of a system? (Yes/No) 10. Does the institution have a foundation? (Yes/No) 11. Is the institution a UVM peer/aspirant? (Yes/No) 12. Does the institution have a hospital? (Yes/No) 13. Total number of headcount students, Fall 2007 14. Full-time equivalent students, Fall 2007 (IPEDS definition) 15. Number of full-time faculty, Fall 2007 16. Headcount number of Executive/Administrative/Managerial employees, Fall 2007 17. Headcount number of Other Professional employees, Fall 2007 18. Headcount number of Technical and Paraprofessional employees, Fall 2007 19. Headcount number of Clerical and Secretarial employees, Fall 2007 20. Headcount number of Skilled Crafts employees, Fall 2007 21. Headcount number of Service/Maintenance employees, Fall 2007 22. Headcount number of Total staff employees, Fall 2007 23. Headcount number of Total faculty and staff, Fall 2007 24. Headcount number of Graduate Assistants, Fall 2007 25. Grand total headcount employees, Fall 2007 26. Total number of full-time equivalent non-medical school employees, Fall 2007 27. Total number of full-time equivalent medical school employees, Fall 2007 28. Total number of full-time equivalent employees, Fall 2007 29. Total revenues from federal, state, local and private grants and contract, FY 2008 30. Total revenues from gifts, including contributions from affiliated organizations, FY 2008 31. Total current year research expenditures, FY 2008 32. Total hospital services expenditures, FY 2008 33. Total revenues, FY 2008 34. Total operating expenses, FY 2008
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It should be noted that not all of these variables were used in these analyses. Of the variables that were used, not all were appropriate or necessary in each of the three analyses. In addition, some variables were combined to create a new variable. The initial data set was composed of all 260 public and private (both not-for-profit and for-profit) Doctoral/Research-Extensive and Intensive institutions in the US, covering all 50 states and Puerto Rico. [Data Source: 2000 Carnegie Classification as reported in the 2007-2008 Integrated Postsecondary Education Data System (IPEDS). Five institutions that were missing revenue and expense information were excluded from the analysis. These five schools were: Antioch University New England; Argosy University-Sarasota; Mayo Graduate School at the Mayo Clinic College of Medicine; Rutgers University-Newark; Walden University. Two schools -- Colorado School of Mines and North Carolina A&T State University -- have been added to this list because they are a Research University - High Activity in the 2005 Carnegie Classification but were neither a Doctoral/Research - Extensive nor Intensive institution in the 2000 Carnegie Classification. [See Appendix A for a complete list of the 260 schools.] It is important to note that IPEDS defines senior administrators somewhat more broadly than just positions with titles that include the nouns of “president” and “provost”. Using the University of Vermont as an example, the employee category “Executive/Administrative/Managerial” includes 101 individuals who have dean, director, or other equivalent titles as well as the individuals with job titles that include president or provost in their job title. [See Appendix B for a complete list of UVM employees listed as executive/administrative/managerial employees.] The IPEDS Executive/Administrative/Managerial employee category – which is also an Equal Employment Opportunity Commission (EEOC) reporting category – is a broad grouping of senior administrator positions based on 2000 Standard Occupational Classification System (SOC). [Note: More detailed information on the IPEDS-EEOC-SOC job category classifications can be found in Appendix C.] The IPEDS Human Resources Survey is the only available and timely source for well-defined and consistent employee census data for all higher education institutions.
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Examining the distributions of the ratio of administrators to faculty members
Data from 2007-08 IPEDS This ratio is calculated by simply dividing the number of FTE3 administrators by the number of FTE faculty for each institution. Percentile rank is then calculated for UVM in each of the 4 population groups: all institutions, public institutions only, Research Universities – High Activity (UVM’s 2005 Carnegie Classification), and peers and aspirants. The strength of this ratio measurement is its clarity and simplicity, albeit it does not account for other possible influencing factors, which will be addressed in subsequent analyses. The results are shown in the following table for the 4 populations:
Public & Private
Universities
Public Universities
Only
Research Universities - High Activity
Peers & Aspirants
Count 260 168 103 21 Mean 0.2430 0.1884 0.2184 0.1967 Maximum 1.1077 1.1077 0.7585 0.4764 75th percentile 0.3307 0.2373 0.2922 0.2502 Median 0.2098 0.1684 0.1865 0.1865 25th percentile 0.1233 0.1031 0.1182 0.0997 Minimum 0.0281 0.0281 0.0283 0.0435 UVM percentile rank 10th 15th 14th 22nd
In the 2007-08 IPEDS dataset, UVM had 100.3 FTE administrators and 1,168 FTE faculty members. The ratio of 100.3/1,168 is 0.0859. In other words, there were approximately 0.086 administrators for each faculty member. UVM’s ratio is well below the means and medians of the four groups and is below the 25th percentile marks. More specifically, UVM’s ratio of FTE administrators to FTE faculty ranks at the bottom 10th, 15th, 14th, and 22nd percentiles, respectively for the 4 populations. There is no evidence of high levels of UVM administrative staffing in proportion to faculty headcount. In fact, compared to our peers and competitors, the number of UVM administrators appears to be very low.
3 IPEDS recognizes that institutions may define full-time equivalencies of faculty, staff, and students differently for different purposes. As a result, they do not collect FTE faculty and staff data. Rather, data are collected by full-time part-time status. IPEDS then suggests the following methodology for computing full-time equivalencies using their data:
a. All full-time individuals are assigned an FTE = 1.00 b. All part-time faculty are given an FTE by either:
1. Assigning each part-time employee an FTE = 0.33 and multiplying by the total number of part-time employees; or, 2. Dividing the total number of part-time employees by 3. This is the FTE calculation used in our Executive Staffing study. It is an FTE defintion widely employed by policy researchers and others involved in higher education reporting and analysis, e.g., US News & World Report’s calculation of student-to-faculty ratios for America’s Best Colleges.
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Modeling the growth in administrative staffing over a 6-year period
Data are obtained for the following three time periods: fall 2003, fall 2005, and fall 2007. These periods were selected because IPEDS reporting of staffing data is only mandatory in odd number fall semesters. This time span largely encompasses President Fogel’s tenure at UVM. The statistical model is a standard analysis of covariance with the number of FTE administrators being the dependent variable, and the number of FTE faculty as the covariate predictor along with “TimePeriod” and institutions, which is represented by “Unit ID” - a categorical variable. The model takes on this form: FTE Administratorij = αi + β1*TimePeriod ij + β2*FTE Facultyij + εij Note: i = 1 to the number of institutions; j = 0, 1, 2.
• The inclusion of the “FTE Faculty” predictor is to help explain away any increase in the size of the administrator group that is the result of institution-wide expansion. “FTE Faculty” is, in a sense, a proxy for overall university size.
• The predictor “Time Period” is recoded as fall 2003 = 0; fall 2005 = 1; and fall 2007 = 2
and means that one “Time Period” unit is the equivalent of two years. This model is applied to the 4 populations and the results are shown below:
Public & Private
Universities
Public Universities
Only
Research Universities - High Activity
Peers & Aspirants
Count 780 504 309 63 Model p-value <.0001 <.0001 <.0001 <.0001 Adjusted Rsquare 0.9484 0.9276 0.9086 0.9597 Time Period p-val 0.0165 0.8596 0.5847 0.9289 FTE Faculty p-val <.0001 <.0001 0.8510 0.0169 UnitID p-val <.0001 <.0001 <.0001 <.0001
Note: the “Count” is three times the previous count for each group because there are 3 time periods.
The “Adjusted R-Square” highlighted above is a measurement of how well the predictors (Time Period, FTE Faculty and UnitID) account for the amount of variation in the dependent variable (FTE Administrator). The “Adjusted R-Square” has a value between 0.0 and 1.0, and is usually referred to as a percentage. In this analysis, the Adjusted R-Square ranges from a low of 91% to a high of 96%, indicating that the model fits the data extremely well, though not all the predictors consistently show a statistical association with “FTE Administrator.” For example, “Time Period” is only statistically associated with “FTE Administrator” (meaning there is a clear trend over time) in the “Public & Private Universities” group; on the other hand, “FTE Faculty” shows no statistical association to “FTE Administrator” in the “Research Universities – High Activity” group. Having established the validity of the above model, the next step is to examine the difference between the actual FTE Administrator and its predicted value in each of the four groups, over the three time periods.
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Public & Private Universities
Time Period Actual UVM FTE Admin
Predicted by Model
Difference (Actual - Pred)
Studentized Residual
0 (fall 2003) 94.0 80.5 13.5 0.164 1 (fall 2005) 93.3 95.2 -1.9 -0.022 2 (fall 2007) 100.3 112.0 -11.7 -0.142
Public Universities Only
Time Period Actual UVM FTE Admin
Predicted by Model
Difference (Actual - Pred)
Studentized Residual
0 (fall 2003) 94.0 90.1 3.9 0.058 1 (fall 2005) 93.3 95.1 -1.7 -0.026 2 (fall 2007) 100.3 102.5 -2.2 -0.032
Research Universities - High Activity
Time Period Actual UVM FTE Admin
Predicted by Model
Difference (Actual - Pred)
Studentized Residual
0 (fall 2003) 94.0 93.7 0.3 0.006 1 (fall 2005) 93.3 96.0 -2.6 -0.054 2 (fall 2007) 100.3 98.0 2.4 0.049
Peers & Aspirants
Time Period Actual UVM FTE Admin
Predicted by Model
Difference (Actual - Pred)
Studentized Residual
0 (fall 2003) 94.0 89.1 4.9 0.067 1 (fall 2005) 93.3 94.5 -1.2 -0.016 2 (fall 2007) 100.3 104.0 -3.7 -0.051
The “Studentized Residual4” (far right column) is an indicator of anomalies. A value close to +/- 2.0 signals an unusual data occurrence. In this case all the studentized residuals are very small, meaning there is no irregularity. The growth rate for UVM administrators over the 6-year period was lower than its peers and competitors in the “Public and Private Research Universities,” “Public Universities Only” and “Peers & Aspirants”, as is indicated by the negative sign of “actual - predicted.” In other words, the rate of UVM administrative growth is slightly lower than the overall trajectory of these comparison groups. When examining the “Research Universities - High Activity,” UVM has a slightly higher growth rate. It is important to stress that this analysis looks only at the rate of growth in administrators. In general, the findings indicate that UVM administrative growth is not significantly dissimilar to the average growth rate of other universities.
4 A “residual” is the difference between the observed value of the dependent variable and its predicted value by the model. The “studentized residual” is the ratio of the residual divided by its own standard deviation.
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Modeling the number of administrators using other institutional characteristics Data from 2007-08 were also used to model the total number of full and part-time administrators with the following 10 predictors:
• Total full and part-time faculty headcount • Total full and part-time non-administrative staff • Total full and part-time student headcount • Total operating expenditure • If a school is in a system • If a school offers a medical degree • If a school is a land-grant institution • If a school has a foundation • Total research expenditure • Hospital expenditure
It should be noted that this analysis does not utilize the “full-time equivalent” conversion for faculty and staff headcounts. The dependent variable, which is the total number of full and part-time administrators, underwent a natural log transformation in the first three groups to partially correct a very skewed distribution. This log transformation, however, is not applied to the last group of 21 peers and aspirants due to the small group size.
Public & Private
Universities
Public Universities
Only
Research Universities - High Activity
Peers & Aspirants
Count 249 165 100 21 Model p-value <.0001 <.0001 <.0001 0.0006 Adjusted Rsquare 0.5813 0.5816 0.3646 0.8183 Faculty p_val 0.0074 0.0172 0.2518 0.0200 Non-Admin Staff p_val 0.5452 0.7224 0.0209 0.6914 Total Students p_val 0.1044 0.0220 0.9347 0.1498 Operating Exp p_val 0.0108 0.2312 0.0003 0.0279 In a System p_val 0.9078 0.2281 0.1742 0.3298 Had Med Degree p_val 0.0305 0.2686 0.8226 0.8938 Is Land Grant Inst p_val 0.3516 0.8415 0.7668 0.8414 Has a Foundation p_val 0.0006 0.2862 0.6529 0.5914 Research Expend p_val 0.3472 0.4144 0.0806 0.1756 Hospital Expend p_val 0.0759 0.6137 0.0096 0.0250
UVM Predicted 223 147 173 315 UVM Actual 101 101 101 101
Actual minus Predicted -122 -46 -72 -214 The adjusted R-Square values are at 58% for the first two groups, “Public and Private Research Universities” and “Public Universities Only.” It falls to a low of 36% for the “Research Universities – High Activity,” indicating poor fit for that model and a lot of unaccounted for variation. The last group, “Peers & Aspirants” has a strong R-Square value of 82%. According to these models, it is predicted that UVM would have anywhere from 46 to 214 more administrators than its actual number.
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Pag
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Sou
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ED
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Sys
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Pag
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App
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stitu
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OIS
: 10
/15/
2009
Sou
rce:
IP
ED
S P
eer A
naly
sis
Sys
tem
Pag
e 8
of 9
App
endi
x A
Uni
tIDIn
stitu
tionN
ame
Con
trol
Car
negi
e C
lass
ifica
tion
2000
Car
negi
e C
lass
ifica
tion
2005
UVM
Peer
s
1371
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Pro
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5231
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den
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ity3_
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nsiv
e
OIS
: 10
/15/
2009
Sou
rce:
IP
ED
S P
eer A
naly
sis
Sys
tem
Pag
e 9
of 9
App
endi
x B
No.
Nam
eJo
b Ti
tleJo
b C
ode
Dep
artm
ent
Dep
t. C
ode
1.
Ash
ikag
a,Ta
kam
aru
Dire
ctor
0964
Med
ical
Bio
stat
istic
s55
080
2.
Aul
tman
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l,Lis
a M
.D
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or U
niv
Tran
spor
tn C
tr08
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Tra
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rtatio
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tr30
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cock
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r Offi
cer G
ovm
nt R
ltn13
89V
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tate
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4.
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lard
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iam
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ston
Ass
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te V
ice
Pre
side
nt08
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dmin
& F
acil
Ser
vice
s11
500
5.
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luke
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res
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l Affr
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livea
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7.
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an,K
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inda
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irect
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63R
adia
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3062
0
8.
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uwie
kel,R
uth
M.
Uni
vers
ity V
eter
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ian
0951
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mal
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e M
anag
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9.
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wn,
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gor
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0860
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10.
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ke,M
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11.
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lacc
ini,P
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2
12.
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lson
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hard
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urD
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ervi
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1158
1
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r,Fra
nces
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rad
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1376
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P &
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vost
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thon
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nie
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Ass
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side
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nanc
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fc11
200
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arel
li,S
alva
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Dire
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0858
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sica
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nt11
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,Kel
vin
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rim A
ssoc
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n11
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rk,J
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ir In
strm
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ech
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v11
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en,J
anie
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ise
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ing
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usan
Ann
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ean
0818
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ran,
Rob
ert
Ass
oc V
P &
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ctor
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letic
s08
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thle
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21.
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rede
ra,E
nriq
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58U
nive
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3055
0
22.
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rian
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3070
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24.
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39In
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atio
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duca
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s30
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25.
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ler,S
hane
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pher
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ctor
0863
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fere
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vent
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s11
570
List
of U
VM E
mpl
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s R
epor
ted
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e EE
OC
Occ
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200
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IPED
S H
uman
Res
ourc
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urve
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OIS
: 10
/15/
2009
Page
1 of
4
App
endi
x B
No.
Nam
eJo
b Ti
tleJo
b C
ode
Dep
artm
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Dep
t. C
ode
26.
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is,J
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0818
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ary
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itt,R
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dmin
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ary
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e P
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0840
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34.
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ell,R
uth
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eA
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e P
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0840
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35.
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dman
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rm A
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36.
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0860
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3043
0
37.
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dber
g,Jo
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er,J
. Mic
hael
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s Fi
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don,
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tratio
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hom
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ames
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s S
tdnt
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3030
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42.
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0840
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0962
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111
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Sen
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ice
Pre
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rovo
st08
52S
enio
r VP
& P
rovo
st30
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45.
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Ass
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te V
ice
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nt08
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105
46.
Jette
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ab &
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47.
John
son,
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side
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uman
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ourc
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48.
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hel K
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y,A
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ces
1160
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52.
Kor
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Exe
cutiv
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s11
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53.
Lant
agne
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glas
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laD
ean
0820
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tate
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ff50
020
OIS
: 10
/15/
2009
Page
2 of
4
App
endi
x B
No.
Nam
eJo
b Ti
tleJo
b C
ode
Dep
artm
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Page
4 of
4
Appendix C
Background to the IPEDS Employee Job Category Classifications The Integrated Postsecondary Education Data System (IPEDS) Human Resources Survey (HRS) consists of three components: Fall Staff; Employees by Assigned Position; and Salaries. The data on staff by employee category are collected in the Fall Staff component of the Survey. The IPEDS Human Resources Survey is open for completion by institutions in the IPEDS winter survey cycle that runs between early December and mid-January of each academic year. The data for the IPEDS HRS survey are to be based on an institutional snapshot from each institution’s human resources system as of November 1st. Jobs are classified by primary job titles, not by secondary appointments. The faculty and staff census data are reported in the following categories:
o Faculty o Executive/administrative/managerial o Other professionals (support/service) o Technical and paraprofessionals o Clerical and secretarial o Skilled Crafts o Service/Maintenance o Graduate Assistants
These categories are the same as those used by the U.S. Equal Employment Opportunity Commission (EEOC). IPEDS employs the Standard Occupational Classification (SOC) codes and categories to identify relevant jobs assigned to the above EEOC categories. More information on the SOC can be found on the web site of U.S. Department of Labor Statistics at: http://www.bls.gov/SOC/ The general description of SOC on the website is as follows:
. . . . . . The 2000 Standard Occupational Classification (SOC) system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of over 820 occupations according to their occupational definition. To facilitate classification, occupations are combined to form 23 major groups, 96 minor groups, and 449 broad occupations. Each broad occupation includes detailed occupation(s) requiring similar job duties, skills, education, or experience.
The list of positions for the SOC category “Education Administrators, Postsecondary” include the following:
Presidents.
Vice Presidents (including assistants and associates).
Deans (including assistants and associates) - if their principal activity is administrative and not primarily instruction, research or public service.
Appendix C
Directors (including assistants and associates).
Department heads (including assistants and associates) - if their principal activity is administrative and not primarily instruction, research or public service.
Assistant, associate managers (including first-line managers of service, production and sales workers who spend more than 80 percent of their time performing supervisory activities).
The University of Vermont’s Office of Human Resources Services classifies UVM job titles into the EEOC categories, including the Executive/Administrative/Managerial category. The UVM Office of Institutional Studies takes those data from the human resources system (formerly HRS, now PeopleSoft) to complete the IPEDS HRS survey. As is the case at UVM, the other institutions completing the IPEDS HRS survey must classify their jobs and job titles based on their understanding of the jobs, their duties, skills, education, or experience. The IPEDS Human Resources Survey is the only available and timely source for well-defined and consistent employee census data for all higher education institutions. OIS: 10/15/2009