Advances in Management & Applied Economics, vol.2, no.1, 2012, 1-23
ISSN: 1792-7544 (print version), 1792-7552 (online)
International Scientific Press, 2012
Managing Operating Efficiencies of Publicly
Owned Universities:
American University Stochastic Frontier
Estimates Using Panel Data
G. Thomas Sav1
Abstract
This paper investigates the extent of operating cost inefficiencies in the public
provision of American higher education. The analysis employs panel data on
American research and doctoral granting universities spanning four academic
years, 2005-06 through 2008-09. Translog cost frontiers are estimated under two
alternative efficiency models whereby institutional specific environmental factors
affect university operating inefficiencies on the one hand and cost frontiers on the
other.
Results support the notion that inefficiency effects are not to be ignored in
modeling the cost structure of American universities. Moreover environmental
factors affect university operating efficiencies. University operating inefficiencies
are found to have increased over time, but the rate of growth slowed substantially
in the 2008-09 academic year. That could be an early managerial response to
1
Department of Economics, Raj Soin College of Business, Wright State University,
Dayton, OH, USA, e-mail: tom.sav@wright.edu
Article Info: Received : October 31, 2011. Revised : December 9, 2011
Published online : February 28, 2012
2
Managing Operating Efficiencies of Public Universities...
budget cuts driven by the financial crisis. Rankings based on individual university
performance indicate fairly wide inefficiency variability and some flagship
universities residing among the more inefficient institutions. With growing interest
in public management reform, the paper should be of interest from both
perspectives of public policy and managerial decision-making.
JEL classification numbers: I21, I22, I23, L3, C33
Keywords: Cost Frontier, Public Universities, Cost Inefficiencies
1
Introduction
Public deficits and increasing debts induced by the financial crisis and the
so-called Great Recession have accelerated international interest in public
management reform and the call for greater efficiency in the delivery of publicly
produced goods. In the United States, publicly funded universities have not
escaped either political or taxpayer scrutiny or the managerial realization of the
need to make internal restructuring decisions, all of which appears to be occurring
in the absence of
rigorous empirical study and evidence regarding the extent to
which there exists university operating inefficiencies. Yet, stochastic frontier
analysis offers a robust methodological approach to the measurement of operating
efficiencies at the industry, sector, and firm or institution level. The methodology
has been fruitfully applied in obtaining efficiency measures within a variety of
industries and sectors. It involves the basic concept that a firm’s production and
costs, while at the peril of uncontrollable random shocks driven by such things as
catastrophic weather events and economy wide recessions, can also be generated
from inefficiencies that are due to characteristics of inputs or managerial
performance. Thus, the main thrust is that these inefficiencies cause firms to
operate below their stochastically maximum production or above their minimum
G. Thomas Sav
3
obtainable costs.
It is the purpose of this paper to determine whether or not and to what extent
there exist such operating cost inefficiencies in the American provision of public
higher education. The focus is on Carnegie classified American doctoral and
research universities that engage in the delivery of multi products, including
undergraduate, graduate, and professional school education along with research.
The university multiproduct cost frontier is estimated via a translog cost function
using panel data pertaining to 159 institutions over four academic years, 2005-09.
Hence, there is the possibility of uncovering the presence of initial university
operating responses to the financial crisis. In addition to providing time varying
inefficiencies over academic years, inefficiency scores and rankings are presented
for individual universities. An exhaustive literature survey indicates that the
research represents the first known application in employing stochastic frontier
analysis in investigating operating cost efficiencies of American universities.
2
Applied Literature
Efficiency estimates based on stochastic frontier analysis are rooted either in
production or costs.
The stochastic production frontiers were pioneered by
Aigner, et al. (1977) and Meeusen and van den Broeck (1977). Aigner, et al. made
empirical application to the U.S. primary metals industry. Other applied studies
followed suit with applications to U.S. dairies (Kumbhakar, et al., 1991), India
paddy farms (Battese and Coelli, 1992 and 1995), international airlines (Coelli, et
al., 1999), and U.S. electricity (Knittel, 2002). On the cost side, stochastic frontier
research has embraced the U.S. airlines industry (Kumbhakar, 1991), insurance
industry (Cummins and Weiss, 1992), hospital care (Bradford, et al., 2001), and
banking (Huang and Wang, 2001), among others.
Of particular interest in the present paper is the fairly new emergence of
4
Managing Operating Efficiencies of Public Universities...
stochastic cost frontier applications to education. At the primary and secondary
school levels, Chakraborty and Poggio (2008) examined the inefficiencies
pertaining to Kansas school districts using panel data for 2001-05. In higher
education, Izadi, et al. (2002) use 1994-95 data to estimate efficiencies for 99
British higher education institutions. Stevens (2005) provided a 1995-99 panel
data study of 80 English and Welsh universities. Johnes and Johnes (2009) also
employ panel data for a three year period, 2000-2001, in their study of 121
English institutions. McMillan and Chan (2006) did a 1992-93 cross section study
of 45 Canadian universities. Abbott and Doucouliagos (2009) examined 7 New
Zealand and 36 Australian universities from 1997 to 2003. Each of these higher
education studies has used different cost and inefficiency specifications that make
comparisons difficult at best. The study by Stevens (2005) represents the closest in
methodology and data for comparison to the current study of American
universities.
3
Cost Frontier and Efficiency Methodology
To obtain empirical measures of university operating efficiencies, we rely on
the stochastic frontier methodology developed by Aigner, Lovell, and Schmidt
(1977) and Meeusen and van den Broech (1977) and extended, in particular, by
Battese and Coelli (1992) and Battese and Coelli (1995).
Following Kumbhakar
and Lovell (2000) and Kumbhakar and Sarker (2005), given that universities
produce multiple products and employ multiple inputs, the superiority of the
stochastic cost frontier is chosen over the production frontier.
For empirical
implementation, the translog, with its well- known functional flexibility, is
selected for the cost specification pertaining to American universities. Thus, for
the multiproduct university, the annual operating costs, C, can be characterized by
the following cost frontier
G. Thomas Sav
5
ln C   0    j ln Q j    k ln wk  12   jl ln Q j ln Ql  12   km ln wk ln wm
j
k
j
l
k
m
  kj ln wk ln Q j  (v  u )
k
(1)
j
where, in the case of longitudinal data, the institutional i and time t subscripts are
omitted for convenience and the standard coefficient ai , j  a j ,i equalities prevail
for  ,  ,
and  . In a broad sense, university outputs, Q, are generally
thought of as including different levels of education and research. On the input
side, universities are considerably labor intensive and the input prices, therefore,
are usually wage rate, w, related. The specific Q outputs and w wage rates used in
the empirical estimations are largely dependent upon data availability. For the
present study, the data details are taken up in the next section of the paper.
With institutional i and time t subscripts renewed for clarity, the cost
specification error term ( vit  uit ) is comprised of two components. First, there is
the usual random error denoted here by vit ; it is assumed to be independently and
identically normally distributed with zero mean and variance  2 : i.e., N (0,  2 ) .
Second is the inefficiency component uit that is nonnegative and independently
distributed. Thus, university operating costs are subject to random variations
beyond direct administrative control but can also be the result of managerial and
other factors creating inefficiencies and causing universities to operate above
( uit  0 ) their minimum cost frontier. Measuring whether or not and to what
extent universities are inefficient in this sense rests on a number of assumptions.
This paper proceeds with two alternative efficiency assumptions.
Following that which is proposed by Battese and Coelli (1995), hereafter
BC95, the managerial or so-called environmental factors, denoted by zit, are
modeled as explicit determinants of the university’s inefficiency effect. Under this
specification,
uit  zit  xit
(2)
6
Managing Operating Efficiencies of Public Universities...
where zit is a (rx1) and  is a (1xr) vector of coefficients with a constant term  0
included and xit is a random variable defined by the truncation of the normal
distribution with zero mean and variance  u 2 truncated at the point zit .
Rather than entering the inefficiency component, the alternative Battese and
Coelli (1992) model, hereafter BC92, assumes that the environmental factors alter
the shape of the university’s cost frontier. In this case, the translog cost frontier
specified by (1) would be amended to directly include zit (without  0 ).
The
inefficiencies can be defined by
uit  ui exp( (t  T ))
(3)
Here, the ui are independently and identically distributed as truncations at zero of
N (  ,  2 ) . The inefficiency monotonically increases (  0 ), decreases (  0 ),
or remains unchanged over time.
Under both BC models, the estimation of the cost frontier will proceed with
four university outputs and two input prices. There will be six environmental
factors along with a time trend employed as determinants of inefficiency in (2) for
the BC95 model and as cost frontier altering determinants in (1) for the BC92
model. Thus, the z’s are neither outputs nor input prices; they are a mix of
environmental factors that have alternative ways of entering and subsequently
affecting costs and operating efficiencies.
4
Panel Data
University level data are drawn from the Integrated Postsecondary Education
Data System (IPEDS) housed at the U.S. National Center for Education Statistics.
The IPEDS annual survey instruments change over time, redefining and deleting
variables, thereby creating difficulties for some longitudinal analyses. Using the
most recent data releases, consistent panel observations on Carnegie doctoral and
G. Thomas Sav
7
research classified public universities were available for four academic years,
2005-06 through 2008-09. Eliminating universities that failed to report any total
cost or enrollment data produced a balanced panel of 159 universities.
To model university costs, outputs and input prices, we rely to a large extent
on the successes of previous research related to university cost structures,
including the seminal work of Cohn, et al. (1989) and subsequent investigations
by Koshal and Koshal (1999), Sav (2004), and Lenton (2008), among others. In
that context, the academic year cost (C) incurred by universities is the reported
total operating expenses. University outputs include undergraduate, graduate, and
professional school education and research. Credit hour production over the
academic year is the output measure for undergraduate (UG) and graduate (GRD)
education. That accounts for students of different classifications (part and
full-time) carrying different course loads throughout the normal academic year,
intersessions, and summer months. That measure was unavailable for professional
school (PRO) output. Instead, it was necessary to use head count enrollments.
That, however, seems reasonable based on medical, dentistry, veterinarian, etc.
professional schools being lock-step full-time student programs.
Here as with
previous studies, the proxy for a university’s aggregate research (RES) output rests
on the receipt of government and private research grants, gifts, and contracts. The
proxy has been widely accepted (e.g., references noted above) based on the
unavailability of quality substitute measures and the notion that external funding
support correlates highly with aggregate research output.
On the input side, two wage variables are included: one representing teaching
and research faculty and one representing administrative faculty. The two wages
are proxied by average salaries, as is common to other cost research. Presently,
faculty wages for teaching and research faculty use the IPEDS salaries for nine
month contracted faculty (SAL9). Administrative faculty on twelve month
contracts such as department chairs, deans, and provosts are assigned the IPEDS
twelve month salary contracts (SAL12).
8
Managing Operating Efficiencies of Public Universities...
Environmental factors included in the inefficiency effects are similar in spirit
to those used by Stevens (2005) in studying English and Welsh universities.
That is, student and faculty characteristics purportedly affect cost efficiencies.
For the former, the university’s percentage of students funded by federally
provided low income grants (FEDGRT) is used as a possible indicator of
underprepared students coming from underfunded school districts. Also included
is the percent of nonwhite students (NWHITE) with the notion that universities
build special courses, programs, and centers related, e.g., to African American,
Hispanic, and Asian experiences. Both enrollment increases could affect a
university’s operating efficiency through the need to engage in remedial or
supplementary education and to allocate resources to fostering educational
diversity.
The remaining four environmental factors relate to the university’s labor
force characteristics and parallel a portion of the variables used in the studies by
Chakraborty and Poggio (2008) and Abbott and Doucouliagos (2009), in addition
to Stevens (2005). Included is the quantity of labor input allocated to the
production of teaching and research and to administration. Teaching and research
faculty are defined as the number of faculty employed on nine month contracts
(FAC9). Administrative faculty are employed on twelve month contracts (FAC12).
Similarly, university cost differences are likely to arise from differences in the
percentage of faculty employed that are in non-tenure track (NTRACK) teaching
positions. That measure includes both part-time adjuncts and non-tenure track
instructors, both of which generally do not hold the Ph.D. and are not required to
engage in research. In contrast, teaching-research faculty are either on tenure
track appointments or have received tenure. To examine the effects on efficiency,
the percentage of faculty that have received tenure (TENURE) is the included
group. Of course, a priori it is difficult to hypothesize the efficiency effects of
NTRACK and TENURE. On the one hand, it is a well- known claim that there can
be direct cost savings realized in the employment of lower wage, non-tenure track
G. Thomas Sav
9
faculty for the purpose of meeting institutional teaching needs. However, the
substitution of non-tenure for tenure-track and eventually tenured faculty has the
potential effect of reducing research funding that carries cost reducing effects
through, e.g., graduate student research stipends and education and university
overhead or administrative expenses. Other claims abound, including the notion
that the tenure system creates inefficiencies due to so-called personnel
inflexibilities imposed on managerial decision-making. That is widely refuted by
other camps that welcome the cost saving efficiencies ingrained in the
employment stability created by the tenure system. Either way, it is hoped that the
present empirical results can help shed some light on the matter.
Table 1 presents a summary of the variables along with their means and
standard deviations. All dollars are in real 2009 year dollars, the most recent
academic year of data available for this study.
Table 1: University Variable Means and Standard Deviations
Variable
Total Operating Cost ($), C
Undergraduate Credit Hours, UG
Graduate Credit Hours, GRD
Professional School Enrollment, PRO
Research Grants ($), RES
Salary of Nine Month Faculty ($), SAL9
Salary of Twelve Month ($), SAL12
Student Low Income Federal Grants, FEDGRT (%)
Non-White Student Enrollments, NWHITE (%)
Employment of Nine Month Faculty, FAC9
Employment of Twelve Month Faculty, FAC12
Non-Tenure Track Faculty, NTRACK (%)
Percent of Faculty that are Tenured, TENURE (%)
Mean
7.66E+08
509319
97934
675
1.88E+08
75184
86047
24.92
36.17
797
144
16.82
47.77
Std. Dev.
7.61E+08
289541
77002
877
2.11E+08
14062
31693
12.68
19.93
424
167
7.40
10.67
In terms of production, undergraduate education is on average approximately
twenty percent of undergraduate education. Administrative faculty salaries are
10
Managing Operating Efficiencies of Public Universities...
fourteen percent greater than teaching and research faculty salaries. On the
employment side, administrative faculty employment averages about eighteen
percent of the employed teaching and research faculty. Almost seventeen percent
of faculty employment is in non-tenure track positions. On average, just under half
of the university faculty is tenured.
5
Empirical Results
Maximum likelihood estimates are provided in Table 2 for both the BC95
and BC92 models. Reference to the statistical significance of individual
coefficients is noted at the 10% and better level of significance. However, the
central focus here as with other stochastic frontier research and as counseled by
Greene (2012), rests with the inefficiency component of the disturbance as
opposed to the cost function parameters. In addition, the cost function is
complicated by the nonlinear structure of the translog specification and the result
that individual output and wage coefficients do not carry a direct economic
interpretation. Rather, coefficients are employed in determining various measures
of scale or scope economies. And that has been rigorously investigated in other
research beginning with Cohn, et al. (1989) and lasting through Lenton (2008),
among others as referenced earlier in this paper. Thus, we do not wish to
re-examine that body of empirical research here but instead focus on the
inefficiency measures offered by frontier analysis. For comparative purposes,
suffice it to note that for the present estimates, the overall mean scale economies,
1   C,Qi , where   is the elasticity of cost with respect to the ith output, are in
the range of 0.54 to 1.77 for the BC95 and BC92 models, respectively. While the
difference can be attributed to the effect of environmental variables in shaping the
cost frontier vs. entering the inefficiency, they are in accord with previous
research that has provided scale estimates over a wide range of output levels.
G. Thomas Sav
11
Table 2: University Translog Stochastic Cost Frontier Estimates
Variables
0
UG
GRD
PRO
RES
SAL9
SAL12
UG^2
GRD^2
PRO^2
RES^2
SAL9^2
SAL12^2
UG-GRD
UG-PRO
UG-RES
UG-SAL9
UG-SAL12
GRD-PRO
GRD-RES
GRD-SAL9
GRD-SAL12
PRO-RES
PRO-SAL9
PRO-SAL12
RES-SAL9
RES-SAL12
SAL9-SAL12
0
FEDGRT
NTRACK
TENURE
NWHITE
FAC9
FAC12
TIME
2


LL
LR Test
BC95 Model
Coefficient t-value
-42.179
1.201
-1.383
-0.308
0.957
7.172
0.627
0.030
-0.017
0.004
0.138
-0.155
0.003
0.038
-0.003
-0.154
0.048
0.022
0.001
-0.040
0.190
-0.006
-0.003
0.035
0.000
-0.280
-0.006
-0.070
-2.904
0.112
0.025
0.060
-0.396
0.223
0.172
0.058
0.082
0.907
197.02
171.94
-1.49
1.25
-1.36
-1.62
1.18
1.30
*3.39
*4.68
-0.72
1.45
*11.32
-0.55
1.02
1.53
-0.53
*-6.37
0.51
*3.69
0.21
*-1.67
*1.79
-1.30
-0.67
*1.69
0.15
*-3.14
*-1.70
*-4.31
*-4.04
*1.75
0.59
1.00
*-1.80
*3.33
*3.30
*2.76
*4.61
*42.38
-
BC92 Model
Coefficient
t-value
-32.220
0.966
-0.665
-0.488
0.765
6.198
-0.010
0.029
-0.017
0.012
0.061
-0.159
0.009
0.053
-0.002
-0.089
-0.038
-0.002
-0.001
-0.030
0.073
0.014
0.020
0.017
-0.007
-0.121
0.000
-0.025
-0.020
-0.025
-0.166
0.058
0.314
0.013
0.036
0.113
0.977
-0.014
653.64
989.93
*-2.17
1.14
-0.91
*-3.14
1.40
*2.24
-0.06
*3.75
-1.05
*3.77
*5.84
-1.09
*2.93
*2.66
-0.24
*-3.61
-0.48
-0.29
-0.09
-1.49
0.99
*2.04
*3.85
1.29
*-3.08
*-2.14
-0.11
*-1.90
-1.55
*-1.69
*-3.92
1.14
*8.21
1.30
*7.33
*3.26
*134.14
-1.50
-
Note: Asterisk, *, denotes significance at the 10% and better level.
12
Managing Operating Efficiencies of Public Universities...
Turning to the overall performance of the stochastic frontiers, the gamma
coefficients under both inefficiency models are greater than 0.9 and statistically
significant above the 1% level of significance. Under the Battese and Corra (1977)
reparameterization of
 2   2   u 2 , the estimation of    u 2 /  2 , 0    1
serves to measure the relevancy of inefficiency effects in university costs. Being
that  exceeds 90%, we can be fairly comfortable in suggesting that inefficiency
effects are a likely determinant of university costs. Moreover, the likelihood ratio
tests individually support each model and rejection of the null hypothesis that
 i ’s    0 for the BC95 model and that       0 for the BC92 model.
Because the BC95 and BC92 models are not nested in one another, a likelihood
ratio test of one being statistically preferred over the other is not feasible. It would
also be artificial to rely on a comparison of the likelihoods of the models. So again,
our focus will remain on the differences in inefficiency estimates generated under
the alternative models.
In examining the influence of environmental variables, as noted in Table 2,
the estimated coefficients in the BC95 model represent effects on cost inefficiency
whereas in the BC92 model they represent effects on the cost frontier. Opposite
signs are not necessarily inconsistent. For example, additional federal grant
student enrollments (FEDGRT) could be cost reducing in that they relieve
universities from using internal subsidies or grants but lead to inefficiency
increases due to the administrative regulations and paper work imposed by the
federal government grant process. Thus, there occurs the negative FEDGRT cost
effect in the BC92 model and positive inefficiency increasing effect in the BC95
model. Similarly non-tenure track, part time faculty employment (NTRACK) can
be cost saving on the payroll side but lead to operating inefficiencies due to
greater labor turnover relative to the stability of a tenured faculty pool; hence, the
negative NTRACK effect in the BC92 cost frontier and positive inefficiency
increasing effect in the BC95 model (although statistically insignificant in the
latter). Interestingly, increases in the proportion of tenured faculty (TENURE)
G. Thomas Sav
13
have a cost reduction effect which could likely be attributed to the increased
research grants brought to the university by tenured faculty (especially relative to
the part-time, non-research, NTRACK faculty). And although the inefficiency
associated with greater TENURE is positive, it is weak and statistically
insignificant.
The BC95 inefficiency effects of additional faculty employment on the
teaching and research nine-month contract side (FAC9) are very similar to that of
administrative faculty employment on twelve-month contracts (FAC12).
However, in the cost frontier, the BC92 model picks up the FAC9 as the only
significant of the two positive cost effects. Because the nine-month faculty
includes both tenured faculty and newer hired tenure-track faculty, the data does
not permit us to untangle all the possible effects associated with the subgroups of
faculty. For example, in some faculty disciplines where shortages have existed,
there has occurred the so-called salary compression where newly hired
tenure-track faculty entered at higher salaries than currently tenured faculty.
That could be a significant cost factor among the FAC9 teaching and research
faculty that could be absent from the FAC12 administrative faculty.
The time variable included in the analysis also has two different effects and
has been employed in other studies beginning with Battese and Coelli (1995). In
the BC95 model it captures the possible linear effects of time on inefficiency. In
the BC92 model, time accounts for the effect of Hicksian type technical change on
costs. In both cases, the estimates provided here are positive and statistically
significant, suggesting that, over these academic years, higher education has
become more costly and more cost inefficient. In the BC92 model, the negative 
parameter reinforces the decay of efficiency, i.e., increased inefficiency, over time.
That estimate is forced to be a monotonic decay but it is somewhat troubling that
it lacks statistical significance, especially given the strong showing of TIME in the
BC95 model. To investigate the possible interaction between  and the inclusion
of TIME, the BC92 model was re-estimated with the time variable removed. As a
14
Managing Operating Efficiencies of Public Universities...
result, the inefficiency decay parameter  emerged statistically significant. This
suggests a closer inspection and comparison of the time varying inefficiencies.
Table 3 presents the comparisons.
Table 3: University Inefficiency Variations Across Time and Specification
BC95 Model
Inefficiency % Change
Mean
Median
Minimum
Maximum
Std. Dev.
2005-06
2006-07
2007-08
2008-09
1.302
1.209
1.022
2.720
0.289
1.253
1.266
1.332
1.357
1.0%
5.2%
1.9%
BC92 Model
Inefficiency % Change
1.416
1.322
1.016
3.050
0.360
1.405
1.412
1.420
1.427
0.54%
0.54%
0.54%
Table 3 contains the mean, median and other descriptive statistics for the
inefficiency scores of the universities over time and in comparison to the model
specification. These are the resulting scores that measure the extent to which
universities operate above their minimum cost frontiers. The scores indicate that
the average is approximately 30% to 42% for the BC95 and BC92 estimates,
respectively. Universities are somewhat skewed in their operating inefficiencies
with the smaller medians being produced under each model specification. Also
provided are the mean inefficiencies for each academic year and the percentage
changes across years. Under the BC92 model the constant decay of efficiency is
just above 0.50% per academic year.
In comparison, the BC95 inefficiency
scores are lower for each academic year, but the annual inefficiency growth rates
are higher. Most notable is the large 5.2% inefficiency bump in 2007-08. In the
present sample, that bump occurs concomitantly with a large graduate education
enrollment growth of 8.1% . That is likely to have been a unanticipated recession
G. Thomas Sav
15
induced enrollment growth that contributed to the inefficiency increase. From a
managerial and public policy perspective, it is reassuring that the subsequent
2008-09 academic year brought a rapid inefficiency adjustment evidenced by the
large reduction from the 5.2% down to 1.9% inefficiency growth. That adjustment
could represent the beginning of managerial responses to budget cuts induced by
the financial crisis.
Examining the efficiency performance and ranking of individual universities
can be useful in providing some additional insights into the cost frontier results.
The Appendix Table A.1 presents the overall ranking (columns 1 and 2) of
universities based on the university’s mean inefficiency score (column 3) as
averaged across the results of both model estimates. In addition, for comparison,
the university rank under the BC95 is presented in column 4 while the BC92 rank
is in column 5. Thus, based on the average inefficiency score, Colorado State
University receives the number one rank. When ranked under the BC95 model, it
slips down one rank to number two and then under the BC92 model it falls to the
fourth ranked university. Similarly, number five ranked University of Alabama at
Huntsville gets the number one spot in the BC95 ranking and slips to number
fifteen in the BC92 ranking. A few institutions experience larger ranking
movements; some of the most notable being the eighteenth ranked University of
Florida, ninety-first ranked University of California, and one hundred fifteenth
ranked CUNY Graduate School. There does tend to be greater uniformity in
inefficiency rankings among the more inefficiently ranked universities, e.g.,
beginning around the one hundred twentieth rank. Of that group of thirty nine
universities with the highest inefficiency scores and lowest rank, almost half of the
Greene and Greene (2001) thirty institutions named as “America’s Flagship
Universities” reside therein (in Table A.1. they are identified by “*”). However,
other “Flagships” are scattered throughout the inefficiency scores. Thus, there
does not appear to be a serious omitted variable problem, but the matter is brought
to the forefront for the usual purpose of noting some potential weaknesses. First
16
Managing Operating Efficiencies of Public Universities...
and foremost is the ever elusive problem of educational quality which has plagued
all higher education cost research. In the present study, that quality measure
would have to available for all three educational levels (undergraduate, graduate
and professional) and possibly across disciplines or schools within an educational
level.
While some specialized small sample educational quality data may be
produced, nothing to date exists for the large sampling of institutions undertaken
here. Yet, it is possible that our results indicate that educational quality is
operationally difficult and sometimes cost inefficient to produce, at least for some
institutions. Second, and related to the first, is the possible inability to capture the
heterogeneity of universities. However, the analysis purposely focused on the
same Carnegie classified group of universities. In that context, the institutions are
generally homogeneous with regard to institutional missions and overall mix of
educational products as required in obtainable the given classification. On other
hand, there are the usual problems associated with institutional rankings, many of
which stem from first hand observations that can generate value judgments. In the
current results, it was somewhat confidence shaking for my colleagues and me to
find our own institution ranked so high among other universities, given that we
observe firsthand what is perceived to be wide-spread institutional misallocations
of resources and associated operating inefficiencies. If the data speak somewhat
accurately, then we must conclude that our own operations are relatively cost
efficient. But for the mix of all our sample universities, it is also true that other
inefficiency influences can arise from the state political machinery and regulatory
constraints. These are all publicly owned and taxpayer supported universities and
some enjoy more higher education friendly environments than others. None,
however, are likely to escape the continuing budgetary pressures brought to bear
by the financial crisis and the changing landscape of public management reform:
both of which suggest that there should be a growing interest in improving public
university operating cost efficiencies.
G. Thomas Sav
6
17
Conclusions
This paper set forth the objective of investigating the possible existence of
operating cost inefficiencies among American public universities. Cost
inefficiency measures were obtained using stochastic frontier analysis. In an
attempt to inject a degree of confidence in any conclusions that could be derived,
empirical estimates were subjected to two alternative inefficiency models. Using
panel data spanning four academic years, 2005-09, the paper offers the following
general observations and conclusions:
1. The empirical estimates indicate that inefficiency effects are not to be
ignored in empirically modeling the operating costs of American public
universities.
2. University cost inefficiencies are affected by environmental conditions
related to student enrollment characteristics and faculty employment
characteristics. The effects, however, depend upon the inefficiency model
and, in particular, whether the environmental factors enter the inefficiency
component (Battese and Coelli, 1995) or the cost frontier (Battese and
Coelli, 1992).
3. Universities exhibit a fairly wide range of inefficiencies. When ranked
accordingly, some universities, relative to others, experience more
sensitivity to modeling assumptions.
4. The cost inefficiency of universities has decayed over the four academic
years, 2005-2009. However, the data tend to attribute that to large
recession induced enrollment increases, particularly in high cost graduate
education. Encouragingly, for the 2008-09, there occurred a significant
slowdown in the rate of inefficiency growth that can therefore be touted as
an efficiency improvement. Whether this constitutes managerial reactions
to the financial crisis, however, will require more years of observations
beyond the 2008-09 academic year.
18
Managing Operating Efficiencies of Public Universities...
5. Somewhat interestingly, a large group of some prestigious “Flagships” (as
crowned by other writings) were found to rest among the more
inefficiently ranked institutions. However, with the usual ranking caveats
in order, the analysis dare not suggest that the path to such prestige lies in
the emulation of operating cost inefficiency. Yet, our results are not
inconsistent with the notion that educational quality can possess operating
efficiency challenges.
The paper is thought to be the first known research to present cost frontier
efficiency estimates for American universities. Thus, there are no direct
comparative benchmarks. However, the average inefficiencies for the English and
Welsh universities as produced by Stevens (2005) vary from 1.007 to 2.01. That
compares to the current American university inefficiencies in the range of 1.022 to
3.050 as summarized in Table 3 and from 1.032 to 2.648 for the mean efficiencies
presented in Table A.1. Given vastly wide differences in data and methodological
approaches employed in other studies, at this juncture it seems best to forgo any
attempt at inter-country comparisons. For that to occur, it would be advantageous
to create more international collaboration on data base construction. Indeed, with
the development of greater international higher education markets, that could
provide a fruitful avenue for future research.
G. Thomas Sav
19
Appendix University Inefficiency Rankings
Appendix Table A.1: University Inefficiency Rankings
Mean
Score
Rank
University
Mean
Score
BC95
Rank
BC92
Rank
Mean
Score
Rank
University
Mean
Score
BC95
Rank
BC92
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
Colorado State
Wright State
Arkansas Little Rock
Loisiana Lafayette
Alabama Huntsville
Nern Colorado
Missori-Rolla
Texas AM Kingsville
George Mason
Texas AM Commerce
Mississippi
Wayne State
S Dakota
Nevada-Las Vegas
Montana
Indiana PA
Michigan Tech
Florida*
S Carolina
Portland State
Louisiana Tech
Wichita
Colorado Bolder*
Houston
Oakland
Texas Austin*
Maryland-Baltimore
Texas Sothern
Ohio
New Jersey Inst Tech
Alabama AM
SUNY Env Sci
San Diego State
Missorui St Loius
Georgia State
Oregon
Mass-Lowell
William & Mary*
N Dakota
New Orleans
Jackson
Central Michigan
S Carolina State
Cleveland
Texas Woman's
Texas Dallas
Wiscon-Madison*
FL International
Missouri Kansas City
Memphis
N Texas
New Mexico Mining
Old Dominion
Tennessee
1.032
1.036
1.041
1.042
1.045
1.048
1.052
1.052
1.055
1.055
1.056
1.065
1.066
1.068
1.069
1.076
1.079
1.079
1.079
1.089
1.093
1.098
1.098
1.099
1.099
1.100
1.108
1.115
1.122
1.123
1.129
1.132
1.132
1.134
1.136
1.137
1.140
1.146
1.147
1.147
1.149
1.161
1.163
1.164
1.166
1.167
1.172
1.176
1.177
1.183
1.186
1.187
1.189
1.199
2
3
14
4
1
16
11
27
29
28
23
33
10
24
17
15
26
55
47
9
49
22
7
52
6
32
8
64
73
20
59
46
30
35
62
21
34
65
58
38
41
42
61
40
19
39
48
36
85
69
56
5
67
75
4
8
2
10
15
9
13
5
3
6
11
12
21
17
20
23
22
1
7
27
16
28
32
18
34
25
35
19
14
38
24
31
39
36
26
46
41
29
33
43
40
50
37
53
64
54
51
60
30
42
55
81
45
47
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
S Florida
Wyoming
N Dakota
Middle Tennessee
Calif-Berkeley*
Arizona*
Florida State
Hawaii Manoa
Mass-Boston
Calif-Riverside
Rhode Island
Maryland -Baltimore
S Dakota State
Idaho
Idaho
Oregon State
Mississippi State
VA Commonwealth
Kansas Main
Alabama
S Mississippi
SUNY Binghamton*
Georgia*
Texas El Paso
Texas Tech
Cincinnati
Nevada-Reno
Akron
Virginia Polytechnic
West Virginia
Auburn
NC Greensboro
Purdue
Minnesota*
Kent State
Indiana State
Calif Santa Barbara*
Kansas State
Texas Arlington
Central Florida
Calif Santa Crz
Loisiana A&M
Texas AM
Illinois Urbana*
Florida Atlantic
Colorado & Hlth Sci
Washington State
U Tennessee
Wiscon-Milwaukee
Montana State
Nebraska Lincoln
SUNY Buffalo
Nern Arizona
Michigan State*
1.199
1.199
1.202
1.210
1.218
1.222
1.223
1.226
1.227
1.228
1.234
1.249
1.249
1.250
1.254
1.254
1.254
1.259
1.264
1.266
1.275
1.275
1.276
1.281
1.282
1.286
1.289
1.299
1.299
1.299
1.300
1.304
1.314
1.315
1.316
1.319
1.324
1.326
1.330
1.335
1.337
1.338
1.340
1.340
1.340
1.363
1.370
1.373
1.375
1.380
1.383
1.391
1.392
1.395
60
74
51
63
37
80
90
76
57
18
68
45
77
99
70
50
71
97
95
88
94
25
110
53
100
93
66
86
89
98
106
105
120
112
87
78
13
103
54
91
31
113
92
108
102
83
115
127
117
84
109
122
107
136
59
49
66
63
82
58
44
62
76
96
71
94
73
52
78
92
79
57
61
70
67
110
48
101
65
74
98
89
85
77
68
72
56
69
97
104
126
86
117
99
125
80
100
84
95
116
90
83
91
119
102
93
107
75
20
Managing Operating Efficiencies of Public Universities...
Appendix Table A.1: Continued.
Mean
Score
Rank
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
University
Indiana -Purdue
Arizona Tempe
Utah State
Indiana Bloomington*
N Carolina*
New Hampshire
CUNY Gradate Schl
SUNY Albany
Louisville
Vermont
Arkansas
New Mexico State
Oklahoma Norman
Nern Illinois
Iowa State
Ball State
Georgia Tech
Maryland Coll Park*
Illinois State
Washington Seattle*
East Tennessee
Western Michigan
Maine
Alaska
Bowling Green
Oklahoma State
Clemson
N Carolina State
Toledo
Rutgers*
Calif San Diego*
Connecticut*
Mass Amherst
Calif-Los Angeles*
Calif Irvine*
Ohio State*
East Carolina
Miami -Oxford*
Southern Illinois
Virginia*
Iowa*
S Alabama
Calif Davis*
New Mexico
Kentucky
Illinois Chicago
Michigan Ann Arbor*
Missouri Colmbia
Alabama Birmingham
Utah
Stony Brook
Mean
Score
BC95
Rank
BC92
Rank
1.396
1.402
1.402
1.408
1.409
1.412
1.431
1.432
1.432
1.434
1.436
1.438
1.449
1.453
1.455
1.455
1.459
1.462
1.465
1.476
1.485
1.485
1.487
1.488
1.492
1.504
1.547
1.555
1.597
1.651
1.670
1.685
1.686
1.772
1.787
1.797
1.853
1.853
1.865
1.872
1.896
1.966
1.987
2.020
2.094
2.094
2.229
2.297
2.522
2.584
2.648
111
129
79
132
116
81
12
43
123
101
119
82
130
124
126
133
44
96
131
114
125
121
118
72
137
134
128
139
142
140
104
144
138
141
135
148
150
143
152
145
151
146
147
149
153
155
156
157
154
159
158
105
88
129
87
106
130
140
135
109
123
113
133
108
115
114
103
139
131
112
128
120
122
127
138
111
118
132
124
121
137
147
134
143
145
149
136
142
148
141
146
144
151
152
153
155
150
154
156
159
157
158
G. Thomas Sav
21
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