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This is to certify that the
dissertation entitled

THE EFFECTS OF TUITION DISCOUNTING AT PRIVATE,
BACCALAUREATE-LEVEL INSTITUTIONS OF HIGHER
EDUCATION

presented by

MELISSA RUTERBUSCH

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PhD. degree in Higher, Adult, and Lifelong
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THE EFFECTS OF TUITION DISCOUNTING
AT PRIVATE, BACCALAUREATE-LEVEL
INSTITUTIONS OF HIGHER EDUCATION

By

Melissa Ruterbusch

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY

Department of Educational Administration

2004

 

 

ABSTRACT
THE EFFECTS OF TUITION DISCOUNTING
AT PRIVATE, BACCALAUREATE-LEVEL
INSTITUTIONS OF HIGHER EDUCATION
By
Melissa Ru‘terbusch

When colleges and universities use their own financial resources to award grants
and scholarships to students it is referred to as tuition discounting. This practice allows
institutions to charge lower than “sticker price” tuition rates to selected students. The
concept of tuition discounting is not new; for years, institutions have offered discounted
tuition rates to students in order to reward merit, to assist financially needy students, and
to expand diversity. In recent years, however, a fourth reason for tuition discounting has
emerged at some institutions. For these institutions, the main purpose of tuition
discounting is to fill otherwise empty seats in classrooms and to enroll enough students to
remain fiscally solvent.

The primary purpose of this study of private, baccalaureate level institutions of
higher education was to explore the pattern of tuition discounting between 1990 and
2000, to analyze variations in tuition discounting by institutional type, and to determine
the relationship between tuition discounting and institutional financial health. The
findings of this study indicate that tuition discounting is on the rise for this sector of
higher education. Between 1990 and 2000, tuition discounting rates rose by
approximately 7 percentage points. Second, tuition discounting does vary based on
institutional demographics such as religious affiliation, size, endowment, and

expenditures. Finally, tuition discounting does appear to have an impact on the financial

 

health of institutions—it appears that tuition discounting is generally negatively related to

institutional financial health.

ACKNOWLEDGEMENTS

I would like to thank my husband Kurt for his love and support in every aspect of
my life! I never could have completed this project without his continuous encouragement
and faith in me. Additionally, I would like to thank my mom, Sandy Hale, for her help
with typing and her constant words of encouragement. Also, thanks to my advisor, Dr.
James Fairweather for his countless hours of assistance, and never-ending patience.
Finally, I would like to thank my dissertation committee—Dr. Marylee Davis, Dr.

Richard Hula, and Dr. David Plank—for their encouragement and support.

 

iv

TABLE OF CONTENTS

List of Tables ........................................................................................ vi
List of Figures ........................................................................................ i:
Chapter One: Statement of Purpose and Research Questions .............................................
Introduction ......................................................................................................................
Confusion with Terminology ...........................................................................................
The Shift and Growth in Tuition Discounting .................................................................
Tuition Discounting Mechanics ..................................................................................... 1
Statement of Purpose/Research Questions .................................................................... 1
Audience ........................................................................................................................ 1
Chapter Two: Literature Review ....................................................................................... 1
Description of the Literature .......................................................................................... 1
I. General Aspects of Tuition Discounting ................................................................... 1
Tuition Discounting Growth ...................................................................................... 1
Factors that Have Contributed to Tuition Discounting Growth ................................. 1
Institutional Participation in Tuition Discounting ...................................................... 2-
II. Outcomes Literature ................................................................................................. 3
The Impact of Tuition Discounting on Enrollment .................................................... 3
Need, Merit, and Willingness ..................................................................................... 3
Financial Aspects of Tuition Discounting .................................................................. 4
Chapter Three: Design and Methodology ......................................................................... 4
Research Questions ........................................................................................................ 4
Data Sources .................................................................................................................. 4
Integrated Postsecondary Education Data Analysis ................................................... 4
Peterson’s Guide to Four-Year Colleges .................................................................... 5
US. News and World Report: America’s Best Colleges ........................................... 5
Selection of Institutions ................................................................................................. 5
Conceptual Framework .................................................................................................. 5
Outcome Variables: Institutional Financial Health ....................................................... 5
Institutional Descriptors ................................................................................................. 5
Indicators of Student Quality ......................................................................................... 6
Indicators of Institutional Quality .................................................................................. 6
The Tuition Discount Rate ............................................................................................ 6
Data Analysis Methods .................................................................................................. 6
Chapter Four: Analysis ofthe Data ............. 6
Rationale for Separate Analysis of BAI and BAH Institutions ...................................... 6
General Description of the BAI Sample ........................................................................ 7
General Description of the BAII Sample ....................................................................... 7
Research Question 1: Patterns of Tuition Discounting and Financial Health .............. 7

Research Question 2: Variations in Tuition Discounting, BAI Institutions .................. 79

Variations in Tuition Discounting, BAII Institutions .................................................... 86
Research Question 3: Tuition Discounting and Institutional Financial Health ............ 92
Tuition Discounting and Institutional Health, BAI Institutions .................................... 93
Regressions with Grouped Data .................................................................................. 107
Chapter Five: Discussion and Conclusions
Summary and Discussion of Findings ......................................................................... 109
Implications for Stakeholders ...................................................................................... 114
Future Research ........................................................................................................... 121
Appendix A: Variable Calculation Grids ........................................................................ 123
Appendix B: Variables .................................................................................................... 128
Appendix C: Descriptive Statistics ................................................................................. 130
Appendix D: List of Institutions by State ....................................................................... 134
Appendix E: Correlations for Institutional Quality Variables ........................................ 141
Appendix F: Regression Results, Grouped Data ............................................................ 143
References ....................................................................................................................... 149

Vi

LIST OF TABLES

Table 1. Tuition and Enrollment Demand, 3 similar colleges ......................................... 12
Table 2. Pricing Strategies (Hypothetical Data) ............................................................... 13
Table 3. Tuition Discounting Rates, Redd’s Study, 2000 ................................................ 25
Table 4. Institutional Aid Policy ...................................................................................... 45
Table 5. Population vs. Sample ........................................................................................ 54
Table 6. Comparison of BAI and BAII Institutions ......................................................... 69
Table 7. Tuition Discount Rates, BAI Institutions ........................................................... 75
Table 8. Financial Health Variables, BAI Institutions ..................................................... 76
Table 9. Tuition Discount Rates, BAH Institutions ......................................................... 77
Table 10. Financial Health Variables, BAII Institutions .................................................. 79
Table 11. Mean Difference in Tuition Discount Rates, 1990-2000, Based on Enrollment,
BAI Institutions .......................................................................................................... 80

Table 12. Mean Tuition Discount Rates, 1990-2000, Based on Geographic Location... 81
Table 13. Mean Tuition Discount Rates, 1990-2000, Based on Institutional Age, BAI. 82
Table 14. Mean Differences in Average Tuition Discount Rates, 1990-2000, Based on

Admissions Competitiveness Rating, BAI Institutions .............................................. 83
Table 15. Mean Tuition Discount Rates, 1990-2000, Based on Academic Reputation
Score, BAI .................................................................................................................. 84
Table 16. Mean Tuition Discount Rates, 1990-2000, Based on Total Educational and
General Expenditures, BAI ........................................................................................ 84
Table 17. Mean Tuition Discount Rates, 1990-2000, Based on Endowment, BAI ......... 85
Table 18. Mean Tuition Discount Rates, 1990-2000, Based on Minority Enrollment,
BAH ............................................................................................................................ 86
Table 19. Mean Tuition Discount Rates, 1990—2000 Based on Geographic Location ..... 87

Table 20. Mean Tuition Discount Rates, 1990-2000, Based on Institutional Age, BA1188
Table 21. Mean Tuition Discount Rates, 1990-2000, Based on Academic Reputation

Score, BAII ................................................................................................................. 89
Table 22. Mean Tuition Discount Rates, 1990-2000, Based on Admissions
Competitiveness Rating, BAII Institutions ................................................................. 89
Table 23. Mean Tuition Discount Rates, 1990-2000, Based on F aculty-Student Ratio,
BAH ............................................................................................................................ 90
Table 24. Mean Tuition Discount Rates, 1990-2000, Based on Endowment, BAH ........ 91
Table 25. Regression Coefficients, 1990, Net Tuition Revenue Per Student as Dependent
Variable, BAI Institutions .......................................................................................... 93
Table 26. Regression Coefficients, 1995, Net Tuition Revenue Per Student as Dependent
Variable, BAI Institutions .......................................................................................... 94
Table 27. Regression Coefficients, 2000, Net Tuition Revenue Per Student as Dependent
Variable, BAI Institutions .......................................................................................... 96
Table 28. Regression Coefficients, 1990, Net Tuition Revenue as Dependent Variable,
BAI Institutions .......................................................................................................... 97
Table 29. Regression Coefficients, 1995, Net Tuition Revenue as Dependent Variable,
BAI Institutions .......................................................................................................... 98

vii

Table 30. Regression Coefficients, 2000, Net Tuition Revenue as Dependent Variable,

BAI Institutions .......................................................................................................... 99
Table 31. Regression Coefficients, 1990, Net Tuition Revenue Per Student as Dependent
Variable, BAII Institutions ....................................................................................... 100
Table 32. Regression Coefficients, 1995, Net Tuition Revenue Per Student as Dependent
Variable, BAII Institutions ....................................................................................... 101
Table 33. Regression Coefficients, 2000, Net Tuition Revenue Per Student as Dependent
Variable, BAH Institutions ....................................................................................... 102
Table 34. Regression Coefficients, 1990, Net Tuition Revenue as Dependent Variable,
BAH Institutions ....................................................................................................... 103
Table 35. Regression Coefficients, 1995, Net Tuition Revenue as Dependent Variable,
BAII Institutions ....................................................................................................... 104
Table 36. Regression Coefficients, 2000, Net Tuition Revenue as Dependent Variable,
BAH Institutions ....................................................................................................... 105
Table 37. Summary of Statistically Significant Regression Coefficients. BAI and BAII
Institutions ................................................................................................................ 106
Table 38. Regression Results, BAI and BAH Institutions, Grouped*. .......................... 108
Table 39. Significant Mean Differences Among Institutional Types ............................ 110
Table 40. Yield Table-Admitted Applicants with Financial Need of $6000-$7000
(Hypothetical Data). ................................................................................................. 1 16

\riii

LIST OF FIGURES

Figure 1. Elastic Demand ................................................................................................. 10
Figure 2. Inelastic Demand .............................................................................................. 11
Figure 3. Graphical Portrayal of Tuition Discounting ..................................................... 14

ix

Chapter One: Statement of Purpose and Research Questions
Introduction

In 2002, nearly $90 billion in financial assistance was awarded to college students
in the United States. Over 8 million students benefited from this assistance at over 9000
post-secondary institutions (Trends in Student Aid, 2002). Financial assistance helps
students defray part of the growing cost of higher education. Earning a college degree
generates an estimated $1 million in extra lifetime earning when compared to high school
graduates (Bureau of Labor Statistics, 2002).

The concept of awarding financial assistance to college students is not new. In
fact, America’s first scholarship dates to 1643, when a wealthy individual donated 100
pounds to start a scholarship fund at Harvard College (Wick, 1997, p. 1). Financial aid is
important because, in different forms, it serves both to reward merit and to assist needy
and deserving students—those capable of, but not able to afford, a higher education.
Financial aid is an investment in human capital—an investment with a significant and
positive rate of return for both student and society.

Student financial assistance comes from three primary sources: the federal
government; state governments; and the colleges and universities. The federal
government awards nearly 72% of the total available student financial aid. The major
federal financial aid programs include Federal Pell Grants, Federal Work Study, and
various federal student loan programs. Most state governments also award student
financial assistance, which comprises about 5% of the total student aid awarded.

Colleges and universities use their own resources to provide about 23% of all financial

aid to students. This aid is usually referred to as institutional aid and can be funded

(meaning the aid is funded by the institution’s endowment earnings or by gifts) or
unfimded (meaning the institution has agreed to forego a portion of the posted tuition
price). Either way, the student pays less than full tuition price. Regardless of whether the
aid is ftmded or unfunded, the “reduction” in the student’s price is called a grant or a
scholarship. From the student’s perspective, there is no distinction.

When colleges use their own institutional resources to offer grants and
scholarships to students, it is referred to as tuition discounting. This practice allows
institutions to charge lower than “sticker price” tuition rates to students who may be
unwilling or unable to pay the full tuition price. The institutions can, through
discounting, charge different prices to different students for the same educational
service—a practice generally known as price discrimination. Price discrimination is a
term used by economists to describe charging different prices to different customers for
the same product or service. This is not an illegal or even an uncommon practice. Long
distance carriers charge different prices based on the time of day in order to charge
businesses (who make calls during normal business hours) a higher price than household
consumers (who normally make calls in the evenings and weekends). Another common
form of price discrimination involves airline fares. Individuals who book early, travel on
weekends, or accept non-refundable tickets usually pay less for the same airline service
than the less flexible air traveler.

Colleges and universities engage in tuition discounting for several reasons. The
first reason is to help financially needy students and families. Historically, this is the
major rationale for tuition discounting (Baum & Schwartz, 1988, p. 127). The earliest

financial aid was intended to assist “poor and pious youth and indigent young men of

merit” (Princeton Weekly Bulletin, 2003, p. 1). Offering discounted tuition rates to lower
and middle income families encourages attendance at institutions that would normally be
out of their reach financially.

A second reason that institutions discount tuition is to attract high quality students
(based on high grades and entrance test scores) because these students help to enhance the
reputation of the institution. Financial assistance awarded to students based on “quality”
are usually referred to as merit-based scholarships. Alfred MacKay (1992) reminds us
that if the market fails to “provide sufficient talent to satisfy a college’s educational aims
[then it is] justified on educational grounds in spending a reasonable amount (i.e.
providing financial aid) to achieve those aims” (p. 50).

A third reason that institutions discount tuition to selected students is to create
ethnic diversity within their student bodies. As with merit based aid, “if the market does
not, without prompting, provide sufficient diversity to satisfy a college’s educational
aims, then if it can afford to, a college is justified on educational grounds in spending a
reasonable amount (i.e., providing financial aid) to achieve those aims” (MacKay, 1992.
p. 50).

The concept of offering reduced tuition rates for these three reasons—financial
need, merit, and diversity—is not new. In recent years, however, a fourth reason for
tuition discounting has emerged at some institutions—a strictly financial reason. For
these institutions, the main purpose for tuition discounting is to fill otherwise empty seats
in classrooms, and to enroll enough students to remain fiscally solvent (Collison, 1992, p.
A28). In other words, tuition discounting has become a means of extracting “the

maximum revenue from every customer” (Graham, 1997, p. Bl). As Lapovsky (1996)

notes, “[t]he price differences for many students aren’t related to ability to pay but rather
willingness to pay and the desire of the institution to enroll that student” (Lapovsky,
1996, p. 25). Recent research supports the claim that discounts are increasingly being
used to entice students to enroll and not necessarily to assist financially needy students, to
reward merit, or to expand diversity (Loomis-Hubbell, 1992). Additionally, the discount
rate is invariably higher for incoming freshmen than for upperclass students, indicating a
focus on enrollment rather than merit, diversity, or need (“Tuition Discounting Findings,”
1997,p.7)

By all measures, the discounting of tuition is a very common and growing
practice, especially at private, non-profit, colleges and universities. Institutional student
aid now represents the fastest growing portion of many institutional budgets (Loomis-
Hubbell & Rush, 1992, p. 24). A 2000 survey administered by the National Association
of College and University Business Officers (NACUBO) also confirms that tuition
discounting is on the rise. In 1990, the average discount rate at small private colleges was
29.5%. This means that, on average, incoming freshmen were given institutional
scholarships amounting to nearly 30% of the actual tuition rate. By 2000, that number
had risen to 43% (Van Der Werf, 2002, p. A25). In 1990, private colleges awarded $3
billion in institutionally funded aid. Three years later that figure had risen to $5 billion
(MacDowell, 1996, p. 24). Fewer than 10% of students at private colleges pay the stated
tuition price. The other 90% receive some form of institutional grant or scholarship
(“Tuition Discounting Findings,” 1997, p. 6). Tuition discounts make the “tuition sticker

price, for most, more symbolic than substantive” (Johnstone, 1999, pp. 1-2).

Confusion with Terminology
There is a great deal of confusion about terminology relating to tuition
discounting. Specifically, four terms are particularly problematic: the tuition discount
itself, tuition “price” (or cost), financial aid, and tuition revenue.
Tuition Discount
First, there is confusion about the way tuition discounting is defined, and what
kind of aid is used when calculating the tuition discount rate. In a recent monograph,
Ronald Allen (1999) articulated an important problem relating to tuition discounting:
“Confusion permeates tuition discounting in both internal and external arenas.
One source of confusion is that college administrators and policy analysts have
different views of the subject and tend to talk past each other when discussing it.
Some administrators focus narrowly on tuition revenue not collected while other
focus on the tuition the students do not pay. These amounts are usually different
because of the role played by institutional and outside grants in the student aid
process. Different groups within higher education use different definitions of
tuition discounting for different purposes, adding to the confusion” (p. 8).
This confusion can be avoided if administrators and researchers are clear about the
definition of tuition discounting they are using. There are essentially three ways to
measure tuition discounting: the simple tuition discount; the scholarship allowance; and
the student tuition discount.
The first tuition discount definition is the simple tuition discount which “consists
solely of the waiver of all or a portion due. . . .It includes no funding from internal sources

SUCh as gifts and endowment, or from external sources such as Federal Pell Grants or

Federal Supplemental Educational Opportunity Grants” (Allen, 1999, p. 2). This
definition only includes what is referred to as “unfunded” institutional aid—aid that is not
really “funded” by any source, but simply represents an institution’s willingness to allow
a student to pay less than full price. The simple tuition discounting rate would be
calculated as the ratio of unfunded institutional aid to gross tuition and fee revenue (the
total amount of revenue the college would have collected if every student paid full tuition
from their own pockets).

The second tuition discounting definition, the scholarship allowance, includes all
institutionally funded aid, including unfunded institutional aid as well as funded
institutional aid (funded by gifts and endowments). This definition is used in an annual
survey administered by the National Association of College and University Business

Oflicers, as well as most of the tuition discounting literature. Irnportantly, “[w]hen gift
and endowment income that is restricted to financial aid is paid from the endowment fund
to the operating find, the college is able to spend operating fund money that would
otherwise be spent on financial aid on other things” (Allen, 1999, p. 3). The scholarship
allowance is measured as the ratio of unfunded plus funded institutional aid to gross
tuition revenue.

Finally, the student tuition discount is the broadest measure of tuition discounting.

It includes all tuition and fees that students do not have to pay out of their own pockets.
It includes all institutional aid (as in the previous definition) as well as any federal, state
01' other aid (grants, loans, or work study) paid on the student’s behalf. The student
tuition discount is calculated as the ratio of all aid the student receives from any source to

gross tuition revenue.

Tuition Price (Cost)

The definition of “price” in higher education can also cause confusion. There are
actually four prices used in higher education. First, there is a base price—the published
tuition and fee price. Second is the sticker price—the base price plus “options” such as
room, board, and fees. Students essentially choose the level of the extras they wish to
purchase for instance a single dorm room or a triple, or 10 meals per week versus 21.
The third price is the discount price, which is essentially the net price—net of
institutional discounts. Finally, the cash price is the actual amount that the student has to
pay out of his or her pocket after all financial aid, including federal, state, and
institutional aid is deducted.

Financial Aid

Two general forms of financial aid must be distinguished in order to understand

tuition discounting: institutional aid and external aid. Institutional aid, not to be confused
with external (federal, state, private) aid, is actually a reduction for the student in the
stated tuition price at an institution. External aid (from sources other than the institution)
is a subsidy from the government to the student, and represents a payment to the
institution on the student’s behalf (Loomis—Hubbell, 1992, p. 8). The following example
illustrates the relationship between the tuition discount and other forms of financial aid.
When a store has a sale, it gives a discount of the stated price. The store has agreed to
forego a certain amount of revenue on the sale of that product. If the customer has a
manufacturer’s coupon (an external discount) in addition to the sale price, he or she is

using an external source to pay a portion of the discounted price.

 

Tuition Revenue

The definition of tuition revenue can also be a bit confusing. Gross tuition
revenue is the total amount of revenue the college would have collected if every student
paid full tuition from his or her own pocket. Net tuition revenue is the actual amount of
revenue the college collects either from students directly or from individuals or
organizations—such as the federal govemment—paying the on the student’s behalf. It is

calculated as the difference between gross revenue and unfunded institutional aid.

The Shift and Growth in Tuition Discounting
The dramatic shift in tuition discounting and its growth can be explained by

demographic, financial, and political forces that have influenced the private,
baccalaureate sector of higher education. First, with regard to demographics, private
colleges rely most heavily on the pool of traditional age students, and nationally, the
traditional age cohort has declined in recent years. During the 1970's, the traditional age
student pool actually grew from 25 million to 30 million. However, starting in 1980, the
pool began to shrink, bottoming out at 23 million in the mid-1990's (Francis, 1991, p.
139). This decline in the applicant pool has created increased competition among
institutions, and has had a substantial impact on enrollment. While the pool of non-
traditional students increased significantly in the 1990’s, this change has mainly affected
public higher education—not private institutions.

Financial forces have also impacted private higher education. Institutional costs
have risen significantly, which has led to increased tuition rates. Tuition increases have

outpaced economic growth and the rate of inflation at both private and public institutions.

Between 1983 and 2000, the higher education price index doubled. In other words, even

after adjusting for inflation, college was twice as expensive in 2000 than it was in 1983
(Research Associates, 1998, p.4) During the 1950's, 1960's, and 1970's, national median
income grew at a rate that equaled or exceeded tuition at private institutions. During the
198 0'5 and 1990’s, however, tuition growth exceeded income growth. According to a
1995 Study by the National Association of Independent Colleges and Universities
(NAICU), the top three factors that contributed to tuition and fee growth were:
technology updates (computers, networks, etc.); institutionally provided student aid; and
faculty compensation increases (“A Commitment to Access,” 1997, p. 2). Kirshstein
(1991 ) adds physical plant maintenance, administrative costs affected by increased
regulatory requirements, and inflation as additional factors (pp. 67-68).

A political factor also has affected these institutions—specifically the slow rate of
growth in government-funded (federal and state) grants for students. When adjusted for
inflation, federal student aid was 5% lower in 1992 than in 1980 (Loomis-Hubbell &
Rush, 1 992, p. 24). In 1975 a federal Pell Grant covered, on average, 38% of tuition costs
at 4 year institutions. By 2000, that percentage had dropped to 15% (Burd, 2001, p. A26).
Not only has the growth in federal funding declined, the makeup of that aid has changed
as well. Loans (as distinct from grants and scholarships) now make up the major portion
of the federal aid budget. The level of student loan borrowing grew 40% in a one year
Period of the early 1990's (Loomis-Hubbell & Rush, 1992). The fear of large

indebtedness also adversely affects enrollment at higher-priced private institutions.

Tuition Discounting Mechanics
Why did tuition discounting emerge as a potential solution to the problems of
lagging enrollment and financial difficulty? Why would institutions be compelled to
spend more money in times of economic hardship? The answer to this question is
couched in economic theory.

Basic economic price theory portrays the “demand” for a particular product to be
negatively related to price. In other words, at a higher price, fewer units of a product will
be demanded than at a lower price. Figure 1 portrays the demand relationship for a
normal good. Notice that at a high price, a relatively low quantity of the product is
demanded (purchased). At a low price, the opposite is true; a higher quantity of the

product is demanded. Similarly, price changes lead to considerably larger changes in

quantities demanded.

Figure 1. Elastic Demand

Price

P1\

 

 

Demand

 

 

 

 

Q1 Q2 Quantity

This relationship, however, does not always hold true. If a product has a special

characteristic, demand may be “inelastic,” and the relationship between price and quantity

lO

 

may not be so pronounced. For instance, the demand for health care is considered to be
inelastic because individuals are willing to purchase it at almost any price; whether $100
or $5000, the individual will, if able, “purchase” the life saving medical treatment.
Figure 2 portrays the relationship between price and quantity demanded for a good
considered to have inelastic demand. The curve illustrates that quantity demanded
changes only a small amount with respect to price changes. Although no good is
perfectly inelastic, it has been argued that demand for higher education at exclusive,
highly competitive institutions, like Harvard or Princeton, is relatively inelastic because

admission to these institutions is highly regarded, and there are few substitutes

(Clotfelter, 1996, pp. 255-257).

Figure 2. Inelastic Demand

Price \
P 1

P2 \

 

 

Demand

 

 

 

Quantity

 

Q1 Q2
Most institutions of higher education face a normal price-demand relationship.

Smaller, less prestigious institutions in which potential students feel there are many

substitutes are good examples of this phenomenon. A recent survey by the Gallup Poll

11

 

 

(1995) indicates the following for three colleges of generally equivalent reputation, size,

and offerings:

Table 1. Tuition and Enrollment Demand, 3 similar colleges

 

 

 

 

Price of tuition for 1 year % of respondents who would
“consider that college”
College A $22,000 17%
College B $18,000 33%
College C $14,000 50%

 

 

 

 

Source: The Gallup Poll, 1995.
These data demonstrate that for most individuals the demand for higher education is
elastic—that is, their demand is responsive, or dependent, upon price. Graphically, we
see a normal, downwardly sloping demand curve for this data set. When demand for a
product exhibits normal behavior, as demonstrated by the numbers above, the product is
considered to be a “normal” good.

The pricing of normal goods is a difficult process. For many institutions of higher
education, particularly small private colleges, the strategy is to find the optimal price
levelxthe level that creates the greatest net revenue. Table 2 shows a standard pricing
analysis for a hypothetical private college. Column 1 represents possible tuition rates.
Column 2 presents corresponding anticipated enrollment at each of those tuition levels.
Column 3, total revenue, is the product of the tuition rate and the anticipated enrolhnent.
Colm 4 presents total fixed costs for the institution (which do not vary based on

enrollment) and total variable costs (which increase as enrollment increases). Column 5

12

presents “after cost” revenue—the amount that remains from tuition revenue once the

institution has met its costs. This money can be used to buy library books, improve lab
etc. The strategy is to find the tuition level that maximizes after cost tuition revenue. I
this example, the optimal tuition level is $16,000 because it produces after cost revenue
of $9.2 million.

Table 2. Pricing Strategies (Hypothetical Data)

 

—|

 

 

 

 

 

 

(1) (2) (3) (4) (5)

Possible Corresponding Total Revenue “After Cost” Tuition
Tuition Anticipated (Col 1 x Col 2) Total Costs“ Revenue

Levels Enrollment (Col 3 - Col 4)
$22,000 1,000 $22 million $20 million $2 million
$20,000 1,400 $28 million $22 million $6 million
$18,000 1,800 $32.4 million $24 million $8.4 million
$16,000 2,200 $35.2 million $26 million $9.2 million
$14,000 2,600 $36.4 million $28 million $8.4 million
$12,000 3,000 $36 million $30 million $6 million 7

 

 

 

 

 

 

*Includes fixed costs (unrelated to the level of enrollment) + variable costs (increase as
enrollment increases).

Suppose, however, the 400 students who would enroll if tuition were $14,000 (I
not $16,000) could be identified. If each of these students were offered a scholarship
(discount) to make up the difference between $16,000 and each individual’s vvillingnes
to pay, then he or she would enroll. Figure 3 is a graphical representation of this
Situation. At a $16,000 tuition rate, a total of 2200 students would enroll. An addition.

400 students can be enticed to enroll only if offered lower tuition rates ranging from

$14,000 to $15,999. The amount of discounting required to enroll 2600 students at
$16,000 is the area of the triangle formed by BCD. Here, that amount is $400,000.
In this scenario, the institution takes in gross tuition of $41 .2 million ($16,000 x

2600 students minus $400,000 in discounts). Its total costs are $28 million (the level of
cost for enrollment of 2600 students). However, after cost tuition revenue is now $13.2
million, a much better scenario than any of those offered in Table 2. Simply put, these
400 additional students contribute more to total revenue than to total cost, even at a lower
tuition rate. As long as “the ‘net revenue’ contributed...exceeds the marginal cost of
enrolling them, it is to the financial advantage of colleges to provide discounts in the form
of scholarships” (Bowen & Breneman, 1993, p.4). Because the students getting the
discounts are filling otherwise empty spaces in classrooms and residence halls, the
additional cost of enrolling them is minimal (“Fact File,” p. A28). This concept is
defined by Glasser (1993) as “no-need” or “empty-seat” financial aid (p. 4).

Figure 3. Graphical Portrayal of Tuition Discounting

 

 

 

 

 

 

Tuition
Price
B
$ 16,000
$14,000
C D
2200 2600 Enrollment

Adapted from: Breneman, D. (1994). Liberal arts colleges: Thriving, surviving. or
MgerLd? Washington, The Brookings Institution.

 

It is easy to understand m private institutions engage in tuition discounting. If
implemented properly, tuition discounting can be an effective tool in influencing an
institution’s financial health, enrollment, and the make-up of its student body. But has
tuition discounting really been an effective practice? This study attempts to address this

and other important questions.

Statement of Purpose/Research Questions
The purpose of this study was to examine tuition discounting at private,
baccalaureate-level institutions of higher education between 1990 and 2000. Specifically,
three main questions were addressed:
1. What is the pattern of tuition discounting for these institutions? Has tuition
discounting increased, decreased, or stayed the same?
2. Are there variations in tuition discounting based on institutional demographics?
For example, does discounting vary geographically, or by institution size or age?
3. Does tuition discounting improve the financial health of institutions? In other
words, what is the relationship between tuition discounting and the financial

health of the institutions?

Audience
The research presented in this study is potentially useful to many different
individuals, institutions, and organizations. First, financial aid administrators at private
institutions need to be aware of the current state of tuition discounting, including the

financial effects. As key players in the administration of tuition discounting programs,

 

they need to understand how tuition discounting works, and how it affects other
institutions.

Similarly, presidents of private institutions need to be aware of the current state (
tuition discounting. Is tuition discounting working for most institutions? Do most
institutions experience enrollment and net tuition growth as tuition discounting expands
When does tuition discounting fail? The Board of Trustees will expect the President to
know the answers to these important questions. In addition, business officers who are

responsible for monitoring the financial status of institutions also have a need for this
information. Business officers are keenly aware of the financial status of the institution,
including the use of resources.

National organizations such as the National Association for Student Financial Ai
Administrators (NASF AA) and the National Association for College and University
Business Officers (NACUBO) have been very supportive of research in the area of tuitic
discounting. Their missions require them to research and disseminate information abou'
emerging issues in higher education finance. Additionally, the donor community and
investors are both interested in making sure their investments are being used wisely by
the institution. If the practice proves effective, and can be attributed to grth and heall
Within the private sector, then donors and creditors may wish to continue their support.
it proves detrimental, donors and creditors may reconsider their support of institutions ii
the private sector.

For many reasons, tuition discounting has been a topic of much interest over the

past decade. Virtually all conferences for financial aid administrators or business office

host a session on tuition discounting. It is emerging as one of the main issues in the are]

16

of higher education finance, with a growing body of literature. A comprehensive review
of this literature, provided in Chapter Two, presents what is known about tuition
discounting, and what is yet to be learned. Chapter Three details the research design and
methodology utilized to answer the research questions. Chapter Four provides data

analysis, and conclusions are presented in Chapter Five.

Chapter Two: Literature Review

Description of the Literature

Expectedly, as the practice of tuition discounting has increased over recent
decades, so has the concomitant literature. Mentioned only sporadically in the literature
of the 1960’s and 1970’s, the subject of tuition discounting grew to a steady and
persistent dialogue by the 1980’s. The tuition discounting literature can generally be
Classified into two categories. The first category includes literature that focuses on
general aspects of tuition discounting including: (1)tuition discounting growth; (2)factors
that have contributed to tuition discounting growth; and (3 )institutional participation in
tuition discounting. The second category of literature focuses on the outcomes of tuition
discounting including (1)the impact of tuition discounting on enrollment; (2)the use of
mition discounting to address merit, need, and willingness to pay; and (3)the financial

aspects of tuition discounting.

 

I. General Aspects of Tuition Discounting

Tuition Discounting Growth

According to published research, tuition discounting grew substantially at
independent (private) institutions in the 1990’s. Articles with titles such as “Tuition
Discounting Continues to Grow” (Lapovsky & Loomis-Hubbell, 2003), and “Tuition
Discounting May Rankle, but it Has Become Widespread” (Chronicle of Higher
Education, 2000) are among many reports of the nationwide surge in tuition discounting
over the past 10-15 years.

The most nationally recognized tuition discounting study is the annual NACUBO
(National Association of College and University Business Officers) Tuition Discounting
Study. According to this multi-year study of 350 independent institutions, fewer and
fewer students are paying the published tuition price. In 2000, only 21% of students paid
the full tuition price—79% received some form of institutional discount, up from 66% in
1990. Also, the average tuition discount for incoming freshmen rose from 28% to about
38% between 1990 and 2000 (Lapovsky & Loomis-Hubbell, 2000, p. 27). Students are
being charged, on average, only 62% of the sticker price. (Recall that this study uses the
scholarship allowance definition of tuition discounting—so the discount includes funded
and unfunded institutional aid). According to Winston and Zimmerman (2000), more
than 60% of private institutions are discounting to more than 80% of their students (p.

10). It seems quite evident that tuition discounting is on the rise. But why?

Mrs thgt Have Contributed to Tuition Discounting Growth

Three factors are cited as major contributors to the rising levels of tuition
discounting: increased competition among institutions for students, high tuition rates, and
decreased federal student financial aid. The first factor is increased competition among
institutions resulting from the declining pool of traditional college age students (Loomis-
Hubbell & Rush, 1992, p. 4). Between 1978 and 1992, the number of 18 year olds in the
US. dropped 25%. Although the number of 18 years olds started to rise again in 1994,
the growth has been very slow, and the numbers are still significantly less than the 1970’s
when the baby-boomers were college age (Day, 1997, p. 2). Additionally, more of
today’s 18 year old students are from less wealthy families (MacDowell, 1996, p. 26).

A second factor contributing to increased tuition discounting is the growth in
tuition rates. Tuition rates, on average, have risen substantially over the past 20 years,
especially at private institutions. From 1981 to 2001 , the average cost of tuition and fees
at private, four-year colleges grew 122% in real terms (“Trends in College Pricing,”
2002). Between 1981 and 1991, average annual tuition and fees at private, four—year
colleges grew 62%, whereas family income grew only 16%. From 1991-2001, private
tuition rates grew 37% while family income grew only 8%. The growth rate in private
tuition and fees was nearly 4 times the growth in income over that 20 year period
(“Trends in College Pricing,” 2002). The percentage of median family income needed to
pay private tuition costs started to rise sharply in 1981 when it increased to 21.7%, from a

level of 16% that had held steady during much of the 1970’s (Halstead, 1989, p. 91).

19

In 1990, a study revealed that 82% of Americans felt tuition prices were rising out
of control—to the point where a college education might be out of reach for their children
(McDuff, 1989-90, p. 21). Additional studies, however, seem to indicate that the average
individual perceives that college costs are higher than they actually are (Loomis-Hubbell
& Rush, 1991; “Straight Talk,” 1998). A 1996 study revealed that individuals over-
estimate the actual tuition price of higher education by about 30% (Loomis-Hubbell &
Rush, 1991, p. 227).

This over-estimation might have been fueled by a series of widely circulated
publications that warned of the increased tuition rates. Even mainstream media, such as
Newsweek, warned of “Those Scary College Costs” (April, 1996). In 1996, Congress
commissioned an inquiry into increasing college costs (“Tuition Increasing Faster then
Household Income and Public Colleges’ Cost”), which received wide media attention. In
1997, an independent advisory board was commissioned by the federal government to
compile and extensive report about college affordability. The report, entitled “Straight
Talk About Colleges Costs and Prices,” was published in 1998. A major finding of this
report was that “rising college tuitions are real” (“Straight Talk,” 1998, p. 1). Whether
there was an over-estimation on the part of families regarding college costs or not,
families were becoming enlightened about the high and growing price of tuition—
particularly at private colleges.

Coupled with rising tuition rates, families have historically saved very little to
finance their children’s educations. According to McDuff(1989-90), “only 50% of
parents who are planning to send their children to college have actually begun to save.

The average savings for these families is slightly over $500” (p. 20). A 1997 survey by

20

Sallie Mae indicates that the vast majority of families save less than 25% of the cost of
higher education.

Even though tuition prices have risen significantly, so have discounts. According
to Doti (1998): “My research indicates that tuition and fees have not increased
significantly over the past decade when we consider discounting and the use of the
appropriate price index” (p. B1). Most students at private institutions pay a discounted
price. However many potential students and families—especially those with lower
incomes—have “sticker shock.” Being unaware of such things as discounts, these
students are deterred from applying at all (Loomis-Hubbell & Rush, 1991, p. 26).

Although some claim that tuition has risen because of “waste,” several substantial
factors appear to explain increased tuition rates (St. John, 1994, p. iii). A 1997 survey of
425 independent institutions cited the following as the major reasons for tuition and fee
growth (“Commitment,” 1997, pp. 1-2):

0 Technology (85% of respondents). Included in the category are academic
computing equipment, administrative computing equipment, telecommunications
upgrades, and wiring dormitories and libraries for intemet access.

0 Institutionally funded student aid (80% of respondents). Survey respondents
indicated that they have to charge more in tuition since they have to discount to so
many students.

0 Faculty salaries (79% of respondents).

o Decreased federal student financial aid (66% of respondents)

0 Administrative salaries (62% of respondents)

0 Increased student recruitment expenditures (57% of respondents)

21

o Increases facilities maintenance (52% of respondents)

Halstead (1989) offers the following as contributors of increased college tuition
costs:

0 Growing input costs. Unlike a firm, however, a college cannot “step up
production” to decrease per unit costs and counteract the increased input costs.

0 Educational quality. Colleges are not willing to sacrifice educational quality by
having inadequate library or laboratory resources.

0 Competition for faculty. “Colleges are continually attempting to maintain and

improve their faculty” (pp. 59-61).

The higher education price index (HEPI)—a measure of inflation in the prices of
the goods and services that colleges and universities purchase (such as salaries, benefits,
services, supplies, equipment, library acquisitions, and utilities)—grew 131% between
1981 and 2000. In contrast, the Consumer Price Index (CPD—a measure of the inflation
in the prices of goods and services that consumers buy—grew 89% over that time period.
The largest areas of inflation for colleges and universities were library acquisitions and
fringe benefits, which tripled between 1981 and 2000 (Research Associates, 1989, p. 27;
Research Associates, 2001, p. 1).

Many colleges and universities have tried to control costs to keep tuition increases
low. These cost-cutting measures include: institution-wide budget cuts, increasing the
efficiency of the physical plant, restructuring institutional debt, defening plant
maintenance, and not filling open faculty positions (“Commitment,” 1997, p. 93).

However, cutting costs is difficult because it can increase the faculty-student ratio, the

22

“personal relationship between students and instructors, which is essential to learning”
(Halstead, 1991, p. 79).

Decreased real values of federal financial aid for students also have increased the
need for tuition discounting. The federal Pell Grant per recipient decreased 12.6% in real
terms between 1987 and 1997. Other federal student grants fell 25.7% in real terms
during that period (“Straight Talk,” 1998, p. 299). The federal Pell Grant now covers
only about 10% of the cost of attending a private college (Kurz, 1995, p. 28). Private
institutions now provide more grant aid to their students than does the federal government
(Glasser, 1993, p. 2).

Since 1980, there has been a shifting of the financial aid burden away fiom the
government toward institutions. The reductions in federal student aid have been very
costly to students who have consequently faced limitations in terms of access and choice.
The reductions have also been costly for institutions who have had to increase
institutional aid to offset the decrease in financial aid. Many institutions have sacrificed
services and deferred maintenance in order to meet the increased demand for financial
assistance (Green, 198 8, p. 58). While federal grants have declined, there has been
significant growth in federal student loans. There also is a growing perception that
federal grants should be “earned by students through national service, rather than as an
entitlement that supports the societal good produced by participation in higher education”

(Kurz, 1995, p. 27).

23

Institutional Participation in Tuition Discounting

Only a few studies have analyzed tuition discounting based on institutional
characteristics. According to Basch (1996), “. . .while discounting has been well
documented overall, fewer efforts have been made to describe and explain tuition
discounting differences. An understanding of such differences is important if one is to
gain a full picture of how colleges differ in the average amount they collect from
students” (p. 41).

In general, the literature reveals five institutional characteristics as related to
tuition discounting: selectivity, price, size, endowment level, and geography. Selectivity
may influence the extent of tuition discounting in several ways. First, selectivity is likely
to affect the types of students that apply. Typically, students from higher income families
are more likely to apply at more selective institutions. Similarly, lower income students
are more likely to attend less selective colleges and universities (Redd, 2000, p. 15):
“Available evidence points to a positive correlation between a family’s wealth and the
student’s academic qualifications. . .and unless more selective colleges make
extraordinary offsetting recruiting efforts, they are apt to attract a less needy population”
(Basch, 1996, p. 48). Prestigious institutions would not need to discount (at least for a
financial need purposes) to the same extent as their less prestigious counterparts.

Additionally, the declining number of 18 year clds is unlikely to affect applications to the
most selective institutions. These institutions can easily achieve their desired class size
by simply dipping deeper into their applicant pools (Basch, 1996, p. 48). For example, an

institution that normally accepts 20% of its applicants can fill the freshman class by

24

 

raising that percentage to 30% . The less selective colleges, in contrast, will be most

affected by the declining numbers of high schools graduates (Basch, 1996, p. 48).

Increased competition among the less selective institutions drives up their discount rates.

Finally, selectivity affects perception of value and willingness to pay. Selective

institutions do not need to discount for competitive/enrollment reasons. They can fill

their freshman classes without discounting at all.

In 2000, Kenneth Redd used the NACUBO database to study tuition discounting

based on institutional selectivity. The study covered the years 1990-1996. Redd grouped
the institutions into 3 categories:

0 Highly selective. Only 30% of applicants are admitted. (7% of the institutions)

0 Selective. Up to 60% of applications are admitted. (18% of the institutions)

0 Less-than-selective. 60% or more applications are admitted. (75% of the

institutions)

Table 3 presents some of the results of Redd’s study. Although tuition discount

rates increased for all selectivity types, the less-than-selective institutions had noticeably

larger increases in institutional aid per student, and in the average award amount. From

this study it is apparent that discounting is growing faster at less-than-selective

institutions—in terms of tuition discount rates over time, award amounts, and the

percentages of students awarded.

Table 3. Tuition Discounting Rates, Redd’s Study, 2000

 

 

 

Selectivity Tuition Tuition % change in % change in
Type Discounting Discounting institutional aid average award
Rate, 1990 Rate, 1996 per student, amount, 1990-
1990-1996 1996
Highly 21% 24% 37% 17.5%

 

 

 

 

 

25

 

 

 

 

 

Selective
Selective 22% 31% 47% 16.1%
Less than 21% 31% 96% 31.7%
Selective

 

 

 

 

 

Another study that analyzed discounts based on selectivity (Basch, 1996) used the
Peterson’s Freshman Financial Aid Database. Basch’s study utilized multi-variate
regression analysis with the discount rate as the dependent variable, and admissions
selectivity, endowment-per-student, tuition prices, and geographic region as independent
variables. This study yielded results similar to Redd’s findings about selectivity. When
compared to their less competitive counterparts, the most competitive institutions had
lower discount rates, gave smaller institutional awards, and had fewer needy students (p.
54).

Tuition price is another factor that may influence the amount of discounting
among institutions. Higher nominal sticker prices are likely to attract a more wealthy
applicant pool, thus reducing the necessity of discounting based on need. Higher prices
may also be associated with better quality, which would influence the mix toward higher
quality applicants who are generally less needy. In contrast, higher tuition prices lead to
higher student financial need. For example a student with a $10,000 family contribution
attending an institution with a $20,000 tuition price would have financial need of
$10,000. That same student at a $30,000 institution would have a $20,000 need.

“. . . [T]he amount of need, and presumably aid [will be higher at the higher price college.
If self-help expectations [work-study and loans] and externally-funded grants are not
sufficiently higher at the higher price college, it will tend to have a higher amount of

college-funded grants” (Basch, 1996, p. 49).

26

 

Basch’s 1996 study found that “higher price is associated with a higher average
college-funded grant, a higher discount rate, and a lower percentage of students judged
needy” (p. 50). Even though these institutions attract wealthier students, the price is
relatively high. This situation creates financial need for many students including those
from families whose income is well the above national average. Meeting this need via
discounting is likely to drive up discount rates at these higher priced institutions.

The NACUBO study, as summarized by Lapovsky and Loomis-Hubbell (2000),
categorizes institutions based on size (“small” means entering freshman class of less than
850) and by tuition cost (“low” means less than $18,000 per year). According to the
NACUBO study, at small colleges with “low” tuition and fees the average incoming
freshman discount grew from 28.5% to 40% between 1991 and 2000. At these colleges,
the percentage of students receiving “some institutional aid” rose from 76% in 1991 to
90% in 2000. For small colleges with “high” tuition the average discount grew from
29.5% to 39% during that same time period. The percentage of students receiving
institutional aid rose from 57% to 70%. For large colleges and universities, the average
discount grew from 21% to 30%. The percentage of students receiving institutional aid
remained steady at 61% between 1991 and 2000. It appears that at the smaller

9

institutions discounting has increased the most, particularly the small colleges with “low’
tuition (Lapovsky & Loomis-Hubbell, 2000).
Another variable that could influence discounting is endowment size. In theory,
institutions with high endowment-per-student ratios have more resources to provide
discounts. In other words, these institutions could use their endowment earnings to

provide tuition discounts (Mac Dowell, 1996, p. 25). It seems logical that the most well

27

endowed institutions would have higher tuition discounting rates (if using the scholarship
allowance definition) because they could discount tuition with little effect on net tuition
revenue. Basch’s 1996 study seems to confirm this hypothesis: “Apparently reflecting
the higher resources available to a college for financial aid, higher endowment per student
has a statistically significant positive effect on average college funded grants and on
discount rates” (p. 50).
Lapovsky and Loomis-Hubbell’s 2000 analysis of the NACUBO study, however,
did not find a relationship between tuition discounting levels and endowment levels:
“Relative institutional wealth or poverty does not sharply affect the level of financial aid.
Institutional aid is an enrollment management tool. The granting of aid to a significant
percentage of the class is a necessary tool to fill the class with the necessary number and
quality of students” (Lapovsky & Loomis-Hubbell, 2000, p. 29). The “source” of the
discount varies by the institution’s endowment level. Well-endowed institutions can use
“restricted” dollars to replace some of the unrestricted dollars being used on discounts:
The “. . .better endowed institution has more resources to spend on
programmatic areas of the college and can compete longer and more
deeply in the discounting/pricing game. Does this indicate a downward
spiral that weakens the less well-endowed institution and continues to
reduce competitiveness? Current levels of tuition discounting have
persisted for much longer than many thought they could be sustained. It
may mean that the very wealthy institutions tend to be among the most

competitive to gain entrance and thus they are not truly in the same market

28

 

with many of the less well-endowed institutions” (Loomis-Hubbell, 2001,
p. 30).

Geographic location may also influence tuition discounting. Regional
characteristics such as family income and wealth, the number of other colleges in the
area, and the number of 18-year olds may influence tuition discounting rates among
institutions. Very little research exists regarding tuition discounting variations by region.
Wick’s 1997 study of no-need/merit awards indicates that institutions in the Midwest
award 39% of the total no-need/merit awards being awarded nationwide, followed by the
Mid-Atlantic region (24%), the South (17%), the West (10%), the Southwest (6%), and
the Northeast (5%) (pp. 21-22).

Although there are many reasons that institutions discount, the “. . .reasons for

using tuition discounting vary by institution. Smaller, less-selective colleges use it as a
tool to achieve enrollment goals. Highly selective colleges that can reach their enrollment
goals with students whose families are in a position to pay full tuition (admittedly a very
small number of institutions) use tuition discounts to enhance the quality and diversity of
their student bodies” (Allen, 1999, p. 9). Recently, these smaller, less-selective
institutions have been the focus of considerable dialogue and debate. Practitioners and
researchers alike have described the private, less prestigious institutions as endangered,
challenged, or threatened. These institutions have been affected by the general decrease
in the numbers of high school graduates as well as competition from lower priced
educational alternatives (McPherson & Schapiro, 1995, p. 19). It is evident that this
private sector of higher education has faced adversity over the past fifty years. In 1950,

private institutions made up 66% of all institutions, and 50% of college enrolhnent. By

29

 

1976, the percentages had fallen to 52% and 24% respectively (Breneman & F inn, 1978,
p. 21-22). By the year 2005, enrollment at private institutions is estimated to be only
20% of total college enrollment (“Fact File,” 1995, p. 38). MacDowell (1996) predicts
that “many [private] colleges which typically operate close to the margin, will go out of
business; already 170 private institutions have closed their doors and more will follow”
(p. 26).

This sector of higher education is very important to society. It “is an integral and
major component of our education system. Its diversity vastly expands the choices
available. Its leadership demonstrates the nature of academic excellence, the ideal of

liberal learning, the necessity of academic freedom and the challenge of innovative and

experimental programs” (Halstead, 1989, p. 48).

II. Outcomes Literature

Tuition discounting is practiced with many outcomes in mind—helping needy
students, increasing student quality, expanding diversity, increasing enrollment, and
improving the financial state of the institution. Tuition discounting does not guarantee
success.

“For most administrators at four-year independent colleges and
universities, achieving student enrollment goals through tuition
discounting requires the skills of an expert juggler. On one hand, campus
officials must balance enrollment targets with their need to increase net
tuition revenue. Spend too much on discounting and you risk losing

precious revenue. Spend too little, and you may not meet your enrollment

30

 

goals. On the other hand, administrators must decide continually whether
to use discounting dollars to assist low-income families or to attract more

academically gifted students” (Redd, 2001, p. 34).

The Impact of Tuition Discounting on Enrollment

According to Leslie & Brinkman (1987), there is a significant positive
relationship between aid offered and enrollment. Institutional financial aid is an
important factor in the numbers and the composition of the student body. Loomis-
Hubbell & Rush (1992) agree: “Institutionally funded financial aid has supported the
participation in higher education of many students, particularly minority students who
may not otherwise have had access” (p. 4).

An underlying assumption of the theory of tuition discounting is that students are
sensitive and responsive to tuition prices. Numerous studies confirm that price,
scholarship offers, and net price are all important factors in enrollment decisions (Seneca
& Taussig, 1987; St. John, 1993; Litten, 1986). An extensive study by Somers and St.
John (1995) assessed the impact of scholarship offers on enrollment decisions. Students
perceive a scholarship as a reward, and an act of good faith on the part of the institution.
The power of scholarship money is demonstrated in the Somers and St. John study.
Applicants who were awarded scholarships were 23.5% more likely to attend for each
$ 1 000 awarded. They found that students were responsive to scholarship offers; even
partial scholarships may influence attendance. The Gallup Poll data presented in Table 1

provides a clear indication that demand for higher education is price sensitive. This poll

indicates that a $4000 tuition discount (from $18,000 to $14,000) would have a

31

significant impact on most families’ college choice decisions. Sixty-one percent said this
would increase their level of interest in the college. More than 80% believed it was a
wise marketing move, and 93% viewed it as a positive action.

Substantial research confirms that both gross and net price affect a student’s

college choice. The size of the tuition discount is reflected in the net price. In a 1993
study it was determined that “students increasingly base their choice of a college or
university on economic considerations...The survey found that 30% selected their
institution on the basis of low tuition, up from 27.7% in 1991. Other factors were the
financial assistance offered, cited by 28.3% of respondents, an increase from 27.8%”
(Burd, year, p. 10). St. John (1993) shows that the tuition discount is the largest factor in
a student’s college choice. Cost has “eclipsed location, academic reputation, and social
life as the #1 factor in choosing a college” (NY Times Higher Education Supplement,

1 996, p. 7). A 1997 study of 3500 high school seniors indicated that cost (particularly not
cost) is now more significant to students than location, academic reputation, or social life
(Marcus, 1997). Another important study found that at mid-selective private colleges, the
percentage change in enrollment dropped .6% for every $100 increase in tuition price
(Brinkman & Leslie, 1987, p. 199).

The effect of price and tuition discounting on retention and graduation only
recently has received attention. The short-term goal of filling a freshman class may
outweigh the long-term goals of retention and graduation. Redd’s 2000 study is the only
one to study the effects of tuition discounting on these outcomes. Redd’s study indicates

that “dramatic discounting may negatively impact graduation rates” (p. 1)

32

Need, Merit, and Willingness

According to Baum (1998), the historical role of tuition discounting has been to
expand educational opportunities for financially needy students (p. 12). Much of the
literature acknowledges that discounts based on need allows lower income students to
enroll at institutions in which they could not otherwise afford. Loomis-Hubbell & Rush
(1991) state that tuition discounts have made private higher education a possibility for
lower and middle class students who would not have been able to attend otherwise (p.
25). Similarly, according to Doti (1998), “the use of discounting has made independent

higher education more affordable for lower-income students” (p. B7). A 2000 study by
Redd analyzed the attendance of Pell Grant recipients at institutions that discount. Pell
grant recipients are the students with the greatest financial need. The percentage of
students receiving Pell Grants “grew by 20 percent at institutions with the largest
increases in discounting rates, and by 16 percent at college and universities with the
smallest increases in discounting. These results suggest that tuition discounting

was. . .successful at helping institutions achieve their goals of providing access to higher

education for low income students...” (p. 3).

Tuition discounts, when based on need, represent a transfer of wealth from higher-
income to lower-income students (Loomis-Hubbell & Rush, 1991, p. 28. According to
Winston, “a tuition policy that makes wealthier families pay more and less wealthy
families pay less for the same education is what’s fashionably called ‘Robin-Hooding’—
uSing tuitions to steal from the rich and give to the poor” (1988, p. B1). But charging

dlfferent customers different prices based on certain personal characteristics (such as age,

33

~

or timing of the purchase) is not uncommon. As detailed in Chapter One, airlines, phone
companies, and movie theaters all engage in this practice. Usually their purpose is to
maximize profit. In contrast, “colleges charge different prices to ensure that able students
can have access to an excellent education, regardless of their families’ incomes”
(Winston, 1998, p. B1). In question here is the concept of income redistribution—
“colleges participate in price discrimination for the social objective of making the best
education available to the best students” (Winston, 1988, p. B1). Winston also points
out that unlike Robin Hood, the colleges and universities are not ‘stealing’ from the
wealthier families—those families are expressing their demand (willingness and ability)
to pay for the education at the full price.

Indeed, “part of what those students who pay full tuition are getting when they
choose to buy a high-quality private education is association with other excellent
students, as well as a breadth and diversity of classmates. Both the quality of the students
and their diversity would be lower if all students had to pay the same price. And quality
and diversity are part of the appeal of these institutions, part of what makes them
educationally excellent and desirable” (Winston, 1988, p. B 1). In short, making

education accessible, regardless of ability to pay, should be part of an institution’s

mission.

Jenkins (1991) wonders whether income redistribution in higher education should
be the responsibility of all of society, rather than just the middle and higher income

families that are already paying for college. She predicts that

“as middle- and upper-middle income families become more sophisticated

in their assessment of educational value (price/outcome), they will be less

34

willing to subsidize lower income students at higher priced private
institutions. There already exists much evidence that many middle-inco
families are selecting high quality public institutions rather that their mc
expensive private counterparts. While access and choice in higher
education are important societal goals, the economics of the marketplacc
are such that, over the long term, they can only be achieved if institution
costs moderate so that federal financial aid programs can bear the major
of cost subsidy” (p. 7).

Winston and Zimmerman (2000) voice concern over the future of need-based
institutional aid, arguing that it “faces an uncertain future, as, more generally, does all
college pricing that serves, idealistically, to redistribute income. It won’t matter, at the
tOp, where students stipends will be paid all around, but it may seriously reduce low
income students’ access everywhere else” (p. 12). Breneman (1996) argues that with

respect to awarding institutional aid based on need... “[e]quity is only the occasional—
and incidental—result” (p. 16). Delbanco (1996) predicts that the concept of funding
based on need is “ancient history” (p. 39).

The future of need-based discounts appears rather tenuous. Much has been
Written about the trend of discounting based on merit rather than discounting based on
need. According to Baum (1998) fewer and fewer colleges are focusing on access for 1

income students when awarding institutional aid, and the percentage of merit based aid
gowing relative to institutional aid based on financial need (p. 12). A 1999 study by
Mulugetta analyzed the growth in institutional need based aid versus growth in

institutional merit based aid. The results of this study indicate that the growth in merit

35

based aid offers was three times the growth in need based aid offers over a 6 year period
in the 1990’s (p. 11). In 1997, Wick synthesized a series of studies that analyzed the
growth in non-need-based institutional aid. The studies included a 1974 study by Robert
Huff, then Director of Financial Aid at Stanford, a 1978 study by the College Board, 1976
and 1984 studies by the American Council on Education (ACE), a 1983 study by the
National Association of College Admissions Counselors (NACAC), and more recent
studies by the College Scholarship Service and the National Association of Student
Financial Aid Administrators (NASFAA). The scope of Wick’s project was to
“determine if there has been a significant increase in institutional gift aid being targeted
towards awards not based on need” (p. 28). He concluded that there definitely has been a
shift toward non-need based aid that has come at the expense of need-based student aid
(p . 28). Discounting based on merit more than need has traditionally been more
prominent at less prestigious institutions. However, the increasing use of merit aid is

now becoming apparent even among more prestigious institutions (McPherson &

Schapiro, 1995, p. 27).
The trend away from need based discounts to discounts based on student quality

( SAT scores or high school grade point average) is “part of an ambitious strategy to create
a virtuous circle, providing irresistible bargains to high achievers in the expectation that
they will, in turn, attract better students and faculty” (Passell, 1997, p. B10). Many

institutions are using no-need scholarships as a mechanism to upgrade student quality,

academic reputation, and status (Sidar, 1976, p. 5).

36

 

In addition, what seems to be emerging is the use of tuition discounting as a
marketing device or bargaining tool (Baum, 1998). As Lapovsky and Loomis-Hubbe
(2001) found:

“more and more institutions are creatively packaging financial aid based on St

marketing devices as familial perceived need, special institutional programs (:

as service scholars or economics scholars) or well defined cohorts. . .. These
structures deviating in part or in whole from any measurement of need, reach
to families unwilling to pay for education without getting some kind of ‘deal’
the competitive battleground among institutions for the top student scholars
contribute to the pressures on the tuition discount at most independent 4-year

colleges and universities” (p. 31).

At some institutions, econometric models are utilized to estimate “willingnes:
pay”——based on characteristics such as anticipated major, whether the student partici]
in a campus visits, or race—rather than “ability to pay.” Many colleges are even hiri
consulting firms to help them understand how to use demographics, credit records, ar
other “predictive modeling techniques” to estimate willingness to pay (Gose, 1999, p
A49). In many cases, any indication of eagerness is taken as an indication of willing]
to pay (Delbanco, 1996, p. 39). The measurement of the responsiveness of consumer
price changes is called price elasticity of demand. Whereas historically institutions h

tried to “determine which students [had] elastic demand and [would] be unable to em
because of limited financial resources. . .under the new enrollment management strata

institutions attempt to identify applicants whose demand is elastic because they are

37

 

unwilling to pay a higher price, even though the resources might be available” (Baum,
1998,p.14)

At many institutions, administrators realize that they may underestimate
willingness to pay. This realization has lead to an unprecedented willingness on the part
of institutions to reconsider financial aid awards—“bargaining” or “negotiating.”
Colleges report a barrage of phone calls, after award letters are sent, from parents hoping
to negotiate a better “deal.” According to Michael Wisniewski, Director of Financial Ai

at Chestnut Hill College in Pennsylvania, “we will match an offer from any other college
(Asinof, 1997, p. C1). He admits, however, that bargaining is unfair to those who aren’t
aware that their award is negotiable. A highly controversial, yet potentially more fair
approach, is ascertaining a family’s willingness to pay. In 1999, a website
(wwwecollegebidcom) was developed that allows families to do just that: “offer to pay
given amount to attend college, then sit back and see whether any colleges are willing to
accept that bid’ (Gose, 1999, p. A49). According to the ecollegebid website, it serves as
‘ ‘brokerage agent between colleges and students. . .that [matches] (1) the family’s ability
or willingness to pay for a college education with (2) the college’s willingness to offer
tuition discounts.” The institutions pay $2,000 annually to participate in ecollegebid.
The list of participating institutions, however, is not made public.
Is it appropriate for colleges to be haggling over price, like used car dealers?
According to Gaudiani (2000), “. . .colleges and universities are not car dealers. As our
Iloll-profit status attests, we have a higher social purpose. Certainly we can learn from t1

f0r—profit sector about efficient management of resources. But if we focus too closely or

38

the bottom line we lose sight of our reason for existence: to educate productive,
compassionate, effective citizens, who will make society a better place” (p. 19).
Rewarding merit, or simply “unwillingness” to pay, rather than need has
concerned many scholars. Generally, the argument is that it “give[s] financial aic
students who would have gone to college anyway, and [takes] those dollars from
students who might not be able to attend” (p. 127). Mulugetta’s 1999 study offer
following conclusion: “recycled tuition revenue—in the form of merit aid——may .
tremendous benefit to a handful of winners,” although the end result is societal in
(pp. 11-12). Baum and Schwartz point out that “to the extent that any new merit
programs replace need-based programs, there may well be a decline in enrollmen
that implied by demographic trends. Merit aid will have little effect on whether
meritorious students enroll, but declining need-based aid will increase the price 0
to college for those with the highest demand elasticity—those from low-income i
(p - 133). Institutions that have focused on need rather than merit “. . .fear that the
attention to the strategic use of financial aid will negate years of working toward
equitable distribution of need based aid...” (Baum, 1998, p. 12).
Baum (1998) wonders if this “strategic” use of financial aid can be “com;
With the goals of equity in higher education, or must we choose between abandor
goals of diversity and educational opportunity and risking institutional solvency”
Gaudiani (2000) points out that institutions must make difficult decision about w
Students to award. They could award $3 0,000 per year (the entire cost of attenda
One highly qualified yet needy applicant. Or, for the same total dollar amount, th

a"Var-d $5000 per year each to six students with no financial need. These six stud

39

 

would still contribute $25,000 per year for four years, whereas the one needy student
contributes no revenue for four years. Many institutions will make the choice to
maximize net tuition revenue—that is, to assist the six no-need students rather than the
one needy student. Therefore, claims Gaudiani (2000), “[i]t is not difficult to see why
strategic use of financial aid might conflict with the goals of equity and access in highe
education” (p. 14).
Financial Aspects of Tuition Discounting
The potential positive effects of tuition discounting are quite clear. First, tuitio:
discounting, at least to some degree, encourages the participation of lower income
students as well as minority students at private institutions of higher education.
Additionally, discounting can serve to “safeguard the flow of net tuition revenues to th
institution” (McPherson & Schapiro, 1995, p. 18). However, as articulated by Loomis-
Hubbell and Rush in a 1991 article, tuition discounting can be a “double edged sword.’
In addition to its many positive aspects, there are less desirable results including possil:
losses in net tuition revenue (if institutions maximize gross tuition revenue rather than
tuition revenue) and excessive increases in gross tuition rates (sticker prices) (p. 25).
As early as the 1960's, researchers were expressing concems about the potentiai
negative side-effects of tuition discounting. A 1960 study of 60 private colleges report
the following “alarming phenomenon:” one of the colleges in the study was diverting 3
Percent of its tuition income to student aid (McPherson & Schapiro, 1998, p. 25). The

high levels of tuition discounting continues to be a source of concern. According to

David Breneman (1994):

40

 

“the sharp increase in student aid provided by the institution itself, I
referred to as unfunded student aid, or as institutionally provided stl
aid, is among the most troubling economic problems confronting lil
arts colleges. It is not unusual to find a college currently devoting 2
more of its budget to unfunded student aid, at the same time that on
on instruction constitute no more than 40% of the budget. One can
understand why Presidents worry when, for every dollar spent on
instruction, 50 cents goes for student aid” (p. 36). Nathan Dickme)
(1993) added that there persists a “gnawing fear that something is v
with uncontrolled growth of the financial aid ratio” (p. 26). Loren
Loomis-Hubbell (1995) argued that “as tuition discounting grows, t
fundamental strength and reliability of institutions’ main revenue st
erode” (p. 31).

There is some evidence that tuition is beginning to erode the financial heal‘
some institutions. According to researchers at the University of Pennsylvania’s In
for Research on Higher Education, many institutions that are discounting heavily V
experience down-graded debt ratings, and consequently will have a more difficult
borrowing funds (“Discounting and Its Discontents,” 1994, p. 34).

There are concerns that tuition discounting can actually be dangerous if

institutions administer it improperly. Improper use of financial aid most often is a
of institutions failing to understand the important inter-relationship between publi:

price, enrolhnent, and discounts.

41

The first piece of this inter-relationship is price. Institutions must choose a
“published” price—even if very few students are actually required to pay the firll price.
Generally, the college has to set a price that generates at least enough revenue to cover
expenses (Hoke, 1993, p. 6), and to “guarantee educational quality” (Shaman & Zemsky,
1984,p.17)

Some institutions utilize what is referred to as a “prestige/quality” pricing policy.
Under this approach, institutions will often raise their tuition prices to improve image or
reputation. In higher education, particularly private higher education, price is generally
regarded as an indicator of quality. A similar pricing policy, “peer” pricing, is an
approach in which an institution sets its tuition price and tuition increases in a manner
that “keeps it comparable with. . .prices at peer institutions” (Shaman & Zemsky, 984, p.
14). These policies, however, are not strategic and fail to recognize the important inter-
relations between price, enrollment, and discounts.

Strategic pricing requires a keen understanding of the inner workings of price,
gross tuition revenue, and net tuition revenue. Recall that gross tuition represents the
amount of tuition revenue that an institution would receive if every student paid the full
published price out of his or her own pocket. Net tuition revenue is the gross tuition
revenue minus institutionally funded aid. Many institutions raise price in order to raise
gross tuition revenue, even though net tuition revenue is more important (Cohen, 1982).
It is entirely possible for gross tuition revenue to increase while net tuition revenue
declines. For example, an institution that raises gross tuition rates 6 percent but
“discounts away” more than the 6 percent increase could face a substantial decrease in net

tuition revenue (Loomis-Hubbell, 1992, p. 17). In other words, if colleges increase their

42

financial aid budgets faster than their tuition rates, their average price per student actually
falls (“Discounting and Its Discontents,” 1994, p. 33).

With this important inter-relationship in mind, some colleges have adopted a
“high-tuition, high-aid” strategy. In 1987, Eamon Kelly, President of Tulane University,
admitted that “[w]e put our tuition as high as possible then put most of the extra money
into financial aid” (Fiske, 1987, p. Bl 3). Others have adopted more of a “low-tuition,
low-aid” strategy. Muskingum College cut its tuition rate with much fanfare in 1995, and
also lowered its discount rate. This approach seems to have worked well for Muskingum:
enrollment increased 31% over 5 years, and both gross and net tuition grew substantially
(Irving, 2001, p. 3).

Bowen and Breneman (1993) distinguish between the use of institutional aid as a
“price discount” and as an “educational investment.” They argue that institutional aid
offered by prestigious schools usually represents an educational investment on the part of
the institution. Institutions such as Harvard and Yale could enroll more than enough
students without offering any institutional aid at all. It is likely that they could double or
triple their tuition rates and still enroll all the students they could want. Yet these schools
do offer institutional scholarships. Why? According to Bowen and Breneman (1993):

“For MIT and similarly situated institutions, student aid enables the college to

attract qualified students who could not come otherwise, including students who

will contribute to the diversity of the student population and, ultimately, to the
needs of the nation for more well educated students from racial minorities and
disadvantaged backgrounds. Decisions to spend some of the college’s own

resources in this way are analogous to decisions to increase spending on the

43

library, on faculty positions, on scientific equipment, or on any number of other

worthwhile activities” (p. 5).

At the other end of the spectrum, however, some colleges cannot get enough filll
pay students to fill an entering class. Many students are willing to attend only if they get
a tuition discount; they are not willing to pay full price. Institutional aid, in this sense, is
functioning as a price discount, not an educational investment.

Bowen and Breneman recommend that institutional leaders answer the following
question to help determine if institutional aid is an investment or a discount: “Does
providing student aid increase or decrease the net resources available to the college to
spend on other purposes?” (Bowen & Breneman, 1993, p. 5). When institutional aid is
being used as a means to create a large enough entering class, it is functioning as a price
discount, and technically, creating net resources for the college. (Recall from the
example using the data in Table 2 that the implementation of tuition discounting actually
increases resources for the institution.) When aid is functioning as an educational
investment, the college could have enrolled enough students without offering any
institutional aid, and the aid represents a decision to use institutional funds instead of
some other use of that money.

It is important that institutions have a clear understanding about being “discount”
schools or “investment” schools. This assessment may require some honest reflection on
the part of administrators, many of whom might be reluctant to admit that their aid policy
is a discount as opposed to an investment. If an institution mis-categorizes its use of aid,
the consequences could be ruinous (Jenny, 1997). For example, institutions that use aid

as an investment should set a predetermined spending cap on the amount of resources

44

devoted to student scholarships. These institutions are assured of a full entering freshmen
class and a definite total tuition revenue amount. Based on these figures, an institution
should set aside a certain dollar amount of expected tuition revenue for scholarships.
However, if an institution that uses aid as a discount set a self-imposed cap (as if it were
an investment school) it may end up with an inappropriate amount of tuition discounting.
In other words, this institutionally imposed level may produce a small level of net tuition
revenue, whereas some other level of awarding may result in a much better financial
picture for the institution. If an institution is not careful about analyzing its position on
the continuum of discount to investment it may create a weak financial situation.

The following table is useful to assess an institution’s institutional student aid:

Table 4. Institutional Aid Policy

 

 

 

 

 

 

Tuition Discount Educational Investment
Freshman Class Would not be full without offering Would be full even if aid
aid not offered
Demand for product Elastic (sensitive to price) Inelastic (Less sensitive to
price)
During economic Increases Decreases
downturns the use of
institutional aid
Resources Creates Economic Resources Uses up Economic
Resources
Purpose Generally: to promote ethnic Diversity, Financial Need

diversity, increase enrollment,
improve financial health, enroll
more students with need, encourage
retention, and enhance prestige

 

 

How it should be treated As a deduction from gross tuition As an expenditure like

on balance sheet revenue other types of spending
with which it competes for
resources

 

 

 

 

Whether discounting should be treated as a “deduction from revenue” or as an

“expense” is no longer in question. In 1997, new accounting practices were developed by

45

the Financial Accounting Standards Board (F ASB 116 and 117) that mandated
institutions treat discounts as a deduction from revenue—not an expense, like
expenditures on salaries, library books, or electricity (Allen, 2001, p. 56). Prior to the
implementation of these new accounting practices, institutions included “[r]evenue that
never existed [tuition funded by institutional grants]. . .in gross tuition while the bottom
line was kept honest by reporting the same dollars as a financial aid expense, a procedure
that accountants call ‘grossing up the accounts.’ Thus, higher education executives were
led to believe that their institutions were generating much more revenue than was the
case” (Allen, 2001, p. 56). The new accounting standards require institutions to deduct
institutional aid when reporting tuition and fee revenue. The “old” rules instructed
institutions to: “report all tuition and fees. . .assessed against students for educational
purposes. Include tuition and fee remissions or exemptions even though there is no
intention of collecting from the student” (IPEDS Finance Survey, 1994). The “new” rules
instruct institutions to report tuition and educational fees “net of any allowances,” where
allowances are defined as: “the difference between the stated charge for goods and
services provided by the institution and the amount which is billed to students and/or
third parties making payments on behalf of students” (IPEDS Finance Survey, 1999).
Some researchers argue that institutions have gone too far in awarding
institutional aid. According to Jenny (1997) “once an institution discounts its prices to
more than 50% of its students, a trend toward a point of no return is set in motion. At
75% or more, the self-defeating process is now well underway” (p. 5). A process that

may have worked at one time may backfire if carried to the extreme.

46

The airline industry is widely known for carrying a discounting pricing strategy to
an extreme in an attempt to “milk its demand curve” (Riggs, 1994, p. 43). However, it is
also known for the massive loss it faced in the 1980's. Riggs argues that the
dysfunctional pricing is probably the main reason for the financial troubles facing the
airline industry (p. 41). Actually, in many ways airlines and higher education are similar.
Both are fixed-cost based so additional customers (passengers or students) add very little
to operating costs, particularly if a plane or a classroom has empty seats. Both scenarios
portray lost revenue and invite the concept of strategic pricing. Riggs (1994) warns that:
“If competition requires all colleges to ‘buy’ their best students with merit scholarships,
that is, if academically able students, even those financially quite able to pay, come to
expect discounted tuition—private higher education is headed down the road defined by
the airlines: price inequities among its ‘customers,’ and increased cynicism” (p. 43).

Another concern about tuition discounting focuses on finance. Jenkins (1991)
warns of the cyclical effect of tuition discounting. She argues that tuition discounting can
tend to spiral—where increases in discounts lead to the necessity for tuition increases,
which leads to further discounting. Tuition discounting needs to be carefully monitored
by an institution’s administrators so as to not spiral out of control (p.6).

For some institutions, tuition discounting decreases the amount of funds available
for student services and other needs (Loomis-Hubbell & Rush, 1992). A 1995 NAICU
study of independent colleges and universities indicates that in response to financial
shortfalls 45% cut their budgets, 37% eliminated administrative positions, and 35% left
faculty positions unfilled (“A Commitment to Affordability,” 1997, p.3). Yet virtually all

of these institutions increased institutionally funded student aid during the same period. It

47

appears that “institutions have been forced to put increasing amounts of their unrestricted
funds into student aid instead of academic programs” (Dunn, 1993, p. 9). It also seems
that an increased portion of cost “is not for instruction, but for everything else” (Werth,
1988, p. 25). At many colleges and universities, spending on institutional aid exceeds
spending on the entire educational program (O’Keefe, 1987, p. 7).

The current body of literature on tuition discounting provides a fairly clear idea
about the history of tuition discounting, the growth in the levels of discounting, reasons
for this growth, and where the growth has mainly taking place. There is also research
regarding the outcomes of discounting. It is easy to understand why institutions have

resorted to tuition discounting. However many questions still remain.

Chapter Three: Design and Methodology
Research Questions
The purpose of this study was to examine tuition discounting at private,
baccalaureate-level institutions of higher education between 1990 and 2000. Specifically,
three main questions were addressed:
1. What is the pattern of tuition discounting for these institutions? Has tuition
discounting increased, decreased, or stayed the same?
2. Are there variations in tuition discounting based on institutional demographics?

For example, does discounting vary geographically, or by institution size or age?

48

3. Does tuition discounting improve the financial health of institutions? In other
words, what is the relationship between tuition discounting and the financial

health of the institutions?

Data Sources

The study included data from three sources: (1) the Integrated Postsecondary
Education Data System (IPEDS); (2) the Peterson’s Guide to 4-year Colleges; and (3) the

US. News and World Report college rankings report.

mgriued Postsecondary Education Data Analysis

Each year, institutions of higher education in the United States are required to
complete a federal survey called the Integrated Postsecondary Data Analysis System
(IPEDS) survey. Completion of the form is mandatory for institutions that participate in
any of the federal financial assistance programs authorized by Title IV of the Higher
Education Act of 1965 (Pell grants, Federal Student Loans, etc). Each year, over 12,000
postsecondary institutions complete the survey. The survey is quite comprehensive and
contains eight survey components: institutional characteristics and activity; completions;
occupationally specific enrollment; finance; staff; libraries; fall enrollment by race, age,
and residence; and faculty salaries. IPEDS represents the single most comprehensive
source of postsecondary educational data in the United States, and is referred to as the
“cornerstone of national postsecondary education data” (Peng & Korb, 1989, p. 76).
Much of the data for this study came from the finance component of the IPEDS survey,

which provides detail about all aspects of an institution’s financial situation. Institutions

49

 

 

typically use their annual general purpose financial statements to complete the IPEDS
survey. An institution’s financial statements are considered highly reliable. The law
requires an annual audit by an independent accounting firm for consistency and accuracy.
Recently, the IPEDS data has been made conveniently accessible to researchers via the
Internet.

Starting in 1997, the finance portion of the IPEDS form was significantly changed
for private institutions. This change was primarily because of tuition discounting. The
“new” form (1997-present) treats institutional financial aid as contra-revenue—that is, a
deduction from tuition and fee revenue, not as an expense. The IPEDS instructions
indicate that institutions should not include “allowances” when reporting tuition and fees,
where an allowance is defined as “the difference between the stated charge for goods and
services provided by the institution and the amount which is billed to students and/or
third parties making payments on behalf of students.” (This definition would include
funded and unfunded institutional aid). Thus, “tuition and fees” on the new IPEDS form
really means net tuition and fees. To obtain gross tuition and fees (the amount of tuition
and fees the institution would receive if every student paid the full tuition price out of his
or her own pocket), allowances must be added to the tuition and fees figure. On the “old”
IPEDS form, “tuition and fees” would reflect gross tuition and fees and the “allowances”
would appear as expenses against that revenue.

This change in the IPEDS form came about after considerable debate in the early
1990’s regarding the reporting of institutional aid. According to Allen (1999), the focus
on gross tuition revenue rather than net tuition revenue was misleading to the users of

institutional financial statements—both within and outside the institution. Institutional

50

leaders were focused on gross tuition revenue rather than the more important figure of net
tuition revenue. Gross tuition revenue ove‘rstates the flow of income to the institution (p.

13).

Peterson’s Guide to F our-Year Colleges

The Peterson’s Guide to Four Year Colleges, published annually since 1970,
provides detailed data about an institution’s students, faculty, facilities, and programs.
The information contained in the Peterson’s Guide is furnished annually by the colleges

to the publisher of the Guide, Thompson Learning.

US. News and World Report: America’s Best Colleges

The third data source was the special college rankings issue of US. News and
World Report, which is published annually. Essentially, the quality of each institution is
analyzed based on indicators purportedly measuring institutional quality. The editors of
this publication recognize that “the college experience consists of a host of intangibles
that cannot be reduced to more numbers. But [they] believe that it is possible to

objectively compare schools on one key attribute: academic excellence” (Morse &

Flanagan, 2001, p. 28).

Selection of Institutions

In 1970, researchers at the Carnegie Foundation for the Advancement of Teaching
developed a classification system for American institutions of higher education. The

system is intended to group or cluster institutions based on characteristics. Although

51

some modifications have been made to the classification system since its inception, the
general principle has not changed; it is a system designed to group institutions (generally
by the highest degree they offer), but not to rank institutions. The classification includes
Research Universities I and II, Doctoral Universities I and 11, Masters Colleges I and II,
Baccalaureate Colleges 1 and H, and Associate of Arts Colleges (Boyer, 1994, p. 1).

The group of institutions analyzed in this study were those private, non-profit
institutions classified by the Carnegie Foundation classification system as Baccalaureate
(Liberal Arts) I or II. The Baccalaureate I (BAI) category includes institutions that focus
on undergraduate liberal arts education and award 40% or more of their degrees in liberal
arts. They tend to be more restrictive in admissions. The Baccalaureate H (BAH)
category includes institutions that also focus on undergraduate education, but award less
than 40% of their degrees in liberal arts. They are less restrictive in admissions than the
BAI institutions (Carnegie Foundation for the Advancement of Teaching, 1994). I
selected this population for study for two reasons: their importance in the American
system of higher education, and the vulnerability of these institutions to economic and
demographic fluctuations. Baccalaureate institutions contribute to maintaining diversity
and variety in American higher education. The US. system of higher education is special
because of this variety (MacKay, 1992). The small private colleges provide an
educational environment unlike any other type of American college. As Breneman
(1994) states, “. . .one can almost View these colleges as standard bearers, holding out the
promise and the reality of education for education’s sake. Without them, American

higher education would lose far more than simply places for 260,000 undergraduates, a

52

miniscule 2 percent of total enrollments. Ten new universities could absorb that
population, but at the cost of a great loss of diversity in our educational system” (p. 3).

These institutions are unique in their single mission: to provide excellent
undergraduate education. Unlike larger institutions, the baccalaureate institutions do not
place emphasis on research, public service or graduate education. They exist for the sole
purpose of educating undergraduate students (Breneman, 1994, p. 94). A study by
Alexander Astin (1977), showed that these institutions excel at providing opportunities
for students to grow intellectually and socially. Students at private institutions tend to
interact more with faculty, tend to be more active on campus, and tend to participate more
in classes. These students also report a great deal of satisfaction with their overall
campus experience (Astin, 1977, p. 230).

Although these institutions play an important role in American higher education,
they are also among the most vulnerable to economic fluctuations (including inflation),
competition for students, and demographic changes. John Nelson, a Senior Vice
President for Moody’s, argues that small institutions (generally, institutions with less than
$20 million in annual revenue) are the most at-risk (Townsley, 2002, p. 25). In sum, the
combination of importance and vulnerability makes this population the ideal focus of a
study of tuition discounting.

I selected institutions for this study if their response to the IPEDS survey question
“institutional control” was “private, non-profit” and if their Carnegie classification was
31 (Baccalaureate I) or 32 (Baccalaureate II) (www.nces.ed.gov/ipeds). This selection
produced a population of 503 institutions, including 157 Baccalaureate I and 346

Baccalaureate H institutions. I analyzed the BAI and BAH institutions separately because

53

of their differences with respect to prestige and admissions restrictiveness, as detailed
previously.

Unfortunately, many institutions made errors in completing the revised version of
the IPEDS form in 1997 and beyond. The new method of reporting institutional aid as an
allowance instead of an expense created a great deal of confirsion among business
officers. Institutions that reported $0 allowances clearly misunderstood the new reporting
system. (A $0 allowance means the institution awarded no funded or unfunded
institutional aid.) I excluded institutions that reported a $0 allowance in 1997 from the
analysis, resulting in a useable sample of 120 BAI’s and 167 BAII’s. I performed a series
of statistical tests (T-tests and Analysis of Variance) on several key variables to ensure
that the resulting samples were statistically representative of their populations. As seen in
Table 5, the sample means for most of the variables were not significantly different from
the corresponding population means for these key variables. For the BAI institutions,
there does not appear to be any significant differences between the sample and the
population. For the BAH institutions, six of the nine variables show no differences
between the sample and the population. Respondents in the sample do appear to be
slightly more selective, with higher reputations, and fewer minority students than the

population at large.

Table 5. Population vs. Sample

 

 

 

 

 

 

 

 

 

BAI BAII
T-Tests Population Sample Sig. Population Sample Sig.
Mean Mean Mean Mean .
Average 1465 1561 0.115 1299 1367 0.319
Enrollment .
Percent Minority 0.0827 0.0842 0.1111 0.192 0.11 .001

 

 

 

54

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Age 140 141 0.73 102 104 0.622 I
Academic 3.5924 3.63 0.482 2.78 2.87 0.004
Reputation Score .
Faculty-Student 13.8086 13.75 0.83 23.28 22.79 0.578
Ratio #
Retention 84.81 85.83 0.443 70.76 71.68 0.236
Average Total Ed 33883028 3600000 0.206 14056062 15000000 0.108
and General

Expenditures I
Average 3669965 3847511 0.82 487115 486524 0.998
Endowment

Analysis of F-Score Significance F-Score Significance
Variance #
Religious 2.164 0.143 0.14 0.708
Affiliation

Gender Affiliation 0.053 0.819 0.891 0.346
Admissions 3.167 0.077 34.58 .001
Competitiveness

Rating

Locale 3.056 0.083 0.288 0.592

 

 

 

 

 

 

 

I downloaded the institutional-level data from the online IPEDS database into

Excel. 1 calculated the study variables from the raw data (see next section). The tuition

discounting rates, (calculated as the ratio of unflmded institutional aid to total tuition and

fees assessed), for example, required the use of 3 separate IPEDS variables—the

institution’s unfunded institutional aid, total tuition and fee revenue, and total allowances

applied to tuition and fees. The Operating Income Ratio calculations required 9 separate

IPEDS variables (which are detailed in the next section). Net tuition revenue per students

was also calculated (net tuition revenue divided by enrollment), as well as Total General

and Educational Expenditures per student. To ensure accuracy in these calculations,

charts were developed to coordinate the calculation process. These charts are included in

55

Appendix A. Once the relevant variables were created, the Excel file was imported into

SPSS for statistical analysis.

Conceptual Framework

In order to develop the conceptual framework for this study, I conducted a series
of interviews with administrators at small private colleges and universities, and also
reviewed models of financial health presented in the research over the past 20 years.
Additionally, I drew upon my decade of experience as a financial aid administrator and
my experiences in that capacity. I spoke with three chief administrators at small private
colleges—Susan Bolt, Vice President for Administration and Finance at Kettering
University in Flint, Michigan, Bob Nichols, Vice President for Enrolhnent Management
at the same institution, and Sharon Maher, VP for Finance at Siena Heights University in
Adrian, Michigan. From these sources I created a conceptual framework—essentially a
model of institutional financial health and the role that tuition discounting plays in
influencing financial health. I included in this framework five basic kinds of measures:
an outcome variable; a set of variables that describe and institution’s general
characteristics; a set of variables that describe student quality; a set of variables that
describe institutional quality; and finally the predictor variable of most interest—the
tuition discount rate. Each of these five measures (and the rationale for their inclusion in
a study of tuition discounting and financial health) are discussed in detail in the

following section.

56

Outcome Variables: Institutional Financial Health

The main outcome analyzed in this study was financial health. After all, if tuiti:
discounting is effective it should serve to bolster an institution’s financial situation. Th
financial health of private colleges is often measured using financial ratios which meaSl
many aspects of an institution’s financial soundness, including the effectiveness in its u
of resources, ability to live within its means, and ability to provide and maintain qualit)
educational services and facilities (“Ratios,” 1982, p. v). For this study I had originally
intended to use the Operating Income Ratio (OIR), to measures an institution’s financie
health. The OIR is calculated as:

(Gross Tuition and Fees-Institutional Student Aid+State/Federal/Local Grants and
Gifts+0ther Revenue+Auxiliary Revenue-Auxiliary Expenses)
(Total Educational and General Expenditures-Auxiliary Expenses-Institutional Studel
Aid)

The numerator includes gross tuition and fee revenue (net of institutional aid) plus grar.
and gifts from federal, state and local sources, plus net auxiliary revenue. The
denominator essentially measures net total expenses of the institution (“Ratios,” 1982, I
16). Unfortunately, after the OIR’s were calculated it became apparent that a pre and p
1996 anomaly existed—likely due to the change of the IPEDS form and the confusion
surrounding that change. Therefore I turned to other sources to find an appropriate
measure of financial health.

Moody’s Investor Services uses an array of financial ratios to measure the
financial performance of private institutions. These ratios are specifically designed to

measure the institution’s ability to “meet debt obligations and are indicative of financia

viability” (Townsley, 2002, p. 142). Two measures used by Moody’s are net tuition

57

revenue and net tuition revenue per student (net tuition revenue/enrollment). These two
measures are important because they analyze both the financial position and the market
position of institutions—both critical factors in overall financial health. Both measures

were utilized in this study as indicators of financial health.

Institutional Descriptors

To examine tuition discounting by different types of BAI and BAII institutions,
and to control for possible intervening influences on the relationship between tuition
discounting and financial health, I included several institutional descriptors: religious
affiliation; enrollment; minority enrollment; locale; gender affiliation; and age of the
institution.

The first of the institutional descriptors was religious affiliation. Many US.
colleges and universities, especially liberal arts colleges, are affiliated with a particular
religion or denomination. There is some evidence to suggest that institutions with
religious affiliations have experienced recent enrollment growth. One reason for this
increase may be the growth of enrollment at Christian high schools in the US, which has
increased the recruiting base for religious affiliated institutions. It appears that many
young people are seeking a college that will provide “moral rigor” and a religious context
in which to learn (Savoye, 2002, p.1). This increase in the demand for religious based
higher education may be related to changes in institutional financial health.

The second institutional descriptor was size (as measured by enrollment). There

is some evidence that size may affect financial health. Smaller colleges may not be able

58

to realize economies of scale—essentially lower per student costs—than larger
institutions. As a result, donors may view the institutions as inefficient, and may be less
likely to assist them . Further, the smallest colleges typically face extreme financial
conditions and must rely on a “trickle” of students to supply revenue. Fewer students
means fewer alumni from which to draw donations. John Nelson, Sr. Vice President for
Moody’s Investor Services cites enrollment as a major indicator of financial viability, and
voices concern that small institutions may experience “long term financial strain”
(Townsley, 2002, p. 202). Size definitely appears to be a factor that influences financial
health, and was therefore included in this study.

The third institutional descriptor—the ethnic make-up of the student body—was
measured for this study as the percentage of the student body that was African American,
Hispanic, or Native American. These specific groups were included because they are
under-represented in higher education. Much has been written about the personal,
institutional, and societal benefits of racial diversity on college campuses (for example
Rudenstine, 2001; Kurlaender & Yun, 2001; Chang, 2001; Hurtado, 2001). A recent
study, entitled “Does Diversity Make a Difference” (2000) by the American Council on
Higher Education and the American Association of University Professors indicates that
all students, not just minorities, benefit from diversity and that these benefits cannot be
duplicated in a homogeneous environment (p. 1). Clearly, diversity is desirable in many
ways. An institution that commits to investing in diversity is likely to experience
enrollment growth, and therefore, improved financial health. However, diversity can be
financially costly. According to Bowen and Bok (1998), “. . .blacks and Hispanics are

much more heavily represented among the poor than they are in the population as a

59

whole. . .Admitting genuinely poor students is very costly since such students have very
few resources of their own” (p. 270). In this sense, diversity may affect financial health
and tuition discounting.

Location, the fourth institutional descriptor, was based on the state in which the
institution was located. The four geographic locations were: North, South, West, and
Midwest. Appendix C provides detail about which states are included in each of the
locations. Geographic location may be affect financial health because of populations
shifts between regions and its implication for enrollment. According to Kodrzycki’s 1999
study three main factors have contributed to geographic shifts in higher education. First,
while the number of public high school graduates declined nationally by 13% between
1972 and 1997, the decline has not been equally distributed geographically. Parts of the
Northeast and Midwest experienced declines as much as 26%, while parts of the Sunbelt
region actually experienced growth in the number of high school graduates. Second, the
qualifications of high school graduates varies demographically. Some areas of the
country are experiencing vast increases in the percentage of high school graduates going
on to college, whereas this percentage has decreased in other parts of the country.
Finally, differences in tuition rates among regions has impacted enrollment as well (p.
28). Many studies of college financial health and tuition discounting include geographic
location as an independent variable (Basch, 1996; Weiler, 1994; Savoca, 1990; Day,
1995; Townsley, 2002).

Gender affiliation was the fifth control variable. There is some evidence of a
waning interest in gender-specific institutions, particularly female-only institutions. Only

about 70 of the 300 women’s colleges that were in existence 100 years ago have persisted

60

 

(Langdon, 2001, p. 5). According to Breneman (1994), “[t]he problem with many
women’s colleges lies in the changing values of young women and the shortcoming of
extracurricular life” (p. 125). Although students at women’s colleges are typically very
vocal about the commitment to remain single-sex, the enrollment at these institutions is,
in general, declining, which clearly impacts financial health.

The final institutional descriptor included in this study was the age of the
institution. Older institutions generally have established histories, secure reputations, and
larger alumni bases from which to draw donations. Therefore, they may have larger
endowments from which to fund discounts, thus reducing the reliance on unfunded

institutional discounts.

Indicators of Student Quality
Many measures of student quality exist. Among them are the test scores, grades,
and high school rank of incoming students. One variable, the admissions competitiveness
rating, as reported by the Peterson’s Guide, indicates the difficulty of entry into the
institution (Petersons, 2000, p. 7) and serves as a proxy for these individual measures of
student quality. The admissions competitiveness rating measures several elements of
entrance difficulty and has 5 values:
0 Most Difficult: “More than 75% of the freshmen were in the top 10% of
their high school classes and scored over 1310 on the SAT I (verbal and
mathematical combined) or over 29 on the ACT (composite); about 30%

or fewer of the applicants were accepted”

61

 

0 Very Difficult: “More than 50% of the freshmen were in the top 10% of
their high school class and scored over 1230 on the SAT I or over 26 on
the ACT; about 60% or fewer applicants were accepted”

0 Moderately Difficult: “More than 75% of the freshmen were in the top half
of their high school classes and scored over 1010 on the SAT I or over 18
on the ACT; about 85% or fewer of the applicants were accepted”

- Minimally Difficult: “Most freshmen were not in the top half of their high
school class and scored below 1010 on the ACT or below 19 on the ACT;
up to 95% of the applicants were accepted”

0 Noncompetitive: “Virtually all applicants were accepted regardless of high
school rank or test scores” (Peterson’s Guide, 2000, pp. 943-949).

Much of the research related to tuition discounting and institutional financial
health recognizes the impact of student quality (or, admissions selectivity) as a factor that
explains institutional variations in tuition discounting and health among institutions.
Basch’s 1996 study found a positive relationship between student preparation for college
and the socioeconomic status of the family (p. 48). Therefore, more selective institutions
are likely to attract more wealthy students, and would not need to discount to the extent of
less selective institutions. Further, institutions that are more selective are more likely to
charge a higher price. There tuition revenue tends to be higher and allows institutions to
offer more administrative and student services, which helps the college to remain

attractive to potential students (Basch, 1996, p. 48).

62

 

Indicators of Institutional Quality

Institutional quality can also be measured with a variety of variables including: (1)
academic reputation; (2) the faculty-student ratio; (3) retention rate; (4) total educational
and general expenditures; and (5) endowment value.

Academic reputation (prestige) is a key driver of institutional financial health.
Many studies confirm that students consider prestige a top determinant of their college
choice, and they specifically seek prestigious colleges. They do so for a number of
reasons. First, they believe that “prestigious colleges generally have all and more of the
characteristics that constitute good quality. Second, students do believe the networking
among alumni at prestigious colleges is an added advantage. Third, external forces [such
as parental or peer influence] convince them to aim for prestige” (Dehne, 1996, p. 23).
Therefore, institutions with prestigious reputations are more likely to have large pools of
applicants from which to select an incoming class, and they are less likely to experience
financial difficulties than less prestigious institutions. For this study, reputation was
measured by the US. News and World Report’s academic reputation score. This score is
based on a survey administered to college presidents, provosts, and deans of admission.
Respondents are asked to rate their peer schools’ academic reputation on a 5 point scale
(1=marginal; 5=distinguished).

The faculty-student ratio is another indicator of institutional quality. Many
institutions have made efforts to improve this ratio—that is, they have attempted to
reduce average classroom enrollment as a strategic measure (Breneman, 1994, p. 38).
Institutions often use the faculty-student ratio as a marketing device to promote small

class sizes and individualized interaction with faculty members. According to Townsley

63

(2002), while small class sizes are appealing to students and lead to a “more intimate
scholarly environment,” they are costly for institutions to maintain. Small class sizes can
negatively impact the financial situation of an institution because they reduce the flow of
revenue to the institution (p. 105).

An institution’s retention rate is another important variable that with a potential
impact on institutional health. The retention rate is measured as the percentage of
freshmen students returning to the institution for their second year. Generally, it can be
thought of as a measure of the institution’s success in offering the educational and other
programs that students need in order to succeed at the institution (Townsley, 2002, p.
103).

An institution’s endowment level is also likely to be a very important factor in its
health. Large endowments allow institutions to endure periods of declining enrollment
and decreasing revenue, and to preserve resources for these periods of decline.
Endowments serve as a safety net or buffer for periods of financial distress (Townsley,
2002, p. 64). Equally important, a larger endowment allows for the awarding of more
funded scholarships (and therefore less reliance on unfunded scholarships), and financial
stability for the institution.

Total educational and general expenditures, the final control variable, measures
institutional quality in that greater spending typically means more and better resources for
students (i.e. student services, library, technology, etc.). These resources not only attract
new students but encourage existing students to return. The level of resources available
to students is a “selling and retention feature” and is likely to influence the outcome of

this study—institutional financial health (O’Keefe, 1987, p. 33).

64

The Tuition Discount Rate

The tuition discounting rate, which is the predictor variable of most interest in this
study, is a policy variable. It is a ratio that institutions can alter, as a matter of policy, to
influence enrollment and financial health. Recall that there are three ways to measure the
tuition discounting rate. The most basic of these measures is the simple tuition discount
which represents the percentage of gross tuition and fees that the institution agrees to
forego—essentially, the percentage of gross tuition and fees waived by the college. It is
measured as:

$ of tuition and fee revenue waived (unfunded institutional aid)
Gross Tuition and Fee Revenue

The scholarship allowance is the second tuition discount measure. It differs from the
simple discount in that the numerator includes unfunded institutional aid as well as
filnded institutional aid (from gifts and endowments). The final tuition discount rate, the
student tuition discount, includes all student aid (institutional, federal, state, etc.) in the
numerator.

These various tuition discount measures have varying degrees of relevance to
different constituencies. Students, parents, and admissions officers are most interested in
the student tuition discount because it represents out-of-pocket cost to the student.
Auditors and analysts are generally most interested in the scholarship allowance because
it provides a measure of institutional resources—whether funded or unfunded—being
used for financial aid. It is an important measure because it represents the opportunity
cost of using funded dollars for financial aid rather than some other purpose. Business

officers, financial aid officers, presidents, and board of directors, however, often focus on

65

 

the simple tuition discount (Allen, 1999, p. 4). This is because “the institution loses the
cash represented by unfunded aid” (Townsley, 2002, p. 31). Funded institutional aid (as
well as external aid such as federal and state aid) represents a discount to the student, but
not a loss in revenue to the institution. When tuition revenue is the major source of
income (which it is for most private colleges), it is easy to understand why the simple
tuition discount is of greatest interest to administrators. For purposes of this study, the
scholarship allowance or the simple tuition discount would have been appropriate
measures. However due to the limitations of the IPEDS data, the simple tuition discount
was the only measure that could be calculated. Therefore, this study uses the simple
tuition discount as the tuition discount measure.

Appendix B contains a full description of all variables used in this study, along

with a complete definition of each, and the source of the data.

Data Analysis Methods

Each of the three research questions were analyzed using statistical techniques as
described below.
Research Question 1: What is the pattern of tuition discounting for these institutions?
Has tuition discounting increased, decreased, or stayed the same?

In order to answer this question, I used SPSS to calculate the means and standard
deviations for each individual year’s tuition discounting rate (1990-2000). I also
calculated the mean and standard deviation for the 11 year average tuition discount rate

and the 11 year change in the tuition discount rate (the 2000 tuition discount rate minus

66

the 1990 rate). I then analyzed these means and standard deviations over time. I
conducted the same analysis for the financial health variables (net tuition revenue per
student and expenditures per student). The calculations were performed for the BAI

institutions and then the BAII institutions.

Research Question 2: Are there variations in tuition discounting based on institutional
demographics? For example, does discounting vary geographically, or by institution size
or age?

Analyses of variance was performed to answer this research question. Starting
with the BAI institutions, I divided the institutions into quartiles for each variable (for
example, with the enrollment variable, I divided the institutions into 4 quartiles: up to
1118 students, 1119 to 1488 students, 1489 to 1933 students; and 1934 or more students).
For each quartile, I found the mean of the 11-year tuition discounting average for the
institutions in that quartile. I then compared quartiles to look for significant mean
differences. This analysis was performed on the following variables: enrollment, age,
admissions competitiveness rating, academic reputation score, total general and
educational expenditures, endowment, gender affiliation, retention, minority enrollment
and the faculty student ratio. After completing this process for the BAI institutions, I

conducted the same analyses for the BAIIs.

Research Question 3: Does tuition discounting improve the financial health of
institutions? In other words, what is the relationship between tuition discounting and the

financial health of institutions?

67

 

 

In order to analyze tuition discounting and its impact on institutional financial
health, I regressed net tuition revenue per student and net tuition revenue (the “health”
measures) on all of the institutional descriptors: religious affiliation; enrollment;
minority enrollment; location; gender affiliation; and the student quality variable

(admissions competitiveness rating). The general form of the regression equations was:

NTRPSX= a +b(RELIG) +c(ENROLL) +d(MINORITY) +e(SOUTH) + f(MIDWEST)
+g(WEST) +h(GENDER) +i(AGE) +j (ACR) +k(ENDOW) +1(TDRATEX) +e

and

NTRX= a +b(RELIG) +c(ENROLL) +d(MINORITY) +e(SOUTH) + f(MHDWEST)
+g(WEST) +h(GENDER) +i(AGE) +j(ACR) +k(ENDOW) +l(TDRATEx) +e

(where NTRPS is net tuition revenue per student and NTR is total net tuition revenue).

I looked at regression results for 3 separate years (x= 1990, X: 1995, x=2000) for the BAI
and BAII institutions separately, to examine the changes in the impact of tuition
discounting on financial health over time. The results of these statistical tests are

provided in the next chapter.

68

Chapter Four: Analysis of the Data

This chapter provides the results of the statistical tests described at the end of the

previous chapter. I start with a rationale for analyzing the BAI and BAII institutions

separately, followed by a general description of the samples (basic descriptive statistics),

and then the results for each of the three research questions.

Rationale for Separate Analysis of BAI and BAII Institutions

My original intention for this study was to analyze the BAI and BAH institutions

together and to draw conclusions about tuition discounting and financial health for the

entire population. However, upon analysis, I found that the differences between the two

groups were too great to treat them as a whole. Table 6 provides the means for the

descriptor variables for the BAI and BAII institutions. Differences were very notable for

nearly every variable, especially the financial and academic quality variables.

Table 6. Comparison of BAI and BAII Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mean BAI BAII Significance
Age 141 104 -001
Percent Religous 48 84 .001
Percent Co-ed 85 91 .069
Percent in Midwest 28 40 .006
Percent in North 43 28 .002
Percent in South 18 20 .474
Percent in West 1 2 1 2 .91 0
Size (enrollment) 1561 1367 .002
Admissions Competitiveness Rating: 3.63 2.87 .001
Percent Most Difficult 10 0 .001
Percent Very Difficult 42 0 .001
Percent Moderately Difficult 48 90 .001
Percent Minimally Difficult 0 9 .001
L Percent Non-Competitive 0 1.8 .001
@ademic Reputation Score 3.07 2.87 .004

 

69

 

 

Table 6 (cont’d).

 

 

 

 

 

Faculty-Student Ratio 13.75 22.8 .001
Retention Rate 85 72 .001
Total Educational and General .001
Expenditures 36,000,000 15,000,000

.001
Endowment 3,700,000 486,524

 

 

 

 

 

General Description of the BAI Sample

Appendix C provides descriptive statistics (minimums, maximums, means, and
standard deviations) for all of the variables included in this study. The following section

is a brief overview of those statistics.

BAI Institutitmal Descriptors (General Institutional Characteristics)

The BAI sample included 120 institutions ranging in age from 35 to 258 years.
The “youngest” institution in the population was Hampshire College in Massachusetts,
founded in 1975. The oldest, The Moravian College and Theological Seminary, in
Pennsylvania, was founded in 1742. The mean institution age was 141 years.

Slightly more than one-half (52%) of the institutions indicated no religious
affiliation, 48% had a religious affiliation. Among these religious affiliations were
Roman Catholic, Southern Baptist, Presbyterian, United Methodist, Mennonite,
Evangelical Lutheran, and Inter-denominational.

Of the 120 institutions, 102 (or, 85%) were co-ed. F emale-only institutions
comprised 11.7% of the sample, and male-only institutions comprised about 3%. There

were 3 male-only institutions, and 14 female-only institutions in the sample.

70

 

The institutions were geographically diverse with 28% of the institutions in the
Midwest, 43% in the North, 18% in the South, and 12% in the West. (Appendix C
contains a description of these geographic classifications).

Institutional size, as measured by enrollment, averaged 1,561 students with a
minimum of 279 students (Marlboro College in Vermont) and a maximum of 3,414
students (at Bucknell University in Pennsylvania). The average percentage minority
students was 8.43% with a range of 1% (several colleges) to 98% (Morehouse College in

Georgia).

BAI Indicagors of Student Quality
All of the 120 institutions had admissions competitiveness ratings (ACR) of 3
(moderately difficult), 4 (very difficult) or 5 (most difficult). Ten percent were

considered most difficult, 42% very difficult, and 48% moderately difficult.

BAI Indicators of Institutional Quality

Indicators of institutional quality included reputation, the faculty student ratio,
student retention, educational and general expenditures, and endowment. Reputation was
measured by the US. News and World Report Academic Reputation Score (ARS). The
Academic Reputation Score is a general rating of the institution’s reputation, as measured
by a 5 point scale where 5 is the highest. The average ARS for the institutions was 3.07
(on a 5 point scale). The range for this variable was 2.0 to 4.8. Three colleges in the
sample had a 4.8 ARS: Williams College in Massachusetts, Swarthrnore College in

Pennsylvania, and Amherst College in Massachusetts.

71

 

The average faculty-student ratio was 13.75 indicating about 14 faculty members
per student. The lowest faculty student ratio was 8—to-1 (several colleges) and the highest
was 25 -to-1 (Ursinus College in Pennsylvania).

The average percentage of students returning for their second year was 85%. The
high was 98% (at Williams College in Massachusetts, Ponoma College in California, and
Colgate University in New York), and the lowest was 40% (St. Andrews Presbyterian in
North Carolina).

The average educational and general expenditures was $36 million with a low of
$6.7 million (Marlboro College in Vermont) and a high of $98 million (Smith College in
Massachusetts). The average expenditures per student was $23,350 with a high of
$42,568 at Swarthmore College in Pennsylvania and a low of $10,578 at Nebraska
Wesleyan.

The average endowment for the 120 institutions (for 1990-1996) was $3.7 million,
with a range from $51,000 (Simon’s Rock College of Bard, Virginia) to $22 million
(Wellesley College in Massachusetts). Endowment data are only available for the 1990-
1996 time period because the IPEDS form changed in 1997, combining the reporting of

“endowment” value with other assets such as real estate.

General Description of the BAII Sample

BAII Institutional Descriptors (General Institutional Characteristics)
The BAH sample included 167 institutions ranging in age from 27 to 213 years.

The “youngest” institution in the population was Wisconsin Lutheran College, founded in

72

1973. The oldest, York College in Pennsylvania, was founded in 1787. The mean
institution age was 104 years.

Only 16% of the institutions indicated no religious affiliation, with the remaining
84% having a religious affiliation. Among these religious affiliations were Roman
Catholic, Southern Baptist, Presbyterian, United Methodist, Mennonite, Evangelical
Lutheran, and Inter-denominational.

Of the 167 institutions, 152 (or, 91%) were co-ed. There were 12 female-only
institutions (about 7% of the sample). There were no male-only institutions in the
sample.

The sample was geographically diverse as well with 40% of the institutions in the
Midwest, 28% in the North, 20% in the South, and 12% in the West. (Appendix C
contains a description of these geographic classifications).

Institutional size, as measured by enrollment, averaged 1,367 students with a
minimum of 347 students (Northwest Christian College in Oregon) and a maximum of
6,449 students (at Columbia College in Missouri). The average percentage of the student
body that was minority was 11% with a range of 0% (Dordt College in Iowa) to 100%

(Morris College in South Carolina).

BAH Indicators of Student Quality

All of the 167 institutions had admissions competitiveness ratings (ACR) of 1
(non-competitive), 2 (minimally difficult), or 3 (moderately difficult). Only 1.8% of the
institutions were classified as non-competitive, with 9% considered minimally difficult,

and the vast majority (90%) considered moderately difficult in terms of admittance.

73

 

BAH Indicators of InstitutionAOuality

Indicators of institutional quality include reputation, the faculty student ratio,
student retention, educational and general expenditures, and endowment.

Reputation was measured by the US. News and World Report Academic
Reputation Score (ARS). The average ARS for the institutions was 2.87 (on a 5 point
scale). The range for this variable was 2.0 to 3.9. Two colleges in the sample had a 3.9
ARS: Susquehanna College in Pennsylvania and George Fox University in Oregon.

The average faculty-student ratio was 22.8 indicating about 23 faculty members
per student. The lowest faculty student ratio was 12-to-1 (several colleges) and the
highest was 125-to-1 (Columbia College in Missouri).

The average percentage of students returning for their second year was 72%. The
high was 94% (at Mt. Ida College in Massachusetts) and the minimum was 44% (Shaw
University in North Carolina).

The average educational and general expenditures was $15 million with a low of
$3.2 million (William Tyndale College in Michigan) and a high of $82 million (Tuoro
College in New York). The average expenditures per student was $11,683 with a high of
$20,975 at Ottawa University in Kansas, and a low of $3,095 at Columbia College in
Missouri.

The average endowment for the 167 institutions (for 1990-1996) was $486,524,
with a range from $0 (several colleges) to $2.7 million (John Brown University in

Arkansas). (Recall that endowment data is only available for the 1990-1996 time period,

74

because the IPEDS form changed in 1997, combining the reporting of “endowment”

value with other assets such as real estate).

Research Question 1: Patterns of Tuition Discounting and Financial Health

Tuition Discount Rate, BA I

Consistent with national trends, tuition discounting did rise, in general, for this
group of institutions. In 1990, the mean tuition discount rate for the BAI institutions was
17.53%. By 1996, the mean tuition discount rate had risen to 27.85, however it jumped
back down to 23.32% in 1997 and increased only slightly by 2000. Table 7 provides the
mean tuition discounting rates and standard deviations for each of the years from 1990 to
2000, as well as the ll-year average and the 1 l-year change in the tuition discount rate

(as measured by the 2000 tuition discount rate minus the 1990 tuition discount rate).

Table 7. Tuition Discount Rates, BAI Institutions

 

 

 

 

 

 

 

 

 

 

 

 

Year Mean Tuition Discount Rates Standard Deviation
1990 17.53% 7.1
1991 19.23% 7.1
1992 21.39% 7.7
1993 23.82% 8.4
1994 24.29% 9.3
1995 26.29% 9.5
1996 27.85% 10.0
1997 23.32% 14.7
1998 22.84% 14.2
1999 24.62% 13.1

 

 

 

 

75

Table ucont’d).

 

 

 

 

 

2000 24.63% 12.7
11 year avg 23.32% 8.5
11 year 7.1 percentage points 11.7
change

 

 

 

F_irlancial Health Variables, BAI

Table 8 provides means and standard deviations for the BAI institutions for the
two financial health variables used in this study—net tuition revenue per student and
expenditures per student. For the BAI institutions, the mean not tuition revenue per
student grew from $9297 in 1990 to $13,529 in 2000—a 46% increase. The average net
tuition revenue per student over the 11 year period (1990-2000) was $11,442. The 11
year change in net tuition revenue per student was also calculated for each institution (the
2000 figure minus the 1990 figure) and the mean of that variable was $4332.

The mean net tuition revenue grew from about $14.6 million in 1990 to $21.9
million in 2000. The average net tuition revenue for the 11 year period was $18.2 million

and the mean of the 11 year change was $5.8 million.

Table 8. Financial Health Variables, BAI Institutions

 

 

 

 

 

 

 

Net Tuition Revenue Per Student Net Tuition Revenue

Mean Standard Dev Mean Standard Dev
1990 9297 3199 14633405 8195215
1991 9948 3739 15687349 8852257
1992 10259 3341 16273593 9504969
1993 10516 3421 16801083 9946772

 

 

 

 

 

 

76

Table 8 (cont’d).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1994 11115 4054 17678953 10556817
1995 11324 3716 18413716 10852991
1996 11583 3840 19084985 11437255
1997 11861 4416 19028585 11335678
1998 12285 4683 19885462 11860399
1999 12795 4741 20837781 12274896
2000 13529 6509 21893577 12766533
11 Yr 11442 3834 18201682 10532231
Avg

11 Yr 4232 4075 7260172 5766870
Change

 

Tuition Discount Rate, BA 11

institutions. In 1990, the mean tuition discount rate for the institutions in the study was
16.31%. By 2000, the mean tuition discount rate had risen to 23.34%. Table 9 provides
the mean tuition discounting rates and standard deviations for each of the years from
1990 to 2000, as well as the 1 1-year average and the ll-year change (as measured by the
2000 tuition discount rate minus the 1990 tuition discount rate). It is interesting to note
that for 1990 and 2000, the tuition discount rates for the BAI and BAII institutions were

becoming quite similar in contrast to years prior when BAI institutions were discounting

significantly more than the BAII institutions.

Table 9. Tuition Discount Rates, BAII Institutions

As with the BAI institutions, tuition discounting rose, in general, for the BAII

 

 

 

 

Year Mean Tuition Discount Rates Standard Deviation
1 990 1 6.3 1% 8 .6
1991 17.09% 8.9

 

 

 

 

 

77

 

 

Table 9 @nt’d).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1992 18.11% 9.8
1993 19.39% 9.7
1994 20.08% 9.9
1995 21.53% 9.7
1996 21.58% 11.0
1997 19.47% 15.1
1998 20.50% 14.2
1999 23.17% 14.6
2000 23.34% 12.3
11 year avg 20.05% 8.9
11 year 7.02 percentage points 12.0
change

 

F_in_ancial Health Variables. BAII

Table 10 provides means and standard deviations for the BAII institutions for the
two financial health variables used in this study—net tuition revenue and net tuition
revenue per student. For the BAII institutions, the mean net tuition revenue per student
grew from $4657 in 1990 to $7686 in 2000—a 65% increase. The average net tuition
revenue per student over the 11 year period (1990-2000) was $6219. The 11 year change
in net tuition revenue per student was also calculated for each institution (the 2000 figure
minus the 1990 figure) and the mean of that variable was $3030.

The mean net tuition revenue grew from about $5.8 million to $11.2 million
between 1990 and 2000. The average net tuition revenue for the 11 year period was

about $8.5 million and the mean of the 11 year change was about $5.5 million.

78

Table 10. Financial Health Variables, BAII Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Net Tuition Revenue Per Student Net Tuition Revenue

Mean Standard Dev Mean Standard Dev
1990 4657 1646 5760878 415056
1991 5059 1809 6343124 4452471
1992 5357 1906 7018255 5120299
1993 5669 1973 7597373 5712425
1994 6012 1955 8146992 5708209
1995 6348 2088 8642696 6690733
1996 6694 2022 9173 855 6414723
1997 6664 2250 9253421 6580126
1998 6934 2332 9835253 6950584
1999 7261 2403 10451317 7330203
2000 7686 2649 1 1223272 7945332
11 Yr 6219 1868 8495130 5912612
Avg
11 Yr 3030 2113 5462395 4885494
Change

 

 

 

 

Research Question 2: Variations in Tuition Discounting, BAI Institutions

To examine differences in the (11 year) mean tuition discounting rates among
institutions based on the institutional descriptors, student quality, and institutional quality,
I used an Analysis of Variance (ANOVA) test for mean differences. For overall
significant mean differences, I carried out a Bonferroni post-hoe test to determine the
nature of the variation. Religious-affiliated institutions had a higher discount rate than
non-religious-affiliated institutions (F=5.365, p<.03, d.f.=118). The 11 year mean tuition
discount rate for the religious-affiliated institutions was 25%. For the non-religious-

affiliated institutions it was 22%, indicating that among the BAI institutions, the

79

religious-affiliated institutions discounted significantly more than the non-religious
affiliated institutions.

Tuition discounting also varied significantly by institutional enrollment (F=6.520,
p<.0001, d.f. = 3, 116). The institutions were divided into quartiles based on enrollment
(with the first quartile being the smallest institutions), and then analyzed for variations in
mean tuition discount rates among those quartiles. Table 11 provides the quartile break-
down and the statistical results. Significant mean differences appeared between
institutions in the highest quartile (the largest institutions) and the institutions in the two
lowest quartiles. The smaller institutions appeared to be discounting, on average, at a
higher rate than the larger schools. When compared to the largest schools (fourth
quartile), the institutions in the lowest quartile discounted nearly 9 percentage points
more. Institutions in the second quartile discounted about 5.6 percentage points more
than the largest institutions. There was no mean difference in tuition discounting rates
between the third and fourth quartiles.

Table 11. Mean Difference in Tuition Discount Rates, 1990-2000, Based on Enrollment,
BAI Institutions

 

 

 

Enrollment (# of students) Mean Difference (in Significance
percentage points)
Column 1 Column 2 [Col 1 — Col 2]

Up to 1118 students 1119-1488 students 3.12 .800
1489-1933 students 580* .035
1934 or more 8.72* .000
students

1119-1488 students Up to 1118 students -3.12 .800
1489-1933 students 2.68 1.00
1934 or more -.5.60* .046
students

 

 

 

 

 

80

 

Table 11 (cont’d).

 

 

 

 

 

 

1489-1933 students Up to 1118 students -5.80"‘ .035
1119-1488 students -2.68 1.00
1934 or more 2.91 .959
students

1934 or more Up to 1118 students -8.72* .000

students 1 1 19-1488 students -5.60* .046
1489-1933 students 292 .959

 

 

*The mean difference is significant at a minimum of the .05 level.

Mean tuition discounting varied by geographic location (F=5.066, p<.003, d.f.=3,
116) primarily because Midwest institutions discounted more heavily than those in the
South. Institutions in the Midwest had an average tuition discount rate 7.5 percentage
points higher than institutions in the South, and 7.79 percentage point higher than

institutions in the West. Table 12 provides the results of the geographic analysis.

Table 12. Mean Tuition Discount Rates, 1990-2000, Based on Geographic Location

 

 

 

 

 

 

Geographic Location Mean Difference (in Significance
percentage points)
Column 1 Column 2 [Col 1 - C012]
North South 3 .82 .43 1
Midwest -3.68 .259
West 4.11 .575
South North -3 .82 .43 1
Midwest -7.50* .007
West 2.90 1.00
Midwest North 3.68 .259
South 7.50* .007
West 7.79 .019
West North -4.1 1 .575
South -2.90 1.00
Midwest -7.79 .019

 

 

 

 

81

 

Mean tuition discounting also varied by age of the institution (F =5.284, p< .003,

d.f. = 3, 116). Table 13 provides the Bonferroni results for the age variable (again with

the institutions divided into quartiles). Although the reason is not readily apparent,

institutions in the third quartile (146 to 168 years of age) discounted more than the other

three groups.

Table 13. Mean Tuition Discount Rates, 1990-2000, Based on Institutional Age, BAI

 

 

 

 

 

 

 

Age (in years) Mean Difference (in Significance
percentage points)
Column 1 Column 2 [Col 1 - Col 2]
Up to 117 years 118-145 years 1.01 1.00
146-168 years -5.63* .043
169 or more years 1.74 1.00
118-145 years Up to 117 years -1.01 1.00
146-168 years -6.64* .009
169 or more years 7.30 1.00
146-168 years Up to 117 years 5.63* .043
118-145 years 6.64* .009
169 or more years 7.37* .004
169 or more years Up to 117 years -1.74 1.00
118-145 years -7.30 1.00
146-168years -7.37* .004

 

 

 

*The mean difference is significant at a minimum of the .05 level.

For two of the institutional descriptor variables, no mean differences in tuition

discounting were significant. The first was gender/co-ed (F=1.511, p<.211, d.f.=l , 118)

and the second was percent minority enrollment (F=.714, p<.546, d.f.=3, 116).

Therefore, tuition discounting does not vary among institutions by co-ed status or

percentage of minority enrollment.

Tuition discounting varies by the Admissions Competitiveness Rating—the

general measure of student quality (F=1 1.161, p < .0001 , d.f = 2, 117). Recall that there

are 5 values for this variable ranging from non-competitive (l) to most difficult (5). All

82

 

of the BAI institutions fell into categories 3, 4, or 5. Table 14 provides the results of the
Bonferroni test for the Admissions Competitiveness Rating variable. Institutions rated as
moderately difficult (3) and very difficult (4) do not appear to vary in tuition discounting
rates. However, there does appear to be a significant difference between tuition
discounting rates for the most competitive institutions (5) and the other two types of
institutions (3 and 4). The mean tuition discount rate for the moderately difficult
institutions was 11 percentage points higher than the mean for the most difficult
institutions. Similarly, the very difficult institutions appear to have discounted, on
average, 7 percentage points more than the most difficult institutions.

Table 14. Mean Differences in Average Tuition Discount Rates, 1990-2000, Based on
Admissions Competitiveness Rating, BAI Institutions

 

 

 

 

 

Admissions Competitiveness Rating Mean Difference Significance
(in percentage
points)
Column 1 Column 2 [Col 1 - Col 2]
Moderately Difficult (3) Very Difficult (4) 3.7 .053
Most Difficult (5) 11.1* .000
Very Difficult (4) Moderately Difficult (3) -3.7 .053
Most Difficult (5) 7.4* .009
Most Difficult (5) Moderately Difficult (3) -11.l* .000
Very Difficult (4) -7.4* .009

 

 

 

 

 

*The mean difference is significant at a minimum of the .05 level.

Mean tuition discounting rates also varied by academic reputation score (ARS)
(F=l2.114, p<.0001, d.f.=3, 116). Again, institutions were divided into quartiles based
on their ARS. Table 15 presents the results. When looking at the last row of this table it
is apparent that the institutions in the lowest three quartiles were discounting at a higher
rate than the institutions with the highest ARS. When compared to the highest quartile,
institutions in the lowest quartile were discounting at a rate over 10 percentage points

higher. There appears to be a diminishing of the mean difference, however, with 8.68

83

percentage points separating quartiles 2 and 4, but only 5.48 percentage points separating
quartiles 3 and 4.

Table 15. Mean Tuition Discount Rates, 1990—2000, Based on Academic Reputation
Score, BAI

 

 

 

 

 

 

Academic Reputation Score (Scale of 1-5; Mean Difference (in Significance
1 is lowest) percentage points)
Column 1 Column 2 [Col 1 - Col 2]

Up to 2.5 2.51 to 2.95 2.08 1.00
2.96 to 3.58 5.28* .033
3.59 or higher 10.76* .000

2.51 to 2.95 Up to 2.5 -2.08 1.00
2.96 to 3.58 3.20 .745
3.59 or higher 868* .000

2.96 to 3.58 Up to 2.5 -5.28* .033
2.51 to 2.95 -3.20 .745
3.59 or higher 549* .034

3.59 or higher Up to 2.5 -10.76* .000
2.51 to 2.95 -8.68* .000
2.96 to 3.58 -5.48* .034

 

 

 

 

*The mean difference is significant at a minimum of the .05 level.

Mean tuition discounting varied by total educational and general expenditures
(F=8.300, p<.0001, d.f. = 3, 116). When separated by quartiles, the institutions with the
highest expenditures discounted less than the institutions with smaller expenditures
(Table 16).

Table 16. Mean Tuition Discount Rates, 1990-2000, Based on Total Educational and
General Expenditures, BAI

 

 

 

 

 

 

Educational and General Expenditures Mean Signifi-
Difference (in canoe
percentage
Column 1 Column 2 points)
[Col 1 - Col 2]
Up to $20.8 million $20.8 million -$33.3 million 1.55 1.00
$33.3-$49 million 4.82 .113
More than $49 million 931* .000
$20.8 million -$33.3 million Up to $20.8 million -1.55 1.000
$33.3 million -$49 million 3.28 .649
More than $49 million 7.78* .001

 

 

84

 

 

Table 16 (cont’d).

 

 

 

$33.3 million-$49 million Up to $20.8 million -4.82 .113
$20.8 million-$33.3 million -3.28 .649
More than $49 million 4.50 .169
More than $49 million Up to $20.8 million -9.32* .000
$20.8 million- $33.3 million -7.77* .001
$33.3 -$49 million -4.50* .169

 

 

 

 

*The mean difference is significant at a minimum of the .05 level.

Mean tuition discounting varied by endowment (F=4.762, p<.005, d.f. = 3, 116).

Institutions in the lowest two quartiles had significantly higher tuition discounting rates

than the more well-endowed institutions (see Table 17). The more well-endowed

institutions are able to fund discounts with endowments rather than simply waiving the

tuition. The simple tuition discount is analyzed here (i.e., the percentage of gross tuition

that is waived for students), so it is not surprising that institutions with higher

endowments would have lower simple tuition discount rates. Simply put, they can still

offer plenty of “discounts” to students, but the discounts are funded by endowment rather

than unfunded.

Table 17. Mean Tuition Discount Rates, 1990-2000, Based on Endowment, BAI

 

 

 

 

 

 

 

Endowment Signifi-
Difference (in canoe
percentage
Column 1 Column 2 points)
[Col 1 - C012]
Up to $1.27 million $1 .27 million - $2.47 million 1.63 1.00
$2.48 million - $4.74 million 2.42 1.00
More than $4.74 million 752* .003
$1 .27 million - $2.47 million Up to $1.27 million -1.62 1.00
$2.48 million - $4.74 million .788 1.00
More than $4.74 million 589* .036
$2.48 million - $4.74 million Up to $1 .27 million -2.42 1.00
$1.27 million - $2.47 million -.788 1.00
More than $4.74 million 511* .101

 

 

85

 

 

Table 17Qont’d).

 

 

 

 

 

More than $4.74 million Up to $1 .27 million -7.52* .003
$1.27 million - $2.47 million -5.89* .036
$2.48 million - $4.74 million -5.11 .101

 

*The mean difference is significant at a minimum of the .05 level.

For two of the institutional quality variables, no mean differences in tuition
discounting were significant. The first was the faculty-student ratio (F=2.132, p<.100,
d.f.=3, 114) and the second was retention (F =2.125, p<.101, d.f.=3, 115). Therefore,
tuition discounting does not vary among the BAI institutions by retention rates or faculty-

student ratios.

Variations in Tuition Discounting, BAII Institutions

Unlike the BAI institutions, mean tuition discounting rates for the BAII
institutions varied by minority student enrollment (F=5.345, p<.003, d.f. =3, 161). Using
quartiles based on the percentage of minority students, institutions with the highest
minority percentage had lower mean tuition discount rates than the institutions in the
other quartiles. Institutions in the highest quartile (those with the highest percentages of
minority students) appear to have discounted about 6 percentage points less that the other

institutions. Table 18 provides the results of this statistical test.

Table 18. Mean Tuition Discount Rates, 1990-2000, Based on Minority Enrollment,
BAH

 

 

 

 

Minority Student Percentage Mean Signifi-
Difference (in cance
Column 1 Column 2 percentage
points)
[Col 1 - C012]

 

86

 

 

Table 18 (cont’d).

 

 

 

 

 

 

 

 

Up to 3.57% minority 3.58% to 6.96% minority .77 1.00
6.97% to 13.54% minority .12 1.00
13.55% or higher minority 6.47* .005
3.58% to 6.96% minority Up to 3.57% minority -.77 1.00
6.97% to 13.54% minority -.65 1.00
13.55% or higher minority 5.70* .033
6.97% to 13.54% minority Up to 3.57% minority -.12 1.00
3.58% to 6.96% minority .065 1.00
13.55% or higher minority 6.35* .004
13.55% or higher minority Up to 3.57% minority -6.47* .005
3.58% to 6.96% minority -5.70* .033
6.97% to 13.54% minority -6.35* .004

 

*The mean difference is significant at a minimum of the .05 level.

Tuition discounting also varied among the BAII institutions by geographic

location (F 2.972 p<.034, d.f.=3,163). In particular, the Midwest institutions discounted

6 percentage points more than the institutions in the West. No other mean differences

were apparent. The results are presented in Table 19.

Table 19. Mean Tuition Discount Rates, 1990-2000 Based on Geographic Location

 

 

 

 

 

 

Geographic Location Mean Difference (in Significance
percentage points)
Column 1 Column 2 [Col 1 - C012]
North South -1.48 1.00
Midwest -3.05 .424
West 3.13 1.00
South North 1 .48 1 .00
Midwest -1.57 1.00
West 4.61 .373
Midwest North 3.05 .424
South 1.57 1.00
West 618* .034
West North -3.13 1.00
South -4.61 .373
Midwest -6.18* .034

 

 

 

 

87

 

 

Mean tuition discounting also varied by institutional age (F=4.282, p<.007, d.f. 3,

160). The oldest institutions (those in the highest quartile) discounted significantly less

that the youngest institutions (those in the lowest two quartiles) by about 6 percentage

points. No significant mean difference was evident in discounting between institutions in

the top 2 quartiles. Table 20 provides the statistical results for this variable.

Table 20. Mean Tuition Discount Rates, 1990-2000, Based on Institutional Age, BAII

 

 

 

 

 

 

 

 

 

Age (in years) Mean Difference (in Significance
percentage points)
Column 1 Column 2 [Col 1 - Col 2j
Up to 73 Years 74-107 Years -.94 1.00
108-133 Years -2.81 .840
134+ Years -6.47* .006
74-107 Years Up to 73 Years .94 1.00
108-133 Years -1.87 1.00
134+ Years -5.53* .033
108-133 Years Up to 73 Years 2.81 .840
74-107 Years 1.86 1.00
134+ Years -3.66 .358
134+ Years Up to 73 Years 647* .006
74-107 Years 553* .033
108-133 Years 3.66* .358

 

*The mean difference is significant at a minimum of the .05 level.

For three of the institutional descriptor variables, no mean differences in tuition

discounting were significant. The first was religious affiliation (F=1.785, p<.l83, d.f.=l ,

163), the second was co-ed/gender (F=.935, p<.335, d.f.=1, 164), and the third was

size/enrollment (F=.774, p<.510, d.f.=3, 162). Therefore, tuition discounting does not

vary among the BAII institutions by co-ed status, religious affiliation, or size.

Mean tuition discounting differed by academic reputation score (ARS) (F=4.445,

P<.006, d.f.=3, 163). Most notable was the mean difference between the institutions in

88

 

 

the highest 2 quartiles versus the institutions in the lowest quartile (Table 21). The

institutions with the lowest academic reputation scores discounted about 6 percentage

points less than those with the highest scores.

Table 21. Mean Tuition Discount Rates, 1990—2000, Based on Academic Reputation

 

 

 

 

 

 

Score, BAH
Academic Reputation Score Mean Difference (in Significance
percentage points)
Column 1 Column 2 [Col 1 - Col 2]

Up to 2.60 2.61 to 2.80 -4.29 .121
2.81 to 3.10 -5.77* .014
3.11 and higher -5.98* .014

2.61 to 2.80 Up to 2.60 4.30 .121
2.81 to 3.10 -1.47 1.00
3.11 and higher -1.68 1.00

2.81 to 3.10 Up to 2.60 5.77* .014
2.60 to 2.80 1.47 1.00
3.11 and higher -.21 1.00

3.11 and higher Up to 2.60 5.98* .014
2.61 to 2.80 1.68 1.00
2.81 to 3.10 .21 1.00

 

 

 

 

*The mean difference is significant at a minimum of the .05 level.

All of the BAII institutions fell into either the noncompetitive(1), minimally

 

 

difficult(2), or moderately difficult(3) categories of the admissions competitiveness
rating. Large, significant mean differences in tuition discounting were evident as shown
in Table 22 (F =10.77 1 , p<.0001, d.f.=2, 164). Noncompetitive institutions discounted, on
average, over 17 percentage points less than the moderately difficult institutions. The
minimally difficult institutions appear to have discounted over 7 percentage points less
than their moderately difficult counterparts.

Table 22. Mean Tuition Discount Rates, 1990-2000, Based on Admissions
Competitiveness Rating, BAH Institutions

 

 

Signifi-
canoe

Mean Difference (in
percentage points)
[Col 1 - C012]

Admissions Competitiveness Rating

Column 1 Column 2

 

 

 

 

89

Table 22 (cont’d).

 

 

 

 

Noncompetitive (1) Minimally Difficult (2) -9.67 .217
Moderately Difficult (3) -17.09* .002
Minimally Difficult (2) Noncompetitive (1) 9.67 .217
Moderately Difficult (3) -7.41 * .004
Moderately Difficult (3) Noncompetitive (1) 17.09* .002
Minimally Difficult(2) 7.41 * .004

 

 

 

 

*The mean difference is significant at a minimum of the .05 level.

Tuition discounting rates varied by the faculty-student ratio (F=9.138, p<.0001,
d.f.: 3,162). As shown in Table 23, the lowest quartile institutions had the lowest
number of students per faculty member. (Quartile 1 included institutions in which the
ratio was 17.03 faculty per student or less). The institutions with the lowest faculty-
student ratios (i.e. smaller class sizes) discounted less than the institutions with higher
faculty-student ratios. Table 23 provides the statistical analysis for the faculty-student
ratio.

Table 23. Mean Tuition Discount Rates, 1990-2000, Based on F aculty-Student Ratio,
BAII

 

 

 

 

 

 

 

Faculty-Student Ratio (# of faculty Mean Difference (in Significance
members per student) percentage points)
Column 1 Column 2 [Col 1 - Col 2]
Up to 17.03 17.04 to 20.25 4.68 .063
20.26 to 24.71 5.19* .027
24.72 and higher 945* .000
17.04 to 20.25 Up to 17.03 -4.68 .063
20.26 to 24.71 .510 1.00
24.72 and higher 4.77 .058
20.26 to 24.71 Up to 17.03 -5.19* .027
17.04 to 20.25 -5.10 1.00
24.72 and higher 4.26 .118
24.72 and higher Up to 17.03 -9.45* .000
17.04 to 20.25 -4.77 .058
20.26 to 24.71 -4.26 .118

 

 

 

 

*The mean difference is significant at a minimum of the .05 level.

90

 

 

Unlike BAI institutions, the more well endowed BAH institutions discounted
more than the BAH institutions with lower endowments (F=9.031, p<.0001, d.f.=3,163).
The mean tuition discounting rates for the highest quartile was approximately 7
percentage points higher than the mean tuition discounting rates for the lowest 2 quartiles
(see Table 24).

Table 24. Mean Tuition Discount Rates, 1990-2000, Based on Endowment, BAII

 

 

 

 

 

 

 

 

Endowment Mean Difference Signifi-
Column 1 Column 2 (in percentage canoe
points)
[Col 1 - C012]
Up to $117,714 $117,715 to $307, 658 -.86 1.00
$307,659 to $699,829 -6.43* .004
$699,830 and higher -7.72* .000
$117,715 to $307,658 Up to $117,714 .86 .000
$307,659 to $699,829 -5.57* .016
$699,830 and higher -6.86* .001
$307,659 to $699,829 Up to $117,714 6.43* .004
$117,715 to $307,658 5.57* .016
$699,830 and higher -1.29 1.00
$699,830 and higher Up to $117,714 7.72* .000
$117,715 to $307, 658 6.86* .001
$307, 659 to $699,829 1.29 1.00

 

 

*The mean difference is significant at a minimum of the .05 level.

For two of the institutional quality variables, no mean differences in tuition
discounting were significant. The first was retention (F=2.199, p<.09, d.f.=3, 163) and
the second was total educational and general expenditures (F=1.210, p<.308, d.f.=3, 163).
Therefore, tuition discounting does not vary among the BAII institutions by retention
rates or by total educational and general expenditures.

In summary, there are several key differences in tuition discounting based on

institutional characteristics, student quality, and institutional quality. In general, among

91

 

BAI institutions, the less prestigious, smaller, older institutions discount more. Among
the BAII institutions it is the younger, more prestigious, higher endowed institutions that
discount more. These results suggest a blurring of the distinction between the lower
status BAIs and the higher status BAIIs, which I will explore in the final regression model

in this chapter.

Research Question 3: Tuition Discounting and Institutional Financial Health
To analyze the influence of tuition discounting on institutional financial health, I
regressed Net Tuition Revenue per Student and Net Tuition Revenue—the institutional
financial health measures—fin the institutional descriptors (religious affiliation,
enrolhnent, minority enrollment, locale, gender affiliation, and institutional age) and the
student quality variable (the admissions competitiveness rating). Because the
institutional quality variables were all very highly correlated (see Appendix E), I only
used one of them, endowment, because of its importance in the descriptive analyses.
The general form of the regression equation was:
- NTRPSX= a +b(RELIG) +c(ENROLL) +d(MINORITY) +e(SOUTH) +
f(MIDWEST) +g(WEST) +h(GENDER) +i(AGE) +j(ACR) +k(ENDOW)

+1(TDRATEX) +e.

. NTRX= a +b(RELIG) +c(ENROLL) +d(MINORITY) +e(SOUTH) +

f(MIDWEST) +g(WEST) +h(GENDER) +i(AGE) +j(ACR) +k(ENDOW)

+1(TDRATEX) +e.

92

Both equations were run for the BAI institutions and then the BAII institutions for each of

the years 1990, 1995, and 2000, for a total of 12 regression equations.

Tuition Discounting and Institutional Health, BAI Institutions

Table 25 provides the results of the 1990 regression equation for the BAI
institutions with net tuition revenue per student as the dependent variable. The R-squared
for this model was .509 indicating that the independent variables in the model account for
just half the variation in net tuition revenue per student. The coefficient for the 1990
tuition discounting rate was statistically significant and indicates that for every 1
percentage point increase in the tuition discounting rate, not tuition revenue per student
declined by $143. For these institutions in 1990, it appears that higher tuition discount
rates lower net tuition revenue per student. Religious affiliation, and percent minority
students were also significantly negatively related to net tuition revenue per student.

Table 25. Regression Coefficients, 1990, Net Tuition Revenue Per Student as Dependent
Variable, BAI Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

R2=.509 Unstandardizetj Standardizegl t Sig. Collinearitj
N=120 Coefficient Coefficient Statistic
B Std. Error Betal Tolerance VIF
(Constant) 11660.927 2090.446 5.578 .000
Religious -2968.332 566.791 -.466 -5.237 .000 .576 1.737
Affiliation
Average -.733 .423 -.153 -1.733 .086 .587 1.704
Enrollment,
1990-2000
Minority -4720.150 2042.142 -.184 -2.311 .023 .715 1.398I
Percentage
Av
Institutio -1084.242 735.260 -.129 -1.475 .143 .592 1.690
Located'
Sou
Institutio -38.681 604.450 -.005 -.O64 .949 .622 1.606
Located'
Midwes

 

 

 

93

 

Table 25 (cont’d).

 

 

 

 

 

 

 

 

 

 

 

 

 

Institution 194.035 746.376 .020 .260 .795 .804 1.243
Locatedirfl

Wes

Coed 598.892 696.357 .067 .860 .392 .747 1.339

Ae .893 5.383 .012 .166 .869 .826 1.210
Admissions: 766.987 460.150 .161 1.667 .098' .486 2.05
Competitive
ness Ratin

Av 1.428E-04 .000 .172 1.726 .087 .460 2.176
Endowmen
1990-199

Tuitiog -14271.403 3485.495 -.349 -4.095 .000 .627 1.595
Discoun
Rate 199

 

 

 

Table 26 provides the results of the regression equation for the BAI institutions

for 1995 with net tuition revenue per student as the dependent variable. The R—squared

for this model was .691. The coefficient for the 1995 tuition discounting rate indicates

that for every 1 percentage point increase in tuition discounting, net tuition revenue per

student fell by $158. This result is similar to the 1990 result, with a slightly higher

decline in net tuition revenue per student associated with each 1 percentage point increase

in the tuition discounting rate. Just as in 1990, religious affiliation and percent minority

students were significantly negatively related to net tuition revenue per student. Co-ed

and average endowment, however, were significantly positively related to net tuition

revenue per student. It is not surprising that endowment and net tuition revenue per

student would be positively related—higher endowments allow institutions to discount

from funded sources, thus essentially increasing net tuition revenue per student.

Table 26. Regression Coefficients, 1995, Net Tuition Revenue Per Student as Dependent
Variable, BAI Institutions

 

 

R2=.691 Unstandardize Standardize t Sig. Collinearirzl
N=120 Coefficient Coefficient Statistic
1% Std. Error Betal Tolerance] VIF

 

 

 

 

 

 

94

Table 26 (cont’d).

 

(Constantj

12842.918

2169.336

5.920

.000

 

Religiousl
Affiliation

£561,130

529.323

-.346

-4.839

.000

.592

1.689

 

Average
Enrollment,
1990-2000

-.107

.398

-.019

-.270

.788

.589

1.698

 

Minority
Percentage
Av

 

-4870.563

190] .7841

-.l68|

-2.561

.012

.707

1.415

 

Institutio
Located '
Sout

-1043.662

677.084I

-.107

-l.541

.126

.625

1.599

 

Institutio
Located '

Midwes

-1022.790

553.006

-.126

-l.850

.067

.6481

1.543

 

Institutio
Located '

Wes

863.8581

722.480

.077

1.196

.235

.738

1.356

 

Coed

1338.268

667.906

.129

2.004l

.048

.733

1.365

 

Age

2.76

5.379

.032

.513

.609

.797

1.255

 

Admissions:
Competitive
ness Ratin

921.0881

472.368I

.160

1.950

.054

.449

2.229

 

Avg}
Endowment
1 990-1 996

2.1 8613-041

.000

.230

2.816

.006

.452

2.211

 

Tuition
Discount
Rate 1995

 

 

-1 5774.9081

 

2652.406

 

-.402

 

-5.947

 

.000

.662

 

 

1.511

 

 

Table 27 provides the results of the 2000 regression equation for the BA]

institutions with net tuition revenue per student as the dependent variable. The R-squared

for this model was .565. This 2000 tuition discounting rate variable proved to be a

significant predictor of financial health with net tuition revenue per student declining by

$359 for every 1 percentage point increase in the tuition discounting rate. Two

variables—religious affiliation and enrollment were also significantly negatively related

to net tuition revenue per student.

95

Table 27. Regression Coefficients, 2000, Net Tuition Revenue Per Student as Dependent
Variable, BAI Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

R2=.565 Unstandardize Standardizej 11 Sig. Collineari
N=120 Coefficien Coefficient Statistic
B Std. Error Bet Tolerance VIF
(Constant) 26927.486 4128.504 6.522 .000
Religiou -3627.920 1104.231 -.280 -3.285 .001 .556 1.79
Affiliation
Average -1.607 .726 -.1641 -2.214 .029 .730 1.369
Enrollment,
1990-2000
Minority -5014.5481 3905.321 -.096 -1.284 .202 .717 1.395
Percentage
Ava
Institution -1016.111 1387.995 -.060 -.732 .466 .609 1.643
Locatedin
South
Institution 416.117 1152.166 .029 .361 .719 .628 1.592
Locatedin
Midwest
Institution 317.480 1429.374 .016 .222 .825 .804 1.244
Locatedin
West
Coed 2224.192 1323.423 .123 1.681 .096 .758 1.319
Age 12.295 10.371 .083 1.186 .238 .816 1.225
Admissions, 226.60 902.925 .023 .251 .802 .463 2.162
Competitive
ness Ram
Avg 1.135E-04l .000 .067 .733 .465 .481 2.081
Endowment
1990-1996
Tuition -38640.658 5038.278 -.592 -7.669 .000 .675 1.481
Discount
Rate 2000

 

 

 

Table 28 provides the results of the 1990 regression equation for the BAI

institutions, with net tuition revenue as the dependent variable measuring financial health.
The R-squared was .855, indicating that the independent variables in the model account
for nearly 86% of the variation in net tuition revenue. The tuition discount rate for 1990
proved to be a significant predictor of financial health with net tuition revenue declining
by $118,573 for every 1 percentage point increase in the tuition discounting rate.

Enrollment and endowment were significantly positively related to net tuition revenue

96

and religious affiliation and minority enrollment were significantly negatively related to

net tuition revenue.

Table 28. Regression Coefficients, 1990, Net Tuition Revenue as Dependent Variable,

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

BAI Institutions
N=120 Unstandardize Standardizej t Sig.
R2=.855 Coefficient Coefficien
B Std. Error Betal
(Constant) 1292081.729 2909224354 .444 .6581
Religious -4404887.797 788788.884 -.270 -5.584 .000
Affiliation
Average 8838.483 588.791 .718 15.011 .000
Enrolhnent,
1990-2000
Minority -13299213.505 2842000657 -.203 -4.680 .000
Percentage
Ava
Institution —1111724.978 1023244351 -.052 -1.086 .280
Locatedin
South
Institution -1074166.959 841 199.378 -.059 -1.277 .204
Locatedin
Midwest
Institution 240771.976 1038714319 .009 .232 .817
Locatedin
West]
Coed 771475.690 969102.922 .034 .796 .4281
Age 4002.619 7491.186 .022 .534 .594
Admission 973 770.034l 640380.483 .080 1.521 .131
Competitivj
ness Ratin
Av? .302 .115 .142 2.622 .010
Endowmen
1990-199
Tuition -11857307.181 4850680396 -.113 -2.444 .016
Discoun
Rate 199

 

 

 

 

 

 

 

Table 29 provides the results of the 1995 regression equation for the BAI
institutions with net tuition revenue as the dependent variable. The R-squared was .876.
The tuition discounting variable was significantly related to net tuition revenue with net

tuition revenue declining by $176,606 for each 1 percentage point increase in the tuition

97

discounting rate. As with the 1990 equation, enrollment and endowment were

significantly positively related to net tuition revenue, and religious affiliation and

enrollment were significantly negatively related to net tuition revenue.

Table 29. Regression Coefficients, 1995, Net Tuition Revenue as Dependent Variable,

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

BAI Institutions
N=120 Unstandardize?! Standardizejl t Sig.
R2=.876 Coefficient Coefficient
B Std. Error Betal
(Constant) 788488.634l 3848708893 .205 .83
Religious: -4381424040 956874.226 -.203 -4.579 .000
Affiliation
Average 1141 1.503 706.124 .700 16.161 .000
Enrollment,
1990-2000
Minority -l3732040.930 3469402266 —.158 -3.958 .000
Percentage.
Avg]
Institution -514927.835 1227351651 -.018 -.420 .676
Locatedin
South
Institution -2152289.555 1013224266 -.090 -2.124 .036
Locatedin
Midwest]
Institutio 1313006687 1286368146 .039 1.021 .310
Located'
Wes
Coed 1674649.484| 1180003344 .055 1.419 .15
Age 5359.691 9124.380 .022 .587 .5581
Admission 1432239300 797376.259 .089 1.796 .075
Competitiv
ness Ratin
Av .461 .138 .1641 3.343 .001
Endowmen
1990-199
Tuitio -17660580.855 4759232768 -.153 -3.711 .000
Discoun
Rate 1995

 

 

 

 

 

 

 

Table 30 provides the results of the regression equation for 2000 for the BAI

institutions with net tuition revenue as the dependent variable. The R-squared for this

98

model was .824. Again, the tuition discounting rate proved to be a significant predictor
of net tuition revenue with net tuition revenue declining by $397,841 for every 1
percentage point increase in the tuition discount rate. As with the 1990 and 1995
equations, enrollment and endowment were significantly positively related to net tuition
revenue and religious affiliation and minority enrollment were significantly negatively
related to net tuition revenue.

Table 30. Regression Coefficients, 2000, Net Tuition Revenue as Dependent Variable,
BAI Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N=120 Unstandardizeil Standardizej t Sig.
R2=.82 Coefficient Coefficient
B Std. Error Betal
(Constant) 9621794557 5155106713 1.866 .065
Religious -5036272.272 137881 1.457 -. 198 -3 .653 .000
Affiliation
Average 1 1781.193 906.683 .614I 12.99 .000
Enrollment,
1990-2000
Minority -15256403.045 4876427 .630 -.149 -3. 129 .002
Percentag
Av;
Institution -862453.544I 1733137521 -.026 -.498I .620
Located '
South
Institution -1710088.310 1438666779 -.061 -1 . 189 .237
Located in
Midwest
Institution 348174.293 1784805943 .009 .195 .846
Located in
West
Coed 3100212535 1652508550 .087 1.876 .063
Age 186845481 12949.817 .065 1.443 .152
Admissionsl 1612006073 1 127448.838l .085 1.430 .156
Competitive
ness Ratin
Av .274 .193 .082 1.415 . 160
Endowmen
1990-199
Tuitio -397841 1 1.523 6291 107.806 -.31 1 -6.324 .000
Discoun
Rate 200

 

 

 

 

 

 

 

99

Table 31 provides the results of the 1990 regression equation for the BAII
institutions with net tuition revenue per student as the dependent variable. The R-squared.
for this model was .261 indicating that many factors other than those captured in this
model influence net tuition revenue per student. For this equation, the tuition discounting
rate was not a significant predictor of financial health as measured by net tuition revenue
per student. However two variables—“South” location and “West” location—were
significantly negatively related to net tuition revenue per student. The admissions
competitiveness rating and average endowment were both significantly positively related

to net tuition revenue per student.

Table 31. Regression Coefficients, 1990, Net Tuition Revenue Per Student as Dependent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable, BAII Institutions
R2=.261 Unstandardizeril Standardized] t Sig. Collinearitil
N=167 Coefficient Coefficient Statistic
B Std. Error Betal Tolerance VIF
Constant) 2201.888 1122.305 1.962 .052
Religiousl -473.118l 347.565 -.105 -1.361 .175 .819 1.221
Affiliation
Average -.159 .144l -.085 -1.104I.271 .825 1.212
Enrollment,
1990-2000
Minority -93.757 879.150 -.008 —.107 .915 .884 1.132
Percentage
Avg
Institution —1575.153 391.263 -.383 —4.026 .000 .536 1.864
Located in
South
Institution -653.270 322.519 -.194 -2.026 .045 .528 1.895
Located in
Midwest
Institution -1449.169 452.931 -.281 -3.200 .002 .628l 1.592
Located in
West
Coed 604.432 462.030 .095 1.308 .193 .912 1.097
_Age 7.374I 3.179 .178| 2.319 .022 .829 1.207
Admissionsa 843.442 332.147 .194 2.539 .012 .833 1.201
Competitive
ness Rm

 

 

 

 

 

 

 

 

100

Table 31 (cont’d).
Avfl 4.894E-04l .000 .155 19841.04 .800 1.251
Endowmen
1990-1996
Tuition -759.496 1425.179 -.043 -.533 .595 .742 1.3481
Discoun
Rate 199

 

 

 

 

 

 

 

 

 

 

 

 

Table 32 provides the results of the 1995 regression equation for the BAII
institutions with net tuition revenue per student as the dependent variable. The R-squared
was .242. The tuition discounting rate was significant in this model and was negatively
related to net tuition revenue per student. Specifically, every 1 percentage point increase
in tuition discounting was associated with a decrease in net tuition revenue per student of
$47. Two variables, enrollment and “South” location, were significantly negatively
related to net tuition revenue per student and two others, age and admissions
competitiveness rating, were positively associated with net tuition revenue per student.

Table 32. Regression Coefficients, 1995, Net Tuition Revenue Per Student as Dependent
Variable, BAH Institutions

 

 

 

 

 

 

 

 

 

 

 

 

R2=.242 Unstandardizetj Standardized] t Sig. Collineari
N=167 Coefficient Coefficient Statistic
B Std. Error Betal Tolerance VIF
Constant) 3473.438 1442.419 2.408 .017
Religiousl -599.864 449.839 -.105 -1.334 .184 .812 1.231
Affiliation
Average -.576 .187 -.241 -3.089 .002 .823 1.216
Enrollment,
1990-2000
Minority -1058.472 1149.240 -.070 -.921 .359 .861 1.162
Percentage
Av
Institution 47844141 508.215 -.342 -3.511 .001 .528 1.892
Locatedin
South
Institution -516680 416.983 -.121 -1.239 .217 .526 1.901
Locatedin
Midwest
Institution -1107.772 571.123 -.170 -1.940 .054 .656 1.52
Locatedin
West
Coe 1326.853 597.465 .165 2.221 .02 .905 1.105

 

 

 

 

 

 

 

 

 

101

Table 32 (cont’d)

Age] 8.549 4.123] .162 2.073 .040 .8181 1.223
Admission 1289.002 425.8041 .234 3.027 .003 .841 1.1881
Competitiv
ness Ratin

Av 6.172E-041 .000 .154 1.908 .058 .773 1.294
Endowment
1990-1996

Tuition -4669.9141 1793.367 -.223 -2.604 .010 .687 1.457
Discount
Rate 1995

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 33 provides the results of the 2000 regression equation for the BAII
institutions with net tuition revenue per student as the dependent variable. The R-squared
was .153. Similar to 1995, a significantly negative relationship was evident between
financial health (as measured by net tuition revenue per student) and the tuition
discounting rate. For every 1 percentage point increase in the tuition discounting rate, net
tuition revenue per student drops by $41. Two other variables were also significantly
negatively related to net tuition revenue per student—“South” location and “Midwest”
location.

Table 33. Regression Coefficients, 2000, Net Tuition Revenue Per Student as Dependent
Variable, BAII Institutions

 

 

 

 

 

 

 

R2=.153 Unstandardizej Standardizeci t Sig. Collinearitj
N=167 Coefficien Coefficient Statistic
B Std. Error Betal Tolerance VIF
(Constant) 8912.857 1943.350 4.586 .000
Religiousl -684.395 601.097 -.094I -1 . 139 .257 .818 1.223
Affiliationl
Average -.377 .253 -.124! -1.490 .138I .801 1.2481
Enrolhnent,
1990-2000
Minority -1564800 1559.068 -.082 -1.004I .317 .839 1.192
Percentage
Av
Institution -1961.3481 663.183 -.296 -2.957 .0041 .5581 1.7941
Located in
South

 

 

 

 

 

 

 

 

 

 

102

Table 33 cont’d).
Institution -1385.601 542.670 -.256 -2.553 .012 .557 1.79
Locatedin
Midwest
Institution -903.288 763.444 -.109 -1.183 .239 .660 1.515
Locatediri
Westl
Coe -438.981 799.695 -.043 -.549 .5841 .909 1.10
Age 1.209E-02 5.573 .000 .002 .998 .805 1.242
Admissions 800.672 561.721 .114 1.425 .156 .869 1.150
Competitive

 

 

 

 

 

 

 

 

ness Ratin
Av? 7.943E-041 .000 .156 1.827 .070 .769 1.300
Endowmen
1990-1996
Tuition -4065.247 1946.190 -.185 -2.089 .038 .708 1.412
Discount
Rate 2000

 

 

 

 

 

 

 

 

 

 

 

Table 34 provides the results of the 1990 regression equation for the BAII
institutions with net tuition revenue as the dependent variable. The R-squared was .628.
In this case, the tuition discounting rate was not significantly related to net tuition
revenue. However, enrollment and endowment were significantly positively related to net
tuition revenue and Southern location and Western location were significantly negatively
related to net tuition revenue.

Table 34. Regression Coefficients, 1990, Net Tuition Revenue as Dependent Variable,
BAH Institutions

 

 

 

 

 

 

 

 

 

N=167 Unstandardizetj Standardized] t1 Sig.
R2=.628 Coefficient Coefficient
B Std. Error Betal
(Constant 510279.508 2000139241 .255 .799
Religious -412553.477 619420.160 -.036 -.666 .506
Affiliatiorl
Average 2932.972 257.239 .621 1 1.402 .000
Enrollment,
1990-2000
Minority -1847608.540 1566795347 -.062 -1.179 .240
Percentage
Av
Institutio -21 10936.517 697297.630 -.204 -3.027 .003
Located '
Sout 7

 

 

 

 

 

 

103

 

Table 34 (cont’d).
Institution -1084287.482 574784.373 -.128I -1.886 .061
Located in
Midwest
Institution -2861453.564 8072005081 -.221 -3.545 .001
Located in
West]
Coed 450857.931 823415.916 .028[ .548 .585
Age 461.360 5666.372 .004J .081 .935
Admissions 718074.088 591943.430 .066 1.213 .227
Competitive
ness Rating
Ava 1.573 .440 .198 3.577 .000
Endowment
1990-1996
Tuition -1582149.677 2539912427 -.036 -.623 .53
Discount
Rate 1990

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 35 provides the results of the regression equation for 1995 for the BAII
institutions with net tuition revenue as the dependent variable. The R-squared for the
model was .686. As with the previous model, the tuition discounting rate does not appear
to be Significantly related to net tuition revenue. Similar to the 1990 results, enrollment
was Significantly positively related to net tuition revenue and both Southern and Western

location were Significantly negatively related to net tuition revenue.

Table 35. Regression Coefficients, 1995, Net Tuition Revenue as Dependent Variable,
BAII Institutions

 

 

 

 

 

 

 

N=167 Unstandardizetj Standardizej 1: Sig.
R2=.686 Coefficient Coefficient
B Std. Error Betal

(Constant) -345938.97 8 2694031.669 -. 128 .8981

Religiou -708063.345 836124.170 -.043 -.847 .3981
Affiliation

Average 5 103.404! 347.367 .73 5 14.692 .000
Enrollment,
1990-2000

Minority -1 194273.063 2146008840 -.027 -.557 .579
Percentage

fl

 

 

 

 

 

 

 

104

Table 35 (cont’d).
Institutio -2869538.072 940155.773 -. 189 -3.052 .003
Located in
South
Institution -1444276810 767229.488 -.1 16 -1.882 .062
Located in
Midwest
Institution -2233449.200 1061932039 -.117 -2.103 .037
Located in
West
Coed 1001340662 1115744311 .043 .897 .371
Age -5274480 7698.232 -.034 -.685 .4941
Admissions 1051997.499 794040.317 .066 1.325 .187
Competitive
ness Rating
Avg .841 .602 .072 1.397 .1641
Endowment
1990-1996
Tuition 1488229851 3310182059 .025 .450 .654
Discount
Rag 1995

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 36 provides the results of the 2000 regression equation for the BAII
institutions with net tuition revenue as the dependent variable. The R-squared, .706,
represents a substantial increase from the models for previous years. Again, the tuition
discounting rate was not a Significant predictor of net tuition revenue. Enrollment,
however, was significantly positively related to net tuition revenue and both Southern
location and Midwestern location were significantly negatively related to net tuition
revenue.

Table 36. Regression Coefficients, 2000, Net Tuition Revenue as Dependent Variable,
BAII Institutions

 

 

 

 

 

 

N=167 Unstandardize Standardized t Sig.
R2=.706 Coefficient Coefficien
B Std. Error BeTal

(Constant) -2221863.006 3419292. 1 88 -.650 .517

Religious -419754490 1057619771 -.019 -.397 .692
Affiliation

Average 6733.120 445.370 .743 15.1 18 .000
Enrollment,
1990-2000

 

 

 

 

 

 

 

105

Table 36 (cont’d).

 

Minori
Percentage
Avg

-25493 94.342

2743153.849

-.045 -.929 .35

 

Institution
Located in
South

-3132239.895

1 166860.108'

-.158 -2.684I .008I

 

Institution
Located in
Midwesq

 

-2344823.916

954819.571

-. 145 -2.456 .015

 

Institutio
Located '

Wes

-1925540.691

1343266730

-.078 -l.433 .154

 

Coed

2209749895

1407050767

.072 1.570 .1181

 

Age

-12264. 165

9806.172

-.061 -1.251 .213

 

Admissions
Competitive
ness Rating

2068836925

988338.275

.099 2.093 .03 81

 

 

Av
Endowmen:
1990- 1 99

1.215 .765

.080 1.589 .114

 

Tuition
Discount
Rate 2000

 

 

-2226233.705 3424288372

 

 

-.034 -.650 .517

 

 

 

 

Finally, Table 37 provides a summary of all of the statistically Significant

regression coefficients for the 3 year longitudinal analysis for both the BAI and BAII

institutions.

Table 37. Summary of Statistically Significant Regression Coefficients. BAI and BAII

 

 

 

 

 

Institutions

BAI Institutions The change in Net Tuition The change in Net Tuition
Revenue Per Student Revenue associated with a 1
associated with a 1 percentage point increase in
percentage point increase in Tuition Discounting*:
Tuition Discounting*:

1990 $-143 $-118,573

1995 $-158 $-116,606

2000 $-3 86 $-397,841

 

 

 

 

106

 

Table 37 (cont’d).

 

BAII Institutions The change in Net Tuition The change in Net Tuition
Revenue Per Student Revenue associated with a 1
associated with a 1 percentage point increase in

percentage point increase in Tuition Discounting*:
Tuition Discounting*:

 

 

 

 

 

 

1990 Not Significant Not Significant
1995 -$47 Not Significant
2000 -$41 Not Significant

 

*Significant at a minimum of the .05 level

Regressions with Grouped Data

Table 38 provides a summary of the regression results for the BAI and BAII
institutions grouped. (Appendix F provides detailed statistical results for the regressions).
The rationale for this analysis was that based on the T-tests, it appeared that the lower
quality (selectivity) BAI institutions and the higher quality BAII institutions were
beginning to exhibit the same behaviors in terms of tuition discounting. Therefore, it
seemed logical to analyze regression results for the BAI and BAII institutions together
while recognizing that overall the two groups of institutions remain quite distinct. It is
interesting to note that for the grouped data there is a notable and significant negative
relationship between financial health (as measured by net tuition revenue and net tuition
revenue per student) and tuition discounting. It does appear that this relationship is
becoming more pronounced over time, with financial health declining by increasing

amounts over the 1990’s.

107

 

 

 

Table 38. Regression Results, BAI and BAH Institutions, Grouped*.

 

F

The change in Net Tuition
Revenue Per Student
associated with a 1
percentage point increase in
Tuition DiscountingRate

The change in Net Tuition
Revenue associated with a 1
percentage point increase in
Tuition Discounting Rate

 

 

 

 

 

 

1990 -$38 -$85,280
1995 -$61 -$84,164
2000 -$ 122 -$137,661

 

108

*Significant at a minimum of the .05 level.

 

Chapter Five: Discussion and Conclusions
This chapter begins with a summary and discussion of the findings of this study,
followed by a discussion of the implications of tuition discounting for stakeholders such
as administrators, trustees, and students (together with parents and counselors). I

conclude with ideas for future research.

Summary and Discussion of Findings

Finding #1: Tuition Discounting is on the Rise

Tuition discounting is on the rise at private, baccalaureate level institutions of
higher education. Between 1990 and 2000, tuition discounting among the BAI
institutions rose from 17.53% to 24.63%, with an average ll-year change of +7.1
percentage points. Among the BAH institutions, tuition discounting rose from 16.31% to
20.05%, with an average ll-year change of +7.02 percentage points. On average, both
types of institutions increased their levels of tuition discounting over the 11-year period.
Students at BAI and BAH institutions saw larger percentages of their tuitions “waived”

through unfunded institutional scholarships (discounts) over that period of time.

Finding #2: Tuition Discounting Does Vm Based on Institutional Type

There were Significant differences among the tuition discounting practices at the

different types of institutions. These significant differences are summarized in Table 39.

109

Table 39. Significant Mean Differences Among Institutional Types

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable BAI Tuition Discount Rate BAH Tuition Discount Rate
Comparisons Comparisons
Religious Affiliation Religious>Non-religious NA
Enrollment Small>Large NA
Location Midwest>South, West Midwest>West
Age 3rd quarti1e>others Youngest>Oldest
Admissions Moderate, Very Moderately
Competitiveness Rating competitive>Most competitive>Minimally or
competitive Non-cogetitive
Academic Reputation Score Lower>Higher Higher>Lower
Total Educational and Lower>Higher NA
General Expenditures
Endowment Lower>Higher Higher>Lower
Gender NA NA
Retention NA NA
Minority Enrollment NA Lowest percentage
minority>highest
percentage minority
F aculty-Student Ratio NA Higher>Lower

 

 

 

Some interesting differences are notable among the BAI institutions. First, the

institutions that appear to be discounting the most can generally be categorized as small

and less prestigious based on the following variables: admissions competitiveness,

academic reputation score, total expenditures, and enrollment. It may be that institutions

with higher levels of prestige may not need to discount as much to attract students.

Additionally, higher prestige institutions generally attract wealthier students who may not

need a discount in order to enroll. The more prestigious institutions generally have larger

endowments and do not need to rely on unfunded (simple) discounts—they can give

students aid funded by their endowment. This is supported by the finding that, in this

study, institutions with smaller endowments appear to have significantly higher tuition

discounting rates than do institutions with larger endowments.

110

 

There were also significant mean differences among the BAI institutions based on
religious affiliation. Overall, the religious affiliated institutions discounted more than the
non-religious institutions. This result may be related to a set of circumstances that has
affected religious-based instruction—particularly small religious-affiliated institutions.
Townsley (2002) indicates that “this set of micro colleges hangs on by a thread,” and
faces a great deal of competition (p. 210). Additionally, the financial support from the
churches on which these institutions have historically relied very heavily has declined
significantly. This set of circumstances may force them to rely more heavily on tuition
discounting than their non-religious based counterparts.

Among the BAI institutions there also appeared to be significant mean differences
based on geographic location with Midwest institutions discounting more than other
geographic areas. This result is likely due to the decline in the number of graduating high
school seniors in the Midwest. Between 1972 and 1997, parts of the Midwest
experienced declines in the number of high school graduates as high as 26% (Kodrzycki,
1999, p. 28). With fierce competition for students, Midwest institutions appear to have
discounted more than other geographic areas.

Finally, older institutions (Specifically those in the 3rd quartile—146 to 168 years)
discounted more than institutions in the other age groups. This finding seems to be
somewhat inconsistent with the prestige findings. One would assume that the younger,
less established institutions would discount more than the older institutions. This
expectation is not born out in this study.

There were also significant mean differences among the BAH institutions (see

Table 38). Although the BAII institutions generally would not be considered prestigious

111

 

 

(none had admissions competitiveness ratings higher that “moderately competitive”), the
institutions with higher admissions competitiveness ratings within this institutional
category discounted more than those with lower admissions competitiveness ratings. In
contrast to the BAI institutions, the more “prestigious” BAII institutions discounted more
than the less prestigious. Similarly, the more well endowed BAH institutions discounted
more than the smaller endowed BAII institutions. One explanation for higher discounting
among more prestigious BAII’S is that the upper tier institutions within the BAII category
may be attempting to compete with the lower tier of the BAI college category, and to do
so must offer Significant discounts. The minimally competitive and non-competitive
institutions may not feel as much pressure to compete with the BAI institutions and may
not feel inclined to discount on the basis of competition.

There were also significant mean differences in tuition discounting rates among
the BAH institutions based on geographic location. As with the BA] institutions, the
BAII institutions in the Midwest discounted more than those in the West, apparently for
the same reason—demographic Shifts related to the number of high school graduates.

Among the BAH institutions there were significant mean differences based on
minority enrollment. Institutions with lower percentages of minority students discounted
more than those with higher percentages of minority students. This result is difficult to
explain as one would expect that institutions committed to expanding diversity would be
doing so via discounting. A similar interesting finding is that BAH institutions with
higher faculty student ratios discounted more than those with lower faculty student ratios.
This finding generally means that institutions with larger average class Sizes discounted

more than institutions with smaller average class sizes.

112

Finding #3: Tuition Discounting Impacts Financial Health

Table 32 in the previous chapter summarizes the findings for the regression
equations, which measured the relationship between tuition discounting and institutional
financial health. Recall that tuition discounting can and Should actually serve to protect
net tuition revenue. That is, net tuition revenue should either increase or remain
unchanged when regressed against tuition discounting. For the BAI institutions, however,
tuition discounting appears to be significantly negatively related to financial health (with
financial health defined as net tuition revenue per student). Tuition discounting is
associated with declining net tuition revenue per student and this pattern has intensified
over time. For the 1990 equation, each 1 percentage point increase in tuition discounting
was associated with a $143 decrease in net tuition revenue per student. By 1995 the
decrease in net tuition revenue per students was $158, and by 2000 it was $386. With net
tuition revenue (total) as the independent (financial health) variable, a significantly
negative relationship also exists between tuition discounting and financial health with a
decrease in net tuition revenue of approximately $119,000, $177,000 and $398,000
related to each 1 percentage point increase in the tuition discounting rate for 1990, 1995,
and 2000 respectively.

The BAII institutions had Similar results. The decrease in net tuition revenue
associated with a 1 percentage point increase in tuition discounting was $47 in 1995 and
$41 in 1991. When net tuition revenue (total) is used as the independent variable,

however, the relationship between tuition discounting and financial health was not

significant.

113

Because the negative relationship exists, at least to some degree, between tuition
discounting and financial health, institutions need to carefully examine their tuition
discounting policies. There are several strategies to help institutions discount more

effectively, or to reduce their levels of tuition discounting.

Implications for Stakeholders

The results of this study indicate that for private, baccalaureate level institutions
of higher education, tuition discounting is on the rise, does vary based on institutional
type, and does impact financial health negatively. These conclusions provide evidence
that tuition discounting is a growing, dynamic practice that can have serious implications
for the financial health of institutions.

Of special interest is the apparent efforts of higher tier BAH colleges to place
themselves through tuition discounting into a higher prestige category. This trend may
mean that efforts to pursue prestige and status at these institutions may be stronger factors
in affecting policy than financial health. It may be that a more complex institutional
typology is needed, one with distinct markets for the top tier of BAI colleges, and a
second market for lower-tier BAIS and top tier BAIIS, and a third market for the least
competitive BAII colleges. Further, the consequences of tuition discounting may vary by
these distinct markets with the most financially vulnerable offering the highest tuition
discount rates.

These results suggest that academic administrators need to carefully analyze their
tuition discounting programs and be prepared to initiate strategic measures ranging from

minor adjustments to major policy changes. First, administrators must start by evaluating

114

the effectiveness of their tuition discounting programs by looking at net tuition revenue
(not gross tuition revenue) longitudinally. Too many institutions focus on gross tuition
revenue, which does not give an accurate picture of the real flow of tuition dollars into the
institution. For tuition discounting to be effective, net tuition revenue should increase
over time. Many institutions set enrollment goals (for example 700 incoming freshmen
students). However, these goals need to be set in conjunction with net tuition revenue
goals (for example 700 new students paying an average of $15,000 each).

If institutions find that their tuition discounting programs are not producing
increases in net tuition revenue, they must work diligently to increase the effectiveness of
their awarding strategy—that is, the determination of which students get discounts, and
how much they get. This strategy requires an extensive analysis of historical data to
determine apprOpriate award amounts (discounts) based on various student factors such
as financial need, test scores, ethnicity, etc. A careful analysis of previous awarding
practices and yields can illuminate the probability of a specific type of student enrolling at
the institution if awarded a Specific discount. In The Effect of Financial Aid Policies on
Admission and Enrollment, Scannell describes a method of creating yield tables (see
Table 40) that display historical data for the institution. The percentages in the body of
the table refer to the percentage of admitted students that actually enrolled at the
institution for a specific year. For example, the first percentage (34%) represents the
number of students in this particular need category ($6000-$7000) that had an ACT score
of less than 20, received no discount, yet still enrolled. The 60% in the bottom right cell
of the table means 60% of students with a 26 or 27 ACT and a $5000-$6000 discount

K

enrolled at the institution. Table 40 is an example of yield data based on quality (ACT

115

score), but Similar tables can be created based on gender, ethnicity, and a host of other
demographic characteristics. Institutions can use this data to help determine discount
amounts that have a high probability of influencing the student to enroll. These tables are
particularly helpful because they “give the institution the opportunity to react strategically
in the use of financial aid” (Scannell, 1992, p. 58).

Table 40. Yield Table-Admitted Applicants with Financial Need of $6000—$7000
(Hypothetical Data).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Discount—r
$0 $1- $1001- $2001— $3001- $4001- $5001-

ACT Score $1000 $2000 $3000 $4000 $5000 $6000

1
<20 34% 41% 42% 56% 61% 70% 77%
20-21 31% 41% 41% 55% 66% 61% 77%
22-23 30% 44% 42% 50% 58% 62% 79%
24-25 28% 29% 31% 41% 50% 51% 70%
26-27 29% 21% 29% 47% 49% 50% 60%
Etc.

 

In addition to increasing the effectiveness of tuition discounting, institutions can
reduce their reliance on, or use of, tuition discounting by incorporating some strategic
measures such as increasing their endowments, containing costs, improving marketing
and recruiting, and collaborating.

Ideally, institutions would like to have endowments large enough to support their
entire financial aid budgets. However, only a few institutions (such as Oberlin or
Macalester) are actually able to achieve this goal. “By not dipping into tuition to pay for
financial aid. . . [institutions] can escape what [some call] a slippery slope in higher

education: raising tuition in order to generate more revenue to support more financial aid”

(Strosnider, 1998, p. A37).

116

Although many of the institutions in this study had sizeable endowments (creating
large pools of funded scholarships), most had relatively low endowments. The average
endowment for the BAII institutions was about $500,000, which generates only about
$25,000 in scholarships annually. As institutions generate higher endowments, they will
be able to replace unfunded discounts with discounts funded by endowment earnings. It
is essential, therefore, that institutions work diligently to increase endowment levels. Too
many institutions have a shortage of donated assets, particularly small, private
institutions. According to Halstead (1991), “[t]here is no Shortage of prospective wealthy
donors and alumni in the country, so the blame must be in part of private institutions for
failing to fully tap this potential” (pp. 23, 26).

Several institutions have been very successful in building large endowments
dedicated to student financial aid. Washington and Lee University, for example, worked
diligently to build an endowment large enough so that virtually all discounts were funded.
Other institutions have undertaken an aggressive approach to building endowment dollars
specifically designated to student aid although many of them are larger research
universities such as Duke and Johns Hopkins (Strosnider, 1998, p. A37). In addition,
institutions can seek annual gifts and donations intended to support financial aid
programs.

Institutions can also work to contain costs. When costs are controlled, tuition
rates can also be controlled. When the tuition rate is under control, the level of tuition
discounting can be decreased or at least not increased. If an institution is unable to

control costs, tuition increases will be large, and so will the need for discounts.

117

Additionally, if costs are growing, institutions become even more reliant on tuition
revenue———making enrollment instability a serious financial issue.

Restructuring is a popular cost cutting measure at universities. Often, academic
programs can be redesigned or restructured. Institutions can also look for ways to lower
unit costs, to increase the productivity of individuals and departments. Although cost
cutting measures may be appropriate at many institutions, they must ensure that they do
not compromise quality.

According to Jenkins (1991), “institutions should begin to develop marketing
strategies. . .based on cost containment and educational value rather than manipulating
price and calling it financial aid if they are to successfully meet consumer demand and
public policy expectations” (p. 4). One way to accomplish this goal is to stress the
positive aspects of the college, or factors that differentiate the college such as quality,
high retention rates, successful programs, low faculty-student ratios, high graduation
rates, or co-op programs, for example. This approach is particularly important for
institutions trying to get away from competition based on price. Institutions need to focus
on the aspects that make it unique and convey those attributes to potential students. They
must market strategically in order to ensure that they do not lose market Share.

There is increasing conversation in the admissions arena regarding student “fit”—
specifically the fit between a student’s needs and desires and the offerings of a college or
university (Hooker-Haring, 1998, p. 32). There is much diversity in higher education
with respect of religious affiliation, gender affiliation, size, scope, and mission. Many
students are lured into enrolling at institutions that are not a good fit simply because of

the tuition discount offered. Parents and students are being encouraged to Shift their

118

focus from getting into a college to finding the best fit and staying there. Colleges can
assist by providing accurate information to students and by creating a profile of their
successful students and recruiting students that fit that profile. Many institutions (such as
Baylor University) have developed models based on current student demographics that
predict the probability that a specific type of potential student will enroll and be
successful at the university. This approach improves retention and minimizes recruiting
and financial aid costs (Gose, 1999, p. A49).

Institutions also need to explore the possibility of expanding programs to non-
traditional markets, for example, adult learners. This effort might be achieved by offering
night or weekend programs. This approach is particularly strategic because it uses
unoccupied facilities (Lapovsky & Loomis-Hubbell, 2001, p. 29). If non-traditional
students can be successfully recruited they could fill empty seats and potentially “generate
sufficient tuition income without resorting to a new round of discounting” (Discounting
and its Discontents, 1994, p. 36).

A final strategy that institutions can explore is that of collaboration. Some experts
believe that for many small colleges and universities, “collaboration is key to survival.”
Under this scenario, institutions that are geographically close, “overcome their natural
impulse to complete, and work together” (Van der Worf, 1999, p. A33). Essentially, the
colleges create a consortium in which students can take courses at any of the member
institutions. This widely expands the number of programs and degrees available to
students at each institution, which enhances enrollment and retention. In central Kansas,

six institutions—Bethany, Bethel, McPherson, Sterling, Tabor, and Kansas Wesleyan—

119

have had great success in creating a consortium knows as the “Associated Colleges of
Central Kansas.”

In addition to college administrators, trustees play an important role in tuition
discounting as well. The trustee’s role in tuition discounting is largely centered around
tuition price. As price-setters, trustees determine half of the institution’s revenue
picture—the other half being enrollment. If the price is too high, enrollment lags and
must be bolstered by discounts thus reducing net tuition revenue. At a lower price,
enrollment will be higher. Trustees must walk that fine line between a tuition rate that
turns students away and a price that is so low that tuition revenue falls short of
institutional needs.

Some institutions have reduced tuition rates in an effort to enroll more students.
Westminster College in Fulton, Missouri, cut tuition by 20% in 2003. Muskingum and
Eureka Colleges have undertaken similar tuition reductions. In a sense, cutting price is
essentially tuition discounting—just in an “across the board” fashion in which every
student gets the same discount. Some institutions may find that a tuition cut with a
reduction (or elimination) of tuition discounting may be preferable to having an
artificially high tuition rate, coupled with deep discounts.

My advice to students, parents, and high school counselors is quite simple: be
aware of the practice of tuition discounting. Students need to understand that tuition
discounting has become quite prevalent among many of the small, private, baccalaureate
level institutions. Students and their parents will want to compare the net prices of the
colleges that they are considering. If the tuition rate at College X is $30,000 and the

“scholarship” (discount) being offered is $10,000, then the net price is $20,000. If the

120

tuition rate at College Y is $20,000 and the discount is $5,000, then the net price is
$15,000. While College Y is ultimately less expensive, many families are more
impressed with College X’s $10,000 “reward,” and may select College X on those
grounds. That said, students Should avoid selecting a college based simply on price, and
focus on “fit.”

Students need to also consider attempting to negotiate their awards. While this
may seem distasteful to many, the majority of institutions do negotiate. In many cases,
the student’s high school counselor may be willing to negotiate on the student’s behalf.
Either way, students need to be prepared to document award amounts from other

institutions as a basis for their award appeal.

Future Research

Future research related to tuition discounting is vital in order to help improve the
financial health and maintain the presence of the private baccalaureate sector of higher
education in America. I would suggest the following four directions for future research.

First, an in—depth analysis of the small private institutions that have closed over
the past 20 years might provide important insights. Among these are Mt. Senario College
in Wisconsin, Aquinas Colleges in Massachusetts, Westmar University in Iowa, and
Notre Dame College in Missouri. It would be interesting to study these institutions to
determine whether tuition discounting was a factor in their closing. There is some
speculation that pricing and discounting problems have led to, or at least significantly

contributed to, the demise of several small institutions.

121

Second, it would be helpful to study tuition discounting under the broader scope
of the scholarship allowance discount as opposed to the Simple tuition discount used in
this study. Although the data needed to calculate the scholarship allowance are not
available via IPEDS, a smaller study of institutions could be undertaken to analyze this
alternative measure of tuition discounting. The results could be Significantly different if
this alternative measure were used.

Third, while this study focused primarily on the financial impact of tuition
discounting, a study of the ethics of tuition discounting is also warranted. Researchers
and stakeholders need to be addressing whether it is appropriate for students and
university officials to be haggling over the price of a university education the way one
would for a used car. IS the process fair, since not all students know about discounts, and
may not be savvy in negotiating an award amount?

Finally, it would be interesting to determine whether tuition discounting is
actually serving to expand diversity and/or allow more needy students to enroll. If not,
researchers must start a dialogue about whether the practice makes sense. If tuition
discounting does not improve financial health, expand diversity, or allow more needy
students to enroll, one must wonder about the usefulness of this practice in small colleges

and universities.

122

 

Appendix A: Variable Calculation Grids

 

1990-1996 IPEDS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Current Funds Revenue Excel IPEDS IPEDS Calculated as

by Source Col Row Column

Tuition and Fees assessed to C A01 3

students

Federal grants, contracts D A06 3

State grants, contracts E A07 3

Local grants, contracts F A08 3

Private gifts, grants, G A09 3

contracts

Sales/Services of H A1 1 3

Educational Activities

Aux Enterprises 1 A12 3

Other Sources-Total J A14 3

Independent Operations K A15 3

Current Funds

Expenditures by Function

Total Educational General L B 1 2 3

Expenditures Transfers

(excluding auxiliary)

Aux Enterprises M B l 3 3

Scholarships and

Fellowships by Source

Unrestricted Institutional N E06 1

Restricted Institutional O E06 2

Calculated Variables

Tuition Discount Rate R N/C

Operating Income Ratio S (C-N-O-
+D+E+F+H+I-
M+J+K)/(L-N-
0)

Enrollment T

Net Tuition Revenue U C-N

Net Tuition Revenue Per V U/T

Student

Expenditures Per Student W (L+M)/T

g

 

 

 

 

 

 

123

 

 

1997 IPEDS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Private, N on-Profit, Revenue and Excel IPEDS IPEDS Calculated as:
Investment Return and Student Aid Col Row Column
Tuition and Fees, assessed to students C A01 3
Federal Grants and Contracts D A06 3
State Grants and Contracts E A07 3
Local Grants and Contracts F A08 3
Private Gifts, Grants, Contr, Cont from Affil G A09+A10 3
Sales/Services of Educational Activities H A12 3
Sales/Services of Auxiliary Enterprises (net of 1 A13 3
allowances to T and F)
Revenue from hospital, indep ops, other source J A15+A14+ 3
A16
Student Aid: Pell Grants K AA01 NA
Student Aid: Other Federal Grants L AA02 NA
Student Aid: State Grants M AA03 NA
Student Aid: Local Grants N AA04 NA
Institutional Grants, Funded O AAOS NA
Institutional Grants, Unfunded P AA06 1+2+3
Student Aid: Portion of Total Stu Aid applied Q AA08 1
to tuition and fees
Student Aid: Portion of Total Stud Aid applied R AA09 1
to Aux Enterprises
Private, Non-Profit, Expenses by Function
Auxiliary Enterprises S B07 1
Hospital Services and Independent Operation T B10 and 1
B9
Total Expenses U B 12 1 includes aux
Total Expenses-Depreciation V NA 5
Calculated Variables
Tuition Discount Rate X P/(C+Q)
Operating Income Ratio Y (C+Q-P-
O+D+E+F+I+
R-S+J)/(U-S-
P-O)
Enrollment Z
Net Tuition Revenue Per Student AA C/Z
Expenditures Per Student AB U/Z

 

 

124

 

 

 

 

 

1998 IPEDS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Private, Non-Profit, Revenue and Excel IPEDS IPEDS Calculated
Investment Return Col Row Colum as:
n

Total Tuition and Fees, Assessed to C A01 3

students

Total Federal Grants and Contracts D A06 3

Total State Grants and Contracts E A07 3

Total Local Grants and Contracts F A08 3

Total Private Gifts, Grants, Contracts G A09 3

Total Contributions from Affiliated H A10 3

Entities

Total Sales/Services of Educational 1 A12 3

Activities

Total Sales/Services of Auxiliary J A13 3

Enterprises

Total Independent Operations Revenue K A15 3

Total Other Revenue L A16 3

Total Pell Grants M AA01 NA

Total Other Federal Grants N AA02 NA

Total State Grants 0 AA03 NA

Total Local Grants P AA04 NA

Total Institutional Grants, Funded Q AA05 NA

Total Institutional Grants, Unfunded R AA06 1+2+3

Total Allowances Applied to Tuition and S AA09 NA

Fees

Total Allowances Applied to Aux T AA10’ NA

Enterprises

Private, Non—Profit, Expenses by

Function

Auxiliary Enterprises-Total U B07 1

Independent Operation Expenditures V B 10 1

Total Expenses-Total (includes auxiliary) W 812 1

Total Expenses-Depreciation X B 12 5

Calculated Variables

Tuition Discount Rate AB R/(C+S)

Operating Income Ratio AC (C+S-R—
Q+D+E+F+
I+K+L+J+T
-U)/(W-U-
R-Q)

Enrollment AD

Net Tuition Revenue Per Student AE C/AD

Expenditures Per Student AF W/AD

 

 

 

 

 

125

 

 

1999 IPEDS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Private, Non-Profit, Revenue and Excel IPEDS IPEDS Calculated as:

Investment Return Col Row Column

Total Tuition and Fees C A01 3

Total Federal Grants and Contracts D A06 3

Total State Grants and Contracts E A07 3

Total Local Grants and Contracts F A08 3

Total Private Gifts, Grants, Contracts G A09 3

Total Contributions from Affiliated H A10 3

Entities

Total Sales/Services of Educational 1 A12 3

Activities

Total Sales/Services of Auxiliary J A13 3

Enterprises

Total Independent Operations Revenue K A15 3

Total Other Revenue L A16 3

Total Pell Grants M AA01 NA

Total Other Federal Grants N AA02 NA

Total State Grants 0 AA03 NA

Total Local Grants P AAO4 NA

Total Institutional Grants, Funded Q AA05 NA

Total Institutional Grants, Unfunded R AA06 1+2+3

Total Allowances Applied to Tuition S AA08 NA

and Fees

Total Allowances Applied to Aux T AA09 NA

Enterprises

Private, Non-Profit, Expenses

Auxiliary Enterprises-Total U B07 1

Independent Operation Expenditures V B 10 1

Total Expenses-Total (includes W B 12 1

auxiliary)

Total Expenses-Depreciation X B 12 5

Calculated Variables

Tuition Discount Rate AB R/(C+S)

Operating Income Ratio AC (C+S-R-Q+ D+E+
F+I+K+ L+J+T-
U) / (W-U-R-Q)

Enrollment AD

Net Tuition Revenue Per Student AE C/AD

Expenditures Per Student AF W/AD

 

 

 

 

 

126

 

 

 

2000 IPEDS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Expenditures Per Student

 

 

 

Private, Non-Profit, Revenue and Excel IPEDS Row Calculated as:
Investment Return Col
Total Tuition and Fees C D01
Total Federal Grants and Contracts D D05
Total State Grants and Contracts E D06
Total Local Grants and Contracts F D07
Total Private Gifts, Grants, Contracts G D08
Total Contributions from Affiliated H D09
Entities
Total Sales/Services of Educational 1 D1 1
Activities
Total Sales/Services of Auxiliary D12
Enterprises
Total Independent Operations Revenue K D14
Total Other Revenue L D15
Student Grants
Total Pell Grants M C01
Total Other Federal Grants N C02
Total State Grants 0 C03
Total Local Grants P C04
Total Institutional Grants, Funded Q C05
Total Institutional Grants, Unfunded R C06
Total Allowances Applied to Tuition and S C08
Fees
Total Allowances Applied to Aux T C09
Enterprises
Private, Non-Profit, Expenses by
Function
- U E07
Auxiliary Enterprises-Total
Independent Operation Expenditures V E10
excluding auxiliary)
TOtal Expenses-Total W E12
Ltd Expenses-Depreciation X E15
Calculated Variables
Eon Discount Rate AB R/(C+S)
Operating Income Ratio AC (C+S-R-
Q+D+E+F+I+K+L+J+T-
U)/(W-U-R-Q)
Enrollment AD
Net Tuition Revenue Per Student AE C/AD
AF W/AD

 

127

 

Appendix B: Variables

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Name Variable values/calculations: Data Source
Age of Institution Peterson’s
Religious Affiliation 0=No 1=YES Peterson’s
Geographic Location N (North) =PA, RI, CT, NY, IPEDS
DC, NH, MA, MD, VT, NJ, ME,
DE
S (South) =GA, LA, VA, FL,
AL, NC, SC, WV, KY, TN, MS,
AR
M (Midwest)=MI, NE, IL, IA,
OH, MO, MN, KS, SD, ND, WI,
IN
W (West) =AK, HI, CA, CO,
TX, NV, AZ, NM, ID, OR, WA,
MT, OK, UT, WY
Male Affiliated 0=NO Peterson’s
1=YES
Female Affiliated 0=NO Peterson’s
1=YES
Co-ed 0=NO Peterson’s
1=YES
Enrollment Total Undergraduate Enrollment; IPEDS
Calculated each year between
1990 and 2000
wage Enrollment Average enrollment, 1990-2000
Percentage of Minority Enrollment/Total IPEDS
Students Minority Enrollment; Calculated each year
between 1990 and 2000
Average Percentage Average Minority Student IPEDS
0f Student Minority, Enrollment between 1990 and
@4000 2000
Endowment Value Endowment Value; Calculated IPEDS
each year between 1990 and
1996
Average endowment, Average Endowment between IPEDS
_19LO-1996 1990 and 1996
Admissions 1=noncompetitive Peterson’s
competitiveness 2=minimally difficult
@g 3=moderately difficult

 

128

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EXpenditures Per
1 Student

 

1990 and 2000

 

4=very difficult
5=most difficult
Percentage of Peterson’s
freshman returning
for 2nd year
Total Educational and Total Educational and General IPEDS
General Expenditures; Calculated each
Expenditures year between 1990 and 2000
Average Total Average Total Educational and IPEDS
Educational and General Exp., 1990-2000
General Exp., 1990-
2000
Academic Reputation Scale of 1-5, 5 is highest US. News and
Score World Report
Student-to- Number of students per faculty IPEDS
undergraduate faculty member; Calculated each year
ratio between 1990 and 2000
Average student-to- IPEDS
faculty ratio, 1990-
1 999
Tuition Discounting Unfunded institutional student IPEDS
Rate aid/ gross tuition and fees;
Calculated each year between
1990 and 2000
Change in tuition Tuition discount rate in 2000 IPEDS
discounting rate minus tuition discount rate in
@2000 1990
Average tuition Mean of the tuition discounting IPEDS
discounting rate, rates, 1990-2000
@2000
Operating Income Calculated each year between IPEDS
Rxatio 1990 and 2000
Net Tuition Revenue Calculated each year between
Per Student 1990 and 2000
Calculated each year between

 

129

 

Appendix C: Descriptive Statistics

 

Descriptive Statistics, BAI Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Minimum Maximum Mean Standard Dev
_Age 35 258 141.28 44.104
Religious Affiliation 0 1 0.48 0.502
Coed O 1 0.85 0.359
Institution Located in North 0 1 0.43 0.496
Institution Located in Midwest 0 l 0.28 0.453
Institution Located in West 0 1 0.12 0.322
Institution Located in South 0 1 0.18 0.382
Average Enrollment, 1990-2000 279.9091 3414.636 1561.47 665.856
Admissions Competitiveness Rating 3 5 3.63 0.673
Academic Reputation Score 2 4.8 3.07 0.740
Faculty Student Ratio Avg 8.532569 24.78351 13.75 2.968
% students returning for 2nd year 40 98 85.38 8.182
Avg. Total EG Expenditures, 1990-2000 6702900 98135063 3610068085 19094119024
Avg Endowment, 1990-1996 51402.14 22284610 375000969 3847511884
Simple Tuition Discount 90 0 0.51 0.22 0.078
Skye Tuition Discount 91 0.09 0.55 0.24 0.075
Simple Tuition Discount 92 0.1 0.57 0.26 0.078
Simple Tuition Discount 93 0.08 0.57 0.29 0.085
Simple Tuition Discount 94 0.11 0.59 0.30 0.092
Simple Tuition Discount 95 0.12 0.65 0.31 0.094
Simple Tuition Discount 96 0.1 0.57 0.32 0.096
Simple Tuition Discount 97 0 0.73 0.34 0.108
Simple Tuition Discount 98 0 0.63 0.32 0.129
Simple Tuition Discount 99 0.17 0.62 0.35 0.098
Simple Tuition Discount 00 0.09 0.65 0.35 0.100
Operating Income Ratio 90 0.26 1 0.75 0.134
Opgating Income Ratio 91 0.33 1.02 0.75 0.130
Operflng Income Ratio 92 0.3 0.99 0.74 0.131
Operating Income Ratio 93 0.3 1.02 0.74 0.135
Operating Income Ratio 94 0.3 1.04 0.74 0.131
Operating Income Ratio 95 0.31 1.02 0.75 0.130
Operatinglncome Ratio 96 0.33 1.19 0.75 0.143
Operating Income Ratio 97 0.346458 3.55418 1.00 0.373
Operating Income Ratio 98 0.27 3.19 1.02 0.379
Operating Income Ratio 99 0.17 3 1.02 0.377
Operating Income Ratio 00 0.12 2.17 1.02 0.369
Net Tuition Revenue Per Student 90 3456.398 27383.27 9297.14 3199.659
Net Tuition Revenue Per Student 91 3527.735 32606.24 9948.27 3739.055
Net Tuition Revenue Per Student 92 3542.881 19384.65 10259.31 3341.726
Net Tuition Revenue Per Student 93 3608.78 20293.43 10516.34 3421.111
Net Tuition Revenue Per Student 94 4065.98 25990.32 11115.20 4054.576

 

 

 

 

 

130

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Net Tuition Revenue Per Student 95 4250.082 21882.72 11324.82 3715.902
Net Tuition Revenue Per Student 96 4958.202 21760.13 11583.73 3840.363
Net Tuition Revenue Per Student 97 4399.644 24422.79 11861.36 4416.175
Net Tuition Revenue Per Student 98 4426.381 25241.34 12285.46 4683.246
Net Tuition Revenue Per Student 99 3907.13 28067.99 12795.51 4741.338
Net Tuition Revenue Per Student 00 3895.026 59150.8 13529.12 6509.728
Expenditures Per Student 90 8890.818 45402.68 20680.08 6929.718
Expenditures Per Student 91 9597.474 47921.91 22049.69 7006.201
Expenditures Per Student 92 9864.046 44814.01 23646.38 7107.710
Expenditures Per Student 93 10759.3 40435.9 24954.45 7121.295
Expenditures Per Student 94 1 1535.53 49826.25 26229.62 7929.817
Expenditures Per Student 95 12403.37 49] 18.45 27047.58 7703.432
Expenditures Per Student 96 12940.03 52687.37 28651.57 8255.830
Expenditures Per Student 97 10244.31 48928.82 24889.90 8182.501
Expenditures Per Student 98 10829.97 53194.15 26061.95 8680.208
Expenditures Per Student 99 11616.13 54509.55 27462.61 9102.226
Expenditures Per Student 00 12911.61 83184.58 29663.82 11204365

 

 

 

 

 

131

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Descriptive Statistics, BAII Institutions
Standard
Minimum Maximum Mean Dev
_Age 27 213 103.54 39.818
Reliflis Affiliation O 1 0.84 0.366
Coed 0 l 0.91 0.287
Institution Located in North 0 1 0.28 0.451
Institution Located in Midwest 0 1 0.40 0.490
Institution Located in West 0 l 0.13 0.333
Institution Located in South 0 1 0.20 0.399
Average Enrolhnent, 1990-2000 347 6449.091 1366.51 877.491
Admissions Competitiveness Rating 1 3 2.87 0.383
Academic Reputation Score 2 3.9 2.87 0.394
Faculty Student Ratio Average 12.19367 125.2002 22.79 11.365
% students returning for 2nd year 44 94 71.68 10.064
152130796

Avg. Total EG Expenditures, 1990-2000 3237693 82204704 5 9240062131
Avg Endowment, 1990-1996 0 2743288 486524.10 519199.990
Simple Tuition Discount 90 0 0.46 0.19 0.093
Simple Tuition Discount 91 0 0.45 0.20 0.096
Simple Tuition Discount 92 0 0.47 0.21 0.093
Simple Tuition Discount 93 0 0.46 0.22 0.099
Simple Tuition Discount 94 0 0.47 0.23 0.101
Simple Tuition Discount 95 0 0.47 0.24 0.100
Simple Tuition Discount 96 0 0.49 0.25 0.109
Simple Tuition Discount 97 0 0.85 0.26 0.138
Simple Tuition Discount 98 0 0.66 0.27 0.138
Simple Tuition Discount 99 0 0.69 0.30 0.134
Simple Tuition Discount 00 0 0.56 0.29 0.121
Operating Income Ratio 90 0.46 1.58 0.81 0.148
Operating Income Ratio 91 0.39 1.35 0.82 0.149
Operating Income Ratio 92 0.29 1.22 0.83 0.132
Operating Income Ratio 93 0.45 1.18 0.84 0.119
Operating Income Ratio 94 0.44 1.53 0.85 0.138
Operating Income Ratio 95 0.45 1.37 0.85 0.129
Operating Income Ratio 96 0.52 1.46 0.85 0.137
Operating Income Ratio 97 0.543815 2.042066 1.10 0.253
Operating Income Ratio 98 0.34 2.52 1.10 0.327
Operatipg Income Ratio 99 0.43 2.28 1.11 0.310
Operatflg Income Ratio 00 0.36 2.23 1.13 0.306
Net Tuition Revenue Per Student 90 1274.56 1 1816.2 4656.56 1646.541
Net Tuition Revenue Per Student 91 1439.962 12465.36 5058.91 1809.739
Net Tuition Revenue Per Student 92 1372.128 13543.52 5357.35 1906.329
Net Tuition Revenue Per Student 93 1599.247 15907.31 5668.94 1972.761
Net Tuition Revenue Per Student 94 1775.039 17559.52 6012.52 1955.863

Lift Tuition Revenue Per Student 95 1715.743 18186.28 6348.57 2088.044

 

132

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Net Tuition Revenue Per Student 96 1928.945 14461.57 6693.84 2022.360
Net Tuition Revenue Per Student 97 1901.629 17740.04 6664.55 2250.535
Net Tuition Revenue Per Student 98 1819.277 18387.24 6934.21 2332.883
Net Tuition Revenue Per Student 99 1734.101 20434.71 7261.24 2403.270
Net Tuition Revenue Per Student 00 2275.568 20940 7686.44 2649.860
Expenditures Per Student 90 3210.174 19607.34 10502.22 3425.626
Expenditures Per Student 91 3168.666 23759.95 11 190.21 3775.196
Expenditures Per Student 92 3273.94 22556.94 11929.77 4007.951
Expenditures Per Student 93 3417.469 24543.46 12428.64 4143.370
Expenditures Per Student 94 3163.472 25214.33 13015.00 4282.813
Expenditures Per Student 95 3649.21 26353.74 13791.31 4313.046
Expenditures Per Student 96 3313.675 26559.9 14591.66 4389.071
Expenditures Per Student 97 2828.965 22600.1 12209.66 3557.874
Expenditures Per Student 98 2678.044 23327.08 12752.51 3720.089
Expenditures Per Student 99 2639.333 25633.92 13483.83 3886.855
Expenditures Per Student 00 3441.587 33769.85 14300.10 4416.749

 

 

 

 

 

133

 

Appendix D: List of Institutions by State

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Institution Name State Carnegie
Code
FAULKNER UNIVERSITY AL BAH
GRAND CANYON UNIVERSITY AZ BAH
HENDRIX COLLEGE AR BAI
JOHN BROWN UNIVERSITY AR BAII
UNIVERSITY OF THE OZARKS AR BAH
WILLIAMS BAPTIST COLLEGE AR BAH
CONCORDIA UNIVERSITY CA BAH
THE MASTER'S COLLEGE AND SEMINARY CA BAII
MH..LS COLLEGE CA BAI
OCCIDENTAL COLLEGE CA BAI
PITZER COLLEGE CA BAI
POMONA COLLEGE CA BAI
SCRIPPS COLLEGE CA BAI
SIMPSON COLLEGE CA BAH
WIHTTIER COLLEGE CA BAI
COLORADO COLLEGE CO BAI
CONNECTICUT COLLEGE CT BAI
TRINITY COLLEGE CT BAI
WESLEYAN UNIVERSITY CT BAI
ECKERD COLLEGE FL BAI
FLORIDA SOUTHERN COLLEGE FL BAH
PALM BEACH ATLANTIC COLLEGE-WEST FL BAH
PALM BEACH
AGNES SCOTT COLLEGE GA BAI
MOREHOUSE COLLEGE GA BAI
SPELMAN COLLEGE GA BAI
ALBERTSON COLLEGE OF IDAHO ID BAH
AUGUSTANA COLLEGE IL BAI
BARAT COLLEGE IL BAH
EUREKA COLLEGE IL BAH
GREENVILLE COLLEGE IL BAH
LAKE FOREST COLLEGE IL BAI
MCKENDREE COLLEGE IL BAH
MILLIKIN UNIVERSITY IL BAH
MONMOUTH COLLEGE IL BAI
NORTH PARK UNIVERSITY IL BAII
QUINCY UNIVERSITY IL BAH
ANDERSON UNIVERSITY IN BAH
BETHEL COLLEGE IN BAH

 

 

 

134

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

FRANKLIN COLLEGE OF INDIANA IN BAI
GOSHEN COLLEGE IN BAI
GRACE COLLEGE AND THEOLOGICAL IN BAII
SEMINARY

HANOVER COLLEGE IN BAI
MANCHESTER COLLEGE 1N BAH
MARIAN COLLEGE IN BAH
SAINT J OSEPHS COLLEGE IN BAH
SAN T MARY-OF-THE-WOODS COLLEGE IN BAII
SAINT MARY'S COLLEGE IN BAII
WABASH COLLEGE IN BAI
BRIAR CLIFF UNIVERSITY IA BAH
COE COLLEGE IA BAI
CORNELL COLLEGE IA BAI
DORDT COLLEGE IA BAH
IOWA WESLEYAN COLLEGE IA BAH
LORAS COLLEGE IA BAH
LUTHER COLLEGE LA BAI
MORNINGSIDE COLLEGE IA BAII
MOUNT MERCY COLLEGE IA BAH
MOUNT ST CLARE COLLEGE IA BAH
NORTHWESTERN COLLEGE IA BAH
SIMPSON COLLEGE IA BAH
WARTBURG COLLEGE IA BAI
WILLIAM PENN UNIVERSITY IA BAII
BENEDICTINE COLLEGE KS BAH
BETHAN Y COLLEGE KS BAH
NEWMAN UNIVERSITY KS BAH
OTTAWA UNIVERSITY KS BAH
SAINT MARY COLLEGE KS BAH
TABOR COLLEGE KS BAH
BRESCIA UNIVERSITY KY BAH
CAMPBELLSVILLE UNIVERSITY KY BAH
GEORGETOWN COLLEGE KY BAI
KENTUCKY WESLEYAN COLLEGE KY BAH
KENTUCKY CHRISTIAN COLLEGE KY BAH
PIKEVILLE COLLEGE KY BAII
TRANSYLVANIA UNIVERSITY KY BAI
BATES COLLEGE ME BAI
BOWDOIN COLLEGE ME BAI
COLBY COLLEGE ME BAI
UNITY COLLEGE ME BAH
ST JOHN'S COLLEGE MD BAI
WESTERN MARYLAND COLLEGE MD BAI

 

135

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AMHERST COLLEGE MA BAI
GORDON COLLEGE MA BAI
HAMPSI-HRE COLLEGE MA BAI
COLLEGE OF THE HOLY CROSS MA BAI
LASELL COLLEGE MA BAH
MERRIMACK COLLEGE MA BAH
MOUNT HOLYOKE COLLEGE MA BAI
MOUNT HDA COLLEGE MA BAH
COLLEGE OF OUR LADY OF THE ELMS MA BAH
REGIS COLLEGE MA BAH
SIMONS ROCK COLLEGE OF BARD MA BAI
SMITH COLLEGE MA BAI
WELLESLEY COLLEGE MA BAI
WHEATON COLLEGE MA BAI
WILLIAMS COLLEGE MA BAI
ADRIAN COLLEGE MI BAH
ALBION COLLEGE MI BAI
HOPE COLLEGE MI BAI
SIENA HEIGHTS UNIVERSITY MI BAH
SPRING ARBOR UNIVERSITY MI BAH
WILLLAM TYNDALE COLLEGE MI BAH
AUGSBURG COLLEGE W BAII
BETHEL COLLEGE MN BAH
CARLETON COLLEGE MN BAI
CONCORDIA COLLEGE AT MOORHEAD MN BAI
GUSTAVUS ADOLPHUS COLLEGE MN BAI
HAMLINE UNIVERSITY MN BAI
MACALESTER COLLEGE MN BAI
NORTHWESTERN COLLEGE MN BAH
COLLEGE OF SAINT BENEDICT MN BAI
SAINT JOHNS UNIVERSITY MN BAI
SAINT OLAF COLLEGE MN BAI
CROWN COLLEGE MN BAH
MILLSAPS COLLEGE MS BAI
COLUMBIA COLLEGE MO BAH
MISSOURI BAPTIST COLLEGE MO BAH
STEPHENS COLLEGE MO BAH
WESTMINSTER COLLEGE MO BAI
WILLIAM J EWELL COLLEGE MO BAI
WILLIAM WOODS UNIVERSITY MO BAH
CARROLL COLLEGE MT BAII
UNIVERSITY OF GREAT FALLS MT BAH
ROCKY MOUNTAIN COLLEGE MT BAII
CONCORDIA UNIVERSITY NE BAH

 

136

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

HASTINGS COLLEGE NE BAI
NEBRASKA WESLEYAN UNIVERSITY NE BAI
COLLEGE OF SAINT MARY NE BAH
COLBY-SAWYER COLLEGE NH BAH
NEW ENGLAND COLLEGE NH BAH
SAH\IT AN SELM COLLEGE NH BAII
CALDWELL COLLEGE NJ BAH
CENTENARY COLLEGE NJ BAH
DREW UNIVERSITY NJ BAI
FELICIAN COLLEGE NJ BAH
COLLEGE OF SAINT ELIZABETH NJ BAH
BARNARD COLLEGE NY BAI
COLGATE UNIVERSITY NY BAI
CONCORDIA COLLEGE NY BAH
DAEMEN COLLEGE NY BAH
HAMILTON COLLEGE NY BAI
HARTWICK COLLEGE NY BAI
I-HLBERT COLLEGE NY BAH
HOBART WILLIAM SMITH COLLEGES NY BAI
HOUGHTON COLLEGE NY BAI
LE MOYNE COLLEGE NY BAH
MANHATTANVILLE COLLEGE NY BAI
MARYMOUNT COLLEGE NY BAH
MARYMOUNT MANHATTAN COLLEGE NY BAH
MEDAILLE COLLEGE NY BAH
COLLEGE OF MOUNT SAINT VINCENT NY BAII
NYACK COLLEGE NY BAII
ROBERTS WESLEYAN COLLEGE NY BAH
ST FRANCIS COLLEGE NY BAII
ST LAWRENCE UNIVERSITY NY BAI
SAINT THOMAS AQUINAS COLLEGE NY BAH
SARAH LAWRENCE COLLEGE NY BAI
SIENA COLLEGE NY BAI
SKIDMORE COLLEGE NY BAI
SAINT J OSEPHS COLLEGE-MAIN CAMPUS NY BAII
TOURO COLLEGE NY BAII
UNION COLLEGE NY BAI
UTICA COLLEGE OF SYRACUSE UNIVERSITY NY BAH
WELLS COLLEGE NY BAI
BELMONT ABBEY COLLEGE NC BAH
DAVIDSON COLLEGE NC BAI
GREENSBORO COLLEGE NC BAII
METHODIST COLLEGE NC BAH
MONTREAT COLLEGE NC BAH

 

 

 

137

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SAINT AUGUSTINES COLLEGE NC BAH
ST ANDREWS PRESBYTERIAN COLLEGE NC BAI
ANTIOCH COLLEGE OH BAI
CEDARVILLE UNIVERSITY OH BAH
DEFIANCE COLLEGE OH BAH
DENISON UNIVERSITY OH BAI
THE UNIVERSITY OF FINDLAY OH BAH
HEIDELBERG COLLEGE OH BAH
HIRAM COLLEGE OH BAI
KENYON COLLEGE OH BAI
LOURDES COLLEGE OH BAH
MARIETTA COLLEGE OH BAH
MOUNT UNION COLLEGE OH BAH
MUSKINGUM COLLEGE OH BAH
NOTRE DAME COLLEGE OF OHIO OH BAII
OHIO DOMINICAN COLLEGE OH BAH
OIHO NORTHERN UNIVERSITY OH BAH
OI~HO WESLEYAN UNIVERSITY OH BAI
OTTERBEIN COLLEGE OH BAH
UNIVERSITY OF RIO GRANDE OH BAH
URSULINE COLLEGE OH BAH
WITTENBERG UNIVERSITY OH BAI
COLLEGE OF WOOSTER OH BAI
OKLAHOMA WESLEYAN UNIVERSITY OK BAII
CONCORDIA UNIVERSITY OR BAH
GEORGE FOX UNIVERSITY OR BAH
LEWIS & CLARK COLLEGE OR BAI
NORTHWEST CHRISTIAN COLLEGE OR BAH
REED COLLEGE OR BAI
WESTERN BAPTIST COLLEGE OR BAH
WILLAMETTE UNIVERSITY OR BAI
ALBRIGHT COLLEGE PA BAI
ALLEGHENY COLLEGE PA BAI
DESALES UNIVERSITY PA BAH
ALVERNIA COLLEGE PA BAH
BRYN MAWR COLLEGE PA BAI
BUCKNELL UNIVERSITY PA BAI
CEDAR CREST COLLEGE PA BAH
CHATHAM COLLEGE PA BAI
DELAWARE VALLEY COLLEGE PA BAH
ELIZABETHTOWN COLLEGE PA BAH
FRANKLIN AND MARSHALL COLLEGE PA BAI
GENEVA COLLEGE PA BAH
GETTYSBURG COLLEGE PA BAI

 

 

 

138

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

HAVERFORD COLLEGE PA BAI
JUNIATA COLLEGE PA BAI
KINGS COLLEGE PA BAH
LAFAYETTE COLLEGE PA BAI
LEBANON VALLEY COLLEGE PA BAH
LYCOMING COLLEGE PA BAH
MERCYHURST COLLEGE PA BAH
MESSIAH COLLEGE PA BAH
MORAVIAN COLLEGE AND THEOLOGICAL PA BAI
SEMINARY

MUHLENBERG COLLEGE PA BAI
NEUMANN COLLEGE PA BAH
ROSEMONT COLLEGE PA BAH
SETON HHL COLLEGE PA BAH
SUSQUEHANNA UNIVERSITY PA BAH
SWARTHMORE COLLEGE PA BAI
THIEL COLLEGE PA BAH
URSINUS COLLEGE PA BAI
WASI-HNGTON & JEFFERSON COLLEGE PA BAI
WESTMINSTER COLLEGE PA BAI
WILSON COLLEGE PA BAH
YORK COLLEGE PENNSYLVANIA PA BAII
ROGER WILLIAMS UNIVERSITY RI BAH
COKER COLLEGE SC BAH
ERSKINE COLLEGE AND SEMINARY SC BAI
FURMAN UNIVERSITY SC BAI
MORRIS COLLEGE SC BAH
NEWBERRY COLLEGE SC BAII
PRESBYTERIAN COLLEGE SC BAI
AUGUSTANA COLLEGE SD BAH
UNIVERSITY OF SIOUX FALLS SD BAH
BRYAN COLLEGE TN BAH
CUMBERLAND UNIVERSITY TN BAH
FREED-HARDEMAN UNIVERSITY TN BAII
KING COLLEGE TN BAII
LAMBUTH UNIVERSITY TN BAH
MARYVILLE COLLEGE TN BAH
UNIVERSITY OF THE SOUTH TN BAI
TENNESSEE WESLEYAN COLLEGE TN BAH
CONCORDIA UNIVERSITY AT AUSTIN TX BAII
UNIVERSITY OF DALLAS TX BAI
EAST TEXAS BAPTIST UNIVERSITY TX BAII
HOWARD PAYNE UNIVERSITY TX BAII
HUSTON-TILLOTSON COLLEGE TX BAH

 

 

 

 

139

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LETOURNEAU UNIVERSITY TX BAH
MCMURRY UNIVERSITY TX BAH
SOUTHWESTERN UNIVERSITY TX BAI
TEXAS LUTHERAN UNIVERSITY TX BAH
WAYLAND BAPTIST UNIVERSITY TX BAH
BENNINGTON COLLEGE VT BAI
MARLBORO COLLEGE VT BAI
MIDDLEBURY COLLEGE VT BAI
BLUEFIELD COLLEGE VA BAH
BRIDGEWATER COLLEGE VA BAH
EMORY AND HENRY COLLEGE VA BAH
EASTERN MENNONITE UNIVERSITY VA BAH
FERRUM COLLEGE VA BAH
HAMPDEN-SYDNEY COLLEGE VA BAI
HOLLINS UNIVERSITY VA BAI
RANDOLPH-MACON COLLEGE VA BAI
ROANOKE COLLEGE VA BAH
SWEET BRIAR COLLEGE VA BAI
VIRGINIA WESLEYAN COLLEGE VA BAI
WASHINGTON AND LEE UNIVERSITY VA BAI
UNIVERSITY OF PUGET SOUND WA BAI
WHITMAN COLLEGE WA BAI
ALDERSON BROADDUS COLLEGE WV BAH
BETHANY COLLEGE WV BAI
BELOIT COLLEGE WI BAI
CONCORDIA UNIVERSITY-WISCONSIN WI BAH
LAWRENCE UNIVERSITY WI BAI
MOUNT MARY COLLEGE WI BAH
MOUNT SENARIO COLLEGE WI BAH
NORTHLAND COLLEGE WI BAH
WISCONSH\1 LUTHERAN COLLEGE WI BAH

 

 

 

140

 

 

Appendix E: Correlations for Institutional Quality Variables

 

Correlations, BAI

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Institutions
Academic Faculty % Avg. Total EG Avg
Reputation Student students Expenditures, 1990- Endowment,
Score Ratio returning 2000 1990-1996
Average for 2nd
year
Academic Pearson 1.00 -0.53 0.67 0.77 0.76
Reputation Score Correlation
Sig. (2- 0.00 0.00 0.00 0.00
tailed)
N 120.00 120.00 120.00 120.00 120.00
Faculty Student Pearson -0.53 1.00 -0.24 -0.30 -0.45
Ratio Average Correlation
Sig. (2- 0.00 0.01 0.00 0.00
tailed)
N 120.00 120.00 120.00 120.00 120.00
% students Pearson 0.67 -0.24 1.00 0.62 0.51
returning for 2nd Correlation
year
Sig. (2- 0.00 0.01 0.00 0.00
tailed)
N 120.00 120.00 120.00 120.00 120.00
Avg. Total EG Pearson 0.77 -0.30 0.62 1.00 0.69
Expenditures, Correlation
1990-2000
Sig. (2- 0.00 0.00 0.00 0.00
tailed)
N 120.00 120.00 120.00 120.00 120.00
Avg Endowment, Pearson 0.76 -045 0.51 0.69 1.00
1990- 1996 Correlation
Sig. (2- 0.00 0.00 0.00 0.00
tailed)
N 120.00 120.00 120.00 120.00 120.00

 

 

 

 

 

 

141

 

 

 

 

Correlations,
BAII

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Institutions
Academic Faculty % Avg. Total Avg
Reputation Student students EG Endowment,
Score Ratio returning Expenditures, 1990-1996
Average for 2nd 1990-2000
year
Academic Pearson 1.00 -0.21 0.43 0.39 0.50
Reputation Correlation
Score
Sig. (2- 0.01 0.00 0.00 0.00
tailed)
N 167.00 167.00 167.00 167.00 167.00
Faculty Pearson -0.21 1.00 -0. 1 0 -0.02 -0. 18
Student Ratio Correlation
Average
Sig. (2- 0.01 0.22 0.76 0.02
tailed)
N 167.00 167.00 167.00 167.00 167.00
% students Pearson 0.43 -0.10 1.00 0.44 0.23
returning for Correlation
2nd year
Sig. (2- 0.00 0.22 0.00 0.00
tailed)
N 167.00 167.00 167.00 167.00 167.00
Avg. Total Pearson 0.39 -002 0.44 1.00 0.32
EG Correlation
Expenditures,
1990-2000
Sig. (2- 0.00 0.76 0.00 0.00
tailed)
N 167.00 167.00 167.00 167.00 167.00
Avg Pearson 0.50 -0.18 0.23 0.32 1.00
Endowment, Correlation
1990-1996
Sig. (2- 0.00 0.02 0.00 0.00
tailed)
N 167.00 167.00 167.00 167.00 167.00

 

 

 

 

 

 

 

142

 

Appendix F: Regression Results, Grouped Data

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients
N=287 Unstandardiz Standardize t Sig.
R2=.561 d Coefficient Coefficien
1 Eonstant) 1831.352 1136.969 1.611 .108
Religiou -2369.649 350.911 -.329 -6753 .000
Afiiliatioj
Average -.240 .185 -.058' -1.296 .196
Enrolhnent,
1990-2000
Minority -2332.616 1086.711 -.094l -2.146 .033
Percentage
Avg
Institution -828.310 427.736 -.098 -1.936 .054
Locatedin
South
Institution -213.001 364.206 -.031 -.585 .559
Locatedin
Midwest
Institution -42.806 479.621 -.004 -.089 .929
Locatedin
West
Coed 413.771 457.503 .038' .9041 .367
Age 12.917 3.31 .177 3.89 .000
Admission 1822.979 299.296 .352 6.091 .000
Competitiven
ess Ratin
Av 1.481E-04 .000 .133 2.263 .024
Endowment
1990-1996
Tuition ~3789.153 1697.023 -.101 -2.233 .026
Discount
Rate 1990

 

 

 

a Dependent Variable: NTRPS9O

143

 

Coefficients

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N=287 Unstandardize‘csil Standardize t Sig.
R2=.615 Coefficien Coefficien
1 (Constant) 1933.048 1248.508 1.548 .123
Religiou -2159.097 376.117 -.263 -5.740 .000
Affiliation
Average -.307 .197 -.06 -1.561 .120
Enrollment,
1990-2000
Minority -2636.030 1170.135 -.095 -2.253 .025
Percentage Avg
Institution -1035.9l9 460.380 -.10 -2.250 .025
Located in
South
Institution -373.441 385.539 -.048 -.969 .334
Locatedin
Midwest
Institution 506.909 507.003 .044 1.000 .318
Located in
West
Coed 935.172 495.999 .076 1.885 .060
Age 14.063 3.671 .168 3.831 .000
Admission: 2399.123 333.772 .395 7.188 .000
Competitivene
s Ratin
Avg 2.630E-04 .000 .211 3.741 .000
Endowment
1990-1996
Tuition -6101.615 1587.746 -.169 -3.843 .000
Discount Rate
1995

 

 

 

 

 

 

 

 

a Dependent Variable: NTRPS95

144

Coefficients

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N=287 Unstandardize Standardized t Sig.
R2=.442 Coefficien Coefficien
1 (Constant) 7170.256 2162.420 3.316 .001
Religious -3 663 .9 1 5 652.906 -.309 ~5.61 2 .000
Affiliation
Average -.669 .337 -.097 -1.984 .048
Enrollment,
1990-2000
Minority —4934.2 1 7 2051.109 -. 120 -2.406 .017
Percentage
Av
Institution -1566.566 798.327 -.1 12 -1.962 .051
Located in
South
Institution -785 .346 667.645 -.068 - l . 176 .241
Located in
Midwest
Institution 491.770 882.553 .029 .557 .578
Located in
West
Coed 699.39 851.171 .039 .822 .412
Age 17.180 6.320 .142 2.7181 .007
Admissions 2665.452 552.082 .312 4.828 .000
Competitive
ness Rating
Avg 8.299E-05 .000 .045 .686 .493
Endowment
1990-1996
Tuition -12199.971 2406.882 -.259 —5.06 .000
Discount
Rate 2000

 

 

a Dependent Variable: NTRPSOO

145

 

Coefficients

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N=287 Unstandardize Standardizej r Sig.
R2=.730 Coefficient Coefficien
1 (Constant) -5735136625 2025559253 -2.831 .005
Religious -2498744.099 625162.878 -.153 -3.997 .000
Affiliation
Average 4397.803 329.54 .465 13.345 .000
Enrollment,
1990-2000
Minority -5649753.0881936022.95 -.100 -2.9181 .00
Percentage Avg
Institution -1510742.955 762029.527 -.078l -1.983 .048
Locatedin
South
Institution -696463.027 648848.463 -.044| -1.073 .284
Locatedin
Midwest
Institution -669609.578 854465.224I -.028 -.784 .434
Located in West
Coed 925804.668 815061.157 .03 1.136 .257
Age 18314610 5903.768 .110 3.102 .002
Admissionj 2926134310 533208.327 .249 5.488 .000
Competitivenes
Ratin
Avg .606 .117 .240 5.200 .000
Endowment
1990-1996
Tuition -8528000800 3023319304 -.100 -2.821 .005
Discount Rate
1990

 

 

 

a Dependent Variable: NTR90

146

 

Coefficients

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

=287 ‘Unstandardized Standardized r Sig.
R2=.767 Coefficients Coefficien
1 (Constant -7936832.992 2438243.]99 -3.255 .001
Religious -2697413.638 742317.02 -.129 -3.634 .000
Affiliation
Average 6709.039 389.416 .553 17.228 .000
Enrolhnent,
1990-2000
Minority -5227629809 2328768200 -.072 -2.245 .026
Percentage
Avgl
Institution -1684031.386 909961.404 -.068 -1.851 .065
Located in
South
Institution -1221259.750 764084.479 -.060 -1.598 .111
Located in
Midwest
Institution -62330.210 1005428573 -.002 -.062 .951
Located in
West
Coed 1630201.767 969497.312 .052 1.681 .09
Age 14435.168 7119.409 .068 2.028 .0441
Admissions 3709977451 630792.490 .24 5.881 .000
Competitive
ness Ratin
Avg .730 .138 .226 5.292 .000
Endowment
1990-1996
Tuition -8416402.006 3110548822 -.090 -2.706 .007
Discoun
Rate 19951

 

 

 

 

 

 

 

 

a Dependent Variable: NTR95

147

Coefficients

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N=287 Unstandardizecj Standardizegl t Sig.
R2=.742 Coefficient Coefficien
1 (Constant) -7850701.503 3081698046 -2.548 .011
Religiou -3640156.658 930467.073 -.146 -3.912 .000
Affiliation
Average 7979.812 480.716 .554 16.600 .000
Enrollment,
1990-2000
Minority -7733657.531 2923067.124 -.090 -2.646 .009
Percentage
Av
Institution -2628630.843 1137708531 -.090 -2.310 .022
Located in
South
Institution -2219001.828 951470.815 -.092 -2.332 .020
Located in
Midwest
Institution -270988623 1257739474 -.008 -.215 .830
Locatedin
West
Coed 3002051001 1213017.]91 .080 2.475 .01
Age 18692.200 9006.789 .07 2.075 .039
Admissions 4735298877 7867801 15 .264r 6.019 .000
Competitive
ness Rating
Avg .484 .172 . 126 2.807 .005
Endowment
1990-1996
Tuition -13766094.546 3430085381 -.139 -4.013 .000
Discount
Rate 2000

 

 

 

a Dependent Variable: NTR00

148

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