A Comparative Empirical Examination of Extent of Disclosure by Private and Public Colleges and Universities in the United States

7/29/2019 A Comparative Empirical Examination of Extent of Disclosure by Private and Public Colleges and Universities in th

1/41

A comparative empirical examinationof extent of disclosure by private

and public colleges and universities inthe United States

Teresa Gordon a, Mary Fischer b,*, David Malone c,Greg Tower d

a University of Idaho, USAb University of Texas at Tyler, College of Business Administration, 3900 University Boulevard,

Tyler, TX 75701, USAc Texas Tech University, USA

d Murdoch University, Australia

Abstract

This study examines the annual reports of 100 United States (US) institutions of

higher education to determine identifiable and measurable factors associated with extent

of disclosure. Each disclosure was weighted by its relative importance to users of college

and university financial statements. The measurement construct for extent of disclosure

was the ratio of an institutions total disclosure score to its total possible disclosure

score. Institution size and public/private status were associated with total extent of

disclosure but leverage and audit firm size were not significant. Extent of disclosure of

non-financial performance information (service efforts and accomplishments) was as-sociated with high tuition rates and low dependence on tuition revenue and with state

auditors as opposed to public accounting firm auditors. The findings are consistent with

accountability and public interest tenets (Coy, D., Fischer, M., Gordon, T., 2001. Public

accountability: a new paradigm for college and university annual reports. Critical

Perspectives on Accounting 12 (1), 131). Highly visible institutions, those larger in size

Journal of Accounting and Public Policy 21 (2002) 235275

www.elsevier.com/locate/jaccpubpol

*Corresponding author. Tel.: +1-903-566-7433; fax: +1-903-566-7372.

E-mail address: [email protected] (M. Fischer).

0278-4254/02/$ - see front matter 2002 Elsevier Science Inc. All rights reserved.

PII: S 0 2 7 8 - 4 2 5 4 ( 0 2 ) 0 0 0 5 1 - 0
http://mail%20to:%[email protected]/http://mail%20to:%[email protected]/


2/41

or audited by the state, disclosed more information. Moreover, some institutions used a

corporate-style report to better promote their interests.

2002 Elsevier Science Inc. All rights reserved.

1. Introduction

The United States (US) has the worlds largest private sector of higher ed-

ucation comprising 55.2% of its 3561 colleges and universities. 1 Private and

public institutions in all categories compete for students, from prestigious re-

search institutions to liberal arts colleges to two-year community colleges.

Accountability by private non-profit colleges and universities entails uniqueissues because these institutions are not subject to the electoral control which

holds government accountable and because they are often insulated from the

market forces that discipline business entities due to their (sometimes) extensive

endowments. Nevertheless, US colleges and universities of all types are faced

with an increasingly adverse and turbulent environment including a declining

college-age population 2 and reductions in federal support for research. Many

institutions are being forced to establish stronger ties with business to replace

federal support. Baron and Grunewald (1995, p. 219) suggest that the line

between profit and non-profit institutions is blurring as choices for academic

institutions narrow.

Despite the similarities among institutions of higher education, financial

reporting for the industry is regulated by two different entities (Fischer, 1997,

p. 255). The Financial Accounting Standards Board (FASB) is responsible for

private not-for-profit institutions (and the small number 3 of proprietary

schools). The Government Accounting Standards Board (GASB) is responsible

for public institutions (Fischer, 1997, p. 254). Objectives for financial reporting

1 According to the US Department of Education, there were 1595 public and 1966 private

institutions of higher education in 1994 (National Center for Education Statistics, 1996, p. 216).

The majority (88%) of public institutions are state operated. Most private institutions are private

not-for-profit entities (84%) rather than proprietary or for-profit entities (16%). Over half (56%) of

the not-for-profit schools are religiously affiliated (Snyder et al., 1996, p. 184).2 High school graduates reached a low in 19931994 (the year of the financial statements used in

this study). High school graduates were projected to increase slowly and then drop back down to

the 19931994 level in 20012002 after which the number of graduates will again begin to grow

steadily and eventually exceed the baby boom numbers by 2009 (Western Interstate Commission

for Higher Education et al., 1993, p. 1).3 Less than 2% of higher education students were enrolled in the 314 proprietary institutions of

higher education in the US in 1994. However, both the number of institutions and proprietaryenrollment had increased 20% between 1985 and 1994, as compared with a 17% overall increase in

enrollment (Snyder et al., 1996, pp. 174227).

236 T. Gordon et al. / Journal of Accounting and Public Policy 21 (2002) 235275


3/41

espoused by the two boards are not identical. The FASBs primary focus is on

providing information that is useful to resource providers in making rational

decisions about the allocation of scarce resources to business and non-profit

organizations (FASB, 1980, para. 35). The GASB also endorses decision use-

fulness but states accountability is the cornerstone of all financial reporting in

government (GASB, 1987, para. 56). From an ethical perspective, account-

ability implies a willingness to endure public scrutiny, even an invitation for

the public to scrutinize the behaviors of the organization s leadership and this

duty to report is not limited only to the minimum required by law (Lawry,

1995, p. 175). The annual financial report produced by most entities is one way

they can meet their duty to be accountable to their stakeholders and to society

at large.In 1995, the FASB implemented Statement of Financial Accounting Stan-

dards (SFAS) Nos. 116 (FASB, 1993a) and 117 (FASB, 1993b) representing a

significant divergence between private and public college and university (C&U)

financial reporting (Fischer, 1997). Prior to implementation, both the FASB

and the GASB recognized the AICPA/NACUBO 4 model for reporting the

financial results of colleges and universities. The current study uses the annual

reports of C&Us from 1994, just prior to the implementation of SFAS Nos.

116 and 117. Various authors have written on the diverse goals represented in

higher education (e.g., Brinkman, 1984, pp. 37; Gardner et al., 1985, pp. 914;

Geiger, 1986, pp. 241249; Zemski, 1996, pp. 114). As reporting practicesdiverge between public and private institutions with the adoption of SFAS Nos.

116 and 117, our study provides a much needed baseline examination of the

factors associated with financial disclosure within these two types of entities. 5

As stated earlier, there are both similarities and dissimilarities between

public and private institutions and their association with financial reporting

practices. The current study falls within a positive theory framework, as that

term is used by Watts and Zimmerman (1986, pp. 49), since it attempts to

explain accounting and reporting practices by (1) identifying those factors

associated with financial reporting common to both public and private C&Us,

4 The earliest recommended uniform reports for colleges and universities date back to the first

decade of the 20th century (Brown, 1993, p. 2). The AICPA/NACUBO model refers specifically to

the industry audit guide published by the American Institute of Certified Public Accountants

(AICPA, 1973) and the manuals and guidelines issued by the National Association of College and

University Business Officers (NACUBO, 1990).5 Changes in the financial reporting requirements for public institutions are underway. See

Engstrom and Esmond-Kiger (1997) for a comparison of the FASB model and the proposed GASB

model for college and university reporting. GASB issued its Statement No. 35 (1999b) amending

Statement No. 34 (1999a) to include public colleges and universities. The new reporting model

based upon full accrual recognition and highly aggregated presentation narrows the divergencebetween public and private college and university financial reporting. Significant differences

continue to exist due to dissimilar definitions, classifications and display criteria.

T. Gordon et al. / Journal of Accounting and Public Policy 21 (2002) 235275 237


4/41

and (2) identifying those factors unique to public and private institutions

respectively.

2. Service efforts and accomplishments

C&Us, whether public or private, do not have a single convenient indicator

of performance comparable to net income, return on investment, or earnings

per share. Many resource providers of C&Us are not direct beneficiaries of the

services provided and are not in a position to ascertain the quality or quantity

of services provided, nor the efficiency with which resources are consumed.

Consequently, both the GASB and FASB emphasize the importance of in-formation about the efficiency and effectiveness of both service efforts and

accomplishments (SEA). Both boards recommend that non-business organi-

zations supplement their financial statements with non-financial SEA mea-

sures. In particular, the GASB (1994) has emphasized the need for

experimentation with SEA measures. Examples and detailed recommendations

(e.g., Gordon and Fischer, 1990, pp. 2931; Fischer and Gordon, 1991, pp. 36

40; Hatry et al., 1990, pp. 5769) as well as documentation of misleading

measures (Cave et al., 1991, pp. 172173; Stecklow, 1995, p. A6) have been

published but there is little information on the extent to which SEA reporting

has been implemented within the context of financial reporting.

3. Extent of disclosure literature

Explanations for variations in the amount of information disclosed by eco-

nomic entities in the US have been approached from several perspectives (e.g.,

firms listing a new stock issue (Copeland and Fredericks, 1968), ventures into

foreign markets (Choi, 1973), or from a more positivistic perspective, exploring

firm characteristics such as size, listing status, leverage, audit firm size, etc.,(Cerf, 1961; Buzby, 1974, 1975; Malone et al., 1993)). Disclosures in other

countries have also been examined (e.g., Baker et al., 1977; Firth, 1979; McNally

et al., 1982; Chow and Wong-Boren, 1987; Wallace and Naser, 1995). 6

There are few studies of C&U reporting in the US (an example is Peat

Marwick Mitchell & Co. (1985) review of annual reports). However, several

studies have examined the quality of reporting practices among public uni-

versities in other countries including Great Britain (Gray and Haslam, 1990),

6

In the area of government reporting, Plewa (1983, p. 124) compared disclosures made bymunicipalities with audited versus unaudited annual reports. A synopsis of the findings of these

earlier studies are provided later in the paper as we develop the hypotheses to be tested.



5/41

New Zealand (Dixon et al., 1991, 1994; Coy et al., 1993, 1994) and Ontario,

Canada (Banks and Nelson, 1994). Unlike typical extent of disclosure studies

which tend to be cross-sectional in nature, these quality of disclosure studies

are longitudinal, designed primarily to assess changes over time for individual

institutions rather than to identify explanations for differences in disclosure

practices among entities.

Our study contributes to the literature in two ways. First, it provides the first

comprehensive study of extent of financial disclosure by US institutions of

higher education. Second, most prior research of C&U disclosures ignored

private institutions. The current study includes both public and private insti-

tutions and hypothesizes a difference in motivations for disclosure between the

two categories, particularly with respect to SEA disclosures. The effect ofdifferences in reporting standards between public and private institutions is

minimized by using 1994 data, prior to the adoption of SFAS 117 by most

private schools. 7

The remainder of the paper is organized as follows: first, hypotheses to be

tested are discussed; second, methods used in the study are outlined; and

finally, results are reported.

4. Hypotheses development

In attempting to assess the determinants of disclosure by C&Us, our study

examines the sample in three different groupings: total sample (i.e., public and

private C&Us), public C&Us, and private C&Us. There are certain charac-

teristics that are common to any economic entity including C&Us that may

help explain the extent of disclosure of that entity. For both public and private

C&Us, those characteristics might include size, various configurations of assets

and debt, governance, and audit status.

Public institutions, while sharing the above characteristics with private ones,

are unique in their relationship to state government, including their relation-ship with the state auditor (Baber, 1983, p. 226). Because of the public support

afforded these institutions, there may be increased monitoring on the part of

the state because of the increased proportionality of their stake in the financial

and operational affairs of the school.

7 Of the 100 institutions included, 90 were following the AICPA audit guide, three were early

adopters of SFAS No. 117 (FASB, 1993b), although not SFAS No. 116 (FASB, 1993a), and four

others were using either the government model or a mix of government and AICPA audit guideformats. The remaining three institutions reports could not be classified and the notes to the

financial statements (if any) were vague with respect to the specific standards being followed.



6/41

Private schools depend heavily on student tuition and fees and on private

donations and gifts. Endowments, therefore, take on a special significance in

the private institution relative to the public institution. Additionally, SEA may

take on special significance to potential contributors and other resource pro-

viders. In our study, SEA disclosures are used as a dependent variable in five of

the hypotheses.

4.1. Total extent of disclosure

A disclosure index comprises items that are expressed as a ratio of the actual

scores of a company compared to the scores which that company is expected to

earn (Cooke, 1991, p. 180). It is created to measure the relative level of dis-closure by an enterprise. Disclosure indices have been used in many voluntary

disclosure studies (for example, Lim and McKinnon, 1993; Hossain et al.,

1994; Hossain and Adams, 1995; Dixon et al., 1994) on the grounds that the

dependent variables used in these studies are not directly measurable (Marston

and Shrives, 1991, pp. 197200). Furthermore, Cheng (1992, p. 2) notes that

early studies of the practices of government entities had relied on measures

such as length of financial report, whether the financial statements were audited

and by whom, the size of the state audit budgets as surrogates for the extent

and quality of disclosure. However, due to the existence of multicollinearity

between and among the independent variables, data reduction methods hademployed different measures for the similar constructs (Ingram, 1984, p. 139;

Banker et al., 1989, p. 37; Giroux, 1989, p. 211). As such, selecting specific

variables to explain disclosure practices, in the public sector, is not desirable

(Cheng, 1992, p. 2). Hence Cheng (1992, p. 36) supports the notion of dis-

closure indices to measure the extent of disclosure by enterprises. This ap-

proach has been widely used in public sector studies (see Ingram, 1984,

pp. 134137; Robbins and Austin, 1986, pp. 413417; Banker et al., 1989, p. 30;

Giroux, 1989, p. 208; Cheng, 1992, p. 28; Lim and McKinnon, 1993, pp. 202

203).

4.1.1. Institution size

Perhaps the most prevalent explanatory variable in the extent of disclosure

literature is size of the organization. 8 Foster (1986) suggests that the positive

relationship that appears to exist between firm size and extent of financial

disclosure can be a function of several possible underlying factors, including

8 For the constructs size and audit firm size, our choice of measurement was influenced

significantly by the extant disclosure literature. Wallace and Naser (1995, pp. 316317) offer anexcellent review of that literature. Rather than replicate that discussion here, we incorporate it by

reference.



7/41

increased political costs associated with larger firms, lower competitive costs of

marginal disclosures by larger firms, and economies of scale enjoyed by larger

firms in the production of information (p. 44). In response to the political cost

problem, Leftwich et al. (1981, p. 57) argue that increased financial disclosure

can help to offset monitoring problems arising from increases in organization

size. Their argument also holds true when there is a dispersion of monitoring

agents in place such as larger governing boards (Ingram, 1998, p. 12).

As an economic construct, size can present difficult measurement issues.

Foster (1986) provides guidance on measure of firm size, listing total assets,

sales, and market capitalization (p. 111) as possible measurable surrogates. For

colleges and universities, market capitalization does not present a measurable

value (with the possible exception of C&U tradable debt securities). However,assets and revenues are measurable in the C&Uswith some caveats. Under

the accounting model used in 19931994, many institutions made no attempt to

eliminate interfund accounts receivable and payable, thus inflating total as-

sets. 9 A more serious problem is the valuation of plant assets. Since a C&U

can be hundreds of years old, with fixed assets dating to the inception of the

institution, total assets of different universities can be fraught with systematic

differences. In addition to the usual problems with historical cost accounting

for assets with long lives, private institutions record depreciation on plant

assets (FASB, 1987) but most public institutions do not (GASB, 1988). This

study uses adjusted gross assets (GASSETS), computed by adding back ac-cumulated depreciation to total assets and removing interfund receivables (if

necessary), in order to make public and private institutions as comparable as

possible. As suggested by Ott (1993, p. 457) the natural log of gross assets was

used because of the original independent variables tendency to produce a non-

linear distribution of error terms in the model. A hypothesis, stated in its al-

ternate form, is suggested by these arguments:

Ha1: Colleges and universities with higher levels of gross assets disclose finan-

cial information to a greater extent than their smaller counterparts.

4.1.2. Governance

The governing boards of colleges and universities are most commonly called

a board of regents or a board of trustees. 10 For the purpose of this study, no

distinction is made among either of these forms, or the others that exist. Of

9 Under GASB (1991, para. 154) Statement No. 14 entity-wide totals were optional and

elimination of interfund receivables and payables was not required.10 The governing board of 66 institutions was named board of trustees, 20 used board of regents,

and three reported a board of governors. Other governing board names reported by an institutionincluded curators, overseers, visitors, managers, supervisors, governor and trustees, board of trust,

agriculture, the corporation, and higher education.



8/41

immediate concern is simply the number of members of the governing board. It

seems clear, however, just from casual observation that boards of different

institutions take on their own character. There may be asymmetries among

boards that could explain different levels of monitoring in place. For example,

some boards are elected, while others are appointed. Some boards have student

and/or faculty representatives while others do not (McGuinness et al., 1994,

pp. 107129). These and other asymmetries are beyond the scope of this study.

Much of the literature on governing boards addresses the effect of outside

directors as a monitoring device (e.g., Rosenstein and Wyatt, 1990; Fosberg,

1989; Fama and Jensen, 1983). Boards of C&Us are composed almost exclu-

sively of outside directors, who are normally either appointed by an external

agent (e.g., the governor of a state) or elected by the existing board members(Madsen, 1998, p. 10). These boards also take on a diverse nature, with board

members coming from diverse racial, gender and experiential backgrounds

(Madsen, 1998, p. 4). Ingram (1998, p. 12) recommends that boards be in-

creased to facilitate improved trusteeship. Supporting that belief is an earlier

finding by Moisan (1992, p. 10) that the general effectiveness of a board was

influenced by its size. The current study proposes that as members are added to

the board, there is a perceived need for higher levels of monitoring. Among

private institutions, large boards may also be desired because of the enhanced

fundraising that may result from having influential and wealthy potential do-

nors serving on the board (Ingram, 1998, p. 14). Both justifications for largeboards are consistent with a positive relationship between board size and extent

of disclosure, resulting in the following hypothesis, again stated in alternate

form:

Ha2: Colleges and universities with more members on the governing board

disclose financial information to a greater extent than do those with few-

er members.

4.1.3. Public versus private

Public institutions are larger than private C&Us in terms of current fund

revenues and enrollment. 11 Therefore, the nature of the population makes it

difficult to avoid this systematic difference. Apart from considerations of size,

however, public institutions may be exposed to greater political costs, due to

11 Geiger (1986, p. 164) reports that only five of the 60 largest universities in the US are privately

controlled. Based on our examination of a primary data source, the Department of Education s

National Center for Educational Statistics, we found that the 19931994 current fund revenues of

private institutions averaged $32,250,000 as compared to $65,519,000 for public institutions. From

the same source, Fall 1993 enrollment in private institutions averaged 1552 as compared to 6886 forpublic institutions. The data from which these averages were computed are available at http://

nces.ed.gov.

http://nces.ed.gov/http://nces.ed.gov/http://nces.ed.gov/http://nces.ed.gov/


9/41

the greater number of constituents to which they are responsible (Kurtenbach

and Roberts, 1994, p. 230). These constituents include taxpayers, the legisla-

ture, and assorted politicians (in addition to students, alumni, bondholders and

accrediting bodiesconstituent interest groups relevant to both public and

private schools). Private institutions are not generally subjected to this addi-

tional scrutiny (with the possible exception of additional incentives to disclose

SEA information, as discussed earlier). As such, when holding all other factors

constant, one would expect public institutions to make more extensive dis-

closures consistent with their multifaceted stewardship roles. The third alter-

nate hypothesis of the study is thus suggested:

Ha3: Public colleges and universities disclose financial information to a greaterextent than do private ones.

Classifying C&Us into the categories of public and private is relatively

straightforward with one exception. One land grant university from each state

was selected to represent public institutions in our study. However, Cornell, a

private non-profit institution, is the land grant institution in New York. The

colleges that comprise the land grant activities at Cornell were established by

New York State statute to participate in the Morrill Act of 1862 (Carren, 1958,

p. 13). For the purposes of our study, Cornell is classified as private.

Using a simple dichotomy for the public versus private dimension has po-tential flaws. The concept of public responsibility for higher education took a

long time to develop in the US. The earliest private institutions (e.g., Harvard

and Yale) received public support and some of the earliest state universities

(e.g., Georgia and North Carolina) were more nearly private than public, often

with self-perpetuating rather than state appointed governing boards (Brub-

acher and Rudy, 1976, p. 145). Regular patterns of tax support for state uni-

versities became more common after the Civil War and the Morrill Act of 1862

(Hofstadter and Smith, 1961, p. 568). Since the second Morrill Act of 1890,

land grant universities have received appropriations from the federal govern-

ment, and state legislatures were spurred to follow suit (Hofstadter and Smith,1961, p. 568). However, the resulting level of state support is far from uni-

form. 12 Both private and public colleges and universities receive student fi-

nancial aid and research grants from federal, state and local governments. 13

12 Based on an examination of an original data source, the US Department of Educations

National Center for Education Statistics (NCES), we found that the 19951996 state appropri-

ations for higher education ranged from $2071 to $6048 per enrolled student with a mean of $3843.

The data from which these averages were computed are available at http://nces.ed.gov.13

According to the NCES, private institutions received 17.6% of their 19931994 revenues fromfederal, state and local governments as compared to 50.9% for public institutions of higher

education. This data is available at http://nces.ed.gov.

http://nces.ed.gov/http://nces.ed.gov/http://nces.ed.gov/http://nces.ed.gov/


10/41

Some states, particularly those with large private sector enrollments, provide

generous state aid to private institutions (Geiger, 1986, p. 212). However, we

were unable to develop a sound alternative construct for the public versus

private dimension. Too many institutions (primarily the private schools) in our

sample commingled private and governmental support in their financial

statements.

4.1.4. Audit firm size

The size of the audit firm and audit status (audited versus unaudited

financial statements) have been shown to impact financial disclosure (Banker

et al., 1989, p. 52; Baber, 1983, p. 221; Rubin, 1992a, p. 162). Wright (1974,

p. 631); DeAngelo (1981, p. 190); Deis and Giroux (1992, p. 477) and Maloneet al. (1993, pp. 254255) argue that smaller certified public accounting (CPA)

firms are more sensitive to client demands than are larger CPA firms and thus

generally are associated with lower levels of disclosure. The sample in this

study is comprised of major universities, and most provide financial statements

audited by public accounting firms (76% were audited by Big-6 CPA firms). 14

However, in some states, governmental units are audited by a state agency. The

type of firm doing the audit may be an important influence on the extent of

disclosure of both financial and non-financial information (Sanders and Allen,

1993, pp. 7375; Raman and Wilson, 1992, pp. 272275). Rubin (1992a, p. 174

and 1992b, p. 35) reported that municipal finance officers had the perceptionthat state auditors were at a disadvantage compared to independent public

auditors with respect to their efficiency and expertise. On the other hand, state

auditors do not routinely weigh the risks of malpractice lawsuits and may be

less concerned about including voluntary (unaudited) non-financial indicators

with the annual reports they audit (Copley, 1989, p. 21; Raman and Wilson,

1994, p. 519). The number of C&Us in our study that was not subjected to an

audit was too small to test. The fourth alternate hypotheses, therefore, is:

Ha4: Colleges and universities that are audited by Big-6 CPA firms disclose

financial information to a greater extent than do those audited by smaller

CPA firms or that are subject to state audit.

4.1.5. Leverage

Jensen and Meckling (1976, p. 338) and Daley and Vigeland (1983, pp. 208

209) argued that firms with a greater proportion of debt, that is more highly

leveraged firms, will incur higher monitoring costs. Myers (1977, p. 161) and

14

At the time of this study, there were six major CPA firms operating in the US (ArthurAndersen LLP, Coopers & Lybrand LLP, Deloitte & Touche LLP, Ernst & Young LLP, KMPG

Peat Marwick LLP, and Price Waterhouse LLP).



11/41

Schipper (1981, p. 87) explained this as a result of conflicts that exist between

debtholders and stockholders. Because stockholders have an incentive to ex-

propriate claims from debtholders, and subsequent debtholders have incentives

to expropriate claims from prior ones, debt covenants arise that may result in

additional disclosure requirements. While universities do not have stockhold-

ers, many issue debt securities, thus suggesting the fifth alternate hypothesis:

Ha5: Highly leveraged colleges and universities disclose financial information

to a greater extent than do less leveraged ones.

4.2. Public colleges and universities

The activities of public institutions are principally focused within the state

and targeted toward the education of state residents (Gardner et al., 1985,

p. 18). To different degrees, members of state legislatures and other oversight

boards can be quite familiar with their constituent institutions, reducing the

need for certain discretionary disclosures. This is due to the many unique re-

lationships that exist across the US between C&Us and their respective state

governments and populations.

In every state, there is but one state legislature. In some states, there is one

governing board for all of the states public institutions of higher learning

(McGuinness et al., 1994, pp. 77102). The extent to which attention is focusedon individual schools and the extent to which governing officials are intimate

with the details of the operations of schools will vary dramatically from state to

state (Brubacher and Rudy, 1976, pp. 143173). For example, in Wyoming,

there is only one four-year schoolthe University of Wyoming. There is not a

legislator or trustee in Wyoming who is not thoroughly familiar with the fi-

nancial condition and budgetary concerns of the university. 15 In a state like

California or New York, however, legislators may be hard pressed even to

name all the public institutions in the state much less be expected to know the

pressing financial issues at each campus.

A single governing board is created in many states to govern all public in-stitutions in the state. This probably results in standardized formats for col-

lecting and compiling extensive higher education financial information for the

state. The reporting and dissemination of financial information by the indi-

vidual institution to the consolidated board should result in added disclosure in

part due to the volume of available data the board needs to govern a large,

15

Information provided by Richard H. Miller, Special Assistant to the President of theUniversity of Wyoming. Mr. Miller was the Director of the Wyoming State Legislative Service

Office from 1988 to 2000. E-mail address: [email protected].



12/41

complex system. By this reasoning, the current study proposes the following

alternate hypothesis:

Ha6: Schools located in states with a single (consolidated) governing board

will disclose financial information to a greater extent than schools in

those states with multiple, relatively decentralized (uncoordinated) gov-

erning boards.

Profiles, roles and responsibilities of governing boards of public higher ed-

ucation institutions in each state was found in the State Postsecondary Edu-

cation Structures Handbook (McGuinness et al., 1994, p. 10).

4.3. Extent of SEA disclosure

Universities strive for educational excellence and institutional prestige and

there is virtually no limit to the amount that can be spent toward these ends

(Geiger, 1986, p. 200). This may cause them to make available certain infor-

mation that is perceived to enhance their reputation. In the US, there have

been three principal ways of feeding higher educations voracious financial

appetiteendowments, taxes, and tuitions (Brubacher and Rudy, 1976,

p. 377).

The remaining hypotheses relate to a subset of the overall extent of dis-closure scoresthose related to SEA and other non-financial indicators in-

cluding enrollment, acceptance rate, etc. As discussed earlier, both the FASB

and GASB have endorsed the need for non-financial indicators (SEA) to make

financial statements fully informative. 16 Nevertheless, this type of reporting is

often published in university fact books rather than annual reports (Fischer

and Gordon, 1991, p. 42). With this caveat in mind, the arguments with respect

to SEA disclosures are developed separately for private and public institutions.

4.3.1. Private colleges and universitiesWhile private C&Us are more dependent on tuition than public institutions,

there is considerable variation among private schools. Many private institu-

tions attract students on the basis of their unique approach to education (in-

cluding sectarian) or their academic prestige (Geiger, 1986, pp. 189195). Such

schools must be able to market their product and justify their claims of su-

periority to a greater extent than public institutions whose tuition rates are

usually much lower (Geiger, 1986, p. 166 and pp. 176185). Geiger (1986,

16

SEA is discussed in both conceptual frameworks (FASB, 1980, para. 47; GASB, 1994)although neither standard setting body has yet mandated SEA disclosures. In addition, both boards

have sponsored research studies on the topic (for example Brace et al., 1980; Hatry et al., 1990).



13/41

p. 169) puts forward the paradoxical generalization 17 that the higher the

tuition a private college or university charges, the less tuition-dependent the

institution is likely to be. The highest tuition rates are charged by private

institutions that have other substantial sources of income such as research

universities and highly selective liberal arts colleges (Geiger, 1986, p. 169).

Conversely, less selective private schools often have few other sources of in-

come and are highly dependent on tuition (Geiger, 1986, p. 169). Many of these

schools were established to satisfy unmet needs for higher education in a

particular area rather than to set standards for academic prestige. 18

The other major source of income for private schools is endowment income

and voluntary gifts. 19 Institutions with relatively high levels of endowment

assets may have developed a trust relationship between the institution and itssupporters and this relationship may be maintained and enhanced by disclo-

sures about the institutions accomplishments. Most contributions to C&Us

come from alumni and almost all institutions have ways of keeping in touch

with their graduates. 20 Alumni support is closely related to the undergraduate,

residential education experience and few commuter students develop deep

attachments to their schools (Geiger, 1986, p. 173). The following alternate

hypotheses are thus suggested:

Ha7: Private colleges and universities whose operations are proportionally less

reliant on tuition and fee revenues will disclose SEA information to agreater extent than do those more reliant on tuition and fees.

Ha8: Private colleges and universities with large endowments will disclose SEA

information to a greater extent than do those with smaller endowments.

Ha9: Private colleges and universities that charge higher tuition fees disclose

SEA information to a greater extent than do those that charge lower

tuition fees.

17 One explanation for the paradox is that high tuition rate schools often provide tuition

discounts that are reported as scholarship expense. Thus the tuition revenue reported may not be

realized in cash making these schools actually less tuition dependent than appears on the surface.18 Geiger (1986, p. 185) notes that before 1960 state legislators had a decided bucolic bias in the

placement of colleges which left unmet needs for higher education in urban areas. The private

institutions that arose to fill the gap were relatively indistinguishable in mission from public

institutions. During the 1960s, a number of these private universities (e.g., Houston and Pittsburgh)

became state controlled.19 According to the NCES, the largest sources of 19931994 current fund revenues for private

institutions were 64.5% from tuition, fees and sales, 17.6% from federal, state and local

governments, and 13.5% from endowment income and private gifts and grants. This data is

available at http://nces.ed.gov.20

Engstrom (1988, p. 14) found that 1.8% of institutions sent complete financial reports to theiralumni. About a third of the institutions (38% of privates) sent condensed reports that did not

include an auditors opinion (Engstrom, 1988, p. 14).

http://nces.ed.gov/http://nces.ed.gov/


14/41

4.3.2. Public versus privateSEA

Public institutions also rely on tuition and many have sizable endowments.

However, they are supported by the third source of fundingtaxesand this

increases their duty to be accountable (Coy et al., 2001). They disclose infor-

mation to satisfy stewardship interests that arise based primarily on a public

service function (i.e., providing higher education to the residents of the state).

Since all land grant schools have similar missions, few systematic differences in

extent of SEA disclosure are anticipated. However, as argued earlier, state

auditors may be more willing than public accounting firms to be associated

with SEA disclosures.

In contrast to public C&Us, private institutions may have a variety of

missions and objectives (e.g., sectarian or single program such as the College ofInsurance in New York). Schools that compete on the basis of prestige (often

indicated by high tuition rates) and those with greater alumni support, have

stronger incentives to provide a more extensive set of non-financial perfor-

mance measurements. One would expect that this would lead to a higher extent

of disclosure among private institutions, ceteris paribus. The following alter-

nate hypotheses are suggested:

Ha10: Public colleges and universities audited by state auditors disclose infor-

mation about SEA to a greater extent than do those audited by public

accounting firms.Ha11: Private colleges and universities disclose SEA information to a greater

extent than do public ones.

5. Methodology

This section discusses the methods employed in testing the hypotheses of the

study. First, the population and sample selection procedures are detailed. We

then discuss the procedures used in the content analysis of the annual reports.

Next, the means by which extent of disclosure by the C&Us was measured are

discussed. Finally, the section specifies the models and statistical methods used

to test the hypotheses.

5.1. Population, working population and sample

There were 3362 institutions of higher learning in the US in 19931994,

defining the population. Of these, 60.3% were four-year institutions that

granted bachelors degrees.21 To examine extent of financial disclosure,

21 Statistics were derived from a primary data source, the National Center for Education

Statistics, available at http://nces.ed.gov.

http://nces.ed.gov/http://nces.ed.gov/


15/41

a national sample, evenly divided between public and private institutions, was

used. Smaller public and private institutions have broadly diverse missions,

necessitating severe restrictions in the working population, loosely defined as

large, nationally prominent institutions. This restriction limits the ability to

extend the results of this study to smaller, systematically different types of

institutions. In selecting a sample, we chose land grant schools 22 to generally

represent public institutions because they had the advantage of their land en-

dowments to make them somewhat more comparable to private institutions

which often have substantial gift endowments. To control as much as possible

for differences in mission and to recognize that certain programs are more

resource intensive than others (e.g., engineering versus history), private insti-

tutions with accredited engineering programs (the mechanical arts portion ofthe Morrill Act) were chosen as the sample of private institutions. All 50 land

grant institutions have accredited engineering programs and offer other gen-

erally comparable programs such as agriculture and forestry. 23 Under the

Carnegie system, 78.0% of the 50 land grant schools were classified as Research

I or II and the remaining 22.0% were classified as Doctoral I or II or Com-

prehensive I or II. 24 Consequently, we did not include in our sample any

private institutions that were classified as specialized engineering programs or

liberal arts colleges. 25 The variable PUBLPRIV is coded 1 for public in-

stitutions and 0 for privates. Letters requesting their annual report for the

fiscal year ending in 1994 were sent to the 50 land grant universities and all 91private institutions with accredited engineering programs and the appropriate

Carnegie classification. The response rate from the land grant schools was

100% and the response rate from the private institutions was 73.6%. Two of the

private schools declined to participate, seven sent 19941995 statements instead

22 The land grant schools had their official origin with the Morrill Act of 1862 which provided

federal lands to endow state universities dedicated to the agricultural and mechanical arts

(Brubacher and Rudy, 1976, p. 63). However, the federal government had already donated

4,000,000 acres of public land to endow universities in 15 states before the Morrill Act (Brubacher

and Rudy, 1976, p. 154). Cornell postponed selling and realized the largest amount ($5 million) and

a few other schools realized almost a million dollars but 17 states realized $150,000 or less

(Brubacher and Rudy, 1976, p. 379). Compare this to the $3.5 million Johns Hopkins bequest and

the $20 million Leland Stanford bequest (Brubacher and Rudy, 1976, p. 377).23 Engineering accreditation is the only program accreditation shared by all 50 land grant

institutions (Higher Education Directory, 1993, pp. 489490). The next most common accreditation

is business with 46 of the 50 land grant institutions having AACSB accreditation (Higher Education

Directory, 1993, pp. 483484).24 Classification was determined based on an original data source, The Carnegie Foundation for

the Advancement of Teaching, 1994. This document is currently available at http://www.carne-

giefoundation.org/Classification/ . A new classification system is being released in 2000 with more

changes anticipated by 2005.25 The private institutions in the final sample included Research (43.1%) and doctoral or

comprehensive (56.9%).

http://www.carnegiefoundation.org/Classification/http://www.carnegiefoundation.org/Classification/http://www.carnegiefoundation.org/Classification/http://www.carnegiefoundation.org/Classification/


16/41

of 19931994, and one school had to be excluded because it had already im-

plemented SFAS Nos. 116 and 117 (FASB, 1993a,b). This left a sample of

annual reports from 49 public land grants, one private land grant, and 57

private institutions. When a state was represented by the annual report of only

one private institution, that school was selected for the study (10 states). When

reports were received from multiple private institutions in a state, one was

randomly selected (19 institutions). The remaining reports provided a pool of

28 schools from which 21 additional private institutions were randomly

selected to bring the total sample to 100 colleges and universities.

As shown in Table 1, private four-year institutions are more numerous than

public institutions of higher learning in the overall population (63.5% versus

36.5%). However, public schools are much larger than the privates, with 69.5%of the total enrollment of all four-year institutions. The private institutions

Table 1

Private versus public comparison: sample institutions, working population of accredited engi-

neering programs, and general population of four-year institutions

Institutions included in

study

Accredited engineering

programs

General

population

Number Percentage Number Percentage Percentage

Enrollmenta

Public institutions 1,479,105 75.8 3,388,122 80.7 69.5

Private institutions 471,965 24.2 808,024 19.3 30.5

Total 1,951,070 100.0 4,196,146 100.0 100.0

Number of institutionsb

Public institutions 49 49.0 193 63.7 36.5

Private institutions 51 51.0 110 36.3 63.5

Total 100 100.0 303 100.0 100.0

Carnegie classification

Doctoral granting schools

(Research I and II and

Doctoral I and II)

80 80.0 153 50.5 11.3

Masters granting schools

(Comprehensive I and II)

20 20.0 114 37.6 25.3

Other four-year institu-

tions (Liberal Arts and

Specialized)

36 11.9 63.4

Total 100 100.0 303 100.0 100.0

a Enrollment figures for Fall 1993 were obtained from the 1993 Higher Education Directory

along with the list of 303 accredited engineering programs. General population percentages and

Carnegie classification percentages were derived from Carnegie Foundation for the Advancement

of Teaching (1994) data.b

Cornell, while a land grant institution, is considered a private school under the Carnegieclassification and its enrollment is included with the other private institutions. Therefore, there are

49 public land grant institutions presented in the table above.



17/41

included in our study are some of the largest private schools in the nation, but

as a group their enrollments are much smaller than those at public land grant

institutions. Public land grant schools included in the study represent 75.8% of

the total enrollment but only 49.0% of the institutions. Private institutions

represent 24.2% of the enrollment but 51.0% of the institutions.

Based on our analysis of primary data sources (see Table 1), schools in-

cluded in our study as well as schools with accredited engineering programs are

heavily skewed toward the more prestigious doctoral granting institutions in

terms of both number and enrollment. Doctoral granting schools comprise

80.0% of the institutions included in our study. In comparison, only half of

accredited engineering programs are offered at doctoral granting institutions

(50.5%). With respect to size of student body (not shown on Table 1), en-rollment at the doctoral granting institutions in our study is 90.3% of the en-

rollment of all the institutions in our study. In comparison, enrollment at

doctoral granting schools that offer accredited engineering programs is 70.8%

of the total enrollment in accredited engineering programs. Enrollment in

doctoral granting institutions is only 44.1% of the enrollment at all four-year

institutions.

Schools included in our study are reasonably representative of accredited

engineering programs with respect to geographic location. While the sample

includes, on average, two institutions from each state, 19 states do not have any

private institutions that offer an accredited engineering program (Higher Ed-ucation Directory, 1993, pp. 489490). Responses were not received from the

private institutions in six other states. Consequently, 25 states are represented

only by their public land grant institution.

5.2. Content analysis procedures

Engstrom (1988) examined the information requirements of a diverse set of

users of college and university annual reports. Through the use of an advisory

board, personal interviews with vested parties, and a mail survey, Engstrom

(1988, pp. 133144) identified 76 disclosures of potential interest to financialstatement users. Subjects in identified user groups were then asked to rate the

relative importance of each of the items in a second mail survey. The disclosure

items rated by Engstroms (1988) respondents provided a list of desirable but

primarily voluntary disclosures that served as the basis for our disclosure index

scores.

The 19931994 annual reports, obtained from the 100 institutions of higher

learning described above, were analyzed using content analysis procedures to

quantify the extent of disclosures (for a thorough discussion of content anal-

ysis, see Holsti, 1969). To guide the content analysis procedure and help assure

consistent coding, we developed an instrument for internal use that included

the relevant items from the Engstrom (1988) study as well as organizational



18/41

characteristics and certain monetary amounts. A binary coding system

was used for all the Engstrom (1988) disclosure items: the item was scored 1 if

the disclosure was present and 0 otherwise. We made no attempt to give ad-

ditional weight for the quality or quantity of the disclosure. For example, an

institution with enrollment data for five years was scored the same as an in-

stitution that reported 10 years of enrollment data with breakdowns by gender

and race.

The instrument was pretested on 10 annual reports and then expanded with

instructions to help ensure consistency among scorers. Each annual report was

then scored by two of the authors and discrepancies were resolved. On average,

the interscorer agreement rate was 84.8%. Most discrepancies were errors of

omission and were easily resolved. To insure consistency over time, the first 10instruments were re-scored at the end of the data collection phase and the

agreement rate was 98.0%.

5.3. Measuring extent of disclosure

Our study focuses on the extent of financial disclosure of public and private

US colleges and universities (C&Us). Certainly, the definition of financial

disclosure is subject to many interpretations, perhaps ranging from the infor-

mation provided in audited notes and financial statements to any information

set that might be employed by a user in arriving at some decision about theeconomic entity at hand. In our study, the financial disclosures examined are

those found in the annual report supplied by the institution. Information in

other documents was not considered.

Measurement of extent of disclosure has a strong foundation in the ac-

counting literature. Most of those studies have used a predetermined list of

disclosures that financial analysts and/or loan officers deemed important in the

investment decision process (see for example Cerf, 1961; Copeland and Fred-

ericks, 1968; Singhvi and Desai, 1971; Choi, 1973; Buzby, 1974, 1975; Plewa,

1983; and Chow and Wong-Boren, 1987.) Both weighted and unweighted in-

dices have been used. In our study, those disclosures identified by Engstrom(1988, pp. 133144), as discussed above, were used as our defined information

set. One advantage of using the Engstrom (1988) list is that it is fairly com-

prehensive in its inclusion of disclosures and is derived from responses pro-

vided by the US users of higher education financial reports. In addition, the

mean importance ratings reported by Engstrom (1988) provide weights for

each of the disclosure variables for a variety of constituent groups (see Eng-

strom, 1988, pp. 6893).

By incorporating user-assigned weighted disclosures, our study attempts to

incorporate the relative importance of different disclosures into the analysis.

Although Robbins and Austin (1986, p. 420) found no material increase in

explanatory power of a compound index over a simple index, conceptually, an



19/41

analysis that incorporates the importance of individual disclosures is superior

to one that does not (Robbins and Austin, 1986, p. 420).

Through the content analysis described above, the disclosures provided by

the sample schools were identified, and weights applied. The sum of these

weighted scores were divided by the total possible weighted score for that school

to arrive at a Total Extent of Disclosure (EXTENT) score, which serves as the

first dependent variable in the study. It is based on all of the relevant items in

Engstrom (1988). 26 The second dependent variable, Extent of SEA Disclosure

(SEA), is calculated in the same way as EXTENT but includes only those

Engstrom (1988) items designated as Enrollment Data (Tables 5 and 6 in Eng-

strom, 1988, p. 82) and Performance Measurement (Tables 57 in Engstrom,

1988, p. 84). Conceptually, EXTENT and SEA could range from 0% to 100%. Aschool that provided none of the Engstrom (1988) disclosures would receive 0%.

A school that provided all possible disclosures would score 100% of the possible

disclosures. Note that the total possible score differed from school to school

because of different economic circumstances. For example, a school with no debt

would be unable to provide disclosures concerning debt maturities, security

provisions, etc. Including those disclosures in the denominator of a school

without such economic circumstances would implicitly penalize those schools.

Appendix A presents descriptive statistics for the EXTENT scores for all sample

schools as well as the SEA disclosure scores which are a subset of EXTENT. 27

5.4. Statistical methods

Ordinary least squares multiple regression models were used in our study to

test the hypotheses detailed above, by category. Table 2 presents the model

used to test the hypotheses related to the overall extent of disclosure scores

(EXTENT) and provides tests for Hypotheses 15. Because debt covenants and

other monitoring requirements are a function of the number of issues out-

standing, as well as size of financing relative to the financing institution s size,

there are several possible measures that could be used to test Hypothesis 5. We

used a ratio of debt to equity (DEQUITY) measured as long-term debt dividedby total fund balance. Hypothesis 6 is tested by examining EXTENT scores for

26 Of the 76 original Engstrom items (1988, pp. 133144), two concerned standard setting and

were not pertinent to individual C&U reports. There was one duplicate item. Certain items were

mutually exclusive, including three types of balance sheet formats and two items related to

depreciation disclosures. Consequently, the maximum possible unweighted score that an institution

could receive was 69. With Engstrom (1988) importance weights attached, the maximum possible

weighted score was 233.04.27

A list of the institutions included in the study can be obtained from the first author. Alsoavailable is a list of the Engstrom (1988) items showing the frequencies as reported by the 100

public and private institutions included in our sample.



20/41

the 49 public institutions using correlation and linear regression (similar to

Model 4 in Table 3) with all public institutions governed by a state-wide

consolidated governing board coded as 1 and all other institutions coded as 0.

Table 2

Total extent of disclosure models (ordinary least-squares regression)

Model 1 (public and private institutions)

EXTENT a b1 LAGASSET b2 BRDMBRS

b3 PUBLPRIV b4 BIG6 b5 DEQUITY e

where

a Standard intercept

b Standard regression coefficient

LAGASSETS natural log of gross assets adjusted for interfund receivables/

payables

BRDMBRS Number of members on governing board

PUBLPRIV Public or private institution (1 public, 0 private)

BIG6 Big-6 or non-Big-6 auditor (1 Big-6 firm, 0 other)

DEQUITY Long-term debt to total fund balance

e Standard normal, randomly distributed error term

Table 3

Extent of SEA disclosure models (ordinary least-squares regression)

Model 2 (private institutions only)

SEA a b1 TUITPCT b2 ENDOW b3 TUITION e

Model 3 (public institutions only)

SEA a b1 CONSBRD b2 STATEAUD e

Model 4 (public and private institutions)

SEA a b1 TUITPCT b2 ENDOW

b3 TUITION b4 BIG6 b5 PUBLPRIV e

where

a Standard intercept

b Standard regression coefficientSEA Extent of disclosure score for enrollment and performance

measurement items only

TUITPCT Tuition and fees expressed as a percentage of current fund

revenues

ENDOW Natural log of fair value of endowment funds (including quasi-

endowments)

TUITION (In-state) Tuition rate charged for a full-time undergraduate

student

STATEAUD State auditor or non-state auditor

BIG6 Big-6 or non-Big-6 auditor

CONSBRD State-wide consolidated governing board (1 yes, 0 no)

PUBLPRIV Public or private institution

e Standard normal, randomly distributed error term



21/41

Table 3 presents the models related to the extent of disclosure of SEA, which

is a subset of the overall disclosure index. Model 2 relates to the private in-

stitutions only and is used to examine Hypotheses 79. Constructs used for the

independent variables for these three hypotheses are TUITPCTtuition and

fees expressed as a proportion of total current fund revenues, ENDOWthe

fair market value of endowment and similar funds including quasi endow-

ments, and TUITIONthe tuition rate for a full-time undergraduate student.

Model 3 relates to the public institutions only and is used to examine Hy-

pothesis 10 by recoding audit firm size (BIG6) to auditor type. For this new

variable (STATEAUD), financial reports audited by state auditors are scored 1

and those audited by any other type of auditor or unaudited are scored 0.

Model 4 is basically a combination of Models 2 and 3 and is used to examineHypothesis 11. The distinctions between public and private institutions are

designated by PUBLPRIV, with public schools coded with a 1 and private

schools coded with a 0. To examine the public versus private dimension for the

overall sample, tuition dependence, tuition rate, endowment and type of au-

ditor were included as control variables.

The disclosure literature is far from conclusive on establishing a dominant

theory on why firms disclose different amounts of information (evidenced by

the predominantly low R2s) (Malone et al., 1993, p. 266) and no previous

studies of colleges and universities exist to guide model specification. Coeffi-

cients for all variables in each model are reported.28

6. Results

The total extent of disclosure scores ranged from a high of 59.1% of the

maximum possible score to a low of 17.6%. On average, each institution re-

ported 19.7 disclosure items with a weighted EXTENT score of 30.5%. The

highest extent of SEA disclosure score was 59.6% of the maximum possible

score but 51 institutions provided none of the disclosures and received zeroscores. On average, each institution reported 2.42 of the SEA items or 14.2% of

the 17 possible disclosures (see Appendix A). There was no significant differ-

ence between public and private colleges and universities on either the weighted

or unweighted scores. An examination of Appendix A reveals that there is

28 Stepwise procedures have also been used to examine extent of disclosure models (e.g., Malone

et al., 1993, p. 265). This is a generally accepted procedure when there is not enough theory

regarding the importance of candidate variables as explanatory variables Cohen (1991, p. 226).

Using stepwise procedures with this data produced models that included only the significantcoefficients. The adjusted R2 of the reduced models were not materially different than those

reported here.



22/41

considerable room for improvement in meeting users expressed information

needs.Table 4 shows the extent to which the annual reports we received were

audited, with a breakdown between private and public institutions. The Big-6

prepared all but one of the 78 annual reports audited by CPA firms. Another

17 annual reports were audited by state auditors. The remaining five reports we

received did not include an auditors opinion letter.

Descriptive statistics about the 100 institutions are presented in Table 5. The

difference in adjusted gross assets between public and private institutions was

Table 5

Selected characteristics of sample institutions

Public institutions

(n 49)

Private institutions

(n 51)

Mean Standard

deviation

Mean Standard

deviation

Panel A: data from financial statements (000 omitted)

Gross assets (adjusted) $1,655,826 $2,561,255 $1,546,481 $1,905,364

Fund balance of endowment $105,499 $182,146 $684,025 $1,104,954

Current fund revenues $890,683 $1,447,924 $440,770 $448,661

Total tuition and fees $133,754 $140,399 $129,429 $103,965

Education and general expenditures

(current funds)$669,181 $955,567 $371,557 $390,315

Long-term debt (all funds) $235,938 $535,968 $186,820 $227,692

Total liabilities (all funds) $380,298 $808,255 $301,002 $340,451

Total fund balance (all funds) $1,202,426 $1,791,377 $1,050,113 $1,438,513

Panel B: other data

Debt equity ratio 21.96% 13.69% 44.93% 25.14%

Annual tuition and fees per student $2758 $1150 $14,964 $3857

Years since founding 128 30 140 59

Total faculty 1618 1285 553 421

Governing board size 15 9 39 13

Enrollment (Fall term, 1993) 40,258 33,101 9861 6682

* Indicates that means are significantly different at p< 0:05 (two-tail t-test).

Table 4

Frequency distribution of auditors responsible for annual audit

Private institutions Public institutions Total

Big-6 accounting firm 49 28 77

Other CPA firm 1 0 1

State auditor 0 17 17

Unaudited report 1 4 5

Total 51 49 100



23/41

not statistically significant, although public institutions were significantly lar-

ger in terms of enrollment, faculty, current fund revenues, and education and

general expenditures. Private institutions endowments were significantly larger

than those at the public institutions and private schools charged significantly

higher tuition and fees. They also had more members serving on their gov-

erning boards. 29 There were no significant differences between public and

private institutions with respect to long-term debt, total liabilities, or total fund

balances. However, the average long-term debt to fund balance ratio (DEQ-

UITY) was 44.9% for private institutions and 22.0% for public institutions

(p< 0:001, two-tail test).

6.1. Total extent of disclosure models

The ordinary least squares multiple regression procedures used to examine

the hypotheses must be interpreted carefully due to a variety of problems

created by correlated independent variables. With correlated independent

variables, coefficients and intercept terms can be unreliable (Ott, 1993, p. 591).

In that circumstance, the regression coefficient measures only the marginal or

partial effect of the specific independent variable on the dependent variable. In

order to address potential effects of multicollinearity, several methods recom-

mended by Neter et al. (1989, pp. 408411) were used including examination of

(a) Pearsons productmoment correlations of independent variables; (b) ei-genvalues and variance inflation factors; (c) changes in estimated regression

coefficients as variables were removed from the model using a backward

elimination stepwise procedure; and (d) estimated coefficients with incorrect

signs.

Table 6 shows correlations among the variables used in Model 1. The size

variable, adjusted gross assets, was strongly skewed to the right and a log

transformation (LAGASSET) was used to normalize the distribution and

improve linearity. The relationship between LAGASSET and EXTENT of

disclosure was significant (r 0:37). Negative correlations between PUBL-

PRIV and BIG6 and between PUBLPRIV and BRDMBRS indicate publicinstitutions (coded 1) are frequently audited by state auditors rather than ac-

counting firms and have smaller governing boards as compared to private

institutions (coded 0). The only apparently problematic correlation was be-

tween status as public or private with number of board members at )0.733.

29 We contacted all 100 institutions by telephone to obtain information on size of governing

board. Only 59 institutions included a list of board members in their 19931994 annual reports. Theinformation obtained by telephone was consistent with the annual report data when it was

available.



24/41

Farrar and Glauber (1967, p. 98) and Judge et al. (1982, p. 620) suggestedthat simple correlations between independent variables should not be consid-

ered harmful until they exceeded 0.80 or 0.90. Simple correlations of 0.80 or

0.90 are usually associated with variance inflation factors of between 6 and 10.

Variance inflation factors in excess of 10, as Neter et al. (1989, p. 409) point

out, should be considered indications of harmful multicollinearity.

Table 7 reports the results of the regression for Model 1. Including all five of

the hypothesized explanatory variables produced a modest but significant

adjusted R2 of 0.131. However, size was the only independent variable with a

coefficient significantly different than zero. The variance inflation factors and

condition index indicated no major problem with multicollinearity. Thus itappears that reporting practices of colleges and universities are consistent with

some findings from the for-profit sector: larger institutions make more exten-

sive disclosures than smaller institutions. However, for this sample, leverage

and audit firm size effects were not evident. Likewise, our hypotheses with

respect to the impact of board size and classification as public or private were

not supported.

To test Hypothesis 6, we looked at just the 49 public institutions. The av-

erage overall extent of disclosure score for institutions in states with a con-

solidated governing board for higher education had a lower level of disclosure

than public institutions in other states (27.5% versus 32.0%). The difference

is significant using both the weighted (p 0:057, two-tail t-test) and the

Table 6

Pearson correlations of variables included in Model 1

EXTENT LAGASSET BRDMBRS PUBLPRIV BIG6

LAGASSET

Natural log of adjusted

gross assets

0.363

BRDMBRS

Number of members on

governing board

0.088 0.084

PUBLPRIV

Public institution 1 )0.078 0.151 )0.733

Private institution

0BIG6

Major accounting firm 1 )0.039 0.056 0.415 )0.463

Others 0

DEQUITY

Long-term debt to total

fund balance

0.120 0.003 0.377 )0.495 0.186

* Correlation is significant at the 0.01 level (two-tailed).



25/41

Table 7Ordinary least-squares linear regression results for Model 1

R R2 Adjusted R2 Standard error of the

estimate

0.419 0.175 0.131 7.2080454%

ANOVA Sum of

squares

df Mean square F Significance

Regression 1037.790 5 207.558 3.995 0.002

Residual 4883.856 94 51.956

Total 5921.647 99

Unstandardized coefficients Standardized

coefficients b

t Significance 95% confid

interval for

b Standard error Lower

bound

Coefficients (dependent variable EXTENT, overall extent of disclosure weighted index)

(Constant) )3.564 8.803 )0.405 0.686 )21.043

LAGASSET 2.818 0.679 0.416 4.152 0.000 1.470

BRDMBRS )4.074E)02 0.068 )0.087 )0.602 0.549 )0.175

PUBLPRIV )3.786 2.492 )0.246 )1.519 0.132 )8.733

BIG6YN )2.744 1.965 )0.150 )1.397 0.166 )6.645

DEQUITY 1.918E)

02 0.036 0.058 0.533 0.596)

0.052


26/41

unweighted disclosure scores (26.5% with a consolidated governing board and

31.5% without, p 0:033, two-tail t-test). This finding was contrary to our

expectations. 30 To further examine the relationship, we ran a ordinary least

squares regression which included the type of auditor as a (dummy) control

variable (equivalent to Model 3 in Table 3 but with EXTENT as the dependent

variable). For this test, we coded STATEAUD 1 if the institutions financial

statements were audited by a state agency and STATEAUD 0 if unaudited

or audited by a CPA firm. Adjusted R2 for the model was 0.078 with a sig-

nificance level ofp 0:057 (two-tail test). The coefficient for STATEAUD was

positive but not significantly different from zero. 31 The coefficient for

CONSBRD was negative and significant (p 0:072, two-tail test). Both the

negative correlation and coefficient would seem to indicate that institutions instates with consolidated governing boards for higher education do not view

their audited financial statements as an important part of their reporting ob-

ligation. Perhaps other information prepared on a uniform basis by all insti-

tutions reduces the need for additional voluntary disclosures within the annual

report.

6.2. Extent of SEA disclosure models

Model 2 included only the 51 private institutions. Examination of the cor-

relation matrix for the variables provided similar results to those shown inTable 8, which includes all 100 institutions. SEA was positively associated

(r 0:549, p 0:000, two-tail test) with the annual tuition and fees charged to

students by private schools (TUITION) and with ENDOW (r 0:491, p

0:001, two-tail test), the natural log of the fair value of the institution s en-

dowment. The hypothesized negative relationship between SEA and TUITPCT

was supported (r 0:441, p 0:001, two-tail test). There was a strong neg-

ative correlation between ENDOW and TUITPCT (r 0:734, p 0:000,

two-tail test) that is a potential source of muftieollinearity for Model 2.

The ordinary least squares regression results for Model 2 were signifi-

cant (p 0:000, two-tail test) with an adjusted R2 of 0.321. As predicted inthe hypotheses, the coefficient for TUITION was positive and significant

(t 3:480, p 0:0011, two-tail test), and the coefficient for TUITPCT was

negative and significant (t 2:071, p 0:0438, two-tail test). The constant

term and the size of the endowment terms were negative but not significantly

30 With respect to the 17 SEA disclosures only, there was no difference in means for either the

weighted or unweighted disclosure index score.31

As an alternative, we used BIG6 as the control variable for type of auditor. This model had asimilar adjusted R2 (p 0:089, two-tail test) and the coefficient for BIG6 was negative but not

significantly different from zero.



27/41

Table 8

Pearson correlations of variables included in Model 4

SEA TUITPCT ENDOW TUITION BIG6

TUITPCT

Tuition and fees as percentage of

current fund revenues)

0.230

ENDOW

Natural log of fair value of all

endowments

0.183 0.059

TUITION

Undergraduate annual tuition

rate (in-state)

0.176 0.498 0.533

BIG6

Major accounting firm 1 )0.152 0.297 0.371 0.459

Others 0

STATEAUD

State auditor 1 0.144 )0.326 )0.227 )0.439 )0.828

Others 0

CONSBRD

Consolidated governing

board 1

)0.041 )0.361 )0.504 )0.537 )0.322

None or private 0

PUBLPRIV

Public institution

1)

0.005)

0.652

)

0.438

)

0.907

)

0.463

Private institution 0

* Correlation is significant at the 0.05 level (two-tailed).** Correlation is significant at the 0.01 level (two-tailed).


28/41

different from zero. Despite the high correlation between ENDOW and TU-

ITPCT, all of the variance inflation factors were


29/41

Table 9 shows the ordinary least squares regression results. The results are

consistent with Models 2 and 3 showing only PUBLPRIV as an additional

independent variable (although the variable representing auditor type is BIG6

rather than STATEAUD). The model is significant (F 6:321, p< 0:000, two-

tail test). Tests for presence of multicollinearity were all satisfactory. As a

group, the variables explained 21.2% of the entire variance in extent of dis-

closure of SEA and enrollment information. All independent variables other

than ENDOW were significant although signs were not always in the hy-

pothesized direction. The results suggest that state auditors may be more

willing than public accounting firms to be associated with SEA disclosures and

that prestigious institutions with high tuition rates tend to provide more SEA

disclosures. Institutions highly dependent on tuition and fees as a source ofoperating revenue tend to provide fewer SEA disclosures.

An alternate way of looking at extent of disclosure would be to compare

institutions which provided a corporate style annual report as compared to

those that provided only a basic set of financial statements. We defined as

corporate style any annual report that included photos, graphics, or a man-

agement discussion and analysis. Table 10 presents some statistics associated

with this dichotomy. The primarily voluntary disclosures rated by the re-

spondents to the Engstrom (1988) study are more likely to be found in cor-

porate style annual reports as indicated by the significant difference in means

(p 0:001, two-tail t-test). This is particularly true for the SEA indicators.33

In addition to the mean disclosure index scores, Table 10 displays the fre-

quencies associated with several significant causal factors. 34 Reports audited

by Big-6 firms were distributed about equally across the two styles but other

auditors and unaudited report were associated with the corporate style far

more often than with the basic report style. Research institutions tended to use

the corporate style while doctoral and comprehensive institutions reported

using the basic report style. Public institutions tended to employ the corporate

style for financial reporting while private institutions used the basic report

format.

6.3. Summary of hypotheses

To test the 11 hypotheses established earlier, the proposed models for extent

of disclosure by public and private colleges and universities were examined for

33 Thanks to an anonymous reviewer who suggested this alternate approach to looking at our

results.34 Thanks to an anonymous reviewer, we collected data by a telephone call to the institution

about the CFOs credentials of all the institutions in the sample. Most CFOs were also CPAs (73%)

and many had advanced degrees. However, contrary to expectation, we found no significant

relationship between credentials or education and extent of disclosure.



30/41

Table 9

Ordinary least squares linear regression results for Model 4R R2 Adjusted R2 Standard error of the esti-

mate

0.502(a) 0.252 0.212 13.0678678

ANOVA Sum of

squares

df Mean

square

F Significance

Regression 5396.816 5 1079.363 6.321 0.000(a)

Residual 16052.302 94 170.769

Total 21449.117 99

Unsaturated coefficients Standard-ized coeffi-

cients b

t Significance 95% confidence intervafor b

b Standard

error

Lower

bound

Upper

bound

Coefficients (dependent variable SEA, extent of disclosure score for enrollment and performance items only)

(Constant) )4.521 11.237 )0.402 0.688 )26.833 17.791

TUITPCT )0.176 0.091 )0.248 )1.939 0.055 )0.355 0.004

ENDOW 0.355 0.601 0.066 0.591 0.556 )0.838 1.549

TUITION 1.816E)03 0.001 0.834 3.610 0.000 0.001 0.003

BIG6 )8.848 3.596 )0.254 )2.460 0.016 )15.989 )1.708

PUBLPRIV 14.678 7.439 0.501 1.973 0.051 )0.092 29.449


31/41

significance. As discussed before, the regression models explained only a small

portion of the variance in level of disclosure. For all models, the observed sign

and significance of the t-statistic for the regression coefficients were examined

to determine whether the research hypothesis associated with each variable was

supported. The following is a summary of the conclusions:

Ha1: (Supported) Colleges and universities with higher levels of gross assets

disclose financial information to a greater extent than their smaller

counterparts (Table 7).

Ha2: (Not supported) Colleges and universities with more members on thegoverning board did not disclose more information than institutions

with fewer members.

Ha3: (Not supported) Public institutions disclosures were less extensive than

those of private institutions (Table 7) when institutional size was also

taken into consideration. However, public institutions were significantly

more likely to provide a corporate style report (Table 10).

Ha4: (Not supported) Annual reports audited by major accounting firms

were associated with a lower overall level of disclosure but the difference

was not statistically significant (Table 7).

Ha5: (Not supported) Leverage as measured by a debt to equity ratio was not

associated with a higher level of disclosure (Table 7).

Table 10

Disclosure as a function of annual report style with causal factors

Mean

Corporate style

(n 57)

Basic report

(n 43)

Total disclosure index 33.5 26.4

SEA disclosure index 18.2 0.6

Cross-tabulations

Corporate style

(Frequency)

Basic report

(Frequency)

Reports audited by Big-6 accounting firms 39 38Other auditors and unaudited reports 18 5

v2 5:509, significance (two-sided) 0:029 57 43

Research I and II institutions 43 18

Doctoral and Comprehensive institutions 14 25


Public institutions 33 16

Private institutions 24 27




32/41

Ha6: (Not supported) The presence of a state-wide consolidated governing

board for public colleges and universities was actually associated with

a lower level of disclosure.

Ha7: (Supported)

Documents

A Comparative Empirical Examination of Extent of Disclosure by Private and Public Colleges and Universities in the United States