The Relationship between Institutional Efficiency and Instructional Quality in Higher Education

doi:10.4236/ce.2012.32035

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Creative Education

2012. Vol.3, No.2, 224-227

Published Online April 2012 in SciRes (http://www.SciRP.org/journal/ce) http://dx.doi.org/10.4236/ce.2012.32035

224

The Relationship between Institutional Efficiency and

Instructional Quality in Higher Education

Susanne Rassouli-Currier

University of Central Oklahoma, Edmond, USA

Email: [email protected]

Received March 4th, 2012; revised April 2nd, 2012; accepted April 12th, 2012

Wage setting methodologies for university faculty may be merit/market based or administered. Failure to

exploit the fact that faculty productivity depends on abilities and wages results in inefficient use of uni-

versity budgets. If such inefficiencies exist it suggests suboptimal productivity of the existing faculty and

the inability to attract new qualified faculty. As motivation for this analysis, a simple model of university

faculty “output” maximization is presented. Efficient budget allocation requires that faculty compensation

be structured so that marginal productivities are equated across faculty. This paper examines and com-

pares the efficiency of several regional universities in the US, identified as “peers”, employing the Data

Envelopment Analysis (DEA) estimation method. The results suggest the existence of inefficiencies and

more notably, that the homogeneity assumption regarding the peers is questionable.

Keywords: Salary; Efficiency; DEA; Higher Education; Education Administration

Introduction and Related Literature

The topic of university faculty salaries has been addressed

frequently through research with varying focal points of interest.

Cohn (1973) develops a multiple regression model to analyze

the factors that might have an effect on faculty salaries. Al-

though the empirical results of this study are dated and of lim-

ited applicability to the current economic situation of university

professors and their pay, many of the model’s independent

variables that are identified still have explanatory power today.

These include the type of institution, whether it is public or

private, geographic location, quality measures, institution size,

and state per capita income. The variable that Cohn finds to

have the greatest effect on faculty salaries is that of institution

quality.

Tuckman and Tuckman (1976) also use regression methods

“to analyze the determinants of salary structure at American

universities”. The explanatory variables of their model empha-

size the rate and amount that faculty publish, faculty personal

characteristics such as gender and age, the university’s geo-

graphic location, and the area of faculty expertise. Two of the

more definitive outcomes are that publishing and research raise

salaries compared to teaching and that academic field correlates

to a significant variation in faculty salaries.

Hoenack (1982) uses a theoretical approach to analyze how

prices affect the choices made by faculty, students, legislators,

and others involved in higher education with regard to the effi-

cient use of resources. He concludes that inefficiency results

from the existing prices, and he suggests how efficiency could

be improved through changes in prices.

Alexander (2001) analyzes the impact that the growing dis-

parity between faculty salaries at private universities compared

to those at public universities is having on the ability of public

institutions to attract and retain top-notch faculty. He describes

the potential for the development of two separate higher educa-

tion systems, one private and the other public. As private uni-

versities are better able to compete financially, there exists a

possible “brain drain” as higher quality faculty migrate toward

better salaries. The differential between faculty salaries at pub-

lic and private universities is reported by Smallwood (2006)

from a survey conducted by the College and University Profes-

sional Association (CUPA) for Human Resources. This survey

reveals that salaries for 2005-2006 increased by 3.7 percent at

private universities compared to only 3.1 percent at public uni-

versities.

In a series of articles contained in the ASHE-ERIC Higher

Education Report (2001) several aspects of faculty compensa-

tion systems are addressed. The first article describes how the

quality of an institution is related to how well the institution

achieves its self-stated mission. Its mission is heavily depend-

ent on the faculty, which in turn is strongly affected by the

faculty compensation system. Faculty compensation is deter-

mined by a variety of factors, both external and internal. Nota-

bly, one of the internal factors cited is “market pay in the disci-

pline”. The second article describes the three main types of

faculty compensation systems: 1) the contract salary system or

merit pay; 2) the single salary system; and 3) nontraditional

faculty compensation systems. The third article describes the

commonly used arguments in favor of either merit pay or the

single salary system. The fourth article describes the advan-

tages and disadvantages of the three compensation systems

mentioned above. Interestingly, one of the primary disadvan-

tages of the single salary compensation system is listed as “a

lack of efficiency in the use of human resources”.

With regard to the methods that have been developed and

utilized to measure technical and/or allocative inefficiency, the

literature is replete with a history of scholarly contributions.

Some of the more notable and most frequently cited of these

include; Farrell (1957), Aigner et al., (1977), Kumbhakar and

Wang (2006) among others.

This paper first describes a simple microeconomic model of

S. RASSOULI-CURRIER

faculty production and budget efficiency. It then presents some

preliminary efficiency results for a select group of Finance

Departments at peer universities.

Theoretical Model

This model is based on the discussion in Rassouli-Currier

and Currier (2008). For a given faculty member, let y denote

“productivity”, i.e., number of publications, and let x = (x1, ···,

xn) denote a vector of university expenditures on productivity

enhancement. The vector x could include expenditures on

wages and salaries, technology support, library facilities, con-

ference travel support etc. The individual faculty member’s

output depends on effort e and the vector x, as summarized by

the “production function”





,yfxe This production func-

tion is an increasing function of e and xi, i = 1, ···, n. In addition,

the faculty member’s utility is





,Uxy e reflecting the “dis-

utility of effort”. We assume that a (small) increase in any xi

will increase the marginal benefit of an increase in y and reduce

the additional effort necessary to achieve this increase in y.

Given the vector x, the faculty member selects effort (and cor-

responding output) to:







Maximize ,

Subject to ,

Uxy e

yfxe





This is illustrated in Figure 1 for the case of n = 1. Utility

increases as we move downward and to the right in the diagram.

The indifference curves are concave, reflecting the assumption

that a faculty member’s willingness to exert additional effort to

increase productivity is highest when effort is low.

For any vector x, individual utility maximization implies so-

lutions





eex and





yyx. Assuming appropriate dif-

ferentiability, we have 0

yx, i = 1, ···, n. Thus, a ceteris

paribus increase in any xi will increase faculty productivity

(output). Suppose now that n = 1, there are m individual faculty

members and that the university has overall resources (e.g.,

salary budget) of $B. Then the university budget constraint is

B



. Under an administered salary program, it is es-

sentially the case that j

, j = 1, ···, m. Thus, differences

in rank not withstanding, each faculty member receives the

same fraction of the total budget allocation. Faculty member j’s

Figure 1.

Utility maximization.

output will be j







with aggregate faculty output











Alternatively, suppose that salaries are set in such a way as to

maximize total faculty output, given the university’s overall

budget constraint. In this case, the university administration

selects individual salaries to solve:



Maximize

Subject to





yielding the vector of optimal salaries



** *

1,,

xx



** *

,,yy y

and

corresponding faculty output levels Since

salaries are chosen to maximize total faculty output, it must be



the case that **

11 .

Yyy













  This is illus-

trated in Figure 2 where it is shown that selection of individual

salary levels on the basis of the optimality criterion permits the

university to operate on the “efficient” production frontier E

whereas any other non-optimal salary assignment rule, meas-

ured by the number of faculty, percentage faculty holding PhD

or equivalent and percentage of the tenured/on tenure-track

faculty, forces the university below the efficient frontier.

The Data Set

The main source of data was the Finance Departments of 27

universities and colleges that were identified as the peers to

University of Central Oklahoma in 2006 (the choice of Finance

Department was arbitrary). However, the data collecting phase

turned out to be extremely challenging. Universities’ published

data on faculty salary and their contribution to the department is

either aggregated at the college/university level or not well

documented. In addition the CUPA website was not particularly

helpful. Ultimately, data was collected on several variables of

interest for 17 out of the 27 institutions. The list of variables

and their summary statistics are presented in Table 1.

DEA Estimation Method

The basic idea of the DEA approach is to view universities as

Decision Making Units (DMUs) with multiple inputs and out-

puts. DEA is a non-parametric productivity analysis model that,

unlike its parametric counter-part, allows for multiple inputs

and outputs to be considered simultaneously. In addition, it

Figure 2.

Efficient production frontier.

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











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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