S. RASSOULI-CURRIER

Table 1.

Summary statistics for the variables (n = 17).

Variable Mean SD Minimum Maximum Sum

RA/AB 42.00 40.16 2.00 134.00 714.00

OPA 32.41 28.83 1.00 96.00 551.00

TEACH 8.82 3.81 4.00 17.00 150.00

PHD 0.80 0.14 0.53 1.00 13.53

TT 0.78 0.21 0.25 1.00 13.21

Where: Y1 = RA/AB is the number of refereed articles/authored books; Y2 = OPA

is other professional activities such as published articles of merit, working papers,

professional presentations etc; X1 = TEACH is the number of teaching staff; X2 =

PHD is the percentage of the faculty holding Ph. Ds or equivalent; X3 = TT is the

percentage of the faculty that is tenured or is on tenure track.

does not require any restrictive assumptions regarding the func-

tional form of the model.

On the down side, DEA assumes that all DMUs have the

same deterministic (as opposed to stochastic) production fron-

tier and that any deviation from the frontier is due to ineffi-

ciency, which may not be realistic. However, it is a reasonably

powerful diagnostic tool that can be used to measure the effi-

ciency of a set of homogenous DMUs individually (e.g., peer

universities) relative to the most efficient unit. Studying the

reasons for any possible inefficiencies and finding remedies to

eliminate them is the responsibility of the unit’s decision maker

(Talluri, 2000).

Following Coelli et al., (1998) in the DEA method, the tech-

nical efficiency is identified as a proportional increase in the

output vector with a given input vector. Therefore, the out-

put-oriented measure of technical efficiency (in case of a pro-

duction function) is the solution to the following constant re-

turns to scale (CRS) DEA linear programming problem:

Maximize ,

,

Subject to0

0

0

i

i

yY

xX

where is a scalar, and yi and xi are column vectors of out-

puts and inputs respectively for the ith university. λ is an N × 1

vector of constants. The variable Y is an M × N output matrix

and X is a K × N input matrix, and the proportional increase in

outputs that could be achieved by the ith university, holding

inputs constant, is , (1) with

1 1 the univer-

sity’s efficiency score, which is between 0 and 1 (For a com-

plete explanation of DEA and its advantages and disadvantages

see Coelli et al., 1998)

Empirics

The empirical model for the DEA estimation is defined as

Output Inputf where Y1, Y2 are considered the outputs

and X1, X2 and X3 are inputs. Due to difficulties obtaining dis-

aggregated salary data, X1, X2 and X3 are proxies for the budget.

The justification here is that the higher the faculty salary

(budget), the better the universities ability to hire more faculty

in general and have a larger body of teaching staff, a higher

percentage of faculty holding PhDs and being tenured or on

tenure track. The DEA efficiency estimation for each institution,

under the assumption of variable return to scale (VRS), was

computed using DEAP 2.1 software developed by T. J. Coelli.

VRS was chosen due to the rather restrictive nature of CRS.

The list of the universities and their efficiency scores are not

included in the paper to preserve both the privacy of the institu-

tion and because the scores in and of themselves are not the

focus of this research.

The summary statistic of the efficiency scores suggests a

mean score of 0.74 with a standard error equal to 0.08. The

95% confidence interval for the mean has a 0.18 margin of

error. There is a wide range of efficiency from 0.023 to 1. The

scores are negatively skewed (skewness = –0.99), as expected

from the efficiency scores, suggesting the existence of ineffi-

ciency in at least some of the universities under study. Based on

the theoretical model here, these inefficiencies stem from the

existence of inefficient allocations of the total budgets.

Qualifications and Concluding Remarks

Historically, universities with larger proportions of budget

allocated to teaching staff (which generally translates to a larger

number of faculty) are assumed to be more productive i.e., to

have higher efficiency. These universities generally have a

higher percentage of faculty holding a Doctorate, tenured or on

tenure track. Some examples among the sample considered here

are University of Colorado-Denver and the University of Texas-

San Antonio. These universities have larger number of faculty

with well above average number of refereed publications. This

observation does not necessarily hold for all universities.

This study attempts to get a preliminary idea of the charac-

teristics of a “peer group” identified by UCO in 2006. The pre-

liminary nature of this research suggests several shortcomings

such as those stemming from the deterministic nature of DEA

and the inability to perform statistical testing etc. However, the

most important shortcoming of results stems from the enor-

mous difficulty of the obtaining disaggregated salary data from

universities. Despite all of this, the study can be considered a

first step and a baseline for future studies. As one potential

example, one can argue that perhaps the criteria that identify

universities as peers should be reexamined since our prelimi-

nary results seem to suggest that the existence of a strong ho-

mogeneity among these universities is highly questionable. It is

noteworthy that since 2007, UCO has adopted a new set of

peers. This change, at least implicitly, could be a validation for

the results of this paper.

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