output and efficiency in the production of business and economics majors

OUTPUT AND EFFICIENCY IN THE PRODUCTION

OF

BUSINESS AND ECONOMICS MAJORS


by

Carlos Asarta



A DISSERTATION



Presented to the Faculty of

The Graduate College at the University of Nebraska

In Partial Fulfillment of Requirements

For the Degree of Doctor of Philosophy


Major: Economics

Under the Supervision of Professor William B. Walstad

Lincoln, Nebraska

May, 2007
UMI Number: 3263485
3263485
2007
Copyright 2007 by
Asarta, Carlos
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Output and Efficiency in the Production of Economics and Business Majors
Carlos Asarta, Ph.D.
University of Nebraska, 2007
Advisor: William Walstad

A linear education production function was estimated to identify the educational
factors that contribute to the production and retention of the core business knowledge and
basic academic abilities of graduating seniors at the college level. The data set used in

this study was comprehensive and included information on the standardized test scores,
demographic characteristics, ability levels, transfer status, major areas of study and core
business course performance of 689 graduating seniors from the College of Business
Administration (CBA) at the University of Nebraska-Lincoln (UNL).
The production and retention of core business knowledge was influenced by a
number of demographic, ability and transfer variables. Male students outperformed
females in all four Major Field Test in Business (MFT-B) models, suggesting that gender
is a significant factor in the production of core business knowledge. Other significant
demographic factors included the age, ethnicity/race and nationality of graduating
seniors. Entry SAT scores and core GPAs were highly significant in explaining the
production of core business knowledge, while the transfer of core business courses from
outside institutions negatively influenced the performance of students on the MFT-B.
Economics major were the only students to exhibit a positive and significant MFT-B
point advantage, while marketing students were the only major to score significantly
lower than their business peers. The performance of students in the Principles of


Macroeconomics, Business Law, and Principles of Finance courses contributed to
significantly higher MFT-B scores. The transfer of Statistics, Principles of Accounting II
and Business Law was detrimental to the production of core business knowledge. Finally,
all majors but economics were less efficient at retaining core business knowledge when
they transferred at least one core business course from an outside institution.
The basic academic abilities of graduating seniors were unrelated to a student’s
age or gender. White students, however, tended to exhibit significantly higher exit ability
levels than students from other races/ethnicities. A student’s nationality and entry SAT
scores were not found to significantly improve his/her basic academic abilities. Student
performance in non-core courses, however, consistently explained student scores on the
Collegiate Learning Assessment (CLA) test. The performance of students in Principles of
Macroeconomics and Principles of Marketing positively influenced their exit academic
abilities, while the transfer of the Business Law course offered at UNL was the only

course transfer to influence the basic academic abilities of graduating seniors in a
negative and significant way.




Acknowledgements

I would like to thank all those who have contributed to my success at the
University of Nebraska-Lincoln during my undergraduate and Ph.D. studies:

Professor William Walstad introduced me to the rich world of economic
education and provided invaluable expertise in the field. Professor Walstad was always
supportive of my dreams financially, intellectually and morally and stood by my side all
along the way. Thank you, Professor Walstad, for your guidance and support.

The members of my committee: Dr. Sam Allgood, for introducing me to the
world of economics and providing guidance and support through my years at UNL; Dr.
Craig MacPhee, for inspiring me to specialize in the field of international economics and
providing timely feedback on my dissertation; and Dr. Fred Luthans, for always
supporting my endeavors.

The faculty of the Department of Economics at the University of Nebraska-
Lincoln: Dr. John Anderson for extending a teaching assistantship to me and for
believing in my intellectual and personal abilities; Dr. John Austin, for being a great
mentor, colleague and friend; Dr. Roger Butters and Dr. Tammie Fischer, for extending
numerous professional opportunities through the Nebraska Council on Economic
Education and the UNL Center for Economic Education, and to all of those who believed
in me and have made a difference in my academic and personal life.

Special thanks go to Jan Hime and Lindsay Kruse for their support in locating the
sources for the data used in this study. Sharon Nemeth was instrumental in proofreading
and formatting the various documents included in this dissertation.

Me gustaria darle las gracias a mis queridos padres, Alberto y Clara, por darme la
education, ayuda y cariño necesarios para poder completar este sueno. Espero que esteis
orgullosos de vuestro hijo.

Finally, I would like to thank my wife, who has been supportive of my dream of
becoming a doctor since day one. Your support, love and care for our family have
allowed us to overcome the many obstacles encountered through the past five years.
This dissertation is dedicated to you and to our beautiful children, Cristian and Kenedi.


Table of Contents
List of Tables i

Chapter 1: The Nature and Objectives of the Research 1

Chapter 2: Literature Review 7

2.1 The Production Function Model 7

2.1.1 The Education Production Function 8
2.1.2 Educational Production Inputs 10
2.1.3 Educational Production Outputs 11
2.1.4 Conceptual, Methodological and Empirical Issues 13

2.2 Outcome Measures and Assessment 18

2.2.1 The Major Field Test in Business (MFT-B) 19
2.2.2 The Core Curriculum Assessment Program (CCAP) 22
2.2.3 The Collegiate Learning Assessment Instrument (CLA) 24
2.2.4 The Association to Advance Collegiate Schools of Business (AACSB) 26

2.3 Findings on Factors Affecting Educational Outcomes in Economics and
Other Business Disciplines 28

2.3.1 Gender 29
2.3.2 Ability 32
2.3.3 Race and Ethnicity 35
2.3.4 Age and Class Standing 39
2.3.5 Transfer Status 41
2.3.6 Business Major 44
2.3.7 Course Grades and Overall Business Performance 47

2.4 Concluding Comments 49

Chapter 3: The University of Nebraska and the
College of Business Administration 50

3.1 The University of Nebraska-Lincoln 50



3.1.1 Admission Requirements 51
3.1.2 Student Body Demographics 52

3.2 The College of Business Administration 57

3.2.1 The Business Senior Assessment Course (BSAD098) 61
3.2.2 Common Body of Knowledge Topics and Sequence 65

3.3 Concluding Comments 66

Chapter 4: Variables and Sample 68

4.1 Dependent Variables 68

4.1.1 MFT-B Score (MFTB) 68
4.1.2 CLA Score (VADDCLA) 69

4.2 Independent Variables 69

4.2.1 Student Gender (MALE) 71
4.2.2 Student Age (AGE) 72
4.2.3 Ethnic Background (WHITE, ASIAN, OTHER) 72
4.2.4 Student Citizenship (ORIGIN) 73
4.2.5 Transfer Status and Credits (TRANSFCORE/OTHER,
TRANSFCORECR) 73
4.2.6 SAT Score (SAT) 74
4.2.7 Overall, Core and Other Grade Point Averages (GPA,
CORE/OTHERGPA) 74
4.2.8 Student Major 76
4.2.9 Course Grades in the Common Body of Knowledge 76
4.2.10 Common Body of Knowledge Course Transfer (TRANSF+COURSE) 78
4.2.11 Student Major and Transfer Status (MAJOR+TRANSFCORE) 78

4.3 Descriptive Statistics 78

4.3.1 Descriptive Statistics on MFT-B Scores 82
4.3.2 Descriptive Statistics by Major 87
4.3.3 Descriptive Statistics on CLA Scores 92

4.4 Concluding Comments 97



Chapter 5: Estimation and Analysis 100

5.1 Main Variables and Descriptive Statistics 100
5.2 Estimation of MFT-B Models 105

5.2.1 Model 1: Choice of Major Effects on MFT-B Performance 105
5.2.2 Model 2: Core Course Achievement Effects on MFT-B Performance 111
5.2.3 Model 3: Transfer of Core Courses Effects on MFT-B Performance 122
5.2.4 Model 4: Choice of Major and Transfer Interaction Effects 131

5.3 Estimation of CLA Models 133

5.3.1 Model 5: Choice of Major Effects on CLA Performance 135
5.3.2 Model 6: Core Course Achievement Effects on CLA Performance 139
5.3.3 Model 7: Transfer of Core Courses Effects on CLA Performance 141

5.4 Concluding Comments 144

Chapter 6: Overview and Conclusions 147

6.1 Literature Review 148

6.2 Results 151

6.3 Implications 157

6.4 Limitations 162

References 165

Appendix 2.1: Major Field Test in Business Sample Questions 176

Appendix 2.2: Major Field Test in Business Content 181

Appendix 2.3: Standards for Business Accreditation 185

Appendix 3.1: CBA Senior Survey 192



Appendix 3.2: Career Placement Assessment Survey 198

Appendix 3.3: Required Core Business Courses, UNL 202

Appendix 4.1: Standard ACT to SAT Table 205

Appendix 5.1: Correlation Coefficients for Models 1-4 206

Appendix 5.2: Transfer Intensity on MFT-B Performance 210

i

List of Tables

Table 3.1: UNL Undergraduate Enrollment by College and Standing, Fall 2006 53
Table 3.2: UNL Undergraduate Enrollment by College and Ethnicity, Fall 2006 54
Table 3.3: UNL Headcount Enrollment by Class Standing and Gender, Fall 2006 56
Table 3.4: UNL Enrollment by Age, Fall 2006 57
Table 3.5: Major and Total Credit Hours Graduation Requirements 61

Table 4.1: List of Main Variables 70
Table 4.2: Letter Grade and GPA Quality Points 75
Table 4.3: Descriptive Statistics 79
Table 4.4: Descriptive Statistics on MFT-B Scores 83
Table 4.5: Difference in Mean MFT-B Scores by Demographics and Ability 86
Table 4.6: Difference in MFT-B Scores by Major 87
Table 4.7: Descriptive Statistics by Major 88
Table 4.8: Descriptive Statistics on CLA Scores 93
Table 4.9: Difference in Mean CLA Scores by Demographics and Ability 96
Table 4.10: Difference in Mean CLA Scores by Major 97

Table 5.1: List of Main Variables 101
Table 5.2: Descriptive Statistics (n = 689) 104
Table 5.3: Choice of Major Effects on MFT-B Performance 107
Table 5.4: Core Course Achievement Effects on MFT-B Performance 113
Table 5.5: Core Course Achievement Effects on MFT-B Performance by Major 118
Table 5.6: Transfer of Core Courses Effects on MFT-B Performance 124
Table 5.7: Transfer of Core Courses Effects on MFT-B Performance by Major 127
Table 5.8: Choice of Major and Transfer Interaction Effects 132
Table 5.9: Descriptive Statistic (n = 191) 134

Table 5.10: Choice of Major Effects of CLA Performance 138
Table 5.11: Core Course Achievement Effects on CLA Performance 141
Table 5.12: Transfer of Core Courses Effects on CLA Performance 143

Appendix 4.1: Standard ACT to ACT Conversion Table 205
ii


Appendix 5.1: Correlation Coefficients for Models 1-4 206
Model 1 206
Model 2 207
Model 3 208
Model 4 209
Appendix 5.2: Transfer Intensity on MFT-B Performance 210

1


Chapter 1
The Nature and Objectives of the Research
The production of education is characterized by choices derived from scarcity of
resources. Due to recent declines in public funding, the burden of education has
increasingly fallen on students and parents, and a greater emphasis has been placed on
streamlining and improving the efficiency of the educational process by carefully
selecting and using the available educational inputs to maximize the creation and
retention of knowledge. Declining enrollments and shrinking market shares have also
created added pressures for legislators and school administrators. As a result, universities
and colleges are expected to assess and continuously improve the quality of their
programs, and accrediting institutions have gain importance in the world of academia
(Becker and Andrews, 2004). Generally, the efficiency of educational inputs and the

returns to human capital investments are measured with quantitative indicators of
institutional, program and student performance (Cohn and Geske, 1990). Nichols and
Nichols (2000a) indicate that program assessment should focus on concrete, verifiable
results, such as how much students have learned upon graduation. The recent use in
education of standardized testing instruments has open the doors to new research that
could further clarify and identify key inputs in the production and retention of
knowledge.
The main purpose of this study is to identify the educational inputs that have a
statistically significant effect in the production and retention of the core business
knowledge and general basic abilities of graduating business students. An educational
production function was used to perform the production and retention analysis. The
2


production function approach enabled the identification of more efficient inputs and
hence more effective production and selection processes. Previous published studies
using standardized testing instruments as the measure for the evaluation of business
programs and student performance have sought to identify key educational factors while
using narrow data sets and incomplete production functions (Allen and Bycio, 1997;
Bean and Bernardi, 2002; Black and Duhon, 2003). The data set used for this study
gathered information from the academic records of 689 graduating seniors from the
University of Nebraska-Lincoln (UNL). These records provided vital information on the
demographic characteristics, ability levels, transfer status and student achievement in
what is known as the “Common Body of Knowledge” sequence of courses. Student
results from the Major Field Test in Business (MFT-B) and the Collegiate Learning
Assessment instrument (CLA) were used as the output measures to draw the conclusion
inferred from the cross-sectional analysis presented in this study.
The first question that this study seeks to answer focuses on the production of
knowledge by graduating business students. First, do students who major in a specific
business area generate more educational output, in terms of core business knowledge,

than similar students who major in other areas within the business curriculum? And if this
is the case, are there other significant factors involved in such production? In other
words, are accountants more productive, in terms of their core business knowledge and
basic general abilities, than economists after controlling for demographic characteristics,
ability levels, and transfer status? There is a possibility that a student’s major may not be
a determinant in explaining student performance on the MFT-B and CLA exams, or that
the difference between the core knowledge and basic abilities accumulated by graduating
3


seniors from different majors not be significant. The repercussions of such findings could
have a direct impact in labor markets. An accounting graduate could become as desirable
as a finance graduate to a prospective general business sector employer if in fact
accountants and finance students generate similar amounts of core business knowledge
and exhibit similar general ability levels after the completion of their undergraduate
programs. On the other hand, if certain majors are found to generate statistically larger
amounts of core business knowledge before graduation, such information should be made
available to students and the majors should be promoted by departments and colleges.
The contributions of specific business courses to the production of core business
knowledge by undergraduate business students are of special interest in answering the
first question asked in this study. The identification of significant courses would allow
administrators to place more emphasis and increase the requirements in those specific
classes so as to improve the student production of core business knowledge and their
basic general abilities. A secondary incentive for institutions to promote learning in these
specific courses includes gaining faster membership or continuous accreditation with
their accrediting agencies (i.e. The Association to Advance Collegiate Schools of
Business (AACSB)).
In recent years, universities in the United States have seen an increasing flow of
students transferring from community and junior colleges, which could be detrimental to
the production of knowledge if such institutions provide a lower undergraduate education

than, for example, research institutions (Vincow, 1997; Noll, 1998). At the same time,
even those implanted into four-year systems seem to attend different universities during
their undergraduate experience. One obvious result of this trend is the heterogeneous
4


nature of the preparation of students for middle and higher level business courses at four-
year institutions. Previous research in economics and other business disciplines has
generally found transfer students at a disadvantage when compared to their native peers
(Borg, Mason and Shapiro, 1989; Laband and Piette, 1995; Borde, Byrd and Modani,
1996; Borde, 1998). Researchers have also tried to account for the ability of the students
and whether the transferred hours were from a two- or four-year institution in order to
shed more light on the effect of transferring courses on undergraduate business
performance. This study, however, attempts to answer a question that has not been
explored in the previous business research: Does the transfer of core courses from outside
institutions impair the production of core business knowledge by graduating seniors at
four-year institutions? And if this is the case, which courses have a more significant
impact on the production of knowledge when transferred? The expectation is that
students who complete their core business education in the same four-year institution will
produce more core business knowledge than those who chose to enroll and transfer
courses from other institutions.
The second question that this study seeks to answer is concerned with the
effectiveness of graduating seniors in retaining the core business knowledge and basic
abilities that they acquired throughout their business education, after controlling for
demographic characteristics, ability levels and transfer status. Students who enroll in
higher education receive basic business training in their first years of attendance. Many
universities require their students to complete several standardized tests before
graduation, including the MFT-B, to assess the amount of knowledge that they have been
able to accumulate and retain over their higher education experience. The performance in
5

these standardized exams is, in a way, a measure of how efficient students are at
maintaining their basic business and general ability levels because there is a time-lag
between the moment they are presented with the materials and the time when they have
to take the assessment instruments. In this study, the efficiency of the different majors
offered at UNL in retaining basic knowledge will be examined. Of special interest is the
retention of knowledge for the group of students majoring and minoring in economics.
The limited availability and recent use of comprehensive business outcome measures, and
the reduced number of students graduating with economic degrees has made it impossible
for researchers to answer this question. Unlike previous research, the dataset used in this
study is large and comprehensive, but the number of students majoring in economics is
still relatively small. Information regarding the minors of graduating seniors, however, is
available and will be included in this study. Students minoring in economics are required
to enroll in the same general economic courses as economic and business majors, but
differ from all other business student because they receive economic training beyond the
basic business requirement. There are no known published research studies in the area of
economic education using production functions where the educational output is the
performance on the MFT-B (or any other comprehensive business output measure) and
the educational inputs belong or are related to students majoring or minoring in
economics.
Finally, this study will measure efficiency by identifying the factors that
contribute to higher levels of basic academic ability after controlling for initial levels of
general ability, demographic characteristics, other ability measures, transfer status and
student majors. The Collegiate Learning Assessment instrument (CLA) is an innovative
6


internet testing instrument designed to simulate complex, ambiguous situations that every
successful college graduate may one day face in the form of written communication,

critical thinking and analytical skills. The CLA is not specifically designed for business
students, but places an emphasis on everyday business situations. The CLA provides a
scaled SAT score for each individual student, and scaled scores take into account
entrance SAT scores to assess the educational value-added of a student’s academic
experience. This approach is innovative for two reasons. First, the presence of an overall
value-added measure in business education studies could not be found in the literature.
Previous studies tend to focus on pre- and post-test results in single courses or areas of
specialization to assess the value-added of education. Most importantly, many in the
academic world argue that a multiple-choice test may not be the most appropriate and
meaningful assessment measure for students and their programs because such testing
instruments fail to assess the skills, attitudes and problem solving capabilities of student
and tend to simply focus on the measurement of cognitive knowledge in an specific field
of study. The two outcome measures used by institutions to assess the overall
performance of business students, namely the Core Curriculum Assessment Program
(CCAP) and the MFT-B are multiple-choice instruments. The CLA will allow for the
comparison of the results arising from two fundamentally different comprehensive testing
instruments and shed more light on the factors contributing to the production and
retention of the basic general abilities of graduating seniors.





7


Chapter Two
Literature Review
This chapter is organized as follows. The first section describes the theoretical
model that will be used to assess the effect of different educational inputs on the

production of academic business knowledge. An overview of the inputs and outputs used
in the literature, as well as a summary of the main conceptual, methodological and
empirical issues frequently encountered in production function studies can be found in
this section. Section two presents several standardized outcome measures available to
business schools to assess the overall performance of their students and programs. A
review of the Association to Advance Collegiate Schools of Business International is
included in this second section. The chapter concludes by examining the main student
characteristics that have been studied in previous education production models. The
emphasis is placed in the economic education literature but reference is made to other
business areas.

2.1 The Production Function Model
The 1964 “Coleman Report” was the first and most influential educational
production function study ever conducted. It included information on over half a million
students and more than 300 schools and concluded that traditional school inputs, as
reflected by per pupil expenditures, class-size and certain teacher attributes have minimal
effects on student achievement. Since then, many researchers have attempted to utilize
production functions to estimate the relationship between educational inputs and student
achievement, both at the pre-college and college and university levels. Considerable
8


confusion remains about how such studies should be conducted and interpreted, as well
as what can be learned from them (Hanushek, 1978; Becker, 2004). More importantly,
there seems to be a series of conceptual, methodological and empirical problems that
have “shadowed” previous research findings and conclusions in the area of education
production functions.
This section is organized as follows. First, the reader can find a brief description
of the theoretical model that will be used to assess the effect of different educational
inputs on the production of academic business knowledge. An overview of the inputs and

outputs used in the literature, as well as a summary of the main conceptual,
methodological and empirical issues frequently encountered in production function
studies follows.

2.1.1 The Education Production Function
In education, the production function is some mathematical relationship
describing how educational resources (inputs) can be transformed into educational
outcomes (outputs) (Cohn and Geske, 1990). The production function then represents the
maximum amount of output that can be produced for given levels of inputs and its
general form can be expressed as

Q = f (X,S) [Equation 1]

For the purposes of this study, the inputs and outputs are related to undergraduate
students and education in universities. Accordingly, in equation 1, Q is a vector of
9


educational outputs (i.e. standardized test score), X is the vector of university related
inputs and S is the vector of non-university inputs. The internal process of transforming
the educational inputs into output is known as the technology of education. This process
is influenced by different variables related to the educational process such as pedagogical
techniques or management.
Although a considerable body of literature has attempted to identify and estimate
the “best” educational production function, most research in the area of economic
education has used a linear production function to estimate the effectiveness of
educational inputs because of its ease of manipulation. Cohn and Geske (1990) note that
a linear specification would be empirically valid to the extent that the curvature of the
total output function is only mildly violated by employing a linear approximation (p.167).
In other words, it is important that the approximation be done in a range where

diminishing marginal returns are mild; linear approximations are not valid if the range
being observed belongs to an area where diminishing marginal returns are considerably
strong. Cohn and Geske also point out that the conclusions derived from the use of linear
analysis should not be applied to input levels beyond the range of the sample observation.
The general form of the ith educational production function using a linear
approximation to the production of knowledge is given by:

ij
m
j
ijh
k
h
ihg
n
g
igii
esdxcqbaq ++++=
∑∑∑
=== 111
[Equation 2]
10


In equation 2,
i
a is the intercept, while scsb
ihig
, and sd
ij

are the coefficients we
wish to estimate, with 0
=
ii
b and
i
e being the stochastic term (Cohn and Geske, 1990).

2.1.2 Educational Production Inputs
There are two general types of inputs that have been identified and used in
previous education studies in an effort to estimate educational production functions. Most
of the early work with educational production functions focused on the pre-college level.
In this case, there are inputs provided by the school (school inputs) and those that are
innate or provided to students through their homes or societal interaction (non-school
inputs). This pre-college approach to education production is similar to the university
approach used for this study.
University inputs can be divided into human inputs such as teacher characteristics
or salaries, and physical inputs such as the condition of universities. Most of the research
using university inputs has been focused on human inputs because a large fraction of
universities’ budgets is spent on the teaching staff, making the efficiency of the “labor”
component of educational production a main subject of study.
The introduction of non-school inputs into the production process is valid because
few would argue that the formation of knowledge by students subject to the educational
process is influenced by factors other than those provided by the school. The largest,
most comprehensive and most hotly debated study of educational production functions
(Equality of Educational Opportunity, 1966) directed by James S. Coleman corroborated
this idea by coming to the conclusion that traditional school inputs as reflected by per
pupil expenditures, class-size and certain teacher attributes have minimal effects on
11

student achievement. This influential study ignited an intense debate in the areas of
education and forced researches to look beyond traditional school inputs when trying to
estimate educational production functions. Watts (1985) included a “poverty index”
variable while testing different specifications of an educational production functions for
student belonging to more than two hundred classes in the state of Indiana. Family
income, number of books at home, the general characteristics of the student body, grade
point average for a students’ section, family size, race or sex are some of the non-
university inputs that have been frequently used in previous production function
estimates (Cohn and Geske, 1990).
It is obvious that although some of the inputs are easily manipulable by university
administrators (i.e. course content) other are not manipulable (i.e. age of students) and
can not be controlled and changed to increase educational output. The non-manipulative
nature of such educational inputs makes the selection process of those entering the
educational system, especially at the higher education level, even more relevant as they
directly relate to the technical and internal process of generating educational knowledge.

2.1.3 Educational Production Outputs
The two types of educational outputs that have been identified and measured in
the economics of education literature are consumption and investment outcomes (Cohn
and Geske, 1990). Consumption outcomes relate to the present utility that students, their
families and society derive from the consumption of education. On the other hand,
investment outcomes relate to the future productive skills and well-being of society.

12


Consumption outcomes include satisfaction from the direct involvement of
students in activities offered by universities or intellectual satisfaction from learning new
materials and skills. Other consumption outcomes include family relieve of responsibility

towards students during school hours, reduced crime rates and lower competition in job
markets by restraining the supply of labor.
The investment aspect of education focuses on the future benefits of the
inculcation of social and moral values as they translate into more civilized and respectful
societies. Other examples of investment outcomes include the acquisition of basic
communication and analytical skills, improvements in health habits and positive changes
in attitudes towards self, family, peers and society.
Educational outcomes can be further divided into cognitive and non-cognitive
outcomes (Cohn and Geske, 1990). From an economic perspective the classification is of
little value because cognitive and non-cognitive outcomes provide both consumption
and/or investment benefits. Obvious examples of previously studied cognitive and non-
cognitive outcomes include basic and vocational skills, creativity and attitudes.
1

Cognitive outcomes, however, have been extensively used in estimations of educational
production functions over non-cognitive outcomes because they are less difficult to
measure. Cohn and Geske (1990) note that “because attitudes are difficult to quantify


1
Several studies examining higher education have extended the list of educational
outputs by including variables pertaining to undergraduate teaching, master and doctoral
graduate level of instruction and research productivity as measured by the number of
publications or monetary spending in research activities (Verry and Layard, 1975; Verry
and Davies, 1976; Psacharopoulos, 1980, 1982; Throsby, 1986; Lloyd et al., 1993;
Johnes, 1993; Lewis and Dundar, 1995; Hashimoto and Cohn, 1997; Cruz et al., 2004).

13

[…] student attitudes have rarely entered a formalized educational input-output model”
(p.165).

Since the production of education differs from “industrial” production in that the
educational industry generates multiple outputs, estimates of educational outcomes
should include as many relevant and reliable measures of educational attainment as
possible, including the widely used and available battery of standardize test score.

2.1.4 Conceptual, Methodological and Empirical Issues
In theory, the production function represents the maximum achievable output for
a given level of inputs and firms make decisions on the optimal amount of inputs to use
in order to maximize their profits. The question is whether production functions, as they
are used in standard production, are a viable method for modeling the creation of
educational output. In reality, the application of production functions to education is
complicated because the technological process of transforming inputs into output is
generally not known and needs to be estimated through observation, the contribution of
similar inputs to the creation of knowledge may vary widely and due to the
heterogeneous nature of the produced output (individuals with different quality
attributes).
Hanushek (1978) and Becker (2004) addressed conceptual and statistical issues
related to research on the estimation of educational production functions and teaching
methods. They observed that student outcomes were generally measured through the use
of standardized tests, but that other plausible outcomes had been studied (i.e. attendance
rates and continuation or dropout rates). The use of different outcome measures, in
conjunction with the variety of inputs introduced in studies of educational production